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Functions for explaining classifiers that use Image data.
import copy
from functools import partial
import numpy as np
import sklearn
import sklearn.preprocessing
from sklearn.utils import check_random_state
from skimage.color import gray2rgb
from . import lime_base
from .wrappers.scikit_image import SegmentationAlgorithm
class ImageExplanation(object):
def __init__(self, image, segments):
"""Init function.
image: 3d numpy array
segments: 2d numpy array, with the output from skimage.segmentation
self.image = image
self.segments = segments
self.intercept = {}
self.local_exp = {}
self.local_pred = None
def get_image_and_mask(self, label, positive_only=True, hide_rest=False,
num_features=5, min_weight=0.):
"""Init function.
label: label to explain
positive_only: if True, only take superpixels that contribute to
the prediction of the label. Otherwise, use the top
num_features superpixels, which can be positive or negative
towards the label
hide_rest: if True, make the non-explanation part of the return
image gray
num_features: number of superpixels to include in explanation
min_weight: TODO
(image, mask), where image is a 3d numpy array and mask is a 2d
numpy array that can be used with
if label not in self.local_exp:
raise KeyError('Label not in explanation')
segments = self.segments
image = self.image
exp = self.local_exp[label]
mask = np.zeros(segments.shape, segments.dtype)
if hide_rest:
temp = np.zeros(self.image.shape)
temp = self.image.copy()
if positive_only:
fs = [x[0] for x in exp
if x[1] > 0 and x[1] > min_weight][:num_features]
for f in fs:
temp[segments == f] = image[segments == f].copy()
mask[segments == f] = 1
return temp, mask
for f, w in exp[:num_features]:
if np.abs(w) < min_weight:
c = 0 if w < 0 else 1
mask[segments == f] = 1 if w < 0 else 2
temp[segments == f] = image[segments == f].copy()
temp[segments == f, c] = np.max(image)
for cp in [0, 1, 2]:
if c == cp:
# temp[segments == f, cp] *= 0.5
return temp, mask
class LimeImageExplainer(object):
"""Explains predictions on Image (i.e. matrix) data.
For numerical features, perturb them by sampling from a Normal(0,1) and
doing the inverse operation of mean-centering and scaling, according to the
means and stds in the training data. For categorical features, perturb by
sampling according to the training distribution, and making a binary
feature that is 1 when the value is the same as the instance being
def __init__(self, kernel_width=.25, kernel=None, verbose=False,
feature_selection='auto', random_state=None):
"""Init function.
kernel_width: kernel width for the exponential kernel.
If None, defaults to sqrt(number of columns) * 0.75.
kernel: similarity kernel that takes euclidean distances and kernel
width as input and outputs weights in (0,1). If None, defaults to
an exponential kernel.
verbose: if true, print local prediction values from linear model
feature_selection: feature selection method. can be
'forward_selection', 'lasso_path', 'none' or 'auto'.
See function 'explain_instance_with_data' in for
details on what each of the options does.
random_state: an integer or numpy.RandomState that will be used to
generate random numbers. If None, the random state will be
initialized using the internal numpy seed.
kernel_width = float(kernel_width)
if kernel is None:
def kernel(d, kernel_width):
return np.sqrt(np.exp(-(d ** 2) / kernel_width ** 2))
kernel_fn = partial(kernel, kernel_width=kernel_width)
self.random_state = check_random_state(random_state)
self.feature_selection = feature_selection
self.base = lime_base.LimeBase(kernel_fn, verbose, random_state=self.random_state)
def explain_instance(self, image, classifier_fn, labels=(1,),
top_labels=5, num_features=100000, num_samples=1000,
"""Generates explanations for a prediction.
First, we generate neighborhood data by randomly perturbing features
from the instance (see __data_inverse). We then learn locally weighted
linear models on this neighborhood data to explain each of the classes
in an interpretable way (see
image: 3 dimension RGB image. If this is only two dimensional,
we will assume it's a grayscale image and call gray2rgb.
classifier_fn: classifier prediction probability function, which
takes a numpy array and outputs prediction probabilities. For
ScikitClassifiers , this is classifier.predict_proba.
labels: iterable with labels to be explained.
hide_color: TODO
top_labels: if not None, ignore labels and produce explanations for
the K labels with highest prediction probabilities, where K is
this parameter.
num_features: maximum number of features present in explanation
num_samples: size of the neighborhood to learn the linear model
batch_size: TODO
distance_metric: the distance metric to use for weights.
model_regressor: sklearn regressor to use in explanation. Defaults
to Ridge regression in LimeBase. Must have model_regressor.coef_
and 'sample_weight' as a parameter to
segmentation_fn: SegmentationAlgorithm, wrapped skimage
segmentation function
random_seed: integer used as random seed for the segmentation
algorithm. If None, a random integer, between 0 and 1000,
will be generated using the internal random number generator.
An Explanation object (see with the corresponding
if len(image.shape) == 2:
image = gray2rgb(image)
if random_seed is None:
random_seed = self.random_state.randint(0, high=1000)
if segmentation_fn is None:
segmentation_fn = SegmentationAlgorithm('quickshift', kernel_size=4,
max_dist=200, ratio=0.2,
segments = segmentation_fn(image)
except ValueError as e:
raise e
fudged_image = image.copy()
if hide_color is None:
for x in np.unique(segments):
fudged_image[segments == x] = (
np.mean(image[segments == x][:, 0]),
np.mean(image[segments == x][:, 1]),
np.mean(image[segments == x][:, 2]))
fudged_image[:] = hide_color
top = labels
data, labels = self.data_labels(image, fudged_image, segments,
classifier_fn, num_samples,
distances = sklearn.metrics.pairwise_distances(
data[0].reshape(1, -1),
ret_exp = ImageExplanation(image, segments)
if top_labels:
top = np.argsort(labels[0])[-top_labels:]
ret_exp.top_labels = list(top)
for label in top:
ret_exp.score, ret_exp.local_pred) = self.base.explain_instance_with_data(
data, labels, distances, label, num_features,
return ret_exp
def data_labels(self,
"""Generates images and predictions in the neighborhood of this image.
image: 3d numpy array, the image
fudged_image: 3d numpy array, image to replace original image when
superpixel is turned off
segments: segmentation of the image
classifier_fn: function that takes a list of images and returns a
matrix of prediction probabilities
num_samples: size of the neighborhood to learn the linear model
batch_size: classifier_fn will be called on batches of this size.
A tuple (data, labels), where:
data: dense num_samples * num_superpixels
labels: prediction probabilities matrix
n_features = np.unique(segments).shape[0]
data = self.random_state.randint(0, 2, num_samples * n_features)\
.reshape((num_samples, n_features))
labels = []
data[0, :] = 1
imgs = []
for row in data:
temp = copy.deepcopy(image)
zeros = np.where(row == 0)[0]
mask = np.zeros(segments.shape).astype(bool)
for z in zeros:
mask[segments == z] = True
temp[mask] = fudged_image[mask]
if len(imgs) == batch_size:
preds = classifier_fn(np.array(imgs))
imgs = []
if len(imgs) > 0:
preds = classifier_fn(np.array(imgs))
return data, np.array(labels)