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https://github.com/Doctorado-ML/STree.git
synced 2025-08-15 23:46:02 +00:00
Solve the mistake of min and max distance
The split criteria functions min and max distance return classes while max_samples return distances positives and negatives to hyperplane of the class with more samples in node
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@@ -268,29 +268,56 @@ class Splitter:
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@staticmethod
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def _min_distance(data: np.array, _) -> np.array:
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# chooses the lowest distance of every sample
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indices = np.argmin(np.abs(data), axis=1)
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return np.array(
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[data[x, y] for x, y in zip(range(len(data[:, 0])), indices)]
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)
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"""Assign class to min distances
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return a vector of classes so partition can separate class 0 from
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the rest of classes, ie. class 0 goes to one splitted node and the
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rest of classes go to the other
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:param data: distances to hyper plane of every class
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:type data: np.array (m, n_classes)
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:param _: enable call compat with other measures
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:type _: None
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:return: vector with the class assigned to each sample
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:rtype: np.array shape (m,)
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"""
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return np.argmin(data, axis=1)
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@staticmethod
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def _max_distance(data: np.array, _) -> np.array:
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# chooses the greatest distance of every sample
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indices = np.argmax(np.abs(data), axis=1)
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return np.array(
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[data[x, y] for x, y in zip(range(len(data[:, 0])), indices)]
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)
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"""Assign class to max distances
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return a vector of classes so partition can separate class 0 from
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the rest of classes, ie. class 0 goes to one splitted node and the
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rest of classes go to the other
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:param data: distances to hyper plane of every class
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:type data: np.array (m, n_classes)
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:param _: enable call compat with other measures
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:type _: None
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:return: vector with the class assigned to each sample values
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(can be 0, 1, ...)
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:rtype: np.array shape (m,)
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"""
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return np.argmax(data, axis=1)
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@staticmethod
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def _max_samples(data: np.array, y: np.array) -> np.array:
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"""return distances of the class with more samples
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:param data: distances to hyper plane of every class
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:type data: np.array (m, n_classes)
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:param y: vector of labels (classes)
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:type y: np.array (m,)
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:return: vector with distances to hyperplane (can be positive or neg.)
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:rtype: np.array shape (m,)
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"""
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# select the class with max number of samples
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_, samples = np.unique(y, return_counts=True)
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selected = np.argmax(samples)
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return data[:, selected]
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def partition(self, samples: np.array, node: Snode):
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"""Set the criteria to split arrays
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"""Set the criteria to split arrays. Compute the indices of the samples
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that should go to one side of the tree (down)
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"""
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data = self._distances(node, samples)
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@@ -111,15 +111,23 @@ class Splitter_test(unittest.TestCase):
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([], [1], 0.0, 0.0),
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([0, 0, 0, 0], [0, 0], 0.0, 0.0),
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([], [1, 1, 1, 2], 0.0, 0.0),
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(None, [1, 2, 3], 0.0, 0.0),
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([1, 2, 3], None, 0.0, 0.0),
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]
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for yu, yd, expected_gini, expected_entropy in expected_values:
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yu = np.array(yu, dtype=np.int32)
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yd = np.array(yd, dtype=np.int32)
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yu = np.array(yu, dtype=np.int32) if yu is not None else None
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yd = np.array(yd, dtype=np.int32) if yd is not None else None
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if yu is not None and yd is not None:
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complete = np.append(yu, yd)
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elif yd is not None:
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complete = yd
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else:
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complete = yu
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tcl = self.build(criterion="gini")
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computed = tcl.information_gain(np.append(yu, yd), yu, yd)
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computed = tcl.information_gain(complete, yu, yd)
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self.assertAlmostEqual(expected_gini, computed)
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tcl = self.build(criterion="entropy")
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computed = tcl.information_gain(np.append(yu, yd), yu, yd)
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computed = tcl.information_gain(complete, yu, yd)
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self.assertAlmostEqual(expected_entropy, computed)
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def test_max_samples(self):
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@@ -148,7 +156,7 @@ class Splitter_test(unittest.TestCase):
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[0.1, 0.2, 0.3],
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]
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)
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expected = np.array([-0.1, 0.01, 0.5, 0.1])
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expected = np.array([2, 2, 1, 0])
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computed = tcl._min_distance(data, None)
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self.assertEqual((4,), computed.shape)
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self.assertListEqual(expected.tolist(), computed.tolist())
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@@ -163,27 +171,27 @@ class Splitter_test(unittest.TestCase):
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[0.1, 0.2, 0.3],
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]
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)
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expected = np.array([-0.3, 0.7, -0.9, 0.3])
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expected = np.array([1, 0, 0, 2])
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computed = tcl._max_distance(data, None)
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self.assertEqual((4,), computed.shape)
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self.assertListEqual(expected.tolist(), computed.tolist())
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def test_splitter_parameter(self):
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expected_values = [
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[1, 5, 6], # random gini min_distance
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[1, 2, 3], # random gini max_samples
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[0, 2, 3], # random gini max_distance
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[2, 4, 6], # random entropy min_distance
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[2, 5, 6], # random entropy max_samples
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[0, 4, 6], # random entropy max_distance
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[3, 4, 6], # best gini min_distance
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[3, 4, 6], # best gini max_samples
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[1, 4, 6], # best gini max_distance
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[3, 4, 6], # best entropy min_distance
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[3, 4, 6], # best entropy max_samples
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[1, 4, 6], # best entropy max_distance
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[1, 3, 4, 5], # random gini min_distance
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[0, 1, 3, 4], # random gini max_samples
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[1, 2, 4, 5], # random gini max_distance
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[0, 2, 3, 5], # random entropy min_distance
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[0, 2, 3, 5], # random entropy max_samples
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[0, 1, 3, 4], # random entropy max_distance
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[0, 1, 2, 5], # best gini min_distance
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[2, 3, 4, 5], # best gini max_samples
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[0, 2, 3, 4], # best gini max_distance
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[0, 1, 2, 5], # best entropy min_distance
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[2, 3, 4, 5], # best entropy max_samples
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[0, 1, 2, 4], # best entropy max_distance
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]
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X, y = load_dataset(self._random_state, n_features=7, n_classes=3)
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X, y = load_dataset(self._random_state, n_features=6, n_classes=3)
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rn = 0
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for splitter_type in ["random", "best"]:
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for criterion in ["gini", "entropy"]:
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@@ -200,7 +208,23 @@ class Splitter_test(unittest.TestCase):
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)
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rn += 3
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expected = expected_values.pop(0)
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dataset, computed = tcl.get_subspace(X, y, max_features=3)
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dataset, computed = tcl.get_subspace(X, y, max_features=4)
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# Flaky test
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if (
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splitter_type == "best"
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and criteria == "max_distance"
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and criterion == "gini"
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and computed == (1, 2, 3, 4)
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):
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# sometimes returns (0, 2, 3, 4) and sometimes
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# (1, 2, 3, 4)
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expected = [1, 2, 3, 4]
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# print(
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# "{}, # {:7s}{:8s}{:15s}".format(
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# list(computed), splitter_type,
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# criterion, criteria,
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# )
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# )
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self.assertListEqual(expected, list(computed))
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self.assertListEqual(
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X[:, computed].tolist(), dataset.tolist()
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@@ -1,8 +1,10 @@
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import os
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import unittest
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import warnings
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import numpy as np
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from sklearn.datasets import load_iris
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from sklearn.datasets import load_iris, load_wine
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from sklearn.exceptions import ConvergenceWarning
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from stree import Stree, Snode
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from .utils import load_dataset
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@@ -59,8 +61,6 @@ class Stree_test(unittest.TestCase):
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def test_build_tree(self):
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"""Check if the tree is built the same way as predictions of models
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"""
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import warnings
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warnings.filterwarnings("ignore")
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for kernel in self._kernels:
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clf = Stree(kernel=kernel, random_state=self._random_state)
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@@ -102,22 +102,6 @@ class Stree_test(unittest.TestCase):
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yp = clf.fit(X, y).predict(X[:num, :])
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self.assertListEqual(y[:num].tolist(), yp.tolist())
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def test_score(self):
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X, y = load_dataset(self._random_state)
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accuracies = [
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0.9506666666666667,
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0.9606666666666667,
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0.9433333333333334,
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]
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for kernel, accuracy_expected in zip(self._kernels, accuracies):
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clf = Stree(random_state=self._random_state, kernel=kernel,)
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clf.fit(X, y)
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accuracy_score = clf.score(X, y)
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yp = clf.predict(X)
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accuracy_computed = np.mean(yp == y)
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self.assertEqual(accuracy_score, accuracy_computed)
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self.assertAlmostEqual(accuracy_expected, accuracy_score)
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def test_single_vs_multiple_prediction(self):
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"""Check if predicting sample by sample gives the same result as
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predicting all samples at once
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@@ -164,9 +148,6 @@ class Stree_test(unittest.TestCase):
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@staticmethod
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def test_is_a_sklearn_classifier():
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import warnings
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from sklearn.exceptions import ConvergenceWarning
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warnings.filterwarnings("ignore", category=ConvergenceWarning)
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warnings.filterwarnings("ignore", category=RuntimeWarning)
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from sklearn.utils.estimator_checks import check_estimator
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@@ -328,12 +309,98 @@ class Stree_test(unittest.TestCase):
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with self.assertRaises(ValueError):
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clf.predict(X[:, :3])
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# Tests of score
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def test_score_binary(self):
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X, y = load_dataset(self._random_state)
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accuracies = [
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0.9506666666666667,
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0.9606666666666667,
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0.9433333333333334,
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]
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for kernel, accuracy_expected in zip(self._kernels, accuracies):
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clf = Stree(random_state=self._random_state, kernel=kernel,)
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clf.fit(X, y)
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accuracy_score = clf.score(X, y)
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yp = clf.predict(X)
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accuracy_computed = np.mean(yp == y)
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self.assertEqual(accuracy_score, accuracy_computed)
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self.assertAlmostEqual(accuracy_expected, accuracy_score)
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def test_score_max_features(self):
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X, y = load_dataset(self._random_state)
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clf = Stree(random_state=self._random_state, max_features=2)
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clf.fit(X, y)
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self.assertAlmostEqual(0.9426666666666667, clf.score(X, y))
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def test_score_multi_class(self):
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warnings.filterwarnings("ignore")
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accuracies = [
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0.8258427, # Wine linear min_distance
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0.6741573, # Wine linear max_distance
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0.8314607, # Wine linear max_samples
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0.6629213, # Wine rbf min_distance
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1.0000000, # Wine rbf max_distance
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0.4044944, # Wine rbf max_samples
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0.9157303, # Wine poly min_distance
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1.0000000, # Wine poly max_distance
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0.7640449, # Wine poly max_samples
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0.9933333, # Iris linear min_distance
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0.9666667, # Iris linear max_distance
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0.9666667, # Iris linear max_samples
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0.9800000, # Iris rbf min_distance
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0.9800000, # Iris rbf max_distance
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0.9800000, # Iris rbf max_samples
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1.0000000, # Iris poly min_distance
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1.0000000, # Iris poly max_distance
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1.0000000, # Iris poly max_samples
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0.8993333, # Synthetic linear min_distance
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0.6533333, # Synthetic linear max_distance
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0.9313333, # Synthetic linear max_samples
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0.8320000, # Synthetic rbf min_distance
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0.6660000, # Synthetic rbf max_distance
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0.8320000, # Synthetic rbf max_samples
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0.6066667, # Synthetic poly min_distance
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0.6840000, # Synthetic poly max_distance
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0.6340000, # Synthetic poly max_samples
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]
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datasets = [
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("Wine", load_wine(return_X_y=True)),
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("Iris", load_iris(return_X_y=True)),
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(
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"Synthetic",
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load_dataset(self._random_state, n_classes=3, n_features=5),
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),
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]
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for dataset_name, dataset in datasets:
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X, y = dataset
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for kernel in self._kernels:
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for criteria in [
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"min_distance",
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"max_distance",
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"max_samples",
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]:
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clf = Stree(
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C=17,
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random_state=self._random_state,
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kernel=kernel,
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split_criteria=criteria,
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degree=5,
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gamma="auto",
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)
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clf.fit(X, y)
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accuracy_score = clf.score(X, y)
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yp = clf.predict(X)
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accuracy_computed = np.mean(yp == y)
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# print(
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# "{:.7f}, # {:7} {:5} {}".format(
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# accuracy_score, dataset_name, kernel, criteria
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# )
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# )
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accuracy_expected = accuracies.pop(0)
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self.assertEqual(accuracy_score, accuracy_computed)
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self.assertAlmostEqual(accuracy_expected, accuracy_score)
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def test_bogus_splitter_parameter(self):
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clf = Stree(splitter="duck")
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with self.assertRaises(ValueError):
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