ValueDifferenceMetric
- class imbens.metrics.ValueDifferenceMetric(*, n_categories='auto', k=1, r=2)[source]
Class implementing the Value Difference Metric.
This metric computes the distance between samples containing only categorical features. The distance between feature values of two samples is defined as:
\[\delta(x, y) = \sum_{c=1}^{C} |p(c|x_{f}) - p(c|y_{f})|^{k} \ ,\]where \(x\) and \(y\) are two samples and \(f\) a given feature, \(C\) is the number of classes, \(p(c|x_{f})\) is the conditional probability that the output class is \(c\) given that the feature value \(f\) has the value \(x\) and \(k\) an exponent usually defined to 1 or 2.
The distance for the feature vectors \(X\) and \(Y\) is subsequently defined as:
\[\Delta(X, Y) = \sum_{f=1}^{F} \delta(X_{f}, Y_{f})^{r} \ ,\]where \(F\) is the number of feature and \(r\) an exponent usually defined equal to 1 or 2.
The definition of this distance was propoed in [1].
Read more in the User Guide.
- Parameters:
- n_categories“auto” or array-like of shape (n_features,), default=”auto”
The number of unique categories per features. If “auto”, the number of categories will be computed from X at fit. Otherwise, you can provide an array-like of such counts to avoid computation. You can use the fitted attribute categories_ of the
OrdinalEncoder
to deduce these counts.- kint, default=1
Exponent used to compute the distance between feature value.
- rint, default=2
Exponent used to compute the distance between the feature vector.
- Attributes:
- n_categories_ndarray of shape (n_features,)
The number of categories per features.
- proba_per_class_list of ndarray of shape (n_categories, n_classes)
List of length n_features containing the conditional probabilities for each category given a class.
Notes
The input data X are expected to be encoded by an
OrdinalEncoder
and the data type is used should be np.int32. If other data types are given, X will be converted to np.int32.References
[1]Stanfill, Craig, and David Waltz. “Toward memory-based reasoning.” Communications of the ACM 29.12 (1986): 1213-1228.
Examples
>>> import numpy as np >>> X = np.array(["green"] * 10 + ["red"] * 10 + ["blue"] * 10).reshape(-1, 1) >>> y = [1] * 8 + [0] * 5 + [1] * 7 + [0] * 9 + [1] >>> from sklearn.preprocessing import OrdinalEncoder >>> encoder = OrdinalEncoder(dtype=np.int32) >>> X_encoded = encoder.fit_transform(X) >>> from imbens.metrics.pairwise import ValueDifferenceMetric >>> vdm = ValueDifferenceMetric().fit(X_encoded, y) >>> pairwise_distance = vdm.pairwise(X_encoded) >>> pairwise_distance.shape (30, 30) >>> X_test = np.array(["green", "red", "blue"]).reshape(-1, 1) >>> X_test_encoded = encoder.transform(X_test) >>> vdm.pairwise(X_test_encoded) array([[ 0. , 0.04, 1.96], [ 0.04, 0. , 1.44], [ 1.96, 1.44, 0. ]])
Methods
fit
(X, y)Compute the necessary statistics from the training set.
Get metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
pairwise
(X[, Y])Compute the VDM distance pairwise.
set_params
(**params)Set the parameters of this estimator.
- fit(X, y)[source]
Compute the necessary statistics from the training set.
- Parameters:
- Xndarray of shape (n_samples, n_features), dtype=np.int32
The input data. The data are expected to be encoded with a
OrdinalEncoder
.- yndarray of shape (n_features,)
The target.
- Returns:
- self
- get_metadata_routing()[source]
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequest
encapsulating routing information.
- get_params(deep=True)[source]
Get parameters for this estimator.
- Parameters:
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
- paramsdict
Parameter names mapped to their values.
- pairwise(X, Y=None)[source]
Compute the VDM distance pairwise.
- Parameters:
- Xndarray of shape (n_samples, n_features), dtype=np.int32
The input data. The data are expected to be encoded with a
OrdinalEncoder
.- Yndarray of shape (n_samples, n_features), dtype=np.int32
The input data. The data are expected to be encoded with a
OrdinalEncoder
.
- Returns:
- distance_matrixndarray of shape (n_samples, n_samples)
The VDM pairwise distance.
- set_params(**params)[source]
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
- **paramsdict
Estimator parameters.
- Returns:
- selfestimator instance
Estimator instance.