def _train(pipeline_name, dev_mode):
if bool(params.overwrite) and os.path.isdir(params.experiment_dir):
shutil.rmtree(params.experiment_dir)
logger.info('reading data in')
if dev_mode:
TRAIN_DAYS, TRAIN_HOURS = cfg.DEV_TRAIN_DAYS, cfg.DEV_TRAIN_HOURS
VALID_DAYS, VALID_HOURS = cfg.DEV_VALID_DAYS, cfg.DEV_VALID_HOURS
else:
TRAIN_DAYS, TRAIN_HOURS = eval(params.train_days), eval(
params.train_hours)
VALID_DAYS, VALID_HOURS = eval(params.valid_days), eval(
params.valid_hours)
meta_train_split = read_csv_time_chunks(params.train_chunks_dir,
days=TRAIN_DAYS,
hours=TRAIN_HOURS,
usecols=cfg.FEATURE_COLUMNS +
cfg.TARGET_COLUMNS,
dtype=cfg.COLUMN_TYPES['train'],
logger=logger)
meta_valid_split = read_csv_time_chunks(params.train_chunks_dir,
days=VALID_DAYS,
hours=VALID_HOURS,
usecols=cfg.FEATURE_COLUMNS +
cfg.TARGET_COLUMNS,
dtype=cfg.COLUMN_TYPES['train'],
logger=logger)
data_hash_channel_send(ctx, 'Training Data Hash', meta_train_split)
data_hash_channel_send(ctx, 'Validation Data Hash', meta_valid_split)
if dev_mode:
meta_train_split = meta_train_split.sample(cfg.DEV_SAMPLE_TRAIN_SIZE,
replace=False)
meta_valid_split = meta_valid_split.sample(cfg.DEV_SAMPLE_VALID_SIZE,
replace=False)
logger.info('Target distribution in train: {}'.format(
meta_train_split['is_attributed'].mean()))
logger.info('Target distribution in valid: {}'.format(
meta_valid_split['is_attributed'].mean()))
logger.info('shuffling data')
meta_train_split = meta_train_split.sample(frac=1)
meta_valid_split = meta_valid_split.sample(frac=1)
data = {
'input': {
'X': meta_train_split[cfg.FEATURE_COLUMNS],
'y': meta_train_split[cfg.TARGET_COLUMNS],
'X_valid': meta_valid_split[cfg.FEATURE_COLUMNS],
'y_valid': meta_valid_split[cfg.TARGET_COLUMNS],
},
}
pipeline = PIPELINES[pipeline_name]['train'](cfg.SOLUTION_CONFIG)
pipeline.clean_cache()
pipeline.fit_transform(data)
pipeline.clean_cache()
def _predict(pipeline_name, dev_mode):
logger.info('reading data in')
if dev_mode:
meta_test = pd.read_csv(params.test_filepath,
usecols=cfg.FEATURE_COLUMNS + cfg.ID_COLUMN,
dtype=cfg.COLUMN_TYPES['inference'],
nrows=cfg.DEV_SAMPLE_TEST_SIZE)
else:
meta_test = pd.read_csv(params.test_filepath,
usecols=cfg.FEATURE_COLUMNS + cfg.ID_COLUMN,
dtype=cfg.COLUMN_TYPES['inference'])
data_hash_channel_send(ctx, 'Test Data Hash', meta_test)
data = {
'input': {
'X': meta_test[cfg.FEATURE_COLUMNS],
'y': None,
},
}
pipeline = PIPELINES[pipeline_name]['inference'](cfg.SOLUTION_CONFIG)
pipeline.clean_cache()
output = pipeline.transform(data)
pipeline.clean_cache()
y_pred = output['y_pred']
logger.info('creating submission')
submission = create_submission(meta_test, y_pred)
submission_filepath = os.path.join(params.experiment_dir, 'submission.csv')
submission.to_csv(submission_filepath, index=None, encoding='utf-8')
logger.info('submission saved to {}'.format(submission_filepath))
logger.info('submission head \n\n{}'.format(submission.head()))
def _evaluate(pipeline_name, dev_mode):
logger.info('reading data in')
if dev_mode:
VALID_DAYS_HOURS = cfg.DEV_VALID_DAYS_HOURS
else:
VALID_DAYS_HOURS = eval(params.valid_days_hours)
meta_valid_split = read_csv_time_chunks(params.train_chunks_dir,
prefix='train',
days_hours=VALID_DAYS_HOURS,
usecols=cfg.FEATURE_COLUMNS +
cfg.TARGET_COLUMNS,
dtype=cfg.COLUMN_TYPES['train'],
logger=logger)
data_hash_channel_send(ctx, 'Evaluation Data Hash', meta_valid_split)
if dev_mode:
meta_valid_split = meta_valid_split.sample(cfg.DEV_SAMPLE_VALID_SIZE,
replace=False)
logger.info('Target distribution in valid: {}'.format(
meta_valid_split['is_attributed'].mean()))
data = {
'input': {
'X': meta_valid_split[cfg.FEATURE_COLUMNS],
'y': None,
},
}
pipeline = PIPELINES[pipeline_name]['inference'](cfg.SOLUTION_CONFIG)
pipeline.clean_cache()
output = pipeline.transform(data)
pipeline.clean_cache()
y_pred = output['y_pred']
y_true = meta_valid_split[cfg.TARGET_COLUMNS].values.reshape(-1)
logger.info('Saving evaluation predictions')
save_evaluation_predictions(params.experiment_dir, y_true, y_pred,
meta_valid_split)
logger.info('Calculating ROC_AUC Full Scores')
score = roc_auc_score(y_true, y_pred)
logger.info('ROC_AUC score on full_validation is {}'.format(score))
ctx.channel_send('ROC_AUC FULL', 0, score)
logger.info('Subsetting on submission times')
index_for_submission_hours = get_submission_hours_index(
meta_valid_split, cfg.TIMESTAMP_COLUMN, eval(params.submission_hours))
y_pred_ = y_pred[index_for_submission_hours]
y_true_ = y_true[index_for_submission_hours]
logger.info('Calculating ROC_AUC Submission Scores')
score = roc_auc_score(y_true_, y_pred_)
logger.info(
'ROC_AUC score on submission subset of validation is {}'.format(score))
ctx.channel_send('ROC_AUC SUBSET', 0, score)
def _evaluate(pipeline_name, dev_mode):
logger.info('reading data in')
if dev_mode:
VALID_DAYS_HOURS = cfg.DEV_VALID_DAYS_HOURS
else:
VALID_DAYS_HOURS = eval(params.valid_days_hours)
meta_valid_split = read_csv_time_chunks(params.train_chunks_dir,
prefix='train',
days_hours=VALID_DAYS_HOURS,
usecols=cfg.FEATURE_COLUMNS + cfg.TARGET_COLUMNS,
dtype=cfg.COLUMN_TYPES['train'],
logger=logger)
data_hash_channel_send(ctx, 'Evaluation Data Hash', meta_valid_split)
if dev_mode:
meta_valid_split = meta_valid_split.sample(cfg.DEV_SAMPLE_VALID_SIZE, replace=False)
logger.info('Target distribution in valid: {}'.format(meta_valid_split['is_attributed'].mean()))
data = {'input': {'X': meta_valid_split[cfg.FEATURE_COLUMNS],
'y': None,
},
}
pipeline = PIPELINES[pipeline_name]['inference'](cfg.SOLUTION_CONFIG)
pipeline.clean_cache()
output = pipeline.transform(data)
pipeline.clean_cache()
y_pred = output['y_pred']
y_true = meta_valid_split[cfg.TARGET_COLUMNS].values.reshape(-1)
logger.info('Saving evaluation predictions')
save_evaluation_predictions(params.experiment_dir, y_true, y_pred, meta_valid_split)
logger.info('Calculating ROC_AUC Full Scores')
score = roc_auc_score(y_true, y_pred)
logger.info('ROC_AUC score on full_validation is {}'.format(score))
ctx.channel_send('ROC_AUC FULL', 0, score)
logger.info('Subsetting on submission times')
index_for_submission_hours = get_submission_hours_index(meta_valid_split,
cfg.TIMESTAMP_COLUMN,
eval(params.submission_hours))
y_pred_ = y_pred[index_for_submission_hours]
y_true_ = y_true[index_for_submission_hours]
logger.info('Calculating ROC_AUC Submission Scores')
score = roc_auc_score(y_true_, y_pred_)
logger.info('ROC_AUC score on submission subset of validation is {}'.format(score))
ctx.channel_send('ROC_AUC SUBSET', 0, score)
def _train(pipeline_name, dev_mode):
if bool(params.overwrite) and os.path.isdir(params.experiment_dir):
shutil.rmtree(params.experiment_dir)
logger.info('reading data in')
if dev_mode:
TRAIN_DAYS_HOURS = cfg.DEV_TRAIN_DAYS_HOURS
VALID_DAYS_HOURS = cfg.DEV_VALID_DAYS_HOURS
else:
TRAIN_DAYS_HOURS = eval(params.train_days_hours)
VALID_DAYS_HOURS = eval(params.valid_days_hours)
meta_train_split = read_csv_time_chunks(params.train_chunks_dir,
prefix='train',
days_hours=TRAIN_DAYS_HOURS,
usecols=cfg.FEATURE_COLUMNS + cfg.TARGET_COLUMNS,
dtype=cfg.COLUMN_TYPES['train'],
logger=logger)
meta_valid_split = read_csv_time_chunks(params.train_chunks_dir,
prefix='train',
days_hours=VALID_DAYS_HOURS,
usecols=cfg.FEATURE_COLUMNS + cfg.TARGET_COLUMNS,
dtype=cfg.COLUMN_TYPES['train'],
logger=logger)
data_hash_channel_send(ctx, 'Training Data Hash', meta_train_split)
data_hash_channel_send(ctx, 'Validation Data Hash', meta_valid_split)
if dev_mode:
meta_train_split = meta_train_split.sample(cfg.DEV_SAMPLE_TRAIN_SIZE, replace=False)
meta_valid_split = meta_valid_split.sample(cfg.DEV_SAMPLE_VALID_SIZE, replace=False)
logger.info('Target distribution in train: {}'.format(meta_train_split['is_attributed'].mean()))
logger.info('Target distribution in valid: {}'.format(meta_valid_split['is_attributed'].mean()))
logger.info('shuffling data')
meta_train_split = meta_train_split.sample(frac=1)
meta_valid_split = meta_valid_split.sample(frac=1)
data = {'input': {'X': meta_train_split[cfg.FEATURE_COLUMNS],
'y': meta_train_split[cfg.TARGET_COLUMNS],
'X_valid': meta_valid_split[cfg.FEATURE_COLUMNS],
'y_valid': meta_valid_split[cfg.TARGET_COLUMNS],
},
}
pipeline = PIPELINES[pipeline_name]['train'](cfg.SOLUTION_CONFIG)
pipeline.clean_cache()
pipeline.fit_transform(data)
pipeline.clean_cache()
def _predict(pipeline_name, dev_mode):
logger.info('reading data in')
if dev_mode:
TEST_DAYS_HOURS = cfg.DEV_TEST_DAYS_HOURS
else:
TEST_DAYS_HOURS = eval(params.test_days_hours)
meta_test_suplement = read_csv_time_chunks(params.test_chunks_dir,
prefix='test',
days_hours=TEST_DAYS_HOURS,
usecols=cfg.FEATURE_COLUMNS + cfg.ID_COLUMN,
dtype=cfg.COLUMN_TYPES['inference'],
logger=logger)
meta_test = pd.read_csv(params.test_filepath,
usecols=cfg.FEATURE_COLUMNS + cfg.ID_COLUMN,
dtype=cfg.COLUMN_TYPES['inference'])
meta_test_full = pd.concat([meta_test_suplement, meta_test], axis=0).reset_index(drop=True)
meta_test_full.drop_duplicates(subset=cfg.ID_COLUMN, keep='last', inplace=True)
meta_test_full['click_time'] = pd.to_datetime(meta_test_full['click_time'], format='%Y-%m-%d %H:%M:%S')
data_hash_channel_send(ctx, 'Test Data Hash', meta_test_full)
if dev_mode:
meta_test_full = meta_test_full.sample(cfg.DEV_SAMPLE_TEST_SIZE, replace=False)
data = {'input': {'X': meta_test_full[cfg.FEATURE_COLUMNS],
'y': None,
},
}
pipeline = PIPELINES[pipeline_name]['inference'](cfg.SOLUTION_CONFIG)
pipeline.clean_cache()
output = pipeline.transform(data)
pipeline.clean_cache()
y_pred = output['y_pred']
logger.info('creating submission full test')
full_submission = create_submission(meta_test_full, y_pred)
full_submission_filepath = os.path.join(params.experiment_dir, 'full_submission.csv')
full_submission.to_csv(full_submission_filepath, index=None, encoding='utf-8')
logger.info('subsetting submission')
submission = pd.merge(full_submission, meta_test[cfg.ID_COLUMN], on=cfg.ID_COLUMN, how='inner')
submission_filepath = os.path.join(params.experiment_dir, 'submission.csv')
submission.to_csv(submission_filepath, index=None, encoding='utf-8')
logger.info('submission saved to {}'.format(submission_filepath))
logger.info('submission head \n\n{}'.format(submission.head()))
def _train(pipeline_name, dev_mode):
if params.use_english:
train_filepath = params.train_en_filepath
else:
train_filepath = params.train_filepath
if bool(params.overwrite) and os.path.isdir(params.experiment_dir):
shutil.rmtree(params.experiment_dir)
logger.info('reading data in')
if dev_mode:
meta_train = pd.read_csv(train_filepath,
usecols=cfg.FEATURE_COLUMNS + cfg.TARGET_COLUMNS + cfg.ITEM_ID_COLUMN,
dtype=cfg.COLUMN_TYPES['train'],
nrows=cfg.DEV_SAMPLE_SIZE)
else:
meta_train = pd.read_csv(train_filepath,
usecols=cfg.FEATURE_COLUMNS + cfg.TARGET_COLUMNS + cfg.ITEM_ID_COLUMN,
dtype=cfg.COLUMN_TYPES['train'])
meta_train_split, meta_valid_split = stratified_train_valid_split(meta_train,
target_column=cfg.TARGET_COLUMNS,
target_bins=params.target_bins,
valid_size=params.validation_size,
random_state=1234)
data_hash_channel_send(ctx, 'Training Data Hash', meta_train_split)
data_hash_channel_send(ctx, 'Validation Data Hash', meta_valid_split)
logger.info('Target distribution in train: {}'.format(meta_train_split[cfg.TARGET_COLUMNS].mean()))
logger.info('Target distribution in valid: {}'.format(meta_valid_split[cfg.TARGET_COLUMNS].mean()))
logger.info('shuffling data')
meta_train_split = meta_train_split.sample(frac=1)
meta_valid_split = meta_valid_split.sample(frac=1)
data = {'input': {'X': meta_train_split[cfg.FEATURE_COLUMNS],
'y': meta_train_split[cfg.TARGET_COLUMNS],
'X_valid': meta_valid_split[cfg.FEATURE_COLUMNS],
'y_valid': meta_valid_split[cfg.TARGET_COLUMNS],
},
'specs': {'is_train': True}
}
pipeline = PIPELINES[pipeline_name]['train'](cfg.SOLUTION_CONFIG)
pipeline.clean_cache()
pipeline.fit_transform(data)
pipeline.clean_cache()
def _evaluate(pipeline_name, dev_mode):
logger.info('reading data in')
if params.use_english:
train_filepath = params.train_en_filepath
else:
train_filepath = params.train_filepath
if dev_mode:
meta_train = pd.read_csv(train_filepath,
usecols=cfg.FEATURE_COLUMNS + cfg.TARGET_COLUMNS + cfg.ITEM_ID_COLUMN,
dtype=cfg.COLUMN_TYPES['train'],
nrows=cfg.DEV_SAMPLE_SIZE)
else:
meta_train = pd.read_csv(train_filepath,
usecols=cfg.FEATURE_COLUMNS + cfg.TARGET_COLUMNS + cfg.ITEM_ID_COLUMN,
dtype=cfg.COLUMN_TYPES['train'])
_, meta_valid_split = stratified_train_valid_split(meta_train,
target_column=cfg.TARGET_COLUMNS,
target_bins=params.target_bins,
valid_size=params.validation_size,
random_state=1234)
data_hash_channel_send(ctx, 'Evaluation Data Hash', meta_valid_split)
logger.info('Target distribution in valid: {}'.format(meta_valid_split[cfg.TARGET_COLUMNS].mean()))
data = {'input': {'X': meta_valid_split[cfg.FEATURE_COLUMNS],
'y': None,
},
'specs': {'is_train': True}
}
pipeline = PIPELINES[pipeline_name]['inference'](cfg.SOLUTION_CONFIG)
pipeline.clean_cache()
output = pipeline.transform(data)
pipeline.clean_cache()
y_pred = output['y_pred']
y_true = meta_valid_split[cfg.TARGET_COLUMNS].values.reshape(-1)
logger.info('Saving evaluation predictions')
save_evaluation_predictions(params.experiment_dir, y_true, y_pred, meta_valid_split)
logger.info('Calculating RMSE')
score = root_mean_squared_error(y_true, y_pred)
logger.info('RMSE score on validation is {}'.format(score))
ctx.channel_send('RMSE', 0, score)
def _predict(pipeline_name, dev_mode):
logger.info('reading data in')
if dev_mode:
TEST_DAYS_HOURS = cfg.DEV_TEST_DAYS_HOURS
else:
TEST_DAYS_HOURS = eval(params.test_days_hours)
meta_test_suplement = read_csv_time_chunks(
params.test_chunks_dir,
prefix='test',
days_hours=TEST_DAYS_HOURS,
usecols=cfg.FEATURE_COLUMNS + cfg.ID_COLUMN,
dtype=cfg.COLUMN_TYPES['inference'],
logger=logger)
meta_test = pd.read_csv(params.test_filepath,
usecols=cfg.FEATURE_COLUMNS + cfg.ID_COLUMN,
dtype=cfg.COLUMN_TYPES['inference'])
meta_test_full = pd.concat([meta_test_suplement, meta_test],
axis=0).reset_index(drop=True)
meta_test_full.drop_duplicates(subset=cfg.ID_COLUMN,
keep='last',
inplace=True)
meta_test_full['click_time'] = pd.to_datetime(meta_test_full['click_time'],
format='%Y-%m-%d %H:%M:%S')
data_hash_channel_send(ctx, 'Test Data Hash', meta_test_full)
if dev_mode:
meta_test_full = meta_test_full.sample(cfg.DEV_SAMPLE_TEST_SIZE,
replace=False)
data = {
'input': {
'X': meta_test_full[cfg.FEATURE_COLUMNS],
'y': None,
},
}
pipeline = PIPELINES[pipeline_name]['inference'](cfg.SOLUTION_CONFIG)
pipeline.clean_cache()
output = pipeline.transform(data)
pipeline.clean_cache()
y_pred = output['y_pred']
logger.info('creating submission full test')
full_submission = create_submission(meta_test_full, y_pred)
full_submission_filepath = os.path.join(params.experiment_dir,
'full_submission.csv')
full_submission.to_csv(full_submission_filepath,
index=None,
encoding='utf-8')
logger.info('subsetting submission')
submission = pd.merge(full_submission,
meta_test[cfg.ID_COLUMN],
on=cfg.ID_COLUMN,
how='inner')
submission_filepath = os.path.join(params.experiment_dir, 'submission.csv')
submission.to_csv(submission_filepath, index=None, encoding='utf-8')
logger.info('submission saved to {}'.format(submission_filepath))
logger.info('submission head \n\n{}'.format(submission.head()))