Plot the sparse projection matrices of an oblique tree

This example shows how projection matrices are generated for an oblique tree, specifically the treeple.tree.ObliqueDecisionTreeClassifier.

The projection matrix here samples a subset of features from the input X controlled by the parameter feature_combinations. The projection matrix is sparse when feature_combinations is small, meaning that it is mostly zero. The non-zero elements of the projection matrix are the features that are sampled from the input X and linearly combined to form candidate split dimensions.

For details on how to use the hyperparameters related to the patches, see treeple.tree.ObliqueDecisionTreeClassifier.

import matplotlib.pyplot as plt

# import modules
# .. note:: We use a private Cython module here to demonstrate what the patches
#           look like. This is not part of the public API. The Cython module used
#           is just a Python wrapper for the underlying Cython code and is not the
#           same as the Cython splitter used in the actual implementation.
#           To use the actual splitter, one should use the public API for the
#           relevant tree/forests class.
import numpy as np
from matplotlib.cm import ScalarMappable
from matplotlib.colors import ListedColormap

from treeple._lib.sklearn.tree._criterion import Gini
from treeple.tree._oblique_splitter import BestObliqueSplitterTester

Initialize patch splitter

The patch splitter is used to generate patches for the projection matrices. We will initialize the patch with some dummy values for the sake of this example.

criterion = Gini(1, np.array((0, 1)))
max_features = 6
min_samples_leaf = 1
min_weight_leaf = 0.0
random_state = np.random.RandomState(1)

feature_combinations = 1.5
monotonic_cst = None
missing_value_feature_mask = None

n_features = 10
n_samples = 5

# initialize some dummy data
X = np.ones((n_samples, n_features), dtype=np.float32)
y = np.array([0, 0, 0, 1, 1]).reshape(-1, 1).astype(np.float64)
sample_weight = np.ones(5)

print("The shape of our dataset is: ", X.shape, y.shape, sample_weight.shape)

The shape of our dataset is:  (5, 10) (5, 1) (5,)

Initialize the splitter

The splitter is initialized in the decision-tree classes, but we expose a testing interface here to demonstrate how the projection matrices are sampled internally.

Warning

Do not use this interface directly in practice.

splitter = BestObliqueSplitterTester(
    criterion,
    max_features,
    min_samples_leaf,
    min_weight_leaf,
    random_state,
    monotonic_cst,
    feature_combinations,
)
splitter.init_test(X, y, sample_weight, missing_value_feature_mask)

Generate projection matrix

Sample the projection matrix that consists of randomly sampled features with an average of feature_combinations * max_features non-zeros in the (max_features, n_features) matrix.

projection_matrix = splitter.sample_projection_matrix_py()
print(projection_matrix.shape)

# Visualize the projection matrix
cmap = ListedColormap(["orange", "white", "green"])

# Create a heatmap to visualize the indices
fig, ax = plt.subplots(figsize=(6, 6))

ax.imshow(projection_matrix, cmap=cmap, aspect=n_features / max_features, interpolation="none")

ax.set(title="Sampled Projection Matrix", xlabel="Feature Index", ylabel="Projection Vector Index")
ax.set_xticks(np.arange(n_features))
ax.set_yticks(np.arange(max_features))
ax.set_yticklabels(np.arange(max_features, dtype=int) + 1)
ax.set_xticklabels(np.arange(n_features, dtype=int) + 1)

# Create a mappable object
sm = ScalarMappable(cmap=cmap)
sm.set_array([])  # You can set an empty array or values here

# Create a color bar with labels for each feature set
colorbar = fig.colorbar(sm, ax=ax, ticks=[0, 0.5, 1], format="%d")
colorbar.set_label("Projection Weight")
colorbar.ax.set_yticklabels(["-1", "0", "1"])

plt.show()

(6, 10)

Discussion

As we can see, the (sparse) oblique splitter samples random features to linearly combine to form candidate split dimensions.

In contrast, the normal splitter in sklearn.tree.DecisionTreeClassifier samples randomly across all n_features features.

For an example of using oblique trees/forests in practice on data, see the following examples:

• Compare the decision surfaces of oblique extra-trees with standard oblique trees

• Plot oblique forest and axis-aligned random forest predictions on cc18 datasets

Estimated memory usage: 227 MB