def _fetch_file(self, input_path, **param): if self.model == None: raise 'Please Train a model before predicting some thing' self.update_param(param) obj_dt = DataTransform(input_path, param) obj_dt.scan_data_type() x, y = obj_dt.fetch_data() return (x, y)
def train(self,input_path,**param): self.update_param(param) obj_dt = DataTransform(input_path,param) obj_dt.scan_data_type() x,y = obj_dt.fetch_data() clf = linear_model.Lasso(alpha=0.01) model = clf.fit(x,y) self.model = model return model
def train(self,input_path,**param): self.update_param(param) obj_dt = DataTransform(input_path,param) obj_dt.scan_data_type() x,y = obj_dt.fetch_data() clf = svm.SVC() model = clf.fit(x,y) self.model = model return model
def train(self,input_path,**param): self.update_param(param) if not self.int_dimension == None: param['n_features'] = self.int_dimension obj_dt = DataTransform(input_path,param) obj_dt.scan_data_type() x,y = obj_dt.fetch_data() clf = tree.DecisionTreeClassifier() model = clf.fit(x,y) self.model = model return model