###########################################
#?Suppress?matplotlib?user?warnings
#?Necessary?for?newer?version?of?matplotlib
import?warnings
warnings.filterwarnings（“ignore“?category?=?UserWarning?module?=?“matplotlib“）
###########################################

import?matplotlib.pyplot?as?pl
import?numpy?as?np
import?sklearn.learning_curve?as?curves
from?sklearn.tree?import?DecisionTreeRegressor
from?sklearn.cross_validation?import?ShuffleSplit?train_test_split

def?ModelLearning（X?y）:
????“““?Calculates?the?performance?of?several?models?with?varying?sizes?of?training?data.
????????The?learning?and?testing?scores?for?each?model?are?then?plotted.?“““
????
????#?Create?10?cross-validation?sets?for?training?and?testing
????cv?=?ShuffleSplit（X.shape[0]?n_iter?=?10?test_size?=?0.2?random_state?=?0）

????#?Generate?the?training?set?sizes?increasing?by?50
????train_sizes?=?np.rint（np.linspace（1?X.shape[0]*0.8?-?1?9））.astype（int）

????#?Create?the?figure?window
????fig?=?pl.figure（figsize=（107））

????#?Create?three?different?models?based?on?max_depth
????for?k?depth?in?enumerate（[13610]）:
????????
????????#?Create?a?Decision?tree?regressor?at?max_depth?=?depth
????????regressor?=?DecisionTreeRegressor（max_depth?=?depth）

????????#?Calculate?the?training?and?testing?scores
????????sizes?train_scores?test_scores?=?curves.learning_curve（regressor?X?y?\
????????????cv?=?cv?train_sizes?=?train_sizes?scoring?=?‘r2‘）
????????
????????#?Find?the?mean?and?standard?deviation?for?smoothing
????????train_std?=?np.std（train_scores?axis?=?1）
????????train_mean?=?np.mean（train_scores?axis?=?1）
????????test_std?=?np.std（test_scores?axis?=?1）
????????test_mean?=?np.mean（test_scores?axis?=?1）

????????#?Subplot?the?learning?curve?
????????ax?=?fig.add_subplot（2?2?k+1）
????????ax.plot（sizes?train_mean?‘o-‘?color?=?‘r‘?label?=?‘Training?Score‘）
????????ax.plot（sizes?test_mean?‘o-‘?color?=?‘g‘?label?=?‘Testing?Score‘）
????????ax.fill_between（sizes?train_mean?-?train_std?\
????????????train_mean?+?train_std?alpha?=?0.15?color?=?‘r‘）
????????ax.fill_between（sizes?test_mean?-?test_std?\
????????????test_mean?+?test_std?alpha?=?0.15?color?=?‘g‘）
????????
????????#?Labels
????????ax.set_title（‘max_depth?=?%s‘%（depth））
????????ax.set_xlabel（‘Number?of?Training?Points‘）
????????ax.set_ylabel（‘Score‘）
????????ax.set_xlim（[0?X.shape[0]*0.8]）
????????ax.set_ylim（[-0.05?1.05]）
????
????#?Visual?aesthetics
????ax.legend（bbox_to_anchor=（1.05?2.05）?loc=‘lower?left‘?borderaxespad?=?0.）
????fig.suptitle（‘Decision?Tree?Regressor?Learning?Performances‘?fontsize?=?16?y?=?1.03）
????fig.tight_layout（）
????fig.show（）


def?ModelComplexity（X?y）:
????“““?Calculates?the?performance?of?the?model?as?model?complexity?increases.
????????The?learning?and?testing?errors?rates?are?then?plotted.?“““
????
????#?Create?10?cross-validation?sets?for?training?and?testing
????cv?=?ShuffleSplit（X.shape[0]?n_iter?=?10?test_size?=?0.2?random_state?=?0）

????#?V

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????目錄???????????0??2018-01-08?15:11??boston_housing\
?????目錄???????????0??2018-01-07?21:16??boston_housing\.ipynb_checkpoints\
?????文件??????146801??2018-01-08?13:51??boston_housing\.ipynb_checkpoints\boston_housing-checkpoint.ipynb
?????文件??????143652??2018-01-08?15:11??boston_housing\boston_housing.ipynb
?????文件???????12435??2016-08-12?03:37??boston_housing\housing.csv
?????文件????????1768??2016-08-12?03:37??boston_housing\README.md
?????文件????????4882??2018-01-07?21:24??boston_housing\visuals.py
?????目錄???????????0??2018-01-07?21:24??boston_housing\__pycache__\
?????文件????????3672??2018-01-07?21:24??boston_housing\__pycache__\visuals.cpython-36.pyc