def main_submit():
start_time = time.time()
train = read_train_data(nrows=None)
test = read_test_data()
train, test = process_data(train, test)
X = train.drop(['ID_code', 'target'], axis=1)
y = train['target']
X_test = test.drop(['ID_code'], axis=1)
oof, predictions, scores, feature_importance = train_model(
X,
X_test,
y,
params,
n_fold=10,
plot_feature_importance=True,
model_type='lgb_sklearn')
str_metric_score = metric + '_0' + str(
int(scores['auc_score'].iloc[0] * 10000))
submit(test, predictions, str_metric_score)
comment = 'add 5 max min feature before standard scale'
storage_src(str_metric_score, scores, feature_importance, comment)
elapsed_time = time.time() - start_time
print(elapsed_time)
def storage_process(submission, str_metric_score, score, second_score, feature_score):
submit(submission, str_metric_score)
comment = 'drop main and second Type, main state East West, States_Federal, metadata_crop, VideoAmt'
storage_src(str_metric_score, score, second_score, feature_score, comment, adoption_shuffle)
def storage_process(submission, str_metric_score, score, second_score,
feature_score):
submit(submission, str_metric_score)
comment = 'remove img height and width feature'
storage_src(str_metric_score, score, second_score, feature_score, comment)
def storage_process(submission, str_metric_score, score, second_score, feature_score):
submit(submission, str_metric_score)
comment = 'test feature importance'
storage_src(str_metric_score, score, second_score, feature_score, comment)
def storage_process(submission, str_metric_score, scores, feature_score):
submit(submission, str_metric_score)
comment = 'Mixed_Breed change'
storage_src(str_metric_score, scores, feature_score, comment)
def storage_process(submission, str_metric_score, score, second_score,
feature_score):
submit(submission, str_metric_score)
comment = 'coefficient not change'
storage_src(str_metric_score, score, second_score, feature_score, comment)
def storage_process(submission, str_metric_score, score, second_score,
feature_score):
submit(submission, str_metric_score)
comment = 'drop main state State,'
storage_src(str_metric_score, score, second_score, feature_score, comment,
adoption_shuffle)
def storage_process(submission, str_metric_score, score, second_score,
feature_score):
submit(submission, str_metric_score)
comment = 'State_factorize to dummies'
storage_src(str_metric_score, score, second_score, feature_score, comment)
def storage_process(submission, str_metric_score, score, second_score,
feature_score):
submit(submission, str_metric_score)
comment = 'new hyper parameter,'
storage_src(str_metric_score, score, second_score, feature_score, comment,
adoption_shuffle)
def storage_process(submission, str_metric_score, score, second_score,
feature_score):
submit(submission, str_metric_score)
comment = 'factrize stateID'
storage_src(str_metric_score, score, second_score, feature_score, comment)
def storage_process(submission, str_metric_score, score, second_score,
feature_score):
submit(submission, str_metric_score)
comment = 'remove size_feature'
storage_src(str_metric_score, score, second_score, feature_score, comment)
def storage_process(submission, str_metric_score, scores, feature_score):
submit(submission, str_metric_score)
comment = 'add 5 max min feature before standard scale'
storage_src(str_metric_score, scores, feature_score, comment)
def storage_process(submission, str_metric_score, score, second_score, feature_score):
submit(submission, str_metric_score)
comment = 'metadata breed 2(1)'
storage_src(str_metric_score, score, second_score, feature_score, comment, adoption_shuffle)
def storage_process(submission, str_metric_score, score, second_score, feature_score):
submit(submission, str_metric_score)
comment = 'main state East West, States_Federal,'
storage_src(str_metric_score, score, second_score, feature_score, comment, adoption_shuffle)
def storage_process(submission, str_metric_score, score, second_score,
feature_score):
submit(submission, str_metric_score)
comment = 'metrics mlogloss'
storage_src(str_metric_score, score, second_score, feature_score, comment)
def storage_process(submission, str_metric_score, score, second_score,
feature_score):
submit(submission, str_metric_score)
comment = 'shaffle_adoptionspeed'
storage_src(str_metric_score, score, second_score, feature_score, comment,
adoption_shuffle)