script.py

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, precision_score, recall_score, f1_score, roc_curve, auc
import sklearn
import joblib
import boto3
import pathlib
from io import StringIO 
import argparse
import joblib
import os
import numpy as np
import pandas as pd

# Loading the model    
def model_fn(model_dir):
    clf = joblib.load(os.path.join(model_dir, "model.joblib"))
    return clf

# script.py will execute line by line    
if __name__ == "__main__":

    print("[INFO] Extracting arguments")
    parser = argparse.ArgumentParser()

    # hyperparameters sent by the client are passed as command-line arguments to the script. Specific to Random Forest classifier
    parser.add_argument("--n_estimators", type=int, default=100)
    parser.add_argument("--random_state", type=int, default=0)

    # Data, model, and output directories. Arguments required to be passed to Sagemaker for model training
    parser.add_argument("--model-dir", type=str, default=os.environ.get("SM_MODEL_DIR")) # default
    parser.add_argument("--train", type=str, default=os.environ.get("SM_CHANNEL_TRAIN")) # default
    parser.add_argument("--test", type=str, default=os.environ.get("SM_CHANNEL_TEST")) # default
    parser.add_argument("--train-file", type=str, default="train-V-1.csv")
    parser.add_argument("--test-file", type=str, default="test-V-1.csv")

    args, _ = parser.parse_known_args()
    
    print("SKLearn Version: ", sklearn.__version__)
    print("Joblib Version: ", joblib.__version__)

    print("[INFO] Reading data")
    print()
    train_df = pd.read_csv(os.path.join(args.train, args.train_file))
    test_df = pd.read_csv(os.path.join(args.test, args.test_file))
    
    features = list(train_df.columns)
    label = features.pop(-1)
    
    print("Building training and testing datasets")
    print()
    X_train = train_df[features]
    X_test = test_df[features]
    y_train = train_df[label]
    y_test = test_df[label]

    print('Column order: ')
    print(features)
    print()
    
    print("Label column is: ",label)
    print()
    
    print("Data Shape: ")
    print()
    print("---- SHAPE OF TRAINING DATA (85%) ----")
    print(X_train.shape)
    print(y_train.shape)
    print()
    print("---- SHAPE OF TESTING DATA (15%) ----")
    print(X_test.shape)
    print(y_test.shape)
    print()
    
  
    print("Training RandomForest Model.....")
    print()
    model =  RandomForestClassifier(n_estimators=args.n_estimators, random_state=args.random_state, verbose = 3,n_jobs=-1)
    model.fit(X_train, y_train)
    print()
    

    model_path = os.path.join(args.model_dir, "model.joblib")
    joblib.dump(model,model_path)
    print("Model persisted at " + model_path)
    print()

    
    y_pred_test = model.predict(X_test)
    test_acc = accuracy_score(y_test,y_pred_test)
    test_rep = classification_report(y_test,y_pred_test)

    print()
    print("---- METRICS RESULTS FOR TESTING DATA ----")
    print()
    print("Total Rows are: ", X_test.shape[0])
    print('[TESTING] Model Accuracy is: ', test_acc)
    print('[TESTING] Testing Report: ')
    print(test_rep)