lime, interpret-ml-models
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How to Use LIME to Understand sklearn Models Predictions?¶
Table of Contents¶
LIME - Local Interpretable Model-Agnostic Explanations ¶
The interpretation of machine learning models has become of prime importance nowadays as we handle big datasets and complicated data types like image, audio, text, structured data with thousands of features, etc. The traditional metrics like accuracy, r2 score, roc AUC curves, precision-recall curves, etc does not give machine learning practitioner enough confidence about model performance as well as reliability. The accuracy of model and interpretability has an inverse relationship for data with lots of samples and features which means that models like neural networks, gradient boosting machines, random forests (Generated using Keras, PyTorch, TensorFlow, sklearn, etc) gives high accuracy but are less interpretable compared to models like linear regression (Generated using sklearn, statsmodels) which might give less accuracy but are easy to interpret. We can have machine learning models that give more than 95% accuracy but fails to recognize some classes of dataset due to underrepresentation or uses of irrelevant features in prediction. It has become the need of an hour to understand better which features are contributing by how much in particular prediction so that we can better understand models that make sense to persons who are not ML practitioners. The python has libraries like lime, SHAP, eli5, interpret, etc which provides different ways to explaining predictions made by models. As a part of this tutorial, we'll be specifically concentrating on lime.
The lime stands for local interpretable model agnostic explanations takes any machine learning models as input and generates explanations about feature contributions in making a prediction. It assumes that is a black box model which means that it does not know the inner workings of models and generates explanation based on this assumption.
How LIME works internally?¶
Below we have tried to explain how LIME works internally. The steps are taken from a presentation given by Kasia Kulma on LIME and the link to the presentation is given in the references section at last.
- LIME takes an
individual sampleandgenerates fake datasetbased on it. It thenpermutesthe fake dataset. - It then
calculates distancemetrics (or similarity metric) between permuted fake data and original observations. This helps to understand how similar permuted fake data is compared to original data. The lime library methods provide us with options to try different similarity metrics for this purpose. - It then
makes a prediction on this new permuted fake datausing our original complex model. - It then picks features that best describe our complex model` performance on permuted fake data. The lime library let us provide how many features to pick up.
- It then
fits simple model(like linear or logistic regression) on the combination ofpermuted data with selected m features and similarity scorescomputed in earlier steps. The lime library lets us provide a simple model that we want to use. Generally, it's linear regression or logistic regression but we can change it. - It then uses
weights derived from that simple model for each feature to explainhow each feature contributed to making a prediction for that sample when predicted using an original complex model.
The lime has three main modules which can be used with different types of datasets:
lime_tabular- It’s used for generating explanations for structured datasets.lime_text- Its used for generating explanations for text datasets.lime_image- It’s used for generating explanations for image datasets.
We'll be explaining the usage of each module with various datasets and ML models. We'll be primarily using models provided from scikit-learn but other library models can be easily incorporated into generating explanations using lime.
We'll start by importing the necessary libraries.
import pandas as pd
import numpy as np
import lime
import matplotlib.pyplot as plt
import random
import warnings
warnings.filterwarnings("ignore")
lime_tabular ¶
The lime has a module named lime_tabular which provides methods that can be used to generate explanations of the model which are trained on structured datasets. We'll be trying regression and classification models on different datasets and then use lime to generate explanations for random samples of dataset.
Example 1 : Regression ¶
As a part of the first example, we'll be using the Boston housing dataset available from scikit-learn. It has information about various houses sold in Boston in past and we'll be predicting the median value of a home 1000 dollars. We have loaded the dataset from the sklearn and printed a description of the dataset as well. We have even loaded the dataset as a pandas dataframe to display the first few samples of data.
from sklearn.datasets import load_boston
boston = load_boston()
for line in boston.DESCR.split("\n")[5:29]:
print(line)
boston_df = pd.DataFrame(data=boston.data, columns = boston.feature_names)
boston_df["Price"] = boston.target
boston_df.head()
Divide Dataset into Train/Test Sets¶
Below we have divided the original dataset into the train (90%) and test (10%) sets.
from sklearn.model_selection import train_test_split
X, Y = boston.data, boston.target
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.90, test_size=0.1, random_state=123, shuffle=True)
X_train.shape, X_test.shape, Y_train.shape, Y_test.shape
We have now fitted a linear regression model from scikit-learn on train dataset and then printed r2 score of the trained model on test & train data.
from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X_train, Y_train)
print("Test R^2 Score : ", lr.score(X_test, Y_test))
print("Train R^2 Score : ", lr.score(X_train, Y_train))
LimeTabularExplainer¶
The lime_tabular module has a class named LimeTabularExplainer which takes as input train data and generated explainer object which can then be used to explain individual prediction. Below is a list of important parameters of the LimeTabularExplainer class.
training_data- It accepts samples (numpy 2D array) that were used to train the model.mode- It accepts one of the below stringsclassification- Default Valueregression
training_labels- It accepts a list of training labels.feature_names- It accepts a list of feature names of data.categorical_features- It accepts list of indices (e.g - [1,4,5,6]) in training data which represents categorical features.categorical_names- It accepts mapping (dict) from integer to list of names. The mapping will have information about all possible values in a particular categorical column. Thecategorical_names[x][y]will be pointing toythvalue of column with index x in dataset.class_names- It accepts a list of class names for the classification tasks.feature_selection- It accepts a list of the below value for feature selection when selecting the m-best feature as described in the internal working of LIME earlier.forward_selectionlasso_pathnoneauto
random_state- It accepts integer ornp.RandomStateobject specifying random state so that we can reproduce the same results each time we rerun the process.
Below we are creating the LimeTabularExplainer object by passing it train data, mode as regression, and feature names.
from lime import lime_tabular
explainer = lime_tabular.LimeTabularExplainer(X_train, mode="regression", feature_names= boston.feature_names)
explainer
explain_instance()¶
The LimeTabularExplainer instance has a method named explain_instance() which takes as input random data sample and method which predicts output as input and generates explanation object. This Explanation object has information about feature contribution to this particular prediction.
Here is a list of important parameters of the method:
data_row- It accepts 1 data sample represented as 1d numpy array or script sparse matrix as input.predict_fn- It accepts prediction function which takes as input sample passed todata_rowas input and generates an actual prediction for regression tasks and class probabilities for classification tasks.labels- It takes as input list of class labels to explain for the multi-class classification tasks.top_labels- It takes as input integer specifying top k class with highest probabilities from prediction to be displayed.num_features- It accepts integer number specifying top k features to keep in explanation. The default is 10.num_samples- It accepts integer specifying the size of samples to use for training a simple linear model. The default is 5000.distance_metric- It accepts distance metric to use to compute the similarity between original and permuted samples. The default value is the stringeuclidean.model_regressor- It accepts a simple model which should be used to train permuted data with m best features. The default is ridge regression from sklearn.
Below we are passing a random sample taken from the test dataset and reference to predict the method of the linear regression model as input to the method and it returns Explanation object.
idx = random.randint(1, len(X_test))
print("Prediction : ", lr.predict(X_test[idx].reshape(1,-1)))
print("Actual : ", Y_test[idx])
explanation = explainer.explain_instance(X_test[idx], lr.predict, num_features=len(boston.feature_names))
explanation
show_in_notebook()¶
The explainer has a method named show_in_notebook() which will explain how we come to a particular prediction based on feature contribution as HTML.
Below the HTML figure shows us the actual predicted value, a bar chart showing weights of how features contributed to this prediction, and a table showing actual feature values.
explanation.show_in_notebook()
as_pyplot_figure()¶
Below we have called the as_pyplot_figure() method to generate a bar chart of feature contribution for this sample.
with plt.style.context("ggplot"):
explanation.as_pyplot_figure()
Below we have printed actual global weights we got from the linear regression model as a matplotlib bar chart.
with plt.style.context("ggplot"):
fig = plt.figure(figsize=(8,5))
plt.barh(range(len(lr.coef_)), lr.coef_, color=["red" if coef<0 else "green" for coef in lr.coef_])
plt.yticks(range(len(lr.coef_)), boston.feature_names);
plt.title("Weights")
as_list()¶
Below we are calling the as_list() method on the Explanation object which returns explanation as a list of tuple where the first value of tuple is condition and the second value contribution of the feature value based on condition.
explanation.as_list()
as_map()¶
Below we have called the as_map() method which is exactly the same as the as_list() method for regression but useful for classification task because it'll return a dictionary where the key is each class of task and value is a list of feature index and their contribution in predicting that class.
explanation.as_map()
as_html()¶
The explanation object has another method named as_html() which returns explanation as HTML stored in a string. We can pass this string to IPython's HTML method for generating HTML output.
from IPython.display import HTML
html_data = explanation.as_html()
HTML(data=html_data)
Below we have printed local prediction and global prediction using explanation. The local prediction is generated by a simple model that was trained on a combination of m best feature permuted data and similarity scores data. We can see that it’s quite close to the actual prediction using our complex model.
print("Explanation Local Prediction : ", explanation.local_pred)
print("Explanation Global Prediction : ", explanation.predicted_value)
save_to_file()¶
We can save explanation as HTML file by calling save_to_file() method on explanation.
explanation.save_to_file("classif_explanation.html")
Example 2 : Binary Classification ¶
The second example that we'll use for explaining the usage of lime_tabular is a binary classification problem. We'll be using a breast cancer dataset available from scikit-learn for this purpose. We have loaded below breast cancer dataset from sklearn and then printed a description of the dataset which explains individual features of the dataset.
We have divided the dataset into the train (90%) and test (10%) sets, fitted logistic regression on train data, and printed metrics like accuracy, confusion matrix, classification report on the test dataset.
from sklearn.datasets import load_breast_cancer
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.linear_model import LogisticRegression
breast_cancer = load_breast_cancer()
for line in breast_cancer.DESCR.split("\n")[5:32]:
print(line)
X, Y = breast_cancer.data, breast_cancer.target
print("Data Size : ", X.shape, Y.shape)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.90, test_size=0.1, stratify=Y, random_state=123)
print("Train/Test Sizes : ", X_train.shape, X_test.shape, Y_train.shape, Y_test.shape)
lr = LogisticRegression()
lr.fit(X_train, Y_train)
print("Test Accuracy : %.2f"%lr.score(X_test, Y_test))
print("Train Accuracy : %.2f"%lr.score(X_train, Y_train))
print()
print("Confusion Matrix : ")
print(confusion_matrix(Y_test, lr.predict(X_test)))
print()
print("Classification Report")
print(classification_report(Y_test, lr.predict(X_test)))
Below we have created a LimeTabularExplainer object based on the training dataset. We'll be using this explainer object to explain a random sample from the test dataset.
explainer = lime_tabular.LimeTabularExplainer(X_train, mode="classification",
class_names=breast_cancer.target_names,
feature_names=breast_cancer.feature_names,
)
explainer
Below we have taken a random sample from the test dataset. We have then called the explain_instance() method on explainer object passing it that random sample and reference to predict_proba() method of logistic regression to generate an explanation object for this random sample. We have then called show_in_notebook() method on explanation object to generate HTML of explanation.
The HTML shows prediction, a bar chart of the contribution of features, and a table with actual feature values. The bar chart is sorted from the most important features to the least important. We can pass a number of important features we want to see to the num_features parameter of the explain_instance() method.
idx = random.randint(1, len(X_test))
print("Prediction : ", breast_cancer.target_names[lr.predict(X_test[idx].reshape(1,-1))[0]])
print("Actual : ", breast_cancer.target_names[Y_test[idx]])
explanation = explainer.explain_instance(X_test[idx], lr.predict_proba,
num_features=len(breast_cancer.feature_names))
explanation.show_in_notebook()
Below we are explaining another random sample from the test dataset for which the model makes the wrong prediction. We are retrieving indices of samples from test data for which model is making mistake. We are then randomly selecting one index from it. We then pass the sample with that index to the explain_instance() method to generate an explanation object. Please make a note that we are only displaying the top 10 features which contribute most to prediction.
preds = lr.predict(X_test)
false_preds = np.argwhere((preds != Y_test)).flatten()
idx = random.choice(false_preds)
print("Prediction : ", breast_cancer.target_names[lr.predict(X_test[idx].reshape(1,-1))[0]])
print("Actual : ", breast_cancer.target_names[Y_test[idx]])
explanation = explainer.explain_instance(X_test[idx], lr.predict_proba)
explanation.show_in_notebook()
Below we have plotted a bar chart of global feature importance based on weights derived from logistic regression. We can use it to compare it with the bar chart generated for individual data samples.
with plt.style.context("ggplot"):
fig = plt.figure(figsize=(8,6))
plt.barh(range(len(lr.coef_[0])), lr.coef_[0], color=["red" if coef<0 else "green" for coef in lr.coef_[0]])
plt.yticks(range(len(lr.coef_[0])), breast_cancer.feature_names);
plt.title("Weights")
print("Explanation Local Prediction : ","malignant" if explanation.local_pred<0.5 else "benign")
print("Explanation Global Prediction Probability : ", explanation.predict_proba)
print("Explanation Global Prediction : ", breast_cancer.target_names[np.argmax(explanation.predict_proba)])
Example 3 : Multi-Class Classification ¶
As a part of our third example for demonstrating usage of the lime_tabular module, we'll be using a multi-class classification problem. We have loaded below wine dataset available from sklearn which has information about various ingredients used in three different types of wine. We have divided data into train/test sets, trained GradientBoostingClassifier on train data, and printed metrics like accuracy, confusion matrix, classification report on the test dataset.
from sklearn.datasets import load_wine
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.ensemble import GradientBoostingClassifier
wine = load_wine()
for line in wine.DESCR.split("\n")[5:29]:
print(line)
X, Y = wine.data, wine.target
print("Data Size : ", X.shape, Y.shape)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.80, test_size=0.2, stratify=Y, random_state=123)
print("Train/Test Sizes : ", X_train.shape, X_test.shape, Y_train.shape, Y_test.shape)
gb = GradientBoostingClassifier()
gb.fit(X_train, Y_train)
print("Test Accuracy : %.2f"%gb.score(X_test, Y_test))
print("Train Accuracy : %.2f"%gb.score(X_train, Y_train))
print()
print("Confusion Matrix : ")
print(confusion_matrix(Y_test, gb.predict(X_test)))
print()
print("Classification Report")
print(classification_report(Y_test, gb.predict(X_test)))
Below we have generation LimeTabularExplainer based on train data.
explainer = lime_tabular.LimeTabularExplainer(X_train, mode="classification",
class_names=wine.target_names,
feature_names=wine.feature_names,
)
explainer
Below we are explaining a random sample from test data using an explanation object. We can see that this time we see three different bar charts showing feature contribution, one for each class. If we want to see bar charts of particular classes only then we can pass class names as a list to the labels parameter of the explain_instance() method. We can also pass an integer to the top_labels method and it'll show that many top classes have a high probability in model prediction.
idx = random.randint(1, len(X_test))
print("Prediction : ", wine.target_names[gb.predict(X_test[idx].reshape(1,-1))[0]])
print("Actual : ", wine.target_names[Y_test[idx]])
explanation = explainer.explain_instance(X_test[idx], gb.predict_proba, top_labels=3)
explanation.show_in_notebook()
Below we are plotting an explanation for a random sample from test data for which model prediction is going wrong.
preds = gb.predict(X_test)
false_preds = np.argwhere((preds != Y_test)).flatten()
idx = random.choice(false_preds)
print("Prediction : ", wine.target_names[gb.predict(X_test[idx].reshape(1,-1))[0]])
print("Actual : ", wine.target_names[Y_test[idx]])
explanation = explainer.explain_instance(X_test[idx], gb.predict_proba, top_labels=3)
explanation.show_in_notebook()
print("Explanation Local Prediction : ", explanation.local_pred)
print("Explanation Global Prediction Probability : ", explanation.predict_proba)
print("Explanation Global Prediction : ", wine.target_names[np.argmax(explanation.predict_proba)])
lime_text ¶
The lime_text module of lime provides explainers that can help us explain unstructured text data. We'll be explaining how to use the text explainer available in the lime_text module of lime as a part of this section.
Example 1 : Binary Classification¶
The first example that we'll use for explaining the usage of the lime_text module is a binary classification problem. We'll be using the spam/ham messages dataset available from UCI to classify whether text data present in the mail is spam or not.
We'll first download data from the UCI ML data directory and then will perform classification by reading a file.
!wget https://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip
!unzip smsspamcollection.zip
Below we have written a simple code that reads one line of data from the SMSSpamCollection text file and retrieves mail content. We have also later counted a number of spam and ham mails.
with open('SMSSpamCollection') as f:
data = [line.strip().split('\t') for line in f.readlines()]
y, text = zip(*data)
import collections
collections.Counter(y)
We have then divided the dataset into the train (75%) and test (25%) sets.
from sklearn.model_selection import train_test_split
text_train, text_test, y_train, y_test = train_test_split(text, y,
random_state=42,
test_size=0.25,
stratify=y)
Below we have first transformed text data from text to float format using TF-IDF vectorizer and then fitted that transformed data to random forest classifier. We have then evaluated model performance on test data printing classification metrics like accuracy, confusion matrix, and classification report.
If you are interested in learning about feature extraction from text data which we have performed here then please feel free to check our tutorial on the same which gives details insight on the topic.
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix, classification_report
tfidf_vectorizer = TfidfVectorizer(analyzer="char")
tfidf_vectorizer.fit(text_train)
X_train_tfidf = tfidf_vectorizer.transform(text_train)
X_test_tfidf = tfidf_vectorizer.transform(text_test)
print(X_train_tfidf.shape, X_test_tfidf.shape)
rf = RandomForestClassifier()
rf.fit(X_train_tfidf, y_train)
print("Test Accuracy : %.2f"%rf.score(X_test_tfidf, y_test))
print("Train Accuracy : %.2f"%rf.score(X_train_tfidf, y_train))
print()
print("Confusion Matrix : ")
print(confusion_matrix(y_test, rf.predict(X_test_tfidf)))
print()
print("Classification Report")
print(classification_report(y_test, rf.predict(X_test_tfidf)))
LimeTextExplainer¶
The LimeTextExplainer class of the lime_text module provides functionality to handle unstructured text data and generate an explanation for it. Below we have first created the LimeTextExplainer object.
Here we have given a list of important parameters of LimeTextExplainer which one can tweak according to their need.
class_names- It accepts a list of class names as input.feature_selection- It accepts a list of the below value for feature selection when selecting the m-best feature as described in the internal working of LIME earlier.forward_selectionlasso_pathnoneauto
split_expression- It accepts the regular expression of a function. The regular expression will be responsible for generating tokens.random_state- It accepts integer ornp.RandomStateobject specifying random state so that we can reproduce the same results each time we rerun the process.
Please make a note that there are few other parameters that we have not mentioned here but can be useful to someone with different scenarios.
Below we have created a LimeTextExplainer object with class names passed to it.
from lime import lime_text
explainer = lime_text.LimeTextExplainer(class_names=["ham", "spam"])
explainer
Below we have created a function that takes as input single sample of text data as input and then returns a prediction about that text is spam or ham. As a part of the function, we are first transforming text using the TF-IDF vectorizer and then returning probabilities for it using random forest. We'll be using this function when creating an explanation for a random sample of test data.
def pred_fn(text):
text_transformed = tfidf_vectorizer.transform(text)
return rf.predict_proba(text_transformed)
pred_fn(text_test[:2])
We have now taken a random test sample and created an explanation object for the same. We have passed function created earlier to classifier_fn parameter of explain_instance() method of LimeTextExplainer object.
There is another way to do the same thing if we don't want to create a function and want to use our default predict_proba() function of random forest. We can pass TF-IDF transformed (X_test_tfidf) random sample instead of actual text sample to explain_instance() method and reference rf.predict_proba to classifier_fn parameter and it'll generate the same results.
idx = random.randint(1, len(text_test))
print("Actual Text : ", text_test[idx])
print("Prediction : ", rf.predict(X_test_tfidf[idx].reshape(1,-1))[0])
print("Actual : ", y_test[idx])
explanation = explainer.explain_instance(text_test[idx], classifier_fn=pred_fn)
explanation.show_in_notebook()
Below we have explained another random sample from test data but this time we have chosen a random sample for which model makes the wrong prediction to understand which words are contributing to the wrong prediction. This can give us more confidence in model performance.
preds = rf.predict(X_test_tfidf)
false_preds = np.argwhere((preds != y_test)).flatten()
idx = random.choice(false_preds)
print("Actual Text : ", text_test[idx])
print("Prediction : ", rf.predict(X_test_tfidf[idx].reshape(1,-1))[0])
print("Actual : ", y_test[idx])
explanation = explainer.explain_instance(text_test[idx], classifier_fn=pred_fn)
explanation.show_in_notebook()
lime_image ¶
The third module that we'll be explaining for the lime library is lime_image which provides an explainer that can help us generate an explanation for images. We'll be using the digits dataset to explain how to generate an explanation using this module.
Example 1 : Digits Classification¶
The example that we'll use for explaining the usage of the lime_image module is a classification of digits dataset. The digits dataset is easily available from scikit-learn. It has images of size 8x8 for digits 0-9. We have below loaded digits dataset, divided it into train/test sets, fitted gradient boosting classifier to train data, and generated classification metrics like accuracy, confusion matrix, and classification report on the test dataset.
from sklearn.datasets import load_digits
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.ensemble import GradientBoostingClassifier
digits = load_digits()
for line in digits.DESCR.split("\n")[5:20]:
print(line)
X, Y = digits.data, digits.target
print("Data Size : ", X.shape, Y.shape)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.80, test_size=0.2, stratify=Y, random_state=123)
print("Train/Test Sizes : ", X_train.shape, X_test.shape, Y_train.shape, Y_test.shape)
gb = GradientBoostingClassifier()
gb.fit(X_train, Y_train)
print("Test Accuracy : %.2f"%gb.score(X_test, Y_test))
print("Train Accuracy : %.2f"%gb.score(X_train, Y_train))
print()
print("Confusion Matrix : ")
print(confusion_matrix(Y_test, gb.predict(X_test)))
print()
print("Classification Report")
print(classification_report(Y_test, gb.predict(X_test)))
LimeImageExplainer¶
The LimeImageExplainer available as a part of the lime_image module can help us explain images by highlighting which parts of the image have contributed to the prediction of a particular class.
Here are some of the important parameters of LimeImageExplainer.
feature_selection- It accepts a list of below value for feature selection when selecting the m-best feature as described in the internal working of LIME earlier.forward_selectionlasso_pathnoneauto
random_state- It accepts integer ornp.RandomStateobject specifying random state so that we can reproduce the same results each time we rerun the process.
Below we have created an instance of LimeImageExplainer which we'll use for explaining images classified using gradient boosting classifier and which part (pixels) of the image contributed to that prediction.
from lime import lime_image
explainer = lime_image.LimeImageExplainer()
Below we have created a function which takes as input list of RGB images, transform them to grayscale images, and then return probabilities of that images using a gradient boosting classifier. The reason for designing this method is that it'll be used when explaining the random sample image.
from skimage.color import gray2rgb, rgb2gray, label2rgb # since the code wants color images
def pred_fn(imgs):
tot_probs = []
for img in imgs:
grayimg = rgb2gray(img)
probs = gb.predict_proba(grayimg.reshape(1, -1))[0]
tot_probs.append(probs)
return tot_probs
pred_fn([X_test[1].reshape(8,8)])
Below we are taking a random sample image from the test dataset. We are then generating an explanation instance for that image by passing the image to the explain_instance() method of explainer object. We have also passed a reference to the prediction function which we designed earlier to the classifier_fn parameter.
Please make a note that the explain_instance() method of LimeImageExplainer requires input image in RGB format whereas our images are 8x8 grayscale images. This is the reason we have first transformed images from grayscale to RGB using the scikit-image function before giving it to the method and the prediction function also transforms this RGB image to grayscale before making prediction because our model works on grayscale images. This simple tweak one needs to understand when working with grayscale and wants to use lime to explain images.
We have then called get_image_and_mask() method on explanation object. This method takes as input actual label for which we want an explanation (highlight part of the image which contributed to predicting that label) and returns two arrays, one is 3D numpy array and another is a 2D numpy array. We can then combine this image and mask to check which pixels contributed to the prediction.
Below are some of the important parameters of get_image_and_mask() method.
label- It accepts the class label that we want to explain.positive_only- It accepts boolean value specifying whether to take only superpixels which contributed positively to the prediction or not. The default is True.negative_only- It accepts boolean value specifying whether to take only superpixels which contributed negatively to the prediction or not. The default is False.hide_rest- It accepts boolean value specifying whether to return pixels that do not contribute to prediction or gray out them. The default is False.num_features- It accepts integer specifying the number of superpixels to include in explanation. The default is 5.
idx = random.randint(1, len(X_test))
print("Prediction : ", gb.predict(X_test[idx].reshape(1,-1))[0])
print("Actual : ", Y_test[idx])
explanation = explainer.explain_instance(gray2rgb(X_test[idx].reshape(8,8)), classifier_fn=pred_fn)
temp, mask = explanation.get_image_and_mask(Y_test[idx], num_features=64)
Below we have combined images generated by get_image_and_mask() to generated a single image highlighting which pixels contributed to prediction.
from skimage.segmentation import mark_boundaries
plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))
Below we have generated an explanation for another random test sample. We have tweaked a few parameters of method get_image_and_mask() for an explanation.
idx = random.randint(1, len(X_test))
print("Prediction : ", gb.predict(X_test[idx].reshape(1,-1))[0])
print("Actual : ", Y_test[idx])
explanation = explainer.explain_instance(gray2rgb(X_test[idx].reshape(8,8)), classifier_fn=pred_fn)
temp, mask = explanation.get_image_and_mask(Y_test[idx], positive_only=True, num_features=10, hide_rest=True, min_weight = 0.01)
plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))
Below we have generated an explanation for another random sample from test data for which our model got the wrong prediction.
preds = gb.predict(X_test)
false_preds = np.argwhere((preds != Y_test)).flatten()
idx = random.choice(false_preds)
print("Prediction : ", gb.predict(X_test[idx].reshape(1,-1))[0])
print("Actual : ", Y_test[idx])
explanation = explainer.explain_instance(gray2rgb(X_test[idx].reshape(8,8)), classifier_fn=pred_fn)
temp, mask = explanation.get_image_and_mask(Y_test[idx])
plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))
This ends our small tutorial explaining various explainers available with lime for explaining model predictions for different kinds of data like structured data, text data, and image data. Please feel free to let us know your views in the comments section.
References ¶
- Interpretable Machine Learning Using LIME Framework - Kasia Kulma (PhD), Data Scientist, Aviva
- Model Evaluation Metrics in Scikit-Learn
- Scikit-Plot: Visualizing Machine Learning Algorithm Results and Performance
- Feature Extraction from Text Data using Scikit-Learn
- How to Use eli5 to Understand sklearn Models, their Performance, and their Predictions?
- Yellowbrick - Visualize Sklearn's Classification & Regression Metrics in Python
- Treeinterpreter - Interpreting Tree-Based Model's Prediction of Individual Sample
- SHAP - Explain Machine Learning Model Predictions using Game Theoretic Approach
- interpret-text - Interpret NLP Models and Their Predictions
- dice-ml - Diverse Counterfactual Explanations for ML Models
- interpret-ml - Explain Machine Learning Models and Their Predictions
- Yellowbrick - Text Data Visualizations
Sunny Solanki
Sunny Solanki
Intro: Coder | Bonsai Enthusiast
About: Sunny Solanki has 8+ years of experience in IT Industry. His experience involves working on projects involving Python & Java with US/Canadian banking clients. He has good hands-on with Python and its ecosystem libraries.
His main areas of interests are Machine Learning, Data Visualization and Concurrent Programming.
Apart from his tech life, he prefers reading biographies and autobiographies.
He spends too much of his time taking care of his 50+ plants.
Contact: sunny.2309@yahoo.in
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