更新CART回归树的Sklearn 和 利用AdaBoost元算法提高分类的md

src/python/09.RegTrees/RTSklearn.py

@@ -1,50 +1,105 @@
 #!/usr/bin/python
 # coding:utf8
 
+# '''
+# Created on 2017-03-10
+# Update on 2017-03-10
+# author: jiangzhonglian
+# content: 回归树
+# '''
+
+# print(__doc__)
+
+
+# # Import the necessary modules and libraries
+# import numpy as np
+# from sklearn.tree import DecisionTreeRegressor
+# import matplotlib.pyplot as plt
+
+
+# # Create a random dataset
+# rng = np.random.RandomState(1)
+# X = np.sort(5 * rng.rand(80, 1), axis=0)
+# y = np.sin(X).ravel()
+# print X, '\n\n\n-----------\n\n\n', y
+# y[::5] += 3 * (0.5 - rng.rand(16))
+
+
+# # Fit regression model
+# regr_1 = DecisionTreeRegressor(max_depth=2, min_samples_leaf=5)
+# regr_2 = DecisionTreeRegressor(max_depth=5, min_samples_leaf=5)
+# regr_1.fit(X, y)
+# regr_2.fit(X, y)
+
+
+# # Predict
+# X_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]
+# y_1 = regr_1.predict(X_test)
+# y_2 = regr_2.predict(X_test)
+
+
+# # Plot the results
+# plt.figure()
+# plt.scatter(X, y, c="darkorange", label="data")
+# plt.plot(X_test, y_1, color="cornflowerblue", label="max_depth=2", linewidth=2)
+# plt.plot(X_test, y_2, color="yellowgreen", label="max_depth=5", linewidth=2)
+# plt.xlabel("data")
+# plt.ylabel("target")
+# plt.title("Decision Tree Regression")
+# plt.legend()
+# plt.show()
+
+
+
+
+
+
+
+
 '''
 Created on 2017-03-10
 Update on 2017-03-10
 author: jiangzhonglian
-content: 回归树
+content: 模型树
 '''
 
 print(__doc__)
 
+# Author: Noel Dawe <noel.dawe@gmail.com>
+#
+# License: BSD 3 clause
 
-# Import the necessary modules and libraries
+# importing necessary libraries
 import numpy as np
-from sklearn.tree import DecisionTreeRegressor
 import matplotlib.pyplot as plt
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.ensemble import AdaBoostRegressor
 
-
-# Create a random dataset
+# Create the dataset
 rng = np.random.RandomState(1)
-X = np.sort(5 * rng.rand(80, 1), axis=0)
-y = np.sin(X).ravel()
-print X, '\n\n\n-----------\n\n\n', y
-y[::5] += 3 * (0.5 - rng.rand(16))
-
+X = np.linspace(0, 6, 100)[:, np.newaxis]
+y = np.sin(X).ravel() + np.sin(6 * X).ravel() + rng.normal(0, 0.1, X.shape[0])
 
 # Fit regression model
-regr_1 = DecisionTreeRegressor(max_depth=2, min_samples_leaf=5)
-regr_2 = DecisionTreeRegressor(max_depth=5, min_samples_leaf=5)
+regr_1 = DecisionTreeRegressor(max_depth=4)
+
+regr_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4),
+                          n_estimators=300, random_state=rng)
+
 regr_1.fit(X, y)
 regr_2.fit(X, y)
 
-
 # Predict
-X_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]
-y_1 = regr_1.predict(X_test)
-y_2 = regr_2.predict(X_test)
-
+y_1 = regr_1.predict(X)
+y_2 = regr_2.predict(X)
 
 # Plot the results
 plt.figure()
-plt.scatter(X, y, c="darkorange", label="data")
-plt.plot(X_test, y_1, color="cornflowerblue", label="max_depth=2", linewidth=2)
-plt.plot(X_test, y_2, color="yellowgreen", label="max_depth=5", linewidth=2)
+plt.scatter(X, y, c="k", label="training samples")
+plt.plot(X, y_1, c="g", label="n_estimators=1", linewidth=2)
+plt.plot(X, y_2, c="r", label="n_estimators=300", linewidth=2)
 plt.xlabel("data")
 plt.ylabel("target")
-plt.title("Decision Tree Regression")
+plt.title("Boosted Decision Tree Regression")
 plt.legend()
 plt.show()