import numpy as np
from scipy.stats import chi2_contingency, ks_2samp
from typing import Callable, Dict, List, Optional, Tuple, Union
from alibi_detect.cd.base import BaseUnivariateDrift
from alibi_detect.utils.warnings import deprecated_alias
import warnings
[docs]
class TabularDrift(BaseUnivariateDrift):
[docs]
@deprecated_alias(preprocess_x_ref='preprocess_at_init')
def __init__(
self,
x_ref: Union[np.ndarray, list],
p_val: float = .05,
categories_per_feature: Dict[int, Optional[int]] = None,
x_ref_preprocessed: bool = False,
preprocess_at_init: bool = True,
update_x_ref: Optional[Dict[str, int]] = None,
preprocess_fn: Optional[Callable] = None,
correction: str = 'bonferroni',
alternative: str = 'two-sided',
n_features: Optional[int] = None,
input_shape: Optional[tuple] = None,
data_type: Optional[str] = None
) -> None:
"""
Mixed-type tabular data drift detector with Bonferroni or False Discovery Rate (FDR)
correction for multivariate data. Kolmogorov-Smirnov (K-S) univariate tests are applied to
continuous numerical data and Chi-Squared (Chi2) univariate tests to categorical data.
Parameters
----------
x_ref
Data used as reference distribution.
p_val
p-value used for significance of the K-S and Chi2 test for each feature.
If the FDR correction method is used, this corresponds to the acceptable q-value.
categories_per_feature
Dictionary with as keys the column indices of the categorical features and optionally as values
the number of possible categorical values for that feature or a list with the possible values.
If you know which features are categorical and simply want to infer the possible values of the
categorical feature from the reference data you can pass a Dict[int, NoneType] such as
{0: None, 3: None} if features 0 and 3 are categorical. If you also know how many categories are
present for a given feature you could pass this in the `categories_per_feature` dict in the
Dict[int, int] format, e.g. *{0: 3, 3: 2}*. If you pass N categories this will assume the possible
values for the feature are [0, ..., N-1]. You can also explicitly pass the possible categories in the
Dict[int, List[int]] format, e.g. {0: [0, 1, 2], 3: [0, 55]}. Note that the categories can be
arbitrary int values.
x_ref_preprocessed
Whether the given reference data `x_ref` has been preprocessed yet. If `x_ref_preprocessed=True`, only
the test data `x` will be preprocessed at prediction time. If `x_ref_preprocessed=False`, the reference
data will also be preprocessed.
preprocess_at_init
Whether to preprocess the reference data when the detector is instantiated. Otherwise, the reference
data will be preprocessed at prediction time. Only applies if `x_ref_preprocessed=False`.
update_x_ref
Reference data can optionally be updated to the last n instances seen by the detector
or via reservoir sampling with size n. For the former, the parameter equals {'last': n} while
for reservoir sampling {'reservoir_sampling': n} is passed.
preprocess_fn
Function to preprocess the data before computing the data drift metrics.
Typically a dimensionality reduction technique.
correction
Correction type for multivariate data. Either 'bonferroni' or 'fdr' (False Discovery Rate).
alternative
Defines the alternative hypothesis for the K-S tests. Options are 'two-sided', 'less' or 'greater'.
n_features
Number of features used in the combined K-S/Chi-Squared tests. No need to pass it if
no preprocessing takes place. In case of a preprocessing step, this can also be inferred
automatically but could be more expensive to compute.
input_shape
Shape of input data.
data_type
Optionally specify the data type (tabular, image or time-series). Added to metadata.
"""
super().__init__(
x_ref=x_ref,
p_val=p_val,
x_ref_preprocessed=x_ref_preprocessed,
preprocess_at_init=preprocess_at_init,
update_x_ref=update_x_ref,
preprocess_fn=preprocess_fn,
correction=correction,
n_features=n_features,
input_shape=input_shape,
data_type=data_type
)
# Set config
self._set_config(locals())
self.alternative = alternative
# Parse categories_per_feature dict
if isinstance(categories_per_feature, dict):
vals = list(categories_per_feature.values())
int_types = (int, np.int16, np.int32, np.int64)
if all(v is None for v in vals): # categories_per_feature = Dict[int, NoneType]
x_flat = self.x_ref.reshape(self.x_ref.shape[0], -1)
categories_per_feature = {f: list(np.unique(x_flat[:, f])) # type: ignore
for f in categories_per_feature.keys()}
elif all(isinstance(v, int_types) for v in vals):
# categories_per_feature = Dict[int, int]
categories_per_feature = {f: list(np.arange(v)) # type: ignore
for f, v in categories_per_feature.items()}
elif not all(isinstance(v, list) for v in vals) and \
all(isinstance(v, int_types) for val in vals for v in val): # type: ignore
raise ValueError('categories_per_feature needs to be None or one of '
'Dict[int, NoneType], Dict[int, int], Dict[int, List[int]]')
self.x_ref_categories = categories_per_feature
self.cat_vars = list(self.x_ref_categories.keys())
# No categories_per_feature dict so assume no categorical features present
else:
self.x_ref_categories, self.cat_vars = {}, []
warnings.warn('No `categories_per_feature` dict provided so all features are assumed to be numerical. '
'`KSDrift` will be applied to all features.')
[docs]
def feature_score(self, x_ref: np.ndarray, x: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
"""
Compute K-S or Chi-Squared test statistics and p-values per feature.
Parameters
----------
x_ref
Reference instances to compare distribution with.
x
Batch of instances.
Returns
-------
Feature level p-values and K-S or Chi-Squared statistics.
"""
x_ref = x_ref.reshape(x_ref.shape[0], -1)
x = x.reshape(x.shape[0], -1)
# apply counts on union of categories per variable in both the reference and test data
if self.cat_vars:
x_categories = {f: list(np.unique(x[:, f])) for f in self.cat_vars}
all_categories = {f: list(set().union(self.x_ref_categories[f], x_categories[f])) # type: ignore
for f in self.cat_vars}
x_ref_count = self._get_counts(x_ref, all_categories)
x_count = self._get_counts(x, all_categories)
p_val = np.zeros(self.n_features, dtype=np.float32)
dist = np.zeros_like(p_val)
for f in range(self.n_features):
if f in self.cat_vars:
contingency_table = np.vstack((x_ref_count[f], x_count[f]))
dist[f], p_val[f], _, _ = chi2_contingency(contingency_table)
else:
dist[f], p_val[f] = ks_2samp(x_ref[:, f], x[:, f], alternative=self.alternative, mode='asymp')
return p_val, dist
def _get_counts(self, x: np.ndarray, categories: Dict[int, List[int]]) -> Dict[int, List[int]]:
"""
Utility method for getting the counts of categories for each categorical variable.
"""
return {f: [(x[:, f] == v).sum() for v in vals] for f, vals in categories.items()}