Implement logging throughout the package Labels #6

createDB.py

@@ -1,14 +1,21 @@
 import numpy as np
 import pandas as pd
+import logging
 
+
+logger=logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
 # Set seed for reproducibility
+logger.debug("Setting the speed...")
 np.random.seed(42)
 
 # Generate 100 rows of data
+logger.debug("Setting up the data...")
 data_size = 100
 
 # Generate 4 features with random values
 # Feature 1: Age (18 to 90)
+logger.debug("Generating the features...")
 age = np.random.randint(18, 90, data_size)
 
 # Feature 2: Tumor size (1 to 10 cm)
@@ -22,11 +29,14 @@
 
 # Generate target column 'Cancer' based on some logic
 # For simplicity, assume higher risk if age > 50, tumor_size > 5, gene_mutation = 1, and smoking_history > 20
+logger.debug("Generating the column...")
 cancer_risk = (age > 50) & (tumor_size > 5) & (gene_mutation == 1) & (smoking_history > 20)
 cancer = np.where(cancer_risk, 1, 0)
 
 # Create a DataFrame
+logger.debug("Creating the DataFrame...")
 df = pd.DataFrame({
+
     'Age': age,
     'Tumor_Size': tumor_size,
     'Gene_Mutation': gene_mutation,
@@ -35,6 +45,7 @@
 })
 
 # Save the DataFrame to a CSV file
+logger.info("Saving the csv file...")
 df.to_csv('cancer.csv', index=False)
 
-print("CSV file 'cancer.csv' has been created.")
+logger.info("CSV file 'cancer.csv' has been created.")

explainableai/__init__.py

@@ -1,4 +1,6 @@
 # explainableai/__init__.py
 from .core import XAIWrapper
 
+import logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
 __all__ = ['XAIWrapper']

explainableai/anomaly_detection.py

@@ -1,6 +1,18 @@
 from sklearn.ensemble import IsolationForest
+import logging
 
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
 def detect_anomalies(X):
-    iso_forest = IsolationForest(contamination=0.1, random_state=42)
-    anomalies = iso_forest.fit_predict(X)
-    return anomalies
+    try:
+        #Creating an Isolation forest
+        logger.debug("Creating isolation forest model...")
+        iso_forest = IsolationForest(contamination=0.1, random_state=42)
+
+        #Prediction
+        logger.debug("Making Prediction...")
+        anomalies = iso_forest.fit_predict(X)
+        logger.info("Prediction Maked...")
+        return anomalies
+    except Exception as e:
+        logger.error(f"Something wrong in the prediction...{str(e)}")