{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# install pycaret\n", "# pip install pycaret\n", "\n", "# pip install pycaret[full]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'2.2.3'" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pycaret.utils import version\n", "version()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Col1Col2Col3Col4Col5Col6Col7Col8Col9Col10
00.2639950.7649290.1384240.9352420.6058670.5187900.9122250.6082340.7237820.733591
10.5460920.6539750.0655750.2277720.8452690.8370660.2723790.3316790.4292970.367422
20.3367140.5388420.1928010.5535630.0745150.3329930.3657920.8613090.8990170.088600
30.0921080.9950170.0144650.1763710.2415300.5147240.5622080.1589630.0737150.208463
40.3252610.8059680.9570330.3316650.3079230.3553150.5018990.5584490.8851690.182754
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" ], "text/plain": [ " Col1 Col2 Col3 Col4 Col5 Col6 Col7 \\\n", "0 0.263995 0.764929 0.138424 0.935242 0.605867 0.518790 0.912225 \n", "1 0.546092 0.653975 0.065575 0.227772 0.845269 0.837066 0.272379 \n", "2 0.336714 0.538842 0.192801 0.553563 0.074515 0.332993 0.365792 \n", "3 0.092108 0.995017 0.014465 0.176371 0.241530 0.514724 0.562208 \n", "4 0.325261 0.805968 0.957033 0.331665 0.307923 0.355315 0.501899 \n", "\n", " Col8 Col9 Col10 \n", "0 0.608234 0.723782 0.733591 \n", "1 0.331679 0.429297 0.367422 \n", "2 0.861309 0.899017 0.088600 \n", "3 0.158963 0.073715 0.208463 \n", "4 0.558449 0.885169 0.182754 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pycaret.datasets import get_data\n", "data = get_data('anomaly')" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countmeanstdmin25%50%75%max
Col11000.00.4913620.2591380.0000000.2874580.4920700.6941920.994431
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Col31000.00.5090770.2566060.0000000.3378020.5100770.6869141.000000
Col41000.00.4973620.2635620.0000000.2561470.4975370.7319491.000000
Col51000.00.5861200.3346580.0000000.1696800.7820190.8479561.000000
Col61000.00.5146360.3174700.0000000.1425870.5379530.8565121.000000
Col71000.00.5082700.2784830.0000000.2460210.4656790.8049351.000000
Col81000.00.4575410.2201290.0000000.2455390.5156190.6267571.000000
Col91000.00.4776850.2414320.0000000.2914520.3877530.7236740.988732
Col101000.00.4957600.2116770.0144950.3299040.4888910.6595281.000000
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" ], "text/plain": [ " count mean std min 25% 50% 75% \\\n", "Col1 1000.0 0.491362 0.259138 0.000000 0.287458 0.492070 0.694192 \n", "Col2 1000.0 0.490200 0.251931 0.000000 0.291449 0.488656 0.686531 \n", "Col3 1000.0 0.509077 0.256606 0.000000 0.337802 0.510077 0.686914 \n", "Col4 1000.0 0.497362 0.263562 0.000000 0.256147 0.497537 0.731949 \n", "Col5 1000.0 0.586120 0.334658 0.000000 0.169680 0.782019 0.847956 \n", "Col6 1000.0 0.514636 0.317470 0.000000 0.142587 0.537953 0.856512 \n", "Col7 1000.0 0.508270 0.278483 0.000000 0.246021 0.465679 0.804935 \n", "Col8 1000.0 0.457541 0.220129 0.000000 0.245539 0.515619 0.626757 \n", "Col9 1000.0 0.477685 0.241432 0.000000 0.291452 0.387753 0.723674 \n", "Col10 1000.0 0.495760 0.211677 0.014495 0.329904 0.488891 0.659528 \n", "\n", " max \n", "Col1 0.994431 \n", "Col2 1.000000 \n", "Col3 1.000000 \n", "Col4 1.000000 \n", "Col5 1.000000 \n", "Col6 1.000000 \n", "Col7 1.000000 \n", "Col8 1.000000 \n", "Col9 0.988732 \n", "Col10 1.000000 " ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.describe().transpose()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1000, 10)" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape" ] }, { "cell_type": "code", "execution_count": 48, "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", "
Description Value
0session_id123
1Original Data(1000, 10)
2Missing ValuesFalse
3Numeric Features10
4Categorical Features0
5Ordinal FeaturesFalse
6High Cardinality FeaturesFalse
7High Cardinality MethodNone
8Transformed Data(1000, 10)
9CPU Jobs-1
10Use GPUFalse
11Log ExperimentTrue
12Experiment Nameanomaly-demo
13USI5118
14Imputation Typesimple
15Iterative Imputation IterationNone
16Numeric Imputermean
17Iterative Imputation Numeric ModelNone
18Categorical Imputermode
19Iterative Imputation Categorical ModelNone
20Unknown Categoricals Handlingleast_frequent
21NormalizeFalse
22Normalize MethodNone
23TransformationFalse
24Transformation MethodNone
25PCAFalse
26PCA MethodNone
27PCA ComponentsNone
28Ignore Low VarianceFalse
29Combine Rare LevelsFalse
30Rare Level ThresholdNone
31Numeric BinningFalse
32Remove OutliersFalse
33Outliers ThresholdNone
34Remove MulticollinearityFalse
35Multicollinearity ThresholdNone
36ClusteringFalse
37Clustering IterationNone
38Polynomial FeaturesFalse
39Polynomial DegreeNone
40Trignometry FeaturesFalse
41Polynomial ThresholdNone
42Group FeaturesFalse
43Feature SelectionFalse
44Features Selection ThresholdNone
45Feature InteractionFalse
46Feature RatioFalse
47Interaction ThresholdNone
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pycaret.anomaly import *\n", "s = setup(data, session_id = 123, log_experiment = True, experiment_name = 'anomaly-demo')" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NameReference
ID
abodAngle-base Outlier Detectionpyod.models.abod.ABOD
clusterClustering-Based Local Outlierpyod.models.cblof.CBLOF
cofConnectivity-Based Local Outlierpyod.models.cof.COF
iforestIsolation Forestpyod.models.iforest.IForest
histogramHistogram-based Outlier Detectionpyod.models.hbos.HBOS
knnK-Nearest Neighbors Detectorpyod.models.knn.KNN
lofLocal Outlier Factorpyod.models.lof.LOF
svmOne-class SVM detectorpyod.models.ocsvm.OCSVM
pcaPrincipal Component Analysispyod.models.pca.PCA
mcdMinimum Covariance Determinantpyod.models.mcd.MCD
sodSubspace Outlier Detectionpyod.models.sod.SOD
sosStochastic Outlier Selectionpyod.models.sos.SOS
\n", "
" ], "text/plain": [ " Name Reference\n", "ID \n", "abod Angle-base Outlier Detection pyod.models.abod.ABOD\n", "cluster Clustering-Based Local Outlier pyod.models.cblof.CBLOF\n", "cof Connectivity-Based Local Outlier pyod.models.cof.COF\n", "iforest Isolation Forest pyod.models.iforest.IForest\n", "histogram Histogram-based Outlier Detection pyod.models.hbos.HBOS\n", "knn K-Nearest Neighbors Detector pyod.models.knn.KNN\n", "lof Local Outlier Factor pyod.models.lof.LOF\n", "svm One-class SVM detector pyod.models.ocsvm.OCSVM\n", "pca Principal Component Analysis pyod.models.pca.PCA\n", "mcd Minimum Covariance Determinant pyod.models.mcd.MCD\n", "sod Subspace Outlier Detection pyod.models.sod.SOD\n", "sos Stochastic Outlier Selection pyod.models.sos.SOS" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "models()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "iforest = create_model('iforest')" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "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)" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iforest" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "IForest(behaviour='new', bootstrap=False, contamination=0.05,\n", " max_features=1.0, max_samples='auto', n_estimators=200, n_jobs=-1,\n", " random_state=123, verbose=0)" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iforest = create_model('iforest', n_estimators=200)\n", "iforest" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KNN(algorithm='auto', contamination=0.05, leaf_size=30, method='largest',\n", " metric='minkowski', metric_params=None, n_jobs=-1, n_neighbors=5, p=2,\n", " radius=1.0)\n" ] } ], "source": [ "knn = create_model('knn')\n", "print(knn)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "customdata": [ [ 0.263995357 ], [ 0.546092303 ], [ 0.336714104 ], [ 0.092107835 ], [ 0.325261175 ], [ 0.212464853 ], [ 0.258565714 ], [ 0.869236755 ], [ 0.197077957 ], [ 0.292984504 ], [ 0.82178316 ], [ 0.796622959 ], [ 0.045577011 ], [ 0.613660351 ], [ 0.950744566 ], [ 0.336346337 ], [ 0.034440667 ], [ 0.741544275 ], [ 0.18443265 ], [ 0.664514943 ], [ 0.162824858 ], [ 0.729838445 ], [ 0.62837389 ], [ 0.057883661 ], [ 0.445534827 ], [ 0.80183695 ], [ 0.27709612 ], [ 0.228634015 ], [ 0.834860657 ], [ 0.669766511 ], [ 0.532527349 ], [ 0.170459071 ], [ 0.510288468 ], [ 0.977561989 ], [ 0.249775993 ], [ 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Col1Col2Col3Col4Col5Col6Col7Col8Col9Col10AnomalyAnomaly_Score
00.2639950.7649290.1384240.9352420.6058670.5187900.9122250.6082340.7237820.7335910-0.030361
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" ], "text/plain": [ " Col1 Col2 Col3 Col4 Col5 Col6 Col7 \\\n", "0 0.263995 0.764929 0.138424 0.935242 0.605867 0.518790 0.912225 \n", "1 0.546092 0.653975 0.065575 0.227772 0.845269 0.837066 0.272379 \n", "2 0.336714 0.538842 0.192801 0.553563 0.074515 0.332993 0.365792 \n", "3 0.092108 0.995017 0.014465 0.176371 0.241530 0.514724 0.562208 \n", "4 0.325261 0.805968 0.957033 0.331665 0.307923 0.355315 0.501899 \n", "\n", " Col8 Col9 Col10 Anomaly Anomaly_Score \n", "0 0.608234 0.723782 0.733591 0 -0.030361 \n", "1 0.331679 0.429297 0.367422 0 -0.078290 \n", "2 0.861309 0.899017 0.088600 1 0.026938 \n", "3 0.158963 0.073715 0.208463 1 0.053551 \n", "4 0.558449 0.885169 0.182754 0 -0.015639 " ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pycaret.anomaly import load_model, predict_model\n", "l = load_model('iforest-abc', platform = 'aws', authentication = .......)\n", "p = predict_model(l, data=data)\n", "p.head()" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Col1Col2Col3Col4Col5Col6Col7Col8Col9Col10AnomalyAnomaly_Score
00.2639950.7649290.1384240.9352420.6058670.5187900.9122250.6082340.7237820.7335910-0.030361
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40.3252610.8059680.9570330.3316650.3079230.3553150.5018990.5584490.8851690.1827540-0.015639
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9980.5024360.9368200.5800620.5407730.1519950.0594520.2252200.2427550.2793850.5387550-0.068005
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1000 rows × 12 columns

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" ], "text/plain": [ " Col1 Col2 Col3 Col4 Col5 Col6 Col7 \\\n", "0 0.263995 0.764929 0.138424 0.935242 0.605867 0.518790 0.912225 \n", "1 0.546092 0.653975 0.065575 0.227772 0.845269 0.837066 0.272379 \n", "2 0.336714 0.538842 0.192801 0.553563 0.074515 0.332993 0.365792 \n", "3 0.092108 0.995017 0.014465 0.176371 0.241530 0.514724 0.562208 \n", "4 0.325261 0.805968 0.957033 0.331665 0.307923 0.355315 0.501899 \n", ".. ... ... ... ... ... ... ... \n", "995 0.305055 0.656837 0.331665 0.822525 0.907127 0.882276 0.855732 \n", "996 0.812627 0.864258 0.616604 0.167966 0.811223 0.938071 0.418462 \n", "997 0.250967 0.138627 0.919703 0.461234 0.886555 0.869888 0.800908 \n", "998 0.502436 0.936820 0.580062 0.540773 0.151995 0.059452 0.225220 \n", "999 0.457991 0.017755 0.714113 0.125992 0.063316 0.154739 0.922974 \n", "\n", " Col8 Col9 Col10 Anomaly Anomaly_Score \n", "0 0.608234 0.723782 0.733591 0 -0.030361 \n", "1 0.331679 0.429297 0.367422 0 -0.078290 \n", "2 0.861309 0.899017 0.088600 1 0.026938 \n", "3 0.158963 0.073715 0.208463 1 0.053551 \n", "4 0.558449 0.885169 0.182754 0 -0.015639 \n", ".. ... ... ... ... ... \n", "995 0.584786 0.808640 0.242762 0 -0.082756 \n", "996 0.472306 0.348347 0.671129 0 -0.065453 \n", "997 0.530324 0.779433 0.234952 0 -0.055405 \n", "998 0.242755 0.279385 0.538755 0 -0.068005 \n", "999 0.692299 0.816777 0.307592 0 -0.012268 \n", "\n", "[1000 rows x 12 columns]" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Col1Col2Col3Col4Col5Col6Col7Col8Col9Col10AnomalyAnomaly_Score
00.2639950.7649290.1384240.9352420.6058670.5187900.9122250.6082340.7237820.7335910-0.035865
10.5460920.6539750.0655750.2277720.8452690.8370660.2723790.3316790.4292970.3674220-0.084927
20.3367140.5388420.1928010.5535630.0745150.3329930.3657920.8613090.8990170.08860010.025356
30.0921080.9950170.0144650.1763710.2415300.5147240.5622080.1589630.0737150.20846310.042415
40.3252610.8059680.9570330.3316650.3079230.3553150.5018990.5584490.8851690.1827540-0.023408
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" ], "text/plain": [ " Col1 Col2 Col3 Col4 Col5 Col6 Col7 \\\n", "0 0.263995 0.764929 0.138424 0.935242 0.605867 0.518790 0.912225 \n", "1 0.546092 0.653975 0.065575 0.227772 0.845269 0.837066 0.272379 \n", "2 0.336714 0.538842 0.192801 0.553563 0.074515 0.332993 0.365792 \n", "3 0.092108 0.995017 0.014465 0.176371 0.241530 0.514724 0.562208 \n", "4 0.325261 0.805968 0.957033 0.331665 0.307923 0.355315 0.501899 \n", "\n", " Col8 Col9 Col10 Anomaly Anomaly_Score \n", "0 0.608234 0.723782 0.733591 0 -0.035865 \n", "1 0.331679 0.429297 0.367422 0 -0.084927 \n", "2 0.861309 0.899017 0.088600 1 0.025356 \n", "3 0.158963 0.073715 0.208463 1 0.042415 \n", "4 0.558449 0.885169 0.182754 0 -0.023408 " ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iforest = create_model('iforest', fraction = 0.05)\n", "results = assign_model(iforest)\n", "results.head()" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "results['Anomaly_Score'].hist(bins=100, figsize=(10,6))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "from sklearn import set_config\n", "set_config(display=None)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Pipeline(memory=None,\n",
       "         steps=[('dtypes',\n",
       "                 DataTypes_Auto_infer(categorical_features=[],\n",
       "                                      display_types=True, features_todrop=[],\n",
       "                                      id_columns=[], ml_usecase='regression',\n",
       "                                      numerical_features=[],\n",
       "                                      target='UNSUPERVISED_DUMMY_TARGET',\n",
       "                                      time_features=[])),\n",
       "                ('imputer',\n",
       "                 Simple_Imputer(categorical_strategy='most frequent',\n",
       "                                fill_value_categorical=None,\n",
       "                                fill_value_numerical=None...\n",
       "                ('fix_perfect', 'passthrough'),\n",
       "                ('clean_names', Clean_Colum_Names()),\n",
       "                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),\n",
       "                ('dfs', 'passthrough'), ('pca', 'passthrough'),\n",
       "                ['trained_model',\n",
       "                 IForest(behaviour='new', bootstrap=False, contamination=0.05,\n",
       "    max_features=1.0, max_samples='auto', n_estimators=200, n_jobs=-1,\n",
       "    random_state=123, verbose=0)]],\n",
       "         verbose=False)
DataTypes_Auto_infer(ml_usecase='regression',\n",
       "                     target='UNSUPERVISED_DUMMY_TARGET')
Simple_Imputer(categorical_strategy='most frequent',\n",
       "               fill_value_categorical=None, fill_value_numerical=None,\n",
       "               numeric_strategy='mean', target_variable=None)
New_Catagorical_Levels_in_TestData(replacement_strategy='least frequent',\n",
       "                                   target='UNSUPERVISED_DUMMY_TARGET')
passthrough
passthrough
passthrough
passthrough
New_Catagorical_Levels_in_TestData(replacement_strategy='least frequent',\n",
       "                                   target='UNSUPERVISED_DUMMY_TARGET')
Make_Time_Features(list_of_features=None,\n",
       "                   time_feature=Index([], dtype='object'))
passthrough
passthrough
passthrough
passthrough
passthrough
passthrough
passthrough
Dummify(target='UNSUPERVISED_DUMMY_TARGET')
passthrough
Clean_Colum_Names()
passthrough
passthrough
passthrough
passthrough
IForest(behaviour='new', bootstrap=False, contamination=0.05,\n",
       "    max_features=1.0, max_samples='auto', n_estimators=200, n_jobs=-1,\n",
       "    random_state=123, verbose=0)
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Col1Col2Col3Col4Col5Col6Col7Col8Col9Col10AnomalyAnomaly_Score
00.2639950.7649290.1384240.9352420.6058670.5187900.9122250.6082340.7237820.7335910-0.035865
10.5460920.6539750.0655750.2277720.8452690.8370660.2723790.3316790.4292970.3674220-0.084927
20.3367140.5388420.1928010.5535630.0745150.3329930.3657920.8613090.8990170.08860010.025356
30.0921080.9950170.0144650.1763710.2415300.5147240.5622080.1589630.0737150.20846310.042415
40.3252610.8059680.9570330.3316650.3079230.3553150.5018990.5584490.8851690.1827540-0.023408
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