def handle_vec(clf, doc, vec, vectorized, feature_names, num_features=None):
if not vectorized:
vec = invert_hashing_and_fit(vec, [doc])
if (vec is None and feature_names is None and
pandas_available and isinstance(doc, pd.Series)):
feature_names = list(doc.index)
# Explaining predictions does not need coef_scale
# because it is handled by the vectorizer.
feature_names = handle_hashing_vec(
vec, feature_names, coef_scale=None, with_coef_scale=False)
feature_names = get_feature_names(
clf, vec, feature_names=feature_names, num_features=num_features)
return vec, feature_names
def explain_linear_regressor_weights(
reg,
vec=None,
top=_TOP,
target_names=None,
targets=None,
feature_names=None,
coef_scale=None,
feature_re=None,
feature_filter=None,
):
"""
Return an explanation of a linear regressor weights.
See :func:`eli5.explain_weights` for description of
``top``, ``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``; you can
pass it instead of ``feature_names``.
``coef_scale`` is a 1D np.ndarray with a scaling coefficient
for each feature; coef[i] = coef[i] * coef_scale[i] if
coef_scale[i] is not nan. Use it if you want to scale coefficients
before displaying them, to take input feature sign or scale in account.
"""
feature_names, coef_scale = handle_hashing_vec(vec, feature_names,
coef_scale)
feature_names = get_feature_names(reg, vec, feature_names=feature_names)
feature_names, flt_indices = feature_names.handle_filter(
feature_filter, feature_re)
_extra_caveats = "\n" + HASHING_CAVEATS if is_invhashing(vec) else ''
def _features(target_id):
coef = get_coef(reg, target_id, scale=coef_scale)
if flt_indices is not None:
coef = coef[flt_indices]
return get_top_features(feature_names, coef, top)
display_names = get_target_display_names(get_default_target_names(reg),
target_names, targets)
if is_multitarget_regressor(reg):
return Explanation(
targets=[
TargetExplanation(target=target_name,
feature_weights=_features(target_id))
for target_id, target_name in display_names
],
description=DESCRIPTION_REGRESSION_MULTITARGET + _extra_caveats,
estimator=repr(reg),
method='linear model',
is_regression=True,
)
else:
return Explanation(
targets=[
TargetExplanation(
target=display_names[0][1],
feature_weights=_features(0),
)
],
description=DESCRIPTION_REGRESSION + _extra_caveats,
estimator=repr(reg),
method='linear model',
is_regression=True,
)
def explain_linear_regressor_weights(reg,
vec=None,
top=_TOP,
target_names=None,
targets=None,
feature_names=None,
coef_scale=None,
feature_re=None):
"""
Return an explanation of a linear regressor weights in the following
format::
Explanation(
estimator="<regressor repr>",
method="<interpretation method>",
description="<human readable description>",
targets=[
TargetExplanation(
target="<target name>",
feature_weights=FeatureWeights(
# positive weights
pos=[
(feature_name, coefficient),
...
],
# negative weights
neg=[
(feature_name, coefficient),
...
],
# A number of features not shown
pos_remaining = <int>,
neg_remaining = <int>,
# Sum of feature weights not shown
# pos_remaining_sum = <float>,
# neg_remaining_sum = <float>,
),
),
...
]
)
To print it use utilities from eli5.formatters.
"""
feature_names, coef_scale = handle_hashing_vec(vec, feature_names,
coef_scale)
feature_names = get_feature_names(reg, vec, feature_names=feature_names)
if feature_re is not None:
feature_names, flt_indices = feature_names.filtered_by_re(feature_re)
_extra_caveats = "\n" + HASHING_CAVEATS if is_invhashing(vec) else ''
def _features(target_id):
coef = get_coef(reg, target_id, scale=coef_scale)
if feature_re is not None:
coef = coef[flt_indices]
return get_top_features(feature_names, coef, top)
display_names = get_display_names(get_default_target_names(reg),
target_names, targets)
if is_multitarget_regressor(reg):
return Explanation(
targets=[
TargetExplanation(target=target_name,
feature_weights=_features(target_id))
for target_id, target_name in display_names
],
description=DESCRIPTION_REGRESSION_MULTITARGET + _extra_caveats,
estimator=repr(reg),
method='linear model',
is_regression=True,
)
else:
return Explanation(
targets=[
TargetExplanation(
target=display_names[0][1],
feature_weights=_features(0),
)
],
description=DESCRIPTION_REGRESSION + _extra_caveats,
estimator=repr(reg),
method='linear model',
is_regression=True,
)
def explain_linear_classifier_weights(
clf,
vec=None,
top=_TOP,
target_names=None,
targets=None,
feature_names=None,
coef_scale=None,
feature_re=None,
feature_filter=None,
):
"""
Return an explanation of a linear classifier weights.
See :func:`eli5.explain_weights` for description of
``top``, ``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``.
``coef_scale`` is a 1D np.ndarray with a scaling coefficient
for each feature; coef[i] = coef[i] * coef_scale[i] if
coef_scale[i] is not nan. Use it if you want to scale coefficients
before displaying them, to take input feature sign or scale in account.
"""
feature_names, coef_scale = handle_hashing_vec(vec, feature_names,
coef_scale)
feature_names = get_feature_names(clf, vec, feature_names=feature_names)
feature_names, flt_indices = feature_names.handle_filter(
feature_filter, feature_re)
_extra_caveats = "\n" + HASHING_CAVEATS if is_invhashing(vec) else ''
def _features(label_id):
coef = get_coef(clf, label_id, scale=coef_scale)
if flt_indices is not None:
coef = coef[flt_indices]
return get_top_features(feature_names, coef, top)
display_names = get_target_display_names(clf.classes_, target_names,
targets)
if is_multiclass_classifier(clf):
return Explanation(
targets=[
TargetExplanation(target=label,
feature_weights=_features(label_id))
for label_id, label in display_names
],
description=DESCRIPTION_CLF_MULTICLASS + _extra_caveats,
estimator=repr(clf),
method='linear model',
)
else:
# for binary classifiers scikit-learn stores a single coefficient
# vector, which corresponds to clf.classes_[1].
return Explanation(
targets=[
TargetExplanation(
target=display_names[1][1],
feature_weights=_features(0),
)
],
description=DESCRIPTION_CLF_BINARY + _extra_caveats,
estimator=repr(clf),
method='linear model',
)
def explain_linear_classifier_weights(clf,
vec=None,
top=_TOP,
target_names=None,
targets=None,
feature_names=None,
coef_scale=None,
feature_re=None):
"""
Return an explanation of a linear classifier weights in the following
format::
Explanation(
estimator="<classifier repr>",
method="<interpretation method>",
description="<human readable description>",
targets=[
TargetExplanation(
target="<class name>",
feature_weights=FeatureWeights(
# positive weights
pos=[
(feature_name, coefficient),
...
],
# negative weights
neg=[
(feature_name, coefficient),
...
],
# A number of features not shown
pos_remaining = <int>,
neg_remaining = <int>,
# Sum of feature weights not shown
# pos_remaining_sum = <float>,
# neg_remaining_sum = <float>,
),
),
...
]
)
To print it use utilities from eli5.formatters.
"""
feature_names, coef_scale = handle_hashing_vec(vec, feature_names,
coef_scale)
feature_names = get_feature_names(clf, vec, feature_names=feature_names)
if feature_re is not None:
feature_names, flt_indices = feature_names.filtered_by_re(feature_re)
_extra_caveats = "\n" + HASHING_CAVEATS if is_invhashing(vec) else ''
def _features(label_id):
coef = get_coef(clf, label_id, scale=coef_scale)
if feature_re is not None:
coef = coef[flt_indices]
return get_top_features(feature_names, coef, top)
display_names = get_display_names(clf.classes_, target_names, targets)
if is_multiclass_classifier(clf):
return Explanation(
targets=[
TargetExplanation(target=label,
feature_weights=_features(label_id))
for label_id, label in display_names
],
description=DESCRIPTION_CLF_MULTICLASS + _extra_caveats,
estimator=repr(clf),
method='linear model',
)
else:
# for binary classifiers scikit-learn stores a single coefficient
# vector, which corresponds to clf.classes_[1].
return Explanation(
targets=[
TargetExplanation(
target=display_names[1][1],
feature_weights=_features(0),
)
],
description=DESCRIPTION_CLF_BINARY + _extra_caveats,
estimator=repr(clf),
method='linear model',
)