Changes files structures

app.py

@@ -48,14 +48,9 @@ import sys
 import glob
 import re
 
-# Add the project root directory to the Python path
-current_dir = os.path.dirname(os.path.abspath(__file__))
-project_root = os.path.abspath(os.path.join(current_dir, '../../'))
-sys.path.append(project_root)
-
 def get_model_modules():
     # Get the list of available model modules
-    models_dir = os.path.join(project_root, 'models', 'supervised', 'regression')
+    models_dir = os.path.join('models', 'supervised', 'regression')
     model_files = glob.glob(os.path.join(models_dir, '*.py'))
 
     # Debugging: Print the models directory and found files
@@ -107,7 +102,7 @@ def train_model(model_module, data_option, data_file, data_path, data_name_kaggl
         return "Invalid data option selected.", None
 
     # Prepare command-line arguments
-    cmd = [sys.executable, os.path.join(project_root, 'scripts', 'train_regression_model.py')]
+    cmd = [sys.executable, os.path.join('scripts', 'train_regression_model.py')]
     cmd.extend(['--model_module', model_module])
     cmd.extend(['--data_path', data_path])
     cmd.extend(['--target_variable', target_variable])
@@ -293,4 +288,4 @@ with gr.Blocks() as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch(share=True)
+    demo.launch()

data/preprocessing/README.md

@@ -1 +0,0 @@
-# preprocessing

data/utils/README.md

@@ -1 +0,0 @@
-# utils

models/computer_vision/README.md

@@ -1 +0,0 @@
-# computer_vision

models/deep_learning/README.md

@@ -1 +0,0 @@
-# deep_learning

models/nlp/README.md

@@ -1 +0,0 @@
-# nlp

models/reinforcement_learning/README.md

@@ -1 +0,0 @@
-# reinforcement_learning

models/supervised/classification/README.md

@@ -1 +0,0 @@
-# classification

models/unsupervised/README.md

@@ -1 +0,0 @@
-# unsupervised

requirements.txt

@@ -8,5 +8,4 @@ catboost==1.2.7
 dask[dataframe]==2024.10.0
 xgboost==2.1.2
 lightgbm==4.5.0
-joblib==1.4.2
-gradio==5.7.1
+joblib==1.4.2

scripts/train_classification_model.py

@@ -1,185 +0,0 @@
-"""
-This script trains classification models using scikit-learn.
-It includes data loading, preprocessing, encoding of target variable,
-hyperparameter tuning, model evaluation, and saving of models, metrics,
-and visualizations.
-
-Usage:
-    python train_classification_model.py --model_module MODEL_MODULE --data_path DATA_PATH/DATA_NAME.csv
-                                         --target_variable TARGET_VARIABLE
-
-Optional arguments:
-    --test_size TEST_SIZE
-    --random_state RANDOM_STATE
-    --cv_folds CV_FOLDS
-    --scoring_metric SCORING_METRIC
-    --model_path MODEL_PATH
-    --results_path RESULTS_PATH
-    --visualize
-    --drop_columns COLUMN_NAMES
-
-Example:
-    python train_classification_model.py --model_module logistic_regression
-                                         --data_path data/titanic/train.csv
-                                         --target_variable Survived --drop_columns PassengerId
-                                         --visualize
-"""
-
-import os
-import sys
-import argparse
-import importlib
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import (accuracy_score, precision_score, recall_score, f1_score,
-                             confusion_matrix, ConfusionMatrixDisplay)
-import joblib
-
-def main(args):
-    # Change to the root directory of the project
-    project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
-    os.chdir(project_root)
-    sys.path.insert(0, project_root)
-
-    # Import the hyperparameter tuning and the model modules
-    from utils.supervised_hyperparameter_tuning import classification_hyperparameter_tuning
-    model_module_path = f"models.supervised.classification.{args.model_module}"
-    model_module = importlib.import_module(model_module_path)
-    
-    # Get the model estimator, parameters grid, and the scoring metric
-    estimator = model_module.estimator
-    param_grid = model_module.param_grid
-    scoring_metric = args.scoring_metric or getattr(model_module, 'default_scoring', 'accuracy')
-    model_name = estimator.__class__.__name__
-
-    # Set default paths if not provided
-    args.model_path = args.model_path or os.path.join('saved_models', model_name)
-    args.results_path = args.results_path or os.path.join('results', model_name)
-    os.makedirs(args.results_path, exist_ok=True)
-
-    # Load the dataset
-    df = pd.read_csv(os.path.join(args.data_path))
-
-    # Drop specified columns
-    if args.drop_columns:
-        columns_to_drop = args.drop_columns.split(',')
-        df = df.drop(columns=columns_to_drop)
-
-    # Define target variable and features
-    target_variable = args.target_variable
-    X = df.drop(columns=[target_variable])
-    y = df[target_variable]
-
-    # Ensure target variable is categorical
-    if np.issubdtype(y.dtype, np.number) and len(np.unique(y)) > 20:
-        raise ValueError(f"The target variable '{target_variable}' seems to be continuous. Please ensure it's categorical for classification tasks.")
-
-    # Encode target variable if not numeric
-    if y.dtype == 'object' or not np.issubdtype(y.dtype, np.number):
-        from sklearn.preprocessing import LabelEncoder
-        le = LabelEncoder()
-        y = le.fit_transform(y)
-        # Save label encoder for inverse transformation
-        joblib.dump(le, os.path.join(args.model_path, 'label_encoder.pkl'))
-
-    # Split the data
-    X_train, X_test, y_train, y_test = train_test_split(
-        X, y, test_size=args.test_size, random_state=args.random_state, stratify=y)
-
-    # Perform hyperparameter tuning
-    best_model, best_params = classification_hyperparameter_tuning(
-        X_train, y_train, estimator, param_grid,
-        cv=args.cv_folds, scoring=scoring_metric)
-
-    # Evaluate the best model on the test set
-    y_pred = best_model.predict(X_test)
-    y_test_actual = y_test
-
-    # Save the trained model
-    model_output_path = os.path.join(args.model_path, 'best_model.pkl')
-    os.makedirs(args.model_path, exist_ok=True)
-    joblib.dump(best_model, model_output_path)
-    print(f"Trained model saved to {model_output_path}")
-
-    # Calculate metrics
-    accuracy = accuracy_score(y_test_actual, y_pred)
-    precision = precision_score(y_test_actual, y_pred, average='weighted', zero_division=0)
-    recall = recall_score(y_test_actual, y_pred, average='weighted', zero_division=0)
-    f1 = f1_score(y_test_actual, y_pred, average='weighted', zero_division=0)
-    print(f"\n{model_name} Classification Metrics on Test Set:")
-    print(f"- Accuracy: {accuracy:.4f}")
-    print(f"- Precision: {precision:.4f}")
-    print(f"- Recall: {recall:.4f}")
-    print(f"- F1 Score: {f1:.4f}")
-    # Save metrics
-    metrics = {'Accuracy': [accuracy], 'Precision': [precision], 'Recall': [recall], 'F1 Score': [f1]}
-
-    # Save metrics to CSV
-    results_df = pd.DataFrame(metrics)
-    results_df.to_csv(os.path.join(args.results_path, 'metrics.csv'), index=False)
-    print(f"\nMetrics saved to {os.path.join(args.results_path, 'metrics.csv')}")
-
-    if args.visualize:
-        # Plot Classification Metrics
-        plt.figure(figsize=(8, 6))
-        # Extract metrics and values
-        metric_names = list(metrics.keys())
-        metric_values = [value[0] for value in metrics.values()]  # Extract the single value from each list
-
-        # Create bar chart
-        plt.bar(metric_names, metric_values, color='skyblue', alpha=0.8)
-        plt.ylim(0, 1)  # Metrics like accuracy, precision, etc., are between 0 and 1
-        plt.xlabel('Metrics')
-        plt.ylabel('Scores')
-        plt.title('Classification Metrics')
-
-        # Save and display the plot
-        plt.savefig(os.path.join(args.results_path, 'classification_metrics.png'))
-        plt.show()
-        print(f"Visualization saved to {os.path.join(args.results_path, 'classification_metrics.png')}")
-
-        # Display and save the confusion matrix
-        conf_matrix = confusion_matrix(y_test_actual, y_pred)
-        disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix)
-        disp.plot(cmap=plt.cm.Blues, values_format='d')  # Format as integers for counts
-        plt.title(f'{model_name} Confusion Matrix')
-
-        # Save the confusion matrix plot
-        conf_matrix_path = os.path.join(args.results_path, 'confusion_matrix.png')
-        plt.savefig(conf_matrix_path)
-        plt.show()
-        print(f"Confusion matrix saved to {conf_matrix_path}")
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Train a classification model.")
-    # Model module argument
-    parser.add_argument('--model_module', type=str, required=True,
-                        help='Name of the classification model module to import.')
-    # Data arguments
-    parser.add_argument('--data_path', type=str, required=True,
-                        help='Path to the dataset file including data name.')
-    parser.add_argument('--target_variable', type=str, required=True,
-                        help='Name of the target variable.')
-    parser.add_argument('--drop_columns', type=str, default='',
-                        help='Columns to drop from the dataset.')
-    # Model arguments
-    parser.add_argument('--test_size', type=float, default=0.2,
-                        help='Proportion for test split.')
-    parser.add_argument('--random_state', type=int, default=42,
-                        help='Random seed.')
-    parser.add_argument('--cv_folds', type=int, default=5,
-                        help='Number of cross-validation folds.')
-    parser.add_argument('--scoring_metric', type=str, default=None,
-                        help='Scoring metric for model evaluation.')
-    # Output arguments
-    parser.add_argument('--model_path', type=str, default=None,
-                        help='Path to save the trained model.')
-    parser.add_argument('--results_path', type=str, default=None,
-                        help='Path to save results and metrics.')
-    parser.add_argument('--visualize', action='store_true',
-                        help='Generate and save visualizations.')
-
-    args = parser.parse_args()
-    main(args)