Added kernel debugging script and updated kernel unit tests

Author: andrewherren

demo/debug/kernel.py

@@ -0,0 +1,33 @@
+import numpy as np
+from stochtree import Dataset, ForestContainer, compute_forest_leaf_indices
+
+# Create dataset
+X = np.array(
+    [[1.5, 8.7, 1.2],
+        [2.7, 3.4, 5.4],
+        [3.6, 1.2, 9.3],
+        [4.4, 5.4, 10.4],
+        [5.3, 9.3, 3.6],
+        [6.1, 10.4, 4.4]]
+)
+n, p = X.shape
+num_trees = 2
+output_dim = 1
+forest_dataset = Dataset()
+forest_dataset.add_covariates(X)
+forest_samples = ForestContainer(num_trees, output_dim, True, False)
+
+# Initialize a forest with constant root predictions
+forest_samples.add_sample(0.)
+
+# Split the root of the first tree in the ensemble at X[,1] > 4.0
+forest_samples.add_numeric_split(0, 0, 0, 0, 4.0, -5., 5.)
+
+# Check that regular and "raw" predictions are the same (since the leaf is constant)
+computed_indices = compute_forest_leaf_indices(forest_samples, X)
+
+# Split the left leaf of the first tree in the ensemble at X[,2] > 4.0
+forest_samples.add_numeric_split(0, 0, 1, 1, 4.0, -7.5, -2.5)
+
+# Check that regular and "raw" predictions are the same (since the leaf is constant)
+computed_indices = compute_forest_leaf_indices(forest_samples, X)

test/python/test_kernel.py

@@ -9,8 +9,8 @@
 )
 
 
-class TestJson:
-    def test_value(self):
+class TestKernel:
+    def test_forest(self):
         # Create dataset
         X = np.array(
             [[1.5, 8.7, 1.2],
@@ -30,19 +30,11 @@ def test_value(self):
         # Initialize a forest with constant root predictions
         forest_samples.add_sample(0.)
 
-        # Check that regular and "raw" predictions are the same (since the leaf is constant)
-        pred = forest_samples.predict(forest_dataset)
-        pred_raw = forest_samples.predict_raw(forest_dataset)
-
-        # Assertion
-        np.testing.assert_almost_equal(pred, pred_raw)
-
         # Split the root of the first tree in the ensemble at X[,1] > 4.0
         forest_samples.add_numeric_split(0, 0, 0, 0, 4.0, -5., 5.)
 
         # Check that regular and "raw" predictions are the same (since the leaf is constant)
         computed = compute_forest_leaf_indices(forest_samples, X)
-        print(computed)
         expected = np.array([
             [0], 
             [0], 
@@ -66,7 +58,6 @@ def test_value(self):
 
         # Check that regular and "raw" predictions are the same (since the leaf is constant)
         computed = compute_forest_leaf_indices(forest_samples, X)
-        print(computed)
         expected = np.array([
             [2], 
             [1],