def train_predict_pool(cls, args):
X, y, tr, ts, textModel_params = args
params = TextModel.params()
textModel_params = {k: v for k, v in textModel_params.items() if k in params}
t = TextModel([X[x] for x in tr], **textModel_params)
m = cls(t).fit([t[X[x]] for x in tr], [y[x] for x in tr])
return ts, np.array(m.predict([t[X[x]] for x in ts]))
def train_predict_pool(cls, args):
X, y, tr, ts, textModel_params = args
params = TextModel.params()
textModel_params = {
k: v
for k, v in textModel_params.items() if k in params
}
t = TextModel([X[x] for x in tr], **textModel_params)
m = cls(t).fit([t[X[x]] for x in tr], [y[x] for x in tr])
return ts, np.array(m.predict([t[X[x]] for x in ts]))
def clean_params(kw):
params = TextModel.params()
return {k: v for k, v in kw.items() if k in params}
