train.py

from sklearn.linear_model import LogisticRegression
import argparse
import os
import numpy as np
from sklearn.metrics import mean_squared_error
import joblib
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
from azureml.core.run import Run
from azureml.data.dataset_factory import TabularDatasetFactory

data_loc = "https://raw.githubusercontent.com/khalidw/Capstone-Project-Azure-Machine-Learning-Engineer/master/divorce.csv"
ds = TabularDatasetFactory.from_delimited_files(data_loc)


#Save model for current iteration

run = Run.get_context()
  
x_df = ds.to_pandas_dataframe().dropna()

y_df = x_df.pop("Class")

# TODO: Split data into train and test sets.

x_train, x_test, y_train, y_test = train_test_split(x_df, y_df, test_size=0.3, random_state=123)

def main():
    # Add arguments to script
    parser = argparse.ArgumentParser()

    parser.add_argument('--C', type=float, default=1.0, help="Inverse of regularization strength. Smaller values cause stronger regularization")
    parser.add_argument('--max_iter', type=int, default=100, help="Maximum number of iterations to converge")

    args = parser.parse_args()

    run.log("Regularization Strength:", np.float(args.C))
    run.log("Max iterations:", np.int(args.max_iter))

    model = LogisticRegression(C=args.C, max_iter=args.max_iter).fit(x_train, y_train)

    accuracy = model.score(x_test, y_test)
    run.log("Accuracy", np.float(accuracy))

    #Save model for current iteration, also include the value for C and max_iter in filename, random_state=
    os.makedirs('outputs', exist_ok=True)
    joblib.dump(model, 'outputs/hyperDrive_{}_{}'.format(args.C,args.max_iter))

if __name__ == '__main__':
    main()