{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PyCaret 2 Regression Example\n", "This notebook is created using PyCaret 2.0. Last updated : 28-07-2020" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pycaret-nightly-0.39\n" ] } ], "source": [ "# check version\n", "from pycaret.utils import version\n", "version()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Loading Dataset" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agesexbmichildrensmokerregioncharges
019female27.9000yessouthwest16884.92400
118male33.7701nosoutheast1725.55230
228male33.0003nosoutheast4449.46200
333male22.7050nonorthwest21984.47061
432male28.8800nonorthwest3866.85520
\n", "
" ], "text/plain": [ " age sex bmi children smoker region charges\n", "0 19 female 27.900 0 yes southwest 16884.92400\n", "1 18 male 33.770 1 no southeast 1725.55230\n", "2 28 male 33.000 3 no southeast 4449.46200\n", "3 33 male 22.705 0 no northwest 21984.47061\n", "4 32 male 28.880 0 no northwest 3866.85520" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pycaret.datasets import get_data\n", "data = get_data('insurance')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Initialize Setup" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " \n", "Setup Succesfully Completed.\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Description Value
0session_id123
1Transform Target False
2Transform Target MethodNone
3Original Data(1338, 7)
4Missing Values False
5Numeric Features 2
6Categorical Features 4
7Ordinal Features False
8High Cardinality Features False
9High Cardinality Method None
10Sampled Data(1338, 7)
11Transformed Train Set(936, 16)
12Transformed Test Set(402, 16)
13Numeric Imputer mean
14Categorical Imputer constant
15Normalize False
16Normalize Method None
17Transformation False
18Transformation Method None
19PCA False
20PCA Method None
21PCA Components None
22Ignore Low Variance False
23Combine Rare Levels False
24Rare Level Threshold None
25Numeric Binning False
26Remove Outliers False
27Outliers Threshold None
28Remove Multicollinearity False
29Multicollinearity Threshold None
30Clustering False
31Clustering Iteration None
32Polynomial Features False
33Polynomial Degree None
34Trignometry Features False
35Polynomial Threshold None
36Group Features False
37Feature Selection False
38Features Selection Threshold None
39Feature Interaction False
40Feature Ratio False
41Interaction Threshold None
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pycaret.regression import *\n", "reg1 = setup(data, target = 'charges', session_id=123, log_experiment=True, experiment_name='insurance1')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Compare Baseline" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Model MAE MSE RMSE R2 RMSLE MAPE TT (Sec)
0Gradient Boosting Regressor2671.592723019681.26614794.60370.83930.44390.31430.1536
1CatBoost Regressor2852.132325408736.96965038.54790.82230.48960.35733.9290
2Random Forest2779.202625351757.15065032.25870.82180.48160.34320.5842
3Light Gradient Boosting Machine3018.989525515012.30515049.84920.81920.55340.38760.2787
4Extra Trees Regressor2755.926528180447.26585299.65660.80430.48750.32550.3242
5AdaBoost Regressor4366.100129298215.00875411.06060.79150.64780.76620.0449
6Extreme Gradient Boosting3257.276731489403.09615610.50790.77740.57230.40720.1843
7Bayesian Ridge4343.500638542310.25366196.46070.73430.64050.44360.0195
8Linear Regression4332.765838549952.00266197.08420.73430.63690.44150.0119
9Lasso Regression4332.632738543897.46926196.60740.73430.64040.44160.0109
10Ridge Regression4339.609338542499.62026196.48910.73430.63480.44290.0088
11TheilSen Regressor4124.365838946435.26316224.89170.73270.53370.37433.0722
12Least Angle Regression4323.457840017870.22866312.01150.72500.56470.42420.0223
13Lasso Least Angle Regression4322.446640023599.45506312.54980.72490.54010.42450.0125
14Decision Tree3184.972844561182.45696663.22480.68260.53430.35230.0142
15Huber Regressor3478.863549170605.58596997.82280.65900.48730.22120.1435
16Random Sample Consensus3467.403652056856.30747203.07740.63820.49700.21750.2620
17Orthogonal Matching Pursuit5760.047557656797.20767580.02240.60260.74260.89960.0121
18Passive Aggressive Regressor4817.272659211213.83237652.97070.59280.75770.43340.0262
19Elastic Net6399.470272811792.65778506.28130.50210.67890.80160.0125
20K Neighbors Regressor6858.1227105272520.336310228.24970.27840.75240.74500.0115
21Support Vector Machine8401.1273163965107.005212732.6249-0.10920.93031.03230.1238
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "best_model = compare_models(fold=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. Create Model" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MAE MSE RMSE R2 RMSLE MAPE
02972.932924058897.43484904.98700.85630.60830.3985
13080.534029299758.44805412.92510.82960.44590.3268
23022.231427624562.66355255.90740.65020.68220.4361
33146.242225018958.48765001.89550.79110.63980.5095
43154.689928894513.08805375.36170.78510.59010.3617
52931.089621432486.19794629.52330.86210.41310.2829
62625.935820785814.44154559.14620.85860.38910.3070
72678.361724232738.55404922.67600.86680.50980.2720
82710.338021418665.48184628.03040.86170.55740.4101
93273.228629598375.85945440.43890.80940.59160.3804
Mean2959.558425236477.06565013.08920.81710.54270.3685
SD210.55993254481.9214324.67550.06280.09430.0703
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lightgbm = create_model('lightgbm')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "lgbm= []\n", "\n", "import numpy as np\n", "for i in np.arange(0.1,1,0.1):\n", " lgbm.append(create_model('lightgbm', learning_rate=i, verbose=False))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "9\n" ] } ], "source": [ "print(len(lgbm))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4.1 Bring your own Model" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MAE MSE RMSE R2 RMSLE MAPE
04350.278634500546.25775873.71660.79390.50890.4944
14641.905044009503.47906633.96590.74410.54550.4477
23648.982928251768.56395315.23930.64230.59410.5031
33827.336425940481.73515093.17990.78340.72110.5688
44542.849441552858.31686446.15070.69100.51380.4358
54076.833434419468.17075866.81070.77860.61570.3785
64157.814439058630.32776249.69040.73420.49050.4696
74943.233745628624.28436754.89630.74910.55500.4572
84509.661839513623.13796285.98630.74480.55800.5444
94829.890545317556.33446731.83160.70810.70590.5245
Mean4352.878637819306.06086125.14680.73700.58080.4824
SD400.77266550504.6006549.43900.04390.07550.0535
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from interpret.glassbox import ExplainableBoostingRegressor\n", "ebm = ExplainableBoostingRegressor()\n", "ebm = create_model('ebm')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from interpret import show\n", "ebm_global = ebm.explain_global()\n", "show(ebm_global)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 5. Tune Hyperparameters" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MAE MSE RMSE R2 RMSLE MAPE
02593.520318901959.35124347.63840.88710.41220.3237
13035.882630123714.30975488.50750.82490.45770.3318
22783.871021407816.74634626.85820.72900.49230.4139
32870.694521243674.28784609.08610.82260.47480.4161
42838.747324370286.38644936.62700.81870.44550.3016
52632.637918784222.60474334.07690.87920.35880.2780
62523.077419451327.41764410.36590.86760.38880.3309
72700.145924627482.80874962.60850.86460.46740.3220
82627.214219952600.02444466.83330.87110.46010.3909
92945.212525414198.63015041.24970.83630.49710.3744
Mean2755.100422427728.25674722.38510.84010.44550.3483
SD158.26643462560.6654356.09940.04410.04290.0452
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tuned_lightgbm = tune_model(lightgbm, n_iter=50, optimize = 'MAE')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n", " importance_type='split', learning_rate=0.3, max_depth=70,\n", " min_child_samples=20, min_child_weight=0.001, min_split_gain=0.2,\n", " n_estimators=10, n_jobs=-1, num_leaves=10, objective=None,\n", " random_state=123, reg_alpha=0.4, reg_lambda=0.1, silent=True,\n", " subsample=1.0, subsample_for_bin=200000, subsample_freq=0)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tuned_lightgbm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 6. Ensemble Model" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MAE MSE RMSE R2 RMSLE MAPE
03130.752839833455.18046311.37510.76210.50670.3295
13104.836945066079.66636713.12740.73800.54100.3104
23315.342047502769.51276892.22530.39860.64520.4540
32869.007840057346.77296329.08740.66550.60430.5635
44039.388164499878.17568031.18160.52030.65900.5106
53324.741941203145.55546418.96760.73500.49160.3224
62579.693338790767.70846228.22350.73610.39110.2259
72727.953035755628.99505979.60110.80340.46980.1807
82863.010638662493.23256217.91710.75030.51130.4390
93207.843447265363.54626874.98100.69560.51210.2716
Mean3116.257043863692.83456599.66870.68050.53320.3608
SD388.32297800317.0820555.03680.11900.07810.1191
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dt = create_model('dt')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MAE MSE RMSE R2 RMSLE MAPE
02689.426422734130.61234768.03220.86420.47560.3477
12850.548930834672.86075552.89770.82070.46830.2826
22767.049924433673.62384943.04300.69060.54590.3887
32842.443824548531.64394954.64750.79500.51650.4156
43020.600230696946.66765540.48250.77170.56020.3738
52818.944222660137.14364760.26650.85420.37120.2631
62617.322022836756.39734778.78190.84460.38740.3035
72684.810124880599.57404988.04570.86320.44510.2565
82334.316118535034.55314305.23340.88030.43570.3510
92820.224929299167.65515412.87060.81130.50720.3478
Mean2744.568625145965.07315000.43010.81960.47130.3330
SD173.16943773482.7721376.38290.05380.05990.0515
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bagged_dt = ensemble_model(dt, n_estimators=50)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MAE MSE RMSE R2 RMSLE MAPE
02088.609825306132.77245030.52010.84890.43600.2089
12694.450037713226.91656141.10960.78070.50230.2522
22580.290431829320.07265641.74800.59700.59540.3725
32026.386722848511.31274780.01160.80920.38170.1531
42783.996936022333.05886001.86080.73210.58990.3035
53304.082042910088.53646550.57930.72400.43980.2454
61691.509121151389.59784599.06400.85610.32260.1226
71941.823122752869.91424769.99680.87490.36080.1170
81842.404420941293.38474576.16580.86470.36410.2280
92618.303336220824.96726018.37390.76670.48300.2274
Mean2357.185629769599.05335410.94300.78540.44760.2231
SD487.10357681159.5886700.92440.08150.09000.0753
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "boosted_dt = ensemble_model(dt, method = 'Boosting')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 7. Blend Models" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MAE MSE RMSE R2 RMSLE MAPE
03588.989327227488.24185217.99660.83740.44740.3922
13940.666337813639.05916149.27960.78010.46620.3596
23269.571423741983.63664872.57460.69940.51310.4489
33244.787621439268.57744630.25580.82100.48920.4796
43830.788334060098.27185836.10300.74670.49800.3891
53401.375927930731.33605284.95330.82030.37180.3010
63336.908229569717.08425437.80440.79880.42170.3678
73917.317232811159.75555728.10260.81960.44310.3370
83581.594428097351.59235300.69350.81850.47620.4443
93799.370232736398.18445721.57310.78910.51250.3934
Mean3591.136929542783.57395417.93360.79310.46390.3913
SD255.62824680386.5098434.48660.04000.04210.0516
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "blender = blend_models()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 8. Stack Models" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MAE MSE RMSE R2 RMSLE MAPE
02380.873218718481.74674326.48610.88820.40860.2976
13109.858231556612.99815617.52730.81650.45300.2955
22702.532120963882.30354578.63320.73460.49290.3608
32692.885418683084.04254322.39330.84400.43680.3712
42868.043126287724.06905127.15560.80450.52030.2937
52820.119319939378.83264465.35320.87170.34930.2744
62318.305119772493.71884446.62720.86550.34290.2549
72731.489724424504.79664942.11540.86570.42180.2370
82415.722018218046.27454268.26030.88230.39870.3020
92965.214627042060.44855200.19810.82580.48100.3027
Mean2700.504322560626.92314729.47500.83990.43050.2990
SD247.05924292155.0822438.96860.04420.05560.0394
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "stacker = stack_models(estimator_list = compare_models(n_select=5, fold = 5, whitelist = models(type='ensemble').index.tolist()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 9. Analyze Model" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_model(dt)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_model(dt, plot = 'error')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_model(dt, plot = 'feature')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d72a4b4dbc6b46189de6c23d167f1004", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Hyperparameters', 'param…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "evaluate_model(dt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 10. Interpret Model" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "interpret_model(lightgbm)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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432male28.8800nonorthwest3866.8552
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" ], "text/plain": [ " age sex bmi children smoker region Label\n", "0 19 female 27.900 0 yes southwest 16884.9240\n", "1 18 male 33.770 1 no southeast 1725.5523\n", "2 28 male 33.000 3 no southeast 5138.2567\n", "3 33 male 22.705 0 no northwest 21984.4706\n", "4 32 male 28.880 0 no northwest 3866.8552" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_data = data.copy()\n", "new_data.drop(['charges'], axis=1, inplace=True)\n", "predict_new = predict_model(best, data=new_data)\n", "predict_new.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 13. Save / Load Model" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Transformation Pipeline and Model Succesfully Saved\n" ] } ], "source": [ "save_model(best, model_name='best-model')" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Transformation Pipeline and Model Sucessfully Loaded\n", "[Pipeline(memory=None,\n", " steps=[('dtypes',\n", " DataTypes_Auto_infer(categorical_features=[],\n", " display_types=True, features_todrop=[],\n", " ml_usecase='regression',\n", " numerical_features=[], target='charges',\n", " time_features=[])),\n", " ('imputer',\n", " Simple_Imputer(categorical_strategy='not_available',\n", " numeric_strategy='mean',\n", " target_variable=None)),\n", " ('new_levels1',\n", " New_Catagorical_Levels...\n", " ('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\n", " ('P_transform', Empty()), ('pt_target', Empty()),\n", " ('binn', Empty()), ('rem_outliers', Empty()),\n", " ('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\n", " ('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\n", " ('feature_select', Empty()), ('fix_multi', Empty()),\n", " ('dfs', Empty()), ('pca', Empty())],\n", " verbose=False), AdaBoostRegressor(base_estimator=DecisionTreeRegressor(ccp_alpha=0.0,\n", " criterion='mse',\n", " max_depth=None,\n", " max_features=None,\n", " max_leaf_nodes=None,\n", " min_impurity_decrease=0.0,\n", " min_impurity_split=None,\n", " min_samples_leaf=1,\n", " min_samples_split=2,\n", " min_weight_fraction_leaf=0.0,\n", " presort='deprecated',\n", " random_state=123,\n", " splitter='best'),\n", " learning_rate=1.0, loss='linear', n_estimators=10,\n", " random_state=123), None]\n" ] } ], "source": [ "loaded_bestmodel = load_model('best-model')\n", "print(loaded_bestmodel)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Pipeline(memory=None,\n",
       "         steps=[('dtypes',\n",
       "                 DataTypes_Auto_infer(categorical_features=[],\n",
       "                                      display_types=True, features_todrop=[],\n",
       "                                      ml_usecase='regression',\n",
       "                                      numerical_features=[], target='charges',\n",
       "                                      time_features=[])),\n",
       "                ('imputer',\n",
       "                 Simple_Imputer(categorical_strategy='not_available',\n",
       "                                numeric_strategy='mean',\n",
       "                                target_variable=None)),\n",
       "                ('new_levels1',\n",
       "                 New_Catagorical_Levels...\n",
       "                ('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\n",
       "                ('P_transform', Empty()), ('pt_target', Empty()),\n",
       "                ('binn', Empty()), ('rem_outliers', Empty()),\n",
       "                ('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\n",
       "                ('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\n",
       "                ('feature_select', Empty()), ('fix_multi', Empty()),\n",
       "                ('dfs', Empty()), ('pca', Empty())],\n",
       "         verbose=False)
DataTypes_Auto_infer(ml_usecase='regression', target='charges')
Simple_Imputer(categorical_strategy='not_available', numeric_strategy='mean',\n",
       "               target_variable=None)
New_Catagorical_Levels_in_TestData(replacement_strategy='least frequent',\n",
       "                                   target='charges')
Empty()
Empty()
Empty()
Empty()
New_Catagorical_Levels_in_TestData(replacement_strategy='least frequent',\n",
       "                                   target='charges')
Make_Time_Features(list_of_features=None)
Empty()
Empty()
Empty()
Empty()
Empty()
Empty()
Empty()
Empty()
Dummify(target='charges')
Empty()
Clean_Colum_Names()
Empty()
Empty()
Empty()
Empty()
" ], "text/plain": [ "Pipeline(memory=None,\n", " steps=[('dtypes',\n", " DataTypes_Auto_infer(categorical_features=[],\n", " display_types=True, features_todrop=[],\n", " ml_usecase='regression',\n", " numerical_features=[], target='charges',\n", " time_features=[])),\n", " ('imputer',\n", " Simple_Imputer(categorical_strategy='not_available',\n", " numeric_strategy='mean',\n", " target_variable=None)),\n", " ('new_levels1',\n", " New_Catagorical_Levels...\n", " ('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\n", " ('P_transform', Empty()), ('pt_target', Empty()),\n", " ('binn', Empty()), ('rem_outliers', Empty()),\n", " ('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\n", " ('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\n", " ('feature_select', Empty()), ('fix_multi', Empty()),\n", " ('dfs', Empty()), ('pca', Empty())],\n", " verbose=False)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn import set_config\n", "set_config(display='diagram')\n", "loaded_bestmodel[0]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "from sklearn import set_config\n", "set_config(display='text')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 14. Deploy Model" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model Succesfully Deployed on AWS S3\n" ] } ], "source": [ "deploy_model(best, model_name = 'best-aws', authentication = {'bucket' : 'pycaret-test'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 15. Get Config / Set Config" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agebmisex_femalesex_malechildren_0children_1children_2children_3children_4children_5smoker_nosmoker_yesregion_northeastregion_northwestregion_southeastregion_southwest
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\n", "
" ], "text/plain": [ " age bmi sex_female sex_male children_0 children_1 children_2 \\\n", "300 36.0 27.55 0.0 1.0 0.0 0.0 0.0 \n", "904 60.0 35.10 1.0 0.0 1.0 0.0 0.0 \n", "670 30.0 31.57 0.0 1.0 0.0 0.0 0.0 \n", "617 49.0 25.60 0.0 1.0 0.0 0.0 1.0 \n", "373 26.0 32.90 0.0 1.0 0.0 0.0 1.0 \n", "\n", " children_3 children_4 children_5 smoker_no smoker_yes \\\n", "300 1.0 0.0 0.0 1.0 0.0 \n", "904 0.0 0.0 0.0 1.0 0.0 \n", "670 1.0 0.0 0.0 1.0 0.0 \n", "617 0.0 0.0 0.0 0.0 1.0 \n", "373 0.0 0.0 0.0 0.0 1.0 \n", "\n", " region_northeast region_northwest region_southeast region_southwest \n", "300 1.0 0.0 0.0 0.0 \n", "904 0.0 0.0 0.0 1.0 \n", "670 0.0 0.0 1.0 0.0 \n", "617 0.0 0.0 0.0 1.0 \n", "373 0.0 0.0 0.0 1.0 " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train = get_config('X_train')\n", "X_train.head()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "123" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_config('seed')" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "from pycaret.regression import set_config\n", "set_config('seed', 999)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "999" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_config('seed')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 16. Get System Logs" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['2020-07-29 09', '47', '14,652', 'INFO', 'PyCaret Regression Module']\n", "['2020-07-29 09', '47', '14,652', 'INFO', 'version pycaret-nightly-0.39']\n", "['2020-07-29 09', '47', '14,652', 'INFO', 'Initializing setup()']\n", "['2020-07-29 09', '47', '14,652', 'INFO', 'USI', 'e640']\n", "['2020-07-29 09', '47', '14,652', 'INFO', 'setup(data=(1338, 7), target=charges, train_size=0.7, sampling=True, sample_estimator=None, categorical_features=None, categorical_imputation=constant, ordinal_features=None,']\n", "['high_cardinality_features=None, high_cardinality_method=frequency, numeric_features=None, numeric_imputation=mean, date_features=None, ignore_features=None, normalize=False,']\n", "['normalize_method=zscore, transformation=False, transformation_method=yeo-johnson, handle_unknown_categorical=True, unknown_categorical_method=least_frequent, pca=False, pca_method=linear,']\n", "['pca_components=None, ignore_low_variance=False, combine_rare_levels=False, rare_level_threshold=0.1, bin_numeric_features=None, remove_outliers=False, outliers_threshold=0.05,']\n", "['remove_multicollinearity=False, multicollinearity_threshold=0.9, remove_perfect_collinearity=False, create_clusters=False, cluster_iter=20,']\n", "['polynomial_features=False, polynomial_degree=2, trigonometry_features=False, polynomial_threshold=0.1, group_features=None,']\n", "['group_names=None, feature_selection=False, feature_selection_threshold=0.8, feature_interaction=False, feature_ratio=False, interaction_threshold=0.01, transform_target=False,']\n", "['transform_target_method=box-cox, data_split_shuffle=True, folds_shuffle=False, n_jobs=-1, html=True, session_id=123, log_experiment=True,']\n", "['experiment_name=insurance1, log_plots=False, log_profile=False, log_data=False, silent=False, verbose=True, profile=False)']\n", "['2020-07-29 09', '47', '14,653', 'INFO', 'Checking environment']\n", "['2020-07-29 09', '47', '14,653', 'INFO', 'python_version', '3.6.10']\n", "['2020-07-29 09', '47', '14,653', 'INFO', 'python_build', \"('default', 'May 7 2020 19\", '46', \"08')\"]\n", "['2020-07-29 09', '47', '14,653', 'INFO', 'machine', 'AMD64']\n", "['2020-07-29 09', '47', '14,653', 'INFO', 'platform', 'Windows-10-10.0.18362-SP0']\n", "['2020-07-29 09', '47', '14,674', 'INFO', 'Memory', 'svmem(total=17032478720, available=5530103808, percent=67.5, used=11502374912, free=5530103808)']\n", "['2020-07-29 09', '47', '14,674', 'INFO', 'Physical Core', '4']\n", "['2020-07-29 09', '47', '14,674', 'INFO', 'Logical Core', '8']\n", "['2020-07-29 09', '47', '14,674', 'INFO', 'Checking libraries']\n", "['2020-07-29 09', '47', '14,674', 'INFO', 'pd==1.0.4']\n", "['2020-07-29 09', '47', '14,674', 'INFO', 'numpy==1.18.5']\n", "['2020-07-29 09', '47', '15,120', 'INFO', 'sklearn==0.23.1']\n", "['2020-07-29 09', '47', '15,204', 'INFO', 'xgboost==1.1.1']\n", "['2020-07-29 09', '47', '15,259', 'INFO', 'lightgbm==2.3.1']\n", "['2020-07-29 09', '47', '15,310', 'INFO', 'catboost==0.23.2']\n", "['2020-07-29 09', '47', '15,876', 'INFO', 'mlflow==1.8.0']\n", "['2020-07-29 09', '47', '15,877', 'INFO', 'Checking Exceptions']\n", "['2020-07-29 09', '47', '15,877', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '47', '15,877', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '47', '15,900', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '47', '18,284', 'INFO', 'Copying data for preprocessing']\n", "['2020-07-29 09', '47', '18,285', 'INFO', 'Declaring global variables']\n", "['2020-07-29 09', '47', '18,296', 'INFO', 'Declaring preprocessing parameters']\n", "['2020-07-29 09', '47', '18,296', 'INFO', 'Importing preprocessing module']\n", "['2020-07-29 09', '47', '19,149', 'INFO', 'Creating preprocessing pipeline']\n", "['2020-07-29 09', '47', '20,310', 'INFO', 'Preprocessing pipeline created successfully']\n", "['2020-07-29 09', '47', '20,310', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '20,310', 'INFO', 'Creating grid variables']\n", "['2020-07-29 09', '47', '20,311', 'INFO', 'Creating global containers']\n", "['2020-07-29 09', '47', '20,410', 'INFO', 'Logging experiment in MLFlow']\n", "['2020-07-29 09', '47', '20,692', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '47', '20,693', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '47', '20,702', 'INFO', 'save_model(model=Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), model_name=Transformation Pipeline, verbose=False)']\n", "['2020-07-29 09', '47', '20,702', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '47', '20,710', 'INFO', 'Transformation Pipeline.pkl saved in current working directory']\n", "['2020-07-29 09', '47', '20,721', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), None]']\n", "['2020-07-29 09', '47', '20,721', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '47', '20,722', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '47', '20,809', 'INFO', 'create_model_container', '0']\n", "['2020-07-29 09', '47', '20,809', 'INFO', 'master_model_container', '0']\n", "['2020-07-29 09', '47', '20,809', 'INFO', 'display_container', '0']\n", "['2020-07-29 09', '47', '20,809', 'INFO', 'setup() succesfully completed......................................']\n", "['2020-07-29 09', '47', '32,141', 'INFO', 'Initializing compare_models()']\n", "['2020-07-29 09', '47', '32,141', 'INFO', 'compare_models(blacklist=None, whitelist=None, fold=5, round=4, sort=R2, n_select=1, turbo=True, verbose=True)']\n", "['2020-07-29 09', '47', '32,141', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '47', '32,141', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '47', '32,141', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '47', '32,174', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '47', '32,176', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '47', '32,186', 'INFO', 'Importing untrained models']\n", "['2020-07-29 09', '47', '32,187', 'INFO', 'Import successful']\n", "['2020-07-29 09', '47', '32,191', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '47', '32,192', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '47', '32,192', 'INFO', 'Initializing Linear Regression']\n", "['2020-07-29 09', '47', '32,198', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '47', '32,206', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '32,210', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '32,212', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '32,212', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '32,229', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '47', '32,235', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '32,239', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '32,240', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '32,241', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '32,248', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '47', '32,254', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '32,257', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '32,259', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '32,260', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '32,266', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '47', '32,271', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '32,275', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '32,276', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '32,277', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '32,282', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '47', '32,288', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '32,291', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '32,293', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '32,293', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '32,302', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '47', '32,302', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '47', '32,313', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '47', '32,365', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '47', '32,366', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '47', '32,366', 'INFO', 'save_model(model=LinearRegression(copy_X=True, fit_intercept=True, n_jobs=-1, normalize=False), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '47', '32,366', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '47', '32,371', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '47', '32,376', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), LinearRegression(copy_X=True, fit_intercept=True, n_jobs=-1, normalize=False), None]']\n", "['2020-07-29 09', '47', '32,376', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '47', '32,376', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '47', '32,731', 'INFO', 'Initializing Lasso Regression']\n", "['2020-07-29 09', '47', '32,736', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '47', '32,742', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '32,747', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '32,749', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '32,749', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '32,756', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '47', '32,762', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '32,766', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '32,768', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '32,768', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '32,775', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '47', '32,781', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '32,786', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '32,788', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '32,788', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '32,795', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '47', '32,801', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '32,804', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '32,805', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '32,805', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '32,812', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '47', '32,818', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '32,820', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '32,822', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '32,822', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '32,829', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '47', '32,829', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '47', '32,841', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '47', '32,901', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '47', '32,901', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '47', '32,901', 'INFO', 'save_model(model=Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,']\n", "['normalize=False, positive=False, precompute=False, random_state=123,']\n", "[\"selection='cyclic', tol=0.0001, warm_start=False), model_name=Trained Model, verbose=False)\"]\n", "['2020-07-29 09', '47', '32,901', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '47', '32,907', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '47', '32,912', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,']\n", "['normalize=False, positive=False, precompute=False, random_state=123,']\n", "[\"selection='cyclic', tol=0.0001, warm_start=False), None]\"]\n", "['2020-07-29 09', '47', '32,912', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '47', '32,912', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '47', '32,961', 'INFO', 'Initializing Ridge Regression']\n", "['2020-07-29 09', '47', '32,967', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '47', '32,972', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '32,974', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '32,976', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '32,976', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '32,983', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '47', '32,988', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '32,991', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '32,993', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '32,993', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '33,001', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '47', '33,006', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '33,008', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '33,010', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '33,010', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '33,017', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '47', '33,022', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '33,025', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '33,027', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '33,027', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '33,035', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '47', '33,041', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '33,043', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '33,045', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '33,045', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '33,051', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '47', '33,051', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '47', '33,062', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '47', '33,129', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '47', '33,129', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '47', '33,129', 'INFO', 'save_model(model=Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,']\n", "[\"normalize=False, random_state=123, solver='auto', tol=0.001), model_name=Trained Model, verbose=False)\"]\n", "['2020-07-29 09', '47', '33,129', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '47', '33,134', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '47', '33,139', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,']\n", "[\"normalize=False, random_state=123, solver='auto', tol=0.001), None]\"]\n", "['2020-07-29 09', '47', '33,139', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '47', '33,139', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '47', '33,188', 'INFO', 'Initializing Elastic Net']\n", "['2020-07-29 09', '47', '33,194', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '47', '33,200', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '33,203', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '33,205', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '33,205', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '33,212', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '47', '33,219', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '33,222', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '33,225', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '33,225', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '33,232', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '47', '33,237', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '33,241', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '33,243', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '33,243', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '33,251', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '47', '33,255', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '33,260', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '33,262', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '33,262', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '33,270', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '47', '33,277', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '33,280', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '33,283', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '33,283', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '33,291', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '47', '33,291', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '47', '33,302', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '47', '33,367', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '47', '33,368', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '47', '33,368', 'INFO', 'save_model(model=ElasticNet(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5,']\n", "['max_iter=1000, normalize=False, positive=False, precompute=False,']\n", "[\"random_state=123, selection='cyclic', tol=0.0001, warm_start=False), model_name=Trained Model, verbose=False)\"]\n", "['2020-07-29 09', '47', '33,368', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '47', '33,373', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '47', '33,379', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), ElasticNet(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5,']\n", "['max_iter=1000, normalize=False, positive=False, precompute=False,']\n", "[\"random_state=123, selection='cyclic', tol=0.0001, warm_start=False), None]\"]\n", "['2020-07-29 09', '47', '33,379', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '47', '33,379', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '47', '33,465', 'INFO', 'Initializing Least Angle Regression']\n", "['2020-07-29 09', '47', '33,470', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '47', '33,476', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '33,482', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '33,483', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '33,484', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '33,490', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '47', '33,496', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '33,502', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '33,504', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '33,504', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '33,510', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '47', '33,517', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '33,522', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '33,523', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '33,523', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '33,531', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '47', '33,536', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '33,542', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '33,543', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '33,544', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '33,552', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '47', '33,564', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '33,572', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '33,574', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '33,574', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '33,580', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '47', '33,581', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '47', '33,593', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '47', '33,653', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '47', '33,654', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '47', '33,654', 'INFO', 'save_model(model=Lars(copy_X=True, eps=2.220446049250313e-16, fit_intercept=True, fit_path=True,']\n", "[\"jitter=None, n_nonzero_coefs=500, normalize=True, precompute='auto',\"]\n", "['random_state=None, verbose=False), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '47', '33,654', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '47', '33,658', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '47', '33,664', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), Lars(copy_X=True, eps=2.220446049250313e-16, fit_intercept=True, fit_path=True,']\n", "[\"jitter=None, n_nonzero_coefs=500, normalize=True, precompute='auto',\"]\n", "['random_state=None, verbose=False), None]']\n", "['2020-07-29 09', '47', '33,664', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '47', '33,664', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '47', '33,714', 'INFO', 'Initializing Lasso Least Angle Regression']\n", "['2020-07-29 09', '47', '33,720', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '47', '33,728', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '33,733', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '33,734', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '33,734', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '33,739', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '47', '33,745', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '33,750', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '33,752', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '33,752', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '33,758', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '47', '33,764', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '33,768', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '33,770', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '33,770', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '33,775', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '47', '33,780', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '33,784', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '33,786', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '33,786', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '33,792', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '47', '33,798', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '33,802', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '33,803', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '33,803', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '33,809', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '47', '33,810', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '47', '33,821', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '47', '33,878', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '47', '33,878', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '47', '33,878', 'INFO', 'save_model(model=LassoLars(alpha=1.0, copy_X=True, eps=2.220446049250313e-16, fit_intercept=True,']\n", "['fit_path=True, jitter=None, max_iter=500, normalize=True,']\n", "[\"positive=False, precompute='auto', random_state=None, verbose=False), model_name=Trained Model, verbose=False)\"]\n", "['2020-07-29 09', '47', '33,878', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '47', '33,884', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '47', '33,889', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), LassoLars(alpha=1.0, copy_X=True, eps=2.220446049250313e-16, fit_intercept=True,']\n", "['fit_path=True, jitter=None, max_iter=500, normalize=True,']\n", "[\"positive=False, precompute='auto', random_state=None, verbose=False), None]\"]\n", "['2020-07-29 09', '47', '33,889', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '47', '33,890', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '47', '33,946', 'INFO', 'Initializing Orthogonal Matching Pursuit']\n", "['2020-07-29 09', '47', '33,952', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '47', '33,961', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '33,964', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '33,966', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '33,966', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '33,974', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '47', '33,983', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '33,985', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '33,987', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '33,988', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '33,997', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '47', '34,002', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '34,006', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '34,008', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '34,008', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '34,017', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '47', '34,023', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '34,026', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '34,029', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '34,029', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '34,037', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '47', '34,044', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '34,048', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '34,051', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '34,051', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '34,058', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '47', '34,058', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '47', '34,073', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '47', '34,193', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '47', '34,193', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '47', '34,194', 'INFO', 'save_model(model=OrthogonalMatchingPursuit(fit_intercept=True, n_nonzero_coefs=None,']\n", "[\"normalize=True, precompute='auto', tol=None), model_name=Trained Model, verbose=False)\"]\n", "['2020-07-29 09', '47', '34,194', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '47', '34,202', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '47', '34,211', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), OrthogonalMatchingPursuit(fit_intercept=True, n_nonzero_coefs=None,']\n", "[\"normalize=True, precompute='auto', tol=None), None]\"]\n", "['2020-07-29 09', '47', '34,211', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '47', '34,211', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '47', '34,263', 'INFO', 'Initializing Bayesian Ridge']\n", "['2020-07-29 09', '47', '34,269', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '47', '34,277', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '34,284', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '34,286', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '34,287', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '34,295', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '47', '34,304', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '34,311', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '34,314', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '34,314', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '34,323', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '47', '34,331', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '34,339', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '34,341', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '34,342', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '34,350', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '47', '34,357', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '34,364', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '34,367', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '34,367', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '34,375', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '47', '34,383', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '34,391', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '34,393', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '34,393', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '34,402', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '47', '34,402', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '47', '34,420', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '47', '34,507', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '47', '34,507', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '47', '34,508', 'INFO', 'save_model(model=BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, alpha_init=None,']\n", "['compute_score=False, copy_X=True, fit_intercept=True,']\n", "['lambda_1=1e-06, lambda_2=1e-06, lambda_init=None, n_iter=300,']\n", "['normalize=False, tol=0.001, verbose=False), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '47', '34,508', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '47', '34,516', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '47', '34,523', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, alpha_init=None,']\n", "['compute_score=False, copy_X=True, fit_intercept=True,']\n", "['lambda_1=1e-06, lambda_2=1e-06, lambda_init=None, n_iter=300,']\n", "['normalize=False, tol=0.001, verbose=False), None]']\n", "['2020-07-29 09', '47', '34,524', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '47', '34,524', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '47', '34,625', 'INFO', 'Initializing Passive Aggressive Regressor']\n", "['2020-07-29 09', '47', '34,633', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '47', '34,640', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '34,651', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '34,654', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '34,654', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '34,664', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '47', '34,672', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '34,683', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '34,685', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '34,686', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '34,696', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '47', '34,705', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '34,715', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '34,717', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '34,717', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '34,726', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '47', '34,735', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '34,746', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '34,749', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '34,749', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '34,759', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '47', '34,767', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '34,777', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '34,779', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '34,779', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '34,790', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '47', '34,790', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '47', '34,808', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '47', '34,904', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '47', '34,904', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '47', '34,905', 'INFO', 'save_model(model=PassiveAggressiveRegressor(C=1.0, average=False, early_stopping=False,']\n", "['epsilon=0.1, fit_intercept=True,']\n", "[\"loss='epsilon_insensitive', max_iter=1000,\"]\n", "['n_iter_no_change=5, random_state=123, shuffle=True,']\n", "['tol=0.001, validation_fraction=0.1, verbose=0,']\n", "['warm_start=False), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '47', '34,905', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '47', '34,914', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '47', '34,922', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), PassiveAggressiveRegressor(C=1.0, average=False, early_stopping=False,']\n", "['epsilon=0.1, fit_intercept=True,']\n", "[\"loss='epsilon_insensitive', max_iter=1000,\"]\n", "['n_iter_no_change=5, random_state=123, shuffle=True,']\n", "['tol=0.001, validation_fraction=0.1, verbose=0,']\n", "['warm_start=False), None]']\n", "['2020-07-29 09', '47', '34,922', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '47', '34,922', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '47', '35,002', 'INFO', 'Initializing Random Sample Consensus']\n", "['2020-07-29 09', '47', '35,011', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '47', '35,023', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '35,179', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '35,182', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '35,183', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '35,194', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '47', '35,203', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '35,358', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '35,361', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '35,361', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '35,371', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '47', '35,381', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '35,533', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '35,535', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '35,535', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '35,547', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '47', '35,556', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '35,711', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '35,714', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '35,715', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '35,727', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '47', '35,737', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '35,875', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '35,878', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '35,878', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '35,889', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '47', '35,889', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '47', '35,911', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '47', '36,020', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '47', '36,020', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '47', '36,020', 'INFO', 'save_model(model=RANSACRegressor(base_estimator=None, is_data_valid=None, is_model_valid=None,']\n", "[\"loss='absolute_loss', max_skips=inf, max_trials=100,\"]\n", "['min_samples=0.5, random_state=123, residual_threshold=None,']\n", "['stop_n_inliers=inf, stop_probability=0.99, stop_score=inf), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '47', '36,020', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '47', '36,029', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '47', '36,039', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), RANSACRegressor(base_estimator=None, is_data_valid=None, is_model_valid=None,']\n", "[\"loss='absolute_loss', max_skips=inf, max_trials=100,\"]\n", "['min_samples=0.5, random_state=123, residual_threshold=None,']\n", "['stop_n_inliers=inf, stop_probability=0.99, stop_score=inf), None]']\n", "['2020-07-29 09', '47', '36,039', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '47', '36,039', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '47', '36,159', 'INFO', 'Initializing TheilSen Regressor']\n", "['2020-07-29 09', '47', '36,168', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '47', '36,178', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '42,054', 'INFO', 'PyCaret Clustering Module']\n", "['2020-07-29 09', '47', '42,054', 'INFO', 'version pycaret-nightly-0.39']\n", "['2020-07-29 09', '47', '42,055', 'INFO', 'Initializing setup()']\n", "['2020-07-29 09', '47', '42,055', 'INFO', 'USI', 'e74c']\n", "['2020-07-29 09', '47', '42,056', 'INFO', 'setup(data=(224, 21), categorical_features=None, categorical_imputation=constant, ordinal_features=None, high_cardinality_features=None,']\n", "[\"numeric_features=None, numeric_imputation=mean, date_features=None, ignore_features=['Country Name'], normalize=False,\"]\n", "['normalize_method=zscore, transformation=False, transformation_method=yeo-johnson, handle_unknown_categorical=True, unknown_categorical_method=least_frequent, pca=False, pca_method=linear,']\n", "['pca_components=None, ignore_low_variance=False, combine_rare_levels=False, rare_level_threshold=0.1, bin_numeric_features=None,']\n", "['remove_multicollinearity=False, multicollinearity_threshold=0.9, group_features=None,']\n", "['group_names=None, supervised=False, supervised_target=None, n_jobs=-1, html=True, session_id=123, log_experiment=True,']\n", "['experiment_name=health1, log_plots=True, log_profile=False, log_data=False, silent=False, verbose=True, profile=False)']\n", "['2020-07-29 09', '47', '42,057', 'INFO', 'Checking environment']\n", "['2020-07-29 09', '47', '42,058', 'INFO', 'python_version', '3.6.10']\n", "['2020-07-29 09', '47', '42,058', 'INFO', 'python_build', \"('default', 'May 7 2020 19\", '46', \"08')\"]\n", "['2020-07-29 09', '47', '42,059', 'INFO', 'machine', 'AMD64']\n", "['2020-07-29 09', '47', '42,060', 'INFO', 'platform', 'Windows-10-10.0.18362-SP0']\n", "['2020-07-29 09', '47', '42,272', 'INFO', 'Memory', 'svmem(total=17032478720, available=5177511936, percent=69.6, used=11854966784, free=5177511936)']\n", "['2020-07-29 09', '47', '42,272', 'INFO', 'Physical Core', '4']\n", "['2020-07-29 09', '47', '42,273', 'INFO', 'Logical Core', '8']\n", "['2020-07-29 09', '47', '42,273', 'INFO', 'Checking libraries']\n", "['2020-07-29 09', '47', '42,273', 'INFO', 'pd==1.0.4']\n", "['2020-07-29 09', '47', '42,283', 'INFO', 'numpy==1.18.5']\n", "['2020-07-29 09', '47', '43,243', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '43,252', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '43,252', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '43,276', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '47', '43,295', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '43,989', 'INFO', 'sklearn==0.23.1']\n", "['2020-07-29 09', '47', '43,991', 'INFO', 'kmodes==0.10.2']\n", "['2020-07-29 09', '47', '45,076', 'INFO', 'PyCaret Anomaly Detection Module']\n", "['2020-07-29 09', '47', '45,076', 'INFO', 'version pycaret-nightly-0.39']\n", "['2020-07-29 09', '47', '45,076', 'INFO', 'Initializing setup()']\n", "['2020-07-29 09', '47', '45,076', 'INFO', 'USI', '9b51']\n", "['2020-07-29 09', '47', '45,077', 'INFO', 'setup(data=(1000, 10), categorical_features=None, categorical_imputation=constant, ordinal_features=None, high_cardinality_features=None,']\n", "['numeric_features=None, numeric_imputation=mean, date_features=None, ignore_features=None, normalize=False,']\n", "['normalize_method=zscore, transformation=False, transformation_method=yeo-johnson, handle_unknown_categorical=True, unknown_categorical_method=least_frequent, pca=False, pca_method=linear,']\n", "['pca_components=None, ignore_low_variance=False, combine_rare_levels=False, rare_level_threshold=0.1, bin_numeric_features=None,']\n", "['remove_multicollinearity=False, multicollinearity_threshold=0.9, group_features=None,']\n", "['group_names=None, supervised=False, supervised_target=None, n_jobs=-1, html=True, session_id=123, log_experiment=True,']\n", "['experiment_name=anomaly1, log_plots=False, log_profile=False, log_data=False, silent=False, verbose=True, profile=False)']\n", "['2020-07-29 09', '47', '45,077', 'INFO', 'Checking environment']\n", "['2020-07-29 09', '47', '45,078', 'INFO', 'python_version', '3.6.10']\n", "['2020-07-29 09', '47', '45,078', 'INFO', 'python_build', \"('default', 'May 7 2020 19\", '46', \"08')\"]\n", "['2020-07-29 09', '47', '45,078', 'INFO', 'machine', 'AMD64']\n", "['2020-07-29 09', '47', '45,079', 'INFO', 'platform', 'Windows-10-10.0.18362-SP0']\n", "['2020-07-29 09', '47', '45,083', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '45,089', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '45,090', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '45,113', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '47', '45,133', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '45,136', 'INFO', 'Memory', 'svmem(total=17032478720, available=5155127296, percent=69.7, used=11877351424, free=5155127296)']\n", "['2020-07-29 09', '47', '45,137', 'INFO', 'Physical Core', '4']\n", "['2020-07-29 09', '47', '45,137', 'INFO', 'Logical Core', '8']\n", "['2020-07-29 09', '47', '45,137', 'INFO', 'Checking libraries']\n", "['2020-07-29 09', '47', '45,137', 'INFO', 'pd==1.0.4']\n", "['2020-07-29 09', '47', '45,138', 'INFO', 'numpy==1.18.5']\n", "['2020-07-29 09', '47', '45,767', 'INFO', 'mlflow==1.8.0']\n", "['2020-07-29 09', '47', '45,768', 'INFO', 'Checking Exceptions']\n", "['2020-07-29 09', '47', '45,769', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '47', '45,850', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '47', '45,903', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '47', '45,903', 'INFO', 'Declaring global variables']\n", "['2020-07-29 09', '47', '45,904', 'INFO', 'Copying data for preprocessing']\n", "['2020-07-29 09', '47', '45,920', 'INFO', 'Declaring preprocessing parameters']\n", "['2020-07-29 09', '47', '45,921', 'INFO', 'Importing preprocessing module']\n", "['2020-07-29 09', '47', '47,044', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '47,054', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '47,055', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '47,083', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '47', '47,108', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '48,011', 'WARNING', 'pyod not found']\n", "['2020-07-29 09', '47', '48,500', 'INFO', 'Creating preprocessing pipeline']\n", "['2020-07-29 09', '47', '49,140', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '49,146', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '49,146', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '49,170', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '47', '49,194', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '49,891', 'INFO', 'Preprocessing pipeline created successfully']\n", "['2020-07-29 09', '47', '49,892', 'INFO', 'Creating grid variables']\n", "['2020-07-29 09', '47', '49,896', 'INFO', 'Creating global containers']\n", "['2020-07-29 09', '47', '49,930', 'INFO', 'mlflow==1.8.0']\n", "['2020-07-29 09', '47', '49,931', 'INFO', 'Checking Exceptions']\n", "['2020-07-29 09', '47', '49,931', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '47', '50,035', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '47', '50,094', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '47', '50,094', 'INFO', 'Declaring global variables']\n", "['2020-07-29 09', '47', '50,094', 'INFO', 'Copying data for preprocessing']\n", "['2020-07-29 09', '47', '50,112', 'INFO', 'Declaring preprocessing parameters']\n", "['2020-07-29 09', '47', '50,112', 'INFO', 'Importing preprocessing module']\n", "['2020-07-29 09', '47', '51,064', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '51,070', 'INFO', 'Logging experiment in MLFlow']\n", "['2020-07-29 09', '47', '51,071', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '51,071', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '51,100', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '47', '51,101', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '47', '51,167', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '47', '51,238', 'INFO', 'Creating preprocessing pipeline']\n", "['2020-07-29 09', '47', '51,321', 'INFO', 'PyCaret NLP Module']\n", "['2020-07-29 09', '47', '51,321', 'INFO', 'version pycaret-nightly-0.39']\n", "['2020-07-29 09', '47', '51,322', 'INFO', 'Initializing setup()']\n", "['2020-07-29 09', '47', '51,322', 'INFO', 'USI', 'ab65']\n", "['2020-07-29 09', '47', '51,322', 'INFO', 'setup(data=(6818, 7), target=en, custom_stopwords=None, html=True, session_id=123, log_experiment=True,']\n", "['experiment_name=kiva1, log_plots=True, log_data=False, verbose=True)']\n", "['2020-07-29 09', '47', '51,323', 'INFO', 'Checking environment']\n", "['2020-07-29 09', '47', '51,323', 'INFO', 'python_version', '3.6.10']\n", "['2020-07-29 09', '47', '51,323', 'INFO', 'python_build', \"('default', 'May 7 2020 19\", '46', \"08')\"]\n", "['2020-07-29 09', '47', '51,323', 'INFO', 'machine', 'AMD64']\n", "['2020-07-29 09', '47', '51,324', 'INFO', 'platform', 'Windows-10-10.0.18362-SP0']\n", "['2020-07-29 09', '47', '51,402', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '47', '51,402', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '47', '51,404', 'INFO', 'save_model(model=TheilSenRegressor(copy_X=True, fit_intercept=True, max_iter=300,']\n", "['max_subpopulation=10000, n_jobs=-1, n_subsamples=None,']\n", "['random_state=123, tol=0.001, verbose=False), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '47', '51,404', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '47', '51,411', 'INFO', 'Memory', 'svmem(total=17032478720, available=5093425152, percent=70.1, used=11939053568, free=5093425152)']\n", "['2020-07-29 09', '47', '51,412', 'INFO', 'Physical Core', '4']\n", "['2020-07-29 09', '47', '51,412', 'INFO', 'Logical Core', '8']\n", "['2020-07-29 09', '47', '51,412', 'INFO', 'Checking libraries']\n", "['2020-07-29 09', '47', '51,412', 'INFO', 'pd==1.0.4']\n", "['2020-07-29 09', '47', '51,413', 'INFO', 'numpy==1.18.5']\n", "['2020-07-29 09', '47', '51,431', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '47', '51,463', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', 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save_model() called ==================================']\n", "['2020-07-29 09', '47', '51,870', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '47', '51,897', 'INFO', 'save_model(model=Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True,']\n", "[\"features_todrop=['Country Name'],\"]\n", "[\"ml_usecase='regression',\"]\n", "['numerical_features=[],']\n", "[\"target='dummy_target',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_...']\n", "[\"target='dummy_target')),\"]\n", "[\"('feature_time',\"]\n", "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n", "[\"('group', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n", "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('dummy', Dummify(target='dummy_target')),\"]\n", "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n", "[\"('pca', Empty())],\"]\n", "['verbose=False), model_name=Transformation Pipeline, verbose=False)']\n", "['2020-07-29 09', '47', '51,897', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '47', '51,912', 'INFO', 'Transformation Pipeline.pkl saved in current working directory']\n", "['2020-07-29 09', '47', '51,922', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '51,926', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '51,926', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '51,942', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True,']\n", "[\"features_todrop=['Country Name'],\"]\n", "[\"ml_usecase='regression',\"]\n", "['numerical_features=[],']\n", 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"[\"target='dummy_target',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_...']\n", "[\"target='dummy_target')),\"]\n", "[\"('feature_time',\"]\n", "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n", "[\"('group', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n", "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('dummy', Dummify(target='dummy_target')),\"]\n", "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n", "[\"('pca', Empty())],\"]\n", "['verbose=False)]']\n", "['2020-07-29 09', '47', '51,942', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '47', '51,943', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '47', '51,951', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '47', '51,969', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '52,110', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '52,118', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '52,118', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '52,131', 'INFO', 'Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True,']\n", "[\"features_todrop=['Country Name'],\"]\n", "[\"ml_usecase='regression',\"]\n", "['numerical_features=[],']\n", "[\"target='dummy_target',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_...']\n", "[\"target='dummy_target')),\"]\n", "[\"('feature_time',\"]\n", "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n", "[\"('group', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n", "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('dummy', Dummify(target='dummy_target')),\"]\n", "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n", "[\"('pca', Empty())],\"]\n", "['verbose=False)']\n", "['2020-07-29 09', '47', '52,132', 'INFO', 'setup() succesfully completed......................................']\n", "['2020-07-29 09', '47', '52,142', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '47', '52,165', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '52,296', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '52,301', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '52,302', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '52,321', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '47', '52,340', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '52,469', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '52,473', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '52,473', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '52,491', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '47', '52,507', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '52,632', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '52,638', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '52,638', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '52,653', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '47', '52,654', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '47', '52,688', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '47', '52,825', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '47', '52,825', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '47', '52,826', 'INFO', 'save_model(model=HuberRegressor(alpha=0.0001, epsilon=1.35, fit_intercept=True, max_iter=100,']\n", "['tol=1e-05, warm_start=False), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '47', '52,826', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '47', '52,841', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '47', '52,854', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), HuberRegressor(alpha=0.0001, epsilon=1.35, fit_intercept=True, max_iter=100,']\n", "['tol=1e-05, warm_start=False), None]']\n", "['2020-07-29 09', '47', '52,854', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '47', '52,854', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '47', '53,009', 'INFO', 'Initializing Support Vector Machine']\n", "['2020-07-29 09', '47', '53,023', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '47', '53,040', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '53,106', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '53,120', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '53,120', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '53,135', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '47', '53,149', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '53,211', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '53,223', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '53,223', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '53,237', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '47', '53,250', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '53,304', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '53,313', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '53,314', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '53,326', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '47', '53,341', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '53,367', 'INFO', 'gensim==3.8.3']\n", "['2020-07-29 09', '47', '53,398', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '53,410', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '53,410', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '53,424', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '47', '53,436', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '53,489', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '53,500', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '53,500', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '53,513', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '47', '53,513', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '47', '53,544', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '47', '53,668', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '47', '53,668', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '47', '53,669', 'INFO', \"save_model(model=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\"]\n", "[\"kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False), model_name=Trained Model, verbose=False)\"]\n", "['2020-07-29 09', '47', '53,669', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '47', '53,680', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '47', '53,690', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\"]\n", "[\"kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False), None]\"]\n", "['2020-07-29 09', '47', '53,690', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '47', '53,690', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '47', '53,805', 'INFO', 'Initializing K Neighbors Regressor']\n", "['2020-07-29 09', '47', '53,817', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '47', '53,831', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '53,838', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '53,953', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '53,953', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '53,968', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '47', '53,981', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '53,989', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '54,057', 'INFO', 'spacy==2.2.4']\n", "['2020-07-29 09', '47', '54,099', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '54,100', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '54,115', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '47', '54,126', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '54,134', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '54,243', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '54,243', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '54,256', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '47', '54,266', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '54,275', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '54,384', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '54,384', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '54,395', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '47', '54,405', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '54,414', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '54,524', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '54,525', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '54,538', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '47', '54,539', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '47', '54,572', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '47', '54,705', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '47', '54,705', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '47', '54,706', 'INFO', \"save_model(model=KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\"]\n", "['metric_params=None, n_jobs=-1, n_neighbors=5, p=2,']\n", "[\"weights='uniform'), model_name=Trained Model, verbose=False)\"]\n", "['2020-07-29 09', '47', '54,707', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '47', '54,723', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '47', '54,740', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\"]\n", "['metric_params=None, n_jobs=-1, n_neighbors=5, p=2,']\n", "[\"weights='uniform'), None]\"]\n", "['2020-07-29 09', '47', '54,740', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '47', '54,740', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '47', '54,857', 'INFO', 'nltk==3.5']\n", "['2020-07-29 09', '47', '54,892', 'INFO', 'Initializing Decision Tree']\n", "['2020-07-29 09', '47', '54,902', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '47', '54,921', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '54,924', 'INFO', 'textblob==0.15.3']\n", "['2020-07-29 09', '47', '54,932', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '54,935', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '54,935', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '54,949', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '47', '54,965', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '54,978', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '54,983', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '54,984', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '55,005', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '47', '55,021', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '55,032', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '55,036', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '47', '55,036', 'INFO', 'create_model(model=kmeans, num_clusters=4, ground_truth=None, verbose=True, system=True)']\n", "['2020-07-29 09', '47', '55,036', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '47', '55,037', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '47', '55,037', 'INFO', 'Setting num_cluster param']\n", "['2020-07-29 09', '47', '55,037', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '47', '55,038', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '55,038', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '55,057', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '47', '55,077', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '55,078', 'INFO', 'Importing untrained model']\n", "['2020-07-29 09', '47', '55,078', 'INFO', 'K-Means Clustering Imported succesfully']\n", "['2020-07-29 09', '47', '55,094', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '55,098', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '55,102', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '55,102', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '55,122', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '47', '55,141', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '55,158', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '55,163', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '55,163', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '55,189', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '47', '55,190', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '47', '55,214', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '55,232', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '47', '55,236', 'INFO', 'Creating Metrics dataframe']\n", "['2020-07-29 09', '47', '55,245', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '47', '55,417', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '47', '55,417', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '47', '55,419', 'INFO', \"save_model(model=DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\"]\n", "['max_features=None, max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0, min_impurity_split=None,']\n", "['min_samples_leaf=1, min_samples_split=2,']\n", "[\"min_weight_fraction_leaf=0.0, presort='deprecated',\"]\n", "[\"random_state=123, splitter='best'), model_name=Trained Model, verbose=False)\"]\n", "['2020-07-29 09', '47', '55,419', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '47', '55,437', 'INFO', 'SubProcess plot_model() called ==================================']\n", "['2020-07-29 09', '47', '55,437', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '47', '55,438', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), plot=cluster, feature=None, label=False, save=True, system=False)']\n", "['2020-07-29 09', '47', '55,439', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '47', '55,439', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '47', '55,439', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '47', '55,465', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\"]\n", "['max_features=None, max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0, min_impurity_split=None,']\n", "['min_samples_leaf=1, min_samples_split=2,']\n", "[\"min_weight_fraction_leaf=0.0, presort='deprecated',\"]\n", "[\"random_state=123, splitter='best'), None]\"]\n", "['2020-07-29 09', '47', '55,466', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '47', '55,466', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '47', '55,656', 'INFO', 'Initializing Random Forest']\n", "['2020-07-29 09', '47', '55,675', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '47', '55,694', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '56,059', 'INFO', 'pyLDAvis==2.1.2']\n", "['2020-07-29 09', '47', '56,271', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '56,275', 'INFO', 'wordcloud==1.7.0']\n", "['2020-07-29 09', '47', '56,383', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '56,384', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '56,401', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '47', '56,417', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '56,825', 'INFO', 'Preprocessing pipeline created successfully']\n", "['2020-07-29 09', '47', '56,826', 'INFO', 'Creating grid variables']\n", "['2020-07-29 09', '47', '56,829', 'INFO', 'Creating global containers']\n", "['2020-07-29 09', '47', '56,945', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '57,059', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '57,060', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '57,086', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '47', '57,104', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '57,516', 'INFO', 'mlflow==1.8.0']\n", "['2020-07-29 09', '47', '57,516', 'INFO', 'Checking Exceptions']\n", "['2020-07-29 09', '47', '57,718', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '57,739', 'INFO', 'Logging experiment in MLFlow']\n", "['2020-07-29 09', '47', '57,833', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '57,834', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '57,859', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '47', '57,881', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '58,479', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '47', '58,479', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '47', '58,508', 'INFO', 'save_model(model=Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "['numerical_features=[],']\n", "[\"target='dummy_target',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_L...']\n", "[\"target='dummy_target')),\"]\n", "[\"('feature_time',\"]\n", "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n", "[\"('group', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n", "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('dummy', Dummify(target='dummy_target')),\"]\n", "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n", "[\"('pca', Empty())],\"]\n", "['verbose=False), model_name=Transformation Pipeline, verbose=False)']\n", "['2020-07-29 09', '47', '58,508', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '47', '58,521', 'INFO', 'Transformation Pipeline.pkl saved in current working directory']\n", "['2020-07-29 09', '47', '58,552', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "['numerical_features=[],']\n", "[\"target='dummy_target',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_L...']\n", "[\"target='dummy_target')),\"]\n", "[\"('feature_time',\"]\n", "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n", "[\"('group', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n", "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('dummy', Dummify(target='dummy_target')),\"]\n", "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n", "[\"('pca', Empty())],\"]\n", "['verbose=False), Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "['numerical_features=[],']\n", "[\"target='dummy_target',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_L...']\n", "[\"target='dummy_target')),\"]\n", "[\"('feature_time',\"]\n", "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n", "[\"('group', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n", "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('dummy', Dummify(target='dummy_target')),\"]\n", "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n", "[\"('pca', Empty())],\"]\n", "['verbose=False)]']\n", "['2020-07-29 09', '47', '58,552', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '47', '58,552', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '47', '58,578', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '58,692', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '58,693', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '58,708', 'INFO', 'Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "['numerical_features=[],']\n", "[\"target='dummy_target',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_L...']\n", "[\"target='dummy_target')),\"]\n", "[\"('feature_time',\"]\n", "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n", "[\"('group', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n", "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('dummy', Dummify(target='dummy_target')),\"]\n", "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n", "[\"('pca', Empty())],\"]\n", "['verbose=False)']\n", "['2020-07-29 09', '47', '58,708', 'INFO', 'setup() succesfully completed......................................']\n", "['2020-07-29 09', '47', '58,714', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '47', '58,735', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '47', '59,302', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '47', '59,421', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '47', '59,422', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '47', '59,443', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '47', '59,444', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '47', '59,489', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '47', '59,672', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '47', '59,672', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '47', '59,674', 'INFO', \"save_model(model=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\"]\n", "[\"max_depth=None, max_features='auto', max_leaf_nodes=None,\"]\n", "['max_samples=None, min_impurity_decrease=0.0,']\n", "['min_impurity_split=None, min_samples_leaf=1,']\n", "['min_samples_split=2, min_weight_fraction_leaf=0.0,']\n", "['n_estimators=100, n_jobs=-1, oob_score=False,']\n", "['random_state=123, verbose=0, warm_start=False), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '47', '59,674', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '47', '59,703', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '47', '59,789', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '47', '59,796', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '47', '59,811', 'INFO', 'plot type', 'cluster']\n", "['2020-07-29 09', '47', '59,812', 'INFO', 'SubProcess assign_model() called ==================================']\n", "['2020-07-29 09', '47', '59,812', 'INFO', 'Initializing assign_model()']\n", "['2020-07-29 09', '47', '59,812', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\"]\n", "[\"max_depth=None, max_features='auto', max_leaf_nodes=None,\"]\n", "['max_samples=None, min_impurity_decrease=0.0,']\n", "['min_impurity_split=None, min_samples_leaf=1,']\n", "['min_samples_split=2, min_weight_fraction_leaf=0.0,']\n", "['n_estimators=100, n_jobs=-1, oob_score=False,']\n", "['random_state=123, verbose=0, warm_start=False), None]']\n", "['2020-07-29 09', '47', '59,812', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '47', '59,812', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '47', '59,812', 'INFO', \"assign_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), transformation=True, verbose=False)']\n", "['2020-07-29 09', '47', '59,812', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '47', '59,813', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '47', '59,813', 'INFO', 'Copying data']\n", "['2020-07-29 09', '47', '59,813', 'INFO', 'Transformation param set to True. Assigned clusters are attached on transformed dataset.']\n", "['2020-07-29 09', '47', '59,814', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '47', '59,849', 'INFO', 'Determining Trained Model']\n", "['2020-07-29 09', '47', '59,850', 'INFO', 'Trained Model', 'K-Means Clustering']\n", "['2020-07-29 09', '47', '59,850', 'INFO', '(224, 21)']\n", "['2020-07-29 09', '47', '59,851', 'INFO', 'assign_model() succesfully completed......................................']\n", "['2020-07-29 09', '47', '59,851', 'INFO', 'SubProcess assign_model() end ==================================']\n", "['2020-07-29 09', '47', '59,858', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '47', '59,859', 'INFO', 'Declaring global variables']\n", "['2020-07-29 09', '47', '59,860', 'INFO', 'Input provided', 'dataframe']\n", "['2020-07-29 09', '47', '59,860', 'INFO', 'session_id set to', '123']\n", "['2020-07-29 09', '47', '59,860', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '47', '59,864', 'INFO', 'Importing stopwords from nltk']\n", "['2020-07-29 09', '47', '59,876', 'INFO', 'Fitting PCA()']\n", "['2020-07-29 09', '47', '59,891', 'INFO', 'Sorting dataframe']\n", "['2020-07-29 09', '47', '59,897', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '47', '59,977', 'INFO', 'Initializing Extra Trees Regressor']\n", "['2020-07-29 09', '47', '59,992', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '48', '00,014', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '48', '00,280', 'INFO', 'No custom stopwords defined']\n", "['2020-07-29 09', '48', '00,282', 'INFO', 'Removing numeric characters from the text']\n", "['2020-07-29 09', '48', '00,441', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '48', '00,553', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '48', '00,554', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '48', '00,576', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '48', '00,594', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '48', '00,820', 'INFO', 'Removing special characters from the text']\n", "['2020-07-29 09', '48', '01,075', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '48', '01,191', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '48', '01,192', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '48', '01,219', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '48', '01,241', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '48', '01,696', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '48', '01,807', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '48', '01,807', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '48', '01,823', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '48', '01,836', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '48', '02,159', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '48', '02,269', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '48', '02,270', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '48', '02,285', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '48', '02,305', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '48', '02,353', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '48', '02,353', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '48', '02,353', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '48', '02,354', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '48', '02,421', 'INFO', 'Importing untrained model']\n", "['2020-07-29 09', '48', '02,422', 'INFO', 'Isolation Forest Imported succesfully']\n", "['2020-07-29 09', '48', '02,449', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '48', '02,813', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '48', '02,928', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '48', '02,929', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '48', '02,953', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '48', '02,954', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '48', '03,011', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '48', '03,241', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '48', '03,242', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '48', '03,244', 'INFO', \"save_model(model=ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\"]\n", "[\"max_depth=None, max_features='auto', max_leaf_nodes=None,\"]\n", "['max_samples=None, min_impurity_decrease=0.0,']\n", "['min_impurity_split=None, min_samples_leaf=1,']\n", "['min_samples_split=2, min_weight_fraction_leaf=0.0,']\n", "['n_estimators=100, n_jobs=-1, oob_score=False,']\n", "['random_state=123, verbose=0, warm_start=False), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '48', '03,244', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '48', '03,413', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '48', '03,441', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\"]\n", "[\"max_depth=None, max_features='auto', max_leaf_nodes=None,\"]\n", "['max_samples=None, min_impurity_decrease=0.0,']\n", "['min_impurity_split=None, min_samples_leaf=1,']\n", "['min_samples_split=2, min_weight_fraction_leaf=0.0,']\n", "['n_estimators=100, n_jobs=-1, oob_score=False,']\n", "['random_state=123, verbose=0, warm_start=False), None]']\n", "['2020-07-29 09', '48', '03,441', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '03,442', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '48', '03,744', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '48', '03,753', 'INFO', 'Initializing AdaBoost Regressor']\n", "['2020-07-29 09', '48', '03,780', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '48', '03,810', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '48', '03,946', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '48', '03,960', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '48', '03,961', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '48', '03,961', 'INFO', 'No inverse transformer found']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "['2020-07-29 09', '48', '03,961', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '48', '03,962', 'INFO', \"save_model(model=IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n", "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n", "['random_state=123, verbose=0), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '48', '03,962', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '48', '03,999', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '48', '04,028', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '48', '04,118', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '48', '04,126', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '48', '04,127', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '48', '04,146', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '48', '04,165', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '48', '04,169', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '48', '04,188', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "['numerical_features=[],']\n", "[\"target='dummy_target',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_L...']\n", "[\"target='dummy_target')),\"]\n", "[\"('feature_time',\"]\n", "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n", "[\"('group', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n", "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('dummy', Dummify(target='dummy_target')),\"]\n", "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n", "[\"('pca', Empty())],\"]\n", "[\"verbose=False), IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n", "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n", "['random_state=123, verbose=0)]']\n", "['2020-07-29 09', '48', '04,188', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '04,189', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '48', '04,228', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '48', '04,238', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '48', '04,238', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '48', '04,265', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '48', '04,290', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '48', '04,301', 'INFO', \"IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n", "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n", "['random_state=123, verbose=0)']\n", "['2020-07-29 09', '48', '04,301', 'INFO', 'create_models() succesfully completed......................................']\n", "['2020-07-29 09', '48', '04,317', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '48', '04,318', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '48', '04,318', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '48', '04,318', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '48', '04,350', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '48', '04,360', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '48', '04,360', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '48', '04,389', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '48', '04,395', 'INFO', 'Importing untrained model']\n", "['2020-07-29 09', '48', '04,396', 'INFO', 'k-Nearest Neighbors Detector Imported succesfully']\n", "['2020-07-29 09', '48', '04,412', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '48', '04,415', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '48', '04,484', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '48', '04,484', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '48', '04,494', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '48', '04,494', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '48', '04,518', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '48', '04,519', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '48', '04,583', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '48', '04,697', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '48', '04,698', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '48', '04,699', 'INFO', \"save_model(model=KNN(algorithm='auto', contamination=0.1, leaf_size=30, method='largest',\"]\n", "[\"metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=5, p=2,\"]\n", "['radius=1.0), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '48', '04,700', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '48', '04,725', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '48', '04,747', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "['numerical_features=[],']\n", "[\"target='dummy_target',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_L...']\n", "[\"target='dummy_target')),\"]\n", "[\"('feature_time',\"]\n", "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n", "[\"('group', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n", "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('dummy', Dummify(target='dummy_target')),\"]\n", "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n", "[\"('pca', Empty())],\"]\n", "[\"verbose=False), KNN(algorithm='auto', contamination=0.1, leaf_size=30, method='largest',\"]\n", "[\"metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=5, p=2,\"]\n", "['radius=1.0)]']\n", "['2020-07-29 09', '48', '04,747', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '04,748', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '48', '04,794', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '48', '04,794', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '48', '04,795', 'INFO', \"save_model(model=AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear',\"]\n", "['n_estimators=50, random_state=123), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '48', '04,796', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '48', '04,831', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '48', '04,855', 'INFO', \"KNN(algorithm='auto', contamination=0.1, leaf_size=30, method='largest',\"]\n", "[\"metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=5, p=2,\"]\n", "['radius=1.0)']\n", "['2020-07-29 09', '48', '04,855', 'INFO', 'create_models() succesfully completed......................................']\n", "['2020-07-29 09', '48', '04,856', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear',\"]\n", "['n_estimators=50, random_state=123), None]']\n", "['2020-07-29 09', '48', '04,856', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '04,856', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '48', '04,871', 'INFO', 'Initializing assign_model()']\n", "['2020-07-29 09', '48', '04,873', 'INFO', \"assign_model(model=IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n", "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n", "['random_state=123, verbose=0), transformation=False, score=True, verbose=True)']\n", "['2020-07-29 09', '48', '04,873', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '48', '04,874', 'INFO', 'Copying data']\n", "['2020-07-29 09', '48', '04,876', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '48', '04,952', 'INFO', 'Determining Trained Model']\n", "['2020-07-29 09', '48', '04,953', 'INFO', 'Trained Model', 'Assigned Isolation Forest']\n", "['2020-07-29 09', '48', '04,956', 'INFO', '(1000, 12)']\n", "['2020-07-29 09', '48', '04,957', 'INFO', 'assign_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '05,009', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '48', '05,010', 'INFO', \"plot_model(model=IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n", "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n", "['random_state=123, verbose=0), plot=tsne, feature=None, save=False, system=True)']\n", "['2020-07-29 09', '48', '05,011', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '48', '05,011', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '48', '05,796', 'INFO', 'Tokenizing Words']\n", "['2020-07-29 09', '48', '06,772', 'INFO', \"Saving 'Cluster.html' in current active directory\"]\n", "['2020-07-29 09', '48', '06,773', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '48', '06,773', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '07,650', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '48', '07,651', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), plot=distribution, feature=None, label=False, save=True, system=False)']\n", "['2020-07-29 09', '48', '07,652', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '48', '07,652', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '48', '07,672', 'INFO', 'plot type', 'distribution']\n", "['2020-07-29 09', '48', '07,672', 'INFO', 'SubProcess assign_model() called ==================================']\n", "['2020-07-29 09', '48', '07,672', 'INFO', 'Initializing assign_model()']\n", "['2020-07-29 09', '48', '07,674', 'INFO', \"assign_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), transformation=False, verbose=False)']\n", "['2020-07-29 09', '48', '07,674', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '48', '07,675', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '48', '07,675', 'INFO', 'Copying data']\n", "['2020-07-29 09', '48', '07,676', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '48', '07,711', 'INFO', 'Determining Trained Model']\n", "['2020-07-29 09', '48', '07,713', 'INFO', 'Trained Model', 'K-Means Clustering']\n", "['2020-07-29 09', '48', '07,714', 'INFO', '(224, 22)']\n", "['2020-07-29 09', '48', '07,714', 'INFO', 'assign_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '07,715', 'INFO', 'SubProcess assign_model() end ==================================']\n", "['2020-07-29 09', '48', '07,715', 'INFO', 'Sorting dataframe']\n", "['2020-07-29 09', '48', '07,732', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '48', '08,329', 'INFO', \"Saving 'Distribution.html' in current active directory\"]\n", "['2020-07-29 09', '48', '08,329', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '48', '08,329', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '08,812', 'INFO', 'plot type', 'tsne']\n", "['2020-07-29 09', '48', '08,813', 'INFO', 'SubProcess assign_model() called ==================================']\n", "['2020-07-29 09', '48', '08,813', 'INFO', 'Initializing assign_model()']\n", "['2020-07-29 09', '48', '08,814', 'INFO', \"assign_model(model=IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n", "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n", "['random_state=123, verbose=0), transformation=True, score=False, verbose=False)']\n", "['2020-07-29 09', '48', '08,814', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '48', '08,815', 'INFO', 'Copying data']\n", "['2020-07-29 09', '48', '08,815', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '48', '08,849', 'INFO', 'Determining Trained Model']\n", "['2020-07-29 09', '48', '08,850', 'INFO', 'Trained Model', 'Assigned Isolation Forest']\n", "['2020-07-29 09', '48', '08,851', 'INFO', '(1000, 11)']\n", "['2020-07-29 09', '48', '08,851', 'INFO', 'assign_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '08,852', 'INFO', 'SubProcess assign_model() end ==================================']\n", "['2020-07-29 09', '48', '08,870', 'INFO', 'Getting dummies to cast categorical variables']\n", "['2020-07-29 09', '48', '08,885', 'INFO', 'Fitting TSNE()']\n", "['2020-07-29 09', '48', '09,534', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '48', '09,535', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), plot=elbow, feature=None, label=False, save=True, system=False)']\n", "['2020-07-29 09', '48', '09,535', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '48', '09,536', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '48', '09,562', 'INFO', 'plot type', 'elbow']\n", "['2020-07-29 09', '48', '09,834', 'INFO', 'Fitting KElbowVisualizer()']\n", "['2020-07-29 09', '48', '11,999', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '48', '13,890', 'INFO', \"Saving 'Elbow.png' in current active directory\"]\n", "['2020-07-29 09', '48', '13,890', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '48', '13,891', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '13,931', 'INFO', 'SubProcess plot_model() end ==================================']\n", "['2020-07-29 09', '48', '13,932', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '48', '13,932', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '48', '13,934', 'INFO', \"save_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '48', '13,934', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '48', '13,953', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '48', '13,975', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True,']\n", "[\"features_todrop=['Country Name'],\"]\n", "[\"ml_usecase='regression',\"]\n", "['numerical_features=[],']\n", "[\"target='dummy_target',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_...']\n", "[\"target='dummy_target')),\"]\n", "[\"('feature_time',\"]\n", "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n", "[\"('group', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n", "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('dummy', Dummify(target='dummy_target')),\"]\n", "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n", "[\"('pca', Empty())],\"]\n", "[\"verbose=False), KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0)]']\n", "['2020-07-29 09', '48', '13,975', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '13,976', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '48', '14,077', 'INFO', \"KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0)']\n", "['2020-07-29 09', '48', '14,078', 'INFO', 'create_models() succesfully completed......................................']\n", "['2020-07-29 09', '48', '14,099', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '48', '14,100', 'INFO', 'create_model(model=kmodes, num_clusters=4, ground_truth=None, verbose=True, system=True)']\n", "['2020-07-29 09', '48', '14,100', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '48', '14,101', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '48', '14,102', 'INFO', 'Setting num_cluster param']\n", "['2020-07-29 09', '48', '14,102', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '48', '14,162', 'INFO', 'Importing untrained model']\n", "['2020-07-29 09', '48', '14,178', 'INFO', 'K-Modes Clustering Imported succesfully']\n", "['2020-07-29 09', '48', '14,201', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '48', '20,873', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '48', '20,911', 'INFO', 'Creating Metrics dataframe']\n", "['2020-07-29 09', '48', '20,919', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '48', '21,190', 'INFO', 'SubProcess plot_model() called ==================================']\n", "['2020-07-29 09', '48', '21,190', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '48', '21,192', 'INFO', \"plot_model(model=KModes(cat_dissim=, init='Cao',\"]\n", "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n", "['verbose=0), plot=cluster, feature=None, label=False, save=True, system=False)']\n", "['2020-07-29 09', '48', '21,192', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '48', '21,192', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '48', '21,221', 'INFO', 'plot type', 'cluster']\n", "['2020-07-29 09', '48', '21,221', 'INFO', 'SubProcess assign_model() called ==================================']\n", "['2020-07-29 09', '48', '21,221', 'INFO', 'Initializing assign_model()']\n", "['2020-07-29 09', '48', '21,223', 'INFO', \"assign_model(model=KModes(cat_dissim=, init='Cao',\"]\n", "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n", "['verbose=0), transformation=True, verbose=False)']\n", "['2020-07-29 09', '48', '21,223', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '48', '21,223', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '48', '21,224', 'INFO', 'Copying data']\n", "['2020-07-29 09', '48', '21,224', 'INFO', 'Transformation param set to True. Assigned clusters are attached on transformed dataset.']\n", "['2020-07-29 09', '48', '21,225', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '48', '21,271', 'INFO', 'Determining Trained Model']\n", "['2020-07-29 09', '48', '21,273', 'INFO', 'Trained Model', 'K-Modes Clustering']\n", "['2020-07-29 09', '48', '21,274', 'INFO', '(224, 21)']\n", "['2020-07-29 09', '48', '21,274', 'INFO', 'assign_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '21,275', 'INFO', 'SubProcess assign_model() end ==================================']\n", "['2020-07-29 09', '48', '21,292', 'INFO', 'Fitting PCA()']\n", "['2020-07-29 09', '48', '21,315', 'INFO', 'Sorting dataframe']\n", "['2020-07-29 09', '48', '21,323', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '48', '21,784', 'INFO', \"Saving 'Cluster.html' in current active directory\"]\n", "['2020-07-29 09', '48', '21,784', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '48', '21,785', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '21,905', 'INFO', 'Removing stopwords']\n", "['2020-07-29 09', '48', '23,356', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '48', '23,357', 'INFO', \"plot_model(model=KModes(cat_dissim=, init='Cao',\"]\n", "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n", "['verbose=0), plot=distribution, feature=None, label=False, save=True, system=False)']\n", "['2020-07-29 09', '48', '23,357', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '48', '23,357', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '48', '23,386', 'INFO', 'plot type', 'distribution']\n", "['2020-07-29 09', '48', '23,387', 'INFO', 'SubProcess assign_model() called ==================================']\n", "['2020-07-29 09', '48', '23,387', 'INFO', 'Initializing assign_model()']\n", "['2020-07-29 09', '48', '23,388', 'INFO', \"assign_model(model=KModes(cat_dissim=, init='Cao',\"]\n", "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n", "['verbose=0), transformation=False, verbose=False)']\n", "['2020-07-29 09', '48', '23,388', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '48', '23,388', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '48', '23,388', 'INFO', 'Copying data']\n", "['2020-07-29 09', '48', '23,391', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '48', '23,427', 'INFO', 'Determining Trained Model']\n", "['2020-07-29 09', '48', '23,428', 'INFO', 'Trained Model', 'K-Modes Clustering']\n", "['2020-07-29 09', '48', '23,429', 'INFO', '(224, 22)']\n", "['2020-07-29 09', '48', '23,429', 'INFO', 'assign_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '23,429', 'INFO', 'SubProcess assign_model() end ==================================']\n", "['2020-07-29 09', '48', '23,430', 'INFO', 'Sorting dataframe']\n", "['2020-07-29 09', '48', '23,439', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '48', '24,069', 'INFO', \"Saving 'Distribution.html' in current active directory\"]\n", "['2020-07-29 09', '48', '24,069', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '48', '24,070', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '25,580', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '48', '25,582', 'INFO', \"plot_model(model=KModes(cat_dissim=, init='Cao',\"]\n", "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n", "['verbose=0), plot=elbow, feature=None, label=False, save=True, system=False)']\n", "['2020-07-29 09', '48', '25,582', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '48', '25,582', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '48', '25,604', 'INFO', 'plot type', 'elbow']\n", "['2020-07-29 09', '48', '25,658', 'INFO', 'Fitting KElbowVisualizer()']\n", "['2020-07-29 09', '48', '32,253', 'INFO', 'Extracting Bigrams']\n", "['2020-07-29 09', '48', '41,062', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '48', '41,646', 'INFO', \"Saving 'Elbow.png' in current active directory\"]\n", "['2020-07-29 09', '48', '41,646', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '48', '41,646', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '41,692', 'INFO', 'SubProcess plot_model() end ==================================']\n", "['2020-07-29 09', '48', '41,692', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '48', '41,693', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '48', '41,694', 'INFO', \"save_model(model=KModes(cat_dissim=, init='Cao',\"]\n", "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n", "['verbose=0), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '48', '41,694', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '48', '42,305', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '48', '42,330', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True,']\n", "[\"features_todrop=['Country Name'],\"]\n", "[\"ml_usecase='regression',\"]\n", "['numerical_features=[],']\n", "[\"target='dummy_target',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_...']\n", "[\"target='dummy_target')),\"]\n", "[\"('feature_time',\"]\n", "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n", "[\"('group', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n", "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('dummy', Dummify(target='dummy_target')),\"]\n", "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n", "[\"('pca', Empty())],\"]\n", "[\"verbose=False), KModes(cat_dissim=, init='Cao',\"]\n", "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n", "['verbose=0)]']\n", "['2020-07-29 09', '48', '42,330', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '42,330', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '48', '42,451', 'INFO', \"KModes(cat_dissim=, init='Cao',\"]\n", "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n", "['verbose=0)']\n", "['2020-07-29 09', '48', '42,451', 'INFO', 'create_models() succesfully completed......................................']\n", "['2020-07-29 09', '48', '42,468', 'INFO', 'Initializing assign_model()']\n", "['2020-07-29 09', '48', '42,469', 'INFO', \"assign_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), transformation=False, verbose=True)']\n", "['2020-07-29 09', '48', '42,470', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '48', '42,470', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '48', '42,470', 'INFO', 'Copying data']\n", "['2020-07-29 09', '48', '42,472', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '48', '42,563', 'INFO', 'Determining Trained Model']\n", "['2020-07-29 09', '48', '42,573', 'INFO', 'Trained Model', 'K-Means Clustering']\n", "['2020-07-29 09', '48', '42,576', 'INFO', '(224, 22)']\n", "['2020-07-29 09', '48', '42,577', 'INFO', 'assign_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '42,694', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '48', '42,695', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), plot=cluster, feature=None, label=False, save=False, system=True)']\n", "['2020-07-29 09', '48', '42,696', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '48', '42,696', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '48', '42,737', 'INFO', 'plot type', 'cluster']\n", "['2020-07-29 09', '48', '42,737', 'INFO', 'SubProcess assign_model() called ==================================']\n", "['2020-07-29 09', '48', '42,738', 'INFO', 'Initializing assign_model()']\n", "['2020-07-29 09', '48', '42,739', 'INFO', \"assign_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), transformation=True, verbose=False)']\n", "['2020-07-29 09', '48', '42,740', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '48', '42,740', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '48', '42,740', 'INFO', 'Copying data']\n", "['2020-07-29 09', '48', '42,741', 'INFO', 'Transformation param set to True. Assigned clusters are attached on transformed dataset.']\n", "['2020-07-29 09', '48', '42,741', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '48', '42,782', 'INFO', 'Determining Trained Model']\n", "['2020-07-29 09', '48', '42,788', 'INFO', 'Trained Model', 'K-Means Clustering']\n", "['2020-07-29 09', '48', '42,789', 'INFO', '(224, 21)']\n", "['2020-07-29 09', '48', '42,789', 'INFO', 'assign_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '42,790', 'INFO', 'SubProcess assign_model() end ==================================']\n", "['2020-07-29 09', '48', '42,805', 'INFO', 'Fitting PCA()']\n", "['2020-07-29 09', '48', '42,824', 'INFO', 'Sorting dataframe']\n", "['2020-07-29 09', '48', '42,833', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '48', '43,140', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '48', '43,140', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '43,154', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '48', '43,156', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), plot=cluster, feature=Country Name, label=True, save=False, system=True)']\n", "['2020-07-29 09', '48', '43,156', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '48', '43,157', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '48', '43,186', 'INFO', 'plot type', 'cluster']\n", "['2020-07-29 09', '48', '43,188', 'INFO', 'SubProcess assign_model() called ==================================']\n", "['2020-07-29 09', '48', '43,188', 'INFO', 'Initializing assign_model()']\n", "['2020-07-29 09', '48', '43,192', 'INFO', \"assign_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), transformation=True, verbose=False)']\n", "['2020-07-29 09', '48', '43,192', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '48', '43,193', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '48', '43,193', 'INFO', 'Copying data']\n", "['2020-07-29 09', '48', '43,194', 'INFO', 'Transformation param set to True. Assigned clusters are attached on transformed dataset.']\n", "['2020-07-29 09', '48', '43,194', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '48', '43,230', 'INFO', 'Determining Trained Model']\n", "['2020-07-29 09', '48', '43,232', 'INFO', 'Trained Model', 'K-Means Clustering']\n", "['2020-07-29 09', '48', '43,233', 'INFO', '(224, 21)']\n", "['2020-07-29 09', '48', '43,233', 'INFO', 'assign_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '43,233', 'INFO', 'SubProcess assign_model() end ==================================']\n", "['2020-07-29 09', '48', '43,246', 'INFO', 'Fitting PCA()']\n", "['2020-07-29 09', '48', '43,268', 'INFO', 'Sorting dataframe']\n", "['2020-07-29 09', '48', '43,274', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '48', '43,585', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '48', '43,585', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '43,599', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '48', '43,601', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), plot=tsne, feature=None, label=False, save=False, system=True)']\n", "['2020-07-29 09', '48', '43,602', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '48', '43,602', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '48', '43,658', 'INFO', 'plot type', 'tsne']\n", "['2020-07-29 09', '48', '43,659', 'INFO', 'SubProcess assign_model() called ==================================']\n", "['2020-07-29 09', '48', '43,659', 'INFO', 'Initializing assign_model()']\n", "['2020-07-29 09', '48', '43,661', 'INFO', \"assign_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), transformation=True, verbose=False)']\n", "['2020-07-29 09', '48', '43,662', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '48', '43,662', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '48', '43,663', 'INFO', 'Copying data']\n", "['2020-07-29 09', '48', '43,664', 'INFO', 'Transformation param set to True. Assigned clusters are attached on transformed dataset.']\n", "['2020-07-29 09', '48', '43,664', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '48', '43,711', 'INFO', 'Determining Trained Model']\n", "['2020-07-29 09', '48', '43,713', 'INFO', 'Trained Model', 'K-Means Clustering']\n", "['2020-07-29 09', '48', '43,714', 'INFO', '(224, 21)']\n", "['2020-07-29 09', '48', '43,714', 'INFO', 'assign_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '43,714', 'INFO', 'SubProcess assign_model() end ==================================']\n", "['2020-07-29 09', '48', '43,718', 'INFO', 'Fitting TSNE()']\n", "['2020-07-29 09', '48', '49,461', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '48', '59,261', 'INFO', 'Sorting dataframe']\n", "['2020-07-29 09', '48', '59,268', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '48', '59,625', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '48', '59,626', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '48', '59,656', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '48', '59,658', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), plot=elbow, feature=None, label=False, save=False, system=True)']\n", "['2020-07-29 09', '48', '59,658', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '48', '59,658', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '48', '59,688', 'INFO', 'plot type', 'elbow']\n", "['2020-07-29 09', '48', '59,688', 'INFO', 'Fitting KElbowVisualizer()']\n", "['2020-07-29 09', '49', '01,501', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '49', '01,501', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '49', '01,532', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '49', '01,533', 'INFO', \"plot_model(model=IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n", "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n", "['random_state=123, verbose=0), plot=umap, feature=None, save=False, system=True)']\n", "['2020-07-29 09', '49', '01,533', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '49', '01,533', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '49', '01,555', 'INFO', 'plot type', 'umap']\n", "['2020-07-29 09', '49', '01,556', 'INFO', 'SubProcess assign_model() called ==================================']\n", "['2020-07-29 09', '49', '01,556', 'INFO', 'Initializing assign_model()']\n", "['2020-07-29 09', '49', '01,556', 'INFO', \"assign_model(model=IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n", "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n", "['random_state=123, verbose=0), transformation=True, score=False, verbose=False)']\n", "['2020-07-29 09', '49', '01,557', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '49', '01,557', 'INFO', 'Copying data']\n", "['2020-07-29 09', '49', '01,558', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '49', '01,588', 'INFO', 'Determining Trained Model']\n", "['2020-07-29 09', '49', '01,589', 'INFO', 'Trained Model', 'Assigned Isolation Forest']\n", "['2020-07-29 09', '49', '01,590', 'INFO', '(1000, 11)']\n", "['2020-07-29 09', '49', '01,590', 'INFO', 'assign_model() succesfully completed......................................']\n", "['2020-07-29 09', '49', '01,590', 'INFO', 'SubProcess assign_model() end ==================================']\n", "['2020-07-29 09', '49', '01,598', 'INFO', 'Getting dummies to cast categorical variables']\n", "['2020-07-29 09', '49', '02,407', 'INFO', 'Extracting Trigrams']\n", "['2020-07-29 09', '49', '05,305', 'INFO', 'Fitting UMAP()']\n", "['2020-07-29 09', '49', '14,048', 'INFO', 'PyCaret Regression Module']\n", "['2020-07-29 09', '49', '14,048', 'INFO', 'version pycaret-nightly-0.39']\n", "['2020-07-29 09', '49', '14,048', 'INFO', 'Initializing setup()']\n", "['2020-07-29 09', '49', '14,049', 'INFO', 'USI', 'd354']\n", "['2020-07-29 09', '49', '14,049', 'INFO', 'setup(data=(1338, 7), target=charges, train_size=0.7, sampling=True, sample_estimator=None, categorical_features=None, categorical_imputation=constant, ordinal_features=None,']\n", "['high_cardinality_features=None, high_cardinality_method=frequency, numeric_features=None, numeric_imputation=mean, date_features=None, ignore_features=None, normalize=False,']\n", "['normalize_method=zscore, transformation=False, transformation_method=yeo-johnson, handle_unknown_categorical=True, unknown_categorical_method=least_frequent, pca=False, pca_method=linear,']\n", "['pca_components=None, ignore_low_variance=False, combine_rare_levels=False, rare_level_threshold=0.1, bin_numeric_features=None, remove_outliers=False, outliers_threshold=0.05,']\n", "['remove_multicollinearity=False, multicollinearity_threshold=0.9, remove_perfect_collinearity=False, create_clusters=False, cluster_iter=20,']\n", "['polynomial_features=False, polynomial_degree=2, trigonometry_features=False, polynomial_threshold=0.1, group_features=None,']\n", "['group_names=None, feature_selection=False, feature_selection_threshold=0.8, feature_interaction=False, feature_ratio=False, interaction_threshold=0.01, transform_target=False,']\n", "['transform_target_method=box-cox, data_split_shuffle=True, folds_shuffle=False, n_jobs=-1, html=True, session_id=123, log_experiment=True,']\n", "['experiment_name=insurance1, log_plots=False, log_profile=False, log_data=False, silent=False, verbose=True, profile=False)']\n", "['2020-07-29 09', '49', '14,049', 'INFO', 'Checking environment']\n", "['2020-07-29 09', '49', '14,049', 'INFO', 'python_version', '3.6.10']\n", "['2020-07-29 09', '49', '14,050', 'INFO', 'python_build', \"('default', 'May 7 2020 19\", '46', \"08')\"]\n", "['2020-07-29 09', '49', '14,050', 'INFO', 'machine', 'AMD64']\n", "['2020-07-29 09', '49', '14,050', 'INFO', 'platform', 'Windows-10-10.0.18362-SP0']\n", "['2020-07-29 09', '49', '14,097', 'INFO', 'Memory', 'svmem(total=17032478720, available=5629382656, percent=66.9, used=11403096064, free=5629382656)']\n", "['2020-07-29 09', '49', '14,097', 'INFO', 'Physical Core', '4']\n", "['2020-07-29 09', '49', '14,097', 'INFO', 'Logical Core', '8']\n", "['2020-07-29 09', '49', '14,097', 'INFO', 'Checking libraries']\n", "['2020-07-29 09', '49', '14,097', 'INFO', 'pd==1.0.4']\n", "['2020-07-29 09', '49', '14,098', 'INFO', 'numpy==1.18.5']\n", "['2020-07-29 09', '49', '14,935', 'INFO', 'sklearn==0.23.1']\n", "['2020-07-29 09', '49', '15,080', 'INFO', 'xgboost==1.1.1']\n", "['2020-07-29 09', '49', '15,220', 'INFO', 'lightgbm==2.3.1']\n", "['2020-07-29 09', '49', '15,339', 'INFO', 'catboost==0.23.2']\n", "['2020-07-29 09', '49', '16,374', 'INFO', 'mlflow==1.8.0']\n", "['2020-07-29 09', '49', '16,375', 'INFO', 'Checking Exceptions']\n", "['2020-07-29 09', '49', '16,375', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '49', '16,375', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '49', '16,408', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '49', '19,757', 'INFO', 'Copying data for preprocessing']\n", "['2020-07-29 09', '49', '19,758', 'INFO', 'Declaring global variables']\n", "['2020-07-29 09', '49', '19,777', 'INFO', 'Declaring preprocessing parameters']\n", "['2020-07-29 09', '49', '19,777', 'INFO', 'Importing preprocessing module']\n", "['2020-07-29 09', '49', '21,005', 'INFO', 'Creating preprocessing pipeline']\n", "['2020-07-29 09', '49', '22,813', 'INFO', 'Preprocessing pipeline created successfully']\n", "['2020-07-29 09', '49', '22,813', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '22,814', 'INFO', 'Creating grid variables']\n", "['2020-07-29 09', '49', '22,818', 'INFO', 'Creating global containers']\n", "['2020-07-29 09', '49', '22,997', 'INFO', 'Logging experiment in MLFlow']\n", "['2020-07-29 09', '49', '23,497', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '49', '23,497', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '49', '23,514', 'INFO', 'save_model(model=Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), model_name=Transformation Pipeline, verbose=False)']\n", "['2020-07-29 09', '49', '23,514', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '49', '23,528', 'INFO', 'Transformation Pipeline.pkl saved in current working directory']\n", "['2020-07-29 09', '49', '23,556', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), None]']\n", "['2020-07-29 09', '49', '23,557', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '49', '23,557', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '49', '24,045', 'INFO', 'create_model_container', '0']\n", "['2020-07-29 09', '49', '24,045', 'INFO', 'master_model_container', '0']\n", "['2020-07-29 09', '49', '24,045', 'INFO', 'display_container', '0']\n", "['2020-07-29 09', '49', '24,045', 'INFO', 'setup() succesfully completed......................................']\n", "['2020-07-29 09', '49', '25,845', 'INFO', 'Initializing compare_models()']\n", "['2020-07-29 09', '49', '25,845', 'INFO', 'compare_models(blacklist=None, whitelist=None, fold=5, round=4, sort=R2, n_select=1, turbo=True, verbose=True)']\n", "['2020-07-29 09', '49', '25,845', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '49', '25,846', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '49', '25,846', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '49', '25,953', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '49', '25,957', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '49', '25,997', 'INFO', 'Importing untrained models']\n", "['2020-07-29 09', '49', '26,001', 'INFO', 'Import successful']\n", "['2020-07-29 09', '49', '26,023', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '49', '26,023', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '49', '26,023', 'INFO', 'Initializing Linear Regression']\n", "['2020-07-29 09', '49', '26,050', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '49', '26,072', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '26,081', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '26,097', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '26,098', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '26,243', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '49', '26,280', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '26,291', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '26,298', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '26,298', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '26,372', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '49', '26,407', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '26,424', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '26,430', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '26,430', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '26,489', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '49', '26,529', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '26,542', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '26,547', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '26,547', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '26,627', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '49', '26,685', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '26,695', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '26,701', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '26,702', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '26,784', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '49', '26,787', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '49', '26,947', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '49', '27,492', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '49', '27,492', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '49', '27,496', 'INFO', 'save_model(model=LinearRegression(copy_X=True, fit_intercept=True, n_jobs=-1, normalize=False), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '49', '27,496', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '49', '27,584', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '49', '27,637', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), LinearRegression(copy_X=True, fit_intercept=True, n_jobs=-1, normalize=False), None]']\n", "['2020-07-29 09', '49', '27,638', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '49', '27,638', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '49', '27,641', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '49', '27,775', 'INFO', 'Initializing Lasso Regression']\n", "['2020-07-29 09', '49', '27,790', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '49', '27,809', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '27,819', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '27,824', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '27,824', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '27,842', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '49', '27,862', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '27,871', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '27,876', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '27,876', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '27,899', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '49', '27,909', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '49', '27,910', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '49', '27,921', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '27,931', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '27,939', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '27,939', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '27,968', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '49', '27,993', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '28,006', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '28,013', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '28,014', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '28,044', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '49', '28,072', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '28,083', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '28,091', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '28,091', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '28,118', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '49', '28,119', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '49', '28,162', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '49', '28,366', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '49', '28,367', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '49', '28,367', 'INFO', 'save_model(model=Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,']\n", "['normalize=False, positive=False, precompute=False, random_state=123,']\n", "[\"selection='cyclic', tol=0.0001, warm_start=False), model_name=Trained Model, verbose=False)\"]\n", "['2020-07-29 09', '49', '28,368', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '49', '28,392', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '49', '28,410', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,']\n", "['normalize=False, positive=False, precompute=False, random_state=123,']\n", "[\"selection='cyclic', tol=0.0001, warm_start=False), None]\"]\n", "['2020-07-29 09', '49', '28,410', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '49', '28,410', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '49', '28,583', 'INFO', 'Initializing Ridge Regression']\n", "['2020-07-29 09', '49', '28,598', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '49', '28,599', 'INFO', \"save_model(model=IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n", "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n", "['random_state=123, verbose=0), model_name=iforest, verbose=True)']\n", "['2020-07-29 09', '49', '28,600', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '49', '28,607', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '49', '28,630', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '28,638', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '28,645', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '28,645', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '28,663', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '49', '28,682', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '28,692', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '28,697', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '28,697', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '28,720', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '49', '28,744', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '28,752', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '28,758', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '28,759', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '28,766', 'INFO', 'iforest.pkl saved in current working directory']\n", "['2020-07-29 09', '49', '28,782', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '49', '28,787', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "['numerical_features=[],']\n", "[\"target='dummy_target',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_L...']\n", "[\"target='dummy_target')),\"]\n", "[\"('feature_time',\"]\n", "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n", "[\"('group', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n", "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('dummy', Dummify(target='dummy_target')),\"]\n", "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n", "[\"('pca', Empty())],\"]\n", "[\"verbose=False), IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n", "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n", "['random_state=123, verbose=0)]']\n", "['2020-07-29 09', '49', '28,788', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '49', '28,808', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '28,818', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '28,826', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '28,826', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '28,853', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '49', '28,878', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '28,886', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '28,893', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '28,893', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '28,917', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '49', '28,918', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '49', '28,963', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '49', '29,109', 'INFO', 'Initializing deploy_model()']\n", "['2020-07-29 09', '49', '29,111', 'INFO', \"deploy_model(model=IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n", "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n", "[\"random_state=123, verbose=0), model_name=iforest-aws, authentication={'bucket'\", \"'pycaret-test'}, platform=aws)\"]\n", "['2020-07-29 09', '49', '29,111', 'INFO', 'Platform', 'AWS S3']\n", "['2020-07-29 09', '49', '29,245', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '49', '29,245', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '49', '29,246', 'INFO', 'save_model(model=Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,']\n", "[\"normalize=False, random_state=123, solver='auto', tol=0.001), model_name=Trained Model, verbose=False)\"]\n", "['2020-07-29 09', '49', '29,247', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '49', '29,272', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '49', '29,288', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,']\n", "[\"normalize=False, random_state=123, solver='auto', tol=0.001), None]\"]\n", "['2020-07-29 09', '49', '29,289', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '49', '29,289', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '49', '29,429', 'INFO', 'Saving model in current working directory']\n", "['2020-07-29 09', '49', '29,430', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '49', '29,430', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '49', '29,430', 'INFO', \"save_model(model=IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n", "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n", "['random_state=123, verbose=0), model_name=iforest-aws, verbose=False)']\n", "['2020-07-29 09', '49', '29,431', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '49', '29,481', 'INFO', 'Initializing Elastic Net']\n", "['2020-07-29 09', '49', '29,503', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '49', '29,528', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '29,540', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '29,545', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '29,546', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '29,573', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '49', '29,595', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '29,606', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '29,614', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '29,615', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '29,654', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '49', '29,677', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '29,689', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '29,697', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '29,697', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '29,698', 'INFO', 'iforest-aws.pkl saved in current working directory']\n", "['2020-07-29 09', '49', '29,722', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "['numerical_features=[],']\n", "[\"target='dummy_target',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_L...']\n", "[\"target='dummy_target')),\"]\n", "[\"('feature_time',\"]\n", "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n", "[\"('group', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n", "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('dummy', Dummify(target='dummy_target')),\"]\n", "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n", "[\"('pca', Empty())],\"]\n", "[\"verbose=False), IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n", "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n", "['random_state=123, verbose=0)]']\n", "['2020-07-29 09', '49', '29,722', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '49', '29,723', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '49', '29,723', 'INFO', 'Initializing S3 client']\n", "['2020-07-29 09', '49', '29,729', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '49', '29,755', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '29,767', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '29,776', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '29,776', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '29,804', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '49', '29,831', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '29,843', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '29,850', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '29,850', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '29,888', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '49', '29,891', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '49', '29,955', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '49', '30,209', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '49', '30,210', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '49', '30,211', 'INFO', 'save_model(model=ElasticNet(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5,']\n", "['max_iter=1000, normalize=False, positive=False, precompute=False,']\n", "[\"random_state=123, selection='cyclic', tol=0.0001, warm_start=False), model_name=Trained Model, verbose=False)\"]\n", "['2020-07-29 09', '49', '30,211', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '49', '30,234', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '49', '30,256', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), ElasticNet(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5,']\n", "['max_iter=1000, normalize=False, positive=False, precompute=False,']\n", "[\"random_state=123, selection='cyclic', tol=0.0001, warm_start=False), None]\"]\n", "['2020-07-29 09', '49', '30,256', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '49', '30,257', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '49', '30,444', 'INFO', 'Initializing Least Angle Regression']\n", "['2020-07-29 09', '49', '30,464', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '49', '30,487', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '30,514', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '30,521', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '30,521', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '30,546', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '49', '30,571', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '30,595', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '30,600', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '30,600', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '30,630', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '49', '30,652', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '30,670', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '30,677', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '30,678', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '30,704', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '49', '30,731', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '30,752', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '30,759', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '30,759', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '30,783', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '49', '30,813', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '30,830', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '30,838', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '30,839', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '30,858', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '49', '30,859', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '49', '30,898', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '49', '31,084', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '49', '31,084', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '49', '31,085', 'INFO', 'save_model(model=Lars(copy_X=True, eps=2.220446049250313e-16, fit_intercept=True, fit_path=True,']\n", "[\"jitter=None, n_nonzero_coefs=500, normalize=True, precompute='auto',\"]\n", "['random_state=None, verbose=False), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '49', '31,085', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '49', '31,100', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '49', '31,113', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), Lars(copy_X=True, eps=2.220446049250313e-16, fit_intercept=True, fit_path=True,']\n", "[\"jitter=None, n_nonzero_coefs=500, normalize=True, precompute='auto',\"]\n", "['random_state=None, verbose=False), None]']\n", "['2020-07-29 09', '49', '31,114', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '49', '31,114', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '49', '31,259', 'INFO', 'Initializing Lasso Least Angle Regression']\n", "['2020-07-29 09', '49', '31,273', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '49', '31,290', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '31,303', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '31,307', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '31,307', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '31,322', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '49', '31,337', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '31,349', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '31,353', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '31,354', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '31,371', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '49', '31,385', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '31,397', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '31,401', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '31,401', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '31,418', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '49', '31,435', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '31,447', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '31,454', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '31,454', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '31,476', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '49', '31,493', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '31,505', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '31,510', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '31,510', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '31,533', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '49', '31,535', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '49', '31,571', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '49', '31,659', 'INFO', \"IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n", "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n", "['random_state=123, verbose=0)']\n", "['2020-07-29 09', '49', '31,660', 'INFO', 'deploy_model() succesfully completed......................................']\n", "['2020-07-29 09', '49', '31,676', 'INFO', 'Initializing get_config()']\n", "['2020-07-29 09', '49', '31,677', 'INFO', 'get_config(variable=X)']\n", "['2020-07-29 09', '49', '31,677', 'INFO', 'Global variable', 'X returned']\n", "['2020-07-29 09', '49', '31,678', 'INFO', 'get_config() succesfully completed......................................']\n", "['2020-07-29 09', '49', '31,728', 'INFO', 'Initializing get_config()']\n", "['2020-07-29 09', '49', '31,729', 'INFO', 'get_config(variable=seed)']\n", "['2020-07-29 09', '49', '31,729', 'INFO', 'Global variable', 'seed returned']\n", "['2020-07-29 09', '49', '31,729', 'INFO', 'get_config() succesfully completed......................................']\n", "['2020-07-29 09', '49', '31,747', 'INFO', 'Initializing set_config()']\n", "['2020-07-29 09', '49', '31,748', 'INFO', 'set_config(variable=seed, value=999)']\n", "['2020-07-29 09', '49', '31,748', 'INFO', 'Global variable', 'seed updated']\n", "['2020-07-29 09', '49', '31,748', 'INFO', 'set_config() succesfully completed......................................']\n", "['2020-07-29 09', '49', '31,762', 'INFO', 'Initializing get_config()']\n", "['2020-07-29 09', '49', '31,763', 'INFO', 'get_config(variable=seed)']\n", "['2020-07-29 09', '49', '31,764', 'INFO', 'Global variable', 'seed returned']\n", "['2020-07-29 09', '49', '31,764', 'INFO', 'get_config() succesfully completed......................................']\n", "['2020-07-29 09', '49', '31,768', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '49', '31,768', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '49', '31,770', 'INFO', 'save_model(model=LassoLars(alpha=1.0, copy_X=True, eps=2.220446049250313e-16, fit_intercept=True,']\n", "['fit_path=True, jitter=None, max_iter=500, normalize=True,']\n", "[\"positive=False, precompute='auto', random_state=None, verbose=False), model_name=Trained Model, verbose=False)\"]\n", "['2020-07-29 09', '49', '31,770', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '49', '31,804', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '49', '31,838', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), LassoLars(alpha=1.0, copy_X=True, eps=2.220446049250313e-16, fit_intercept=True,']\n", "['fit_path=True, jitter=None, max_iter=500, normalize=True,']\n", "[\"positive=False, precompute='auto', random_state=None, verbose=False), None]\"]\n", "['2020-07-29 09', '49', '31,838', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '49', '31,839', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '49', '32,048', 'INFO', 'Initializing Orthogonal Matching Pursuit']\n", "['2020-07-29 09', '49', '32,071', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '49', '32,100', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '32,114', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '32,120', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '32,121', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '32,149', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '49', '32,171', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '32,182', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '32,189', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '32,190', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '32,214', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '49', '32,239', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '32,251', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '32,259', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '32,259', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '32,293', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '49', '32,320', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '32,332', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '32,339', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '32,340', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '32,364', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '49', '32,391', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '32,401', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '32,410', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '32,410', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '32,438', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '49', '32,439', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '49', '32,496', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '49', '32,721', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '49', '32,721', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '49', '32,722', 'INFO', 'save_model(model=OrthogonalMatchingPursuit(fit_intercept=True, n_nonzero_coefs=None,']\n", "[\"normalize=True, precompute='auto', tol=None), model_name=Trained Model, verbose=False)\"]\n", "['2020-07-29 09', '49', '32,723', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '49', '32,747', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '49', '32,777', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), OrthogonalMatchingPursuit(fit_intercept=True, n_nonzero_coefs=None,']\n", "[\"normalize=True, precompute='auto', tol=None), None]\"]\n", "['2020-07-29 09', '49', '32,778', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '49', '32,778', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '49', '33,009', 'INFO', 'Initializing Bayesian Ridge']\n", "['2020-07-29 09', '49', '33,039', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '49', '33,069', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '33,090', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '33,097', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '33,097', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '33,123', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '49', '33,148', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '33,166', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '33,174', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '33,174', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '33,201', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '49', '33,231', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '33,253', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '33,263', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '33,264', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '33,299', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '49', '33,329', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '33,346', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '33,355', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '33,356', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '33,388', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '49', '33,414', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '33,433', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '33,441', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '33,441', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '33,467', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '49', '33,469', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '49', '33,543', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '49', '33,846', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '49', '33,846', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '49', '33,848', 'INFO', 'save_model(model=BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, alpha_init=None,']\n", "['compute_score=False, copy_X=True, fit_intercept=True,']\n", "['lambda_1=1e-06, lambda_2=1e-06, lambda_init=None, n_iter=300,']\n", "['normalize=False, tol=0.001, verbose=False), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '49', '33,848', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '49', '33,884', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '49', '33,914', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, alpha_init=None,']\n", "['compute_score=False, copy_X=True, fit_intercept=True,']\n", "['lambda_1=1e-06, lambda_2=1e-06, lambda_init=None, n_iter=300,']\n", "['normalize=False, tol=0.001, verbose=False), None]']\n", "['2020-07-29 09', '49', '33,914', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '49', '33,914', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '49', '34,122', 'INFO', 'Initializing Passive Aggressive Regressor']\n", "['2020-07-29 09', '49', '34,147', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '49', '34,176', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '34,202', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '34,208', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '34,208', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '34,230', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '49', '34,248', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '34,269', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '34,274', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '34,275', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '34,299', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '49', '34,327', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '34,354', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '34,362', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '34,363', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '34,396', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '49', '34,425', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '34,454', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '34,458', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '34,459', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '34,488', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '49', '34,517', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '34,543', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '34,551', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '34,552', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '34,582', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '49', '34,584', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '49', '34,638', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '49', '34,855', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '49', '34,855', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '49', '34,857', 'INFO', 'save_model(model=PassiveAggressiveRegressor(C=1.0, average=False, early_stopping=False,']\n", "['epsilon=0.1, fit_intercept=True,']\n", "[\"loss='epsilon_insensitive', max_iter=1000,\"]\n", "['n_iter_no_change=5, random_state=123, shuffle=True,']\n", "['tol=0.001, validation_fraction=0.1, verbose=0,']\n", "['warm_start=False), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '49', '34,857', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '49', '34,884', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '49', '34,912', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), PassiveAggressiveRegressor(C=1.0, average=False, early_stopping=False,']\n", "['epsilon=0.1, fit_intercept=True,']\n", "[\"loss='epsilon_insensitive', max_iter=1000,\"]\n", "['n_iter_no_change=5, random_state=123, shuffle=True,']\n", "['tol=0.001, validation_fraction=0.1, verbose=0,']\n", "['warm_start=False), None]']\n", "['2020-07-29 09', '49', '34,912', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '49', '34,912', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '49', '35,070', 'INFO', 'Initializing Random Sample Consensus']\n", "['2020-07-29 09', '49', '35,086', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '49', '35,110', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '35,317', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '35,323', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '35,324', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '35,341', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '49', '35,357', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '35,576', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '35,580', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '35,581', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '35,600', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '49', '35,617', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '35,900', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '35,907', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '35,908', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '35,938', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '49', '35,965', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '36,277', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '36,283', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '36,283', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '36,307', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '49', '36,328', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '36,615', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '36,620', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '36,620', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '36,638', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '49', '36,639', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '49', '36,682', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '49', '36,866', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '49', '36,866', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '49', '36,867', 'INFO', 'save_model(model=RANSACRegressor(base_estimator=None, is_data_valid=None, is_model_valid=None,']\n", "[\"loss='absolute_loss', max_skips=inf, max_trials=100,\"]\n", "['min_samples=0.5, random_state=123, residual_threshold=None,']\n", "['stop_n_inliers=inf, stop_probability=0.99, stop_score=inf), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '49', '36,867', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '49', '36,881', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '49', '36,893', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), RANSACRegressor(base_estimator=None, is_data_valid=None, is_model_valid=None,']\n", "[\"loss='absolute_loss', max_skips=inf, max_trials=100,\"]\n", "['min_samples=0.5, random_state=123, residual_threshold=None,']\n", "['stop_n_inliers=inf, stop_probability=0.99, stop_score=inf), None]']\n", "['2020-07-29 09', '49', '36,893', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '49', '36,894', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '49', '37,043', 'INFO', 'Initializing TheilSen Regressor']\n", "['2020-07-29 09', '49', '37,062', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '49', '37,081', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '45,284', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '45,289', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '45,289', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '45,308', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '49', '45,327', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '47,087', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '47,092', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '47,093', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '47,110', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '49', '47,130', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '48,914', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '48,918', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '48,919', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '48,938', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '49', '48,960', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '50,969', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '50,974', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '50,974', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '50,997', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '49', '51,022', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '52,626', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '52,633', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '52,633', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '52,651', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '49', '52,651', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '49', '52,706', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '49', '53,189', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '49', '53,190', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '49', '53,191', 'INFO', 'save_model(model=TheilSenRegressor(copy_X=True, fit_intercept=True, max_iter=300,']\n", "['max_subpopulation=10000, n_jobs=-1, n_subsamples=None,']\n", "['random_state=123, tol=0.001, verbose=False), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '49', '53,191', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '49', '53,205', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '49', '53,219', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), TheilSenRegressor(copy_X=True, fit_intercept=True, max_iter=300,']\n", "['max_subpopulation=10000, n_jobs=-1, n_subsamples=None,']\n", "['random_state=123, tol=0.001, verbose=False), None]']\n", "['2020-07-29 09', '49', '53,220', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '49', '53,220', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '49', '53,380', 'INFO', 'Initializing Huber Regressor']\n", "['2020-07-29 09', '49', '53,393', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '49', '53,418', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '53,583', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '53,587', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '53,587', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '53,608', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '49', '53,627', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '53,750', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '53,755', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '53,755', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '53,775', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '49', '53,797', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '53,937', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '53,943', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '53,944', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '53,964', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '49', '53,987', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '54,134', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '54,141', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '54,141', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '54,160', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '49', '54,180', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '54,320', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '54,325', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '54,326', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '54,347', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '49', '54,348', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '49', '54,389', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '49', '54,547', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '49', '54,547', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '49', '54,547', 'INFO', 'save_model(model=HuberRegressor(alpha=0.0001, epsilon=1.35, fit_intercept=True, max_iter=100,']\n", "['tol=1e-05, warm_start=False), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '49', '54,548', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '49', '54,560', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '49', '54,572', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), HuberRegressor(alpha=0.0001, epsilon=1.35, fit_intercept=True, max_iter=100,']\n", "['tol=1e-05, warm_start=False), None]']\n", "['2020-07-29 09', '49', '54,573', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '49', '54,573', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '49', '54,742', 'INFO', 'Initializing Support Vector Machine']\n", "['2020-07-29 09', '49', '54,758', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '49', '54,775', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '54,906', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '54,927', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '54,928', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '54,956', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '49', '54,983', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '55,088', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '55,109', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '55,109', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '55,136', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '49', '55,173', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '55,286', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '55,308', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '55,309', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '55,353', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '49', '55,376', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '55,499', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '55,525', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '55,525', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '55,579', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '49', '55,643', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '55,789', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '55,814', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '55,815', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '55,874', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '49', '55,877', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '49', '55,972', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '49', '56,261', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '49', '56,261', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '49', '56,263', 'INFO', \"save_model(model=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\"]\n", "[\"kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False), model_name=Trained Model, verbose=False)\"]\n", "['2020-07-29 09', '49', '56,263', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '49', '56,290', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '49', '56,309', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', 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'49', '56,490', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '56,608', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '56,608', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '56,633', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '49', '56,648', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '56,658', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '56,771', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '56,772', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '56,792', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '49', '56,812', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '56,829', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '56,946', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '56,946', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '56,965', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '49', '56,984', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '56,995', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '57,106', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '57,107', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '57,121', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '49', '57,136', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '57,143', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '57,254', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '57,254', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '57,273', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '49', '57,273', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '49', '57,305', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '49', '57,442', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '49', '57,442', 'INFO', 'Initializing 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"['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\"]\n", "['metric_params=None, n_jobs=-1, n_neighbors=5, p=2,']\n", "[\"weights='uniform'), None]\"]\n", "['2020-07-29 09', '49', '57,469', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '49', '57,469', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '49', '57,561', 'INFO', 'Initializing Decision Tree']\n", "['2020-07-29 09', '49', '57,572', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '49', '57,586', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '57,595', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '57,599', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '57,599', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '57,611', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '49', '57,626', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '57,641', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '57,646', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '57,647', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '57,671', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '49', '57,697', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '57,714', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '57,720', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '57,721', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '57,747', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '49', '57,776', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '57,794', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '57,802', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '57,802', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '57,827', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '49', '57,844', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '57,856', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '57,861', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '57,861', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '57,880', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '49', '57,882', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '49', '57,920', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '49', '58,087', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '49', '58,088', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '49', '58,089', 'INFO', \"save_model(model=DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\"]\n", "['max_features=None, max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0, min_impurity_split=None,']\n", "['min_samples_leaf=1, min_samples_split=2,']\n", "[\"min_weight_fraction_leaf=0.0, presort='deprecated',\"]\n", "[\"random_state=123, splitter='best'), model_name=Trained Model, verbose=False)\"]\n", "['2020-07-29 09', '49', '58,090', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '49', '58,105', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '49', '58,125', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\"]\n", "['max_features=None, max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0, min_impurity_split=None,']\n", "['min_samples_leaf=1, min_samples_split=2,']\n", "[\"min_weight_fraction_leaf=0.0, presort='deprecated',\"]\n", "[\"random_state=123, splitter='best'), None]\"]\n", "['2020-07-29 09', '49', '58,126', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '49', '58,126', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '49', '58,253', 'INFO', 'Initializing Random Forest']\n", "['2020-07-29 09', '49', '58,267', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '49', '58,286', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '58,838', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '58,950', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '58,951', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '58,967', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '49', '58,988', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '49', '59,551', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '49', '59,666', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '49', '59,667', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '49', '59,693', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '49', '59,726', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '00,434', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '00,551', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '00,552', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '00,569', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '50', '00,585', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '01,120', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '01,231', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '01,232', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '01,249', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '50', '01,269', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '01,827', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '01,937', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '01,937', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '01,951', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '50', '01,952', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '50', '01,989', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '50', '02,150', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '50', '02,150', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '50', '02,151', 'INFO', \"save_model(model=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\"]\n", "[\"max_depth=None, max_features='auto', max_leaf_nodes=None,\"]\n", "['max_samples=None, min_impurity_decrease=0.0,']\n", "['min_impurity_split=None, min_samples_leaf=1,']\n", "['min_samples_split=2, min_weight_fraction_leaf=0.0,']\n", "['n_estimators=100, n_jobs=-1, oob_score=False,']\n", "['random_state=123, verbose=0, warm_start=False), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '50', '02,151', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '50', '02,248', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '50', '02,262', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", 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'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '50', '02,392', 'INFO', 'Initializing Extra Trees Regressor']\n", "['2020-07-29 09', '50', '02,406', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '50', '02,420', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '02,827', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '02,938', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '02,938', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '02,953', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '50', '02,969', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '03,270', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '03,380', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '03,380', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '03,395', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '50', '03,411', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '03,707', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '03,821', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '03,821', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '03,841', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '50', '03,855', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '04,177', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '04,286', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '04,286', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '04,302', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '50', '04,315', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '04,609', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '04,719', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '04,719', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '04,731', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '50', '04,732', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '50', '04,773', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '50', '04,942', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '50', '04,942', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '50', '04,943', 'INFO', \"save_model(model=ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\"]\n", "[\"max_depth=None, max_features='auto', max_leaf_nodes=None,\"]\n", "['max_samples=None, min_impurity_decrease=0.0,']\n", "['min_impurity_split=None, min_samples_leaf=1,']\n", "['min_samples_split=2, min_weight_fraction_leaf=0.0,']\n", "['n_estimators=100, n_jobs=-1, oob_score=False,']\n", "['random_state=123, verbose=0, warm_start=False), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '50', '04,943', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '50', '05,031', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '50', '05,043', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\"]\n", "[\"max_depth=None, max_features='auto', max_leaf_nodes=None,\"]\n", "['max_samples=None, min_impurity_decrease=0.0,']\n", "['min_impurity_split=None, min_samples_leaf=1,']\n", "['min_samples_split=2, min_weight_fraction_leaf=0.0,']\n", "['n_estimators=100, n_jobs=-1, oob_score=False,']\n", "['random_state=123, verbose=0, warm_start=False), None]']\n", "['2020-07-29 09', '50', '05,043', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '50', '05,043', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '50', '05,162', 'INFO', 'Initializing AdaBoost Regressor']\n", "['2020-07-29 09', '50', '05,174', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '50', '05,191', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '05,255', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '05,260', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '05,260', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '05,273', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '50', '05,288', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '05,331', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '05,337', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '05,337', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '05,349', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '50', '05,363', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '05,396', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '05,402', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '05,402', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '05,416', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '50', '05,434', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '05,466', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '05,471', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '05,472', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '05,486', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '50', '05,500', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '05,548', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '05,555', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '05,555', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '05,569', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '50', '05,570', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '50', '05,608', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '50', '05,786', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '50', '05,786', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '50', '05,786', 'INFO', \"save_model(model=AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear',\"]\n", "['n_estimators=50, random_state=123), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '50', '05,786', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '50', '05,802', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '50', '05,813', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear',\"]\n", "['n_estimators=50, random_state=123), None]']\n", "['2020-07-29 09', '50', '05,813', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '50', '05,814', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '50', '05,897', 'INFO', 'Initializing Gradient Boosting Regressor']\n", "['2020-07-29 09', '50', '05,908', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '50', '05,922', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '06,084', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '06,089', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '06,089', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '06,101', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '50', '06,113', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '06,270', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '06,273', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '06,273', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '06,285', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '50', '06,299', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '06,448', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '06,452', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '06,452', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '06,464', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '50', '06,479', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '06,630', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '06,634', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '06,634', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '06,645', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '50', '06,659', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '06,809', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '06,813', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '06,813', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '06,824', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '50', '06,825', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '50', '06,856', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '50', '07,002', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '50', '07,002', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '50', '07,003', 'INFO', \"save_model(model=GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',\"]\n", "[\"init=None, learning_rate=0.1, loss='ls', max_depth=3,\"]\n", "['max_features=None, max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0, min_impurity_split=None,']\n", "['min_samples_leaf=1, min_samples_split=2,']\n", "['min_weight_fraction_leaf=0.0, n_estimators=100,']\n", "[\"n_iter_no_change=None, presort='deprecated',\"]\n", "['random_state=123, subsample=1.0, tol=0.0001,']\n", "['validation_fraction=0.1, verbose=0, warm_start=False), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '50', '07,003', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '50', '07,018', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '50', '07,028', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',\"]\n", "[\"init=None, learning_rate=0.1, loss='ls', max_depth=3,\"]\n", "['max_features=None, max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0, min_impurity_split=None,']\n", "['min_samples_leaf=1, min_samples_split=2,']\n", "['min_weight_fraction_leaf=0.0, n_estimators=100,']\n", "[\"n_iter_no_change=None, presort='deprecated',\"]\n", "['random_state=123, subsample=1.0, tol=0.0001,']\n", "['validation_fraction=0.1, verbose=0, warm_start=False), None]']\n", "['2020-07-29 09', '50', '07,029', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '50', '07,029', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '50', '07,161', 'INFO', 'Initializing Extreme Gradient Boosting']\n", "['2020-07-29 09', '50', '07,173', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '50', '07,190', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '07,362', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '07,368', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '07,369', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '07,394', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '50', '07,416', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '07,586', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '07,592', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '07,592', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '07,620', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '50', '07,643', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '07,828', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '07,834', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '07,834', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '07,863', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '50', '07,887', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '08,074', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '08,081', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '08,081', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '08,110', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '50', '08,135', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '08,339', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '08,346', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '08,346', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '08,375', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '50', '08,376', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '50', '08,448', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '50', '08,679', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '50', '08,680', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '50', '08,686', 'INFO', \"save_model(model=XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\"]\n", "['colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,']\n", "[\"importance_type='gain', interaction_constraints='',\"]\n", "['learning_rate=0.300000012, max_delta_step=0, max_depth=6,']\n", "[\"min_child_weight=1, missing=nan, monotone_constraints='()',\"]\n", "['n_estimators=100, n_jobs=-1, num_parallel_tree=1,']\n", "[\"objective='reg\", \"squarederror', random_state=123, reg_alpha=0,\"]\n", "[\"reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\"]\n", "['validate_parameters=1, verbosity=0), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '50', '08,686', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '50', '08,711', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '50', '08,730', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\"]\n", "['colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,']\n", "[\"importance_type='gain', interaction_constraints='',\"]\n", "['learning_rate=0.300000012, max_delta_step=0, max_depth=6,']\n", "[\"min_child_weight=1, missing=nan, monotone_constraints='()',\"]\n", "['n_estimators=100, n_jobs=-1, num_parallel_tree=1,']\n", "[\"objective='reg\", \"squarederror', random_state=123, reg_alpha=0,\"]\n", "[\"reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\"]\n", "['validate_parameters=1, verbosity=0), None]']\n", "['2020-07-29 09', '50', '08,730', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '50', '08,730', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '50', '08,868', 'INFO', 'Initializing Light Gradient Boosting Machine']\n", "['2020-07-29 09', '50', '08,881', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '50', '08,901', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '09,213', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '09,226', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '09,226', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '09,258', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '50', '09,282', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '09,525', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '09,535', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '09,535', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '09,567', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '50', '09,592', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '09,871', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '09,882', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '09,882', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '09,913', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '50', '09,938', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '10,228', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '10,241', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '10,241', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '10,271', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '50', '10,299', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '10,568', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '10,579', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '10,579', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '10,610', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '50', '10,612', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '50', '10,683', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '50', '10,933', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '50', '10,933', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '50', '10,936', 'INFO', \"save_model(model=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '50', '10,936', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '50', '10,982', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '50', '11,014', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", 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min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), None]']\n", "['2020-07-29 09', '50', '11,015', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '50', '11,015', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '50', '11,186', 'INFO', 'Initializing CatBoost Regressor']\n", "['2020-07-29 09', '50', '11,198', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '50', '11,213', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '15,134', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '15,140', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '15,140', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '15,156', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '50', '15,174', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '19,173', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '19,181', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '19,181', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '19,199', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '50', '19,214', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '20,317', 'INFO', 'PyCaret Clustering Module']\n", "['2020-07-29 09', '50', '20,317', 'INFO', 'version pycaret-nightly-0.39']\n", "['2020-07-29 09', '50', '20,317', 'INFO', 'Initializing setup()']\n", "['2020-07-29 09', '50', '20,317', 'INFO', 'USI', 'a262']\n", "['2020-07-29 09', '50', '20,318', 'INFO', 'setup(data=(224, 21), categorical_features=None, categorical_imputation=constant, ordinal_features=None, high_cardinality_features=None,']\n", "[\"numeric_features=None, numeric_imputation=mean, date_features=None, ignore_features=['Country 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'20,318', 'INFO', 'machine', 'AMD64']\n", "['2020-07-29 09', '50', '20,319', 'INFO', 'platform', 'Windows-10-10.0.18362-SP0']\n", "['2020-07-29 09', '50', '20,400', 'INFO', 'Memory', 'svmem(total=17032478720, available=4871823360, percent=71.4, used=12160655360, free=4871823360)']\n", "['2020-07-29 09', '50', '20,402', 'INFO', 'Physical Core', '4']\n", "['2020-07-29 09', '50', '20,402', 'INFO', 'Logical Core', '8']\n", "['2020-07-29 09', '50', '20,402', 'INFO', 'Checking libraries']\n", "['2020-07-29 09', '50', '20,402', 'INFO', 'pd==1.0.4']\n", "['2020-07-29 09', '50', '20,402', 'INFO', 'numpy==1.18.5']\n", "['2020-07-29 09', '50', '22,550', 'INFO', 'sklearn==0.23.1']\n", "['2020-07-29 09', '50', '22,554', 'INFO', 'kmodes==0.10.2']\n", "['2020-07-29 09', '50', '23,217', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '23,224', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '23,224', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '23,238', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '50', '23,257', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '25,615', 'INFO', 'mlflow==1.8.0']\n", "['2020-07-29 09', '50', '25,615', 'INFO', 'Checking Exceptions']\n", "['2020-07-29 09', '50', '25,616', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '50', '25,827', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '50', '25,889', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '50', '25,889', 'INFO', 'Declaring global variables']\n", "['2020-07-29 09', '50', '25,889', 'INFO', 'Copying data for preprocessing']\n", "['2020-07-29 09', '50', '25,902', 'INFO', 'Declaring preprocessing parameters']\n", "['2020-07-29 09', '50', '25,902', 'INFO', 'Importing preprocessing module']\n", "['2020-07-29 09', '50', '27,230', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '27,237', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '27,237', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '27,252', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '50', '27,271', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '29,232', 'INFO', 'Creating preprocessing pipeline']\n", "['2020-07-29 09', '50', '31,016', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '31,022', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '50', '31,022', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '31,035', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '50', '31,036', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '50', '31,082', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '50', '31,207', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '50', '31,207', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '50', '31,207', 'INFO', 'save_model(model=, model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '50', '31,207', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '50', '31,227', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '50', '31,240', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), , None]']\n", "['2020-07-29 09', '50', '31,240', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '50', '31,240', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '50', '31,614', 'INFO', 'Finalizing top_n models']\n", "['2020-07-29 09', '50', '31,615', 'INFO', 'SubProcess create_model() called ==================================']\n", "['2020-07-29 09', '50', '31,630', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '50', '31,630', 'INFO', 'create_model(estimator=gbr, ensemble=False, method=None, fold=10, round=4, cross_validation=True, verbose=False, system=False)']\n", "['2020-07-29 09', '50', '31,630', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '50', '31,630', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '50', '31,631', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '50', '31,652', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '50', '31,653', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '50', '31,655', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '50', '31,655', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '50', '31,656', 'INFO', 'Gradient Boosting Regressor Imported succesfully']\n", "['2020-07-29 09', '50', '31,657', 'INFO', 'Checking ensemble method']\n", "['2020-07-29 09', '50', '31,659', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '50', '31,662', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '31,827', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '31,831', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '31,832', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '31,846', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '50', '31,848', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '32,015', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '32,017', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '32,018', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '32,029', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '50', '32,031', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '32,183', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '32,187', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '32,187', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '32,198', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '50', '32,201', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '32,348', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '32,351', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '32,352', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '32,362', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '50', '32,364', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '32,528', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '32,532', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '32,532', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '32,544', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '50', '32,547', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '32,700', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '32,704', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '32,704', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '32,714', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '50', '32,716', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '32,866', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '32,869', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '32,869', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '32,881', 'INFO', 'Initializing Fold 8']\n", "['2020-07-29 09', '50', '32,885', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '33,045', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '33,049', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '33,050', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '33,061', 'INFO', 'Initializing Fold 9']\n", "['2020-07-29 09', '50', '33,064', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '33,222', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '33,225', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '33,226', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '33,235', 'INFO', 'Initializing Fold 10']\n", "['2020-07-29 09', '50', '33,237', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '33,386', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '33,389', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '33,389', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '33,399', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '50', '33,400', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '50', '33,408', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '50', '33,570', 'INFO', 'Uploading results into container']\n", "['2020-07-29 09', '50', '33,570', 'INFO', 'Uploading model into container']\n", "['2020-07-29 09', '50', '33,570', 'INFO', 'create_model_container', '1']\n", "['2020-07-29 09', '50', '33,571', 'INFO', 'master_model_container', '1']\n", "['2020-07-29 09', '50', '33,571', 'INFO', 'display_container', '1']\n", "['2020-07-29 09', '50', '33,572', 'INFO', \"GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',\"]\n", "[\"init=None, learning_rate=0.1, loss='ls', max_depth=3,\"]\n", "['max_features=None, max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0, min_impurity_split=None,']\n", "['min_samples_leaf=1, min_samples_split=2,']\n", "['min_weight_fraction_leaf=0.0, n_estimators=100,']\n", "[\"n_iter_no_change=None, presort='deprecated',\"]\n", "['random_state=123, subsample=1.0, tol=0.0001,']\n", "['validation_fraction=0.1, verbose=0, warm_start=False)']\n", "['2020-07-29 09', '50', '33,572', 'INFO', 'create_model() succesfully completed......................................']\n", "['2020-07-29 09', '50', '33,572', 'INFO', 'SubProcess create_model() end ==================================']\n", "['2020-07-29 09', '50', '33,736', 'INFO', 'create_model_container', '1']\n", "['2020-07-29 09', '50', '33,736', 'INFO', 'master_model_container', '1']\n", "['2020-07-29 09', '50', '33,736', 'INFO', 'display_container', '2']\n", "['2020-07-29 09', '50', '33,737', 'INFO', \"GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',\"]\n", "[\"init=None, learning_rate=0.1, loss='ls', max_depth=3,\"]\n", "['max_features=None, max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0, min_impurity_split=None,']\n", "['min_samples_leaf=1, min_samples_split=2,']\n", "['min_weight_fraction_leaf=0.0, n_estimators=100,']\n", "[\"n_iter_no_change=None, presort='deprecated',\"]\n", "['random_state=123, subsample=1.0, tol=0.0001,']\n", "['validation_fraction=0.1, verbose=0, warm_start=False)']\n", "['2020-07-29 09', '50', '33,737', 'INFO', 'compare_models() succesfully completed......................................']\n", "['2020-07-29 09', '50', '38,592', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '50', '38,593', 'INFO', 'create_model(estimator=lightgbm, ensemble=False, method=None, fold=10, round=4, cross_validation=True, verbose=True, system=True)']\n", "['2020-07-29 09', '50', '38,593', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '50', '38,593', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '50', '38,593', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '50', '38,629', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '50', '38,630', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '50', '38,631', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '50', '38,631', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '50', '38,639', 'INFO', 'Light Gradient Boosting Machine Imported succesfully']\n", "['2020-07-29 09', '50', '38,640', 'INFO', 'Checking ensemble method']\n", "['2020-07-29 09', '50', '38,647', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '50', '38,656', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '38,849', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '38,857', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '38,857', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '38,908', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '50', '38,925', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '39,151', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '39,159', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '39,159', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '39,219', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '50', '39,239', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '39,508', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '39,518', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '39,518', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '39,580', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '50', '39,603', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '39,908', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '39,919', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '39,920', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '39,994', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '50', '40,018', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '40,307', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '40,315', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '40,316', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '40,402', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '50', '40,424', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '40,753', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '40,763', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '40,764', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '40,860', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '50', '40,885', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '41,203', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '41,212', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '41,213', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '41,289', 'INFO', 'Initializing Fold 8']\n", "['2020-07-29 09', '50', '41,312', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '41,654', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '41,665', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '41,665', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '41,742', 'INFO', 'Initializing Fold 9']\n", "['2020-07-29 09', '50', '41,764', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '42,039', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '42,049', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '42,050', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '42,129', 'INFO', 'Initializing Fold 10']\n", "['2020-07-29 09', '50', '42,151', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '42,228', 'INFO', 'Preprocessing pipeline created successfully']\n", "['2020-07-29 09', '50', '42,228', 'INFO', 'Creating grid variables']\n", "['2020-07-29 09', '50', '42,233', 'INFO', 'Creating global containers']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "['2020-07-29 09', '50', '42,511', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '42,521', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '42,521', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '42,601', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '50', '42,606', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '50', '42,633', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '50', '42,983', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '50', '43,314', 'INFO', 'Logging experiment in MLFlow']\n", "['2020-07-29 09', '50', '43,501', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '50', '43,502', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '50', '43,503', 'INFO', \"save_model(model=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '50', '43,504', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '50', '43,542', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '50', '43,572', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", 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completed......................................']\n", "['2020-07-29 09', '50', '43,573', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '50', '43,664', 'INFO', 'Uploading results into container']\n", "['2020-07-29 09', '50', '43,664', 'INFO', 'Uploading model into container']\n", "['2020-07-29 09', '50', '43,819', 'INFO', 'create_model_container', '2']\n", "['2020-07-29 09', '50', '43,819', 'INFO', 'master_model_container', '2']\n", "['2020-07-29 09', '50', '43,819', 'INFO', 'display_container', '3']\n", "['2020-07-29 09', '50', '43,821', 'INFO', \"LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0)']\n", "['2020-07-29 09', '50', '43,821', 'INFO', 'create_model() succesfully completed......................................']\n", "['2020-07-29 09', '50', '43,841', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '50', '43,842', 'INFO', 'create_model(estimator=lightgbm, ensemble=False, method=None, fold=10, round=4, cross_validation=True, verbose=False, system=True)']\n", "['2020-07-29 09', '50', '43,842', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '50', '43,843', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '50', '43,843', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '50', '43,885', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '50', '43,886', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '50', '43,889', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '50', '43,889', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '50', '43,890', 'INFO', 'Light Gradient Boosting Machine Imported succesfully']\n", "['2020-07-29 09', '50', '43,893', 'INFO', 'Checking ensemble method']\n", "['2020-07-29 09', '50', '43,895', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '50', '43,901', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '44,135', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '50', '44,136', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '50', '44,171', 'INFO', 'save_model(model=Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True,']\n", "[\"features_todrop=['Country Name'],\"]\n", "[\"ml_usecase='regression',\"]\n", "['numerical_features=[],']\n", "[\"target='dummy_target',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_...']\n", "[\"target='dummy_target')),\"]\n", "[\"('feature_time',\"]\n", "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n", "[\"('group', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n", "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('dummy', Dummify(target='dummy_target')),\"]\n", "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n", "[\"('pca', Empty())],\"]\n", "['verbose=False), model_name=Transformation Pipeline, verbose=False)']\n", "['2020-07-29 09', '50', '44,172', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '50', '44,203', 'INFO', 'Transformation Pipeline.pkl saved in current working directory']\n", "['2020-07-29 09', '50', '44,258', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True,']\n", "[\"features_todrop=['Country 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'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '50', '44,437', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '44,448', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '44,448', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '44,477', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '50', '44,483', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '44,521', 'INFO', 'Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True,']\n", "[\"features_todrop=['Country Name'],\"]\n", "[\"ml_usecase='regression',\"]\n", "['numerical_features=[],']\n", "[\"target='dummy_target',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_...']\n", "[\"target='dummy_target')),\"]\n", "[\"('feature_time',\"]\n", "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n", "[\"('group', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n", "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('dummy', Dummify(target='dummy_target')),\"]\n", "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n", "[\"('pca', Empty())],\"]\n", "['verbose=False)']\n", "['2020-07-29 09', '50', '44,521', 'INFO', 'setup() succesfully completed......................................']\n", "['2020-07-29 09', '50', '44,623', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '50', '44,624', 'INFO', 'create_model(model=kmeans, num_clusters=4, ground_truth=None, verbose=True, system=True)']\n", "['2020-07-29 09', '50', '44,625', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '50', '44,625', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '50', '44,625', 'INFO', 'Setting num_cluster param']\n", "['2020-07-29 09', '50', '44,626', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '50', '44,690', 'INFO', 'Importing untrained model']\n", "['2020-07-29 09', '50', '44,690', 'INFO', 'K-Means Clustering Imported succesfully']\n", "['2020-07-29 09', '50', '44,711', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '44,901', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '44,946', 'INFO', 'Creating Metrics dataframe']\n", "['2020-07-29 09', '50', '44,956', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '50', '45,024', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '45,037', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '45,038', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '45,068', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '50', '45,074', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '45,271', 'INFO', 'SubProcess plot_model() called ==================================']\n", "['2020-07-29 09', '50', '45,271', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '50', '45,273', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), plot=cluster, feature=None, label=False, save=True, system=False)']\n", "['2020-07-29 09', '50', '45,273', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '50', '45,273', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '50', '45,551', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '45,564', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '45,565', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '45,597', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '50', '45,608', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '46,101', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '46,111', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '46,112', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '46,140', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '50', '46,145', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '46,487', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '46,498', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '46,499', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '46,530', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '50', '46,538', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '46,999', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '47,011', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '47,011', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '47,042', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '50', '47,047', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '47,395', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '47,408', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '47,408', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '47,437', 'INFO', 'Initializing Fold 8']\n", "['2020-07-29 09', '50', '47,442', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '47,856', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '47,866', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '47,867', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '47,897', 'INFO', 'Initializing Fold 9']\n", "['2020-07-29 09', '50', '47,905', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '48,279', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '48,290', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '48,291', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '48,325', 'INFO', 'Initializing Fold 10']\n", "['2020-07-29 09', '50', '48,330', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '48,701', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '48,712', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '48,713', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '48,743', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '50', '48,748', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '50', '48,768', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '50', '49,134', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '50', '49,651', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '50', '49,651', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '50', '49,652', 'INFO', \"save_model(model=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '50', '49,653', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '50', '49,697', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '50', '49,727', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), None]']\n", "['2020-07-29 09', '50', '49,728', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '50', '49,728', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '50', '49,815', 'INFO', 'Uploading results into container']\n", "['2020-07-29 09', '50', '49,815', 'INFO', 'Uploading model into container']\n", "['2020-07-29 09', '50', '49,815', 'INFO', 'create_model_container', '3']\n", "['2020-07-29 09', '50', '49,815', 'INFO', 'master_model_container', '3']\n", "['2020-07-29 09', '50', '49,815', 'INFO', 'display_container', '4']\n", "['2020-07-29 09', '50', '49,817', 'INFO', \"LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0)']\n", "['2020-07-29 09', '50', '49,817', 'INFO', 'create_model() succesfully completed......................................']\n", "['2020-07-29 09', '50', '49,818', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '50', '49,818', 'INFO', 'create_model(estimator=lightgbm, ensemble=False, method=None, fold=10, round=4, cross_validation=True, verbose=False, system=True)']\n", "['2020-07-29 09', '50', '49,818', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '50', '49,818', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '50', '49,819', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '50', '49,851', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '50', '49,852', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '50', '49,855', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '50', '49,855', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '50', '49,857', 'INFO', 'Light Gradient Boosting Machine Imported succesfully']\n", "['2020-07-29 09', '50', '49,860', 'INFO', 'Checking ensemble method']\n", "['2020-07-29 09', '50', '49,862', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '50', '49,868', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '50,229', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '50,237', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '50,238', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '50,268', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '50', '50,273', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '50,640', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '50,650', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '50,650', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '50,680', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '50', '50,687', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '51,379', 'INFO', 'plot type', 'cluster']\n", "['2020-07-29 09', '50', '51,379', 'INFO', 'SubProcess assign_model() called ==================================']\n", "['2020-07-29 09', '50', '51,379', 'INFO', 'Initializing assign_model()']\n", "['2020-07-29 09', '50', '51,381', 'INFO', \"assign_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), transformation=True, verbose=False)']\n", "['2020-07-29 09', '50', '51,381', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '50', '51,382', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '50', '51,382', 'INFO', 'Copying data']\n", "['2020-07-29 09', '50', '51,383', 'INFO', 'Transformation param set to True. Assigned clusters are attached on transformed dataset.']\n", "['2020-07-29 09', '50', '51,383', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '50', '51,441', 'INFO', 'Determining Trained Model']\n", "['2020-07-29 09', '50', '51,443', 'INFO', 'Trained Model', 'K-Means Clustering']\n", "['2020-07-29 09', '50', '51,443', 'INFO', '(224, 21)']\n", "['2020-07-29 09', '50', '51,444', 'INFO', 'assign_model() succesfully completed......................................']\n", "['2020-07-29 09', '50', '51,444', 'INFO', 'SubProcess assign_model() end ==================================']\n", "['2020-07-29 09', '50', '51,481', 'INFO', 'Fitting PCA()']\n", "['2020-07-29 09', '50', '51,501', 'INFO', 'Sorting dataframe']\n", "['2020-07-29 09', '50', '51,510', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '50', '51,800', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '51,811', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '51,811', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '51,840', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '50', '51,855', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '52,980', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '52,992', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '52,993', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '53,024', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '50', '53,029', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '53,902', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '53,914', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '53,914', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '53,950', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '50', '53,955', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '54,501', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '54,512', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '54,512', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '54,544', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '50', '54,549', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '55,374', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '55,384', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '55,384', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '55,410', 'INFO', 'Initializing Fold 8']\n", "['2020-07-29 09', '50', '55,415', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '55,458', 'INFO', 'PyCaret NLP Module']\n", "['2020-07-29 09', '50', '55,459', 'INFO', 'version pycaret-nightly-0.39']\n", "['2020-07-29 09', '50', '55,459', 'INFO', 'Initializing setup()']\n", "['2020-07-29 09', '50', '55,460', 'INFO', 'USI', '2c3d']\n", "['2020-07-29 09', '50', '55,460', 'INFO', 'setup(data=(6818, 7), target=en, custom_stopwords=None, html=True, session_id=123, log_experiment=True,']\n", "['experiment_name=kiva1, log_plots=True, log_data=False, verbose=True)']\n", "['2020-07-29 09', '50', '55,460', 'INFO', 'Checking environment']\n", "['2020-07-29 09', '50', '55,461', 'INFO', 'python_version', '3.6.10']\n", "['2020-07-29 09', '50', '55,461', 'INFO', 'python_build', \"('default', 'May 7 2020 19\", '46', \"08')\"]\n", "['2020-07-29 09', '50', '55,461', 'INFO', 'machine', 'AMD64']\n", "['2020-07-29 09', '50', '55,462', 'INFO', 'platform', 'Windows-10-10.0.18362-SP0']\n", "['2020-07-29 09', '50', '55,546', 'INFO', 'Memory', 'svmem(total=17032478720, available=4700733440, percent=72.4, used=12331745280, free=4700733440)']\n", "['2020-07-29 09', '50', '55,546', 'INFO', 'Physical Core', '4']\n", "['2020-07-29 09', '50', '55,547', 'INFO', 'Logical Core', '8']\n", "['2020-07-29 09', '50', '55,547', 'INFO', 'Checking libraries']\n", "['2020-07-29 09', '50', '55,547', 'INFO', 'pd==1.0.4']\n", "['2020-07-29 09', '50', '55,547', 'INFO', 'numpy==1.18.5']\n", "['2020-07-29 09', '50', '55,982', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '55,993', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '55,993', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '56,032', 'INFO', 'Initializing Fold 9']\n", "['2020-07-29 09', '50', '56,038', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '56,505', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '56,516', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '56,516', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '56,545', 'INFO', 'Initializing Fold 10']\n", "['2020-07-29 09', '50', '56,551', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '57,714', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '50', '57,725', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '50', '57,725', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '50', '57,754', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '50', '57,759', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '50', '57,779', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '50', '58,308', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '50', '58,410', 'INFO', 'gensim==3.8.3']\n", "['2020-07-29 09', '50', '58,896', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '50', '58,897', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '50', '58,899', 'INFO', \"save_model(model=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '50', '58,899', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '50', '58,945', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '50', '58,978', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), None]']\n", "['2020-07-29 09', '50', '58,978', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '50', '58,979', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '50', '59,072', 'INFO', 'Uploading results into container']\n", "['2020-07-29 09', '50', '59,072', 'INFO', 'Uploading model into container']\n", "['2020-07-29 09', '50', '59,072', 'INFO', 'create_model_container', '4']\n", "['2020-07-29 09', '50', '59,072', 'INFO', 'master_model_container', '4']\n", "['2020-07-29 09', '50', '59,073', 'INFO', 'display_container', '5']\n", "['2020-07-29 09', '50', '59,074', 'INFO', \"LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0)']\n", "['2020-07-29 09', '50', '59,075', 'INFO', 'create_model() succesfully completed......................................']\n", "['2020-07-29 09', '50', '59,075', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '50', '59,075', 'INFO', 'create_model(estimator=lightgbm, ensemble=False, method=None, fold=10, round=4, cross_validation=True, verbose=False, system=True)']\n", "['2020-07-29 09', '50', '59,076', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '50', '59,076', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '50', '59,076', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '50', '59,110', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '50', '59,112', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '50', '59,115', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '50', '59,115', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '50', '59,116', 'INFO', 'Light Gradient Boosting Machine Imported succesfully']\n", "['2020-07-29 09', '50', '59,120', 'INFO', 'Checking ensemble method']\n", "['2020-07-29 09', '50', '59,122', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '50', '59,129', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '50', '59,817', 'INFO', 'spacy==2.2.4']\n", "['2020-07-29 09', '51', '00,027', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '00,037', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '00,037', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '00,072', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '51', '00,081', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '00,692', 'INFO', \"Saving 'Cluster.html' in current active directory\"]\n", "['2020-07-29 09', '51', '00,692', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '51', '00,692', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '00,907', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '00,919', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '00,919', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '00,948', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '51', '00,954', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '01,313', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '01,323', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '01,323', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '01,349', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '51', '01,354', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '01,554', 'INFO', 'nltk==3.5']\n", "['2020-07-29 09', '51', '01,706', 'INFO', 'textblob==0.15.3']\n", "['2020-07-29 09', '51', '01,774', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '01,783', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '01,783', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '01,814', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '51', '01,819', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '02,155', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '02,165', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '02,165', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '02,189', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '51', '02,190', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), plot=distribution, feature=None, label=False, save=True, system=False)']\n", "['2020-07-29 09', '51', '02,191', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '02,191', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '51', '02,194', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '51', '02,200', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '02,220', 'INFO', 'plot type', 'distribution']\n", "['2020-07-29 09', '51', '02,221', 'INFO', 'SubProcess assign_model() called ==================================']\n", "['2020-07-29 09', '51', '02,221', 'INFO', 'Initializing assign_model()']\n", "['2020-07-29 09', '51', '02,223', 'INFO', \"assign_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), transformation=False, verbose=False)']\n", "['2020-07-29 09', '51', '02,224', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '02,224', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '51', '02,224', 'INFO', 'Copying data']\n", "['2020-07-29 09', '51', '02,225', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '51', '02,271', 'INFO', 'Determining Trained Model']\n", "['2020-07-29 09', '51', '02,273', 'INFO', 'Trained Model', 'K-Means Clustering']\n", "['2020-07-29 09', '51', '02,274', 'INFO', '(224, 22)']\n", "['2020-07-29 09', '51', '02,274', 'INFO', 'assign_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '02,274', 'INFO', 'SubProcess assign_model() end ==================================']\n", "['2020-07-29 09', '51', '02,275', 'INFO', 'Sorting dataframe']\n", "['2020-07-29 09', '51', '02,294', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '51', '02,690', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '02,701', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '02,701', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '02,728', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '51', '02,734', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '03,122', 'INFO', \"Saving 'Distribution.html' in current active directory\"]\n", "['2020-07-29 09', '51', '03,122', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '51', '03,122', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '03,236', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '03,240', 'INFO', 'pyLDAvis==2.1.2']\n", "['2020-07-29 09', '51', '03,244', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '03,244', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '03,273', 'INFO', 'Initializing Fold 8']\n", "['2020-07-29 09', '51', '03,278', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '03,482', 'INFO', 'wordcloud==1.7.0']\n", "['2020-07-29 09', '51', '03,624', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '03,636', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '03,637', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '03,663', 'INFO', 'Initializing Fold 9']\n", "['2020-07-29 09', '51', '03,668', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '03,979', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '03,989', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '03,989', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '04,017', 'INFO', 'Initializing Fold 10']\n", "['2020-07-29 09', '51', '04,023', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '04,361', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '04,371', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '04,372', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '04,400', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '51', '04,406', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '51', '04,424', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '51', '04,475', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '51', '04,477', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), plot=elbow, feature=None, label=False, save=True, system=False)']\n", "['2020-07-29 09', '51', '04,477', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '04,477', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '51', '04,503', 'INFO', 'plot type', 'elbow']\n", "['2020-07-29 09', '51', '04,642', 'INFO', 'Fitting KElbowVisualizer()']\n", "['2020-07-29 09', '51', '04,861', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '51', '05,101', 'INFO', 'mlflow==1.8.0']\n", "['2020-07-29 09', '51', '05,102', 'INFO', 'Checking Exceptions']\n", "['2020-07-29 09', '51', '05,449', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '51', '05,450', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '51', '05,452', 'INFO', \"save_model(model=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '51', '05,452', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '51', '05,498', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '51', '05,521', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), None]']\n", "['2020-07-29 09', '51', '05,522', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '05,522', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '51', '05,602', 'INFO', 'Uploading results into container']\n", "['2020-07-29 09', '51', '05,602', 'INFO', 'Uploading model into container']\n", "['2020-07-29 09', '51', '05,602', 'INFO', 'create_model_container', '5']\n", "['2020-07-29 09', '51', '05,603', 'INFO', 'master_model_container', '5']\n", "['2020-07-29 09', '51', '05,603', 'INFO', 'display_container', '6']\n", "['2020-07-29 09', '51', '05,605', 'INFO', \"LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0)']\n", "['2020-07-29 09', '51', '05,606', 'INFO', 'create_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '05,606', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '51', '05,606', 'INFO', 'create_model(estimator=lightgbm, ensemble=False, method=None, fold=10, round=4, cross_validation=True, verbose=False, system=True)']\n", "['2020-07-29 09', '51', '05,607', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '05,607', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '51', '05,607', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '51', '05,637', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '51', '05,638', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '51', '05,641', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '51', '05,641', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '51', '05,642', 'INFO', 'Light Gradient Boosting Machine Imported succesfully']\n", "['2020-07-29 09', '51', '05,643', 'INFO', 'Checking ensemble method']\n", "['2020-07-29 09', '51', '05,645', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '51', '05,651', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '05,963', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '05,971', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '05,971', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '05,997', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '51', '06,002', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '06,313', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '06,321', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '06,322', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '06,345', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '51', '06,349', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '06,432', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '51', '06,700', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '06,708', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '06,709', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '06,740', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '51', '06,744', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '07,071', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '07,080', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '07,080', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '07,101', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '51', '07,106', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '07,403', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '07,411', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '07,411', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '07,438', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '51', '07,443', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '07,767', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '07,781', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '07,782', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '07,808', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '51', '07,813', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '08,123', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '51', '08,170', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '08,179', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '08,180', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '08,189', 'INFO', \"Saving 'Elbow.png' in current active directory\"]\n", "['2020-07-29 09', '51', '08,189', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '51', '08,189', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '08,207', 'INFO', 'Initializing Fold 8']\n", "['2020-07-29 09', '51', '08,211', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '08,234', 'INFO', 'SubProcess plot_model() end ==================================']\n", "['2020-07-29 09', '51', '08,234', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '51', '08,234', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '51', '08,236', 'INFO', \"save_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '51', '08,236', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '51', '08,262', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '51', '08,273', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '51', '08,285', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True,']\n", "[\"features_todrop=['Country Name'],\"]\n", "[\"ml_usecase='regression',\"]\n", "['numerical_features=[],']\n", "[\"target='dummy_target',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_...']\n", "[\"target='dummy_target')),\"]\n", "[\"('feature_time',\"]\n", "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n", "[\"('group', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n", "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('dummy', Dummify(target='dummy_target')),\"]\n", "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n", "[\"('pca', Empty())],\"]\n", "[\"verbose=False), KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0)]']\n", "['2020-07-29 09', '51', '08,285', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '08,285', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '51', '08,344', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '51', '08,344', 'INFO', 'Declaring global variables']\n", "['2020-07-29 09', '51', '08,345', 'INFO', 'Input provided', 'dataframe']\n", "['2020-07-29 09', '51', '08,345', 'INFO', 'session_id set to', '123']\n", "['2020-07-29 09', '51', '08,347', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '51', '08,351', 'INFO', 'Importing stopwords from nltk']\n", "['2020-07-29 09', '51', '08,398', 'INFO', \"KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0)']\n", "['2020-07-29 09', '51', '08,399', 'INFO', 'create_models() succesfully completed......................................']\n", "['2020-07-29 09', '51', '08,419', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '51', '08,419', 'INFO', 'create_model(model=kmodes, num_clusters=4, ground_truth=None, verbose=True, system=True)']\n", "['2020-07-29 09', '51', '08,419', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '08,420', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '51', '08,421', 'INFO', 'Setting num_cluster param']\n", "['2020-07-29 09', '51', '08,421', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '51', '08,484', 'INFO', 'Importing untrained model']\n", "['2020-07-29 09', '51', '08,493', 'INFO', 'K-Modes Clustering Imported succesfully']\n", "['2020-07-29 09', '51', '08,516', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '08,689', 'INFO', 'No custom stopwords defined']\n", "['2020-07-29 09', '51', '08,692', 'INFO', 'Removing numeric characters from the text']\n", "['2020-07-29 09', '51', '10,128', 'INFO', 'Removing special characters from the text']\n", "['2020-07-29 09', '51', '14,072', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '14,083', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '14,083', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '14,112', 'INFO', 'Initializing Fold 9']\n", "['2020-07-29 09', '51', '14,120', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '14,712', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '14,721', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '14,722', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '14,747', 'INFO', 'Initializing Fold 10']\n", "['2020-07-29 09', '51', '14,751', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '15,185', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '15,195', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '15,196', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '15,225', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '51', '15,233', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '51', '15,250', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '51', '15,429', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '15,460', 'INFO', 'Creating Metrics dataframe']\n", "['2020-07-29 09', '51', '15,467', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '51', '15,686', 'INFO', 'SubProcess plot_model() called ==================================']\n", "['2020-07-29 09', '51', '15,687', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '51', '15,687', 'INFO', \"plot_model(model=KModes(cat_dissim=, init='Cao',\"]\n", "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n", "['verbose=0), plot=cluster, feature=None, label=False, save=True, system=False)']\n", "['2020-07-29 09', '51', '15,687', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '15,687', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '51', '15,712', 'INFO', 'plot type', 'cluster']\n", "['2020-07-29 09', '51', '15,712', 'INFO', 'SubProcess assign_model() called ==================================']\n", "['2020-07-29 09', '51', '15,712', 'INFO', 'Initializing assign_model()']\n", "['2020-07-29 09', '51', '15,714', 'INFO', \"assign_model(model=KModes(cat_dissim=, init='Cao',\"]\n", "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n", "['verbose=0), transformation=True, verbose=False)']\n", "['2020-07-29 09', '51', '15,714', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '15,714', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '51', '15,714', 'INFO', 'Copying data']\n", "['2020-07-29 09', '51', '15,715', 'INFO', 'Transformation param set to True. Assigned clusters are attached on transformed dataset.']\n", "['2020-07-29 09', '51', '15,715', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '51', '15,750', 'INFO', 'Determining Trained Model']\n", "['2020-07-29 09', '51', '15,751', 'INFO', 'Trained Model', 'K-Modes Clustering']\n", "['2020-07-29 09', '51', '15,752', 'INFO', '(224, 21)']\n", "['2020-07-29 09', '51', '15,752', 'INFO', 'assign_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '15,753', 'INFO', 'SubProcess assign_model() end ==================================']\n", "['2020-07-29 09', '51', '15,768', 'INFO', 'Fitting PCA()']\n", "['2020-07-29 09', '51', '15,785', 'INFO', 'Sorting dataframe']\n", "['2020-07-29 09', '51', '15,791', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '51', '15,848', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '51', '16,197', 'INFO', \"Saving 'Cluster.html' in current active directory\"]\n", "['2020-07-29 09', '51', '16,197', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '51', '16,198', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '16,439', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '51', '16,439', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '51', '16,441', 'INFO', \"save_model(model=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '51', '16,441', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '51', '16,481', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '51', '16,508', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), None]']\n", "['2020-07-29 09', '51', '16,508', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '16,508', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '51', '16,609', 'INFO', 'Uploading results into container']\n", "['2020-07-29 09', '51', '16,609', 'INFO', 'Uploading model into container']\n", "['2020-07-29 09', '51', '16,610', 'INFO', 'create_model_container', '6']\n", "['2020-07-29 09', '51', '16,610', 'INFO', 'master_model_container', '6']\n", "['2020-07-29 09', '51', '16,610', 'INFO', 'display_container', '7']\n", "['2020-07-29 09', '51', '16,612', 'INFO', \"LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0)']\n", "['2020-07-29 09', '51', '16,612', 'INFO', 'create_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '16,613', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '51', '16,613', 'INFO', 'create_model(estimator=lightgbm, ensemble=False, method=None, fold=10, round=4, cross_validation=True, verbose=False, system=True)']\n", "['2020-07-29 09', '51', '16,613', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '16,613', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '51', '16,613', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '51', '16,641', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '51', '16,642', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '51', '16,644', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '51', '16,645', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '51', '16,646', 'INFO', 'Light Gradient Boosting Machine Imported succesfully']\n", "['2020-07-29 09', '51', '16,648', 'INFO', 'Checking ensemble method']\n", "['2020-07-29 09', '51', '16,650', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '51', '16,655', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '16,986', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '16,996', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '16,996', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '17,026', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '51', '17,032', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '17,396', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '17,407', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '17,407', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '17,433', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '51', '17,438', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '17,523', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '51', '17,525', 'INFO', \"plot_model(model=KModes(cat_dissim=, init='Cao',\"]\n", "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n", "['verbose=0), plot=distribution, feature=None, label=False, save=True, system=False)']\n", "['2020-07-29 09', '51', '17,525', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '17,525', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '51', '17,550', 'INFO', 'plot type', 'distribution']\n", "['2020-07-29 09', '51', '17,550', 'INFO', 'SubProcess assign_model() called ==================================']\n", "['2020-07-29 09', '51', '17,551', 'INFO', 'Initializing assign_model()']\n", "['2020-07-29 09', '51', '17,552', 'INFO', \"assign_model(model=KModes(cat_dissim=, init='Cao',\"]\n", "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n", "['verbose=0), transformation=False, verbose=False)']\n", "['2020-07-29 09', '51', '17,553', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '17,553', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '51', '17,553', 'INFO', 'Copying data']\n", "['2020-07-29 09', '51', '17,554', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '51', '17,595', 'INFO', 'Determining Trained Model']\n", "['2020-07-29 09', '51', '17,597', 'INFO', 'Trained Model', 'K-Modes Clustering']\n", "['2020-07-29 09', '51', '17,597', 'INFO', '(224, 22)']\n", "['2020-07-29 09', '51', '17,597', 'INFO', 'assign_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '17,598', 'INFO', 'SubProcess assign_model() end ==================================']\n", "['2020-07-29 09', '51', '17,598', 'INFO', 'Sorting dataframe']\n", "['2020-07-29 09', '51', '17,611', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '51', '17,786', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '17,795', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '17,795', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '17,822', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '51', '17,828', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '18,047', 'INFO', 'Tokenizing Words']\n", "['2020-07-29 09', '51', '18,251', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '18,259', 'INFO', \"Saving 'Distribution.html' in current active directory\"]\n", "['2020-07-29 09', '51', '18,259', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '18,259', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '18,259', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '51', '18,261', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '18,286', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '51', '18,290', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '18,650', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '18,657', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '18,658', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '18,680', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '51', '18,685', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '19,010', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '19,019', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '19,019', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '19,041', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '51', '19,046', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '19,353', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '19,361', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '19,361', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '19,383', 'INFO', 'Initializing Fold 8']\n", "['2020-07-29 09', '51', '19,387', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '19,654', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '51', '19,655', 'INFO', \"plot_model(model=KModes(cat_dissim=, init='Cao',\"]\n", "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n", "['verbose=0), plot=elbow, feature=None, label=False, save=True, system=False)']\n", "['2020-07-29 09', '51', '19,656', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '19,656', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '51', '19,680', 'INFO', 'plot type', 'elbow']\n", "['2020-07-29 09', '51', '19,722', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '19,743', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '19,744', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '19,776', 'INFO', 'Fitting KElbowVisualizer()']\n", "['2020-07-29 09', '51', '19,780', 'INFO', 'Initializing Fold 9']\n", "['2020-07-29 09', '51', '19,789', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '20,182', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '20,190', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '20,191', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '20,218', 'INFO', 'Initializing Fold 10']\n", "['2020-07-29 09', '51', '20,223', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '20,568', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '20,579', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '20,579', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '20,608', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '51', '20,614', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '51', '20,633', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '51', '20,990', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '51', '21,496', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '51', '21,496', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '51', '21,498', 'INFO', \"save_model(model=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '51', '21,498', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '51', '21,545', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '51', '21,572', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), None]']\n", "['2020-07-29 09', '51', '21,572', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '21,572', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '51', '21,660', 'INFO', 'Uploading results into container']\n", "['2020-07-29 09', '51', '21,660', 'INFO', 'Uploading model into container']\n", "['2020-07-29 09', '51', '21,660', 'INFO', 'create_model_container', '7']\n", "['2020-07-29 09', '51', '21,661', 'INFO', 'master_model_container', '7']\n", "['2020-07-29 09', '51', '21,661', 'INFO', 'display_container', '8']\n", "['2020-07-29 09', '51', '21,663', 'INFO', \"LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0)']\n", "['2020-07-29 09', '51', '21,663', 'INFO', 'create_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '21,664', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '51', '21,664', 'INFO', 'create_model(estimator=lightgbm, ensemble=False, method=None, fold=10, round=4, cross_validation=True, verbose=False, system=True)']\n", "['2020-07-29 09', '51', '21,664', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '21,664', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '51', '21,664', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '51', '21,704', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '51', '21,705', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '51', '21,707', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '51', '21,707', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '51', '21,708', 'INFO', 'Light Gradient Boosting Machine Imported succesfully']\n", "['2020-07-29 09', '51', '21,711', 'INFO', 'Checking ensemble method']\n", "['2020-07-29 09', '51', '21,714', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '51', '21,719', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '22,073', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '22,082', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '22,082', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '22,110', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '51', '22,114', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '22,485', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '22,494', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '22,495', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '22,521', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '51', '22,526', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '22,854', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '22,863', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '22,863', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '22,886', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '51', '22,892', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '23,201', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '23,209', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '23,209', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '23,234', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '51', '23,239', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '23,566', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '23,577', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '23,578', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '23,604', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '51', '23,609', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '24,001', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '24,010', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '24,011', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '24,039', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '51', '24,045', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '24,458', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '24,469', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '24,470', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '24,497', 'INFO', 'Initializing Fold 8']\n", "['2020-07-29 09', '51', '24,502', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '24,843', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '24,854', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '24,855', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '24,878', 'INFO', 'Initializing Fold 9']\n", "['2020-07-29 09', '51', '24,883', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '25,229', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '25,237', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '25,238', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '25,260', 'INFO', 'Initializing Fold 10']\n", "['2020-07-29 09', '51', '25,264', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '25,610', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '25,619', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '25,620', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '25,649', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '51', '25,654', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '51', '25,676', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '51', '26,031', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '51', '26,558', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '51', '26,558', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '51', '26,560', 'INFO', \"save_model(model=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '51', '26,561', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '51', '26,610', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '51', '26,641', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), None]']\n", "['2020-07-29 09', '51', '26,641', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '26,641', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '51', '26,727', 'INFO', 'Uploading results into container']\n", "['2020-07-29 09', '51', '26,728', 'INFO', 'Uploading model into container']\n", "['2020-07-29 09', '51', '26,728', 'INFO', 'create_model_container', '8']\n", "['2020-07-29 09', '51', '26,728', 'INFO', 'master_model_container', '8']\n", "['2020-07-29 09', '51', '26,728', 'INFO', 'display_container', '9']\n", "['2020-07-29 09', '51', '26,729', 'INFO', \"LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0)']\n", "['2020-07-29 09', '51', '26,729', 'INFO', 'create_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '26,730', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '51', '26,730', 'INFO', 'create_model(estimator=lightgbm, ensemble=False, method=None, fold=10, round=4, cross_validation=True, verbose=False, system=True)']\n", "['2020-07-29 09', '51', '26,730', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '26,730', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '51', '26,730', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '51', '26,757', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '51', '26,758', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '51', '26,761', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '51', '26,761', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '51', '26,762', 'INFO', 'Light Gradient Boosting Machine Imported succesfully']\n", "['2020-07-29 09', '51', '26,765', 'INFO', 'Checking ensemble method']\n", "['2020-07-29 09', '51', '26,767', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '51', '26,772', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '27,137', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '27,145', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '27,146', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '27,180', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '51', '27,185', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '27,525', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '27,537', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '27,537', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '27,568', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '51', '27,574', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '27,918', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '27,927', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '27,927', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '27,957', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '51', '27,964', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '28,283', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '28,292', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '28,292', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '28,317', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '51', '28,323', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '28,657', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '28,666', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '28,667', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '28,694', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '51', '28,698', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '29,025', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '29,033', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '29,033', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '29,060', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '51', '29,064', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '29,407', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '29,418', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '29,418', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '29,447', 'INFO', 'Initializing Fold 8']\n", "['2020-07-29 09', '51', '29,454', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '29,799', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '29,810', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '29,810', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '29,839', 'INFO', 'Initializing Fold 9']\n", "['2020-07-29 09', '51', '29,845', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '30,163', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '30,170', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '30,171', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '30,197', 'INFO', 'Initializing Fold 10']\n", "['2020-07-29 09', '51', '30,202', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '30,523', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '30,533', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '30,533', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '30,559', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '51', '30,562', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '51', '30,580', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '51', '30,932', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '51', '31,478', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '51', '31,479', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '51', '31,481', 'INFO', \"save_model(model=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '51', '31,481', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '51', '31,512', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '51', '31,536', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), None]']\n", "['2020-07-29 09', '51', '31,536', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '31,536', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '51', '31,619', 'INFO', 'Uploading results into container']\n", "['2020-07-29 09', '51', '31,620', 'INFO', 'Uploading model into container']\n", "['2020-07-29 09', '51', '31,620', 'INFO', 'create_model_container', '9']\n", "['2020-07-29 09', '51', '31,620', 'INFO', 'master_model_container', '9']\n", "['2020-07-29 09', '51', '31,620', 'INFO', 'display_container', '10']\n", "['2020-07-29 09', '51', '31,622', 'INFO', \"LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0)']\n", "['2020-07-29 09', '51', '31,623', 'INFO', 'create_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '31,623', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '51', '31,624', 'INFO', 'create_model(estimator=lightgbm, ensemble=False, method=None, fold=10, round=4, cross_validation=True, verbose=False, system=True)']\n", "['2020-07-29 09', '51', '31,624', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '31,624', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '51', '31,624', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '51', '31,656', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '51', '31,658', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '51', '31,660', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '51', '31,661', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '51', '31,662', 'INFO', 'Light Gradient Boosting Machine Imported succesfully']\n", "['2020-07-29 09', '51', '31,664', 'INFO', 'Checking ensemble method']\n", "['2020-07-29 09', '51', '31,667', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '51', '31,672', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '32,036', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '32,044', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '32,044', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '32,068', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '51', '32,073', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '32,380', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '32,391', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '32,391', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '32,418', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '51', '32,423', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '32,741', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '32,749', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '32,750', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '32,774', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '51', '32,779', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '33,089', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '33,096', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '33,096', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '33,122', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '51', '33,127', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '33,448', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '33,456', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '33,456', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '33,478', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '51', '33,482', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '33,801', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '33,811', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '33,812', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '33,838', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '51', '33,843', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '33,875', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '51', '34,159', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '34,168', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '34,168', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '34,193', 'INFO', 'Initializing Fold 8']\n", "['2020-07-29 09', '51', '34,198', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '34,434', 'INFO', \"Saving 'Elbow.png' in current active directory\"]\n", "['2020-07-29 09', '51', '34,435', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '51', '34,435', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '34,471', 'INFO', 'SubProcess plot_model() end ==================================']\n", "['2020-07-29 09', '51', '34,472', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '51', '34,472', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '51', '34,473', 'INFO', \"save_model(model=KModes(cat_dissim=, init='Cao',\"]\n", "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n", "['verbose=0), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '51', '34,473', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '51', '34,506', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '34,514', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '34,514', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '34,537', 'INFO', 'Initializing Fold 9']\n", "['2020-07-29 09', '51', '34,542', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '34,842', 'INFO', 'Removing stopwords']\n", "['2020-07-29 09', '51', '34,868', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '34,879', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '34,880', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '34,904', 'INFO', 'Initializing Fold 10']\n", "['2020-07-29 09', '51', '34,909', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '35,141', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '51', '35,166', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True,']\n", "[\"features_todrop=['Country Name'],\"]\n", "[\"ml_usecase='regression',\"]\n", "['numerical_features=[],']\n", "[\"target='dummy_target',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_...']\n", "[\"target='dummy_target')),\"]\n", "[\"('feature_time',\"]\n", "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n", "[\"('group', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n", "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('dummy', Dummify(target='dummy_target')),\"]\n", "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n", "[\"('pca', Empty())],\"]\n", "[\"verbose=False), KModes(cat_dissim=, init='Cao',\"]\n", "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n", "['verbose=0)]']\n", "['2020-07-29 09', '51', '35,166', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '35,167', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '51', '35,226', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '35,245', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '35,246', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '35,271', 'INFO', \"KModes(cat_dissim=, init='Cao',\"]\n", "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n", "['verbose=0)']\n", "['2020-07-29 09', '51', '35,272', 'INFO', 'create_models() succesfully completed......................................']\n", "['2020-07-29 09', '51', '35,275', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '51', '35,280', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '51', '35,295', 'INFO', 'Initializing assign_model()']\n", "['2020-07-29 09', '51', '35,297', 'INFO', \"assign_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), transformation=False, verbose=True)']\n", "['2020-07-29 09', '51', '35,297', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '35,298', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '51', '35,298', 'INFO', 'Copying data']\n", "['2020-07-29 09', '51', '35,299', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '51', '35,300', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '51', '35,373', 'INFO', 'Determining Trained Model']\n", "['2020-07-29 09', '51', '35,375', 'INFO', 'Trained Model', 'K-Means Clustering']\n", "['2020-07-29 09', '51', '35,378', 'INFO', '(224, 22)']\n", "['2020-07-29 09', '51', '35,378', 'INFO', 'assign_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '35,468', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '51', '35,470', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), plot=cluster, feature=None, label=False, save=False, system=True)']\n", "['2020-07-29 09', '51', '35,470', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '35,470', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '51', '35,494', 'INFO', 'plot type', 'cluster']\n", "['2020-07-29 09', '51', '35,494', 'INFO', 'SubProcess assign_model() called ==================================']\n", "['2020-07-29 09', '51', '35,494', 'INFO', 'Initializing assign_model()']\n", "['2020-07-29 09', '51', '35,496', 'INFO', \"assign_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), transformation=True, verbose=False)']\n", "['2020-07-29 09', '51', '35,496', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '35,496', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '51', '35,496', 'INFO', 'Copying data']\n", "['2020-07-29 09', '51', '35,497', 'INFO', 'Transformation param set to True. Assigned clusters are attached on transformed dataset.']\n", "['2020-07-29 09', '51', '35,497', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '51', '35,530', 'INFO', 'Determining Trained Model']\n", "['2020-07-29 09', '51', '35,532', 'INFO', 'Trained Model', 'K-Means Clustering']\n", "['2020-07-29 09', '51', '35,533', 'INFO', '(224, 21)']\n", "['2020-07-29 09', '51', '35,533', 'INFO', 'assign_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '35,534', 'INFO', 'SubProcess assign_model() end ==================================']\n", "['2020-07-29 09', '51', '35,549', 'INFO', 'Fitting PCA()']\n", "['2020-07-29 09', '51', '35,565', 'INFO', 'Sorting dataframe']\n", "['2020-07-29 09', '51', '35,570', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '51', '35,796', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '51', '35,796', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '35,812', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '51', '35,813', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), plot=cluster, feature=Country Name, label=True, save=False, system=True)']\n", "['2020-07-29 09', '51', '35,814', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '35,814', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '51', '35,840', 'INFO', 'plot type', 'cluster']\n", "['2020-07-29 09', '51', '35,841', 'INFO', 'SubProcess assign_model() called ==================================']\n", "['2020-07-29 09', '51', '35,841', 'INFO', 'Initializing assign_model()']\n", "['2020-07-29 09', '51', '35,842', 'INFO', \"assign_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), transformation=True, verbose=False)']\n", "['2020-07-29 09', '51', '35,842', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '35,843', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '51', '35,843', 'INFO', 'Copying data']\n", "['2020-07-29 09', '51', '35,844', 'INFO', 'Transformation param set to True. Assigned clusters are attached on transformed dataset.']\n", "['2020-07-29 09', '51', '35,844', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '51', '35,878', 'INFO', 'Determining Trained Model']\n", "['2020-07-29 09', '51', '35,880', 'INFO', 'Trained Model', 'K-Means Clustering']\n", "['2020-07-29 09', '51', '35,880', 'INFO', '(224, 21)']\n", "['2020-07-29 09', '51', '35,881', 'INFO', 'assign_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '35,881', 'INFO', 'SubProcess assign_model() end ==================================']\n", "['2020-07-29 09', '51', '35,897', 'INFO', 'Fitting PCA()']\n", "['2020-07-29 09', '51', '35,916', 'INFO', 'Sorting dataframe']\n", "['2020-07-29 09', '51', '35,923', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '51', '36,095', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '51', '36,190', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '51', '36,190', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '36,205', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '51', '36,206', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), plot=tsne, feature=None, label=False, save=False, system=True)']\n", "['2020-07-29 09', '51', '36,207', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '36,207', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '51', '36,240', 'INFO', 'plot type', 'tsne']\n", "['2020-07-29 09', '51', '36,240', 'INFO', 'SubProcess assign_model() called ==================================']\n", "['2020-07-29 09', '51', '36,240', 'INFO', 'Initializing assign_model()']\n", "['2020-07-29 09', '51', '36,242', 'INFO', \"assign_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), transformation=True, verbose=False)']\n", "['2020-07-29 09', '51', '36,242', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '36,243', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '51', '36,243', 'INFO', 'Copying data']\n", "['2020-07-29 09', '51', '36,244', 'INFO', 'Transformation param set to True. Assigned clusters are attached on transformed dataset.']\n", "['2020-07-29 09', '51', '36,244', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '51', '36,287', 'INFO', 'Determining Trained Model']\n", "['2020-07-29 09', '51', '36,289', 'INFO', 'Trained Model', 'K-Means Clustering']\n", "['2020-07-29 09', '51', '36,289', 'INFO', '(224, 21)']\n", "['2020-07-29 09', '51', '36,290', 'INFO', 'assign_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '36,290', 'INFO', 'SubProcess assign_model() end ==================================']\n", "['2020-07-29 09', '51', '36,294', 'INFO', 'Fitting TSNE()']\n", "['2020-07-29 09', '51', '36,736', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '51', '36,737', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '51', '36,739', 'INFO', \"save_model(model=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '51', '36,739', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '51', '36,784', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '51', '36,809', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), None]']\n", "['2020-07-29 09', '51', '36,809', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '36,810', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '51', '36,901', 'INFO', 'Uploading results into container']\n", "['2020-07-29 09', '51', '36,901', 'INFO', 'Uploading model into container']\n", "['2020-07-29 09', '51', '36,901', 'INFO', 'create_model_container', '10']\n", "['2020-07-29 09', '51', '36,901', 'INFO', 'master_model_container', '10']\n", "['2020-07-29 09', '51', '36,902', 'INFO', 'display_container', '11']\n", "['2020-07-29 09', '51', '36,903', 'INFO', \"LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0)']\n", "['2020-07-29 09', '51', '36,904', 'INFO', 'create_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '36,904', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '51', '36,904', 'INFO', 'create_model(estimator=lightgbm, ensemble=False, method=None, fold=10, round=4, cross_validation=True, verbose=False, system=True)']\n", "['2020-07-29 09', '51', '36,905', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '36,905', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '51', '36,905', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '51', '36,943', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '51', '36,944', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '51', '36,947', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '51', '36,948', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '51', '36,949', 'INFO', 'Light Gradient Boosting Machine Imported succesfully']\n", "['2020-07-29 09', '51', '36,952', 'INFO', 'Checking ensemble method']\n", "['2020-07-29 09', '51', '36,954', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '51', '36,958', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '37,684', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '37,692', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '37,692', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '37,716', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '51', '37,721', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '38,429', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '38,437', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '38,437', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '38,460', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '51', '38,464', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '39,178', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '39,186', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '39,186', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '39,210', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '51', '39,215', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '39,960', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '39,968', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '39,968', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '39,992', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '51', '39,997', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '40,712', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '40,720', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '40,720', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '40,743', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '51', '40,748', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '41,445', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '41,454', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '41,454', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '41,478', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '51', '41,483', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '41,653', 'INFO', 'Sorting dataframe']\n", "['2020-07-29 09', '51', '41,659', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '51', '41,956', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '41,965', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '41,965', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '41,967', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '51', '41,967', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '41,981', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '51', '41,982', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), plot=elbow, feature=None, label=False, save=False, system=True)']\n", "['2020-07-29 09', '51', '41,983', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '41,984', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '51', '41,994', 'INFO', 'Initializing Fold 8']\n", "['2020-07-29 09', '51', '41,997', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '42,027', 'INFO', 'plot type', 'elbow']\n", "['2020-07-29 09', '51', '42,028', 'INFO', 'Fitting KElbowVisualizer()']\n", "['2020-07-29 09', '51', '42,381', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '42,393', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '42,394', 'INFO', 'Compiling Metrics']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "['2020-07-29 09', '51', '42,420', 'INFO', 'Initializing Fold 9']\n", "['2020-07-29 09', '51', '42,425', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '42,790', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '42,799', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '42,799', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '42,822', 'INFO', 'Initializing Fold 10']\n", "['2020-07-29 09', '51', '42,827', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '43,263', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '43,283', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '43,284', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '43,307', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '51', '43,311', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '51', '43,331', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '51', '43,369', 'INFO', 'Extracting Bigrams']\n", "['2020-07-29 09', '51', '43,937', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '51', '43,967', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '51', '44,615', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '51', '44,615', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '51', '44,617', 'INFO', \"save_model(model=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '51', '44,618', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '51', '44,634', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '51', '44,635', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '44,650', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '51', '44,652', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), plot=silhouette, feature=None, label=False, save=False, system=True)']\n", "['2020-07-29 09', '51', '44,656', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '44,656', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '51', '44,669', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '51', '44,688', 'INFO', 'plot type', 'silhouette']\n", "['2020-07-29 09', '51', '44,689', 'INFO', 'Fitting SilhouetteVisualizer()']\n", "['2020-07-29 09', '51', '44,701', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), None]']\n", "['2020-07-29 09', '51', '44,701', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '44,701', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '51', '44,794', 'INFO', 'Uploading results into container']\n", "['2020-07-29 09', '51', '44,794', 'INFO', 'Uploading model into container']\n", "['2020-07-29 09', '51', '44,795', 'INFO', 'create_model_container', '11']\n", "['2020-07-29 09', '51', '44,795', 'INFO', 'master_model_container', '11']\n", "['2020-07-29 09', '51', '44,795', 'INFO', 'display_container', '12']\n", "['2020-07-29 09', '51', '44,798', 'INFO', \"LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0)']\n", "['2020-07-29 09', '51', '44,798', 'INFO', 'create_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '44,800', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '51', '44,906', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '51', '44,908', 'INFO', \"create_model(estimator=ExplainableBoostingRegressor(binning='quantile', early_stopping_rounds=50,\"]\n", "['early_stopping_tolerance=0, feature_names=None,']\n", "['feature_types=None, inner_bags=0, interactions=0,']\n", "[\"learning_rate=0.01, mains='all', max_bins=255,\"]\n", "['max_leaves=3, max_rounds=5000, min_samples_leaf=2,']\n", "['n_jobs=-2, outer_bags=16, random_state=42,']\n", "['validation_size=0.15), ensemble=False, method=None, fold=10, round=4, cross_validation=True, verbose=True, system=True)']\n", "['2020-07-29 09', '51', '44,909', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '44,909', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '51', '44,909', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '51', '45,006', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '51', '45,008', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '51', '45,011', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '51', '45,012', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '51', '45,033', 'INFO', 'Declaring custom model']\n", "['2020-07-29 09', '51', '45,035', 'INFO', 'ExplainableBoostingRegressor Imported succesfully']\n", "['2020-07-29 09', '51', '45,039', 'INFO', 'Checking ensemble method']\n", "['2020-07-29 09', '51', '45,060', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '51', '45,083', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '45,326', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '51', '45,327', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '45,342', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '51', '45,344', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), plot=distance, feature=None, label=False, save=False, system=True)']\n", "['2020-07-29 09', '51', '45,344', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '45,345', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '51', '45,377', 'INFO', 'plot type', 'distance']\n", "['2020-07-29 09', '51', '45,487', 'INFO', 'Fitting InterclusterDistance()']\n", "['2020-07-29 09', '51', '45,564', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '51', '46,194', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '51', '46,194', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '46,209', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '51', '46,212', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), plot=distribution, feature=None, label=False, save=False, system=True)']\n", "['2020-07-29 09', '51', '46,212', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '46,213', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '51', '46,239', 'INFO', 'plot type', 'distribution']\n", "['2020-07-29 09', '51', '46,240', 'INFO', 'SubProcess assign_model() called ==================================']\n", "['2020-07-29 09', '51', '46,240', 'INFO', 'Initializing assign_model()']\n", "['2020-07-29 09', '51', '46,242', 'INFO', \"assign_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), transformation=False, verbose=False)']\n", "['2020-07-29 09', '51', '46,242', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '51', '46,243', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '51', '46,243', 'INFO', 'Copying data']\n", "['2020-07-29 09', '51', '46,244', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '51', '46,295', 'INFO', 'Determining Trained Model']\n", "['2020-07-29 09', '51', '46,297', 'INFO', 'Trained Model', 'K-Means Clustering']\n", "['2020-07-29 09', '51', '46,297', 'INFO', '(224, 22)']\n", "['2020-07-29 09', '51', '46,297', 'INFO', 'assign_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '46,298', 'INFO', 'SubProcess assign_model() end ==================================']\n", "['2020-07-29 09', '51', '46,298', 'INFO', 'Sorting dataframe']\n", "['2020-07-29 09', '51', '46,312', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '51', '46,914', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '51', '46,914', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '47,206', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '51', '47,208', 'INFO', \"save_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), model_name=kmeans, verbose=True)']\n", "['2020-07-29 09', '51', '47,209', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '51', '47,243', 'INFO', 'kmeans.pkl saved in current working directory']\n", "['2020-07-29 09', '51', '47,273', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True,']\n", "[\"features_todrop=['Country Name'],\"]\n", "[\"ml_usecase='regression',\"]\n", "['numerical_features=[],']\n", "[\"target='dummy_target',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_...']\n", "[\"target='dummy_target')),\"]\n", "[\"('feature_time',\"]\n", "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n", "[\"('group', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n", "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('dummy', Dummify(target='dummy_target')),\"]\n", "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n", "[\"('pca', Empty())],\"]\n", "[\"verbose=False), KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0)]']\n", "['2020-07-29 09', '51', '47,275', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '47,520', 'INFO', 'Initializing deploy_model()']\n", "['2020-07-29 09', '51', '47,522', 'INFO', \"deploy_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "[\"random_state=123, tol=0.0001, verbose=0), model_name=kmeans-aws, authentication={'bucket'\", \"'pycaret-test'}, platform=aws)\"]\n", "['2020-07-29 09', '51', '47,522', 'INFO', 'Platform', 'AWS S3']\n", "['2020-07-29 09', '51', '49,570', 'INFO', 'Saving model in current working directory']\n", "['2020-07-29 09', '51', '49,571', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '51', '49,571', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '51', '49,573', 'INFO', \"save_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0), model_name=kmeans-aws, verbose=False)']\n", "['2020-07-29 09', '51', '49,574', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '51', '49,638', 'INFO', 'kmeans-aws.pkl saved in current working directory']\n", "['2020-07-29 09', '51', '49,661', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True,']\n", "[\"features_todrop=['Country Name'],\"]\n", "[\"ml_usecase='regression',\"]\n", "['numerical_features=[],']\n", "[\"target='dummy_target',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_...']\n", "[\"target='dummy_target')),\"]\n", "[\"('feature_time',\"]\n", "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n", "[\"('group', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n", "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('dummy', Dummify(target='dummy_target')),\"]\n", "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n", "[\"('pca', Empty())],\"]\n", "[\"verbose=False), KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0)]']\n", "['2020-07-29 09', '51', '49,661', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '49,661', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '51', '49,661', 'INFO', 'Initializing S3 client']\n", "['2020-07-29 09', '51', '51,288', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '51,296', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '51,296', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '51,340', 'INFO', \"KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n", "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n", "['random_state=123, tol=0.0001, verbose=0)']\n", "['2020-07-29 09', '51', '51,340', 'INFO', 'deploy_model() succesfully completed......................................']\n", "['2020-07-29 09', '51', '51,362', 'INFO', 'Initializing get_config()']\n", "['2020-07-29 09', '51', '51,363', 'INFO', 'get_config(variable=X)']\n", "['2020-07-29 09', '51', '51,363', 'INFO', 'Global variable', 'X returned']\n", "['2020-07-29 09', '51', '51,364', 'INFO', 'get_config() succesfully completed......................................']\n", "['2020-07-29 09', '51', '51,382', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '51', '51,410', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '51,471', 'INFO', 'Initializing get_config()']\n", "['2020-07-29 09', '51', '51,472', 'INFO', 'get_config(variable=seed)']\n", "['2020-07-29 09', '51', '51,472', 'INFO', 'Global variable', 'seed returned']\n", "['2020-07-29 09', '51', '51,472', 'INFO', 'get_config() succesfully completed......................................']\n", "['2020-07-29 09', '51', '51,496', 'INFO', 'Initializing set_config()']\n", "['2020-07-29 09', '51', '51,496', 'INFO', 'set_config(variable=seed, value=999)']\n", "['2020-07-29 09', '51', '51,497', 'INFO', 'Global variable', 'seed updated']\n", "['2020-07-29 09', '51', '51,497', 'INFO', 'set_config() succesfully completed......................................']\n", "['2020-07-29 09', '51', '51,566', 'INFO', 'Initializing get_config()']\n", "['2020-07-29 09', '51', '51,567', 'INFO', 'get_config(variable=seed)']\n", "['2020-07-29 09', '51', '51,567', 'INFO', 'Global variable', 'seed returned']\n", "['2020-07-29 09', '51', '51,567', 'INFO', 'get_config() succesfully completed......................................']\n", "['2020-07-29 09', '51', '55,073', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '55,081', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '55,082', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '55,166', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '51', '55,192', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '51', '58,548', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '51', '58,557', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '51', '58,558', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '51', '58,658', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '51', '58,681', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '02,035', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '02,042', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '52', '02,042', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '02,134', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '52', '02,165', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '05,861', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '05,867', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '52', '05,868', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '05,957', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '52', '05,982', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '09,038', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '09,047', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '52', '09,047', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '09,126', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '52', '09,151', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '11,650', 'INFO', 'Extracting Trigrams']\n", "['2020-07-29 09', '52', '13,611', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '13,615', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '52', '13,616', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '13,680', 'INFO', 'Initializing Fold 8']\n", "['2020-07-29 09', '52', '13,704', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '16,878', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '16,883', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '52', '16,883', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '16,939', 'INFO', 'Initializing Fold 9']\n", "['2020-07-29 09', '52', '16,958', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '20,101', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '20,104', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '52', '20,105', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '20,170', 'INFO', 'Initializing Fold 10']\n", "['2020-07-29 09', '52', '20,191', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '23,876', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '23,881', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '52', '23,882', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '23,948', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '52', '23,953', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '52', '23,983', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '52', '26,603', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '52', '27,300', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '52', '27,301', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '52', '27,303', 'INFO', \"save_model(model=ExplainableBoostingRegressor(binning='quantile', early_stopping_rounds=50,\"]\n", "['early_stopping_tolerance=0,']\n", "[\"feature_names=['age', 'bmi', 'sex_female',\"]\n", "[\"'sex_male', 'children_0',\"]\n", "[\"'children_1', 'children_2',\"]\n", "[\"'children_3', 'children_4',\"]\n", "[\"'children_5', 'smoker_no',\"]\n", "[\"'smoker_yes', 'region_northeast',\"]\n", "[\"'region_northwest',\"]\n", "[\"'region_southeast',\"]\n", "[\"'region_southwest'],\"]\n", "[\"feature_types=['con...\"]\n", "[\"'categorical', 'categorical',\"]\n", "[\"'categorical', 'categorical',\"]\n", "[\"'categorical', 'categorical',\"]\n", "[\"'categorical', 'categorical',\"]\n", "[\"'categorical', 'categorical',\"]\n", "[\"'categorical', 'categorical'],\"]\n", "['inner_bags=0, interactions=0, learning_rate=0.01,']\n", "[\"mains='all', max_bins=255, max_leaves=3,\"]\n", "['max_rounds=5000, min_samples_leaf=2, n_jobs=-2,']\n", "['outer_bags=16, random_state=42,']\n", "['validation_size=0.15), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '52', '27,303', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '52', '27,337', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '52', '27,355', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), ExplainableBoostingRegressor(binning='quantile', early_stopping_rounds=50,\"]\n", "['early_stopping_tolerance=0,']\n", "[\"feature_names=['age', 'bmi', 'sex_female',\"]\n", "[\"'sex_male', 'children_0',\"]\n", "[\"'children_1', 'children_2',\"]\n", "[\"'children_3', 'children_4',\"]\n", "[\"'children_5', 'smoker_no',\"]\n", "[\"'smoker_yes', 'region_northeast',\"]\n", "[\"'region_northwest',\"]\n", "[\"'region_southeast',\"]\n", "[\"'region_southwest'],\"]\n", "[\"feature_types=['con...\"]\n", "[\"'categorical', 'categorical',\"]\n", "[\"'categorical', 'categorical',\"]\n", "[\"'categorical', 'categorical',\"]\n", "[\"'categorical', 'categorical',\"]\n", "[\"'categorical', 'categorical',\"]\n", "[\"'categorical', 'categorical'],\"]\n", "['inner_bags=0, interactions=0, learning_rate=0.01,']\n", "[\"mains='all', max_bins=255, max_leaves=3,\"]\n", "['max_rounds=5000, min_samples_leaf=2, n_jobs=-2,']\n", "['outer_bags=16, random_state=42,']\n", "['validation_size=0.15), None]']\n", "['2020-07-29 09', '52', '27,356', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '52', '27,356', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '52', '27,417', 'INFO', 'Uploading results into container']\n", "['2020-07-29 09', '52', '27,418', 'INFO', 'Uploading model into container']\n", "['2020-07-29 09', '52', '27,557', 'INFO', 'create_model_container', '12']\n", "['2020-07-29 09', '52', '27,557', 'INFO', 'master_model_container', '12']\n", "['2020-07-29 09', '52', '27,558', 'INFO', 'display_container', '13']\n", "['2020-07-29 09', '52', '27,561', 'INFO', \"ExplainableBoostingRegressor(binning='quantile', early_stopping_rounds=50,\"]\n", "['early_stopping_tolerance=0,']\n", "[\"feature_names=['age', 'bmi', 'sex_female',\"]\n", "[\"'sex_male', 'children_0',\"]\n", "[\"'children_1', 'children_2',\"]\n", "[\"'children_3', 'children_4',\"]\n", "[\"'children_5', 'smoker_no',\"]\n", "[\"'smoker_yes', 'region_northeast',\"]\n", "[\"'region_northwest',\"]\n", "[\"'region_southeast',\"]\n", "[\"'region_southwest'],\"]\n", "[\"feature_types=['con...\"]\n", "[\"'categorical', 'categorical',\"]\n", "[\"'categorical', 'categorical',\"]\n", "[\"'categorical', 'categorical',\"]\n", "[\"'categorical', 'categorical',\"]\n", "[\"'categorical', 'categorical',\"]\n", "[\"'categorical', 'categorical'],\"]\n", "['inner_bags=0, interactions=0, learning_rate=0.01,']\n", "[\"mains='all', max_bins=255, max_leaves=3,\"]\n", "['max_rounds=5000, min_samples_leaf=2, n_jobs=-2,']\n", "['outer_bags=16, random_state=42,']\n", "['validation_size=0.15)']\n", "['2020-07-29 09', '52', '27,562', 'INFO', 'create_model() succesfully completed......................................']\n", "['2020-07-29 09', '52', '30,619', 'INFO', 'Initializing tune_model()']\n", "['2020-07-29 09', '52', '30,622', 'INFO', \"tune_model(estimator=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), fold=10, round=4, n_iter=50, custom_grid=None, optimize=MAE, choose_better=False, verbose=True)']\n", "['2020-07-29 09', '52', '30,622', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '52', '30,623', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '52', '30,623', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '52', '30,693', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '52', '30,695', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '52', '30,702', 'INFO', 'Checking base model']\n", "['2020-07-29 09', '52', '30,703', 'INFO', 'Base model', 'Light Gradient Boosting Machine']\n", "['2020-07-29 09', '52', '30,704', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '52', '30,705', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '52', '30,721', 'INFO', 'Defining Hyperparameters']\n", "['2020-07-29 09', '52', '30,722', 'INFO', 'Initializing RandomizedSearchCV']\n", "['2020-07-29 09', '52', '39,151', 'INFO', 'Random search completed']\n", "['2020-07-29 09', '52', '39,167', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '52', '39,190', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '39,219', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '39,229', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '39,312', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '52', '39,335', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '39,371', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '39,381', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '39,457', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '52', '39,477', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '39,510', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '39,519', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '39,596', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '52', '39,618', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '39,651', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '39,659', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '39,737', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '52', '39,758', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '39,790', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '39,797', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '39,867', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '52', '39,889', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '39,917', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '39,927', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '40,004', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '52', '40,025', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '40,054', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '40,063', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '40,146', 'INFO', 'Initializing Fold 8']\n", "['2020-07-29 09', '52', '40,167', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '40,195', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '40,204', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '40,280', 'INFO', 'Initializing Fold 9']\n", "['2020-07-29 09', '52', '40,303', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '40,331', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '40,339', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '40,416', 'INFO', 'Initializing Fold 10']\n", "['2020-07-29 09', '52', '40,435', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '40,464', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '40,472', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '40,554', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '52', '40,558', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '52', '40,581', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '52', '40,608', 'INFO', 'Uploading results into container']\n", "['2020-07-29 09', '52', '40,609', 'INFO', 'Uploading model into container']\n", "['2020-07-29 09', '52', '40,609', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '52', '40,851', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '52', '40,852', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '52', '40,853', 'INFO', \"save_model(model=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.3, max_depth=70,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.2,']\n", "['n_estimators=10, n_jobs=-1, num_leaves=10, objective=None,']\n", "['random_state=123, reg_alpha=0.4, reg_lambda=0.1, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '52', '40,854', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '52', '40,872', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '52', '40,893', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', 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"['2020-07-29 09', '52', '41,437', 'INFO', 'master_model_container', '13']\n", "['2020-07-29 09', '52', '41,437', 'INFO', 'display_container', '14']\n", "['2020-07-29 09', '52', '41,439', 'INFO', \"LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.3, max_depth=70,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.2,']\n", "['n_estimators=10, n_jobs=-1, num_leaves=10, objective=None,']\n", "['random_state=123, reg_alpha=0.4, reg_lambda=0.1, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0)']\n", "['2020-07-29 09', '52', '41,439', 'INFO', 'tune_model() succesfully completed......................................']\n", "['2020-07-29 09', '52', '41,472', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '52', '41,472', 'INFO', 'create_model(estimator=dt, ensemble=False, method=None, fold=10, round=4, cross_validation=True, verbose=True, system=True)']\n", "['2020-07-29 09', '52', '41,473', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '52', '41,473', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '52', '41,474', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '52', '41,532', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '52', '41,533', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '52', '41,536', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '52', '41,536', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '52', '41,549', 'INFO', 'Decision Tree Regressor Imported succesfully']\n", "['2020-07-29 09', '52', '41,551', 'INFO', 'Checking ensemble method']\n", "['2020-07-29 09', '52', '41,567', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '52', '41,583', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '41,601', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '41,607', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '52', '41,607', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '41,659', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '52', '41,676', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '41,690', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '41,695', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '52', '41,696', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '41,738', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '52', '41,752', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '41,766', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '41,770', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '52', '41,770', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '41,808', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '52', '41,821', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '41,835', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '41,840', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '52', '41,841', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '41,883', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '52', '41,896', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '41,909', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '41,913', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '52', '41,913', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '41,951', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '52', '41,966', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '41,988', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '41,993', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '52', '41,994', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '42,036', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '52', '42,053', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '42,066', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 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'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '42,307', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '52', '42,308', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '42,349', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '52', '42,354', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '52', '42,375', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '52', '42,392', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '52', '42,792', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '52', '42,792', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '52', '42,793', 'INFO', \"save_model(model=DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\"]\n", "['max_features=None, max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0, min_impurity_split=None,']\n", "['min_samples_leaf=1, min_samples_split=2,']\n", "[\"min_weight_fraction_leaf=0.0, 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09', '52', '43,112', 'INFO', 'BaggingRegressor() succesfully imported']\n", "['2020-07-29 09', '52', '43,124', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '52', '43,126', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '52', '43,144', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '43,512', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '43,526', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '43,566', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '52', '43,581', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '43,931', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '43,945', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '43,985', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '52', '43,998', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '44,363', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '44,379', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '44,415', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '52', '44,427', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '44,804', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '44,820', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '44,866', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '52', '44,881', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '45,255', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '45,269', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '45,314', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '52', '45,328', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '45,699', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '45,713', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '45,756', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '52', '45,772', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '46,138', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '46,152', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '46,190', 'INFO', 'Initializing Fold 8']\n", "['2020-07-29 09', '52', '46,202', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '46,527', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '46,541', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '46,581', 'INFO', 'Initializing Fold 9']\n", "['2020-07-29 09', '52', '46,595', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '46,788', 'INFO', 'Lemmatizing tokens']\n", "['2020-07-29 09', '52', '46,970', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '46,984', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '47,028', 'INFO', 'Initializing Fold 10']\n", "['2020-07-29 09', '52', '47,042', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '47,402', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '47,424', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '47,473', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '52', '47,474', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '52', '47,494', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '52', '47,880', 'INFO', 'Uploading results into container']\n", "['2020-07-29 09', '52', '47,880', 'INFO', 'Uploading model into container']\n", "['2020-07-29 09', '52', '47,882', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '52', '48,062', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '52', '48,062', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '52', '48,065', 'INFO', 'save_model(model=BaggingRegressor(base_estimator=DecisionTreeRegressor(ccp_alpha=0.0,']\n", "[\"criterion='mse',\"]\n", "['max_depth=None,']\n", "['max_features=None,']\n", "['max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0,']\n", "['min_impurity_split=None,']\n", "['min_samples_leaf=1,']\n", "['min_samples_split=2,']\n", "['min_weight_fraction_leaf=0.0,']\n", 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bootstrap_features=False, max_features=1.0,']\n", "['max_samples=1.0, n_estimators=50, n_jobs=None, oob_score=False,']\n", "['random_state=123, verbose=0, warm_start=False), None]']\n", "['2020-07-29 09', '52', '48,139', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '52', '48,139', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '52', '48,502', 'INFO', 'create_model_container', '15']\n", "['2020-07-29 09', '52', '48,503', 'INFO', 'master_model_container', '15']\n", "['2020-07-29 09', '52', '48,503', 'INFO', 'display_container', '16']\n", "['2020-07-29 09', '52', '48,505', 'INFO', 'BaggingRegressor(base_estimator=DecisionTreeRegressor(ccp_alpha=0.0,']\n", "[\"criterion='mse',\"]\n", "['max_depth=None,']\n", "['max_features=None,']\n", "['max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0,']\n", "['min_impurity_split=None,']\n", "['min_samples_leaf=1,']\n", "['min_samples_split=2,']\n", "['min_weight_fraction_leaf=0.0,']\n", "[\"presort='deprecated',\"]\n", "['random_state=123,']\n", "[\"splitter='best'),\"]\n", "['bootstrap=True, bootstrap_features=False, max_features=1.0,']\n", "['max_samples=1.0, n_estimators=50, n_jobs=None, oob_score=False,']\n", "['random_state=123, verbose=0, warm_start=False)']\n", "['2020-07-29 09', '52', '48,505', 'INFO', 'ensemble_model() succesfully completed......................................']\n", "['2020-07-29 09', '52', '48,515', 'INFO', 'Initializing ensemble_model()']\n", "['2020-07-29 09', '52', '48,517', 'INFO', \"ensemble_model(estimator=DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\"]\n", "['max_features=None, max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0, min_impurity_split=None,']\n", "['min_samples_leaf=1, min_samples_split=2,']\n", "[\"min_weight_fraction_leaf=0.0, presort='deprecated',\"]\n", "[\"random_state=123, splitter='best'), method=Boosting, fold=10, n_estimators=10, round=4, choose_better=False, optimize=R2, verbose=True)\"]\n", "['2020-07-29 09', '52', '48,517', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '52', '48,517', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '52', '48,517', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '52', '48,570', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '52', '48,571', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '52', '48,573', 'INFO', 'Checking base model']\n", "['2020-07-29 09', '52', '48,575', 'INFO', 'Base model', 'Decision Tree']\n", "['2020-07-29 09', '52', '48,585', 'INFO', 'AdaBoostRegressor() succesfully imported']\n", "['2020-07-29 09', '52', '48,596', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '52', '48,598', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '52', '48,612', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '48,718', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '48,726', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '48,781', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '52', '48,799', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '48,894', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '48,902', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '48,955', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '52', '48,969', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '49,065', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '49,075', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '49,121', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '52', '49,135', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '49,219', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '49,226', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '49,271', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '52', '49,284', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '49,367', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '49,374', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '49,415', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '52', '49,429', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '49,507', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '49,514', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '49,553', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '52', '49,566', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '49,652', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '49,659', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '49,705', 'INFO', 'Initializing Fold 8']\n", "['2020-07-29 09', '52', '49,718', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '49,799', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '49,804', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '49,848', 'INFO', 'Initializing Fold 9']\n", "['2020-07-29 09', '52', '49,861', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '49,941', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '49,947', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '49,991', 'INFO', 'Initializing Fold 10']\n", "['2020-07-29 09', '52', '50,004', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '52', '50,086', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '52', '50,092', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '52', '50,133', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '52', '50,135', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '52', '50,162', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '52', '50,250', 'INFO', 'Uploading results into container']\n", "['2020-07-29 09', '52', '50,250', 'INFO', 'Uploading model into container']\n", "['2020-07-29 09', '52', '50,252', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '52', '50,411', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '52', '50,411', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '52', '50,414', 'INFO', 'save_model(model=AdaBoostRegressor(base_estimator=DecisionTreeRegressor(ccp_alpha=0.0,']\n", "[\"criterion='mse',\"]\n", "['max_depth=None,']\n", "['max_features=None,']\n", "['max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0,']\n", "['min_impurity_split=None,']\n", "['min_samples_leaf=1,']\n", "['min_samples_split=2,']\n", "['min_weight_fraction_leaf=0.0,']\n", "[\"presort='deprecated',\"]\n", "['random_state=123,']\n", "[\"splitter='best'),\"]\n", "[\"learning_rate=1.0, loss='linear', n_estimators=10,\"]\n", "['random_state=123), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '52', '50,414', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '52', '50,439', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '52', '50,456', 'INFO', 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'17']\n", "['2020-07-29 09', '52', '50,790', 'INFO', 'AdaBoostRegressor(base_estimator=DecisionTreeRegressor(ccp_alpha=0.0,']\n", "[\"criterion='mse',\"]\n", "['max_depth=None,']\n", "['max_features=None,']\n", "['max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0,']\n", "['min_impurity_split=None,']\n", "['min_samples_leaf=1,']\n", "['min_samples_split=2,']\n", "['min_weight_fraction_leaf=0.0,']\n", "[\"presort='deprecated',\"]\n", "['random_state=123,']\n", "[\"splitter='best'),\"]\n", "[\"learning_rate=1.0, loss='linear', n_estimators=10,\"]\n", "['random_state=123)']\n", "['2020-07-29 09', '52', '50,790', 'INFO', 'ensemble_model() succesfully completed......................................']\n", "['2020-07-29 09', '52', '50,799', 'INFO', 'Initializing blend_models()']\n", "['2020-07-29 09', '52', '50,799', 'INFO', 'blend_models(estimator_list=All, fold=10, round=4, choose_better=False, optimize=R2, turbo=True, verbose=True)']\n", "['2020-07-29 09', '52', '50,799', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '52', '50,800', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '52', '50,800', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '52', '50,875', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '52', '50,876', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '52', '50,878', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '52', '50,879', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '52', '50,889', 'INFO', 'Importing untrained models']\n", "['2020-07-29 09', '52', '50,890', 'INFO', 'Import successful']\n", "['2020-07-29 09', '52', '50,892', 'INFO', 'Defining model names in estimator_list']\n", "['2020-07-29 09', '52', '59,294', 'INFO', 'n_jobs multiple passed']\n", "['2020-07-29 09', '52', '59,312', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '52', '59,335', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '53', '08,537', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '53', '08,967', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '53', '09,033', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '53', '09,057', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '53', '18,122', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '53', '18,568', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '53', '18,625', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '53', '18,651', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '53', '26,960', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '53', '27,385', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '53', '27,447', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '53', '27,466', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '53', '35,608', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '53', '36,057', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '53', '36,122', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '53', '36,146', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '53', '44,142', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '53', '44,556', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '53', '44,612', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '53', '44,634', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '53', '51,795', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '53', '52,209', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '53', '52,272', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '53', '52,294', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '01,346', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '01,769', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '01,828', 'INFO', 'Initializing Fold 8']\n", "['2020-07-29 09', '54', '01,853', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '08,836', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '09,256', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '09,324', 'INFO', 'Initializing Fold 9']\n", "['2020-07-29 09', '54', '09,344', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '15,456', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '15,888', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '15,954', 'INFO', 'Initializing Fold 10']\n", "['2020-07-29 09', '54', '15,976', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '22,725', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '23,173', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '23,247', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '54', '23,251', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '54', '23,281', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '54', '29,838', 'INFO', 'Uploading results into container']\n", "['2020-07-29 09', '54', '29,838', 'INFO', 'Uploading model into container']\n", "['2020-07-29 09', '54', '29,839', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '54', '29,941', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '54', '29,941', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '54', '29,996', 'INFO', \"save_model(model=VotingRegressor(estimators=[('Linear Regression_0',\"]\n", "['LinearRegression(copy_X=True, fit_intercept=True,']\n", "['n_jobs=-1, normalize=False)),']\n", "[\"('Lasso_1',\"]\n", "['Lasso(alpha=1.0, copy_X=True, fit_intercept=True,']\n", "['max_iter=1000, normalize=False,']\n", "['positive=False, precompute=False,']\n", "[\"random_state=123, selection='cyclic',\"]\n", "['tol=0.0001, warm_start=False)),']\n", "[\"('Ridge_2',\"]\n", "['Ridge(alpha=1.0, copy_X=True...']\n", "['min_child_samples=20,']\n", "['min_child_weight=0.001,']\n", "['min_split_gain=0.0, n_estimators=100,']\n", "['n_jobs=-1, num_leaves=31,']\n", "['objective=None, random_state=123,']\n", "['reg_alpha=0.0, reg_lambda=0.0,']\n", "['silent=True, subsample=1.0,']\n", "['subsample_for_bin=200000,']\n", "['subsample_freq=0)),']\n", "[\"('CatBoost Regressor_21',\"]\n", "[')],']\n", "['n_jobs=-1, verbose=False, weights=None), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '54', '29,996', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '54', '30,258', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '54', '30,381', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), VotingRegressor(estimators=[('Linear Regression_0',\"]\n", "['LinearRegression(copy_X=True, fit_intercept=True,']\n", "['n_jobs=-1, normalize=False)),']\n", "[\"('Lasso_1',\"]\n", "['Lasso(alpha=1.0, copy_X=True, fit_intercept=True,']\n", "['max_iter=1000, normalize=False,']\n", "['positive=False, precompute=False,']\n", "[\"random_state=123, selection='cyclic',\"]\n", "['tol=0.0001, warm_start=False)),']\n", "[\"('Ridge_2',\"]\n", "['Ridge(alpha=1.0, copy_X=True...']\n", "['min_child_samples=20,']\n", "['min_child_weight=0.001,']\n", "['min_split_gain=0.0, n_estimators=100,']\n", "['n_jobs=-1, num_leaves=31,']\n", "['objective=None, random_state=123,']\n", "['reg_alpha=0.0, reg_lambda=0.0,']\n", "['silent=True, subsample=1.0,']\n", "['subsample_for_bin=200000,']\n", "['subsample_freq=0)),']\n", "[\"('CatBoost Regressor_21',\"]\n", "[')],']\n", "['n_jobs=-1, verbose=False, weights=None), None]']\n", "['2020-07-29 09', '54', '30,381', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '54', '30,382', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '54', '31,533', 'INFO', 'create_model_container', '17']\n", "['2020-07-29 09', '54', '31,534', 'INFO', 'master_model_container', '17']\n", "['2020-07-29 09', '54', '31,534', 'INFO', 'display_container', '18']\n", "['2020-07-29 09', '54', '31,606', 'INFO', \"VotingRegressor(estimators=[('Linear Regression_0',\"]\n", "['LinearRegression(copy_X=True, fit_intercept=True,']\n", "['n_jobs=-1, normalize=False)),']\n", "[\"('Lasso_1',\"]\n", "['Lasso(alpha=1.0, copy_X=True, fit_intercept=True,']\n", "['max_iter=1000, normalize=False,']\n", "['positive=False, precompute=False,']\n", "[\"random_state=123, selection='cyclic',\"]\n", "['tol=0.0001, warm_start=False)),']\n", "[\"('Ridge_2',\"]\n", "['Ridge(alpha=1.0, copy_X=True...']\n", "['min_child_samples=20,']\n", "['min_child_weight=0.001,']\n", "['min_split_gain=0.0, n_estimators=100,']\n", "['n_jobs=-1, num_leaves=31,']\n", "['objective=None, random_state=123,']\n", "['reg_alpha=0.0, reg_lambda=0.0,']\n", "['silent=True, subsample=1.0,']\n", "['subsample_for_bin=200000,']\n", "['subsample_freq=0)),']\n", "[\"('CatBoost Regressor_21',\"]\n", "[')],']\n", "['n_jobs=-1, verbose=False, weights=None)']\n", "['2020-07-29 09', '54', '31,606', 'INFO', 'blend_models() succesfully completed......................................']\n", "['2020-07-29 09', '54', '31,626', 'INFO', 'Initializing compare_models()']\n", "['2020-07-29 09', '54', '31,626', 'INFO', \"compare_models(blacklist=None, whitelist=['rf', 'et', 'ada', 'gbr', 'xgboost', 'lightgbm', 'catboost'], fold=5, round=4, sort=R2, n_select=5, turbo=True, verbose=True)\"]\n", "['2020-07-29 09', '54', '31,627', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '54', '31,627', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '54', '31,628', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '54', '31,684', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '54', '31,688', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '54', '31,707', 'INFO', 'Importing untrained models']\n", "['2020-07-29 09', '54', '31,708', 'INFO', 'Import successful']\n", "['2020-07-29 09', '54', '31,723', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '54', '31,724', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '54', '31,724', 'INFO', 'Initializing Random Forest']\n", "['2020-07-29 09', '54', '31,744', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '54', '31,761', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '32,221', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '32,336', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '32,336', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '32,351', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '54', '32,372', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '32,788', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '32,899', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '32,900', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '32,920', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '54', '32,937', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '33,373', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '33,483', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '33,483', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '33,498', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '54', '33,517', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '33,935', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '34,045', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '34,045', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '34,060', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '54', '34,076', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '34,497', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '34,607', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '34,607', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '34,621', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '54', '34,622', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '54', '34,649', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '54', '34,817', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '54', '34,818', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '54', '34,819', 'INFO', \"save_model(model=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\"]\n", "[\"max_depth=None, max_features='auto', max_leaf_nodes=None,\"]\n", "['max_samples=None, min_impurity_decrease=0.0,']\n", "['min_impurity_split=None, min_samples_leaf=1,']\n", "['min_samples_split=2, min_weight_fraction_leaf=0.0,']\n", "['n_estimators=100, n_jobs=-1, oob_score=False,']\n", "['random_state=123, verbose=0, warm_start=False), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '54', '34,819', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '54', '34,913', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '54', '34,925', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\"]\n", "[\"max_depth=None, max_features='auto', max_leaf_nodes=None,\"]\n", "['max_samples=None, min_impurity_decrease=0.0,']\n", "['min_impurity_split=None, min_samples_leaf=1,']\n", "['min_samples_split=2, min_weight_fraction_leaf=0.0,']\n", "['n_estimators=100, n_jobs=-1, oob_score=False,']\n", "['random_state=123, verbose=0, warm_start=False), None]']\n", "['2020-07-29 09', '54', '34,925', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '54', '34,925', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '54', '35,050', 'INFO', 'Initializing Extra Trees Regressor']\n", "['2020-07-29 09', '54', '35,066', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '54', '35,083', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '35,412', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '35,522', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '35,522', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '35,537', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '54', '35,556', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '35,982', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '36,092', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '36,093', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '36,108', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '54', '36,122', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '36,567', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '36,680', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '36,680', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '36,696', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '54', '36,715', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '37,023', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '37,133', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '37,133', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '37,153', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '54', '37,170', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '37,597', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '37,707', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '37,707', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '37,722', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '54', '37,723', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '54', '37,751', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '54', '37,926', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '54', '37,927', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '54', '37,928', 'INFO', \"save_model(model=ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\"]\n", "[\"max_depth=None, max_features='auto', max_leaf_nodes=None,\"]\n", "['max_samples=None, min_impurity_decrease=0.0,']\n", "['min_impurity_split=None, min_samples_leaf=1,']\n", "['min_samples_split=2, min_weight_fraction_leaf=0.0,']\n", "['n_estimators=100, n_jobs=-1, oob_score=False,']\n", "['random_state=123, verbose=0, warm_start=False), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '54', '37,929', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '54', '38,029', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '54', '38,040', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\"]\n", "[\"max_depth=None, max_features='auto', max_leaf_nodes=None,\"]\n", "['max_samples=None, min_impurity_decrease=0.0,']\n", "['min_impurity_split=None, min_samples_leaf=1,']\n", "['min_samples_split=2, min_weight_fraction_leaf=0.0,']\n", "['n_estimators=100, n_jobs=-1, oob_score=False,']\n", "['random_state=123, verbose=0, warm_start=False), None]']\n", "['2020-07-29 09', '54', '38,041', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '54', '38,041', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '54', '38,175', 'INFO', 'Initializing AdaBoost Regressor']\n", "['2020-07-29 09', '54', '38,187', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '54', '38,207', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '38,279', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '38,286', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '38,286', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '38,301', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '54', '38,314', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '38,368', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '38,376', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '38,376', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '38,390', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '54', '38,406', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '38,444', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '38,449', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '38,449', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '38,464', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '54', '38,480', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '38,526', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '38,533', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '38,533', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '38,549', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '54', '38,569', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '38,624', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '38,630', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '38,631', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '38,645', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '54', '38,646', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '54', '38,682', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '54', '38,896', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '54', '38,896', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '54', '38,897', 'INFO', \"save_model(model=AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear',\"]\n", "['n_estimators=50, random_state=123), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '54', '38,897', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '54', '38,927', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '54', '38,946', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear',\"]\n", "['n_estimators=50, random_state=123), None]']\n", "['2020-07-29 09', '54', '38,947', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '54', '38,947', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '54', '39,064', 'INFO', 'Initializing Gradient Boosting Regressor']\n", "['2020-07-29 09', '54', '39,081', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '54', '39,100', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '39,295', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '39,301', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '39,301', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '39,316', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '54', '39,331', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '39,513', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '39,518', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '39,519', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '39,533', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '54', '39,550', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '39,746', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '39,751', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '39,752', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '39,768', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '54', '39,787', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '39,999', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '40,004', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '40,004', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '40,018', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '54', '40,039', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '40,228', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '40,232', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '40,233', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '40,247', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '54', '40,248', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '54', '40,280', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '54', '40,466', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '54', '40,466', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '54', '40,468', 'INFO', \"save_model(model=GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',\"]\n", "[\"init=None, learning_rate=0.1, loss='ls', max_depth=3,\"]\n", "['max_features=None, max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0, min_impurity_split=None,']\n", "['min_samples_leaf=1, min_samples_split=2,']\n", "['min_weight_fraction_leaf=0.0, n_estimators=100,']\n", "[\"n_iter_no_change=None, presort='deprecated',\"]\n", "['random_state=123, subsample=1.0, tol=0.0001,']\n", "['validation_fraction=0.1, verbose=0, warm_start=False), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '54', '40,468', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '54', '40,489', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '54', '40,501', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',\"]\n", "[\"init=None, learning_rate=0.1, loss='ls', max_depth=3,\"]\n", "['max_features=None, max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0, min_impurity_split=None,']\n", "['min_samples_leaf=1, min_samples_split=2,']\n", "['min_weight_fraction_leaf=0.0, n_estimators=100,']\n", "[\"n_iter_no_change=None, presort='deprecated',\"]\n", "['random_state=123, subsample=1.0, tol=0.0001,']\n", "['validation_fraction=0.1, verbose=0, warm_start=False), None]']\n", "['2020-07-29 09', '54', '40,502', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '54', '40,502', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '54', '40,622', 'INFO', 'Initializing Extreme Gradient Boosting']\n", "['2020-07-29 09', '54', '40,638', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '54', '40,656', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '40,847', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '40,854', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '40,854', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '40,883', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '54', '40,909', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '41,117', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '41,123', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '41,123', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '41,155', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '54', '41,181', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '41,415', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '41,421', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '41,422', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '41,447', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '54', '41,475', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '41,708', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '41,715', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '41,715', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '41,746', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '54', '41,771', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '41,983', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '41,989', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '41,989', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '42,022', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '54', '42,023', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '54', '42,085', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '54', '42,348', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '54', '42,349', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '54', '42,358', 'INFO', \"save_model(model=XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\"]\n", "['colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,']\n", "[\"importance_type='gain', interaction_constraints='',\"]\n", "['learning_rate=0.300000012, max_delta_step=0, max_depth=6,']\n", "[\"min_child_weight=1, missing=nan, monotone_constraints='()',\"]\n", "['n_estimators=100, n_jobs=-1, num_parallel_tree=1,']\n", "[\"objective='reg\", \"squarederror', random_state=123, reg_alpha=0,\"]\n", "[\"reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\"]\n", "['validate_parameters=1, verbosity=0), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '54', '42,358', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '54', '42,385', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '54', '42,416', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\"]\n", "['colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,']\n", "[\"importance_type='gain', interaction_constraints='',\"]\n", "['learning_rate=0.300000012, max_delta_step=0, max_depth=6,']\n", "[\"min_child_weight=1, missing=nan, monotone_constraints='()',\"]\n", "['n_estimators=100, n_jobs=-1, num_parallel_tree=1,']\n", "[\"objective='reg\", \"squarederror', random_state=123, reg_alpha=0,\"]\n", "[\"reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\"]\n", "['validate_parameters=1, verbosity=0), None]']\n", "['2020-07-29 09', '54', '42,416', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '54', '42,416', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '54', '42,564', 'INFO', 'Initializing Light Gradient Boosting Machine']\n", "['2020-07-29 09', '54', '42,580', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '54', '42,603', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '42,880', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '42,891', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '42,891', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '42,923', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '54', '42,950', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '43,227', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '43,234', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '43,235', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '43,267', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '54', '43,293', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '43,562', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '43,571', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '43,572', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '43,596', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '54', '43,624', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '43,919', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '43,929', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '43,929', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '43,953', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '54', '43,978', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '44,253', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '44,264', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '44,264', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '44,294', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '54', '44,296', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '54', '44,355', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '54', '44,565', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '54', '44,565', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '54', '44,566', 'INFO', \"save_model(model=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '54', '44,567', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '54', '44,593', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '54', '44,622', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "[\"verbose=False), LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), None]']\n", "['2020-07-29 09', '54', '44,623', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '54', '44,623', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '54', '44,769', 'INFO', 'Initializing CatBoost Regressor']\n", "['2020-07-29 09', '54', '44,781', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '54', '44,803', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '48,223', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '48,231', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '48,231', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '48,249', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '54', '48,268', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '51,685', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '51,691', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '51,692', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '51,710', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '54', '51,728', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '55,194', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '55,200', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '55,200', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '55,221', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '54', '55,236', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '54', '58,639', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '54', '58,644', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '54', '58,644', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '54', '58,660', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '54', '58,675', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '02,043', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '02,049', 'INFO', 'No inverse transformer found']\n", "['2020-07-29 09', '55', '02,050', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '02,064', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '55', '02,065', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '55', '02,101', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '55', '02,280', 'INFO', 'SubProcess save_model() called ==================================']\n", "['2020-07-29 09', '55', '02,280', 'INFO', 'Initializing save_model()']\n", "['2020-07-29 09', '55', '02,280', 'INFO', 'save_model(model=, model_name=Trained Model, verbose=False)']\n", "['2020-07-29 09', '55', '02,280', 'INFO', 'Appending prep pipeline']\n", "['2020-07-29 09', '55', '02,301', 'INFO', 'Trained Model.pkl saved in current working directory']\n", "['2020-07-29 09', '55', '02,315', 'INFO', '[Pipeline(memory=None,']\n", "[\"steps=[('dtypes',\"]\n", "['DataTypes_Auto_infer(categorical_features=[],']\n", "['display_types=True, features_todrop=[],']\n", "[\"ml_usecase='regression',\"]\n", "[\"numerical_features=[], target='charges',\"]\n", "['time_features=[])),']\n", "[\"('imputer',\"]\n", "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n", "[\"numeric_strategy='mean',\"]\n", "['target_variable=None)),']\n", "[\"('new_levels1',\"]\n", "['New_Catagorical_Levels...']\n", "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n", "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n", "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n", "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n", "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n", "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n", "[\"('dfs', Empty()), ('pca', Empty())],\"]\n", "['verbose=False), , None]']\n", "['2020-07-29 09', '55', '02,315', 'INFO', 'save_model() succesfully completed......................................']\n", "['2020-07-29 09', '55', '02,316', 'INFO', 'SubProcess save_model() end ==================================']\n", "['2020-07-29 09', '55', '02,472', 'INFO', 'Finalizing top_n models']\n", "['2020-07-29 09', '55', '02,473', 'INFO', 'SubProcess create_model() called ==================================']\n", "['2020-07-29 09', '55', '02,488', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '55', '02,489', 'INFO', 'create_model(estimator=gbr, ensemble=False, method=None, fold=10, round=4, cross_validation=True, verbose=False, system=False)']\n", "['2020-07-29 09', '55', '02,489', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '55', '02,490', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '55', '02,490', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '55', '02,519', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '55', '02,520', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '55', '02,521', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '55', '02,522', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '55', '02,523', 'INFO', 'Gradient Boosting Regressor Imported succesfully']\n", "['2020-07-29 09', '55', '02,524', 'INFO', 'Checking ensemble method']\n", "['2020-07-29 09', '55', '02,527', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '55', '02,532', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '02,758', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '02,766', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '02,766', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '02,788', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '55', '02,790', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '03,021', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '03,028', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '03,029', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '03,043', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '55', '03,047', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '03,237', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '03,241', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '03,241', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '03,255', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '55', '03,258', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '03,444', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '03,449', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '03,449', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '03,469', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '55', '03,473', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '03,705', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '03,713', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '03,713', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '03,732', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '55', '03,736', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '03,983', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '03,988', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '03,988', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '04,008', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '55', '04,012', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '04,217', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '04,223', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '04,223', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '04,241', 'INFO', 'Initializing Fold 8']\n", "['2020-07-29 09', '55', '04,243', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '04,465', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '04,470', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '04,470', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '04,494', 'INFO', 'Initializing Fold 9']\n", "['2020-07-29 09', '55', '04,498', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '04,748', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '04,754', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '04,755', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '04,775', 'INFO', 'Initializing Fold 10']\n", "['2020-07-29 09', '55', '04,779', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '05,002', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '05,008', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '05,008', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '05,024', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '55', '05,026', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '55', '05,039', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '55', '05,288', 'INFO', 'Uploading results into container']\n", "['2020-07-29 09', '55', '05,289', 'INFO', 'Uploading model into container']\n", "['2020-07-29 09', '55', '05,289', 'INFO', 'create_model_container', '18']\n", "['2020-07-29 09', '55', '05,289', 'INFO', 'master_model_container', '18']\n", "['2020-07-29 09', '55', '05,290', 'INFO', 'display_container', '19']\n", "['2020-07-29 09', '55', '05,291', 'INFO', \"GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',\"]\n", "[\"init=None, learning_rate=0.1, loss='ls', max_depth=3,\"]\n", "['max_features=None, max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0, min_impurity_split=None,']\n", "['min_samples_leaf=1, min_samples_split=2,']\n", "['min_weight_fraction_leaf=0.0, n_estimators=100,']\n", "[\"n_iter_no_change=None, presort='deprecated',\"]\n", "['random_state=123, subsample=1.0, tol=0.0001,']\n", "['validation_fraction=0.1, verbose=0, warm_start=False)']\n", "['2020-07-29 09', '55', '05,291', 'INFO', 'create_model() succesfully completed......................................']\n", "['2020-07-29 09', '55', '05,303', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '55', '05,304', 'INFO', 'create_model(estimator=catboost, ensemble=False, method=None, fold=10, round=4, cross_validation=True, verbose=False, system=False)']\n", "['2020-07-29 09', '55', '05,304', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '55', '05,305', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '55', '05,305', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '55', '05,345', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '55', '05,347', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '55', '05,350', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '55', '05,350', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '55', '05,352', 'INFO', 'CatBoost Regressor Imported succesfully']\n", "['2020-07-29 09', '55', '05,354', 'INFO', 'Checking ensemble method']\n", "['2020-07-29 09', '55', '05,356', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '55', '05,362', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '08,956', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '08,962', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '08,962', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '08,982', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '55', '08,986', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '12,438', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '12,444', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '12,444', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '12,461', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '55', '12,464', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '16,098', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '16,105', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '16,105', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '16,122', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '55', '16,125', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '19,576', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '19,581', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '19,581', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '19,596', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '55', '19,599', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '20,759', 'INFO', 'Removing stopwords after lemmatizing']\n", "['2020-07-29 09', '55', '23,020', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '23,026', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '23,026', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '23,039', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '55', '23,044', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '25,402', 'INFO', 'Creating corpus and dictionary']\n", "['2020-07-29 09', '55', '26,262', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '26,268', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '26,268', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '26,281', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '55', '26,285', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '29,637', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '29,643', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '29,643', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '29,657', 'INFO', 'Initializing Fold 8']\n", "['2020-07-29 09', '55', '29,661', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '29,860', 'INFO', 'Compiling processed text']\n", "['2020-07-29 09', '55', '29,892', 'INFO', 'Compiling information grid']\n", "['2020-07-29 09', '55', '31,213', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '55', '32,285', 'INFO', 'SubProcess plot_model() called ==================================']\n", "['2020-07-29 09', '55', '32,285', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '55', '32,286', 'INFO', 'plot_model(model=None, plot=frequency, topic_num=None, save=True, system=False)']\n", "['2020-07-29 09', '55', '32,286', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '55', '32,286', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '55', '33,014', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '33,021', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '33,021', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '33,035', 'INFO', 'Initializing Fold 9']\n", "['2020-07-29 09', '55', '33,039', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '36,232', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '36,238', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '36,238', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '36,254', 'INFO', 'Initializing Fold 10']\n", "['2020-07-29 09', '55', '36,258', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '37,542', 'INFO', 'save_param set to True']\n", "['2020-07-29 09', '55', '37,543', 'INFO', 'plot type', 'frequency']\n", "['2020-07-29 09', '55', '37,543', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '55', '37,543', 'WARNING', 'topic_num set to None. Plot generated at corpus level.']\n", "['2020-07-29 09', '55', '37,545', 'INFO', 'Fitting CountVectorizer()']\n", "['2020-07-29 09', '55', '39,540', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '39,547', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '39,547', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '39,561', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '55', '39,563', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '55', '39,575', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '55', '40,917', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '55', '41,103', 'INFO', \"Saving 'Word Frequency.html' in current active directory\"]\n", "['2020-07-29 09', '55', '41,103', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '55', '42,527', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '55', '42,527', 'INFO', 'plot_model(model=None, plot=bigram, topic_num=None, save=True, system=False)']\n", "['2020-07-29 09', '55', '42,528', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '55', '42,528', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '55', '42,554', 'INFO', 'save_param set to True']\n", "['2020-07-29 09', '55', '42,555', 'INFO', 'plot type', 'bigram']\n", "['2020-07-29 09', '55', '42,555', 'WARNING', 'topic_num set to None. Plot generated at corpus level.']\n", "['2020-07-29 09', '55', '42,555', 'INFO', 'Fitting CountVectorizer()']\n", "['2020-07-29 09', '55', '43,017', 'INFO', 'Uploading results into container']\n", "['2020-07-29 09', '55', '43,017', 'INFO', 'Uploading model into container']\n", "['2020-07-29 09', '55', '43,017', 'INFO', 'create_model_container', '19']\n", "['2020-07-29 09', '55', '43,018', 'INFO', 'master_model_container', '19']\n", "['2020-07-29 09', '55', '43,018', 'INFO', 'display_container', '20']\n", "['2020-07-29 09', '55', '43,018', 'INFO', '']\n", "['2020-07-29 09', '55', '43,018', 'INFO', 'create_model() succesfully completed......................................']\n", "['2020-07-29 09', '55', '43,032', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '55', '43,032', 'INFO', 'create_model(estimator=rf, ensemble=False, method=None, fold=10, round=4, cross_validation=True, verbose=False, system=False)']\n", "['2020-07-29 09', '55', '43,032', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '55', '43,032', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '55', '43,033', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '55', '43,062', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '55', '43,064', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '55', '43,066', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '55', '43,066', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '55', '43,067', 'INFO', 'Random Forest Regressor Imported succesfully']\n", "['2020-07-29 09', '55', '43,069', 'INFO', 'Checking ensemble method']\n", "['2020-07-29 09', '55', '43,072', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '55', '43,077', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '43,672', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '43,782', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '43,782', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '43,797', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '55', '43,800', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '44,214', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '44,325', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '44,325', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '44,342', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '55', '44,345', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '44,759', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '44,870', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '44,870', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '44,883', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '55', '44,886', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '45,322', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '45,433', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '45,433', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '45,448', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '55', '45,452', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '45,877', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '45,991', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '45,991', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '46,005', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '55', '46,009', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '46,411', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '46,521', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '46,521', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '46,535', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '55', '46,539', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '46,969', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '47,078', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '47,078', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '47,092', 'INFO', 'Initializing Fold 8']\n", "['2020-07-29 09', '55', '47,095', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '47,334', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '55', '47,387', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '55', '47,494', 'INFO', \"Saving 'Bigram.html' in current active directory\"]\n", "['2020-07-29 09', '55', '47,494', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '55', '47,496', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '47,608', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '47,608', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '47,623', 'INFO', 'Initializing Fold 9']\n", "['2020-07-29 09', '55', '47,627', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '48,136', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '48,247', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '48,247', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '48,259', 'INFO', 'Initializing Fold 10']\n", "['2020-07-29 09', '55', '48,262', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '48,263', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '55', '48,264', 'INFO', 'plot_model(model=None, plot=trigram, topic_num=None, save=True, system=False)']\n", "['2020-07-29 09', '55', '48,264', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '55', '48,264', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '55', '48,278', 'INFO', 'save_param set to True']\n", "['2020-07-29 09', '55', '48,278', 'INFO', 'plot type', 'trigram']\n", "['2020-07-29 09', '55', '48,279', 'WARNING', 'topic_num set to None. Plot generated at corpus level.']\n", "['2020-07-29 09', '55', '48,660', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '48,775', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '48,776', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '48,792', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '55', '48,796', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '55', '48,807', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '55', '49,221', 'INFO', 'Uploading results into container']\n", "['2020-07-29 09', '55', '49,222', 'INFO', 'Uploading model into container']\n", "['2020-07-29 09', '55', '49,222', 'INFO', 'create_model_container', '20']\n", "['2020-07-29 09', '55', '49,222', 'INFO', 'master_model_container', '20']\n", "['2020-07-29 09', '55', '49,222', 'INFO', 'display_container', '21']\n", "['2020-07-29 09', '55', '49,224', 'INFO', \"RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\"]\n", "[\"max_depth=None, max_features='auto', max_leaf_nodes=None,\"]\n", "['max_samples=None, min_impurity_decrease=0.0,']\n", "['min_impurity_split=None, min_samples_leaf=1,']\n", "['min_samples_split=2, min_weight_fraction_leaf=0.0,']\n", "['n_estimators=100, n_jobs=-1, oob_score=False,']\n", "['random_state=123, verbose=0, warm_start=False)']\n", "['2020-07-29 09', '55', '49,224', 'INFO', 'create_model() succesfully completed......................................']\n", "['2020-07-29 09', '55', '49,234', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '55', '49,234', 'INFO', 'create_model(estimator=lightgbm, ensemble=False, method=None, fold=10, round=4, cross_validation=True, verbose=False, system=False)']\n", "['2020-07-29 09', '55', '49,234', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '55', '49,235', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '55', '49,235', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '55', '49,260', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '55', '49,262', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '55', '49,264', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '55', '49,264', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '55', '49,265', 'INFO', 'Light Gradient Boosting Machine Imported succesfully']\n", "['2020-07-29 09', '55', '49,266', 'INFO', 'Checking ensemble method']\n", "['2020-07-29 09', '55', '49,267', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '55', '49,273', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '49,550', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '49,560', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '49,560', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '49,586', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '55', '49,591', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '49,856', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '49,866', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '49,867', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '49,893', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '55', '49,898', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '50,169', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '50,178', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '50,179', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '50,202', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '55', '50,206', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '50,475', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '50,484', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '50,484', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '50,509', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '55', '50,514', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '50,791', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '50,801', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '50,801', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '50,827', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '55', '50,832', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '51,109', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '51,119', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '51,119', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '51,143', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '55', '51,149', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '51,422', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '51,432', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '51,433', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '51,462', 'INFO', 'Initializing Fold 8']\n", "['2020-07-29 09', '55', '51,467', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '51,751', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '51,758', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '51,758', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '51,784', 'INFO', 'Initializing Fold 9']\n", "['2020-07-29 09', '55', '51,788', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '52,038', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '52,048', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '52,048', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '52,072', 'INFO', 'Initializing Fold 10']\n", "['2020-07-29 09', '55', '52,076', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '52,359', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '52,369', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '52,369', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '52,393', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '55', '52,396', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '55', '52,414', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '55', '52,685', 'INFO', 'Uploading results into container']\n", "['2020-07-29 09', '55', '52,685', 'INFO', 'Uploading model into container']\n", "['2020-07-29 09', '55', '52,685', 'INFO', 'create_model_container', '21']\n", "['2020-07-29 09', '55', '52,686', 'INFO', 'master_model_container', '21']\n", "['2020-07-29 09', '55', '52,686', 'INFO', 'display_container', '22']\n", "['2020-07-29 09', '55', '52,688', 'INFO', \"LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0)']\n", "['2020-07-29 09', '55', '52,688', 'INFO', 'create_model() succesfully completed......................................']\n", "['2020-07-29 09', '55', '52,704', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '55', '52,704', 'INFO', 'create_model(estimator=et, ensemble=False, method=None, fold=10, round=4, cross_validation=True, verbose=False, system=False)']\n", "['2020-07-29 09', '55', '52,704', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '55', '52,704', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '55', '52,705', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '55', '52,745', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '55', '52,746', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '55', '52,749', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '55', '52,750', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '55', '52,751', 'INFO', 'Extra Trees Regressor Imported succesfully']\n", "['2020-07-29 09', '55', '52,754', 'INFO', 'Checking ensemble method']\n", "['2020-07-29 09', '55', '52,756', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '55', '52,761', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '53,075', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '53,186', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '53,186', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '53,201', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '55', '53,205', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '53,502', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '53,567', 'INFO', 'Fitting CountVectorizer()']\n", "['2020-07-29 09', '55', '53,613', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '53,613', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '53,627', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '55', '53,631', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '53,924', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '54,033', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '54,033', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '54,047', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '55', '54,050', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '54,359', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '54,471', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '54,471', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '54,484', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '55', '54,486', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '54,781', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '54,890', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '54,890', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '54,905', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '55', '54,909', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '55,219', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '55,330', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '55,330', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '55,343', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '55', '55,347', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '55,644', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '55,753', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '55,753', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '55,766', 'INFO', 'Initializing Fold 8']\n", "['2020-07-29 09', '55', '55,769', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '56,059', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '56,079', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '55', '56,111', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '55', '56,170', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '56,170', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '56,186', 'INFO', \"Saving 'Trigram.html' in current active directory\"]\n", "['2020-07-29 09', '55', '56,186', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '55', '56,188', 'INFO', 'Initializing Fold 9']\n", "['2020-07-29 09', '55', '56,191', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '56,496', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '56,606', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '56,606', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '56,620', 'INFO', 'Initializing Fold 10']\n", "['2020-07-29 09', '55', '56,623', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '55', '56,941', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '55', '57,046', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '55', '57,046', 'INFO', 'plot_model(model=None, plot=pos, topic_num=None, save=True, system=False)']\n", "['2020-07-29 09', '55', '57,046', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '55', '57,046', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '55', '57,050', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '55', '57,050', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '55', '57,062', 'INFO', 'save_param set to True']\n", "['2020-07-29 09', '55', '57,062', 'INFO', 'plot type', 'pos']\n", "['2020-07-29 09', '55', '57,062', 'INFO', 'Fitting TextBlob()']\n", "['2020-07-29 09', '55', '57,066', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '55', '57,070', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '55', '57,079', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '55', '57,402', 'INFO', 'Uploading results into container']\n", "['2020-07-29 09', '55', '57,403', 'INFO', 'Uploading model into container']\n", "['2020-07-29 09', '55', '57,403', 'INFO', 'create_model_container', '22']\n", "['2020-07-29 09', '55', '57,403', 'INFO', 'master_model_container', '22']\n", "['2020-07-29 09', '55', '57,403', 'INFO', 'display_container', '23']\n", "['2020-07-29 09', '55', '57,405', 'INFO', \"ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\"]\n", "[\"max_depth=None, max_features='auto', max_leaf_nodes=None,\"]\n", "['max_samples=None, min_impurity_decrease=0.0,']\n", "['min_impurity_split=None, min_samples_leaf=1,']\n", "['min_samples_split=2, min_weight_fraction_leaf=0.0,']\n", "['n_estimators=100, n_jobs=-1, oob_score=False,']\n", "['random_state=123, verbose=0, warm_start=False)']\n", "['2020-07-29 09', '55', '57,406', 'INFO', 'create_model() succesfully completed......................................']\n", "['2020-07-29 09', '55', '57,406', 'INFO', 'SubProcess create_model() end ==================================']\n", "['2020-07-29 09', '55', '57,512', 'INFO', 'create_model_container', '22']\n", "['2020-07-29 09', '55', '57,513', 'INFO', 'master_model_container', '22']\n", "['2020-07-29 09', '55', '57,513', 'INFO', 'display_container', '24']\n", "['2020-07-29 09', '55', '57,518', 'INFO', \"[GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',\"]\n", "[\"init=None, learning_rate=0.1, loss='ls', max_depth=3,\"]\n", "['max_features=None, max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0, min_impurity_split=None,']\n", "['min_samples_leaf=1, min_samples_split=2,']\n", "['min_weight_fraction_leaf=0.0, n_estimators=100,']\n", "[\"n_iter_no_change=None, presort='deprecated',\"]\n", "['random_state=123, subsample=1.0, tol=0.0001,']\n", "[\"validation_fraction=0.1, verbose=0, warm_start=False), , RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\"]\n", "[\"max_depth=None, max_features='auto', max_leaf_nodes=None,\"]\n", "['max_samples=None, min_impurity_decrease=0.0,']\n", "['min_impurity_split=None, min_samples_leaf=1,']\n", "['min_samples_split=2, min_weight_fraction_leaf=0.0,']\n", "['n_estimators=100, n_jobs=-1, oob_score=False,']\n", "[\"random_state=123, verbose=0, warm_start=False), LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "[\"subsample=1.0, subsample_for_bin=200000, subsample_freq=0), ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\"]\n", "[\"max_depth=None, max_features='auto', max_leaf_nodes=None,\"]\n", "['max_samples=None, min_impurity_decrease=0.0,']\n", "['min_impurity_split=None, min_samples_leaf=1,']\n", "['min_samples_split=2, min_weight_fraction_leaf=0.0,']\n", "['n_estimators=100, n_jobs=-1, oob_score=False,']\n", "['random_state=123, verbose=0, warm_start=False)]']\n", "['2020-07-29 09', '55', '57,519', 'INFO', 'compare_models() succesfully completed......................................']\n", "['2020-07-29 09', '55', '57,529', 'INFO', 'Initializing stack_models()']\n", "['2020-07-29 09', '55', '57,535', 'INFO', \"stack_models(estimator_list=[GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',\"]\n", "[\"init=None, learning_rate=0.1, loss='ls', max_depth=3,\"]\n", "['max_features=None, max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0, min_impurity_split=None,']\n", "['min_samples_leaf=1, min_samples_split=2,']\n", "['min_weight_fraction_leaf=0.0, n_estimators=100,']\n", "[\"n_iter_no_change=None, presort='deprecated',\"]\n", "['random_state=123, subsample=1.0, tol=0.0001,']\n", "[\"validation_fraction=0.1, verbose=0, warm_start=False), , RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\"]\n", "[\"max_depth=None, max_features='auto', max_leaf_nodes=None,\"]\n", "['max_samples=None, min_impurity_decrease=0.0,']\n", "['min_impurity_split=None, min_samples_leaf=1,']\n", "['min_samples_split=2, min_weight_fraction_leaf=0.0,']\n", "['n_estimators=100, n_jobs=-1, oob_score=False,']\n", "[\"random_state=123, verbose=0, warm_start=False), LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "[\"subsample=1.0, subsample_for_bin=200000, subsample_freq=0), ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\"]\n", "[\"max_depth=None, max_features='auto', max_leaf_nodes=None,\"]\n", "['max_samples=None, min_impurity_decrease=0.0,']\n", "['min_impurity_split=None, min_samples_leaf=1,']\n", "['min_samples_split=2, min_weight_fraction_leaf=0.0,']\n", "['n_estimators=100, n_jobs=-1, oob_score=False,']\n", "['random_state=123, verbose=0, warm_start=False)], meta_model=None, fold=10, round=4, restack=True, plot=False, choose_better=False, optimize=R2, finalize=False, verbose=True)']\n", "['2020-07-29 09', '55', '57,536', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '55', '57,536', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '55', '57,536', 'INFO', 'Copying estimator list']\n", "['2020-07-29 09', '55', '57,704', 'INFO', 'Defining meta model']\n", "['2020-07-29 09', '55', '57,708', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '55', '57,777', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '55', '57,780', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '55', '57,782', 'INFO', 'Getting model names']\n", "['2020-07-29 09', '55', '57,790', 'INFO', 'Checking base model', 'GradientBoostingRegressor']\n", "['2020-07-29 09', '55', '57,805', 'INFO', 'Fitting base model']\n", "['2020-07-29 09', '55', '58,031', 'INFO', 'Generating cross val predictions']\n", "['2020-07-29 09', '55', '59,784', 'INFO', 'Checking base model', 'CatBoostRegressor']\n", "['2020-07-29 09', '55', '59,792', 'INFO', 'Fitting base model']\n", "['2020-07-29 09', '56', '02,959', 'INFO', 'Generating cross val predictions']\n", "['2020-07-29 09', '56', '04,096', 'INFO', 'Rendering Visual']\n", "['2020-07-29 09', '56', '04,161', 'INFO', 'Visual Rendered Sucessfully']\n", "['2020-07-29 09', '56', '04,315', 'INFO', \"Saving 'POS.html' in current active directory\"]\n", "['2020-07-29 09', '56', '04,317', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '56', '05,704', 'INFO', 'SubProcess plot_model() end ==================================']\n", "['2020-07-29 09', '56', '05,751', 'INFO', 'setup() succesfully completed......................................']\n", "['2020-07-29 09', '56', '05,816', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '56', '05,817', 'INFO', 'create_model(model=lda, multi_core=False, num_topics=None, verbose=True, system=True)']\n", "['2020-07-29 09', '56', '05,817', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '56', '05,817', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '56', '05,817', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '56', '05,862', 'INFO', 'Defining topic model']\n", "['2020-07-29 09', '56', '05,863', 'INFO', 'Model', 'Latent Dirichlet Allocation']\n", "['2020-07-29 09', '56', '05,863', 'INFO', 'Defining num_topics parameter']\n", "['2020-07-29 09', '56', '05,863', 'INFO', 'num_topics set to', '4']\n", "['2020-07-29 09', '56', '05,878', 'INFO', 'LdaModel imported successfully']\n", "['2020-07-29 09', '56', '22,940', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '56', '22,941', 'INFO', \"plot_model(estimator=DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\"]\n", "['max_features=None, max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0, min_impurity_split=None,']\n", "['min_samples_leaf=1, min_samples_split=2,']\n", "[\"min_weight_fraction_leaf=0.0, presort='deprecated',\"]\n", "[\"random_state=123, splitter='best'), plot=residuals, save=False, verbose=True, system=True)\"]\n", "['2020-07-29 09', '56', '22,941', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '56', '22,942', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '56', '22,942', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '56', '22,959', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '56', '22,960', 'INFO', 'plot type', 'residuals']\n", "['2020-07-29 09', '56', '23,078', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '56', '23,141', 'INFO', 'Scoring test/hold-out set']\n", "['2020-07-29 09', '56', '24,409', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '56', '24,409', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '56', '24,584', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '56', '24,586', 'INFO', \"plot_model(estimator=DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\"]\n", "['max_features=None, max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0, min_impurity_split=None,']\n", "['min_samples_leaf=1, min_samples_split=2,']\n", "[\"min_weight_fraction_leaf=0.0, presort='deprecated',\"]\n", "[\"random_state=123, splitter='best'), plot=error, save=False, verbose=True, system=True)\"]\n", "['2020-07-29 09', '56', '24,586', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '56', '24,587', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '56', '24,587', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '56', '24,608', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '56', '24,610', 'INFO', 'plot type', 'error']\n", "['2020-07-29 09', '56', '24,611', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '56', '24,613', 'INFO', 'Scoring test/hold-out set']\n", "['2020-07-29 09', '56', '24,957', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '56', '24,957', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '56', '25,013', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '56', '25,014', 'INFO', \"plot_model(estimator=DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\"]\n", "['max_features=None, max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0, min_impurity_split=None,']\n", "['min_samples_leaf=1, min_samples_split=2,']\n", "[\"min_weight_fraction_leaf=0.0, presort='deprecated',\"]\n", "[\"random_state=123, splitter='best'), plot=feature, save=False, verbose=True, system=True)\"]\n", "['2020-07-29 09', '56', '25,015', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '56', '25,015', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '56', '25,015', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '56', '25,032', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '56', '25,034', 'INFO', 'plot type', 'feature']\n", "['2020-07-29 09', '56', '25,331', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '56', '25,332', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '56', '25,669', 'INFO', 'Initializing plot_model()']\n", "['2020-07-29 09', '56', '25,671', 'INFO', \"plot_model(estimator=DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\"]\n", "['max_features=None, max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0, min_impurity_split=None,']\n", "['min_samples_leaf=1, min_samples_split=2,']\n", "[\"min_weight_fraction_leaf=0.0, presort='deprecated',\"]\n", "[\"random_state=123, splitter='best'), plot=parameter, save=False, verbose=True, system=True)\"]\n", "['2020-07-29 09', '56', '25,671', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '56', '25,672', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '56', '25,672', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '56', '25,692', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '56', '25,693', 'INFO', 'plot type', 'parameter']\n", "['2020-07-29 09', '56', '25,707', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '56', '25,707', 'INFO', 'plot_model() succesfully completed......................................']\n", "['2020-07-29 09', '56', '27,376', 'INFO', 'Initializing interpret_model()']\n", "['2020-07-29 09', '56', '27,378', 'INFO', \"interpret_model(estimator=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), plot=summary, feature=None, observation=None)']\n", "['2020-07-29 09', '56', '27,378', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '56', '27,379', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '56', '27,526', 'INFO', 'plot type', 'summary']\n", "['2020-07-29 09', '56', '27,527', 'INFO', 'Creating TreeExplainer']\n", "['2020-07-29 09', '56', '27,945', 'INFO', 'Compiling shap values']\n", "['2020-07-29 09', '56', '28,774', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '56', '28,775', 'INFO', 'interpret_model() succesfully completed......................................']\n", "['2020-07-29 09', '56', '29,395', 'INFO', 'Initializing interpret_model()']\n", "['2020-07-29 09', '56', '29,396', 'INFO', \"interpret_model(estimator=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), plot=correlation, feature=None, observation=None)']\n", "['2020-07-29 09', '56', '29,397', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '56', '29,399', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '56', '29,399', 'INFO', 'plot type', 'correlation']\n", "['2020-07-29 09', '56', '29,399', 'WARNING', 'No feature passed. Default value of feature used for correlation plot', 'age']\n", "['2020-07-29 09', '56', '29,399', 'INFO', 'Creating TreeExplainer']\n", "['2020-07-29 09', '56', '29,775', 'INFO', 'Compiling shap values']\n", "['2020-07-29 09', '56', '30,270', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '56', '30,270', 'INFO', 'interpret_model() succesfully completed......................................']\n", "['2020-07-29 09', '56', '30,281', 'INFO', 'Initializing interpret_model()']\n", "['2020-07-29 09', '56', '30,283', 'INFO', \"interpret_model(estimator=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\"]\n", "[\"importance_type='split', learning_rate=0.1, max_depth=-1,\"]\n", "['min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,']\n", "['n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,']\n", "['random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent=True,']\n", "['subsample=1.0, subsample_for_bin=200000, subsample_freq=0), plot=reason, feature=None, observation=12)']\n", "['2020-07-29 09', '56', '30,283', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '56', '30,284', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '56', '30,284', 'INFO', 'plot type', 'reason']\n", "['2020-07-29 09', '56', '30,285', 'INFO', 'Creating TreeExplainer']\n", "['2020-07-29 09', '56', '30,727', 'INFO', 'Compiling shap values']\n", "['2020-07-29 09', '56', '30,919', 'INFO', 'Visual Rendered Successfully']\n", "['2020-07-29 09', '56', '30,919', 'INFO', 'interpret_model() succesfully completed......................................']\n", "['2020-07-29 09', '56', '33,482', 'INFO', 'Initializing automl()']\n", "['2020-07-29 09', '56', '33,483', 'INFO', 'automl(optimize=MAE, use_holdout=False)']\n", "['2020-07-29 09', '56', '33,483', 'INFO', 'Model Selection Basis', 'CV Results on Training set']\n", "['2020-07-29 09', '56', '33,491', 'INFO', 'SubProcess finalize_model() called ==================================']\n", "['2020-07-29 09', '56', '33,492', 'INFO', 'Initializing finalize_model()']\n", "['2020-07-29 09', '56', '33,495', 'INFO', 'finalize_model(estimator=AdaBoostRegressor(base_estimator=DecisionTreeRegressor(ccp_alpha=0.0,']\n", "[\"criterion='mse',\"]\n", "['max_depth=None,']\n", "['max_features=None,']\n", "['max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0,']\n", "['min_impurity_split=None,']\n", "['min_samples_leaf=1,']\n", "['min_samples_split=2,']\n", "['min_weight_fraction_leaf=0.0,']\n", "[\"presort='deprecated',\"]\n", "['random_state=123,']\n", "[\"splitter='best'),\"]\n", "[\"learning_rate=1.0, loss='linear', n_estimators=10,\"]\n", "['random_state=123))']\n", "['2020-07-29 09', '56', '33,495', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '56', '33,495', 'INFO', 'Getting model name']\n", "['2020-07-29 09', '56', '33,497', 'INFO', 'Finalizing AdaBoost Regressor']\n", "['2020-07-29 09', '56', '33,585', 'INFO', 'Creating MLFlow logs']\n", "['2020-07-29 09', '56', '33,669', 'INFO', 'SubProcess create_model() called ==================================']\n", "['2020-07-29 09', '56', '33,669', 'INFO', 'Initializing create_model()']\n", "['2020-07-29 09', '56', '33,671', 'INFO', 'create_model(estimator=AdaBoostRegressor(base_estimator=DecisionTreeRegressor(ccp_alpha=0.0,']\n", "[\"criterion='mse',\"]\n", "['max_depth=None,']\n", "['max_features=None,']\n", "['max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0,']\n", "['min_impurity_split=None,']\n", "['min_samples_leaf=1,']\n", "['min_samples_split=2,']\n", "['min_weight_fraction_leaf=0.0,']\n", "[\"presort='deprecated',\"]\n", "['random_state=123,']\n", "[\"splitter='best'),\"]\n", "[\"learning_rate=1.0, loss='linear', n_estimators=10,\"]\n", "['random_state=123), ensemble=False, method=None, fold=10, round=4, cross_validation=True, verbose=False, system=False)']\n", "['2020-07-29 09', '56', '33,671', 'INFO', 'Checking exceptions']\n", "['2020-07-29 09', '56', '33,671', 'INFO', 'Preloading libraries']\n", "['2020-07-29 09', '56', '33,671', 'INFO', 'Preparing display monitor']\n", "['2020-07-29 09', '56', '33,690', 'INFO', 'Copying training dataset']\n", "['2020-07-29 09', '56', '33,691', 'INFO', 'Importing libraries']\n", "['2020-07-29 09', '56', '33,693', 'INFO', 'Defining folds']\n", "['2020-07-29 09', '56', '33,693', 'INFO', 'Declaring metric variables']\n", "['2020-07-29 09', '56', '33,694', 'INFO', 'Declaring custom model']\n", "['2020-07-29 09', '56', '33,696', 'INFO', 'AdaBoost Regressor Imported succesfully']\n", "['2020-07-29 09', '56', '33,697', 'INFO', 'Checking ensemble method']\n", "['2020-07-29 09', '56', '33,699', 'INFO', 'Initializing Fold 1']\n", "['2020-07-29 09', '56', '33,702', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '56', '33,758', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '56', '33,762', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '56', '33,762', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '56', '33,775', 'INFO', 'Initializing Fold 2']\n", "['2020-07-29 09', '56', '33,777', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '56', '33,842', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '56', '33,847', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '56', '33,848', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '56', '33,864', 'INFO', 'Initializing Fold 3']\n", "['2020-07-29 09', '56', '33,868', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '56', '33,928', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '56', '33,934', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '56', '33,934', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '56', '33,945', 'INFO', 'Initializing Fold 4']\n", "['2020-07-29 09', '56', '33,948', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '56', '34,016', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '56', '34,021', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '56', '34,021', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '56', '34,032', 'INFO', 'Initializing Fold 5']\n", "['2020-07-29 09', '56', '34,035', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '56', '34,100', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '56', '34,104', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '56', '34,104', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '56', '34,115', 'INFO', 'Initializing Fold 6']\n", "['2020-07-29 09', '56', '34,116', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '56', '34,179', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '56', '34,183', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '56', '34,183', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '56', '34,198', 'INFO', 'Initializing Fold 7']\n", "['2020-07-29 09', '56', '34,201', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '56', '34,257', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '56', '34,262', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '56', '34,263', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '56', '34,274', 'INFO', 'Initializing Fold 8']\n", "['2020-07-29 09', '56', '34,275', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '56', '34,335', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '56', '34,340', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '56', '34,340', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '56', '34,352', 'INFO', 'Initializing Fold 9']\n", "['2020-07-29 09', '56', '34,354', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '56', '34,416', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '56', '34,421', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '56', '34,421', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '56', '34,434', 'INFO', 'Initializing Fold 10']\n", "['2020-07-29 09', '56', '34,436', 'INFO', 'Fitting Model']\n", "['2020-07-29 09', '56', '34,520', 'INFO', 'Evaluating Metrics']\n", "['2020-07-29 09', '56', '34,525', 'INFO', 'No inverse transformation']\n", "['2020-07-29 09', '56', '34,525', 'INFO', 'Compiling Metrics']\n", "['2020-07-29 09', '56', '34,538', 'INFO', 'Calculating mean and std']\n", "['2020-07-29 09', '56', '34,540', 'INFO', 'Creating metrics dataframe']\n", "['2020-07-29 09', '56', '34,549', 'INFO', 'Finalizing model']\n", "['2020-07-29 09', '56', '34,642', 'INFO', 'Uploading results into container']\n", "['2020-07-29 09', '56', '34,642', 'INFO', 'Uploading model into container']\n", "['2020-07-29 09', '56', '34,642', 'INFO', 'create_model_container', '23']\n", "['2020-07-29 09', '56', '34,642', 'INFO', 'master_model_container', '23']\n", "['2020-07-29 09', '56', '34,643', 'INFO', 'display_container', '25']\n", "['2020-07-29 09', '56', '34,645', 'INFO', 'AdaBoostRegressor(base_estimator=DecisionTreeRegressor(ccp_alpha=0.0,']\n", "[\"criterion='mse',\"]\n", "['max_depth=None,']\n", "['max_features=None,']\n", "['max_leaf_nodes=None,']\n", "['min_impurity_decrease=0.0,']\n", 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... ... ... \n", "61 Elastic Net e640 12.16 46e4ab7a \n", "62 Ridge Regression e640 11.99 46e4ab7a \n", "63 Lasso Regression e640 12.16 46e4ab7a \n", "64 Linear Regression e640 12.16 46e4ab7a \n", "65 Session Initialized e640 e640 11.44 ab172480 \n", "\n", " tags.Source tags.Final tags.Run ID \\\n", "0 finalize_model True 3ceaff2c0be247bca703bf99030e7385 \n", "1 compare_models None a7f177d89880493e9947f619d39e6c64 \n", "2 compare_models None 9bceee47fc3f425e9e82259f03381d6d \n", "3 compare_models None 7b7973e270194411aee1c67b49a258e8 \n", "4 compare_models None 8d91720808a74f0ca95a1beafa02af52 \n", ".. ... ... ... \n", "61 compare_models None ca41825ba51049cca2980db4b14f0803 \n", "62 compare_models None 220347e92d6d4e15a0689f1996f65be1 \n", "63 compare_models None 92449063af5b4b7f889b49049567e4fb \n", "64 compare_models None 6c6d422e6ab84f6489075246e84b5b88 \n", "65 setup None 076cde0252cb41c9a57a55908c8f3aa3 \n", "\n", " tags.mlflow.user tags.Run Time \\\n", "0 moezs 0.09 \n", "1 moezs 17.33 \n", "2 moezs 1.79 \n", "3 moezs 1.46 \n", "4 moezs 1.22 \n", ".. ... ... \n", "61 moezs 0.11 \n", "62 moezs 0.1 \n", "63 moezs 0.11 \n", "64 moezs 0.12 \n", "65 moezs 4.53 \n", "\n", " tags.mlflow.source.name \n", "0 C:\\Users\\moezs\\Anaconda3\\envs\\pycaret-nightly-... \n", "1 C:\\Users\\moezs\\Anaconda3\\envs\\pycaret-nightly-... \n", "2 C:\\Users\\moezs\\Anaconda3\\envs\\pycaret-nightly-... \n", "3 C:\\Users\\moezs\\Anaconda3\\envs\\pycaret-nightly-... \n", "4 C:\\Users\\moezs\\Anaconda3\\envs\\pycaret-nightly-... \n", ".. ... \n", "61 C:\\Users\\moezs\\Anaconda3\\envs\\pycaret-nightly-... \n", "62 C:\\Users\\moezs\\Anaconda3\\envs\\pycaret-nightly-... \n", "63 C:\\Users\\moezs\\Anaconda3\\envs\\pycaret-nightly-... \n", "64 C:\\Users\\moezs\\Anaconda3\\envs\\pycaret-nightly-... \n", "65 C:\\Users\\moezs\\Anaconda3\\envs\\pycaret-nightly-... \n", "\n", "[66 rows x 201 columns]" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_logs()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Running the mlflow server failed. Please see the logs above for details.\n" ] } ], "source": [ "!mlflow ui" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# End\n", "Thank you. For more information / tutorials on PyCaret, please visit https://www.pycaret.org" ] } ], "metadata": { "kernelspec": { "display_name": "pycaret-nightly-env", "language": "python", "name": "pycaret-nightly-env" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.10" } }, "nbformat": 4, "nbformat_minor": 2 }