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train.py
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train.py
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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
import pandas as pd
from azureml.core.run import Run
from azureml.data.dataset_factory import TabularDatasetFactory
dataset_url = "https://raw.githubusercontent.com/shubhrasachdev/AzureML-Nanodegree-Capstone/main/diabetes.csv"
ds = TabularDatasetFactory.from_delimited_files(path = dataset_url)
x_df = ds.to_pandas_dataframe().dropna()
y_df = x_df.pop("Outcome")
# Split data into train and test sets.
x_train, x_test, y_train, y_test = train_test_split(x_df, y_df, test_size = 0.2)
run = Run.get_context()
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))
os.makedirs('outputs', exist_ok=True)
# files saved in the "outputs" folder are automatically uploaded into run history
joblib.dump(model, 'outputs/model.joblib')
if __name__ == '__main__':
main()