本文整理汇总了Python中neuralnet.NeuralNet.fit方法的典型用法代码示例。如果您正苦于以下问题：Python NeuralNet.fit方法的具体用法？Python NeuralNet.fit怎么用？Python NeuralNet.fit使用的例子？那么恭喜您, 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neuralnet.NeuralNet的用法示例。
在下文中一共展示了NeuralNet.fit方法的1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: BlendedModel
# 需要导入模块: from neuralnet import NeuralNet [as 别名]
# 或者: from neuralnet.NeuralNet import fit [as 别名]
class BlendedModel(BaseEnsemble):
def __init__(self, models=[], blending='average',nbFeatures=4):
self.models = models
self.blending = blending
self.logR = LogisticRegression(C=10)#,multi_class='multinomial',solver='lbfgs', max_iter=10000)
self.logRT= LogisticRegression(C=10)#,multi_class='multinomial',solver='lbfgs', max_iter=10000)
self.nn=NeuralNet(nbFeatures)
self.XGB=ModifiedXGBClassifier()
if self.blending not in ['average', 'most_confident']:
raise Exception('Wrong blending method')
##fit the stochastic gradient boosting trees classifier
def fit(self, X, y):
for model in self.models:
print 'Training model :'
print model.get_params()
model.fit(X, y)
return self
##predict the outputs according to the average of the classifier (or according an entropy based voting scheme that does not work well)
def predict_proba(self, X):
preds = np.array(
[model.predict_proba(X) for model in self.models]
)
if self.blending == 'average':
return np.mean(preds , axis=0 )
elif self.blending == 'most_confident':
def dirac_weights(entropies):
w = (entropies == np.min(entropies)).astype(float)
return w/np.sum(w)
def shannon_entropy(l):
l = [min(max(1e-5,p),1-1e-5) for p in l]
l = np.array(l)/sum(l)
return sum([-p*math.log(p) for p in l])
shannon_entropy_array = lambda l : np.apply_along_axis(shannon_entropy, 1, l)
entropies = np.array([shannon_entropy_array(pred) for pred in preds])
weights = np.apply_along_axis(dirac_weights, 0, entropies)
return np.sum(np.multiply(weights.T, preds.T).T, axis = 0)
##fit the logistic regression stacking
def fitLog(self,X,y,mod=0):
if mod==0: ##without features engineering
preds = np.array(
[model.predict_proba(X) for model in self.models]
)
features=np.array([np.array([preds[j][i] for j in range(len(self.models))]).flatten() for i in range(len(X))])
self.logR.fit(features, y)
elif mod==1: ##with features engineering
preds = np.array(
[model.predict_proba(X) for model in self.models]
)
features=np.array([np.array([[math.log(preds[j][i][k]/(1-preds[j][i][k])) for k in range(4)] for j in range(len(self.models))]).flatten() for i in range(len(X))])
self.logRT.fit(features, y)
return self
##predict the outputs of the logistic regression stack
def predict_Logproba(self, X,mod=0):
if mod==0:
preds = np.array(
[model.predict_proba(X) for model in self.models]
)
features=np.array([np.array([preds[j][i] for j in range(len(self.models))]).flatten() for i in range(len(X))])
preds=self.logR.predict_proba(features)
return preds
elif mod==1:
preds = np.array(
[model.predict_proba(X) for model in self.models]
)
features=np.array([np.array([[math.log(preds[j][i][k]/(1-preds[j][i][k])) for k in range(4)] for j in range(len(self.models))]).flatten() for i in range(len(X))])
preds=self.logRT.predict_proba(features)
return preds
##I also try to use a gradient boosting as a stacking classifier, but it does not work well
def fitXGB(self,X,y):
preds = np.array(
[model.predict_proba(X) for model in self.models]
)
features=np.array([np.array([[math.log(preds[j][i][k]/(1-preds[j][i][k])) for k in range(4)] for j in range(len(self.models))]).flatten() for i in range(len(X))])
#features= np.append(features, X, axis=1)
self.XGB.fit(features,y)
return self
def predict_XGBproba(self,X):
preds = np.array(
[model.predict_proba(X) for model in self.models]
)
features=np.array([np.array([[math.log(preds[j][i][k]/(1-preds[j][i][k])) for k in range(4)] for j in range(len(self.models))]).flatten() for i in range(len(X))])
#features= np.append(features, X, axis=1)
return self.XGB.predict_proba(features)
##neural network stack classifier
def fitNN(self,X,y,lambda1=0.00000001,lambda2=0.00005,new=0,teX=[],teY=[],lr=0.001):
preds = np.array(
[model.predict_proba(X) for model in self.models]
)
features=np.array([np.array([preds[j][i] for j in range(len(self.models))]).flatten() for i in range(len(X))])
features= np.append(features, X, axis=1)
#.........这里部分代码省略.........