thon_sklearn机器学习库学习笔记（四）decision_tree（决策树）

import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.cross_validation import train_test_split
from sklearn.metrics import classification_report
from sklearn.pipeline import Pipeline
from sklearn.grid_search import GridSearchCV

import zipfile
#压缩节省空间
z=zipfile.ZipFile('ad-dataset.zip')
# df=pd.read_csv(z.open(z.namelist()[0]),header=None,low_memory=False)
# df = pd.read_csv(z.open(z.namelist()[0]), header=None, low_memory=False)

df=pd.read_csv('.\\tree_data\\ad.data',header=None)
explanatory_variable_columns=set(df.columns.values)
response_variable_column=df[len(df.columns.values)-1]
#最后一列是代表的标签类型
explanatory_variable_columns.remove(len(df.columns)-1)

y=[1 if e =='ad.' else 0 for e in response_variable_column]
X=df.loc[:,list(explanatory_variable_columns)]

#匹配？字符，并把值转化为-1
X.replace(to_replace=' *\?', value=-1, regex=True, inplace=True)

X_train,X_test,y_train,y_test=train_test_split(X,y)
#用信息增益启发式算法建立决策树
pipeline=Pipeline([('clf',DecisionTreeClassifier(criterion='entropy'))])
parameters = {
'clf__max_depth': (150, 155, 160),
'clf__min_samples_split': (1, 2, 3),
'clf__min_samples_leaf': (1, 2, 3)
}
#f1查全率和查准率的调和平均
grid_search=GridSearchCV(pipeline,parameters,n_jobs=-1,
verbose=1,scoring='f1')
grid_search.fit(X_train,y_train)
print '最佳效果：%0.3f'%grid_search.best_score_
print '最优参数'
best_parameters=grid_search.best_estimator_.get_params()
best_parameters

for param_name in sorted(parameters.keys()):
print ('\t%s:%r'%(param_name,best_parameters[param_name]))
predictions=grid_search.predict(X_test)
print classification_report(y_test,predictions)

df.head()

#coding:utf-8
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import train_test_split
from sklearn.metrics import classification_report
from sklearn.pipeline import Pipeline
from sklearn.grid_search import GridSearchCVdf=pd.read_csv('.\\tree_data\\ad.data',header=None,low_memory=False)
explanatory_variable_columns=set(df.columns.values)
response_variable_column=df[len(df.columns.values)-1]

df.head()

#The last column describes the targets(去掉最后一列)
explanatory_variable_columns.remove(len(df.columns.values)-1)
y=[1 if e=='ad.' else 0 for e in response_variable_column]
X=df.loc[:,list(explanatory_variable_columns)]
#置换有？的为-1
X.replace(to_replace=' *\?', value=-1, regex=True, inplace=True)
X_train,X_test,y_train,y_test=train_test_split(X,y)
pipeline=Pipeline([('clf',RandomForestClassifier(criterion='entropy'))])
parameters = {
'clf__n_estimators': (5, 10, 20, 50),
'clf__max_depth': (50, 150, 250),
'clf__min_samples_split': (1, 2, 3),
'clf__min_samples_leaf': (1, 2, 3)
}
grid_search = GridSearchCV(pipeline,parameters,n_jobs=-1,verbose=1,scoring='f1')
grid_search.fit(X_train,y_train)

print(u'最佳效果：%0.3f'%grid_search.best_score_)
print u'最优的参数：'
best_parameters=grid_search.best_estimator_.get_params()
for param_name in sorted(parameters.keys()):
print('\t%s:%r'%(param_name,best_parameters[param_name]))

predictions=grid_search.predict(X_test)
print classification_report(y_test,predictions)