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/root/.cache/pip/wheels/63/d0/4f/3deeca1f4c47a6aca7c2c6a6e2bf272391565dc86a7718a59b\n", " Building wheel for querystring-parser (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for querystring-parser: filename=querystring_parser-1.2.4-cp36-none-any.whl size=7079 sha256=4362b33092620c2d85d89b741bc01e3f9993e2571d1caa57ca41d5da53210357\n", " Stored in directory: /root/.cache/pip/wheels/1e/41/34/23ebf5d1089a9aed847951e0ee375426eb4ad0a7079d88d41e\n", " Building wheel for imagehash (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for imagehash: filename=ImageHash-4.1.0-py2.py3-none-any.whl size=291990 sha256=35274b1a839753923467aba0a29ce8cc7eeb5422791c9a612591c17c2eceb19d\n", " Stored in directory: /root/.cache/pip/wheels/07/1c/dc/6831446f09feb8cc199ec73a0f2f0703253f6ae013a22f4be9\n", "Successfully built pycaret pyLDAvis pyod funcy combo suod htmlmin sqlalchemy prometheus-flask-exporter databricks-cli querystring-parser imagehash\n", "\u001b[31mERROR: pandas-profiling 2.8.0 has requirement tqdm>=4.43.0, but you'll have tqdm 4.41.1 which is incompatible.\u001b[0m\n", "Installing collected packages: threadpoolctl, scikit-learn, yellowbrick, lightgbm, funcy, pyLDAvis, combo, suod, pyod, catboost, tangled-up-in-unicode, imagehash, visions, confuse, htmlmin, phik, pandas-profiling, kmodes, datefinder, zope.interface, DateTime, sqlalchemy, Mako, python-editor, alembic, gunicorn, smmap, gitdb, gitpython, websocket-client, docker, prometheus-flask-exporter, databricks-cli, azure-core, isodate, msrest, cryptography, azure-storage-blob, querystring-parser, gorilla, mlflow, pycaret\n", " Found existing installation: scikit-learn 0.22.2.post1\n", " Uninstalling scikit-learn-0.22.2.post1:\n", " Successfully uninstalled scikit-learn-0.22.2.post1\n", " Found existing installation: yellowbrick 0.9.1\n", " Uninstalling yellowbrick-0.9.1:\n", " Successfully uninstalled yellowbrick-0.9.1\n", " Found existing installation: lightgbm 2.2.3\n", " 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threadpoolctl-2.1.0 visions-0.4.4 websocket-client-0.57.0 yellowbrick-1.1 zope.interface-5.1.0\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "lUvE187JEQm3", "colab": { "base_uri": "https://localhost:8080/", "height": 204 }, "outputId": "e6083dca-71b1-40b6-fc25-3256960e4dfb" }, "source": [ "from pycaret.datasets import get_data\n", "dataset = get_data('diamond')" ], "execution_count": 2, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "
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Carat WeightCutColorClarityPolishSymmetryReportPrice
01.10IdealHSI1VGEXGIA5169
10.83IdealHVS1IDIDAGSL3470
20.85IdealHSI1EXEXGIA3183
30.91IdealESI1VGVGGIA4370
40.83IdealGSI1EXEXGIA3171
\n", "
" ], "text/plain": [ " Carat Weight Cut Color Clarity Polish Symmetry Report Price\n", "0 1.10 Ideal H SI1 VG EX GIA 5169\n", "1 0.83 Ideal H VS1 ID ID AGSL 3470\n", "2 0.85 Ideal H SI1 EX EX GIA 3183\n", "3 0.91 Ideal E SI1 VG VG GIA 4370\n", "4 0.83 Ideal G SI1 EX EX GIA 3171" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "hXmaL1xFEQnj", "colab": { "base_uri": "https://localhost:8080/", "height": 53 }, "outputId": "da4af24e-212b-4c5f-a4ba-42dbbd7a2953" }, "source": [ "data = dataset.sample(frac=0.95, random_state=786).reset_index(drop=True)\n", "data_unseen = dataset.drop(data.index).reset_index(drop=True)\n", "\n", "print('Data for Modeling: ' + str(data.shape))\n", "print('Unseen Data For Predictions: ' + str(data_unseen.shape))" ], "execution_count": 3, "outputs": [ { "output_type": "stream", "text": [ "Data for Modeling: (5700, 8)\n", "Unseen Data For Predictions: (300, 8)\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "3sXuMNuqG6PG", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 958, "referenced_widgets": [ "efaff962f0a94c8d8f5d06311e023506", "c2e2060613d444078b48b60e30a112e7", "09690916f8634c01b8b289d83119e178", "dbb0c8ed8d2f4b37a492d777d1cfcd48", "5db0cd1626b044b6ada0e461e2d7c94b", "8a5e8367e06b49dcbe1550b7ce9a8380" ] }, "outputId": "0bd58ff5-896d-4656-96c4-535089378636" }, "source": [ "from pycaret.regression import *\n", "exp_reg101 = setup(data = data, target = 'Price', session_id=123)" ], "execution_count": 4, "outputs": [ { "output_type": "stream", "text": [ " \n", "Setup Succesfully Completed.\n" ], "name": "stdout" }, { "output_type": "display_data", "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(5700, 8)
4Missing Values False
5Numeric Features 1
6Categorical Features 6
7Ordinal Features False
8High Cardinality Features False
9High Cardinality Method None
10Sampled Data(5700, 8)
11Transformed Train Set(3989, 29)
12Transformed Test Set(1711, 29)
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": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "FGCoUiQpEQpz", "colab": { "base_uri": "https://localhost:8080/", "height": 292, "referenced_widgets": [ "f82b0cd6d54b427ea21cf2a5b2958c21", "0f57ccbc1ce543ad9b7d837c7fe03595", "2dae5a1a055343ea88a268f14cc53a49" ] }, "outputId": "79640529-8cdd-4875-b6c7-ce0544252663" }, "source": [ "lightgbm = create_model('lightgbm')" ], "execution_count": 5, "outputs": [ { "output_type": "display_data", "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
0762.66533673803.82341916.71690.97010.07280.0523
1955.52677425168.55222724.91620.94000.08500.0588
2852.91265146188.15412268.52110.95790.08340.0610
3699.48261730518.03451315.49160.98110.07660.0557
4633.40701505126.50841226.83600.98520.06810.0523
5758.11282698170.19781642.61080.97270.07840.0581
6846.18886214502.54172492.89040.93950.08110.0582
7729.43422328849.09081526.05670.97280.07650.0560
8837.56042614949.94351617.08070.97380.08240.0622
9715.89502427369.38911558.00170.97580.08380.0580
Mean779.11853576464.62361828.91220.96690.07880.0573
SD89.01161910918.8685481.19090.01520.00510.0031
" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "gmaIfnBMEQrE", "colab": { "base_uri": "https://localhost:8080/", "height": 292, "referenced_widgets": [ "1de1da0d08c64148935f305cafdeaadc", "577eb34fbd6f4bdea66489337463c354", "f9312a23e9af43e183ebd777b133a2a9" ] }, "outputId": "bc3b8389-0387-44d4-9fd9-6d71903e2230" }, "source": [ "tuned_lightgbm = tune_model(lightgbm)" ], "execution_count": 6, "outputs": [ { "output_type": "display_data", "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
0823.06622453046.61281566.22050.98010.08650.0632
1912.88325993852.27322448.23450.95160.08560.0623
2832.14673108349.33941763.05110.97450.08620.0651
3691.43281452486.18371205.19130.98420.08430.0595
4722.35781775038.59271332.30570.98260.08050.0599
5738.39891958978.39361399.63510.98020.07720.0603
6843.29425019902.11732240.51380.95110.08600.0625
7756.85542315238.97261521.59090.97300.08010.0592
8837.29922257821.78561502.60500.97740.08350.0642
9740.57262154216.09631467.72480.97850.09010.0650
Mean789.83072848893.03671644.70730.97330.08400.0621
SD65.98671408540.1118379.25050.01140.00360.0022
" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "nwaZk6oTEQsi", "colab": { "base_uri": "https://localhost:8080/", "height": 80 }, "outputId": "594ed7b7-0b2d-4a90-ab31-a0bace294024" }, "source": [ "predict_model(tuned_lightgbm );" ], "execution_count": 7, "outputs": [ { "output_type": "display_data", "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", "
ModelMAEMSERMSER2RMSLEMAPE
0Light Gradient Boosting Machine707.80431.828776e+061352.32230.98210.07760.0571
\n", "
" ], "text/plain": [ " Model MAE ... RMSLE MAPE\n", "0 Light Gradient Boosting Machine 707.8043 ... 0.0776 0.0571\n", "\n", "[1 rows x 7 columns]" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "r79BGjIfEQs1" }, "source": [ "# 12.0 Finalize Model for Deployment" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "_--tO4KGEQs-", "colab": {} }, "source": [ "final_lightgbm = finalize_model(tuned_lightgbm )" ], "execution_count": 8, "outputs": [] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "U9W6kXsSEQtQ", "colab": { "base_uri": "https://localhost:8080/", "height": 125 }, "outputId": "4bfc1789-f50e-4dc5-aa76-8ec80e7fe6b4" }, "source": [ "print(final_lightgbm)" ], "execution_count": 9, "outputs": [ { "output_type": "stream", "text": [ "LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n", " importance_type='split', learning_rate=0.4, max_depth=10,\n", " min_child_samples=20, min_child_weight=0.001, min_split_gain=0.9,\n", " n_estimators=90, n_jobs=-1, num_leaves=10, objective=None,\n", " random_state=123, reg_alpha=0.9, reg_lambda=0.2, silent=True,\n", " subsample=1.0, subsample_for_bin=200000, subsample_freq=0)\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "NJDk3I-EEQtg", "colab": { "base_uri": "https://localhost:8080/", "height": 80 }, "outputId": "18b12734-b0fb-4270-daca-c177d4dbb16c" }, "source": [ "predict_model(final_lightgbm);" ], "execution_count": 10, "outputs": [ { "output_type": "display_data", "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", "
ModelMAEMSERMSER2RMSLEMAPE
0Light Gradient Boosting Machine568.6295880420.4651938.30720.99140.06660.05
\n", "
" ], "text/plain": [ " Model MAE MSE ... R2 RMSLE MAPE\n", "0 Light Gradient Boosting Machine 568.6295 880420.4651 ... 0.9914 0.0666 0.05\n", "\n", "[1 rows x 7 columns]" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "dWU2Dmdx2UNZ", "colab_type": "text" }, "source": [ "# 13.0 Deploy Model on Microsoft Azure\n", "\n", "This is the code to deploy model on Microsft azure using `pycaret` functionalities." ] }, { "cell_type": "code", "metadata": { "id": "PtdFIPJJ0zHX", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 485 }, "outputId": "58dee8d4-02f9-4018-c3ba-3dd3209628ff" }, "source": [ "# ! pip install azure-storage-blob\n", "! pip install awscli\n", "\n" ], "execution_count": 11, "outputs": [ { "output_type": "stream", "text": [ "Collecting awscli\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/db/87/d390c07c9f761c682b71d0c5e99fe46193b91e6140a4dde04044c70fdeb6/awscli-1.18.117-py2.py3-none-any.whl (3.3MB)\n", "\u001b[K |████████████████████████████████| 3.3MB 8.3MB/s \n", "\u001b[?25hCollecting botocore==1.17.40\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/3d/77/4f1f409c9c454ae798cff20744efacd5ca79059159272857636b6b560bf6/botocore-1.17.40-py2.py3-none-any.whl (6.5MB)\n", "\u001b[K |████████████████████████████████| 6.5MB 45.2MB/s \n", "\u001b[?25hCollecting rsa<=4.5.0,>=3.1.2; python_version != \"3.4\"\n", " Downloading https://files.pythonhosted.org/packages/26/f8/8127fdda0294f044121d20aac7785feb810e159098447967a6103dedfb96/rsa-4.5-py2.py3-none-any.whl\n", "Requirement already satisfied: s3transfer<0.4.0,>=0.3.0 in /usr/local/lib/python3.6/dist-packages (from awscli) (0.3.3)\n", "Requirement already satisfied: PyYAML<5.4,>=3.10; python_version != \"3.4\" in /usr/local/lib/python3.6/dist-packages (from awscli) (3.13)\n", "Requirement already satisfied: docutils<0.16,>=0.10 in /usr/local/lib/python3.6/dist-packages (from awscli) (0.15.2)\n", "Collecting colorama<0.4.4,>=0.2.5; python_version != \"3.4\"\n", " Downloading https://files.pythonhosted.org/packages/c9/dc/45cdef1b4d119eb96316b3117e6d5708a08029992b2fee2c143c7a0a5cc5/colorama-0.4.3-py2.py3-none-any.whl\n", "Requirement already satisfied: urllib3<1.26,>=1.20; python_version != \"3.4\" in /usr/local/lib/python3.6/dist-packages (from botocore==1.17.40->awscli) (1.24.3)\n", "Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /usr/local/lib/python3.6/dist-packages (from botocore==1.17.40->awscli) (0.10.0)\n", "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.6/dist-packages (from botocore==1.17.40->awscli) (2.8.1)\n", "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<=4.5.0,>=3.1.2; python_version != \"3.4\"->awscli) (0.4.8)\n", "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil<3.0.0,>=2.1->botocore==1.17.40->awscli) (1.15.0)\n", "Installing collected packages: botocore, rsa, colorama, awscli\n", " Found existing installation: botocore 1.17.37\n", " Uninstalling botocore-1.17.37:\n", " Successfully uninstalled botocore-1.17.37\n", " Found existing installation: rsa 4.6\n", " Uninstalling rsa-4.6:\n", " Successfully uninstalled rsa-4.6\n", "Successfully installed awscli-1.18.117 botocore-1.17.40 colorama-0.4.3 rsa-4.5\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "ImFnwpb52iDl", "colab_type": "code", "colab": {} }, "source": [ "# Enter connection string when running in google colab\n", "connect_str = '' #@param {type:\"string\"}\n", "print(connect_str)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "4FolddlO2iTK", "colab_type": "code", "colab": {} }, "source": [ "#! export AZURE_STORAGE_CONNECTION_STRING=connect_str" ], "execution_count": 13, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "q_MZPZ4271g3", "colab_type": "code", "colab": {} }, "source": [ "import os\n", "os.environ['AZURE_STORAGE_CONNECTION_STRING']= connect_str" ], "execution_count": 14, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "wz0YIfLb6iVK", "colab_type": "code", "colab": {} }, "source": [ "! echo $AZURE_STORAGE_CONNECTION_STRING" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "cUOqSvi63m01", "colab_type": "code", "colab": {} }, "source": [ "os.getenv('AZURE_STORAGE_CONNECTION_STRING')" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "H3C-nMpF2iZg", "colab_type": "code", "colab": {} }, "source": [ "authentication = {'container': 'pycaret-reg-1011'}\n", "model_name = 'lightgbm-reg-101'\n", "deploy_model(final_lightgbm, model_name, authentication, platform = 'azure')" ], "execution_count": 17, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "iuBz98UT2icD", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 107 }, "outputId": "946b34d4-aadf-470b-bac5-5fd49d8f2198" }, "source": [ "authentication = {'container': 'pycaret-reg-1011'}\n", "model_name = 'lightgbm-reg-101'\n", "model_azure = load_model(model_name, \n", " platform = 'azure', \n", " authentication = authentication,\n", " verbose=True)" ], "execution_count": 18, "outputs": [ { "output_type": "stream", "text": [ "Loading model from Microsoft Azure\n", "\n", "Downloading blob to \n", "\tlightgbm-reg-101.pkl\n", "Transformation Pipeline and Model Successfully Loaded\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "aiP_EiLm2iWk", "colab_type": "code", "colab": {} }, "source": [ "\n", "unseen_predictions = predict_model(model_azure, data=data_unseen, verbose=True)" ], "execution_count": 19, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "G9s2LdGIbIlV", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 519 }, "outputId": "1a3ce935-f82c-40ff-b7f9-1fb816e7e090" }, "source": [ "predict_model(model_azure)" ], "execution_count": 20, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "
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ModelMAEMSERMSER2RMSLEMAPE
0Light Gradient Boosting Machine568.6295880420.4651938.30720.99140.06660.05
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" ], "text/plain": [ " Model MAE MSE ... R2 RMSLE MAPE\n", "0 Light Gradient Boosting Machine 568.6295 880420.4651 ... 0.9914 0.0666 0.05\n", "\n", "[1 rows x 7 columns]" ] }, "metadata": { "tags": [] } }, { "output_type": "execute_result", "data": { "text/html": [ "
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Carat WeightCut_FairCut_GoodCut_IdealCut_Signature-IdealCut_Very GoodColor_DColor_EColor_FColor_GColor_HColor_IClarity_FLClarity_IFClarity_SI1Clarity_VS1Clarity_VS2Clarity_VVS1Clarity_VVS2Polish_EXPolish_GPolish_IDPolish_VGSymmetry_EXSymmetry_GSymmetry_IDSymmetry_VGReport_AGSLReport_GIAPriceLabel
01.600.00.01.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.01.00.00.00.01.00.01.00.01294213087.2057
11.150.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.01.00.01.00.00.00.01.00.061106159.4249
22.370.00.01.00.00.00.00.00.00.01.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.01.00.01.02806326176.5884
32.010.00.00.00.01.00.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.01.00.00.00.01.00.01.02594824200.7832
40.910.00.00.00.01.00.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.01.00.01.00.00.00.01.043814480.3517
................................................................................................
17060.900.00.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.01.01.00.00.00.00.01.072686947.5704
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" ], "text/plain": [ " Carat Weight Cut Color ... Report Price Label\n", "0 1.23 Very Good G ... GIA 8445 9072.6351\n", "1 0.90 Fair I ... GIA 3526 3554.2894\n", "2 0.77 Very Good G ... AGSL 3966 4229.3503\n", "3 1.51 Very Good D ... GIA 14416 14623.5531\n", "4 2.33 Ideal H ... AGSL 21618 20527.9639\n", ".. ... ... ... ... ... ... ...\n", "295 1.03 Ideal D ... GIA 6250 6742.9309\n", "296 1.00 Very Good D ... GIA 5328 5621.3273\n", "297 1.02 Ideal D ... GIA 6157 6679.0715\n", "298 1.27 Signature-Ideal G ... GIA 11206 11642.7346\n", "299 2.19 Ideal E ... GIA 30507 35164.7663\n", "\n", "[300 rows x 9 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 24 } ] }, { "cell_type": "code", "metadata": { "id": "2CRqugcz2h5a", "colab_type": "code", "colab": {} }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "0ZxYxszDBqJh", "colab_type": "text" }, "source": [ "# 13.0 Deploy Model on Google Cloud" ] }, { "cell_type": "markdown", "metadata": { "id": "N5qy_gsfB1rA", "colab_type": "text" }, "source": [ "After the model is finalised and you are happy with the model, you can deploy the model on your cloud of choice. In this section, we deploy the model on the google cloud platform. " ] }, { "cell_type": "code", "metadata": { "id": "2eJdBC3EClnW", "colab_type": "code", "colab": {} }, "source": [ "from google.colab import auth\n", "auth.authenticate_user()" ], "execution_count": 21, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "9L31JPblEPG6", "colab_type": "code", "colab": {} }, "source": [ "! pip install awscli" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "i8xWrcliQCz1", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "outputId": "77c554fd-d401-4186-c755-741465ba1806" }, "source": [ "# GCP project name, Change the name based on your own GCP project.\n", "CLOUD_PROJECT = 'gcpessentials-rz' # GCP project name\n", "bucket_name = 'pycaret-reg101-test1' # bucket name for storage of your model\n", "BUCKET = 'gs://' + CLOUD_PROJECT + '-{}'.format(bucket_name)\n", "# Set the gcloud consol to $CLOUD_PROJECT Environment Variable for your Desired Project)\n", "!gcloud config set project $CLOUD_PROJECT" ], "execution_count": 22, "outputs": [ { "output_type": "stream", "text": [ "Updated property [core/project].\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "fq7-Su1iQuHl", "colab_type": "code", "colab": {} }, "source": [ "authentication = {'project': CLOUD_PROJECT, 'bucket' : bucket_name}\n", "model_name = 'lightgbm-reg'\n", "deploy_model(final_lightgbm, model_name, authentication, platform = 'gcp')" ], "execution_count": 23, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "CN0CkUXKRAlc", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 53 }, "outputId": "9abf26f2-377f-4327-89af-07ffbab14bc5" }, "source": [ "authentication = {'project': CLOUD_PROJECT, 'bucket' : bucket_name}\n", "model_name = 'lightgbm-reg'\n", "model_gcp = load_model(model_name, \n", " platform = 'gcp', \n", " authentication = authentication,\n", " verbose=True)" ], "execution_count": 24, "outputs": [ { "output_type": "stream", "text": [ "loading model from GCP\n", "Transformation Pipeline and Model Successfully Loaded\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "bIMlREBHXTtF", "colab_type": "code", "colab": {} }, "source": [ "\n", "unseen_predictions = predict_model(model_gcp, data=data_unseen, verbose=True)" ], "execution_count": 25, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "CFxn0KJ_ebGz", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 419 }, "outputId": "cffebb43-47cb-4482-fb6d-777d65ac1e5d" }, "source": [ "unseen_predictions" ], "execution_count": 26, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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31.51Very GoodDVS2EXEXGIA1441614623.5531
42.33IdealHSI1IDIDAGSL2161820527.9639
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2992.19IdealEVS1EXEXGIA3050735164.7663
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" ], "text/plain": [ " Carat Weight Cut Color ... Report Price Label\n", "0 1.23 Very Good G ... GIA 8445 9072.6351\n", "1 0.90 Fair I ... GIA 3526 3554.2894\n", "2 0.77 Very Good G ... AGSL 3966 4229.3503\n", "3 1.51 Very Good D ... GIA 14416 14623.5531\n", "4 2.33 Ideal H ... AGSL 21618 20527.9639\n", ".. ... ... ... ... ... ... ...\n", "295 1.03 Ideal D ... GIA 6250 6742.9309\n", "296 1.00 Very Good D ... GIA 5328 5621.3273\n", "297 1.02 Ideal D ... GIA 6157 6679.0715\n", "298 1.27 Signature-Ideal G ... GIA 11206 11642.7346\n", "299 2.19 Ideal E ... GIA 30507 35164.7663\n", "\n", "[300 rows x 9 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 26 } ] }, { "cell_type": "code", "metadata": { "id": "tzpXE4Jmbull", "colab_type": "code", "colab": {} }, "source": [ "" ], "execution_count": null, "outputs": [] } ] }