def shap_values(self, X, **kwargs):
""" Estimate the SHAP values for a set of samples.
Parameters
----------
X : numpy.array or pandas.DataFrame
A matrix of samples (# samples x # features) on which to explain the model's output.
nsamples : "auto" or int
Number of times to re-evaluate the model when explaining each prediction. More samples
lead to lower variance estimates of the SHAP values.
l1_reg : "auto" or float
The l1 regularization to use for feature selection (the estimation procedure is based on
a debiased lasso). Set this to zero to remove the feature selection step before estimation.
Returns
-------
For models with a single output this returns a matrix of SHAP values
(# samples x # features). Each row sums to the difference between the model output for that
sample and the expected value of the model output (which is stored as expected_value
attribute of the explainer). For models with vector outputs this returns a list
of such matrices, one for each output.
"""
# convert dataframes
if str(type(X)).endswith("pandas.core.series.Series'>"):
X = X.values
elif str(type(X)).endswith("'pandas.core.frame.DataFrame'>"):
if self.keep_index:
index_value = X.index.values
index_name = X.index.name
column_name = list(X.columns)
X = X.values
assert str(type(X)).endswith("'numpy.ndarray'>"), "Unknown instance type: " + str(type(X))
assert len(X.shape) == 1 or len(X.shape) == 2, "Instance must have 1 or 2 dimensions!"
# single instance
if len(X.shape) == 1:
data = X.reshape((1, X.shape[0]))
if self.keep_index:
data = convert_to_instance_with_index(data, column_name, index_name, index_value)
explanation = self.explain(data, **kwargs)
# vector-output
s = explanation.effects.shape
if len(s) == 2:
outs = [np.zeros(s[0]) for j in range(s[1])]
for j in range(s[1]):
outs[j] = explanation.effects[:, j]
assert abs(self.link.f(self.expected_value[j]) - explanation.base_value[j]) < 1e-6
return outs
# single-output
else:
out = np.zeros(s[0])
out[:] = explanation.effects
assert abs(self.link.f(self.expected_value) - explanation.base_value) < 1e-6
return out
# explain the whole dataset
elif len(X.shape) == 2:
explanations = []
for i in tqdm(range(X.shape[0]), disable=kwargs.get("silent", False)):
data = X[i:i + 1, :]
if self.keep_index:
data = convert_to_instance_with_index(data, column_name, index_value[i:i + 1], index_name)
explanations.append(self.explain(data, **kwargs))
# vector-output
s = explanations[0].effects.shape
if len(s) == 2:
outs = [np.zeros((X.shape[0], s[0])) for j in range(s[1])]
for i in range(X.shape[0]):
for j in range(s[1]):
outs[j][i] = explanations[i].effects[:, j]
assert abs(self.link.f(self.expected_value[j]) - explanations[i].base_value[j]) < 1e-6
return outs
# single-output
else:
out = np.zeros((X.shape[0], s[0]))
for i in range(X.shape[0]):
out[i] = explanations[i].effects
assert abs(self.link.f(self.expected_value) - explanations[i].base_value) < 1e-6
return out
def shap_values(self, X, **kwargs):
# convert dataframes
if str(type(X)).endswith("pandas.core.series.Series'>"):
X = X.as_matrix()
elif str(type(X)).endswith("'pandas.core.frame.DataFrame'>"):
if self.keep_index:
index_value = X.index.values
index_name = X.index.name
column_name = list(X.columns)
X = X.as_matrix()
assert str(type(X)).endswith(
"'numpy.ndarray'>"), "Unknown instance type: " + str(type(X))
assert len(X.shape) == 1 or len(
X.shape) == 2, "Instance must have 1 or 2 dimensions!"
# single instance
if len(X.shape) == 1:
data = X.reshape((1, X.shape[0]))
if self.keep_index:
data = convert_to_instance_with_index(data, column_name,
index_name, index_value)
explanation = self.explain(data, **kwargs)
# vector-output
s = explanation.effects.shape
if len(s) == 2:
outs = [np.zeros(s[0] + 1) for j in range(s[1])]
for j in range(s[1]):
outs[j][:-1] = explanation.effects[:, j]
outs[j][-1] = explanation.base_value[j]
return outs
# single-output
else:
out = np.zeros(s[0] + 1)
out[:-1] = explanation.effects
out[-1] = explanation.base_value
return out
# explain the whole dataset
elif len(X.shape) == 2:
explanations = []
for i in tqdm(range(X.shape[0])):
data = X[i:i + 1, :]
if self.keep_index:
data = convert_to_instance_with_index(
data, column_name, index_value[i:i + 1], index_name)
explanations.append(self.explain(data, **kwargs))
# vector-output
s = explanations[0].effects.shape
if len(s) == 2:
outs = [np.zeros((X.shape[0], s[0] + 1)) for j in range(s[1])]
for i in range(X.shape[0]):
for j in range(s[1]):
outs[j][i, :-1] = explanations[i].effects[:, j]
outs[j][i, -1] = explanations[i].base_value[j]
return outs
# single-output
else:
out = np.zeros((X.shape[0], s[0] + 1))
for i in range(X.shape[0]):
out[i, :-1] = explanations[i].effects
out[i, -1] = explanations[i].base_value
return out