444

function.py

@@ -4,7 +4,6 @@ from sklearn.preprocessing import StandardScaler
 from sklearn.metrics import accuracy_score, roc_auc_score, f1_score  
 from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier  
 from sklearn.linear_model import LogisticRegression  
-from sklearn.svm import SVC  
 
 """  
 请在这里填写学号和姓名，不填写即成绩无效。  
@@ -13,95 +12,122 @@ id = "202210423221"
 name = "廖子豪"  
 
 def load_data(filepath):  
-    """Load data from a text file for model training."""  
+    """Load and preprocess data from specified file."""  
     with open(filepath) as file:  
         fr = file.readlines()  
+        
     data_list = []  
     y = []  
+    
     for data in fr:  
         data = data.strip().split(' ')  
-        data_25 = data[24]  # Preserving the 25th element for later use  
-        y.append(data[22])  # Using 23rd element as the target value  
-
-        # Remove unnecessary elements and convert to float, handle missing values  
-        numerical_data = [0 if elem == '?' else float(elem) for i, elem in enumerate(data) if i not in [24, 22, 26]]  
-        data_list.append(numerical_data)  
-
-    data_number = len(y)  
-    y_train = np.array([1 if label in ['2', '3'] else 0 for label in y])  # Binary classification  
+        
+        # Keep the necessary fields  
+        y.append(data[22])  # Target value  
+        # Remove unnecessary columns  
+        data = [data[i] for i in range(len(data)) if i not in [2, 22, 24, 25, 26]]  
+        
+        # Convert to float, replacing '?' with 0  
+        data_processed = [0 if elem == '?' else float(elem) for elem in data]  
+        data_list.append(data_processed)  
+    
+    y_train = np.array([1 if label in ['2', '3'] else 0 for label in y])  
     X = np.array(data_list)  
+    
+    # Add bias term (intercept)  
+    X = np.insert(X, 0, 1, axis=1)  
+    
     return X, y_train  
 
-def train_logreg(X, y, numIter=1000, learning_rate=0.06, batch_size=128, lambda_reg=0.01):  
+def train_logreg(X, y, numIter=1000, learning_rate=0.01, batch_size=128, lambda_reg=0.01):  
     """Train a logistic regression model using mini-batch gradient descent."""  
     m, n = X.shape  
     weights = np.zeros(n)  
     
     for j in range(numIter):  
+        # Shuffle data indices  
         dataIndex = np.random.permutation(m)  
+        
         for i in range(0, m, batch_size):  
             end = min(i + batch_size, m)  
             batch_X = X[dataIndex[i:end]]  
             batch_y = y[dataIndex[i:end]]  
+            
+            # Compute predictions  
             z = np.dot(batch_X, weights)  
-            h = 1 / (1 + np.exp(-z))  # Sigmoid function  
+            h = 1 / (1 + np.exp(-z))  # Sigmoid  
+            
+            # Compute gradient  
             error = batch_y - h  
             gradient = np.dot(batch_X.T, error) / len(batch_y)  
             reg_term = 2 * lambda_reg * weights  
             gradient -= reg_term / len(batch_y)  # Regularization  
+            
+            # Update weights  
             weights += learning_rate * gradient  
+            
     return weights  
 
 def predict_logreg(inX, weights):  
-    """Make a prediction using the logistics regression model."""  
+    """Make a prediction using the logistic regression model."""  
     z = np.dot(inX, weights)  
     prob = 1 / (1 + np.exp(-z))  
     return 1 if prob >= 0.5 else 0  
 
 if __name__ == "__main__":  
-    # Load train and test data  
+    # Data loading  
     X_train, y_train = load_data("./data/horse-colic.data")  
     X_test, y_test = load_data("./data/horse-colic.test")  
 
-    # Standardize the data  
+    # Standardize the features  
     scaler = StandardScaler()  
     X_train = scaler.fit_transform(X_train)  
     X_test = scaler.transform(X_test)  
 
-    # Split train data into training and validation sets  
+    # Split training data into training and validation sets  
     X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)  
 
-    # Train the logistic regression model  
+    # Logistic regression training and evaluation  
     best_weights = None  
     best_auc = 0  
+    
     for iter in range(1, 1001):  
         weights = train_logreg(X_train, y_train, numIter=iter, learning_rate=0.01, lambda_reg=0.1)  
         y_pred = [predict_logreg(x, weights) for x in X_val]  
+        
+        # AUC score for validation predictions  
         auc = roc_auc_score(y_val, y_pred)  
+        
         if auc > best_auc:  
             best_auc = auc  
             best_weights = weights  
+            
         if iter > 10 and auc == best_auc:  
             break  
 
-    # Make predictions and evaluate test accuracy  
+    # Predictions on the test set  
     y_pred_test = [predict_logreg(x, best_weights) for x in X_test]  
     accuracy_test = accuracy_score(y_test, y_pred_test)  
     print(f"Test Accuracy: {accuracy_test}")  
 
-    # Example for a Random Forest model  
+    # Random Forest model example  
     rf = RandomForestClassifier(n_estimators=100, random_state=42)  
     rf.fit(X_train, y_train)  
     rf_accuracy = rf.score(X_test, y_test)  
     print(f"Random Forest Test Accuracy: {rf_accuracy}")  
 
-    # Example for Gradient Boosting model  
+    # Gradient Boosting model example  
     gb_clf = GradientBoostingClassifier()  
     gb_clf.fit(X_train, y_train)  
-    y_pred_gb = gb_clf.predict(X_test)  
-    gb_accuracy = accuracy_score(y_test, y_pred_gb)  
+    yf_pred_gb = gb_clf.predict(X_test)  
+    gb_accuracy = accuracy_score(y_test, yf_pred_gb)  
     print(f"Gradient Boosting Test Accuracy: {gb_accuracy}")  
 
-    # Evaluate performance with F1 score  
-    f1 = f1_score(y_test, y_pred_gb)  
-    print(f"F1 Score: {f1}")
+    # Cross-validation for Gradient Boosting  
+    scores = cross_val_score(gb_clf, X_train, y_train, cv=5, scoring='accuracy')  
+    print("Cross-validation scores:", scores)  
+    print("Average cross-validation score:", np.mean(scores))  
+
+    # F1 score evaluation  
+    f1 = f1_score(y_test, yf_pred_gb)  
+    print("F1 Score:", f1)