Consolidate and use the same function

Signed-off-by: Adam Li <[email protected]>

.spin/cmds.py

@@ -137,7 +137,7 @@ def setup_submodule(forcesubmodule=False):
 def build(ctx, meson_args, jobs=None, clean=False, forcesubmodule=False, verbose=False):
     """Build scikit-tree using submodules.
 
-    git submodule update --recursive --remote
+        git submodule update --recursive --remote
 
     To update submodule wrt latest commits:
 

sktree/tests/test_honest_forest.py

@@ -1,7 +1,6 @@
 import numpy as np
 import pytest
 from numpy.testing import assert_allclose, assert_array_almost_equal
-from scipy.stats import entropy
 from sklearn import datasets
 from sklearn.metrics import accuracy_score, r2_score, roc_auc_score
 from sklearn.model_selection import cross_val_score
@@ -358,16 +357,13 @@ def test_honest_forest_with_sklearn_trees_with_mi():
         Forest.fit(X, y)
 
         # compute MI
-        _, counts = np.unique(y, return_counts=True)
-        H_Y = entropy(counts, base=np.exp(1))
-
+        # _, counts = np.unique(y, return_counts=True)
+        # H_Y = entropy(counts, base=np.exp(1))
         sk_posterior = skForest.predict_proba(X)
-        H_YX = np.mean(entropy(sk_posterior, base=np.exp(1), axis=1))
-        sk_score = max(H_Y - H_YX, 0)
+        sk_score = _mutual_information(y, sk_posterior)
 
         posterior = Forest.predict_proba(X)
-        H_YX = np.mean(entropy(posterior, base=np.exp(1), axis=1))
-        score = max(H_Y - H_YX, 0)
+        score = _mutual_information(y, posterior)
 
         scores.append(score)
         sk_scores.append(sk_score)