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

def clean_data(data):
    # Cleaning the data
    x_df = data.to_pandas_dataframe().dropna()
    x_df.drop("name", inplace=True, axis=1)
    y_df = x_df.pop("status")
    return x_df, y_df
def main():
    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()
    
    link='https://raw.githubusercontent.com/sg7801/Parkinsons-Disease-Prediction/main/parkinsons.txt'
    ds = TabularDatasetFactory.from_delimited_files(path=link)
    x, y = clean_data(ds)
    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.27)

    run = Run.get_context()
    
    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))

if __name__ == '__main__':
    main()