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+# -*- encoding:utf-8 -*-
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+from sklearn import datasets
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+from sklearn.linear_model import LogisticRegression
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+from numpy import shape
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+from sklearn import metrics
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+import numpy as np
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+
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+
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+def read_data(path):
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+ with open(path) as f:
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+ lines = f.readlines()
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+ lines = [eval(line.strip()) for line in lines]
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+ X, y = zip(*lines)
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+ X = np.array(X)
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+ y = np.array(y)
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+ return X, y
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+
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+
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+X_train, y_train = read_data("cancer_train_data")
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+X_test, y_test = read_data("cancer_test_data")
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+
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+
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+def train_model(reg):
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+ print reg
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+ model = LogisticRegression(penalty=reg)
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+ model.fit(X_train, y_train)
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+ print "w", model.coef_
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+ # print (model.intercept_)
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+ y_pred_train = model.predict(X_train)
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+ y_pred_test = model.predict(X_test)
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+ e_train = metrics.mean_squared_error(y_train, y_pred_train)
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+ e_test = metrics.mean_squared_error(y_test, y_pred_test)
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+
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+ kl_train = metrics.log_loss(y_train, y_pred_train)
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+ kl_test = metrics.log_loss(y_test, y_pred_test)
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+
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+ print "训练集MSE:{}, KL:{}".format(e_train, kl_train)
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+ print "测试集MSE:{}, KL:{}".format(e_test, kl_test)
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+ print "训练测试差异{}".format(e_test-e_train)
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+ print
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+
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+
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+
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+# train_model(reg="None")
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+train_model(reg="l1")
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+train_model(reg="l2")
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