I am trying to explain the outputs of my convolutional neural network bult in Keras with LIME.
My neural network is a multi-class text classifier where every class is independent. Thus, a text can contain class 1 and 2 or only 1 etc. A fifth "class" (None) for cases where no classes are in the text.
However, while i managed to explain a binary classification case with Keras and Lime, I just cannot get the multi-class case with independent classes. A first help was found here:
However, my code does not work, I get internal errors from Lime such as: "ValueError: Found input variables with inconsistent numbers of samples: [5000, 100000]"
from lime.lime_text import LimeTextExplainer, TextDomainMapper
explainer = LimeTextExplainer(class_names=encoder.classes_)
chosen_text = 2
def flatten_predict(i):
global model
# catch single string inputs and convert them to list
if i.__class__ != list:
i = [i]
print("## Caught and transformed single string.")
# list for predictions
predStorage = []
# loop through input list and predict
for textInput in i:
textInput = preprocess(textInput)
textInput = make_predictable(textInput)
pred = model.predict(textInput)
pred = np.append(pred, 1-pred, axis=1)
# control output of function
predStorage.extend(pred)
return np.asarray(predStorage)
def get_predict_proba_fn_of_class(label):
"""assuming wrapped_predict outputs an (n, d) array of prediction probabilities, where d is the number of labels"""
def rewrapped_predict(strings):
preds = flatten_predict(strings)[:, np.where(flatten_predict(strings)==label)].reshape(-1, 1)
ret = np.asarray(np.hstack([(1 - preds), preds]))
return ret
return rewrapped_predict
str = 'Ein sehr freundlicher Arzt.'
preds = flatten_predict(str)
labels_to_explain = preds#
print(labels_to_explain)
explanation_for_label = {}
for label in labels_to_explain:
wrapped = get_predict_proba_fn_of_class(label)
explanation_for_label[label] = explainer.explain_instance(str, wrapped)
explanation_for_label[label].show_in_notebook()
Error Message:
ValueError Traceback (most recent call last)
<ipython-input-26-8df61aaa23f4> in <module>()
53 for label in labels_to_explain:
54 wrapped = get_predict_proba_fn_of_class(label)
---> 55 explanation_for_label[label] = explainer.explain_instance(str, wrapped)
56 explanation_for_label[label].show_in_notebook()
57
/usr/local/lib/python3.6/dist-packages/lime/lime_text.py in explain_instance(self, text_instance, classifier_fn, labels, top_labels, num_features, num_samples, distance_metric, model_regressor)
405 data, yss, distances, label, num_features,
406 model_regressor=model_regressor,
--> 407 feature_selection=self.feature_selection)
408 return ret_exp
409
/usr/local/lib/python3.6/dist-packages/lime/lime_base.py in explain_instance_with_data(self, neighborhood_data, neighborhood_labels, distances, label, num_features, feature_selection, model_regressor)
155 weights,
156 num_features,
--> 157 feature_selection)
158
159 if model_regressor is None:
/usr/local/lib/python3.6/dist-packages/lime/lime_base.py in feature_selection(self, data, labels, weights, num_features, method)
104 n_method = 'highest_weights'
105 return self.feature_selection(data, labels, weights,
--> 106 num_features, n_method)
107
108 def explain_instance_with_data(self,
/usr/local/lib/python3.6/dist-packages/lime/lime_base.py in feature_selection(self, data, labels, weights, num_features, method)
78 clf = Ridge(alpha=0, fit_intercept=True,
79 random_state=self.random_state)
---> 80 clf.fit(data, labels, sample_weight=weights)
81 feature_weights = sorted(zip(range(data.shape[0]),
82 clf.coef_ * data[0]),
/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/ridge.py in fit(self, X, y, sample_weight)
678 self : returns an instance of self.
679 """
--> 680 return super(Ridge, self).fit(X, y, sample_weight=sample_weight)
681
682
/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/ridge.py in fit(self, X, y, sample_weight)
489
490 X, y = check_X_y(X, y, ['csr', 'csc', 'coo'], dtype=_dtype,
--> 491 multi_output=True, y_numeric=True)
492
493 if ((sample_weight is not None) and
/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)
764 y = y.astype(np.float64)
765
--> 766 check_consistent_length(X, y)
767
768 return X, y
/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py in check_consistent_length(*arrays)
233 if len(uniques) > 1:
234 raise ValueError("Found input variables with inconsistent numbers of"
--> 235 " samples: %r" % [int(l) for l in lengths])
236
237
ValueError: Found input variables with inconsistent numbers of samples: [5000, 100000]
Does anyone know what I am doing wrong? I am pretty sure it has to do with the input format.
I managed to solve the problem now. Here comes the solution for those who might have an interest in it.
In short, the trick was to pick the right columns from the numpy arrays in get_predict_proba_fn_of_class. While I had five independent classification scores that do not add up to one, I had to add the negative scores for every label's classification scores in a new column (e.g. for 0.67 I added 1-0.67) and then pick the original and new column.
from lime.lime_text import LimeTextExplainer, TextDomainMapper
print(encoder.classes_)
##https://lime-ml.readthedocs.io/en/latest/lime.html#module-lime.lime_text
def flatten_predict(i):
global model
# catch single string input and convert to list
if i.__class__ != list:
i = [i]
# list for predictions
predStorage = []
# loop through input list and predict
for textInput in i:
textInput = preprocess(textInput)
textInput = make_predictable(textInput)
pred = model.predict(textInput)
pred = np.append(pred, 1-pred, axis=1)
predStorage.extend(pred)
return np.asarray(predStorage)
def get_predict_proba_fn_of_class(strings):
def rewrapped_predict(strings):
pred = flatten_predict(strings)
index = np.where(pred==label)[1][0]
preds = pred[:, index::5]
return preds
return rewrapped_predict
string="Der Arzt weiß, was er tut und hat mir alles genau erklärt."
print("Simple model prediction:", model.predict(make_predictable(preprocess(string))))
labels_to_explain = flatten_predict(string)
print("labels_to_explain:", labels_to_explain)
explanation_for_label = {}
for index, label in enumerate(labels_to_explain[0]):
if index < (len(labels_to_explain[0])/2):
actual_classes = [encoder.classes_[index], 'None']
explainer = LimeTextExplainer(class_names=actual_classes)
wrapped = get_predict_proba_fn_of_class(string) # function returns function!
explanation_for_label[label] = explainer.explain_instance(string, wrapped)
explanation_for_label[label].show_in_notebook()