def predict_t2(self, grid_search = True):
if self.docs == {}:
raise ValueError("docs have not been created. call set_prediction_docs first!")
# get features and target
X, Y, info = self.get_feature('t2')
# init svm classifier
svm = SVM(self._model_path, "trig-theme1-2", "linear", grid_search = grid_search, class_weight = 'auto')
svm.load()
return svm.predict(X), Y, info
def predict_tt(self, grid_search = True):
"""
return prediction of given docid_list
"""
if self.docs == {}:
raise ValueError("docs have not been created. call set_prediction_docs first!")
# get list of file
#doc_ids = self.get_docid_list(docid_list_fname)
# get features and target
X, Y, info = self.get_feature('tt')
# init svm classifier
svm = SVM(self._model_path, "trig-trig", "linear", grid_search = grid_search, class_weight = 'auto')
svm.load()
return svm.predict(X), Y, info