Search is not available for this dataset
identifier stringlengths 1 155 | parameters stringlengths 2 6.09k | docstring stringlengths 11 63.4k | docstring_summary stringlengths 0 63.4k | function stringlengths 29 99.8k | function_tokens list | start_point list | end_point list | language stringclasses 1
value | docstring_language stringlengths 2 7 | docstring_language_predictions stringlengths 18 23 | is_langid_reliable stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|---|
test_model_finder_dummy_regression | (model_finder_regression, test_input) | Testing if DummyModel (for regression) is created correctly. | Testing if DummyModel (for regression) is created correctly. | def test_model_finder_dummy_regression(model_finder_regression, test_input):
"""Testing if DummyModel (for regression) is created correctly."""
expected_model = DummyRegressor(strategy="median")
median = 35.619966243279364
expected_model_scores = {"mean_squared_error": 487.0142795860736, "r2_score": -0.... | [
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test_model_finder_regression_dummy_model_results | (model_finder_regression) | Testing if dummy_model_results() function returns correct DataFrame (regression). | Testing if dummy_model_results() function returns correct DataFrame (regression). | def test_model_finder_regression_dummy_model_results(model_finder_regression):
"""Testing if dummy_model_results() function returns correct DataFrame (regression)."""
_ = {
"model": "DummyRegressor",
"fit_time": np.nan,
"params": "{'constant': None, 'quantile': None, 'strategy': 'median'... | [
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test_model_finder_set_model_regression | (model_finder_regression, seed) | Testing if set_model() function correctly sets chosen Model and corresponding properties (regression).
Additionally checks if the set Model wasn't fitted in the process. | Testing if set_model() function correctly sets chosen Model and corresponding properties (regression).
Additionally checks if the set Model wasn't fitted in the process. | def test_model_finder_set_model_regression(model_finder_regression, seed):
"""Testing if set_model() function correctly sets chosen Model and corresponding properties (regression).
Additionally checks if the set Model wasn't fitted in the process."""
model = DecisionTreeRegressor(max_depth=10, criterion="ma... | [
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69,
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test_model_finder_regression_search | (model_finder_regression, mode, expected_model, seed) | Testing if search() function returns expected Model (for regression). | Testing if search() function returns expected Model (for regression). | def test_model_finder_regression_search(model_finder_regression, mode, expected_model, seed):
"""Testing if search() function returns expected Model (for regression)."""
model_finder_regression._quicksearch_limit = 1
actual_model = model_finder_regression.search(models=None, scoring=mean_squared_error, mode... | [
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test_model_finder_search_and_fit_regression | (model_finder_regression, mode, expected_model, expected_scores, seed) | Testing if search_and_fit() function correctly searches for and sets and fits chosen model (regression).
Additionally checks if the model is correctly wrapped in TransformedTargetRegressor. | Testing if search_and_fit() function correctly searches for and sets and fits chosen model (regression).
Additionally checks if the model is correctly wrapped in TransformedTargetRegressor. | def test_model_finder_search_and_fit_regression(model_finder_regression, mode, expected_model, expected_scores, seed):
"""Testing if search_and_fit() function correctly searches for and sets and fits chosen model (regression).
Additionally checks if the model is correctly wrapped in TransformedTargetRegressor."... | [
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test_model_finder_regression_search_defined_models | (model_finder_regression, models, expected_model) | Testing if models provided explicitly are being scored and chosen properly in regression
(including models not present in default models collection). | Testing if models provided explicitly are being scored and chosen properly in regression
(including models not present in default models collection). | def test_model_finder_regression_search_defined_models(model_finder_regression, models, expected_model):
"""Testing if models provided explicitly are being scored and chosen properly in regression
(including models not present in default models collection)."""
actual_model = model_finder_regression.search(m... | [
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test_model_finder_perform_gridsearch_regression | (model_finder_regression, chosen_regressors_grid, seed) | Testing if gridsearch works and returns correct Models and result dict (in regression). | Testing if gridsearch works and returns correct Models and result dict (in regression). | def test_model_finder_perform_gridsearch_regression(model_finder_regression, chosen_regressors_grid, seed):
"""Testing if gridsearch works and returns correct Models and result dict (in regression)."""
expected_models = [
(DecisionTreeRegressor, {"max_depth": 10, "criterion": "mae", "random_state": seed... | [
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186,
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test_model_finder_perform_quicksearch_regression | (model_finder_regression, chosen_regressors_grid, seed) | Testing if quicksearch works and returns correct Models and result dict (in regression). | Testing if quicksearch works and returns correct Models and result dict (in regression). | def test_model_finder_perform_quicksearch_regression(model_finder_regression, chosen_regressors_grid, seed):
"""Testing if quicksearch works and returns correct Models and result dict (in regression)."""
expected_models = [
(DecisionTreeRegressor, 1801.9747017092634),
(Ridge, 1530.5096036958037)... | [
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test_model_finder_quicksearch_regression | (
model_finder_regression, chosen_regressors_grid, limit, expected_models
) | Testing if quicksearch correctly chooses only a limited number of found Models based on the limit
(in regression). | Testing if quicksearch correctly chooses only a limited number of found Models based on the limit
(in regression). | def test_model_finder_quicksearch_regression(
model_finder_regression, chosen_regressors_grid, limit, expected_models
):
"""Testing if quicksearch correctly chooses only a limited number of found Models based on the limit
(in regression)."""
model_finder_regression._quicksearch_limit = limit
act... | [
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test_model_finder_assess_models_regression | (model_finder_regression, seed) | Testing if assess_model function returns correct Models and result dict (in regression). | Testing if assess_model function returns correct Models and result dict (in regression). | def test_model_finder_assess_models_regression(model_finder_regression, seed):
"""Testing if assess_model function returns correct Models and result dict (in regression)."""
models = [
DecisionTreeRegressor(**{"max_depth": 10, "criterion": "mae", "random_state": seed}),
Ridge(**{"alpha": 0.0001,... | [
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test_model_finder_regression_search_results_dataframe | (model_finder_regression_fitted, limit, seed) | Testing if search_results_dataframe is being correctly filtered out to a provided
model_limit (in regression) | Testing if search_results_dataframe is being correctly filtered out to a provided
model_limit (in regression) | def test_model_finder_regression_search_results_dataframe(model_finder_regression_fitted, limit, seed):
"""Testing if search_results_dataframe is being correctly filtered out to a provided
model_limit (in regression)"""
models = ["SVR", "Ridge", "DecisionTreeRegressor"]
dummy = ["DummyRegressor"]
ex... | [
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test_model_finder_regression_prediction_errors | (model_finder_regression_fitted, limit, seed) | Testing if calculated prediction errors are correct (for regression). | Testing if calculated prediction errors are correct (for regression). | def test_model_finder_regression_prediction_errors(model_finder_regression_fitted, limit, seed):
"""Testing if calculated prediction errors are correct (for regression)."""
results = [
SVR(**{"C": 0.1, "tol": 1.0}),
Ridge(**{"alpha": 0.0001, "random_state": seed}),
DecisionTreeRegressor(... | [
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test_model_finder_regression_prediction_errors_error | (model_finder_regression) | Testing if prediction_errors raises an error when there are no search results available (regression). | Testing if prediction_errors raises an error when there are no search results available (regression). | def test_model_finder_regression_prediction_errors_error(model_finder_regression):
"""Testing if prediction_errors raises an error when there are no search results available (regression)."""
with pytest.raises(ModelsNotSearchedError) as excinfo:
_ = model_finder_regression.prediction_errors(1)
asser... | [
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test_model_finder_regression_residuals | (model_finder_regression_fitted, limit, seed) | Testing if calculated residuals are correct (for regression). | Testing if calculated residuals are correct (for regression). | def test_model_finder_regression_residuals(model_finder_regression_fitted, limit, seed):
"""Testing if calculated residuals are correct (for regression)."""
results = [
SVR(**{"C": 0.1, "tol": 1.0}),
Ridge(**{"alpha": 0.0001, "random_state": seed}),
DecisionTreeRegressor(**{"max_depth": ... | [
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test_model_finder_regression_residual_error | (model_finder_regression) | Testing if prediction_errors raises an error when there are no search results available (regression). | Testing if prediction_errors raises an error when there are no search results available (regression). | def test_model_finder_regression_residual_error(model_finder_regression):
"""Testing if prediction_errors raises an error when there are no search results available (regression)."""
with pytest.raises(ModelsNotSearchedError) as excinfo:
_ = model_finder_regression.residuals(1)
assert "Search Results... | [
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test_model_finder_predict_X_test_regression | (model_finder_regression_fitted, split_dataset_numerical, limit, seed) | Testing if predictions of X_test split from found models are correct (in regression). | Testing if predictions of X_test split from found models are correct (in regression). | def test_model_finder_predict_X_test_regression(model_finder_regression_fitted, split_dataset_numerical, limit, seed):
"""Testing if predictions of X_test split from found models are correct (in regression)."""
models = [
SVR(**{"C": 0.1, "tol": 1.0}),
Ridge(**{"alpha": 0.0001, "random_state": s... | [
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test_model_finder_wrap_model_regression | (model_finder_regression, expected_model) | Testing if wrapping a chosen model with custom TransformedTargetRegressor works correctly (regression). | Testing if wrapping a chosen model with custom TransformedTargetRegressor works correctly (regression). | def test_model_finder_wrap_model_regression(model_finder_regression, expected_model):
"""Testing if wrapping a chosen model with custom TransformedTargetRegressor works correctly (regression)."""
actual_model = model_finder_regression._wrap_model(expected_model)
assert str(actual_model) == str(expected_mod... | [
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test_model_finder_wrap_results_dataframe_regression | (model_finder_regression, test_data) | Testing if adding additional column to the results dataframe works correctly (in regression). | Testing if adding additional column to the results dataframe works correctly (in regression). | def test_model_finder_wrap_results_dataframe_regression(model_finder_regression, test_data):
"""Testing if adding additional column to the results dataframe works correctly (in regression)."""
df = pd.DataFrame(data=test_data)
title = model_finder_regression._transformed_target_name
params = str(model_f... | [
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test_model_finder_wrap_params_regression | (model_finder_regression, test_params) | Testing if adding params from Target Transformer to params dict works correctly (in regression). | Testing if adding params from Target Transformer to params dict works correctly (in regression). | def test_model_finder_wrap_params_regression(model_finder_regression, test_params):
"""Testing if adding params from Target Transformer to params dict works correctly (in regression)."""
expected_params = copy.deepcopy(test_params)
title = model_finder_regression._transformed_target_name
transformer = m... | [
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test_model_finder_calculate_model_score_multiclass_regular_scoring | (
model_finder_regression, split_dataset_numerical, model
) | Testing if calculating model score works correctly in multiclass with scoring != roc_auc_score. | Testing if calculating model score works correctly in multiclass with scoring != roc_auc_score. | def test_model_finder_calculate_model_score_multiclass_regular_scoring(
model_finder_regression, split_dataset_numerical, model
):
"""Testing if calculating model score works correctly in multiclass with scoring != roc_auc_score."""
scoring = mean_squared_error
X_train = split_dataset_numerical[0]
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split_first | (s, delims) |
.. deprecated:: 1.25
Given a string and an iterable of delimiters, split on the first found
delimiter. Return two split parts and the matched delimiter.
If not found, then the first part is the full input string.
Example::
>>> split_first('foo/bar?baz', '?/=')
('foo', 'bar?baz',... |
.. deprecated:: 1.25 | def split_first(s, delims):
"""
.. deprecated:: 1.25
Given a string and an iterable of delimiters, split on the first found
delimiter. Return two split parts and the matched delimiter.
If not found, then the first part is the full input string.
Example::
>>> split_first('foo/bar?baz'... | [
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_encode_invalid_chars | (component, allowed_chars, encoding="utf-8") | Percent-encodes a URI component without reapplying
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| Percent-encodes a URI component without reapplying
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_encode_target | (target) | Percent-encodes a request target so that there are no invalid characters | Percent-encodes a request target so that there are no invalid characters | def _encode_target(target):
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path, query = TARGET_RE.match(target).groups()
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parse_url | (url) |
Given a url, return a parsed :class:`.Url` namedtuple. Best-effort is
performed to parse incomplete urls. Fields not provided will be None.
This parser is RFC 3986 compliant.
The parser logic and helper functions are based heavily on
work done in the ``rfc3986`` module.
:param str url: URL to... |
Given a url, return a parsed :class:`.Url` namedtuple. Best-effort is
performed to parse incomplete urls. Fields not provided will be None.
This parser is RFC 3986 compliant. | def parse_url(url):
"""
Given a url, return a parsed :class:`.Url` namedtuple. Best-effort is
performed to parse incomplete urls. Fields not provided will be None.
This parser is RFC 3986 compliant.
The parser logic and helper functions are based heavily on
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get_host | (url) |
Deprecated. Use :func:`parse_url` instead.
|
Deprecated. Use :func:`parse_url` instead.
| def get_host(url):
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Url.hostname | (self) | For backwards-compatibility with urlparse. We're nice like that. | For backwards-compatibility with urlparse. We're nice like that. | def hostname(self):
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Url.request_uri | (self) | Absolute path including the query string. | Absolute path including the query string. | def request_uri(self):
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uri = self.path or "/"
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uri += "?" + self.query
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Url.netloc | (self) | Network location including host and port | Network location including host and port | def netloc(self):
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Url.url | (self) |
Convert self into a url
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Convert self into a url | def url(self):
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feature_descriptions | () | Descriptions for test data. | Descriptions for test data. | def feature_descriptions():
"""Descriptions for test data."""
d = FeatureDescriptor._description
m = FeatureDescriptor._mapping
c = FeatureDescriptor._category
descriptions = {
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feature_descriptor | (feature_descriptions) | Fixture FeatureDescriptor. | Fixture FeatureDescriptor. | def feature_descriptor(feature_descriptions):
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fd = FeatureDescriptor(feature_descriptions)
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feature_descriptor_forced_categories | (feature_descriptions) | Fixture FeatureDescriptor with forced categories. | Fixture FeatureDescriptor with forced categories. | def feature_descriptor_forced_categories(feature_descriptions):
"""Fixture FeatureDescriptor with forced categories."""
c = FeatureDescriptor._category
cat_cols = ["Height", "Price"]
num_cols = ["bool", "AgeGroup", "Target"]
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feature_descriptor_broken | (feature_descriptions) | Fixture FeatureDescriptor with str keys instead of int. | Fixture FeatureDescriptor with str keys instead of int. | def feature_descriptor_broken(feature_descriptions):
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data_classification_balanced | () | Test data with 'balanced' ratio of 0s and 1s in 'Target' variable. | Test data with 'balanced' ratio of 0s and 1s in 'Target' variable. | def data_classification_balanced():
"""Test data with 'balanced' ratio of 0s and 1s in 'Target' variable."""
random_seed = 56
columns = ["Sex", "AgeGroup", "Height", "Date", "Product", "Price", "bool", "Target"]
length = 100
random.seed(random_seed)
np.random.seed(seed=random_seed)
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categorical_features | () | Categorical Features names in data_classification_balanced. | Categorical Features names in data_classification_balanced. | def categorical_features():
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numerical_features | () | Numerical Features names in data_classification_balanced. | Numerical Features names in data_classification_balanced. | def numerical_features():
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fixture_features | (data_classification_balanced, feature_descriptor) | Fixture Features object for data_classification_balanced test data. | Fixture Features object for data_classification_balanced test data. | def fixture_features(data_classification_balanced, feature_descriptor):
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X, y = data_classification_balanced
f = Features(X, y, feature_descriptor)
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expected_raw_mapping | () | Expected 'raw' mapping of values in data_classification_balanced test data. | Expected 'raw' mapping of values in data_classification_balanced test data. | def expected_raw_mapping():
"""Expected 'raw' mapping of values in data_classification_balanced test data."""
expected_raw_mapping = {
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expected_mapping | () | Expected final mapping (from FeaturesDescriptor) for data_classification_balanced test data. | Expected final mapping (from FeaturesDescriptor) for data_classification_balanced test data. | def expected_mapping():
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html_test_table | () | Test table for data_classification_balanced test_data. | Test table for data_classification_balanced test_data. | def html_test_table():
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_ = """
<table>
<thead><tr><th></th><th></th></tr></thead>
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analyzer_fixture | (fixture_features) | Fixture Analyzer for data_classification_balanced test data. | Fixture Analyzer for data_classification_balanced test data. | def analyzer_fixture(fixture_features):
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root_path_to_package | () | Path to modules (package) and modules (package) name. | Path to modules (package) and modules (package) name. | def root_path_to_package():
"""Path to modules (package) and modules (package) name."""
package_name = "data_dashboard"
root_path = os.path.split(os.getcwd())[0]
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seed | () | Fixture random seed. | Fixture random seed. | def seed():
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data_regression | (data_classification_balanced) | Test data with Price feature as a target variable. | Test data with Price feature as a target variable. | def data_regression(data_classification_balanced):
"""Test data with Price feature as a target variable."""
df = pd.concat([data_classification_balanced[0], data_classification_balanced[1]], axis=1)
target = "Price"
feats = df.columns.to_list()
feats.remove(target)
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data_multiclass | (data_classification_balanced) | Test data with multiclass 'Product Type' target variable. | Test data with multiclass 'Product Type' target variable. | def data_multiclass(data_classification_balanced):
"""Test data with multiclass 'Product Type' target variable."""
X = pd.concat([data_classification_balanced[0], data_classification_balanced[1]], axis=1)
y = pd.Series(np.where(
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fixture_features_multiclass | (data_multiclass, feature_descriptor) | Fixture Features for multiclass problem. | Fixture Features for multiclass problem. | def fixture_features_multiclass(data_multiclass, feature_descriptor):
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X, y = data_multiclass
f = Features(X, y, feature_descriptor)
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preprocessor_X | (categorical_features, numerical_features, seed) | Base Fixture preprocessor X. | Base Fixture preprocessor X. | def preprocessor_X(categorical_features, numerical_features, seed):
"""Base Fixture preprocessor X."""
numeric_transformer = make_pipeline(
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transformer_classification | (categorical_features, numerical_features, seed, preprocessor_X) | Transformer for data_classification_balanced test data. | Transformer for data_classification_balanced test data. | def transformer_classification(categorical_features, numerical_features, seed, preprocessor_X):
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categorical_features.remove("Target")
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transformer_regression | (categorical_features, numerical_features, seed, preprocessor_X) | Transformer for data_regression test data. | Transformer for data_regression test data. | def transformer_regression(categorical_features, numerical_features, seed, preprocessor_X):
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numerical_features.remove("Price")
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transformer_multiclass | (categorical_features, numerical_features, seed, preprocessor_X) | Transformer for data_multiclass test data. | Transformer for data_multiclass test data. | def transformer_multiclass(categorical_features, numerical_features, seed, preprocessor_X):
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transformer_classification_fitted | (transformer_classification, data_classification_balanced) | Fitted Transformer for data_classification_balanced test data. | Fitted Transformer for data_classification_balanced test data. | def transformer_classification_fitted(transformer_classification, data_classification_balanced):
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transformer_classification.fit(data_classification_balanced[0])
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transformed_classification_data | (data_classification_balanced, transformer_classification) | Transformed data_classification_balanced test data. | Transformed data_classification_balanced test data. | def transformed_classification_data(data_classification_balanced, transformer_classification):
"""Transformed data_classification_balanced test data."""
X = data_classification_balanced[0]
y = data_classification_balanced[1]
X = X.drop(["Date"], axis=1)
transformer_classification.fit(X)
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transformed_regression_data | (data_regression, transformer_regression) | Transformed data_regression test data. | Transformed data_regression test data. | def transformed_regression_data(data_regression, transformer_regression):
"""Transformed data_regression test data."""
X = data_regression[0]
y = data_regression[1]
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transformed_multiclass_data | (data_multiclass, transformer_multiclass) | Transformed data_multiclass test data. | Transformed data_multiclass test data. | def transformed_multiclass_data(data_multiclass, transformer_multiclass):
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y = data_multiclass[1]
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split_dataset_classification | (data_classification_balanced, transformer_classification, seed) | Train/test split of data_classification_balanced test data. | Train/test split of data_classification_balanced test data. | def split_dataset_classification(data_classification_balanced, transformer_classification, seed):
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split_dataset_numerical | (data_regression, transformer_regression, seed) | Train/test split of data_regression test data. | Train/test split of data_regression test data. | def split_dataset_numerical(data_regression, transformer_regression, seed):
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X = data_regression[0]
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split_dataset_multiclass | (data_multiclass, transformer_multiclass, seed) | Train/test split of data_multiclass test data. | Train/test split of data_multiclass test data. | def split_dataset_multiclass(data_multiclass, transformer_multiclass, seed):
"""Train/test split of data_multiclass test data."""
X = data_multiclass[0]
y = data_multiclass[1]
X = X.drop(["Date"], axis=1)
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chosen_classifiers_grid | () | Test 'Model': parameters pairs for classification. | Test 'Model': parameters pairs for classification. | def chosen_classifiers_grid():
"""Test 'Model': parameters pairs for classification."""
_ = {
LogisticRegression: {
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chosen_regressors_grid | () | Test 'Model': parameters pairs for regression. | Test 'Model': parameters pairs for regression. | def chosen_regressors_grid():
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multiclass_scorings | () | Wrapped scoring functions for multiclass problem. | Wrapped scoring functions for multiclass problem. | def multiclass_scorings():
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scorings = [
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model_finder_classification | (
transformed_classification_data, split_dataset_classification, chosen_classifiers_grid, seed
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transformed_classification_data, split_dataset_classification, chosen_classifiers_grid, seed
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model_finder_regression | (transformed_regression_data, split_dataset_numerical, chosen_regressors_grid, seed) | Fixture ModelFinder for regression problem. | Fixture ModelFinder for regression problem. | def model_finder_regression(transformed_regression_data, split_dataset_numerical, chosen_regressors_grid, seed):
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model_finder_multiclass | (
transformed_multiclass_data, split_dataset_multiclass, chosen_classifiers_grid, multiclass_scorings, seed
) | Fixture ModelFinder for multiclass problem. | Fixture ModelFinder for multiclass problem. | def model_finder_multiclass(
transformed_multiclass_data, split_dataset_multiclass, chosen_classifiers_grid, multiclass_scorings, seed
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"""Fixture ModelFinder for multiclass problem."""
X = transformed_multiclass_data[0]
y = transformed_multiclass_data[1]
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model_finder_classification_fitted | (model_finder_classification) | Fixture fitted ModelFinder for classification problem. | Fixture fitted ModelFinder for classification problem. | def model_finder_classification_fitted(model_finder_classification):
"""Fixture fitted ModelFinder for classification problem."""
model_finder_classification.search_and_fit(mode="quick")
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model_finder_regression_fitted | (model_finder_regression) | Fixture fitted ModelFinder for regression problem. | Fixture fitted ModelFinder for regression problem. | def model_finder_regression_fitted(model_finder_regression):
"""Fixture fitted ModelFinder for regression problem."""
model_finder_regression.search_and_fit(mode="quick")
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model_finder_multiclass_fitted | (model_finder_multiclass) | Fixture fitted ModelFinder for multiclass problem. | Fixture fitted ModelFinder for multiclass problem. | def model_finder_multiclass_fitted(model_finder_multiclass):
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output | (
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output_multiclass | (
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dashboard | (data_classification_balanced, tmpdir, seed) | Fixture Dashboard object. | Fixture Dashboard object. | def dashboard(data_classification_balanced, tmpdir, seed):
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template | () | Fixture test template. | Fixture test template. | def template():
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detect | (byte_str) |
Detect the encoding of the given byte string.
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register.check_metadata | (self) | Deprecated API. | Deprecated API. | def check_metadata(self):
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register._set_config | (self) | Reads the configuration file and set attributes.
| Reads the configuration file and set attributes.
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register.classifiers | (self) | Fetch the list of classifiers from the server.
| Fetch the list of classifiers from the server.
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register.verify_metadata | (self) | Send the metadata to the package index server to be checked.
| Send the metadata to the package index server to be checked.
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register.send_metadata | (self) | Send the metadata to the package index server.
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register.post_to_server | (self, data, auth=None) | Post a query to the server, and return a string response.
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248,
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SequencesLibTest.testApplySustainControlChanges | (self) | Verify sustain controls extend notes until the end of the control. | Verify sustain controls extend notes until the end of the control. | def testApplySustainControlChanges(self):
"""Verify sustain controls extend notes until the end of the control."""
sequence = copy.copy(self.note_sequence)
testing_lib.add_control_changes_to_sequence(
sequence, 0,
[(0.0, 64, 127), (0.75, 64, 0), (2.0, 64, 127), (3.0, 64, 0),
(3.75, ... | [
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SequencesLibTest.testApplySustainControlChangesWithRepeatedNotes | (self) | Verify that sustain control handles repeated notes correctly.
For example, a single pitch played before sustain:
x-- x-- x--
After sustain:
x---x---x--
Notes should be extended until either the end of the sustain control or the
beginning of another note of the same pitch.
| Verify that sustain control handles repeated notes correctly. | def testApplySustainControlChangesWithRepeatedNotes(self):
"""Verify that sustain control handles repeated notes correctly.
For example, a single pitch played before sustain:
x-- x-- x--
After sustain:
x---x---x--
Notes should be extended until either the end of the sustain control or the
... | [
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SequencesLibTest.testApplySustainControlChangesWithRepeatedNotesBeforeSustain | (self) | Repeated notes before sustain can overlap and should not be modified.
Once a repeat happens within the sustain, any active notes should end
before the next one starts.
This is kind of an edge case because a note overlapping a note of the same
pitch may not make sense, but apply_sustain_control_changes... | Repeated notes before sustain can overlap and should not be modified. | def testApplySustainControlChangesWithRepeatedNotesBeforeSustain(self):
"""Repeated notes before sustain can overlap and should not be modified.
Once a repeat happens within the sustain, any active notes should end
before the next one starts.
This is kind of an edge case because a note overlapping a n... | [
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SequencesLibTest.testApplySustainControlChangesSimultaneousOnOff | (self) | Test sustain on and off events happening at the same time.
The off event should be processed last, so this should be a no-op.
| Test sustain on and off events happening at the same time. | def testApplySustainControlChangesSimultaneousOnOff(self):
"""Test sustain on and off events happening at the same time.
The off event should be processed last, so this should be a no-op.
"""
sequence = copy.copy(self.note_sequence)
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SequencesLibTest.testApplySustainControlChangesExtendNotesToEnd | (self) | Test sustain control extending the duration of the final note. | Test sustain control extending the duration of the final note. | def testApplySustainControlChangesExtendNotesToEnd(self):
"""Test sustain control extending the duration of the final note."""
sequence = copy.copy(self.note_sequence)
testing_lib.add_control_changes_to_sequence(
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SequencesLibTest.testApplySustainControlChangesExtraneousSustain | (self) | Test applying extraneous sustain control at the end of the sequence. | Test applying extraneous sustain control at the end of the sequence. | def testApplySustainControlChangesExtraneousSustain(self):
"""Test applying extraneous sustain control at the end of the sequence."""
sequence = copy.copy(self.note_sequence)
testing_lib.add_control_changes_to_sequence(
sequence, 0, [(4.0, 64, 127), (5.0, 64, 0)])
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SequencesLibTest.testApplySustainControlChangesWithIdenticalNotes | (self) | In the case of identical notes, one should be dropped.
This is an edge case because in most cases, the same pitch should not sound
twice at the same time on one instrument.
| In the case of identical notes, one should be dropped. | def testApplySustainControlChangesWithIdenticalNotes(self):
"""In the case of identical notes, one should be dropped.
This is an edge case because in most cases, the same pitch should not sound
twice at the same time on one instrument.
"""
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SequencesLibTest.testApplySustainControlChangesWithDrumNotes | (self) | Drum notes should not be modified when applying sustain changes. | Drum notes should not be modified when applying sustain changes. | def testApplySustainControlChangesWithDrumNotes(self):
"""Drum notes should not be modified when applying sustain changes."""
sequence = copy.copy(self.note_sequence)
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SequencesLibTest.testApplySustainControlChangesProcessSustainBeforeNotes | (self) | Verify sustain controls extend notes until the end of the control. | Verify sustain controls extend notes until the end of the control. | def testApplySustainControlChangesProcessSustainBeforeNotes(self):
"""Verify sustain controls extend notes until the end of the control."""
sequence = copy.copy(self.note_sequence)
testing_lib.add_control_changes_to_sequence(
sequence, 0,
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get_cmap | (n, name="hsv") | Returns a function that maps each index in 0, 1, ..., n-1 to a distinct
RGB color; the keyword argument name must be a standard mpl colormap name. | Returns a function that maps each index in 0, 1, ..., n-1 to a distinct
RGB color; the keyword argument name must be a standard mpl colormap name. | def get_cmap(n, name="hsv"):
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WebElementWrapper._reload_element | (self) | Method for starting reload process on current instance. | Method for starting reload process on current instance. | def _reload_element(self):
"""Method for starting reload process on current instance."""
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WebElementWrapper._execute | (self, command, params=None) | Overriding in order to catch StaleElementReferenceException. | Overriding in order to catch StaleElementReferenceException. | def _execute(self, command, params=None):
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parse_requirements | (
filename, # type: str
session, # type: PipSession
finder=None, # type: Optional[PackageFinder]
comes_from=None, # type: Optional[str]
options=None, # type: Optional[optparse.Values]
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) | Parse a requirements file and yield ParsedRequirement instances.
:param filename: Path or url of requirements file.
:param session: PipSession instance.
:param finder: Instance of pip.index.PackageFinder.
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:param options: cli op... | Parse a requirements file and yield ParsedRequirement instances. | def parse_requirements(
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preprocess | (content) | Split, filter, and join lines, and return a line iterator
:param content: the content of the requirements file
| Split, filter, and join lines, and return a line iterator | def preprocess(content):
# type: (Text) -> ReqFileLines
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handle_line | (
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finder=None, # type: Optional[PackageFinder]
session=None, # type: Optional[PipSession]
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break_args_options | (line) | Break up the line into an args and options string. We only want to shlex
(and then optparse) the options, not the args. args can contain markers
which are corrupted by shlex.
| Break up the line into an args and options string. We only want to shlex
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| def break_args_options(line):
# type: (Text) -> Tuple[str, Text]
"""Break up the line into an args and options string. We only want to shlex
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build_parser | () |
Return a parser for parsing requirement lines
|
Return a parser for parsing requirement lines
| def build_parser():
# type: () -> optparse.OptionParser
"""
Return a parser for parsing requirement lines
"""
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join_lines | (lines_enum) | Joins a line ending in '\' with the previous line (except when following
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| Joins a line ending in '\' with the previous line (except when following
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ignore_comments | (lines_enum) |
Strips comments and filter empty lines.
|
Strips comments and filter empty lines.
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expand_env_variables | (lines_enum) | Replace all environment variables that can be retrieved via `os.getenv`.
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get_file_content | (url, session, comes_from=None) | Gets the content of a file; it may be a filename, file: URL, or
http: URL. Returns (location, content). Content is unicode.
Respects # -*- coding: declarations on the retrieved files.
:param url: File path or url.
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http: URL. Returns (location, content). Content is unicode.
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RequirementsFileParser.parse | (self, filename, constraint) | Parse a given file, yielding parsed lines.
| Parse a given file, yielding parsed lines.
| def parse(self, filename, constraint):
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copyfileobj | (fsrc, fdst, length=16*1024) | copy data from file-like object fsrc to file-like object fdst | copy data from file-like object fsrc to file-like object fdst | def copyfileobj(fsrc, fdst, length=16*1024):
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while 1:
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] | [
69,
0
] | [
75,
23
] | python | en | ['en', 'en', 'en'] | True |
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