A short example for train.py

train.py

import os
import sys
import argparse
import joblib
import pandas as pd

from azureml.core import Run
from azureml.core.run import Run

from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler

# All the imports go here...

def getRuntimeArgs():
    parser = argparse.ArgumentParser()
    parser.add_argument('--data-path', type=str) # Make sure the data path can be passed in dynamically
    # Add more arguments here
    args = parser.parse_args()
    return args

def main():
  
    # Get arguments from command line
    args = getRuntimeArgs()

    # Get the current experiment run context from Azure Machine Learning
    run = Run.get_context()

    # Use whatever function you require to load the data from the data directory
    credit_data_df = pd.read_csv(os.path.join(args.data_path, 'german_credit_data.csv'))

    # Do some model training
    clf = model_train(credit_data_df, run)

    #copying to "outputs" directory, automatically uploads it to Azure ML
    output_dir = './outputs/'
    os.makedirs(output_dir, exist_ok=True)
    joblib.dump(value=clf, filename=os.path.join(output_dir, 'model.pkl'))

# Do your training here
def model_train(ds_df, run):

    ds_df.drop("Sno", axis=1, inplace=True)

    y_raw = ds_df['Risk']
    X_raw = ds_df.drop('Risk', axis=1)

    categorical_features = X_raw.select_dtypes(include=['object']).columns
    numeric_features = X_raw.select_dtypes(include=['int64', 'float']).columns

    categorical_transformer = Pipeline(steps=[
        ('imputer', SimpleImputer(strategy='constant', fill_value="missing")),
        ('onehotencoder', OneHotEncoder(categories='auto', sparse=False))])

    numeric_transformer = Pipeline(steps=[
        ('scaler', StandardScaler())])

    feature_engineering_pipeline = ColumnTransformer(
        transformers=[
            ('numeric', numeric_transformer, numeric_features),
            ('categorical', categorical_transformer, categorical_features)
        ], remainder="drop")

    # Encode Labels
    le = LabelEncoder()
    encoded_y = le.fit_transform(y_raw)

    # Train test split
    X_train, X_test, y_train, y_test = train_test_split(X_raw, encoded_y, test_size=0.20, stratify=encoded_y, random_state=42)

    # Create sklearn pipeline
    lr_clf = Pipeline(steps=[('preprocessor', feature_engineering_pipeline),
                             ('classifier', LogisticRegression(solver="lbfgs"))])
    # Train the model
    lr_clf.fit(X_train, y_train)

    # Capture metrics
    train_acc = lr_clf.score(X_train, y_train)
    test_acc = lr_clf.score(X_test, y_test)
    print("Training accuracy: %.3f" % train_acc)
    print("Test data accuracy: %.3f" % test_acc)

    # Logmetrics to Azure ML
    run.log('Train accuracy', train_acc)
    run.log('Test accuracy', test_acc)

    return lr_clf

if __name__ == "__main__":
    main()