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import copy
import gc
import itertools
import logging
import time
import warnings
from typing import Any, Literal
import numpy as np
import numpy.typing as npt
import pandas as pd
import scipy.sparse
import sklearn
from packaging import version
from scipy.special import binom
from sklearn.linear_model import Lasso, LassoLarsIC, lars_path
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from tqdm.auto import tqdm
from .._cutils import compute_exp_val
from .._explanation import Explanation
from ..utils import safe_isinstance
from ..utils._exceptions import DimensionError
from ..utils._legacy import (
DenseData,
SparseData,
convert_to_data,
convert_to_instance,
convert_to_instance_with_index,
convert_to_link,
convert_to_model,
match_instance_to_data,
match_model_to_data,
)
from ._explainer import Explainer
log = logging.getLogger("shap")
class KernelExplainer(Explainer):
"""Uses the Kernel SHAP method to explain the output of any function.
Kernel SHAP is a method that uses a special weighted linear regression
to compute the importance of each feature. The computed importance values
are Shapley values from game theory and also coefficients from a local linear
regression.
Parameters
----------
model : function or iml.Model
User supplied function that takes a matrix of samples (# samples x # features) and
computes the output of the model for those samples. The output can be a vector
(# samples) or a matrix (# samples x # model outputs).
data : numpy.array or pandas.DataFrame or shap.common.DenseData or any scipy.sparse matrix
The background dataset to use for integrating out features. To determine the impact
of a feature, that feature is set to "missing" and the change in the model output
is observed. Since most models aren't designed to handle arbitrary missing data at test
time, we simulate "missing" by replacing the feature with the values it takes in the
background dataset. So if the background dataset is a simple sample of all zeros, then
we would approximate a feature being missing by setting it to zero. For small problems,
this background dataset can be the whole training set, but for larger problems consider
using a single reference value or using the ``kmeans`` function to summarize the dataset.
Note: for the sparse case, we accept any sparse matrix but convert to lil format for
performance.
feature_names : list
The names of the features in the background dataset. If the background dataset is
supplied as a pandas.DataFrame, then ``feature_names`` can be set to ``None`` (default),
and the feature names will be taken as the column names of the dataframe.
link : "identity" or "logit"
A generalized linear model link to connect the feature importance values to the model
output. Since the feature importance values, phi, sum up to the model output, it often makes
sense to connect them to the output with a link function where link(output) = sum(phi).
Default is "identity" (a no-op).
If the model output is a probability, then "logit" can be used to transform the SHAP values
into log-odds units.
Examples
--------
See :ref:`Kernel Explainer Examples <kernel_explainer_examples>`.
"""
data_feature_names: list[str]
link: Any
keep_index: bool
keep_index_ordered: bool
model: Any
data: DenseData | SparseData
N: int
P: int
linkfv: np.vectorize
nsamplesAdded: int
nsamplesRun: int
fnull: npt.NDArray[np.floating[Any]]
expected_value: float | npt.NDArray[np.floating[Any]]
vector_out: bool
D: int
varyingInds: npt.NDArray[np.intp]
varyingFeatureGroups: npt.NDArray[Any] | list[Any]
M: int
fx: npt.NDArray[np.floating[Any]]
l1_reg: str | float | bool
nsamples: int
max_samples: int
synth_data: npt.NDArray[Any] | scipy.sparse.lil_matrix
maskMatrix: npt.NDArray[np.floating[Any]]
kernelWeights: npt.NDArray[np.floating[Any]]
y: npt.NDArray[np.floating[Any]]
ey: npt.NDArray[np.floating[Any]]
lastMask: npt.NDArray[np.floating[Any]]
synth_data_index: npt.NDArray[Any]
def __init__(
self,
model: Any,
data: Any,
feature_names: list[str] | None = None,
link: Literal["identity", "logit"] | Any = "identity",
**kwargs: Any,
) -> None:
if feature_names is not None:
self.data_feature_names = feature_names
elif isinstance(data, pd.DataFrame):
self.data_feature_names = list(data.columns)
# convert incoming inputs to standardized iml objects
self.link = convert_to_link(link)
self.keep_index = kwargs.get("keep_index", False)
self.keep_index_ordered = kwargs.get("keep_index_ordered", False)
self.model = convert_to_model(model, keep_index=self.keep_index)
self.data = convert_to_data(data, keep_index=self.keep_index)
model_null = match_model_to_data(self.model, self.data)
# enforce our current input type limitations
if not isinstance(self.data, (DenseData, SparseData)):
emsg = "Shap explainer only supports the DenseData and SparseData input currently."
raise TypeError(emsg)
if self.data.transposed:
emsg = "Shap explainer does not support transposed DenseData or SparseData currently."
raise DimensionError(emsg)
# warn users about large background data sets
if len(self.data.weights) > 100:
log.warning(
"Using "
+ str(len(self.data.weights))
+ " background data samples could cause "
+ "slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to "
+ "summarize the background as K samples."
)
# init our parameters
self.N = self.data.data.shape[0]
self.P = self.data.data.shape[1]
self.linkfv = np.vectorize(self.link.f)
self.nsamplesAdded = 0
self.nsamplesRun = 0
# find E_x[f(x)]
if isinstance(model_null, (pd.DataFrame, pd.Series)):
model_null = np.squeeze(model_null.values)
if safe_isinstance(model_null, "tensorflow.python.framework.ops.EagerTensor"):
model_null = model_null.numpy()
elif safe_isinstance(model_null, "tensorflow.python.framework.ops.SymbolicTensor"):
model_null = self._convert_symbolic_tensor(model_null)
self.fnull = np.sum((model_null.T * self.data.weights).T, 0)
self.expected_value = self.linkfv(self.fnull)
# see if we have a vector output
self.vector_out = True
if len(self.fnull.shape) == 0:
self.vector_out = False
self.fnull = np.array([self.fnull])
self.D = 1
self.expected_value = float(self.expected_value)
else:
self.D = self.fnull.shape[0]
@staticmethod
def _convert_symbolic_tensor(symbolic_tensor: Any) -> npt.NDArray[Any]:
import tensorflow as tf
if tf.__version__ >= "2.0.0":
with tf.compat.v1.Session() as sess:
sess.run(tf.compat.v1.global_variables_initializer())
tensor_as_np_array = sess.run(symbolic_tensor)
else:
# this is untested
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
tensor_as_np_array = sess.run(symbolic_tensor)
return tensor_as_np_array
def __call__( # type: ignore[override]
self,
X: npt.NDArray[Any] | pd.DataFrame | scipy.sparse.spmatrix,
l1_reg: str | float | bool = "num_features(10)",
silent: bool = False,
) -> Explanation:
start_time = time.time()
if isinstance(X, pd.DataFrame):
feature_names = list(X.columns)
else:
feature_names = getattr(self, "data_feature_names", None) # type: ignore[assignment]
v = self.shap_values(X, l1_reg=l1_reg, silent=silent)
if isinstance(v, list):
v = np.stack(v, axis=-1) # put outputs at the end
# the explanation object expects an expected value for each row
if hasattr(self.expected_value, "__len__"):
ev_tiled = np.tile(self.expected_value, (v.shape[0], 1))
else:
ev_tiled = np.tile(self.expected_value, v.shape[0])
return Explanation(
v,
base_values=ev_tiled,
data=X.to_numpy() if isinstance(X, pd.DataFrame) else X,
feature_names=feature_names,
compute_time=time.time() - start_time,
)
def shap_values(
self,
X: npt.NDArray[Any] | pd.DataFrame | pd.Series | scipy.sparse.spmatrix,
**kwargs: Any,
) -> npt.NDArray[Any]:
"""Estimate the SHAP values for a set of samples.
Parameters
----------
X : numpy.array or pandas.DataFrame or any scipy.sparse matrix
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. The "auto" setting uses
`nsamples = 2 * X.shape[1] + 2048`.
l1_reg : "num_features(int)", "aic", "bic", or float
The l1 regularization to use for feature selection. The estimation
procedure is based on a debiased lasso.
* "num_features(int)" selects a fixed number of top features.
* "aic" and "bic" options use the AIC and BIC rules for regularization.
* Passing a float directly sets the "alpha" parameter of the
``sklearn.linear_model.Lasso`` model used for feature selection.
* "auto" (deprecated): uses "aic" when less than
20% of the possible sample space is enumerated, otherwise it uses
no regularization.
.. versionchanged:: 0.47.0
The default value changed from ``"auto"`` to ``"num_features(10)"``.
silent: bool
If True, hide tqdm progress bar. Default False.
gc_collect : bool
Run garbage collection after each explanation round. Sometime needed for memory intensive explanations (default False).
Returns
-------
np.array or list
Estimated SHAP values, usually of shape ``(# 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 the ``expected_value``
attribute of the explainer).
The type and shape of the return value depends on the number of model inputs and outputs:
* one input, one output: array of shape ``(#num_samples, *X.shape[1:])``.
* one input, multiple outputs: array of shape ``(#num_samples, *X.shape[1:], #num_outputs)``
* multiple inputs: list of arrays of corresponding shape above.
.. versionchanged:: 0.45.0
Return type for models with multiple outputs and one input changed from list to np.ndarray.
"""
# convert dataframes
if isinstance(X, pd.Series):
X = X.values
elif isinstance(X, pd.DataFrame):
if self.keep_index:
index_value = X.index.values
index_name = X.index.name
column_name = list(X.columns)
X = X.values
x_type = str(type(X))
arr_type = "'numpy.ndarray'>"
# if sparse, convert to lil for performance
if scipy.sparse.issparse(X) and not scipy.sparse.isspmatrix_lil(X):
X = X.tolil() # type: ignore[union-attr]
assert x_type.endswith(arr_type) or scipy.sparse.isspmatrix_lil(X), "Unknown instance type: " + x_type
# 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.shape
out = np.zeros(s)
out[:] = explanation
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))
if kwargs.get("gc_collect", False):
gc.collect()
# vector-output
s = explanations[0].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][:, j]
outs = np.stack(outs, axis=-1) # type: ignore[assignment]
return outs # type: ignore[return-value]
# single-output
else:
out = np.zeros((X.shape[0], s[0]))
for i in range(X.shape[0]):
out[i] = explanations[i]
return out
else:
emsg = "Instance must have 1 or 2 dimensions!"
raise DimensionError(emsg)
def explain(self, incoming_instance: Any, **kwargs: Any) -> npt.NDArray[Any]:
# convert incoming input to a standardized iml object
instance = convert_to_instance(incoming_instance)
match_instance_to_data(instance, self.data)
# find the feature groups we will test. If a feature does not change from its
# current value then we know it doesn't impact the model
self.varyingInds = self.varying_groups(instance.x)
if self.data.groups is None:
self.varyingFeatureGroups = np.array([i for i in self.varyingInds])
self.M = self.varyingFeatureGroups.shape[0]
else:
self.varyingFeatureGroups = [self.data.groups[i] for i in self.varyingInds]
self.M = len(self.varyingFeatureGroups)
groups = self.data.groups
# convert to numpy array as it is much faster if not jagged array (all groups of same length)
if self.varyingFeatureGroups and all(len(groups[i]) == len(groups[0]) for i in self.varyingInds):
self.varyingFeatureGroups = np.array(self.varyingFeatureGroups)
# further performance optimization in case each group has a single value
if self.varyingFeatureGroups.shape[1] == 1:
self.varyingFeatureGroups = self.varyingFeatureGroups.flatten()
# find f(x)
if self.keep_index:
model_out = self.model.f(instance.convert_to_df())
else:
model_out = self.model.f(instance.x)
if isinstance(model_out, (pd.DataFrame, pd.Series)):
model_out = model_out.values
elif safe_isinstance(model_out, "tensorflow.python.framework.ops.SymbolicTensor"):
model_out = self._convert_symbolic_tensor(model_out)
self.fx = model_out[0]
if not self.vector_out:
self.fx = np.array([self.fx])
# if no features vary then no feature has an effect
if self.M == 0:
phi = np.zeros((self.data.groups_size, self.D))
phi_var = np.zeros((self.data.groups_size, self.D))
# if only one feature varies then it has all the effect
elif self.M == 1:
phi = np.zeros((self.data.groups_size, self.D))
phi_var = np.zeros((self.data.groups_size, self.D))
diff = self.link.f(self.fx) - self.link.f(self.fnull)
for d in range(self.D):
phi[self.varyingInds[0], d] = diff[d]
# if more than one feature varies then we have to do real work
else:
self.l1_reg = kwargs.get("l1_reg", "num_features(10)")
# pick a reasonable number of samples if the user didn't specify how many they wanted
self.nsamples = kwargs.get("nsamples", "auto")
if self.nsamples == "auto":
self.nsamples = 2 * self.M + 2**11
# if we have enough samples to enumerate all subsets then ignore the unneeded samples
self.max_samples = 2**30
if self.M <= 30:
self.max_samples = 2**self.M - 2
if self.nsamples > self.max_samples:
self.nsamples = self.max_samples
# reserve space for some of our computations
self.allocate()
# weight the different subset sizes
num_subset_sizes = int(np.ceil((self.M - 1) / 2.0))
num_paired_subset_sizes = int(np.floor((self.M - 1) / 2.0))
weight_vector = np.array([(self.M - 1.0) / (i * (self.M - i)) for i in range(1, num_subset_sizes + 1)])
weight_vector[:num_paired_subset_sizes] *= 2
weight_vector /= np.sum(weight_vector)
log.debug(f"{weight_vector = }")
log.debug(f"{num_subset_sizes = }")
log.debug(f"{num_paired_subset_sizes = }")
log.debug(f"{self.M = }")
# fill out all the subset sizes we can completely enumerate
# given nsamples*remaining_weight_vector[subset_size]
num_full_subsets = 0
num_samples_left = self.nsamples
group_inds = np.arange(self.M, dtype="int64")
mask = np.zeros(self.M)
remaining_weight_vector = copy.copy(weight_vector)
for subset_size in range(1, num_subset_sizes + 1):
# determine how many subsets (and their complements) are of the current size
nsubsets = binom(self.M, subset_size)
if subset_size <= num_paired_subset_sizes:
nsubsets *= 2
log.debug(f"{subset_size = }")
log.debug(f"{nsubsets = }")
log.debug(
"self.nsamples*weight_vector[subset_size-1] = "
f"{num_samples_left * remaining_weight_vector[subset_size - 1]}"
)
log.debug(
"self.nsamples*weight_vector[subset_size-1]/nsubsets = "
f"{num_samples_left * remaining_weight_vector[subset_size - 1] / nsubsets}"
)
# see if we have enough samples to enumerate all subsets of this size
if num_samples_left * remaining_weight_vector[subset_size - 1] / nsubsets >= 1.0 - 1e-8:
num_full_subsets += 1
num_samples_left -= nsubsets
# rescale what's left of the remaining weight vector to sum to 1
if remaining_weight_vector[subset_size - 1] < 1.0:
remaining_weight_vector /= 1 - remaining_weight_vector[subset_size - 1]
# add all the samples of the current subset size
w = weight_vector[subset_size - 1] / binom(self.M, subset_size)
if subset_size <= num_paired_subset_sizes:
w /= 2.0
for inds in itertools.combinations(group_inds, subset_size):
mask[:] = 0.0
mask[np.array(inds, dtype="int64")] = 1.0
self.addsample(instance.x, mask, w)
if subset_size <= num_paired_subset_sizes:
mask[:] = np.abs(mask - 1)
self.addsample(instance.x, mask, w)
else:
break
log.info(f"{num_full_subsets = }")
# add random samples from what is left of the subset space
nfixed_samples = self.nsamplesAdded
samples_left = self.nsamples - self.nsamplesAdded
log.debug(f"{samples_left = }")
if num_full_subsets != num_subset_sizes:
remaining_weight_vector = copy.copy(weight_vector)
remaining_weight_vector[:num_paired_subset_sizes] /= 2 # because we draw two samples each below
remaining_weight_vector = remaining_weight_vector[num_full_subsets:]
remaining_weight_vector /= np.sum(remaining_weight_vector)
log.info(f"{remaining_weight_vector = }")
log.info(f"{num_paired_subset_sizes = }")
ind_set = np.random.choice(len(remaining_weight_vector), 4 * samples_left, p=remaining_weight_vector)
ind_set_pos = 0
used_masks = {}
while samples_left > 0 and ind_set_pos < len(ind_set):
mask.fill(0.0)
ind = ind_set[ind_set_pos] # we call np.random.choice once to save time and then just read it here
ind_set_pos += 1
subset_size = ind + num_full_subsets + 1
mask[np.random.permutation(self.M)[:subset_size]] = 1.0
# only add the sample if we have not seen it before, otherwise just
# increment a previous sample's weight
mask_tuple = tuple(mask)
new_sample = False
if mask_tuple not in used_masks:
new_sample = True
used_masks[mask_tuple] = self.nsamplesAdded
samples_left -= 1
self.addsample(instance.x, mask, 1.0)
else:
self.kernelWeights[used_masks[mask_tuple]] += 1.0
# add the compliment sample
if samples_left > 0 and subset_size <= num_paired_subset_sizes:
mask[:] = np.abs(mask - 1)
# only add the sample if we have not seen it before, otherwise just
# increment a previous sample's weight
if new_sample:
samples_left -= 1
self.addsample(instance.x, mask, 1.0)
else:
# we know the compliment sample is the next one after the original sample, so + 1
self.kernelWeights[used_masks[mask_tuple] + 1] += 1.0
# normalize the kernel weights for the random samples to equal the weight left after
# the fixed enumerated samples have been already counted
weight_left = np.sum(weight_vector[num_full_subsets:])
log.info(f"{weight_left = }")
self.kernelWeights[nfixed_samples:] *= weight_left / self.kernelWeights[nfixed_samples:].sum()
# execute the model on the synthetic samples we have created
self.run()
# solve then expand the feature importance (Shapley value) vector to contain the non-varying features
phi = np.zeros((self.data.groups_size, self.D))
phi_var = np.zeros((self.data.groups_size, self.D))
for d in range(self.D):
vphi, vphi_var = self.solve(self.nsamples / self.max_samples, d)
phi[self.varyingInds, d] = vphi
phi_var[self.varyingInds, d] = vphi_var
if not self.vector_out:
phi = np.squeeze(phi, axis=1)
phi_var = np.squeeze(phi_var, axis=1)
return phi
@staticmethod
def not_equal(i: Any, j: Any) -> int:
number_types = (int, float, np.number)
if isinstance(i, number_types) and isinstance(j, number_types):
return 0 if np.allclose(i, j, equal_nan=True) else 1
elif hasattr(i, "dtype") and hasattr(j, "dtype"):
if np.issubdtype(i.dtype, np.number) and np.issubdtype(j.dtype, np.number):
return 0 if np.allclose(i, j, equal_nan=True) else 1
if np.issubdtype(i.dtype, np.bool_) and np.issubdtype(j.dtype, np.bool_):
return 0 if np.allclose(i, j, equal_nan=True) else 1
return 0 if all(i == j) else 1
else:
return 0 if i == j else 1
def varying_groups(self, x: npt.NDArray[Any] | scipy.sparse.spmatrix) -> npt.NDArray[np.intp]:
if not scipy.sparse.issparse(x):
varying = np.zeros(self.data.groups_size)
for i in range(self.data.groups_size):
inds = self.data.groups[i] # type: ignore[index]
x_group = x[0, inds]
if scipy.sparse.issparse(x_group):
if all(j not in x.nonzero()[1] for j in inds):
varying[i] = False
continue
x_group = x_group.todense()
varying[i] = self.not_equal(x_group, self.data.data[:, inds])
varying_indices = np.nonzero(varying)[0]
return varying_indices
else:
varying_indices = [] # type: ignore[assignment]
# go over all nonzero columns in background and evaluation data
# if both background and evaluation are zero, the column does not vary
varying_indices = np.unique(np.union1d(self.data.data.nonzero()[1], x.nonzero()[1]))
remove_unvarying_indices = []
for i in range(len(varying_indices)):
varying_index = varying_indices[i]
# now verify the nonzero values do vary
data_rows = self.data.data[:, [varying_index]]
nonzero_rows = data_rows.nonzero()[0]
if nonzero_rows.size > 0:
background_data_rows = data_rows[nonzero_rows]
if scipy.sparse.issparse(background_data_rows):
background_data_rows = background_data_rows.toarray()
num_mismatches = np.sum(np.abs(background_data_rows - x[0, varying_index]) > 1e-7)
# Note: If feature column non-zero but some background zero, can't remove index
if num_mismatches == 0 and not (
np.abs(x[0, [varying_index]][0, 0]) > 1e-7 and len(nonzero_rows) < data_rows.shape[0]
):
remove_unvarying_indices.append(i)
mask = np.ones(len(varying_indices), dtype=bool)
mask[remove_unvarying_indices] = False
varying_indices = varying_indices[mask]
return varying_indices
def allocate(self) -> None:
if scipy.sparse.issparse(self.data.data):
# We tile the sparse matrix in csr format but convert it to lil
# for performance when adding samples
shape = self.data.data.shape
nnz = self.data.data.nnz
data_rows, data_cols = shape
rows = data_rows * self.nsamples
shape = rows, data_cols
if nnz == 0:
self.synth_data = scipy.sparse.csr_matrix(shape, dtype=self.data.data.dtype).tolil()
else:
data = self.data.data.data
indices = self.data.data.indices
indptr = self.data.data.indptr
last_indptr_idx = indptr[len(indptr) - 1]
indptr_wo_last = indptr[:-1]
new_indptrs = []
for i in range(self.nsamples - 1):
new_indptrs.append(indptr_wo_last + (i * last_indptr_idx))
new_indptrs.append(indptr + ((self.nsamples - 1) * last_indptr_idx))
new_indptr = np.concatenate(new_indptrs)
new_data = np.tile(data, self.nsamples)
new_indices = np.tile(indices, self.nsamples)
self.synth_data = scipy.sparse.csr_matrix((new_data, new_indices, new_indptr), shape=shape).tolil()
else:
self.synth_data = np.tile(self.data.data, (self.nsamples, 1))
self.maskMatrix = np.zeros((self.nsamples, self.M))
self.kernelWeights = np.zeros(self.nsamples)
self.y = np.zeros((self.nsamples * self.N, self.D))
self.ey = np.zeros((self.nsamples, self.D))
self.lastMask = np.zeros(self.nsamples)
self.nsamplesAdded = 0
self.nsamplesRun = 0
if self.keep_index:
self.synth_data_index = np.tile(self.data.index_value, self.nsamples) # type: ignore[union-attr]
def addsample(
self,
x: npt.NDArray[Any] | scipy.sparse.spmatrix,
m: npt.NDArray[Any],
w: float,
) -> None:
offset = self.nsamplesAdded * self.N
if isinstance(self.varyingFeatureGroups, (list,)):
for j in range(self.M):
for k in self.varyingFeatureGroups[j]:
if m[j] == 1.0:
self.synth_data[offset : offset + self.N, k] = x[0, k]
else:
# for non-jagged numpy array we can significantly boost performance
mask = m == 1.0
groups = self.varyingFeatureGroups[mask]
if len(groups.shape) == 2:
for group in groups:
self.synth_data[offset : offset + self.N, group] = x[0, group]
else:
# further performance optimization in case each group has a single feature
evaluation_data = x[0, groups]
# In edge case where background is all dense but evaluation data
# is all sparse, make evaluation data dense
if scipy.sparse.issparse(x) and not scipy.sparse.issparse(self.synth_data):
evaluation_data = evaluation_data.toarray()
self.synth_data[offset : offset + self.N, groups] = evaluation_data
self.maskMatrix[self.nsamplesAdded, :] = m
self.kernelWeights[self.nsamplesAdded] = w
self.nsamplesAdded += 1
def run(self) -> None:
num_to_run = self.nsamplesAdded * self.N - self.nsamplesRun * self.N
data = self.synth_data[self.nsamplesRun * self.N : self.nsamplesAdded * self.N, :]
if self.keep_index:
index = self.synth_data_index[self.nsamplesRun * self.N : self.nsamplesAdded * self.N]
index = pd.DataFrame(index, columns=[self.data.index_name]) # type: ignore[union-attr]
data = pd.DataFrame(data, columns=self.data.group_names)
data = pd.concat([index, data], axis=1).set_index(self.data.index_name) # type: ignore[union-attr]
if self.keep_index_ordered:
data = data.sort_index()
modelOut = self.model.f(data)
if isinstance(modelOut, (pd.DataFrame, pd.Series)):
modelOut = modelOut.values
elif safe_isinstance(modelOut, "tensorflow.python.framework.ops.SymbolicTensor"):
modelOut = self._convert_symbolic_tensor(modelOut)
self.y[self.nsamplesRun * self.N : self.nsamplesAdded * self.N, :] = np.reshape(modelOut, (num_to_run, self.D))
# find the expected value of each output
self.nsamplesRun = compute_exp_val(
self.nsamplesRun, self.nsamplesAdded, self.D, self.N, self.data.weights, self.y, self.ey
)
def solve(
self,
fraction_evaluated: float,
dim: int,
) -> tuple[npt.NDArray[Any], npt.NDArray[Any]]:
eyAdj = self.linkfv(self.ey[:, dim]) - self.link.f(self.fnull[dim])
s = np.sum(self.maskMatrix, 1)
# do feature selection if we have not well enumerated the space
nonzero_inds = np.arange(self.M)
log.debug(f"{fraction_evaluated = }")
if self.l1_reg == "auto":
warnings.warn("l1_reg='auto' is deprecated and will be removed in a future version.", DeprecationWarning)
if (self.l1_reg not in ["auto", False, 0]) or (fraction_evaluated < 0.2 and self.l1_reg == "auto"):
w_aug = np.hstack((self.kernelWeights * (self.M - s), self.kernelWeights * s))
log.info(f"{np.sum(w_aug) = }")
log.info(f"{np.sum(self.kernelWeights) = }")
w_sqrt_aug = np.sqrt(w_aug)
eyAdj_aug = np.hstack((eyAdj, eyAdj - (self.link.f(self.fx[dim]) - self.link.f(self.fnull[dim]))))
eyAdj_aug *= w_sqrt_aug
mask_aug = np.transpose(w_sqrt_aug * np.transpose(np.vstack((self.maskMatrix, self.maskMatrix - 1))))
# var_norms = np.array([np.linalg.norm(mask_aug[:, i]) for i in range(mask_aug.shape[1])])
# select a fixed number of top features
if isinstance(self.l1_reg, str) and self.l1_reg.startswith("num_features("):
r = int(self.l1_reg[len("num_features(") : -1])
nonzero_inds = lars_path(mask_aug, eyAdj_aug, max_iter=r)[1]
# use an adaptive regularization method
elif self.l1_reg in ("auto", "bic", "aic"):
c = "aic" if self.l1_reg == "auto" else self.l1_reg
# "Normalize" parameter of LassoLarsIC was deprecated in sklearn version 1.2
if version.parse(sklearn.__version__) < version.parse("1.2.0"):
kwg = dict(normalize=False)
else:
kwg = {}
model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC(criterion=c, **kwg))
nonzero_inds = np.nonzero(model.fit(mask_aug, eyAdj_aug)[1].coef_)[0]
# use a fixed regularization coefficient
else:
nonzero_inds = np.nonzero(Lasso(alpha=self.l1_reg).fit(mask_aug, eyAdj_aug).coef_)[0]
if len(nonzero_inds) == 0:
return np.zeros(self.M), np.ones(self.M)
# eliminate one variable with the constraint that all features sum to the output
eyAdj2 = eyAdj - self.maskMatrix[:, nonzero_inds[-1]] * (
self.link.f(self.fx[dim]) - self.link.f(self.fnull[dim])
)
etmp = np.transpose(np.transpose(self.maskMatrix[:, nonzero_inds[:-1]]) - self.maskMatrix[:, nonzero_inds[-1]])
log.debug(f"{etmp[:4, :] = }")
# solve a weighted least squares equation to estimate phi
# least squares:
# phi = min_w ||W^(1/2) (y - X w)||^2
# the corresponding normal equation:
# (X' W X) phi = X' W y
# with
# X = etmp
# W = np.diag(self.kernelWeights)
# y = eyAdj2
#
# We could just rely on sciki-learn
# from sklearn.linear_model import LinearRegression
# lm = LinearRegression(fit_intercept=False).fit(etmp, eyAdj2, sample_weight=self.kernelWeights)
# Under the hood, as of scikit-learn version 1.3, LinearRegression still uses np.linalg.lstsq and
# there are more performant options. See https://github.com/scikit-learn/scikit-learn/issues/22855.
y = np.asarray(eyAdj2)
X = etmp
WX = self.kernelWeights[:, None] * X
try:
w = np.linalg.solve(X.T @ WX, WX.T @ y)
except np.linalg.LinAlgError:
warnings.warn(
"Linear regression equation is singular, a least squares solutions is used instead.\n"
"To avoid this situation and get a regular matrix do one of the following:\n"
"1) turn up the number of samples,\n"
"2) turn up the L1 regularization with num_features(N) where N is less than the number of samples,\n"
"3) group features together to reduce the number of inputs that need to be explained."
)
# XWX = np.linalg.pinv(X.T @ WX)
# w = np.dot(XWX, np.dot(np.transpose(WX), y))
sqrt_W = np.sqrt(self.kernelWeights)
w = np.linalg.lstsq(sqrt_W[:, None] * X, sqrt_W * y, rcond=None)[0]
log.debug(f"{np.sum(w) = }")
log.debug(
f"self.link(self.fx) - self.link(self.fnull) = {self.link.f(self.fx[dim]) - self.link.f(self.fnull[dim])}"
)
log.debug(f"self.fx = {self.fx[dim]}")
log.debug(f"self.link(self.fx) = {self.link.f(self.fx[dim])}")
log.debug(f"self.fnull = {self.fnull[dim]}")
log.debug(f"self.link(self.fnull) = {self.link.f(self.fnull[dim])}")
phi = np.zeros(self.M)
phi[nonzero_inds[:-1]] = w
phi[nonzero_inds[-1]] = (self.link.f(self.fx[dim]) - self.link.f(self.fnull[dim])) - sum(w)
log.info(f"{phi = }")
# clean up any rounding errors
for i in range(self.M):
if np.abs(phi[i]) < 1e-10:
phi[i] = 0
return phi, np.ones(len(phi))