<filename>train.py
import os
import warnings
import sys

import pandas as pd
import numpy as np
from itertools import cycle
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import ElasticNet
from sklearn.linear_model import lasso_path, enet_path
filePath = "PD_Commercial_Train.csv"
os.environ['MLFLOW_TRACKING_URI'] = 'http://localhost:5000'
os.environ['GIT_PYTHON_REFRESH'] = 'quiet'

df_credit = pd.read_csv(filePath)

cols = df_credit.columns
data = df_credit[cols].apply(pd.to_numeric, errors='coerce')
data = data.fillna(0)

X = data.drop(["PD"], axis=1)
y = data[["PD"]]

# Import mlflow
import mlflow
import mlflow.sklearn


# Evaluate metrics
def eval_metrics(actual, pred):
    rmse = np.sqrt(mean_squared_error(actual, pred))
    mae = mean_absolute_error(actual, pred)
    r2 = r2_score(actual, pred)
    return rmse, mae, r2
#new
if __name__ == "__main__":
    warnings.filterwarnings("ignore")
    np.random.seed(40)
    mlflow.set_experiment('riskAnalysis')
    # Split the data into training and test sets. (0.75, 0.25) split.
    train, test = train_test_split(data)

    #Predict PD
    train_x = train.drop(["PD"], axis=1)
    test_x = test.drop(["PD"], axis=1)
    train_y = train[["PD"]]
    test_y = test[["PD"]]

    alpha = float(sys.argv[1]) if len(sys.argv) > 1 else 0.05
    l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.05

    # Run ElasticNet
    lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)
    lr.fit(train_x, train_y)
    predicted_qualities = lr.predict(test_x)
    (rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)

    # Print out ElasticNet model metrics
    print("Elasticnet model (alpha=%f, l1_ratio=%f):" % (alpha, l1_ratio))
    print("  RMSE: %s" % rmse)
    print("  MAE: %s" % mae)
    print("  R2: %s" % r2)

    # Log mlflow attributes for mlflow UI
    mlflow.log_param("alpha", alpha)
    mlflow.log_param("l1_ratio", l1_ratio)
    mlflow.log_metric("rmse", rmse)
    mlflow.log_metric("r2", r2)
    mlflow.log_metric("mae", mae)
    mlflow.sklearn.log_model(lr, "model")
    
   