generated from LufsX/Experiment-LogReg
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import numpy as np
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# function.py
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from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
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import numpy as np
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from sklearn.preprocessing import StandardScaler
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from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
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from sklearn.metrics import accuracy_score, roc_auc_score, f1_score
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from sklearn.preprocessing import StandardScaler
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from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier
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from sklearn.metrics import accuracy_score, roc_auc_score, f1_score
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from sklearn.linear_model import LogisticRegression
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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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实验说明:
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- 自行编写实现数据加载与预测病马死亡的对率回归算法。
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"""
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注意事项:
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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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- 在 function.py 中实现对率回归算法。
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"""
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- 可以自行导入其他库，需要在 requirements.txt 添加依赖，若未在 requirements.txt 中添加使用到的依赖，则无法自动评测，成绩为零。
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id = "202210423221"
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- 自动评测脚本仅会加载 train_logreg、predict_logreg 和 load_data 函数，请勿修改函数名。
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name = "廖子豪"
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def load_data(filepath):
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测评环境:
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"""Load and preprocess data from specified file."""
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- CPU: Intel Xeon(R) CPU E3-1231 v3 @ 3.40GHz
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with open(filepath) as file:
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- OS: Ubuntu 22.04.4 LTS
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fr = file.readlines()
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- Python: 3.10.12
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- numpy: 2.1.1
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data_list = []
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"""
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y = []
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for data in fr:
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"""
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data = data.strip().split(' ')
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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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# Keep the necessary fields
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id = "202210423221"
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y.append(data[22]) # Target value
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name = "廖子豪"
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# Remove unnecessary columns
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def load_data(filepath):
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data = [data[i] for i in range(len(data)) if i not in [2, 22, 24, 25, 26]]
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file = open(filepath)
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fr = file.readlines()
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# Convert to float, replacing '?' with 0
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data_list = []
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data_processed = [0 if elem == '?' else float(elem) for elem in data]
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y = []
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data_list.append(data_processed)
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for data in fr:
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data = data.strip()
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y_train = np.array([1 if label in ['2', '3'] else 0 for label in y])
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data = data.split(' ')
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X = np.array(data_list)
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data_25 = data[24]
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y.append(data[22])
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# Add bias term (intercept)
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del data[-1]
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X = np.insert(X, 0, 1, axis=1)
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del data[26]
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del data[25]
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del data[24]
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del data[22]
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del data[2]
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for i in data_25:
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data.append(i)
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data = [0 if data_elem == '?' else float(data_elem) for data_elem in data]
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data_list.append(data)
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data_number = len(y)
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y_train = np.array([1 if y[j] == '2' or y[j] == '3' else 0 for j in range(data_number)])
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X = np.array(data_list)
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X_train = np.insert(X, 0, 1, axis=1) # 添加偏置项
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scaler = StandardScaler()
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scaler = StandardScaler()
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X = scaler.fit_transform(X)
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X= scaler.fit_transform(Xa)
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return X, y_train
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return X_train, y_train
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def train_logreg(X, y, numIter=1000, learning_rate=0.01, batch_size=128, lambda_reg=0.01):
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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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m, n = X.shape
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weights = np.zeros(n)
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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 j in range(numIter):
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for i in range(0, m, batch_size):
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# Shuffle data indices
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end = min(i + batch_size, m)
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dataIndex = np.random.permutation(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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for i in range(0, m, batch_size):
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z = np.dot(batch_X, weights)
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end = min(i + batch_size, m)
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h = 1 / (1 + np.exp(-z))
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batch_X = X[dataIndex[i:end]]
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error = batch_y - h
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batch_y = y[dataIndex[i:end]]
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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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# Compute predictions
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gradient -= reg_term / len(batch_y)
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z = np.dot(batch_X, weights)
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weights += learning_rate * gradient
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h = 1 / (1 + np.exp(-z)) # Sigmoid
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return weights
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# Compute gradient
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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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# Update weights
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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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def predict_logreg(inX, weights):
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"""Make a prediction using the logistic regression model."""
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z = np.dot(inX, weights)
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z = np.dot(inX, weights)
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prob = 1 / (1 + np.exp(-z))
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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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return 1 if prob >= 0.5 else 0
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if __name__ == "__main__":
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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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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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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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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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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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# 随机森林模型
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param_grid = {
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'n_estimators': [100, 200, 300],
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'max_depth': [5, 10, 15],
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'learning_rate': [0.01, 0.05, 0.1]
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}
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rf = RandomForestClassifier()
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grid_search = GridSearchCV(estimator=rf, param_grid=param_grid, cv=5, scoring='accuracy')
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grid_search.fit(X_train, y_train)
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best_rf = grid_search.best_estimator_
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print("Best parameters:", grid_search.best_params_)
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print("Best accuracy:", grid_search.best_score_)
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