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