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Simulate RF Categorical Variable Encoding Bias
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import random | |
import sys | |
import numpy as np | |
import matplotlib.pyplot as plt | |
from sklearn.ensemble import RandomForestClassifier | |
N_SAMPLES = 1000 | |
N_TREES = 100 | |
MAX_CATEGORIES = 32 | |
N_SIMS = 100 | |
def generate_onehot(n_samples, max_categories): | |
n_features = 0 | |
variable_indices = [0] | |
for i in xrange(2, max_categories + 1): | |
n_features += i | |
variable_indices.append(n_features) | |
features = np.zeros((n_samples, n_features)) | |
labels = np.zeros((n_samples)) | |
for r in xrange(n_samples): | |
labels[r] = random.randint(0, 1) | |
for c in xrange(n_features): | |
features[r, c] = random.randint(0, 1) | |
return features, labels, variable_indices | |
def generate_stacked(n_samples, max_categories): | |
n_features = max_categories - 1 | |
features = np.zeros((n_samples, n_features)) | |
labels = np.zeros((n_samples)) | |
for r in xrange(n_samples): | |
labels[r] = random.randint(0, 1) | |
for c in xrange(n_features): | |
n_categories = c + 2 | |
features[r, c] = random.randint(0, n_categories - 1) | |
return features, labels | |
def plot_variable_importances(flname, variable_importances, title): | |
plt.clf() | |
plt.boxplot(x=variable_importances) | |
plt.xlabel("Variable", fontsize=16) | |
plt.ylabel("Gini Importance", fontsize=16) | |
plt.title(title, fontsize=18) | |
plt.savefig(flname, DPI=200) | |
if __name__ == "__main__": | |
# burn in | |
for i in xrange(100): | |
random.random() | |
variable_importances = [[] for i in xrange(MAX_CATEGORIES - 1)] | |
for i in xrange(N_SIMS): | |
print "Round", i | |
X, y, variable_indices = generate_onehot(N_SAMPLES, MAX_CATEGORIES) | |
rf = RandomForestClassifier(n_estimators = N_TREES) | |
rf.fit(X, y) | |
feature_importances = rf.feature_importances_ | |
for i, (start, end) in enumerate(zip(variable_indices[:-1], variable_indices[1:])): | |
variable_importances[i].extend(feature_importances[start:end]) | |
plot_variable_importances(sys.argv[1], variable_importances, "One-Hot Encoded") | |
variable_importances = [[] for i in xrange(MAX_CATEGORIES - 1)] | |
for i in xrange(N_SIMS): | |
print "Round", i | |
X, y = generate_stacked(N_SAMPLES, MAX_CATEGORIES) | |
rf = RandomForestClassifier(n_estimators = N_TREES) | |
rf.fit(X, y) | |
feature_importances = rf.feature_importances_ | |
for i in xrange(MAX_CATEGORIES - 1): | |
variable_importances[i].append(feature_importances[i]) | |
plot_variable_importances(sys.argv[2], variable_importances, "Integer Encoded") |
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