python sklearn庫實現簡單邏輯迴歸
阿新 • • 發佈：2019-02-01
import xlrd import matplotlib.pyplot as plt import numpy as np from sklearn import model_selection from sklearn.linear_model import LogisticRegression from sklearn import metrics data = xlrd.open_workbook('gua.xlsx') sheet = data.sheet_by_index(0) Density = sheet.col_values(6) Sugar = sheet.col_values(7) Res = sheet.col_values(8) # 讀取原始資料 X = np.array([Density, Sugar]) # y的尺寸為(17,) y = np.array(Res) X = X.reshape(17,2) # 繪製分類資料 f1 = plt.figure(1) plt.title('watermelon_3a') plt.xlabel('density') plt.ylabel('ratio_sugar') # 繪製散點圖（x軸為密度，y軸為含糖率） plt.scatter(X[y == 0,0], X[y == 0,1], marker = 'o', color = 'k', s=100, label = 'bad') plt.scatter(X[y == 1,0], X[y == 1,1], marker = 'o', color = 'g', s=100, label = 'good') plt.legend(loc = 'upper right') plt.show() # 從原始資料中選取一半資料進行訓練，另一半資料進行測試 X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.5, random_state=0) # 邏輯迴歸模型 log_model = LogisticRegression() # 訓練邏輯迴歸模型 log_model.fit(X_train, y_train) # 預測y的值 y_pred = log_model.predict(X_test) # 檢視測試結果 print(metrics.confusion_matrix(y_test, y_pred)) print(metrics.classification_report(y_test, y_pred))