def transform(self, X):
result = []
for x in X:
event = array2json(x)
result.append(self.stream.transform(event))
return result
def fit(self, X, y=None, headers=None, verbose=False):
X = array2d(X)
if (X.ndim != 2):
raise ValueError('X must have dimension 2, ndim='+X.ndim)
# n_samples, self.n_features_ = X.shape
y = np.atleast_1d(y)
# y = y.astype(DOUBLE)
if self.target is not None:
if y is None:
y = [None]*len(X)
if (len(y) != len(X)):
raise ValueError('y must be same shape as X, len(X)='+str(len(X))+', len(y)='+str(len(y)))
if headers is not None:
if (len(headers) != len(X)):
raise ValueError('headers must be same shape as X, len(X)='+str(len(X))+', len(headers)='+str(len(headers)))
for x,t in zip(X,y):
if verbose: print x,t
event = array2json(x,headers)
if self.target is not None:
event[self.target] = t
self.stream.train(event)
def predict(self, X):
result = []
for x in X:
event = array2json(x)
# should this be a numpy type?
result.append(float(self.stream.predict(event)['prediction']))
return result
