我创建了一个自定义手工编码分类器，它实现了标准的sklearn分类器函数(
编辑1： 根据Cel的建议，我尝试直接应用它。
第一步是按照这里的说明添加get_params和set_params。不出所料，完整的交叉验证过程确实运行了，但最终出现以下错误。
fit()、predict()和predict_proba())。这个分类器可以直接与sklearn工具GridSearchCV()一起使用吗？还是应该添加其他内容？编辑1： 根据Cel的建议，我尝试直接应用它。
第一步是按照这里的说明添加get_params和set_params。不出所料，完整的交叉验证过程确实运行了，但最终出现以下错误。
return self._fit(X, y, ParameterGrid(self.param_grid))
best_estimator.fit(X, y, **self.fit_params)
AttributeError: 'NoneType' object has no attribute 'fit'
编辑2：添加分类器代码（它是基于Theano的逻辑回归分类器）
class LogisticRegression:
""" Apply minibatch logistic regression
:type n_in: int
:param n_in: number of input units, the dimension of the space in
which the datapoints lie
:type n_out: int
:param n_out: number of output units, the dimension of the space in
which the labels lie
"""
def __init__(self,n_in,n_out,batch_size=600,learning_rate=0.13,iters=500,verbose=0):
self.n_in = n_in
self.n_out = n_out
self.batch_size = batch_size
self.learning_rate = learning_rate
self.iters = iters
self.verbose = verbose
self.single_layer = Layer(self.n_in,self.n_out,T.nnet.softmax)
self.minibatch_count = 0
def get_params(self,deep=True):
return {"n_in" : self.n_in,"n_out" : self.n_out,"batch_size" : self.batch_size,
"learning_rate" : self.learning_rate,"iters" : self.iters,
"verbose" : self.verbose}
def set_params(self, **parameters):
for parameter, value in parameters.items():
setattr(self, parameter, value)
def minibatch_trainer(self,data_x,data_y):
n_batches = data_x.get_value(borrow=True).shape[0]/self.batch_size
tensor_x = T.matrix('x')
tensor_y = T.ivector('y')
index = T.lscalar('index')
cost = self.single_layer.negative_log_likelihood(tensor_x, tensor_y)
g_W = T.grad(cost,self.single_layer.W)
g_b = T.grad(cost,self.single_layer.b)
updates = [(self.single_layer.W,self.single_layer.W - g_W*self.learning_rate),
(self.single_layer.b,self.single_layer.b - g_b*self.learning_rate)]
train_batch = theano.function([index],[cost],
updates=updates,
givens={tensor_x : data_x[index*self.batch_size : (index + 1)*self.batch_size],
tensor_y : data_y[index*self.batch_size : (index + 1)*self.batch_size]})
return np.mean([train_batch(i) for i in xrange(n_batches)])
def fit(self,data_x,data_y):
data_x,data_y = shared_dataset(data_x,data_y)
start = time.clock()
for iter in xrange(self.iters):
train_err = self.minibatch_trainer(data_x,data_y)
if self.verbose==1: print "Iter %d --> %f" % (iter,train_err)
end = time.clock()
print "Finished Training Logistic Regression Model\n" \
"Iterations %d\n" \
"Time Taken : %d secs" % (self.iters,end - start)
return self
def partial_fit(self,data_x,data_y):
data_x,data_y = shared_dataset(data_x,data_y)
self.minibatch_count += 1
err = self.minibatch_trainer(data_x, data_y)
print "MiniBatch %d --> %f" % (self.minibatch_count,err)
def predict(self,data_x):
data_x = shared_dataset(data_x)
n_batches = data_x.get_value(borrow=True).shape[0]/self.batch_size
tensor_x = T.matrix('x')
index = T.lscalar('index')
tensor_ypred = self.prediction_tensor(tensor_x)
predictor = theano.function([index],tensor_ypred,
givens={tensor_x : data_x[index*self.batch_size:(index + 1)*self.batch_size]})
ypred = [predictor(i) for i in xrange(n_batches)]
return np.hstack(ypred)
def predict_proba(self,data_x):
data_x = shared_dataset(data_x)
tensor_x = T.matrix('x')
tensor_ypredproba = self.single_layer.decision_function_tensor(tensor_x)
predproba_func = theano.function([],tensor_ypredproba,
givens={tensor_x : data_x})
return predproba_func()
def prediction_tensor(self,tensor_x):
"""
Returns the predicted y value as a tensor variable
:param tensor_x: TensorType matrix on input data
:return: TensorType tensor_ypred output
"""
return T.argmax(self.single_layer.decision_function_tensor(tensor_x),axis=1)
编辑3：添加GridSearchCV的精确用法
clf_cv = GridSearchCV(LogisticRegression(n_in=200,n_out=2),{"iters" : [3]},cv=4,scoring="roc_auc",n_jobs=-1,verbose=1)
我也尝试添加了BaseEstimator和ClassifierMixin; sklearn.base.clone没有输出任何错误
sklearn.base.clone来查看它是否正常工作吗？为什么不让你的类直接继承sklearn.base.BaseEstimator呢？这里有说明。 - eickenberg