def rating_prediction_switch(self, dataset, dataset_switch, model_manager, force):
from graphlab.data_structures.sframe import SFrame
from graphlab.data_structures.sarray import SArray
import os
for folder in dataset.folders:
rating_prediction_file = self._get_rating_prediction_file(dataset_switch, folder)
class_prediction_file = self._get_class_prediction_file(dataset_switch, folder)
if os.path.exists(rating_prediction_file) and not force:
print "Model " + self.id + " in " + dataset_switch.id + " " + folder.id + " already tested."
continue
cf_predictions = model_manager.get_predictions(dataset, folder)
if self.id == 'best':
test_sframe = folder.test_sframe
target = test_sframe.select_column(key = 'rating')
rating_predictions = map(lambda t, *p: self._get_best_prediction(t, *p), target, *cf_predictions)
rating_array = SArray(rating_predictions)
rating_array.save(filename = rating_prediction_file)
else:
sf = SFrame.read_csv(class_prediction_file, header = True, quote_char = '"',
column_type_hints = [int, str])
switch_predictions = sf.select_column(key = 'x')
index_switch_predictions = model_manager.get_index_model(switch_predictions)
rating_predictions = map(lambda t, *p: self._get_switch_prediction(t, *p),
index_switch_predictions, *cf_predictions)
rating_array = SArray(rating_predictions)
rating_array.save(filename = rating_prediction_file)
