RandomForest issue with apply() when using Python interface.

[geektoni]

If we train a RandomForest object for a regression problem, the apply() method won't work as expected and it will throw the error below. If we use apply_regression() instead everything works perfectly.

Traceback (most recent call last):
  File "serialization.py", line 26, in <module>
    labels_predict = rand_forest.apply(features_test)
SystemError: Labels (RegressionLabels) are not compatible with multiclass.

Code to replicate the issue:

#!/usr/bin/env python
# coding: utf-8

import shogun as sg
import numpy as np

# Create random features
X_train = np.random.normal(0, 1, (100, 5))
betas = np.random.normal(0,1, 5)
y_train = np.dot(X_train, betas)

X_test = np.random.normal(0, 1, (10, 5))
y_test = np.dot(X_test, betas)

features_train = sg.create_features(X_train.T)
features_test = sg.create_features(X_test.T)
labels_train = sg.create_labels(y_train)
labels_test = sg.create_labels(y_test)

# Create the random forest object
mean_rule = sg.create_combination_rule("MeanRule")
rand_forest = sg.create_machine("RandomForest", labels=labels_train, num_bags=5,
                                seed=1, combination_rule=mean_rule)

rand_forest.train(features_train)
labels_predict = rand_forest.apply(features_test)

[gf712]

Hmmm, this should be fixed once someone picks up on #5054

[stale]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.

[stale]

This issue is now being closed due to a lack of activity. Feel free to reopen it.

[stale]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.

[stale]

This issue is now being closed due to a lack of activity. Feel free to reopen it.