def fit_resample(self, X, y):
"""Resampling technique for this sampler.
Arguments:
X (pd.DataFrame): Training data to fit and resample
y (pd.Series): Training data targets to fit and resample
Returns:
list: Indices to keep for training data
"""
X_ww = infer_feature_types(X)
y_ww = infer_feature_types(y)
X = _convert_woodwork_types_wrapper(X_ww.to_dataframe())
y = _convert_woodwork_types_wrapper(y_ww.to_series())
result = self._find_ideal_samples(y)
indices_to_drop = []
if len(result):
# iterate through the classes we need to undersample and remove the number of samples we need to remove
for key, value in result.items():
indices = y.index[y == key].values
indices_to_remove = self.random_state.choice(indices,
value,
replace=False)
indices_to_drop.extend(indices_to_remove)
return list(set(list(y.index.values)).difference(set(indices_to_drop)))
def validate(self, X, y):
"""Check if any of the features are highly correlated with the target by using mutual information or Pearson correlation.
If `method='mutual'`, supports all target and feature types. Otherwise, if `method='pearson'` only supports binary with numeric and boolean dtypes.
Pearson correlation returns a value in [-1, 1], while mutual information returns a value in [0, 1].
Arguments:
X (ww.DataTable, pd.DataFrame, np.ndarray): The input features to check
y (ww.DataColumn, pd.Series, np.ndarray): The target data
Returns:
dict (DataCheckWarning): dict with a DataCheckWarning if target leakage is detected.
Example:
>>> import pandas as pd
>>> X = pd.DataFrame({
... 'leak': [10, 42, 31, 51, 61],
... 'x': [42, 54, 12, 64, 12],
... 'y': [13, 5, 13, 74, 24],
... })
>>> y = pd.Series([10, 42, 31, 51, 40])
>>> target_leakage_check = TargetLeakageDataCheck(pct_corr_threshold=0.95)
>>> assert target_leakage_check.validate(X, y) == {"warnings": [{"message": "Column 'leak' is 95.0% or more correlated with the target",\
"data_check_name": "TargetLeakageDataCheck",\
"level": "warning",\
"code": "TARGET_LEAKAGE",\
"details": {"column": "leak"}}],\
"errors": [],\
"actions": [{"code": "DROP_COL",\
"metadata": {"column": "leak"}}]}
"""
results = {"warnings": [], "errors": [], "actions": []}
X = infer_feature_types(X)
y = infer_feature_types(y)
if self.method == 'pearson':
highly_corr_cols = self._calculate_pearson(X, y)
else:
X = _convert_woodwork_types_wrapper(X.to_dataframe())
y = _convert_woodwork_types_wrapper(y.to_series())
highly_corr_cols = self._calculate_mutual_information(X, y)
warning_msg = "Column '{}' is {}% or more correlated with the target"
results["warnings"].extend([
DataCheckWarning(message=warning_msg.format(
col_name, self.pct_corr_threshold * 100),
data_check_name=self.name,
message_code=DataCheckMessageCode.TARGET_LEAKAGE,
details={
"column": col_name
}).to_dict() for col_name in highly_corr_cols
])
results["actions"].extend([
DataCheckAction(DataCheckActionCode.DROP_COL,
metadata={
"column": col_name
}).to_dict() for col_name in highly_corr_cols
])
return results
def _calculate_pearson(self, X, y):
highly_corr_cols = []
X_num = X.select(include=numeric_and_boolean_ww)
if y.logical_type not in numeric_and_boolean_ww or len(X_num.columns) == 0:
return highly_corr_cols
X_num = _convert_woodwork_types_wrapper(X_num.to_dataframe())
y = _convert_woodwork_types_wrapper(y.to_series())
highly_corr_cols = [label for label, col in X_num.iteritems() if abs(y.corr(col)) >= self.pct_corr_threshold]
return highly_corr_cols
def fit_resample(self, X, y):
"""Resampling technique for this sampler.
Arguments:
X (pd.DataFrame): Training data to fit and resample
y (pd.Series): Training data targets to fit and resample
Returns:
list: Indices to keep for training data
"""
y_ww = infer_feature_types(y)
y = _convert_woodwork_types_wrapper(y_ww.to_series())
# if we have a dictionary provided, opt to use that
if len(self.sampling_ratio_dict):
result = self._sampling_dict_to_remove_dict(y)
else:
result = self._find_ideal_samples(y)
indices_to_drop = []
if len(result):
# iterate through the classes we need to undersample and remove the number of samples we need to remove
for key, value in result.items():
indices = y.index[y == key].values
indices_to_remove = self.random_state.choice(indices,
value,
replace=False)
indices_to_drop.extend(indices_to_remove)
# indices of the y datacolumn
original_indices = list(
set(y.index.values).difference(set(indices_to_drop)))
return original_indices
def validate(self, X, y=None):
"""Checks if there are any columns in the input that are too unique in the case of classification
problems or not unique enough in the case of regression problems.
Arguments:
X (ww.DataTable, pd.DataFrame, np.ndarray): Features.
y (ww.DataColumn, pd.Series, np.ndarray): Ignored. Defaults to None.
Returns:
dict: dict with a DataCheckWarning if there are any too unique or not
unique enough columns.
Example:
>>> import pandas as pd
>>> df = pd.DataFrame({
... 'regression_unique_enough': [float(x) for x in range(100)],
... 'regression_not_unique_enough': [float(1) for x in range(100)]
... })
>>> uniqueness_check = UniquenessDataCheck(problem_type="regression", threshold=0.8)
>>> assert uniqueness_check.validate(df) == {"errors": [],\
"warnings": [{"message": "Input columns (regression_not_unique_enough) for regression problem type are not unique enough.",\
"data_check_name": "UniquenessDataCheck",\
"level": "warning",\
"code": "NOT_UNIQUE_ENOUGH",\
"details": {"column": "regression_not_unique_enough", 'uniqueness_score': 0.0}}],\
"actions": []}
"""
results = {"warnings": [], "errors": [], "actions": []}
X = infer_feature_types(X)
X = _convert_woodwork_types_wrapper(X.to_dataframe())
res = X.apply(UniquenessDataCheck.uniqueness_score)
if is_regression(self.problem_type):
not_unique_enough_cols = list(res.index[res < self.threshold])
results["warnings"].extend([
DataCheckWarning(
message=warning_not_unique_enough.format(
col_name, self.problem_type),
data_check_name=self.name,
message_code=DataCheckMessageCode.NOT_UNIQUE_ENOUGH,
details={
"column": col_name,
"uniqueness_score": res.loc[col_name]
}).to_dict() for col_name in not_unique_enough_cols
])
elif is_multiclass(self.problem_type):
too_unique_cols = list(res.index[res > self.threshold])
results["warnings"].extend([
DataCheckWarning(message=warning_too_unique.format(
col_name, self.problem_type),
data_check_name=self.name,
message_code=DataCheckMessageCode.TOO_UNIQUE,
details={
"column": col_name,
"uniqueness_score": res.loc[col_name]
}).to_dict() for col_name in too_unique_cols
])
return results
def _prepare_data(self, X, y):
"""Transforms the input data to pandas data structure that our sampler can ingest.
Arguments:
X (ww.DataFrame): Training features
y (ww.DataColumn): Target features
Returns:
ww.DataTable, ww.DataColumn, pd.DataFrame, pd.Series: Prepared X and y data, both woodwork and pandas
"""
X = infer_feature_types(X)
if y is None:
raise ValueError("y cannot be none")
y = infer_feature_types(y)
X_pd = _convert_woodwork_types_wrapper(X.to_dataframe())
y_pd = _convert_woodwork_types_wrapper(y.to_series())
return X, y, X_pd, y_pd
def split(self, X, y):
"""Splits and returns the indices of the training and testing using the data sampler provided.
Arguments:
X (ww.DataTable): DataTable of points to split
y (ww.DataTable): DataColumn of points to split
Returns:
tuple(train, test): A tuple containing the resulting train and test indices, post sampling.
"""
X_ww = infer_feature_types(X)
y_ww = infer_feature_types(y)
X = _convert_woodwork_types_wrapper(X_ww.to_dataframe())
y = _convert_woodwork_types_wrapper(y_ww.to_series())
index_df = pd.Series(y.index)
for train, test in self.splitter.split(X, y):
X_train, y_train = X.iloc[train], y.iloc[train]
train_index_drop = self.sampler.fit_resample(X_train, y_train)
# convert the indices of the y column into index indices of the original pre-split y
train_indices = index_df[index_df.isin(train_index_drop)].dropna().index.values.tolist()
yield iter([train_indices, test])
def validate(self, X, y=None):
"""Calculates what percentage of each column's unique values exceed the count threshold and compare
that percentage to the sparsity threshold stored in the class instance.
Arguments:
X (ww.DataTable, pd.DataFrame, np.ndarray): Features.
y (ww.DataColumn, pd.Series, np.ndarray): Ignored.
Returns:
dict: dict with a DataCheckWarning if there are any sparse columns.
Example:
>>> import pandas as pd
>>> df = pd.DataFrame({
... 'sparse': [float(x) for x in range(100)],
... 'not_sparse': [float(1) for x in range(100)]
... })
>>> sparsity_check = SparsityDataCheck(problem_type="multiclass", threshold=0.5, unique_count_threshold=10)
>>> assert sparsity_check.validate(df) == {"errors": [],\
"warnings": [{"message": "Input columns (sparse) for multiclass problem type are too sparse.",\
"data_check_name": "SparsityDataCheck",\
"level": "warning",\
"code": "TOO_SPARSE",\
"details": {"column": "sparse", 'sparsity_score': 0.0}}],\
"actions": [{"code": "DROP_COL",\
"metadata": {"column": "sparse"}}]}
"""
results = {"warnings": [], "errors": [], "actions": []}
X = infer_feature_types(X)
X = _convert_woodwork_types_wrapper(X.to_dataframe())
res = X.apply(SparsityDataCheck.sparsity_score,
count_threshold=self.unique_count_threshold)
too_sparse_cols = [col for col in res.index[res < self.threshold]]
results["warnings"].extend([
DataCheckWarning(message=warning_too_unique.format(
col_name, self.problem_type),
data_check_name=self.name,
message_code=DataCheckMessageCode.TOO_SPARSE,
details={
"column": col_name,
"sparsity_score": res.loc[col_name]
}).to_dict() for col_name in too_sparse_cols
])
results["actions"].extend([
DataCheckAction(action_code=DataCheckActionCode.DROP_COL,
metadata={
"column": col_name
}).to_dict() for col_name in too_sparse_cols
])
return results
def transform_sample(self, X, y):
"""Transforms the input data with the balancing strategy.
Arguments:
X (ww.DataTable): DataTable of points to split
y (ww.DataTable): DataColumn of points to split
Returns:
list: List of indices to keep
"""
y_ww = infer_feature_types(y)
y = _convert_woodwork_types_wrapper(y_ww.to_series())
index_df = pd.Series(y.index)
train_index_drop = self.sampler.fit_resample(X, y)
# convert the indices of the y column into index indices of the original pre-split y
train_indices = index_df[index_df.isin(train_index_drop)].dropna().index.values.tolist()
return train_indices
def validate(self, X, y=None):
"""Checks if any datetime columns contain NaN values.
Arguments:
X (ww.DataTable, pd.DataFrame, np.ndarray): Features.
y (ww.DataColumn, pd.Series, np.ndarray): Ignored. Defaults to None.
Returns:
dict: dict with a DataCheckError if NaN values are present in datetime columns.
Example:
>>> import pandas as pd
>>> import woodwork as ww
>>> import numpy as np
>>> dates = np.arange(np.datetime64('2017-01-01'), np.datetime64('2017-01-08'))
>>> dates[0] = np.datetime64('NaT')
>>> ww_input = ww.DataTable(pd.DataFrame(dates, columns=['index']))
>>> dt_nan_check = DateTimeNaNDataCheck()
>>> assert dt_nan_check.validate(ww_input) == {"warnings": [],
... "actions": [],
... "errors": [DataCheckError(message='Input datetime column(s) (index) contains NaN values. Please impute NaN values or drop these rows or columns.',
... data_check_name=DateTimeNaNDataCheck.name,
... message_code=DataCheckMessageCode.DATETIME_HAS_NAN,
... details={"columns": 'index'}).to_dict()]}
"""
results = {
"warnings": [],
"errors": [],
"actions": []
}
X = infer_feature_types(X)
datetime_cols = _convert_woodwork_types_wrapper(X.select("datetime").to_dataframe())
nan_columns = datetime_cols.columns[datetime_cols.isna().any()].tolist()
if len(nan_columns) > 0:
nan_columns = [str(col) for col in nan_columns]
cols_str = ', '.join(nan_columns) if len(nan_columns) > 1 else nan_columns[0]
results["errors"].append(DataCheckError(message=error_contains_nan.format(cols_str),
data_check_name=self.name,
message_code=DataCheckMessageCode.DATETIME_HAS_NAN,
details={"columns": cols_str}).to_dict())
return results
def train_and_score_pipeline(pipeline, automl, full_X_train, full_y_train):
"""Given a pipeline, config and data, train and score the pipeline and return the CV or TV scores
Arguments:
pipeline (PipelineBase): The pipeline to score
automl (AutoMLSearch): The AutoML search, used to access config and for the error callback
full_X_train (ww.DataTable): Training features
full_y_train (ww.DataColumn): Training target
Returns:
dict: A dict containing cv_score_mean, cv_scores, training_time and a cv_data structure with details.
"""
start = time.time()
cv_data = []
logger.info("\tStarting cross validation")
X_pd = _convert_woodwork_types_wrapper(full_X_train.to_dataframe())
y_pd = _convert_woodwork_types_wrapper(full_y_train.to_series())
y_pd_encoded = y_pd
# Encode target for classification problems so that we can support float targets. This is okay because we only use split to get the indices to split on
if is_classification(automl.problem_type):
y_mapping = {
original_target: encoded_target
for (encoded_target,
original_target) in enumerate(y_pd.value_counts().index)
}
y_pd_encoded = y_pd.map(y_mapping)
for i, (train, valid) in enumerate(
automl.data_splitter.split(X_pd, y_pd_encoded)):
if pipeline.model_family == ModelFamily.ENSEMBLE and i > 0:
# Stacked ensembles do CV internally, so we do not run CV here for performance reasons.
logger.debug(
f"Skipping fold {i} because CV for stacked ensembles is not supported."
)
break
logger.debug(f"\t\tTraining and scoring on fold {i}")
X_train, X_valid = full_X_train.iloc[train], full_X_train.iloc[
valid]
y_train, y_valid = full_y_train.iloc[train], full_y_train.iloc[
valid]
if is_binary(automl.problem_type) or is_multiclass(
automl.problem_type):
diff_train = set(
np.setdiff1d(full_y_train.to_series(),
y_train.to_series()))
diff_valid = set(
np.setdiff1d(full_y_train.to_series(),
y_valid.to_series()))
diff_string = f"Missing target values in the training set after data split: {diff_train}. " if diff_train else ""
diff_string += f"Missing target values in the validation set after data split: {diff_valid}." if diff_valid else ""
if diff_string:
raise Exception(diff_string)
objectives_to_score = [automl.objective
] + automl.additional_objectives
cv_pipeline = None
try:
logger.debug(f"\t\t\tFold {i}: starting training")
cv_pipeline = EngineBase.train_pipeline(
pipeline, X_train, y_train, automl.optimize_thresholds,
automl.objective)
logger.debug(f"\t\t\tFold {i}: finished training")
if automl.optimize_thresholds and pipeline.can_tune_threshold_with_objective(
automl.objective
) and automl.objective.can_optimize_threshold:
logger.debug(
f"\t\t\tFold {i}: Optimal threshold found ({cv_pipeline.threshold:.3f})"
)
logger.debug(f"\t\t\tFold {i}: Scoring trained pipeline")
scores = cv_pipeline.score(X_valid,
y_valid,
objectives=objectives_to_score)
logger.debug(
f"\t\t\tFold {i}: {automl.objective.name} score: {scores[automl.objective.name]:.3f}"
)
score = scores[automl.objective.name]
except Exception as e:
if automl.error_callback is not None:
automl.error_callback(exception=e,
traceback=traceback.format_tb(
sys.exc_info()[2]),
automl=automl,
fold_num=i,
pipeline=pipeline)
if isinstance(e, PipelineScoreError):
nan_scores = {
objective: np.nan
for objective in e.exceptions
}
scores = {**nan_scores, **e.scored_successfully}
scores = OrderedDict({
o.name: scores[o.name]
for o in [automl.objective] +
automl.additional_objectives
})
score = scores[automl.objective.name]
else:
score = np.nan
scores = OrderedDict(
zip([n.name for n in automl.additional_objectives],
[np.nan] * len(automl.additional_objectives)))
ordered_scores = OrderedDict()
ordered_scores.update({automl.objective.name: score})
ordered_scores.update(scores)
ordered_scores.update({"# Training": y_train.shape[0]})
ordered_scores.update({"# Validation": y_valid.shape[0]})
evaluation_entry = {
"all_objective_scores": ordered_scores,
"score": score,
'binary_classification_threshold': None
}
if is_binary(
automl.problem_type
) and cv_pipeline is not None and cv_pipeline.threshold is not None:
evaluation_entry[
'binary_classification_threshold'] = cv_pipeline.threshold
cv_data.append(evaluation_entry)
training_time = time.time() - start
cv_scores = pd.Series([fold['score'] for fold in cv_data])
cv_score_mean = cv_scores.mean()
logger.info(
f"\tFinished cross validation - mean {automl.objective.name}: {cv_score_mean:.3f}"
)
return {
'cv_data': cv_data,
'training_time': training_time,
'cv_scores': cv_scores,
'cv_score_mean': cv_score_mean
}
def validate(self, X, y):
"""Checks if the target data contains missing or invalid values.
Arguments:
X (ww.DataTable, pd.DataFrame, np.ndarray): Features. Ignored.
y (ww.DataColumn, pd.Series, np.ndarray): Target data to check for invalid values.
Returns:
dict (DataCheckError): List with DataCheckErrors if any invalid values are found in the target data.
Example:
>>> import pandas as pd
>>> X = pd.DataFrame({"col": [1, 2, 3, 1]})
>>> y = pd.Series([0, 1, None, None])
>>> target_check = InvalidTargetDataCheck('binary', 'Log Loss Binary')
>>> assert target_check.validate(X, y) == {"errors": [{"message": "2 row(s) (50.0%) of target values are null",\
"data_check_name": "InvalidTargetDataCheck",\
"level": "error",\
"code": "TARGET_HAS_NULL",\
"details": {"num_null_rows": 2, "pct_null_rows": 50}}],\
"warnings": [],\
"actions": [{'code': 'IMPUTE_COL', 'metadata': {'column': None, 'impute_strategy': 'most_frequent', 'is_target': True}}]}
"""
results = {"warnings": [], "errors": [], "actions": []}
if y is None:
results["errors"].append(
DataCheckError(
message="Target is None",
data_check_name=self.name,
message_code=DataCheckMessageCode.TARGET_IS_NONE,
details={}).to_dict())
return results
y = infer_feature_types(y)
is_supported_type = y.logical_type in numeric_and_boolean_ww + [
ww.logical_types.Categorical
]
if not is_supported_type:
results["errors"].append(
DataCheckError(
message=
"Target is unsupported {} type. Valid Woodwork logical types include: {}"
.format(
y.logical_type, ", ".join([
ltype.type_string
for ltype in numeric_and_boolean_ww
])),
data_check_name=self.name,
message_code=DataCheckMessageCode.TARGET_UNSUPPORTED_TYPE,
details={
"unsupported_type": y.logical_type.type_string
}).to_dict())
y_df = _convert_woodwork_types_wrapper(y.to_series())
null_rows = y_df.isnull()
if null_rows.all():
results["errors"].append(
DataCheckError(message="Target is either empty or fully null.",
data_check_name=self.name,
message_code=DataCheckMessageCode.
TARGET_IS_EMPTY_OR_FULLY_NULL,
details={}).to_dict())
return results
elif null_rows.any():
num_null_rows = null_rows.sum()
pct_null_rows = null_rows.mean() * 100
results["errors"].append(
DataCheckError(
message="{} row(s) ({}%) of target values are null".format(
num_null_rows, pct_null_rows),
data_check_name=self.name,
message_code=DataCheckMessageCode.TARGET_HAS_NULL,
details={
"num_null_rows": num_null_rows,
"pct_null_rows": pct_null_rows
}).to_dict())
impute_strategy = "mean" if is_regression(
self.problem_type) else "most_frequent"
results["actions"].append(
DataCheckAction(DataCheckActionCode.IMPUTE_COL,
metadata={
"column": None,
"is_target": True,
"impute_strategy": impute_strategy
}).to_dict())
value_counts = y_df.value_counts()
unique_values = value_counts.index.tolist()
if is_binary(self.problem_type) and len(value_counts) != 2:
if self.n_unique is None:
details = {"target_values": unique_values}
else:
details = {
"target_values":
unique_values[:min(self.n_unique, len(unique_values))]
}
results["errors"].append(
DataCheckError(
message=
"Binary class targets require exactly two unique values.",
data_check_name=self.name,
message_code=DataCheckMessageCode.
TARGET_BINARY_NOT_TWO_UNIQUE_VALUES,
details=details).to_dict())
if self.problem_type == ProblemTypes.REGRESSION and "numeric" not in y.semantic_tags:
results["errors"].append(
DataCheckError(
message=
"Target data type should be numeric for regression type problems.",
data_check_name=self.name,
message_code=DataCheckMessageCode.TARGET_UNSUPPORTED_TYPE,
details={}).to_dict())
if is_multiclass(self.problem_type):
if value_counts.min() <= 1:
least_populated = value_counts[value_counts <= 1]
details = {
"least_populated_class_labels":
least_populated.index.tolist()
}
results["errors"].append(
DataCheckError(
message=
"Target does not have at least two instances per class which is required for multiclass classification",
data_check_name=self.name,
message_code=DataCheckMessageCode.
TARGET_MULTICLASS_NOT_TWO_EXAMPLES_PER_CLASS,
details=details).to_dict())
if len(unique_values) <= 2:
details = {"num_classes": len(unique_values)}
results["errors"].append(
DataCheckError(
message=
"Target has two or less classes, which is too few for multiclass problems. Consider changing to binary.",
data_check_name=self.name,
message_code=DataCheckMessageCode.
TARGET_MULTICLASS_NOT_ENOUGH_CLASSES,
details=details).to_dict())
num_class_to_num_value_ratio = len(unique_values) / len(y)
if num_class_to_num_value_ratio >= self.multiclass_continuous_threshold:
details = {
"class_to_value_ratio": num_class_to_num_value_ratio
}
results["warnings"].append(
DataCheckWarning(
message=
"Target has a large number of unique values, could be regression type problem.",
data_check_name=self.name,
message_code=DataCheckMessageCode.
TARGET_MULTICLASS_HIGH_UNIQUE_CLASS,
details=details).to_dict())
any_neg = not (y_df > 0).all() if y.logical_type in [
ww.logical_types.Integer, ww.logical_types.Double
] else None
if any_neg and self.objective.positive_only:
details = {
"Count of offending values":
sum(val <= 0 for val in y_df.values.flatten())
}
results["errors"].append(
DataCheckError(
message=
f"Target has non-positive values which is not supported for {self.objective.name}",
data_check_name=self.name,
message_code=DataCheckMessageCode.
TARGET_INCOMPATIBLE_OBJECTIVE,
details=details).to_dict())
if X is not None:
X = infer_feature_types(X)
X_index = list(X.to_dataframe().index)
y_index = list(y_df.index)
X_length = len(X_index)
y_length = len(y_index)
if X_length != y_length:
results["warnings"].append(
DataCheckWarning(
message=
"Input target and features have different lengths",
data_check_name=self.name,
message_code=DataCheckMessageCode.MISMATCHED_LENGTHS,
details={
"features_length": X_length,
"target_length": y_length
}).to_dict())
if X_index != y_index:
if set(X_index) == set(y_index):
results["warnings"].append(
DataCheckWarning(
message=
"Input target and features have mismatched indices order",
data_check_name=self.name,
message_code=DataCheckMessageCode.
MISMATCHED_INDICES_ORDER,
details={}).to_dict())
else:
index_diff_not_in_X = list(set(y_index) -
set(X_index))[:10]
index_diff_not_in_y = list(set(X_index) -
set(y_index))[:10]
results["warnings"].append(
DataCheckWarning(
message=
"Input target and features have mismatched indices",
data_check_name=self.name,
message_code=DataCheckMessageCode.
MISMATCHED_INDICES,
details={
"indices_not_in_features": index_diff_not_in_X,
"indices_not_in_target": index_diff_not_in_y
}).to_dict())
return results
