def predict_libfm(self):
print('predict: %s' % time.strftime("%Y/%m/%d-%H:%M:%S"))
run_libfm(self.context_train_file, self.context_test_file,
self.context_predictions_file, self.context_log_file)
self.predictions = rmse_calculator.read_targets_from_txt(
self.context_predictions_file)
def main_evaluate():
I = my_i
records = ETLUtils.load_json_file(RECORDS_FILE)
# print('num_records', len(records))
test_file = RECORDS_FILE + '_test'
test_records = ETLUtils.load_json_file(test_file)
top_n_evaluator = TopNEvaluator(records, test_records, DATASET, 10, I)
top_n_evaluator.find_important_records()
# top_n_evaluator.initialize()
# records_to_predict_file = DATASET_FOLDER + 'generated/records_to_predict_' + DATASET + '.json'
top_n_evaluator.load_records_to_predict(RECORDS_TO_PREDICT_FILE)
predictions_file = GENERATED_FOLDER + 'predictions_' + DATASET + '.txt'
predictions = rmse_calculator.read_targets_from_txt(predictions_file)
# print('total predictions', len(predictions))
top_n_evaluator.evaluate(predictions)
# print('precision', top_n_evaluator.precision)
print('recall', top_n_evaluator.recall)
return top_n_evaluator.recall
def evaluate(self):
print('evaluate: %s' % time.strftime("%Y/%d/%m-%H:%M:%S"))
predictions = rmse_calculator.read_targets_from_txt(
self.no_context_predictions_file)
self.top_n_evaluator.evaluate(predictions)
no_context_recall = self.top_n_evaluator.recall
predictions = rmse_calculator.read_targets_from_txt(
self.context_predictions_file)
self.top_n_evaluator.evaluate(predictions)
context_recall = self.top_n_evaluator.recall
print('No context recall: %f' % no_context_recall)
print('Context recall: %f' % context_recall)
return context_recall, no_context_recall
def predict_libfm(self):
print('predict: %s' % time.strftime("%Y/%m/%d-%H:%M:%S"))
run_libfm(
self.context_train_file, self.context_test_file,
self.context_predictions_file, self.context_log_file)
self.predictions = rmse_calculator.read_targets_from_txt(
self.context_predictions_file)
def evaluate(self):
print('evaluate: %s' % time.strftime("%Y/%m/%d-%H:%M:%S"))
predictions = rmse_calculator.read_targets_from_txt(
self.context_predictions_file)
self.top_n_evaluator.evaluate(predictions)
recall = self.top_n_evaluator.recall
print('Recall: %f' % recall)
print('Specific recall: %f' % self.top_n_evaluator.specific_recall)
print('Generic recall: %f' % self.top_n_evaluator.generic_recall)
return recall
