def run_single_validation(self, grid_size, validation_date, time_unit):
data = ProcessData(self.dataset_info['name'], self.dataset_info['path'])
df = data.get_formated_df()
self.dataset_info['dict'] = data.dataset_dict #update dataset dictionary on experiment instance
df_filtered = ProcessData.filter_by_field(df, self.custom_filter['field'], self.custom_filter['value'])
validation = ValidateModel(df_filtered, self.dataset_info['dict'], time_unit, None)
prediction_results = validation.inner_loop_validation(self.model, grid_size, self.train_dates, datetime.strptime(validation_date,'%Y-%m-%d'), self.metrics)
return prediction_results
def run_ncv_experiment(self, time_unit, grid_size, region):
""" Run nested-cross validation
:region: An instance of :class: open_cp.data.RectangularRegion,
if 'None', the region will dfined based on training points
:return: An array with prediction results
"""
self.check_exp_params()
data = ProcessData(self.dataset_info['name'], self.dataset_info['path'])
df = data.get_formated_df()
self.dataset_info['dict'] = data.dataset_dict #update dataset dictionary on experiment instance
dates_interval = {'initial': self.train_dates['initial'], 'final': self.validation_dates['final']}
df_train_validation = ProcessData.select_data(df, self.dataset_info['dict'], self.custom_filter, dates_interval)
validation = ValidateModel(df_train_validation, self.dataset_info['dict'], time_unit, region)
prediction_results = validation.walk_fwd_chain(self.model, grid_size, self.train_dates, self.validation_dates, self.metrics)
return prediction_results