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130 lines
5.7 KiB
Python
130 lines
5.7 KiB
Python
# ---------------------------------------------------------
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# ---------------------------------------------------------
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import os
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import pandas as pd
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import zipfile
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from sklearn.model_selection import train_test_split
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from sklearn.externals import joblib
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from sklearn.preprocessing import StandardScaler, OneHotEncoder
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from sklearn.impute import SimpleImputer
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from sklearn.pipeline import Pipeline
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from sklearn.linear_model import LogisticRegression
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from sklearn_pandas import DataFrameMapper
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from azureml.core.run import Run
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from interpret.ext.blackbox import TabularExplainer
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from azureml.contrib.interpret.explanation.explanation_client import ExplanationClient
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from azureml.interpret.scoring.scoring_explainer import LinearScoringExplainer, save
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OUTPUT_DIR = './outputs/'
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os.makedirs(OUTPUT_DIR, exist_ok=True)
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# get the IBM employee attrition dataset
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outdirname = 'dataset.6.21.19'
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try:
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from urllib import urlretrieve
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except ImportError:
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from urllib.request import urlretrieve
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zipfilename = outdirname + '.zip'
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urlretrieve('https://publictestdatasets.blob.core.windows.net/data/' + zipfilename, zipfilename)
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with zipfile.ZipFile(zipfilename, 'r') as unzip:
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unzip.extractall('.')
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attritionData = pd.read_csv('./WA_Fn-UseC_-HR-Employee-Attrition.csv')
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# dropping Employee count as all values are 1 and hence attrition is independent of this feature
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attritionData = attritionData.drop(['EmployeeCount'], axis=1)
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# dropping Employee Number since it is merely an identifier
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attritionData = attritionData.drop(['EmployeeNumber'], axis=1)
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attritionData = attritionData.drop(['Over18'], axis=1)
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# since all values are 80
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attritionData = attritionData.drop(['StandardHours'], axis=1)
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# converting target variables from string to numerical values
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target_map = {'Yes': 1, 'No': 0}
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attritionData["Attrition_numerical"] = attritionData["Attrition"].apply(lambda x: target_map[x])
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target = attritionData["Attrition_numerical"]
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attritionXData = attritionData.drop(['Attrition_numerical', 'Attrition'], axis=1)
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# creating dummy columns for each categorical feature
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categorical = []
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for col, value in attritionXData.iteritems():
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if value.dtype == 'object':
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categorical.append(col)
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# store the numerical columns
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numerical = attritionXData.columns.difference(categorical)
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numeric_transformations = [([f], Pipeline(steps=[
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('imputer', SimpleImputer(strategy='median')),
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('scaler', StandardScaler())])) for f in numerical]
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categorical_transformations = [([f], OneHotEncoder(handle_unknown='ignore', sparse=False)) for f in categorical]
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transformations = numeric_transformations + categorical_transformations
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# append classifier to preprocessing pipeline
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clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)),
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('classifier', LogisticRegression(solver='lbfgs'))])
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# get the run this was submitted from to interact with run history
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run = Run.get_context()
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# create an explanation client to store the explanation (contrib API)
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client = ExplanationClient.from_run(run)
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# Split data into train and test
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x_train, x_test, y_train, y_test = train_test_split(attritionXData,
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target,
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test_size=0.2,
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random_state=0,
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stratify=target)
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# write x_test out as a pickle file for later visualization
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x_test_pkl = 'x_test.pkl'
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with open(x_test_pkl, 'wb') as file:
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joblib.dump(value=x_test, filename=os.path.join(OUTPUT_DIR, x_test_pkl))
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run.upload_file('x_test_ibm.pkl', os.path.join(OUTPUT_DIR, x_test_pkl))
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# preprocess the data and fit the classification model
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clf.fit(x_train, y_train)
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model = clf.steps[-1][1]
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# save model for use outside the script
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model_file_name = 'log_reg.pkl'
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with open(model_file_name, 'wb') as file:
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joblib.dump(value=clf, filename=os.path.join(OUTPUT_DIR, model_file_name))
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# register the model with the model management service for later use
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run.upload_file('original_model.pkl', os.path.join(OUTPUT_DIR, model_file_name))
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original_model = run.register_model(model_name='amlcompute_deploy_model',
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model_path='original_model.pkl')
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# create an explainer to validate or debug the model
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tabular_explainer = TabularExplainer(model,
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initialization_examples=x_train,
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features=attritionXData.columns,
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classes=["Not leaving", "leaving"],
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transformations=transformations)
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# explain overall model predictions (global explanation)
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# passing in test dataset for evaluation examples - note it must be a representative sample of the original data
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# more data (e.g. x_train) will likely lead to higher accuracy, but at a time cost
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global_explanation = tabular_explainer.explain_global(x_test)
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# uploading model explanation data for storage or visualization
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comment = 'Global explanation on classification model trained on IBM employee attrition dataset'
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client.upload_model_explanation(global_explanation, comment=comment, model_id=original_model.id)
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# also create a lightweight explainer for scoring time
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scoring_explainer = LinearScoringExplainer(tabular_explainer)
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# pickle scoring explainer locally
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save(scoring_explainer, directory=OUTPUT_DIR, exist_ok=True)
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# register scoring explainer
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run.upload_file('IBM_attrition_explainer.pkl', os.path.join(OUTPUT_DIR, 'scoring_explainer.pkl'))
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scoring_explainer_model = run.register_model(model_name='IBM_attrition_explainer',
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model_path='IBM_attrition_explainer.pkl')
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