106 lines
3.4 KiB
Python
106 lines
3.4 KiB
Python
"""
|
|
===================================================
|
|
Hashing feature transformation using Random Forests
|
|
===================================================
|
|
|
|
RandomForestEmbedding provide a way to map data to a
|
|
very high-dimensional, sparse representation, which might
|
|
be beneficial for classification.
|
|
The mapping is completely unsupervised and very efficient.
|
|
|
|
This example visualizes the partitionings given by several
|
|
trees and shows how the transformation can also be used for
|
|
non-linear dimensionality reduction or manifold learning.
|
|
|
|
Points that are neighboring often share the same leaf of a tree will share
|
|
large parts of their hashed representation. This allows to
|
|
separate two circles simply based on the principal components of the
|
|
transformed data.
|
|
|
|
In the high-dimensional space, a simple classifier if often
|
|
enough for a good fit. For sparse binary data, BernoulliNB
|
|
is particularly well-suited. The bottom row compares the
|
|
decision boundary obtained by BernoulliNB in the transformed
|
|
space with an ExtraTreesClassifier forests learned on the
|
|
original data.
|
|
"""
|
|
import pylab as pl
|
|
import numpy as np
|
|
|
|
from sklearn.datasets import make_circles
|
|
from sklearn.ensemble import RandomForestEmbedding, ExtraTreesClassifier
|
|
from sklearn.decomposition import RandomizedPCA
|
|
from sklearn.naive_bayes import BernoulliNB
|
|
|
|
# make a synthetic dataset
|
|
X, y = make_circles(factor=0.5, random_state=0, noise=0.05)
|
|
|
|
# use RandomForestEmbedding to transform data
|
|
hasher = RandomForestEmbedding(n_estimators=10, random_state=0, max_depth=3)
|
|
X_transformed = hasher.fit_transform(X)
|
|
|
|
# Visualize result using PCA
|
|
pca = RandomizedPCA(n_components=2)
|
|
X_reduced = pca.fit_transform(X_transformed)
|
|
|
|
# Learn a Naive Bayes classifier on the transformed data
|
|
nb = BernoulliNB()
|
|
nb.fit(X_transformed, y)
|
|
|
|
|
|
# Learn an ExtraTreesClassifier for comparison
|
|
trees = ExtraTreesClassifier(max_depth=3, n_estimators=10, random_state=0)
|
|
trees.fit(X, y)
|
|
|
|
|
|
# scatter plot of original and reduced data
|
|
fig = pl.figure(figsize=(8, 8))
|
|
|
|
ax = pl.subplot(221)
|
|
ax.scatter(X[:, 0], X[:, 1], c=y, s=50)
|
|
ax.set_title("Original Data")
|
|
ax.set_xticks(())
|
|
ax.set_yticks(())
|
|
|
|
ax = pl.subplot(222)
|
|
ax.scatter(X_reduced[:, 0], X_reduced[:, 1], c=y, s=50)
|
|
ax.set_title("PCA reduction of transformed data")
|
|
ax.set_xticks(())
|
|
ax.set_yticks(())
|
|
|
|
# Plot the decision in original space. For that, we will asign a color to each
|
|
# point in the mesh [x_min, m_max] x [y_min, y_max].
|
|
h = .01
|
|
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
|
|
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
|
|
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
|
|
np.arange(y_min, y_max, h))
|
|
|
|
# transform grid using RandomForestEmbedding
|
|
transformed_grid = hasher.transform(np.c_[xx.ravel(), yy.ravel()])
|
|
y_grid_pred = nb.predict_proba(transformed_grid)[:, 1]
|
|
|
|
ax = pl.subplot(223)
|
|
ax.set_title("Naive Bayes on Transformed data")
|
|
ax.pcolormesh(xx, yy, y_grid_pred.reshape(xx.shape))
|
|
ax.scatter(X[:, 0], X[:, 1], c=y, s=50)
|
|
ax.set_ylim(-1.4, 1.4)
|
|
ax.set_xlim(-1.4, 1.4)
|
|
ax.set_xticks(())
|
|
ax.set_yticks(())
|
|
|
|
# transform grid using ExtraTreesClassifier
|
|
y_grid_pred = trees.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
|
|
|
|
ax = pl.subplot(224)
|
|
ax.set_title("ExtraTrees predictions")
|
|
ax.pcolormesh(xx, yy, y_grid_pred.reshape(xx.shape))
|
|
ax.scatter(X[:, 0], X[:, 1], c=y, s=50)
|
|
ax.set_ylim(-1.4, 1.4)
|
|
ax.set_xlim(-1.4, 1.4)
|
|
ax.set_xticks(())
|
|
ax.set_yticks(())
|
|
fig.subplots_adjust(left=0.02, right=0.98, top=0.94, bottom=0.02)
|
|
|
|
pl.show()
|