本文整理汇总了Python中etl.ETLUtils.write_row_to_json方法的典型用法代码示例。如果您正苦于以下问题：Python ETLUtils.write_row_to_json方法的具体用法？Python ETLUtils.write_row_to_json怎么用？Python ETLUtils.write_row_to_json使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类etl.ETLUtils的用法示例。
在下文中一共展示了ETLUtils.write_row_to_json方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: full_cycle
# 需要导入模块: from etl import ETLUtils [as 别名]
# 或者: from etl.ETLUtils import write_row_to_json [as 别名]
def full_cycle(metric):
csv_file_name = Constants.generate_file_name(
metric, 'csv', Constants.RESULTS_FOLDER, None,
None, False)
json_file_name = Constants.generate_file_name(
metric, 'json', Constants.RESULTS_FOLDER, None,
None, False)
print(json_file_name)
print(csv_file_name)
properties = Constants.get_properties_copy()
results = evaluate_topic_model(metric)
print(results)
results.update(properties)
ETLUtils.write_row_to_csv(csv_file_name, results)
ETLUtils.write_row_to_json(json_file_name, results)示例2: full_cycle
# 需要导入模块: from etl import ETLUtils [as 别名]
# 或者: from etl.ETLUtils import write_row_to_json [as 别名]
def full_cycle():
plant_random_seeds()
my_records = load_records()
preprocess_records(my_records)
x_matrix, y_vector = transform(my_records)
count_specific_generic(my_records)
# Error estimation
error_estimation_results = []
best_classifier = None
best_score = 0.0
for classifier, params in PARAM_GRID_MAP.items():
# print('Classifier: %s' % classifier)
cv = StratifiedKFold(Constants.CROSS_VALIDATION_NUM_FOLDS)
score = error_estimation(x_matrix, y_vector, params, cv, SCORE_METRIC).mean()
error_estimation_results.append(
{
'classifier': classifier,
'accuracy': score,
Constants.BUSINESS_TYPE_FIELD: Constants.ITEM_TYPE
}
)
print('%s score: %f' % (classifier, score))
if score > best_score:
best_score = score
best_classifier = classifier
# Model selection
cv = StratifiedKFold(Constants.CROSS_VALIDATION_NUM_FOLDS)
grid_search_cv = model_selection(
x_matrix, y_vector, PARAM_GRID_MAP[best_classifier], cv, SCORE_METRIC)
# best_model = grid_search_cv.best_estimator_.get_params()['classifier']
# features_importance = best_model.coef_
print('%s: %f' % (SCORE_METRIC, grid_search_cv.best_score_))
print('best params', grid_search_cv.best_params_)
# for key, value in grid_search_cv.best_params_.items():
# print(key, value)
# print('best estimator', grid_search_cv.best_estimator_)
# print('features importance', features_importance)
# csv_file_name = Constants.generate_file_name(
# 'classifier_results', 'csv', Constants.RESULTS_FOLDER, None,
# None, False)
# json_file_name = Constants.generate_file_name(
# 'classifier_results', 'json', Constants.RESULTS_FOLDER, None,
# None, False)
csv_file_name2 = Constants.RESULTS_FOLDER + 'classifier_results.csv'
json_file_name2 = Constants.RESULTS_FOLDER + 'classifier_results.json'
# results = get_scores(final_grid_search_cv.cv_results_)
# csv_file = '/Users/fpena/tmp/' + Constants.ITEM_TYPE + '_new_reviews_classifier_results.csv'
# ETLUtils.save_csv_file(
# csv_file_name, error_estimation_results,
# error_estimation_results[0].keys())
# ETLUtils.save_json_file(json_file_name, error_estimation_results)
for result in error_estimation_results:
ETLUtils.write_row_to_csv(
csv_file_name2, result)
ETLUtils.write_row_to_json(json_file_name2, result)
#
# print(csv_file)
best_hyperparams_file_name = Constants.generate_file_name(
'best_hyperparameters', 'json', Constants.CACHE_FOLDER, None,
None, False)
save_parameters(best_hyperparams_file_name, grid_search_cv.best_params_)