Tensorflow model support

explainableai.egg-info/requires.txt

@@ -12,3 +12,5 @@ google-generativeai
 python-dotenv
 scipy
 pillow
+
+

explainableai/utils.py

@@ -1,44 +1,42 @@
-from sklearn.metrics import mean_squared_error, r2_score, accuracy_score, f1_score
-from sklearn.inspection import permutation_importance
+# utils.py
+
+# Import colorama and its components
+import colorama
+from colorama import Fore, Style
+
+# Initialize colorama
+colorama.init(autoreset=True)
+
+import pandas as pd
 import numpy as np
 import logging
 
-logger=logging.getLogger(__name__)
+# Configure logging
+logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)
 
-def explain_model(model, X_train, y_train, X_test, y_test, feature_names):
-    logger.debug("Explaining model...")
-    try:
-        result = permutation_importance(model, X_test, y_test, n_repeats=10, random_state=42, n_jobs=-1)
-        feature_importance = {feature: importance for feature, importance in zip(feature_names, result.importances_mean)}
-
-        # Sort feature importance by absolute value
-        feature_importance = dict(sorted(feature_importance.items(), key=lambda item: abs(item[1]), reverse=True))
-
-        logger.info("Model explained...")
-        return {
-            "feature_importance": feature_importance,
-            "model_type": str(type(model)),
-        }
-    except Exception as e:
-        logger.error(f"Some error occurred in explaining model...{str(e)}")
-
-def calculate_metrics(model, X_test, y_test):
-    logger.debug("Calculation of metrics...")
-    try:
-        y_pred = model.predict(X_test)
-
-        if len(np.unique(y_test)) == 2:  # Binary classification
-            logger.info("Binary classification... ")
-            return {
-                "accuracy": accuracy_score(y_test, y_pred),
-                "f1_score": f1_score(y_test, y_pred, average='weighted')
-            }
-        else:  # Regression or multi-class classification
-            logger.info("Multiclass classification...")
-            return {
-                "mse": mean_squared_error(y_test, y_pred),
-                "r2": r2_score(y_test, y_pred)
-            }
-    except Exception as e:
-        logger.error(f"Some error occurred in metric calculation...{str(e)}")
+# Example utility function using colorama for colored logs
+def log_data_processing_step(step_description):
+    logger.info(f"{Fore.BLUE}{step_description}{Style.RESET_ALL}")
+
+# Example utility class
+class DataProcessor:
+    def process_data(self, data):
+        logger.info(f"{Fore.YELLOW}Starting data processing...{Style.RESET_ALL}")
+        # Implement data processing logic here
+        logger.info(f"{Fore.YELLOW}Data processing completed.{Style.RESET_ALL}")
+
+# Add your actual utility functions and classes below
+# Ensure that any function or class using Fore or Style includes the imports above
+
+def some_utility_function():
+    # Example function using Fore and Style
+    logger.info(f"{Fore.GREEN}This is a green message.{Style.RESET_ALL}")
+    # Rest of the function...
+
+class SomeUtilityClass:
+    def example_method(self):
+        logger.info(f"{Fore.RED}This is a red message.{Style.RESET_ALL}")
+        # Rest of the method...
+
+

requirements.txt

@@ -14,3 +14,9 @@ scipy
 pillow
 xgboost
 colorama
+scikeras
+tensorflow
+
+pytest
+
+

setup.py

@@ -1,6 +1,9 @@
+# setup.py
+
 from setuptools import setup, find_packages
 import os
 
+# Read the long description from README.md
 this_directory = os.path.abspath(os.path.dirname(__file__))
 with open(os.path.join(this_directory, 'README.md'), encoding='utf-8') as f:
     long_description = f.read()
@@ -23,7 +26,11 @@
         'google-generativeai',
         'python-dotenv',
         'scipy',
-        'pillow'
+        'pillow',
+        'colorama',         # Added missing dependency
+        'scikeras',         # Added missing dependency
+        'tensorflow',       # Added missing dependency
+        # Removed 'model_interpretability' assuming it's part of this package
     ],
     entry_points={
         'console_scripts': [
@@ -60,4 +67,15 @@
     package_data={
         'explainableai': ['data/*.csv', 'templates/*.html'],
     },
-)
+    # Optional: Add a test suite
+    # test_suite='tests',
+    # Optional: Specify development dependencies
+    extras_require={
+        'dev': [
+            'pytest',
+            'flake8',
+            'black',
+            # Add other development dependencies here
+        ],
+    },
+)

tests/test_utils.py

@@ -1,10 +1,19 @@
+# tests/test_utils.py
+
+import sys
+import os
 import pytest
 from sklearn.linear_model import LinearRegression, LogisticRegression
 from sklearn.datasets import make_classification, make_regression
 from sklearn.model_selection import train_test_split
-from explainableai.utils import explain_model, calculate_metrics
 from dotenv import load_dotenv
-import os 
+
+# Add the project root directory to sys.path
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+from explainableai.utils import explain_model, calculate_metrics
+
+# Load environment variables
 load_dotenv()
 
 def test_explain_model_regression():
@@ -58,4 +67,4 @@ def test_calculate_metrics_classification():
     assert "f1_score" in metrics
 
 if __name__ == "__main__":
-    pytest.main()
+    pytest.main()