reporting.py

"""
This script used to generate a confusion matrix.

Name: Nedal Altiti
Date: 09 / 15 2023
"""
import json
import os
import logging
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
from diagnostics import model_predictions

logging.basicConfig(level=logging.INFO, format="%(asctime)-15s %(message)s")
logger = logging.getLogger()


# Load config.json and get path variables
with open('config.json','r') as f:
    config = json.load(f) 

test_data_path = os.path.join(config['test_data_path'], 'testdata.csv') 
output_model_path = os.path.join(config['output_model_path'])

# Function for reporting
def score_model():
    """
    calculate a confusion matrix using the test data and the deployed model
    """
    # Read test data
    data = pd.read_csv(test_data_path)
    y = data['exited']

    # Get model predictions
    pred = model_predictions(data)

    # Calculate confusion matrix
    cm = confusion_matrix(y, pred)

    # Plot confusion matrix
    plt.figure(figsize=(6, 6))
    ax = sns.heatmap(cm, annot=True, cmap='Blues')
    ax.set_title('Confusion matrix')
    ax.set_xlabel('\nPredicted Label')
    ax.set_ylabel('True Label')
    ax.xaxis.set_ticklabels(['False', 'True'])
    ax.yaxis.set_ticklabels(['False', 'True'])

    # Save confusion matrix plot
    plt.savefig(os.path.join(output_model_path, 'confusionmatrix.png'))
    logger.info(f"confusion matrix saved in {config['output_model_path']}")
    return cm

if __name__ == '__main__':
    score_model()