train.py

"""
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"""

import os
import pandas as pd
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split


# Split the dataframe into test and train data
def split_data(df):
    X = df.drop('Y', axis=1).values
    y = df['Y'].values

    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=0)
    data = {"train": {"X": X_train, "y": y_train},
            "test": {"X": X_test, "y": y_test}}
    return data


# Train the model, return the model
def train_model(data, ridge_args):
    reg_model = Ridge(**ridge_args)
    reg_model.fit(data["train"]["X"], data["train"]["y"])
    return reg_model


# Evaluate the metrics for the model
def get_model_metrics(model, data):
    preds = model.predict(data["test"]["X"])
    mse = mean_squared_error(data["test"]["y"],preds)
    metrics = {"mse": mse}
    return metrics


def main():
    print("Running train.py")

    # Define training parameters
    ridge_args = {"alpha": 0.5}

    # Load the training data as dataframe
    data_dir = "data"
    data_file = os.path.join(data_dir, 'diabetes.csv')
    train_df = pd.read_csv(data_file)

    data = split_data(train_df)

    # Train the model
    model = train_model(data, ridge_args)

    # Log the metrics for the model
    metrics = get_model_metrics(model, data)
    for (k, v) in metrics.items():
        print(f"{k}: {v}")


if __name__ == '__main__':
    main()