本文整理汇总了Python中sklearn.ensemble.GradientBoostingClassifier.fit_transform方法的典型用法代码示例。如果您正苦于以下问题：Python GradientBoostingClassifier.fit_transform方法的具体用法？Python GradientBoostingClassifier.fit_transform怎么用？Python GradientBoostingClassifier.fit_transform使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.ensemble.GradientBoostingClassifier的用法示例。
在下文中一共展示了GradientBoostingClassifier.fit_transform方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: GradientBoostingClassifier
# 需要导入模块: from sklearn.ensemble import GradientBoostingClassifier [as 别名]
# 或者: from sklearn.ensemble.GradientBoostingClassifier import fit_transform [as 别名]
gb = GradientBoostingClassifier()
gb.fit(x_train,y_train)
gb.score(x_test,y_test)
proba=pd.DataFrame(gb.predict_proba(x_test))[1]
false_positive_rate, true_positive_rate, thresholds = skrc(y_test,proba)
auc(false_positive_rate, true_positive_rate)
#find best features for Gradient Boost
#Feature selection based on AUC
X,X_test,y,y_test=train_test_split(X,y,train_size=.9)
model=GradientBoostingClassifier()
features=[]
scores=[]
for i in X:
features.append(i)
model.fit_transform(X[[i]],y)
proba=model.predict_proba(X_test[[i]])
proba=pd.DataFrame(proba)[1]
false_positive_rate, true_positive_rate, thresholds = skrc(y_test,proba)
scores.append(auc(false_positive_rate, true_positive_rate))
df_f=pd.DataFrame({'features':features, 'scores':scores})
df_f=df_f.sort_values(by='scores',ascending=False)
best=df_f.features
#Find best AUC
#build new train and test sets
train,test=train_test_split(df,train_size=.9)
y_train=train['2015h']
x_train=train.drop('2015h',axis=1)
y_test=test['2015h']示例2: write2X
# 需要导入模块: from sklearn.ensemble import GradientBoostingClassifier [as 别名]
# 或者: from sklearn.ensemble.GradientBoostingClassifier import fit_transform [as 别名]
X = write2X(aspects_1)[:1000]
clear_trainning_set(X,y)
#clear_trainning_set(X2,y2)
#balance_trainning_set(X,y)
y1 = 0
y0 = 0
for i in range(len(y)):
if y[i] == 1:
y1 += 1
else:
y0 += 1
print "We got X for " + str(len(X)) +" and Y for " + str(len(y))
print "we have " + str(y1) + "for 1 and " + str(y0) + " for 0"
clf = GradientBoostingClassifier(n_estimators=47, learning_rate=0.03,max_depth=3,random_state=0)
test_X = clf.fit_transform(X,y)
#clf.fit(X,y)
importances = clf.feature_importances_
position_propotion = 0.0 # 0-8
vertical_propotion = 0.0 # 9-74
query_propotion =0.0 #75-77
text_propotion = 0.0 #78 - last
#print "size of importances " + str(len(importances))
#indices = np.argsort(importances)[::-1]
#for f in range(10):
# print("%d. feature %d (%f)" % (f + 1, indices[f], importances[indices[f]]))
#print len(test_X[0])
#clf2 = svm.SVC(C=1.0, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, random_state=None)
test = ['accuracy','recall_macro','f1_macro','roc_auc']