mirror of
https://github.com/Doctorado-ML/STree.git
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2f6ae648a1
* Add complete classes counts to node and tests * Implement optimized predict and new predict_proba * Add predict_proba test * Add python 3.10 to CI
130 lines
4.7 KiB
Python
130 lines
4.7 KiB
Python
import os
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import unittest
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import numpy as np
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from stree import Stree
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from stree.Splitter import Snode
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from .utils import load_dataset
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class Snode_test(unittest.TestCase):
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def __init__(self, *args, **kwargs):
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self._random_state = 1
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self._clf = Stree(
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random_state=self._random_state,
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kernel="liblinear",
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multiclass_strategy="ovr",
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)
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self._clf.fit(*load_dataset(self._random_state))
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super().__init__(*args, **kwargs)
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@classmethod
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def setUp(cls):
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os.environ["TESTING"] = "1"
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def test_attributes_in_leaves(self):
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"""Check if the attributes in leaves have correct values so they form a
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predictor
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"""
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def check_leave(node: Snode):
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if not node.is_leaf():
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check_leave(node.get_down())
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check_leave(node.get_up())
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return
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# Check Belief in leave
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classes, card = np.unique(node._y, return_counts=True)
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max_card = max(card)
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min_card = min(card)
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if len(classes) > 1:
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belief = max_card / (max_card + min_card)
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else:
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belief = 1
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self.assertEqual(belief, node._belief)
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# Check Class
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class_computed = classes[card == max_card]
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self.assertEqual(class_computed, node._class)
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# Check Partition column
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self.assertEqual(node._partition_column, -1)
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check_leave(self._clf.tree_)
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def test_nodes_coefs(self):
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"""Check if the nodes of the tree have the right attributes filled"""
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def run_tree(node: Snode):
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if node._belief < 1:
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# only exclude pure leaves
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self.assertIsNotNone(node._clf)
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self.assertIsNotNone(node._clf.coef_)
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if node.is_leaf():
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return
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run_tree(node.get_up())
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run_tree(node.get_down())
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model = Stree(self._random_state)
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model.fit(*load_dataset(self._random_state, 3, 4))
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run_tree(model.tree_)
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def test_make_predictor_on_leaf(self):
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test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
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test.make_predictor(2)
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self.assertEqual(1, test._class)
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self.assertEqual(0.75, test._belief)
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self.assertEqual(-1, test._partition_column)
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self.assertListEqual([1, 3], test._proba.tolist())
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def test_make_predictor_on_not_leaf(self):
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test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
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test.set_up(Snode(None, [1], [1], [], 0.0, "another_test"))
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test.make_predictor(2)
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self.assertIsNone(test._class)
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self.assertEqual(0, test._belief)
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self.assertEqual(-1, test._partition_column)
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self.assertEqual(-1, test.get_up()._partition_column)
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self.assertIsNone(test._proba)
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def test_make_predictor_on_leaf_bogus_data(self):
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test = Snode(None, [1, 2, 3, 4], [], [], 0.0, "test")
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test.make_predictor(2)
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self.assertIsNone(test._class)
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self.assertEqual(-1, test._partition_column)
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self.assertListEqual([0, 0], test._proba.tolist())
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def test_set_title(self):
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test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
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self.assertEqual("test", test.get_title())
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test.set_title("another")
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self.assertEqual("another", test.get_title())
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def test_set_classifier(self):
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test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
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clf = Stree()
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self.assertIsNone(test.get_classifier())
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test.set_classifier(clf)
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self.assertEqual(clf, test.get_classifier())
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def test_set_impurity(self):
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test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
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self.assertEqual(0.0, test.get_impurity())
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test.set_impurity(54.7)
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self.assertEqual(54.7, test.get_impurity())
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def test_set_features(self):
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test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [0, 1], 0.0, "test")
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self.assertListEqual([0, 1], test.get_features())
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test.set_features([1, 2])
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self.assertListEqual([1, 2], test.get_features())
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def test_copy_node(self):
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px = [1, 2, 3, 4]
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py = [1]
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test = Snode(Stree(), px, py, [], 0.0, "test")
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computed = Snode.copy(test)
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self.assertListEqual(computed._X, px)
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self.assertListEqual(computed._y, py)
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self.assertEqual("test", computed._title)
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self.assertIsInstance(computed._clf, Stree)
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self.assertEqual(test._partition_column, computed._partition_column)
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self.assertEqual(test._sample_weight, computed._sample_weight)
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self.assertEqual(test._scaler, computed._scaler)
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