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def test_prepare_data_pick_regexp():
'Test picking columns by regexp.'
data = np.random.rand(5, 10)
columns = ['_a_b_c1_', '_a_b_c2_', '_a_b2_c3_', '_a_b2_c4_', '_a_b3_c5_', '_a_b3_c6_', '_a3_b2_c7_', '_a2_b_c7_', '_a2_b_c8_', '_a2_b_c9_']
X = columns[:(- 1)]
y = columns[(- 1)]
df = pd.DataFra... |
def test_check_consistency() -> None:
'Test check_consistency function.'
y = pd.Series(np.random.randint(0, 2, size=10))
problem_type = 'classification'
groups = None
cv = 5
with warnings.catch_warnings():
warnings.simplefilter('error')
check_consistency(y=y, cv=cv, groups=grou... |
def test__check_x_types() -> None:
'Test checking for valid X types.'
X = ['a', 'b', 'c']
X_types = {'categorical': ['a', 'b'], 'continuous': ['c']}
with warnings.catch_warnings():
warnings.simplefilter('error')
checked_X_types = _check_x_types(X=X, X_types=X_types)
assert (X_t... |
def test__check_x_types_regexp() -> None:
'Test checking for valid X types using regexp.'
X = ['_a_b_c1_', '_a_b_c2_', '_a_b2_c3_', '_a_b2_c4_', '_a_b3_c5_', '_a_b3_c6_', '_a3_b2_c7_', '_a2_b_c7_', '_a2_b_c8_', '_a2_b_c9_']
X_types = {'categorical': ['.*a_b.*', '_a2.*'], 'continuous': ['_a3.*']}
with ... |
def list_transformers() -> List[str]:
'List all the available transformers.\n\n Returns\n -------\n list of str\n A list will all the available transformer names.\n\n '
return list(_available_transformers.keys())
|
def get_transformer(name: str, **params: Any) -> TransformerLike:
'Get a transformer.\n\n Parameters\n ----------\n name : str\n The transformer name.\n **params : dict\n Parameters to get transformer.\n\n Returns\n -------\n scikit-learn compatible transformer\n The tran... |
def register_transformer(transformer_name, transformer_cls, overwrite=None):
'Register a transformer to julearn.\n\n This function allows you to add a transformer to julearn.\n Afterwards, it behaves like every other julearn transformer and can\n be referred to by name.\n\n Parameters\n ----------\... |
def reset_transformer_register():
'Reset the transformer register to its initial state.'
global _available_transformers
_available_transformers = deepcopy(_available_transformers_reset)
return _available_transformers
|
class ChangeColumnTypes(JuTransformer):
"Transformer to change the column types.\n\n Parameters\n ----------\n X_types : dict, optional\n A dictionary with the column types to set. The keys are the column\n types and the values are the columns to set the type to. If None, will\n set ... |
class DropColumns(JuTransformer):
"Drop columns of a DataFrame.\n\n Parameters\n ----------\n apply_to : ColumnTypesLike\n The feature types ('X_types') to drop.\n row_select_col_type : str or list of str or set of str or ColumnTypes\n The column types needed to select rows (default is N... |
class FilterColumns(JuTransformer):
"Filter columns of a DataFrame.\n\n Parameters\n ----------\n keep : ColumnTypesLike, optional\n Which feature types ('X_types') to keep. If not specified, 'keep'\n defaults to 'continuous'.\n row_select_col_type : str or list of str or set of str or C... |
class SetColumnTypes(JuTransformer):
'Transformer to set the column types.\n\n Parameters\n ----------\n X_types : dict, optional\n A dictionary with the column types to set. The keys are the column\n types and the values are the columns to set the type to. If None, will\n set all th... |
def test_DropColumns() -> None:
'Test DropColumns.'
drop_columns = DropColumns(apply_to=['confound'])
drop_columns.fit(X_with_types)
X_trans = drop_columns.transform(X_with_types)
support = drop_columns.get_support()
non_confound = ['a__:type:__continuous', 'b__:type:__continuous', 'e__:type:_... |
def test_FilterColumns() -> None:
'Test FilterColumns.'
filter = FilterColumns(keep=['continuous'])
kept_columns = ['a__:type:__continuous', 'b__:type:__continuous']
filter.set_output(transform='pandas').fit(X_with_types)
X_expected = X_with_types.copy()[kept_columns]
X_trans = filter.transfor... |
def test_SetColumnTypes(X_iris: pd.DataFrame, X_types_iris: Optional[Dict]) -> None:
'Test SetColumnTypes.\n\n Parameters\n ----------\n X_iris : pd.DataFrame\n The iris dataset.\n X_types_iris : dict, optional\n The types to set in the iris dataset.\n '
_X_types_iris = ({} if (X_... |
def test_SetColumnTypes_input_validation(X_iris: pd.DataFrame) -> None:
'Test SetColumnTypes input validation.\n\n Parameters\n ----------\n X_iris : pd.DataFrame\n The iris dataset.\n\n '
with pytest.raises(ValueError, match='Each value of X_types must be a list.'):
SetColumnTypes(... |
def test_SetColumnTypes_array(X_iris: pd.DataFrame, X_types_iris: Optional[Dict]) -> None:
'Test SetColumnTypes.\n\n Parameters\n ----------\n X_iris : pd.DataFrame\n The iris dataset.\n X_types_iris : dict, optional\n The types to set in the iris dataset.\n '
_X_types_iris = ({} ... |
class JuColumnTransformer(JuTransformer):
'Column transformer that can be used in a julearn pipeline.\n\n This column transformer is a wrapper around the sklearn column transformer,\n so it can be used directly with julearn pipelines.\n\n Parameters\n ----------\n name : str\n Name of the tr... |
def list_target_transformers() -> List[str]:
'List all the available target transformers.\n\n Returns\n -------\n out : list of str\n A list will all the available transformer names.\n '
return list(_available_target_transformers.keys())
|
def get_target_transformer(name: str, **params: Any) -> JuTargetTransformer:
'Get a target transformer by name.\n\n Parameters\n ----------\n name : str\n The target transformer name\n **params\n Parameters for the transformer.\n\n Returns\n -------\n JuTargetTransformer\n ... |
def register_target_transformer(transformer_name: str, transformer_cls: Type[JuTargetTransformer], overwrite: Optional[bool]=None):
'Register a target transformer to julearn.\n\n Parameters\n ----------\n transformer_name : str\n Name by which the transformer will be referenced by\n transformer... |
def reset_target_transformer_register() -> None:
'Reset the target transformer register to its initial state.'
global _available_target_transformers
_available_target_transformers = deepcopy(_available_target_transformers_reset)
|
class JuTargetTransformer():
'Base class for target transformers.\n\n Unlike the scikit-learn transformer, this fits and transforms using both\n X and y. This is useful for pipelines that work on the target but require\n information from the input data, such as the TargetConfoundRemover or\n a target ... |
def _wrapped_model_has(attr):
'Create a function to check if self.model_ has a given attribute.\n\n This function is usable by\n :func:`sklearn.utils.metaestimators.available_if`\n\n Parameters\n ----------\n attr : str\n The attribute to check for.\n\n Returns\n -------\n check : f... |
class TransformedTargetWarning(RuntimeWarning):
'Warning used to notify the user that the target has been transformed.'
|
class JuTransformedTargetModel(JuBaseEstimator):
'Class that provides a model that supports transforming the target.\n\n This _model_ is a wrapper that will transform the target before fitting.\n\n Parameters\n ----------\n model : ModelLike\n The model to be wrapped. Can be a pipeline.\n tr... |
def test_register_target_transformer() -> None:
'Test registering target transformers.'
with pytest.raises(ValueError, match='\\(useless\\) is not available'):
get_target_transformer('useless')
first = list_target_transformers()
class MyTransformer(JuTargetTransformer):
pass
regis... |
def test_reset_target_transformer() -> None:
'Test resetting the target transformers registry.'
with pytest.raises(ValueError, match='\\(useless\\) is not available'):
get_target_transformer('useless')
class MyTransformer(JuTargetTransformer):
pass
register_target_transformer('useless... |
def test_JuTargetTransformer_abstractness() -> None:
'Test JuTargetTransformer is abstract base class.'
with pytest.raises(NotImplementedError, match='fit'):
JuTargetTransformer().fit('1', '2')
|
class Fish(BaseEstimator, TransformerMixin):
'A (flying) fish.\n\n Parameters\n ----------\n can_it_fly : bool\n Whether the fish can fly.\n\n '
def __init__(self, can_it_fly: bool):
self.can_it_fly = can_it_fly
def fit(self, X: DataLike, y: Optional[DataLike]=None) -> 'Fish':... |
def test_register_reset() -> None:
'Test the register reset.'
reset_transformer_register()
with pytest.raises(ValueError, match='The specified transformer'):
get_transformer('passthrough')
register_transformer('passthrough', PassThroughTransformer)
assert (get_transformer('passthrough').__... |
def test_register_class_no_default_params():
'Test the register with a class that has no default params.'
reset_transformer_register()
register_transformer('fish', Fish)
get_transformer('fish', can_it_fly='dont_be_stupid')
|
def test_register_warnings_errors():
'Test the register warning / error.'
with pytest.warns(RuntimeWarning, match='Transformer name'):
register_transformer('zscore', Fish)
reset_transformer_register()
with pytest.raises(ValueError, match='Transformer name'):
register_transformer('zscor... |
def test_CBPM_posneg_correlated_features(X_iris: pd.DataFrame, y_iris: pd.DataFrame) -> None:
'Test the CBPM transformer with posneg correlated features.\n\n Parameters\n ----------\n X_iris : pd.DataFrame\n The iris dataset features\n y_iris : pd.Series\n The iris dataset target\n '
... |
def test_CBPM_pos_correlated_features(X_iris: pd.DataFrame, y_iris: pd.DataFrame) -> None:
'Test the CBPM transformer with positive correlated features.\n\n Parameters\n ----------\n X_iris : pd.DataFrame\n The iris dataset features\n y_iris : pd.Series\n The iris dataset target\n '
... |
def test_CBPM_neg_correlated_features(X_iris: pd.DataFrame, y_iris: pd.DataFrame) -> None:
'Test the CBPM transformer with positive correlated features.\n\n Parameters\n ----------\n X_iris : pd.DataFrame\n The iris dataset features\n y_iris : pd.Series\n The iris dataset target\n '
... |
def test_CBPM_warnings(X_iris: pd.DataFrame, y_iris: pd.DataFrame) -> None:
'Test the CBPM transformer warnings.\n\n Parameters\n ----------\n X_iris : pd.DataFrame\n The iris dataset features\n y_iris : pd.Series\n The iris dataset target\n '
X_pos = ['sepal_length', 'petal_lengt... |
def test_CBPM_lower_sign_threshhold(X_iris: pd.DataFrame, y_iris: pd.DataFrame) -> None:
'Test the CBPM transformer with lower significance threshold.\n\n Parameters\n ----------\n X_iris : pd.DataFrame\n The iris dataset features\n y_iris : pd.Series\n The iris dataset target\n '
... |
def test_CBPM_lower_sign_threshhold_no_sig(X_iris: pd.DataFrame, y_iris: pd.DataFrame) -> None:
'Test the CBPM transformer with an even lower significance threshold.\n\n Parameters\n ----------\n X_iris : pd.DataFrame\n The iris dataset features\n y_iris : pd.Series\n The iris dataset ta... |
def test_CBPM_spearman(X_iris: pd.DataFrame, y_iris: pd.DataFrame) -> None:
'Test the CBPM transformer with spearman correlation.\n\n Parameters\n ----------\n X_iris : pd.DataFrame\n The iris dataset features\n y_iris : pd.Series\n The iris dataset target\n '
X_pos = ['sepal_leng... |
def test_CBPM_set_output_posneg(X_iris: pd.DataFrame, y_iris: pd.DataFrame) -> None:
'Test the CBPM transformer for setting posneg output.\n\n Parameters\n ----------\n X_iris : pd.DataFrame\n The iris dataset features.\n y_iris : pd.Series\n The iris dataset target.\n\n '
X_pos =... |
def test_CBPM_set_output_pos(X_iris: pd.DataFrame, y_iris: pd.DataFrame) -> None:
'Test the CBPM transformer for setting pos output.\n\n Parameters\n ----------\n X_iris : pd.DataFrame\n The iris dataset features.\n y_iris : pd.Series\n The iris dataset target.\n\n '
X_pos = ['sep... |
def test_CBPM_set_output_neg(X_iris: pd.DataFrame, y_iris: pd.DataFrame) -> None:
'Test the CBPM transformer for setting neg output.\n\n Parameters\n ----------\n X_iris : pd.DataFrame\n The iris dataset features.\n y_iris : pd.Series\n The iris dataset target.\n\n '
X_neg = ['sep... |
@fixture
def df_X_confounds() -> pd.DataFrame:
'Create a dataframe with confounds.\n\n Returns\n -------\n pd.DataFrame\n A dataframe with confounds.\n\n '
X = pd.DataFrame({'a__:type:__continuous': (np.arange(10) + np.random.rand(10)), 'b__:type:__continuous': (np.arange(10, 20) + np.rando... |
@pytest.mark.parametrize('name, klass, params', [('zscore', StandardScaler, {}), ('scaler_robust', RobustScaler, {}), ('scaler_minmax', MinMaxScaler, {}), ('scaler_maxabs', MaxAbsScaler, {}), ('scaler_normalizer', Normalizer, {}), ('scaler_quantile', QuantileTransformer, {'n_quantiles': 10}), ('scaler_power', PowerTr... |
def test_JuColumnTransformer_row_select():
'Test row selection for JuColumnTransformer.'
X = pd.DataFrame({'a__:type:__continuous': [0, 0, 1, 1], 'b__:type:__healthy': [1, 1, 0, 0]})
transformer_healthy = JuColumnTransformer(name='zscore', transformer=StandardScaler(), apply_to='continuous', row_select_co... |
def _recurse_to_list(a):
'Recursively convert a to a list.'
if isinstance(a, (list, tuple)):
return [_recurse_to_list(i) for i in a]
elif isinstance(a, np.ndarray):
return a.tolist()
else:
return a
|
def _compute_cvmdsum(cv):
'Compute the sum of the CV generator.'
params = dict(vars(cv).items())
params['class'] = cv.__class__.__name__
out = None
if ('random_state' in params):
if (params['random_state'] is None):
if (params.get('shuffle', True) is True):
out ... |
def is_nonoverlapping_cv(cv) -> bool:
_valid_instances = (KFold, GroupKFold, RepeatedKFold, RepeatedStratifiedKFold, StratifiedKFold, LeaveOneOut, LeaveOneGroupOut, StratifiedGroupKFold, ContinuousStratifiedGroupKFold, RepeatedContinuousStratifiedGroupKFold)
return isinstance(cv, _valid_instances)
|
def check_scores_df(*scores: pd.DataFrame, same_cv: bool=False) -> pd.DataFrame:
'Check the output of `run_cross_validation`.\n\n Parameters\n ----------\n *scores : pd.DataFrame\n DataFrames containing the scores of the models. The DataFrames must\n be the output of `run_cross_validation`\... |
def _get_git_head(path: Path) -> str:
'Aux function to read HEAD from git.\n\n Parameters\n ----------\n path : pathlib.Path\n The path to read git HEAD from.\n\n Returns\n -------\n str\n Empty string if timeout expired for subprocess command execution else\n git HEAD infor... |
def get_versions() -> Dict:
'Import stuff and get versions if module.\n\n Returns\n -------\n module_versions : dict\n The module names and corresponding versions.\n\n '
module_versions = {}
for (name, module) in sys.modules.items():
if ('.' in name):
continue
... |
def _safe_log(versions: Dict, name: str) -> None:
'Log with safety.\n\n Parameters\n ----------\n versions : dict\n The dictionary with keys as dependency names and values as the\n versions.\n name : str\n The dependency to look up in `versions`.\n\n '
if (name in versions)... |
def log_versions() -> None:
'Log versions of the core libraries, for reproducibility purposes.'
versions = get_versions()
logger.info('===== Lib Versions =====')
_safe_log(versions, 'numpy')
_safe_log(versions, 'scipy')
_safe_log(versions, 'sklearn')
_safe_log(versions, 'pandas')
_safe... |
def configure_logging(level: Union[(int, str)]='WARNING', fname: Optional[Union[(str, Path)]]=None, overwrite: Optional[bool]=None, output_format=None) -> None:
'Configure the logging functionality.\n\n Parameters\n ----------\n level : int or {"DEBUG", "INFO", "WARNING", "ERROR"}\n The level of t... |
def _close_handlers(logger: logging.Logger) -> None:
'Safely close relevant handlers for logger.\n\n Parameters\n ----------\n logger : logging.logger\n The logger to close handlers for.\n\n '
for handler in list(logger.handlers):
if isinstance(handler, (logging.FileHandler, logging... |
def raise_error(msg: str, klass: Type[Exception]=ValueError, exception: Optional[Exception]=None) -> NoReturn:
'Raise error, but first log it.\n\n Parameters\n ----------\n msg : str\n The message for the exception.\n klass : subclass of Exception, optional\n The subclass of Exception to... |
def warn_with_log(msg: str, category: Optional[Type[Warning]]=RuntimeWarning) -> None:
'Warn, but first log it.\n\n Parameters\n ----------\n msg : str\n Warning message.\n category : subclass of Warning, optional\n The warning subclass (default RuntimeWarning).\n\n '
this_filters... |
class WrapStdOut(logging.StreamHandler):
'Dynamically wrap to sys.stdout.\n\n This makes packages that monkey-patch sys.stdout (e.g.doctest,\n sphinx-gallery) work properly.\n\n '
def __getattr__(self, name: str) -> str:
'Implement attribute fetch.'
if hasattr(sys.stdout, name):
... |
def compare_models(clf1: EstimatorLike, clf2: EstimatorLike) -> None:
'Compare two models.\n\n Parameters\n ----------\n clf1 : EstimatorLike\n The first model.\n clf2 : EstimatorLike\n The second model.\n\n Raises\n ------\n AssertionError\n If the models are not equal.\... |
def do_scoring_test(X: List[str], y: str, data: pd.DataFrame, api_params: Dict[(str, Any)], sklearn_model: EstimatorLike, scorers: List[str], groups: Optional[str]=None, X_types: Optional[Dict[(str, List[str])]]=None, cv: int=5, sk_y: Optional[np.ndarray]=None, decimal: int=5):
'Test scoring for a model, using th... |
class PassThroughTransformer(TransformerMixin, BaseEstimator):
'A transformer doing nothing.'
def __init__(self):
pass
def fit(self, X: DataLike, y: Optional[DataLike]=None) -> 'PassThroughTransformer':
'Fit the transformer.\n\n Parameters\n ----------\n X : DataLike... |
class TargetPassThroughTransformer(PassThroughTransformer):
'A target transformer doing nothing.'
def __init__(self):
super().__init__()
def transform(self, X: Optional[DataLike]=None, y: Optional[DataLike]=None) -> Optional[DataLike]:
'Transform the data.\n\n Parameters\n ... |
def _get_coef_over_versions(clf: EstimatorLike) -> np.ndarray:
'Get the coefficients of a model, skipping warnings.\n\n Parameters\n ----------\n clf : EstimatorLike\n The model.\n\n Returns\n -------\n np.ndarray\n The coefficients.\n '
if isinstance(clf, (BernoulliNB, Comp... |
@pytest.mark.filterwarnings('ignore::DeprecationWarning')
def test_log_file() -> None:
'Test logging to a file.'
with tempfile.TemporaryDirectory() as tmp:
tmpdir = Path(tmp)
configure_logging(fname=(tmpdir / 'test1.log'))
logger.debug('Debug message')
logger.info('Info message... |
@pytest.mark.filterwarnings('ignore::DeprecationWarning')
def test_log() -> None:
'Simple log test.'
configure_logging()
logger.info('Testing')
|
@pytest.mark.filterwarnings('ignore::DeprecationWarning')
def test_lib_logging() -> None:
'Test logging versions.'
import numpy as np
import pandas
import scipy
import sklearn
with tempfile.TemporaryDirectory() as tmp:
tmpdir = Path(tmp)
configure_logging(fname=(tmpdir / 'test1... |
@pytest.mark.filterwarnings('ignore::DeprecationWarning')
def test_log_file_warning_filter() -> None:
'Test filtering warning when logging to a file.'
with tempfile.TemporaryDirectory() as tmp:
tmpdir = Path(tmp)
configure_logging(fname=(tmpdir / 'test_filter.log'))
warn_with_log('Warn... |
def test_major_true() -> None:
'Test major version check.'
assert check_version('3.5.1', (lambda x: (int(x) > 1)))
|
def test_major_false() -> None:
'Test major version check false.'
assert (check_version('1.5.1', (lambda x: (int(x) > 1))) is False)
|
def test_minor_true() -> None:
'Test minor version check.'
assert check_version('3.5.1', minor_check=(lambda x: (int(x) > 2)))
|
def test_minor_false() -> None:
'Test minor version check false.'
assert (check_version('3.1.1', minor_check=(lambda x: (int(x) >= 2))) is False)
|
def test_patch_true() -> None:
'Test patch version check.'
assert check_version('3.1.5', patch_check=(lambda x: (int(x) > 2)))
|
def test_patch_false() -> None:
'Test patch version check false.'
assert (check_version('3.1.1', patch_check=(lambda x: (int(x) >= 2))) is False)
|
def test_multiple_true() -> None:
'Test multiple checks.'
assert check_version('3.2.1', major_check=(lambda x: (int(x) == 3)), minor_check=(lambda x: (int(x) == 2)), patch_check=(lambda x: (int(x) >= 1)))
|
def test_multiple_false() -> None:
'Test multiple checks false.'
assert (check_version('3.2.1', major_check=(lambda x: (int(x) == 3)), minor_check=(lambda x: (int(x) == 3)), patch_check=(lambda x: (int(x) >= 2))) is False)
|
def test_joblib_args_higer_1(monkeypatch: MonkeyPatch) -> None:
'Test joblib args for sklearn >= 1.0.'
with monkeypatch.context() as m:
m.setattr('sklearn.__version__', '2.2.11')
kwargs = _joblib_parallel_args(prefer='threads')
assert (kwargs['prefer'] == 'threads')
|
def test_joblib_args_lower_1(monkeypatch: MonkeyPatch) -> None:
'Test joblib args for sklearn < 1.0.'
with monkeypatch.context() as m:
import sklearn
m.setattr('sklearn.__version__', '0.24.2')
m.setattr(sklearn.utils.fixes, '_joblib_parallel_args', (lambda prefer: {'backend': 'threads'... |
@runtime_checkable
class EstimatorLikeFit1(Protocol):
'Class for estimator-like fit 1.'
def fit(self, X: List[str], y: str, **kwargs: Any) -> 'EstimatorLikeFit1':
'Fit estimator.\n\n Parameters\n ----------\n X : list of str\n The features to use.\n y : str\n ... |
@runtime_checkable
class EstimatorLikeFit2(Protocol):
'Class for estimator-like fit 2.'
def fit(self, X: List[str], y: str) -> 'EstimatorLikeFit2':
'Fit estimator.\n\n Parameters\n ----------\n X : list of str\n The features to use.\n y : str\n The ta... |
@runtime_checkable
class EstimatorLikeFity(Protocol):
'Class for estimator-like fit y.'
def fit(self, y: str) -> 'EstimatorLikeFity':
'Fit estimator.\n\n Parameters\n ----------\n y : str\n The target to use.\n\n Returns\n -------\n EstimatorLikeFi... |
@runtime_checkable
class TransformerLike(EstimatorLikeFit1, Protocol):
'Class for transformer-like.'
def fit(self, X: List[str], y: Optional[str]=None, **fit_params: Any) -> None:
'Fit transformer.\n\n Parameters\n ----------\n X : list of str\n The features to use.\n ... |
@runtime_checkable
class ModelLike(EstimatorLikeFit1, Protocol):
'Class for model-like.'
classes_: np.ndarray
def predict(self, X: pd.DataFrame) -> DataLike:
'Predict using the model.\n\n Parameters\n ----------\n X : pd.DataFrame\n The data to predict on.\n\n ... |
@runtime_checkable
class JuEstimatorLike(EstimatorLikeFit1, Protocol):
'Class for juestimator-like.'
def get_needed_types(self) -> ColumnTypes:
'Get the column types needed by the estimator.\n\n Returns\n -------\n ColumnTypes\n The column types needed by the estimator... |
@runtime_checkable
class JuModelLike(ModelLike, Protocol):
'Class for jumodel-like.'
def get_needed_types(self) -> ColumnTypes:
'Get the column types needed by the estimator.\n\n Returns\n -------\n ColumnTypes\n The column types needed by the estimator.\n\n '
... |
def check_version(version: str, major_check: Optional[Callable]=None, minor_check: Optional[Callable]=None, patch_check: Optional[Callable]=None):
'Check a version following major.minor.patch version numbers.\n\n The version is checked according to checks as functions major, minor and\n patch. This function... |
def _joblib_parallel_args(**kwargs: Any) -> Any:
'Get joblib parallel args depending on scikit-learn version.\n\n Parameters\n ----------\n **kwargs : dict\n keyword arguments to pass to joblib.Parallel\n '
sklearn_version = sklearn.__version__
higher_than_11 = check_version(sklearn_ver... |
class _JulearnScoresViewer(param.Parameterized):
'A class to visualize the scores for model comparison.\n\n Parameters\n ----------\n *scores : pd.DataFrame\n DataFrames containing the scores of the models. The DataFrames must\n be the output of `run_cross_validation`\n width : int\n ... |
def plot_scores(*scores: pd.DataFrame, width: int=800, height: int=600, ci: float=0.95) -> pn.layout.Panel:
'Plot the scores of the models on a panel dashboard.\n\n Parameters\n ----------\n *scores : pd.DataFrame\n DataFrames containing the scores of the models. The DataFrames must\n be th... |
class Normalize(nn.Module):
def __init__(self, mean, std):
super(Normalize, self).__init__()
self.register_buffer('mean', torch.Tensor(mean))
self.register_buffer('std', torch.Tensor(std))
def forward(self, x):
mean = self.mean.reshape(1, 3, 1, 1)
std = self.std.resha... |
def add_data_normalization(model, mean, std):
norm_layer = Normalize(mean=mean, std=std)
model_ = torch.nn.Sequential(norm_layer, model)
return model_
|
def apply_attack_on_dataset(model, dataloader, attack, epsilons, device, verbose=True):
robust_accuracy = []
c_a = []
for (images, labels) in dataloader:
(images, labels) = (images.to(device), labels.to(device))
outputs = model(images)
(_, pre) = torch.max(outputs.data, 1)
... |
def apply_attack_on_batch(model, images, labels, attack, device):
(images, labels) = (images.to(device), labels.to(device))
outputs = model(images)
(_, pre) = torch.max(outputs.data, 1)
correct_predictions = (pre == labels)
correct_predictions = correct_predictions.cpu().numpy()
clean_accuracy... |
def plot_accuracy(x, accuracy, methods, title, xlabel='x', ylabel='accuracy'):
for i in range(len(methods)):
plt.plot(x, accuracy[i], label=methods[i])
plt.legend()
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.show()
|
def imshow(img, title):
npimg = img.numpy()
fig = plt.figure(figsize=(15, 15))
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.title(title)
plt.show()
|
class Conv2dGrad(autograd.Function):
@staticmethod
def forward(context, input, weight, bias, stride, padding, dilation, groups):
(context.stride, context.padding, context.dilation, context.groups) = (stride, padding, dilation, groups)
context.save_for_backward(input, weight, bias)
out... |
class LinearGrad(autograd.Function):
@staticmethod
def forward(context, input, weight, bias=None):
context.save_for_backward(input, weight, bias)
output = torch.nn.functional.linear(input, weight, bias)
return output
@staticmethod
def backward(context, grad_output):
(... |
class Conv2dGrad(autograd.Function):
'\n Autograd Function that Does a backward pass using the weight_backward matrix of the layer\n '
@staticmethod
def forward(context, input, weight, weight_backward, bias, bias_backward, stride, padding, dilation, groups):
(context.stride, context.padding... |
class LinearGrad(autograd.Function):
'\n Autograd Function that Does a backward pass using the weight_backward matrix of the layer\n '
@staticmethod
def forward(context, input, weight, weight_backward, bias=None, bias_backward=None):
context.save_for_backward(input, weight, weight_backward,... |
def select_loss_function(loss_function_config):
if (loss_function_config['name'] == 'cross_entropy'):
return torch.nn.CrossEntropyLoss()
|
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