I have a dataset which follows the one-hot encoding pattern and my dependent variable is also binary. The first part of my code lists the important variables for the entire dataset. I used the method as mentioned in this stackoverflow post, "Using scikit to determine contributions of each feature to a specific class prediction". I am unsure as to what output I am getting. The feature importance ranks the most important feature for the entire model, "Delay Related DMS With Advice", in my case. I interpret it as that, this variable should be important either in Class 0 or Class 1 but from the output I get, it is unimportant in both Classes. The code in the stackoverflow I shared above, also shows that when the DV is binary, the output of Class 0 is the exact opposite (in terms of sign +/-) of Class 1. In my case, the values are different in both classes.
Here is how the plots look like:-
Feature Importance - Overall Model
Feature Importance - Class 0
Feature Importance - Class 1
The 2nd part of my code shows cumulative feature importances but looking at the [plot] shows that none of the variables are important. Is my formula wrong or my interpretation wrong or both?
plot
Here is my code;
import pandas as pd
import numpy as np
import json
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import scale
from sklearn.ensemble import ExtraTreesClassifier
##get_ipython().run_line_magic('matplotlib', 'inline')
file = r'RCM_Binary.csv'
data = pd.read_csv()
print("data loaded successfully ...")
# Define features and target
X = data.iloc[:,:-1]
y = data.iloc[:,-1]
#split to training and testing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=41)
# define classifier and fitting data
forest = ExtraTreesClassifier(random_state=1)
forest.fit(X_train, y_train)
# predict and get confusion matrix
y_pred = forest.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
print(cm)
#Applying 10-fold cross validation
accuracies = cross_val_score(estimator=forest, X=X_train, y=y_train, cv=10)
print("accuracy (10-fold): ", np.mean(accuracies))
# Features importances
importances = forest.feature_importances_
std = np.std([tree.feature_importances_ for tree in forest.estimators_],
axis=0)
indices = np.argsort(importances)[::-1]
feature_list = [X.columns[indices[f]] for f in range(X.shape[1])] #names of features.
ff = np.array(feature_list)
# Print the feature ranking
print("Feature ranking:")
for f in range(X.shape[1]):
print("%d. feature %d (%f) name: %s" % (f + 1, indices[f], importances[indices[f]], ff[indices[f]]))
# Plot the feature importances of the forest
plt.figure()
plt.rcParams['figure.figsize'] = [16, 6]
plt.title("Feature importances")
plt.bar(range(X.shape[1]), importances[indices],
color="r", yerr=std[indices], align="center")
plt.xticks(range(X.shape[1]), ff[indices], rotation=90)
plt.xlim([-1, X.shape[1]])
plt.show()
## The new additions to get feature importance to classes:
# To get the importance according to each class:
def class_feature_importance(X, Y, feature_importances):
N, M = X.shape
X = scale(X)
out = {}
for c in set(Y):
out[c] = dict(
zip(range(N), np.mean(X[Y==c, :], axis=0)*feature_importances)
)
return out
result = class_feature_importance(X, y, importances)
print (json.dumps(result,indent=4))
# Plot the feature importances of the forest
titles = ["Did not Divert", "Diverted"]
for t, i in zip(titles, range(len(result))):
plt.figure()
plt.rcParams['figure.figsize'] = [16, 6]
plt.title(t)
plt.bar(range(len(result[i])), result[i].values(),
color="r", align="center")
plt.xticks(range(len(result[i])), ff[list(result[i].keys())], rotation=90)
plt.xlim([-1, len(result[i])])
plt.show()
The 2nd part of the code
# List of tuples with variable and importance
feature_importances = [(feature, round(importance, 2)) for feature, importance in zip(feature_list, importances)]
# Sort the feature importances by most important first
feature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = True)
# Print out the feature and importances
[print('Variable: {:20} Importance: {}'.format(*pair)) for pair in feature_importances]
# list of x locations for plotting
x_values = list(range(len(importances)))
# Make a bar chart
plt.bar(x_values, importances, orientation = 'vertical', color = 'r', edgecolor = 'k', linewidth = 1.2)
# Tick labels for x axis
plt.xticks(x_values, feature_list, rotation='vertical')
# Axis labels and title
plt.ylabel('Importance'); plt.xlabel('Variable'); plt.title('Variable Importances');
# List of features sorted from most to least important
sorted_importances = [importance[1] for importance in feature_importances]
sorted_features = [importance[0] for importance in feature_importances]
# Cumulative importances
cumulative_importances = np.cumsum(sorted_importances)
# Make a line graph
plt.plot(x_values, cumulative_importances, 'g-')
# Draw line at 95% of importance retained
plt.hlines(y = 0.95, xmin=0, xmax=len(sorted_importances), color = 'r', linestyles = 'dashed')
# Format x ticks and labels
plt.xticks(x_values, sorted_features, rotation = 'vertical')
# Axis labels and title
plt.xlabel('Variable'); plt.ylabel('Cumulative Importance'); plt.title('Cumulative Importances');
plt.show()
# Find number of features for cumulative importance of 95%
# Add 1 because Python is zero-indexed
print('Number of features for 95% importance:', np.where(cumulative_importances > 0.95)[0][0] + 1)
