Ive created my own manually classified classifier, which implements the standard functions of the sklearn classifier ( fit() , predict() and predict_proba() ). Can this be directly used with the sklearn utility GridSearchCV GridSearchCV() or are there any add-ons that need to be made?
EDIT 1: On suggestion, I tried to apply it directly
The first step was to add get_params and set_params, as described here . Of course, the full cross-validation procedure was actually performed, but ends with the following error
return self._fit(X, y, ParameterGrid(self.param_grid)) best_estimator.fit(X, y, **self.fit_params) AttributeError: 'NoneType' object has no attribute 'fit'
EDIT 2: adding a classifier code (its anano-based logistic regression classifier)
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)
EDIT 3: Adding Exact Use of GridSearchCV
clf_cv = GridSearchCV(LogisticRegression(n_in=200,n_out=2),{"iters" : [3]},cv=4,scoring="roc_auc",n_jobs=-1,verbose=1)
Ive also tried adding BaseEstimator and ClassifierMixin; sklearn.base.clone does not display any errors