def profile_table(table, group_by=None, **params):
check_required_parameters(_profile_table, params, ['table'])
if group_by is not None:
return _function_by_group(_profile_table,
table,
group_by=group_by,
**params)
else:
return _profile_table(table, **params)
def tukeys_range_test(table, group_by=None, **params):
check_required_parameters(_tukeys_range_test, params, ['table'])
if group_by is not None:
return _function_by_group(_tukeys_range_test,
table,
group_by=group_by,
**params)
else:
return _tukeys_range_test(table, **params)
def svm_classification_train(table, group_by=None, **params):
check_required_parameters(_svm_classification_train, params, ['table'])
if group_by is not None:
return _function_by_group(_svm_classification_train,
table,
group_by=group_by,
**params)
else:
return _svm_classification_train(table, **params)
def xgb_classification_predict(table, model, **params):
check_required_parameters(_xgb_classification_predict, params,
['table', 'model'])
if '_grouped_data' in model:
return _function_by_group(_xgb_classification_predict, table, model,
**params)
else:
return _xgb_classification_predict(table, model, **params)
def statistic_derivation(table, group_by=None, **params):
check_required_parameters(_statistic_derivation, params, ['table'])
if group_by is not None:
return _function_by_group(_statistic_derivation,
table,
group_by=group_by,
**params)
else:
return _statistic_derivation(table, **params)
def ftest_for_stacked_data(table, group_by=None, **params):
check_required_parameters(_ftest_for_stacked_data, params, ['table'])
if group_by is not None:
return _function_by_group(_ftest_for_stacked_data,
table,
group_by=group_by,
**params)
else:
return _ftest_for_stacked_data(table, **params)
def unit_root_test(table, group_by=None, **params):
check_required_parameters(_unit_root_test, params, ['table'])
if group_by is not None:
return _function_by_group(_unit_root_test,
table,
group_by=group_by,
**params)
else:
return _unit_root_test(table, **params)
def association_rule(table, group_by=None, **params):
check_required_parameters(_association_rule, params, ['table'])
if group_by is not None:
return _function_by_group(_association_rule,
table,
group_by=group_by,
**params)
else:
return _association_rule(table, **params)
def replace_missing_string(table, group_by=None, **params):
check_required_parameters(_replace_missing_string, params, ['table'])
params = get_default_from_parameters_if_required(params, _replace_missing_string)
param_validation_check = [greater_than_or_equal_to(params, 1, 'limit')]
validate(*param_validation_check)
if group_by is not None:
return _function_by_group(_replace_missing_string, table, group_by=group_by, **params)
else:
return _replace_missing_string(table, **params)
def moving_average(table, group_by=None, **params):
check_required_parameters(_moving_average, params, ['table'])
params = get_default_from_parameters_if_required(params,_moving_average)
param_validation_check = [greater_than_or_equal_to(params, 1, 'window_size')]
validate(*param_validation_check)
if group_by is not None:
return _function_by_group(_moving_average, table, group_by=group_by, **params)
else:
return _moving_average(table, **params)
def mann_whitney_test(table, group_by=None, **params):
check_required_parameters(_mann_whitney_test, params, ['table'])
params = get_default_from_parameters_if_required(params, _mann_whitney_test)
if group_by is not None:
return _function_by_group(_mann_whitney_test, table, group_by=group_by, **params)
else:
return _mann_whitney_test(table, **params)
def paired_ttest(table, group_by=None, **params):
check_required_parameters(_paired_ttest, params, ['table'])
params = get_default_from_parameters_if_required(params, _paired_ttest)
param_validation_check = [from_to(params, 0, 1, 'confidence_level')]
validate(*param_validation_check)
if group_by is not None:
return _function_by_group(_paired_ttest, table, group_by=group_by, **params)
else:
return _paired_ttest(table, **params)
def hierarchical_clustering(table, group_by=None, **params):
check_required_parameters(_hierarchical_clustering, params, ['table'])
if group_by is not None:
return _function_by_group(_hierarchical_clustering,
table,
group_by=group_by,
**params)
else:
return _hierarchical_clustering(table, **params)
def gaussian_mixture_train(table, group_by=None, **params):
check_required_parameters(_gaussian_mixture_train, params, ['table'])
if group_by is not None:
return _function_by_group(_gaussian_mixture_train,
table,
group_by=group_by,
**params)
else:
return _gaussian_mixture_train(table, **params)
def kruskal_wallis_test(table, group_by=None, **params):
check_required_parameters(_kruskal_wallis_test, params, ['table'])
params = get_default_from_parameters_if_required(params, _kruskal_wallis_test)
if group_by is not None:
return _function_by_group(_kruskal_wallis_test, table, group_by=group_by, **params)
else:
return _kruskal_wallis_test(table, **params)
def evaluate_regression(table, group_by=None, **params):
check_required_parameters(_evaluate_regression, params, ['table'])
if group_by is not None:
return _function_by_group(_evaluate_regression,
table,
group_by=group_by,
**params)
else:
return _evaluate_regression(table, **params)
def sparse_naive_bayes_train(table, group_by=None, **params):
check_required_parameters(_sparse_naive_bayes_train, params, ['table'])
if group_by is not None:
return _function_by_group(_sparse_naive_bayes_train,
table,
group_by=group_by,
**params)
else:
return _sparse_naive_bayes_train(table, **params)
def one_sample_ttest(table, group_by=None, **params):
check_required_parameters(_one_sample_ttest, params, ['table'])
if group_by is not None:
return _function_by_group(_one_sample_ttest,
table,
group_by=group_by,
**params)
else:
return _one_sample_ttest(table, **params)
def word2vec_similarity2(table, model, **params):
check_required_parameters(_word2vec_similarity2, params,
['table', 'model'])
params = get_default_from_parameters_if_required(params,
_word2vec_similarity2)
param_validation_check = [greater_than_or_equal_to(params, 1, 'topn')]
validate(*param_validation_check)
return _word2vec_similarity2(table, model, **params)
def string_summary(table, group_by=None, **params):
check_required_parameters(_string_summary, params, ['table'])
if group_by is not None:
return _function_by_group(_string_summary,
table,
group_by=group_by,
**params)
else:
return _string_summary(table, **params)
def transpose(table, group_by=None, **params):
check_required_parameters(_transpose, params, ['table'])
if group_by is not None:
return _function_by_group(_transpose,
table,
group_by=group_by,
**params)
else:
return _transpose(table, **params)
def outlier_detection_lof(table, group_by=None, **params):
check_required_parameters(_outlier_detection_lof, params, ['table'])
if group_by is not None:
return _function_by_group(_outlier_detection_lof,
table,
group_by=group_by,
**params)
else:
return _outlier_detection_lof(table, **params)
def distinct(table, group_by=None, **params):
check_required_parameters(_distinct, params, ['table'])
if group_by is not None:
return _function_by_group(_distinct,
table,
group_by=group_by,
**params)
else:
return _distinct(table, **params)
def split_data(table, group_by=None, **params):
check_required_parameters(_split_data, params, ['table'])
if group_by is not None:
return _function_by_group(_split_data,
table,
group_by=group_by,
**params)
else:
return _split_data(table, **params)
def correlation(table, group_by=None, **params):
check_required_parameters(_correlation, params, ['table'])
if group_by is not None:
return _function_by_group(_correlation,
table,
group_by=group_by,
**params)
else:
return _correlation(table, **params)
def replace_missing_number(table, group_by=None, **params):
check_required_parameters(_replace_missing_number, params, ['table'])
if group_by is not None:
return _function_by_group(_replace_missing_number,
table,
group_by=group_by,
**params)
else:
return _replace_missing_number(table, **params)
def oneway_anova(table, group_by=None, **params):
check_required_parameters(_oneway_anova, params, ['table'])
if group_by is not None:
return _function_by_group(_oneway_anova,
table,
group_by=group_by,
**params)
else:
return _oneway_anova(table, **params)
def linear_regression_train(table, group_by=None, **params):
check_required_parameters(_linear_regression_train, params, ['table'])
if group_by is not None:
return _function_by_group(_linear_regression_train,
table,
group_by=group_by,
**params)
else:
return _linear_regression_train(table, **params)
def svc_predict(table, model, group_by=None, **params):
check_required_parameters(_svc_predict, params, ['table', 'model'])
if group_by is not None:
return _function_by_group(_svc_predict,
table,
model,
group_by=group_by,
**params)
else:
return _svc_predict(table, model, **params)
def doc_summarizer_eng(table, **params):
check_required_parameters(_doc_summarizer_eng, params, ['table'])
params = get_default_from_parameters_if_required(params,
_doc_summarizer_eng)
param_validation_check = [
greater_than(params, 0, 'ratio'),
greater_than_or_equal_to(params, 1, 'num_sentence')
]
validate(*param_validation_check)
return _doc_summarizer_eng(table, **params)
