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Get only 3 sets for best split

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Fix flaky test in Splitter_test
This commit is contained in:
Ricardo Montañana Gómez
2020-06-27 18:29:40 +02:00
parent 4b7e4a3fb0
commit 554ec03c32
4 changed files with 159 additions and 76 deletions
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2
.gitignore vendored
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@@ -131,3 +131,5 @@ dmypy.json
.idea .idea
.vscode .vscode
.pre-commit-config.yaml .pre-commit-config.yaml
**.csv

174
notebooks/features.ipynb
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File diff suppressed because one or more lines are too long

6
stree/Strees.py
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@@ -218,7 +218,7 @@ class Splitter:
imp_dn = self.criterion_function(labels_dn) imp_dn = self.criterion_function(labels_dn)
samples = card_up + card_dn samples = card_up + card_dn
if samples == 0: if samples == 0:
return 0 return 0.0
else: else:
result = ( result = (
imp_prev imp_prev
@@ -244,7 +244,6 @@ class Splitter:
if gain > max_gain: if gain > max_gain:
max_gain = gain max_gain = gain
selected = feature_set selected = feature_set
return selected if selected is not None else feature_set return selected if selected is not None else feature_set
def _get_subspaces_set( def _get_subspaces_set(
@@ -257,6 +256,9 @@ class Splitter:
index = random.randint(0, len(features_sets) - 1) index = random.randint(0, len(features_sets) - 1)
return features_sets[index] return features_sets[index]
else: else:
# get only 3 sets at most
if len(features_sets) > 3:
features_sets = random.sample(features_sets, 3)
return self._select_best_set(dataset, labels, features_sets) return self._select_best_set(dataset, labels, features_sets)
else: else:
return features_sets[0] return features_sets[0]

51
stree/tests/Splitter_test.py
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@@ -1,11 +1,11 @@
import os import os
import unittest import unittest
import random
import numpy as np import numpy as np
from sklearn.svm import LinearSVC from sklearn.svm import SVC
from sklearn.datasets import load_wine
from stree import Splitter from stree import Splitter
from .utils import load_dataset
class Splitter_test(unittest.TestCase): class Splitter_test(unittest.TestCase):
@@ -15,7 +15,7 @@ class Splitter_test(unittest.TestCase):
@staticmethod @staticmethod
def build( def build(
clf=LinearSVC(), clf=SVC,
min_samples_split=0, min_samples_split=0,
splitter_type="random", splitter_type="random",
criterion="gini", criterion="gini",
@@ -23,7 +23,7 @@ class Splitter_test(unittest.TestCase):
random_state=None, random_state=None,
): ):
return Splitter( return Splitter(
clf=clf, clf=clf(random_state=random_state, kernel="rbf"),
min_samples_split=min_samples_split, min_samples_split=min_samples_split,
splitter_type=splitter_type, splitter_type=splitter_type,
criterion=criterion, criterion=criterion,
@@ -43,7 +43,7 @@ class Splitter_test(unittest.TestCase):
with self.assertRaises(ValueError): with self.assertRaises(ValueError):
self.build(criteria="duck") self.build(criteria="duck")
with self.assertRaises(ValueError): with self.assertRaises(ValueError):
self.build(clf=None) _ = Splitter(clf=None)
for splitter_type in ["best", "random"]: for splitter_type in ["best", "random"]:
for criterion in ["gini", "entropy"]: for criterion in ["gini", "entropy"]:
for criteria in [ for criteria in [
@@ -178,26 +178,23 @@ class Splitter_test(unittest.TestCase):
def test_splitter_parameter(self): def test_splitter_parameter(self):
expected_values = [ expected_values = [
[1, 2], # random gini min_distance [2, 3, 5, 7], # best entropy min_distance
[0, 2], # random gini max_samples [0, 2, 4, 5], # best entropy max_samples
[1, 3], # random gini max_distance [0, 2, 8, 12], # best entropy max_distance
[1, 2], # random entropy min_distance [1, 2, 5, 12], # best gini min_distance
[1, 2], # random entropy max_samples [0, 3, 4, 10], # best gini max_samples
[0, 2], # random entropy max_distance [1, 2, 9, 12], # best gini max_distance
[1, 2], # best gini min_distance [3, 9, 11, 12], # random entropy min_distance
[0, 2], # best gini max_samples [1, 5, 6, 9], # random entropy max_samples
[0, 2], # best gini max_distance [1, 2, 4, 8], # random entropy max_distance
[0, 1], # best entropy min_distance [2, 6, 7, 12], # random gini min_distance
[0, 1], # best entropy max_samples [3, 9, 10, 11], # random gini max_samples
[0, 1], # best entropy max_distance [2, 5, 8, 12], # random gini max_distance
] ]
X, y = load_dataset(self._random_state, n_features=6, n_classes=3) X, y = load_wine(return_X_y=True)
from sklearn.datasets import load_iris
X, y = load_iris(return_X_y=True)
rn = 0 rn = 0
for splitter_type in ["random", "best"]: for splitter_type in ["best", "random"]:
for criterion in ["gini", "entropy"]: for criterion in ["entropy", "gini"]:
for criteria in [ for criteria in [
"min_distance", "min_distance",
"max_samples", "max_samples",
@@ -207,11 +204,11 @@ class Splitter_test(unittest.TestCase):
splitter_type=splitter_type, splitter_type=splitter_type,
criterion=criterion, criterion=criterion,
criteria=criteria, criteria=criteria,
random_state=rn,
) )
rn += 3
expected = expected_values.pop(0) expected = expected_values.pop(0)
dataset, computed = tcl.get_subspace(X, y, max_features=2) random.seed(rn)
rn += 1
dataset, computed = tcl.get_subspace(X, y, max_features=4)
# print( # print(
# "{}, # {:7s}{:8s}{:15s}".format( # "{}, # {:7s}{:8s}{:15s}".format(
# list(computed), splitter_type, criterion, # list(computed), splitter_type, criterion,