随机森林

integr/random_forest.py

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+
+# -*- encoding:utf-8 -*-
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.datasets import load_wine
+from sklearn.model_selection import train_test_split
+import numpy as np
+from sklearn.tree import DecisionTreeRegressor
+from sklearn import metrics
+
+'''
+参数	含义
+criterion	不纯度的衡量指标，有基尼系数和信息熵两种选择
+max_depth	树的最大深度，超过最大深度的树枝都会被剪掉
+min_samples_leaf	一个节点在分枝后的每个子节点都必须包含至少min_samples_leaf个训练样本，否则分枝就不会发生
+min_samples_split	一个节点必须要包含至少min_samples_split个训练样本，这个节点才允许被分枝，否则分枝就不会发生
+max_features	max_features限制分枝时考虑的特征个数，超过限制个数的特征都会被舍弃，默认值为总特征个数开平方取整
+min_impurity_decrease	限制信息增益的大小，信息增益小于设定数值的分枝不会发生
+'''
+def demo_wine():
+    wine = load_wine()
+    #print wine.data
+    #print wine.target
+    Xtrain, Xtest, Ytrain, Ytest = train_test_split(wine.data,wine.target,test_size=0.3)
+    rfc = RandomForestClassifier(random_state=0,n_estimators=10)
+    rfc = rfc.fit(Xtrain,Ytrain)
+    print(rfc.score(Xtest,Ytest))
+
+
+def read_data(path):
+    with open(path) as f :
+        lines=f.readlines()
+    lines=[eval(line.strip()) for line in lines]
+    X,y=zip(*lines)
+    X=np.array(X)
+    y=np.array(y)
+    return X,y
+
+
+def demo():
+    X_train, y_train = read_data("../bbztx/train_data")
+    X_test, y_test = read_data("../bbztx/test_data")
+    Xtrain, Xtest, Ytrain, Ytest = train_test_split(X_train, y_train, test_size=0.3)
+    rfc = RandomForestRegressor(random_state=0, n_estimators=10, max_depth=10)
+    rfc = rfc.fit(Xtrain, Ytrain)
+    print(rfc.score(Xtest, Ytest))
+
+    print(rfc.predict(X_test))
+    # print(rfc.score(Xtest, y_test))
+
+    y_pred_test = rfc.predict(X_test)
+    test_mse = metrics.mean_squared_error(y_test, y_pred_test)
+    print("测试集MSE:", test_mse)
+
+
+if __name__ == '__main__':
+	demo()
+
+
+