# -*- coding: utf-8 -*-
from functools import partial
import numpy as np
import scipy.sparse as sp
from sklearn.base import BaseEstimator
from sklearn.ensemble import (
ExtraTreesClassifier,
ExtraTreesRegressor,
GradientBoostingClassifier,
GradientBoostingRegressor,
RandomForestClassifier,
RandomForestRegressor,
)
from sklearn.linear_model import (
ElasticNet, # includes Lasso, MultiTaskElasticNet, etc.
ElasticNetCV,
HuberRegressor,
Lars,
LassoCV,
LinearRegression,
LogisticRegression,
LogisticRegressionCV,
OrthogonalMatchingPursuit,
OrthogonalMatchingPursuitCV,
PassiveAggressiveClassifier,
PassiveAggressiveRegressor,
Perceptron,
Ridge,
RidgeCV,
RidgeClassifier,
RidgeClassifierCV,
SGDClassifier,
SGDRegressor,
TheilSenRegressor,
)
from sklearn.svm import (
LinearSVC,
LinearSVR,
SVC,
SVR,
NuSVC,
NuSVR,
OneClassSVM,
)
from sklearn.multiclass import OneVsRestClassifier
from sklearn.tree import (
DecisionTreeClassifier,
DecisionTreeRegressor
)
from eli5.base import Explanation, TargetExplanation
from eli5.base_utils import singledispatch
from eli5.utils import (
get_target_display_names,
get_binary_target_scale_label_id
)
from eli5.sklearn.utils import (
add_intercept,
get_coef,
get_default_target_names,
get_X,
get_X0,
is_multiclass_classifier,
is_multitarget_regressor,
predict_proba,
has_intercept,
handle_vec,
)
from eli5.sklearn.text import add_weighted_spans
from eli5.explain import explain_prediction
from eli5._decision_path import DECISION_PATHS_CAVEATS
from eli5._feature_weights import get_top_features_filtered
[docs]@singledispatch
def explain_prediction_sklearn(estimator, doc,
vec=None,
top=None,
top_targets=None,
target_names=None,
targets=None,
feature_names=None,
feature_re=None,
feature_filter=None,
vectorized=False):
""" Return an explanation of a scikit-learn estimator """
return explain_prediction_sklearn_not_supported(estimator, doc)
@explain_prediction.register(BaseEstimator)
def explain_prediction_sklearn_not_supported(
estimator, doc,
vec=None,
top=None,
top_targets=None,
target_names=None,
targets=None,
feature_names=None,
feature_re=None,
feature_filter=None,
vectorized=False):
return Explanation(
estimator=repr(estimator),
error="estimator %r is not supported" % estimator,
)
def register(cls):
def deco(f):
return explain_prediction.register(cls)(
explain_prediction_sklearn.register(cls)(f))
return deco
@explain_prediction.register(OneVsRestClassifier)
def explain_prediction_ovr(clf, doc, **kwargs):
estimator = clf.estimator
func = explain_prediction.dispatch(estimator.__class__)
return func(clf, doc, **kwargs)
@explain_prediction_sklearn.register(OneVsRestClassifier)
def explain_prediction_ovr_sklearn(clf, doc, **kwargs):
# dispatch OvR to eli5.sklearn
# if explain_prediction_sklearn is called explicitly
estimator = clf.estimator
func = explain_prediction_sklearn.dispatch(estimator.__class__)
return func(clf, doc, **kwargs)
[docs]@register(LogisticRegression)
@register(LogisticRegressionCV)
@register(SGDClassifier)
@register(PassiveAggressiveClassifier)
@register(Perceptron)
@register(LinearSVC)
@register(RidgeClassifier)
@register(RidgeClassifierCV)
def explain_prediction_linear_classifier(clf, doc,
vec=None,
top=None,
top_targets=None,
target_names=None,
targets=None,
feature_names=None,
feature_re=None,
feature_filter=None,
vectorized=False,
):
"""
Explain prediction of a linear classifier.
See :func:`eli5.explain_prediction` for description of
``top``, ``top_targets``, ``target_names``, ``targets``,
``feature_names``, ``feature_re`` and ``feature_filter`` parameters.
``vec`` is a vectorizer instance used to transform
raw features to the input of the classifier ``clf``
(e.g. a fitted CountVectorizer instance); you can pass it
instead of ``feature_names``.
``vectorized`` is a flag which tells eli5 if ``doc`` should be
passed through ``vec`` or not. By default it is False, meaning that
if ``vec`` is not None, ``vec.transform([doc])`` is passed to the
classifier. Set it to True if you're passing ``vec``, but ``doc``
is already vectorized.
"""
vec, feature_names = handle_vec(clf, doc, vec, vectorized, feature_names)
X = get_X(doc, vec=vec, vectorized=vectorized, to_dense=True)
proba = predict_proba(clf, X)
score, = clf.decision_function(X)
if has_intercept(clf):
X = add_intercept(X)
x = get_X0(X)
feature_names, flt_indices = feature_names.handle_filter(
feature_filter, feature_re, x)
res = Explanation(
estimator=repr(clf),
method='linear model',
targets=[],
)
assert res.targets is not None
_weights = _linear_weights(clf, x, top, feature_names, flt_indices)
classes = getattr(clf, "classes_", ["-1", "1"]) # OneClassSVM support
display_names = get_target_display_names(classes, target_names,
targets, top_targets, score)
if is_multiclass_classifier(clf):
for label_id, label in display_names:
target_expl = TargetExplanation(
target=label,
feature_weights=_weights(label_id),
score=score[label_id],
proba=proba[label_id] if proba is not None else None,
)
add_weighted_spans(doc, vec, vectorized, target_expl)
res.targets.append(target_expl)
else:
if len(display_names) == 1: # target is passed explicitly
label_id, target = display_names[0]
else:
label_id = 1 if score >= 0 else 0
target = display_names[label_id][1]
scale = -1 if label_id == 0 else 1
target_expl = TargetExplanation(
target=target,
feature_weights=_weights(0, scale=scale),
score=score,
proba=proba[label_id] if proba is not None else None,
)
add_weighted_spans(doc, vec, vectorized, target_expl)
res.targets.append(target_expl)
return res
@register(NuSVC)
@register(SVC)
@register(OneClassSVM)
def test_explain_prediction_libsvm_linear(clf, doc, *args, **kwargs):
if clf.kernel != 'linear':
return Explanation(
estimator=repr(clf),
error="only kernel='linear' is currently supported for "
"libsvm-based classifiers",
)
if len(getattr(clf, 'classes_', [])) > 2:
return Explanation(
estimator=repr(clf),
error="only binary libsvm-based classifiers are supported",
)
return explain_prediction_linear_classifier(clf, doc, *args, **kwargs)
[docs]@register(ElasticNet)
@register(ElasticNetCV)
@register(HuberRegressor)
@register(Lars)
@register(LassoCV)
@register(LinearRegression)
@register(LinearSVR)
@register(OrthogonalMatchingPursuit)
@register(OrthogonalMatchingPursuitCV)
@register(PassiveAggressiveRegressor)
@register(Ridge)
@register(RidgeCV)
@register(SGDRegressor)
@register(TheilSenRegressor)
@register(SVR)
@register(NuSVR)
def explain_prediction_linear_regressor(reg, doc,
vec=None,
top=None,
top_targets=None,
target_names=None,
targets=None,
feature_names=None,
feature_re=None,
feature_filter=None,
vectorized=False):
"""
Explain prediction of a linear regressor.
See :func:`eli5.explain_prediction` for description of
``top``, ``top_targets``, ``target_names``, ``targets``,
``feature_names``, ``feature_re`` and ``feature_filter`` parameters.
``vec`` is a vectorizer instance used to transform
raw features to the input of the classifier ``clf``;
you can pass it instead of ``feature_names``.
``vectorized`` is a flag which tells eli5 if ``doc`` should be
passed through ``vec`` or not. By default it is False, meaning that
if ``vec`` is not None, ``vec.transform([doc])`` is passed to the
regressor ``reg``. Set it to True if you're passing ``vec``,
but ``doc`` is already vectorized.
"""
if isinstance(reg, (SVR, NuSVR)) and reg.kernel != 'linear':
return explain_prediction_sklearn_not_supported(reg, doc)
vec, feature_names = handle_vec(reg, doc, vec, vectorized, feature_names)
X = get_X(doc, vec=vec, vectorized=vectorized, to_dense=True)
score, = reg.predict(X)
if has_intercept(reg):
X = add_intercept(X)
x = get_X0(X)
feature_names, flt_indices = feature_names.handle_filter(
feature_filter, feature_re, x)
res = Explanation(
estimator=repr(reg),
method='linear model',
targets=[],
is_regression=True,
)
assert res.targets is not None
_weights = _linear_weights(reg, x, top, feature_names, flt_indices)
names = get_default_target_names(reg)
display_names = get_target_display_names(names, target_names, targets,
top_targets, score)
if is_multitarget_regressor(reg):
for label_id, label in display_names:
target_expl = TargetExplanation(
target=label,
feature_weights=_weights(label_id),
score=score[label_id],
)
add_weighted_spans(doc, vec, vectorized, target_expl)
res.targets.append(target_expl)
else:
target_expl = TargetExplanation(
target=display_names[0][1],
feature_weights=_weights(0),
score=score,
)
add_weighted_spans(doc, vec, vectorized, target_expl)
res.targets.append(target_expl)
return res
DECISION_PATHS_CAVEATS = """
Feature weights are calculated by following decision paths in trees
of an ensemble (or a single tree for DecisionTreeClassifier).
Each node of the tree has an output score, and contribution of a feature
on the decision path is how much the score changes from parent to child.
Weights of all features sum to the output score or proba of the estimator.
""" + DECISION_PATHS_CAVEATS
DESCRIPTION_TREE_CLF_BINARY = """
Features with largest coefficients.
""" + DECISION_PATHS_CAVEATS
DESCRIPTION_TREE_CLF_MULTICLASS = """
Features with largest coefficients per class.
""" + DECISION_PATHS_CAVEATS
DESCRIPTION_TREE_REG = """
Features with largest coefficients.
""" + DECISION_PATHS_CAVEATS
DESCRIPTION_TREE_REG_MULTITARGET = """
Features with largest coefficients per target.
""" + DECISION_PATHS_CAVEATS
[docs]@register(DecisionTreeClassifier)
@register(ExtraTreesClassifier)
@register(GradientBoostingClassifier)
@register(RandomForestClassifier)
def explain_prediction_tree_classifier(
clf, doc,
vec=None,
top=None,
top_targets=None,
target_names=None,
targets=None,
feature_names=None,
feature_re=None,
feature_filter=None,
vectorized=False):
""" Explain prediction of a tree classifier.
See :func:`eli5.explain_prediction` for description of
``top``, ``top_targets``, ``target_names``, ``targets``,
``feature_names``, ``feature_re`` and ``feature_filter`` parameters.
``vec`` is a vectorizer instance used to transform
raw features to the input of the classifier ``clf``
(e.g. a fitted CountVectorizer instance); you can pass it
instead of ``feature_names``.
``vectorized`` is a flag which tells eli5 if ``doc`` should be
passed through ``vec`` or not. By default it is False, meaning that
if ``vec`` is not None, ``vec.transform([doc])`` is passed to the
classifier. Set it to True if you're passing ``vec``,
but ``doc`` is already vectorized.
Method for determining feature importances follows an idea from
http://blog.datadive.net/interpreting-random-forests/.
Feature weights are calculated by following decision paths in trees
of an ensemble (or a single tree for DecisionTreeClassifier).
Each node of the tree has an output score, and contribution of a feature
on the decision path is how much the score changes from parent to child.
Weights of all features sum to the output score or proba of the estimator.
"""
vec, feature_names = handle_vec(clf, doc, vec, vectorized, feature_names)
X = get_X(doc, vec=vec, vectorized=vectorized)
if feature_names.bias_name is None:
# Tree estimators do not have an intercept, but here we interpret
# them as having an intercept
feature_names.bias_name = '<BIAS>'
proba = predict_proba(clf, X)
if hasattr(clf, 'decision_function'):
score, = clf.decision_function(X)
else:
score = None
is_multiclass = clf.n_classes_ > 2
feature_weights = _trees_feature_weights(
clf, X, feature_names, clf.n_classes_)
x = get_X0(add_intercept(X))
flt_feature_names, flt_indices = feature_names.handle_filter(
feature_filter, feature_re, x)
def _weights(label_id, scale=1.0):
weights = feature_weights[:, label_id]
return get_top_features_filtered(x, flt_feature_names, flt_indices,
weights, top, scale)
res = Explanation(
estimator=repr(clf),
method='decision path',
targets=[],
description=(DESCRIPTION_TREE_CLF_MULTICLASS if is_multiclass
else DESCRIPTION_TREE_CLF_BINARY),
)
assert res.targets is not None
display_names = get_target_display_names(
clf.classes_, target_names, targets, top_targets,
score=score if score is not None else proba)
if is_multiclass:
for label_id, label in display_names:
target_expl = TargetExplanation(
target=label,
feature_weights=_weights(label_id),
score=score[label_id] if score is not None else None,
proba=proba[label_id] if proba is not None else None,
)
add_weighted_spans(doc, vec, vectorized, target_expl)
res.targets.append(target_expl)
else:
target, scale, label_id = get_binary_target_scale_label_id(
score, display_names, proba)
target_expl = TargetExplanation(
target=target,
feature_weights=_weights(label_id, scale=scale),
score=score if score is not None else None,
proba=proba[label_id] if proba is not None else None,
)
add_weighted_spans(doc, vec, vectorized, target_expl)
res.targets.append(target_expl)
return res
[docs]@register(DecisionTreeRegressor)
@register(ExtraTreesRegressor)
@register(GradientBoostingRegressor)
@register(RandomForestRegressor)
def explain_prediction_tree_regressor(
reg, doc,
vec=None,
top=None,
top_targets=None,
target_names=None,
targets=None,
feature_names=None,
feature_re=None,
feature_filter=None,
vectorized=False):
""" Explain prediction of a tree regressor.
See :func:`eli5.explain_prediction` for description of
``top``, ``top_targets``, ``target_names``, ``targets``,
``feature_names``, ``feature_re`` and ``feature_filter`` parameters.
``vec`` is a vectorizer instance used to transform
raw features to the input of the regressor ``reg``
(e.g. a fitted CountVectorizer instance); you can pass it
instead of ``feature_names``.
``vectorized`` is a flag which tells eli5 if ``doc`` should be
passed through ``vec`` or not. By default it is False, meaning that
if ``vec`` is not None, ``vec.transform([doc])`` is passed to the
regressor. Set it to True if you're passing ``vec``,
but ``doc`` is already vectorized.
Method for determining feature importances follows an idea from
http://blog.datadive.net/interpreting-random-forests/.
Feature weights are calculated by following decision paths in trees
of an ensemble (or a single tree for DecisionTreeRegressor).
Each node of the tree has an output score, and contribution of a feature
on the decision path is how much the score changes from parent to child.
Weights of all features sum to the output score of the estimator.
"""
vec, feature_names = handle_vec(reg, doc, vec, vectorized, feature_names)
X = get_X(doc, vec=vec, vectorized=vectorized)
if feature_names.bias_name is None:
# Tree estimators do not have an intercept, but here we interpret
# them as having an intercept
feature_names.bias_name = '<BIAS>'
score, = reg.predict(X)
num_targets = getattr(reg, 'n_outputs_', 1)
is_multitarget = num_targets > 1
feature_weights = _trees_feature_weights(reg, X, feature_names, num_targets)
x = get_X0(add_intercept(X))
flt_feature_names, flt_indices = feature_names.handle_filter(
feature_filter, feature_re, x)
def _weights(label_id, scale=1.0):
weights = feature_weights[:, label_id]
return get_top_features_filtered(x, flt_feature_names, flt_indices,
weights, top, scale)
res = Explanation(
estimator=repr(reg),
method='decision path',
description=(DESCRIPTION_TREE_REG_MULTITARGET if is_multitarget
else DESCRIPTION_TREE_REG),
targets=[],
is_regression=True,
)
assert res.targets is not None
names = get_default_target_names(reg, num_targets=num_targets)
display_names = get_target_display_names(names, target_names, targets,
top_targets, score)
if is_multitarget:
for label_id, label in display_names:
target_expl = TargetExplanation(
target=label,
feature_weights=_weights(label_id),
score=score[label_id],
)
add_weighted_spans(doc, vec, vectorized, target_expl)
res.targets.append(target_expl)
else:
target_expl = TargetExplanation(
target=display_names[0][1],
feature_weights=_weights(0),
score=score,
)
add_weighted_spans(doc, vec, vectorized, target_expl)
res.targets.append(target_expl)
return res
def _trees_feature_weights(clf, X, feature_names, num_targets):
""" Return feature weights for a tree or a tree ensemble.
"""
feature_weights = np.zeros([len(feature_names), num_targets])
is_grad_boost = isinstance(clf, (GradientBoostingClassifier,
GradientBoostingRegressor))
if hasattr(clf, 'tree_'):
_update_tree_feature_weights(X, feature_names, clf, feature_weights)
else:
if is_grad_boost:
weight = clf.learning_rate
else:
weight = 1. / len(clf.estimators_)
for _clfs in clf.estimators_:
_update = partial(_update_tree_feature_weights, X, feature_names)
if isinstance(_clfs, np.ndarray):
if len(_clfs) == 1:
_update(_clfs[0], feature_weights)
else:
for idx, _clf in enumerate(_clfs):
_update(_clf, feature_weights[:, idx])
else:
_update(_clfs, feature_weights)
feature_weights *= weight
if hasattr(clf, 'init_'):
if clf.init_ == 'zero':
bias_init = 0
elif is_grad_boost and hasattr(clf.loss_, 'get_init_raw_predictions'):
bias_init = clf.loss_.get_init_raw_predictions(
X, clf.init_).astype(np.float64)[0]
else:
bias_init = clf.init_.predict(X)[0]
feature_weights[feature_names.bias_idx] += bias_init
return feature_weights
def _update_tree_feature_weights(X, feature_names, clf, feature_weights):
""" Update tree feature weights using decision path method.
"""
tree_value = clf.tree_.value
if tree_value.shape[1] == 1:
squeeze_axis = 1
else:
assert tree_value.shape[2] == 1
squeeze_axis = 2
tree_value = np.squeeze(tree_value, axis=squeeze_axis)
tree_feature = clf.tree_.feature
_, indices = clf.decision_path(X).nonzero()
if isinstance(clf, DecisionTreeClassifier):
norm = lambda x: x / x.sum()
else:
norm = lambda x: x
feature_weights[feature_names.bias_idx] += norm(tree_value[0])
for parent_idx, child_idx in zip(indices, indices[1:]):
assert tree_feature[parent_idx] >= 0
feature_idx = tree_feature[parent_idx]
diff = norm(tree_value[child_idx]) - norm(tree_value[parent_idx])
feature_weights[feature_idx] += diff
def _multiply(X, coef):
""" Multiple X by coef element-wise, preserving sparsity. """
if sp.issparse(X):
return X.multiply(sp.csr_matrix(coef))
else:
return np.multiply(X, coef)
def _linear_weights(clf, x, top, flt_feature_names, flt_indices):
""" Return top weights getter for label_id.
"""
def _weights(label_id, scale=1.0):
coef = get_coef(clf, label_id)
scores = _multiply(x, coef)
return get_top_features_filtered(x, flt_feature_names, flt_indices,
scores, top, scale)
return _weights