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# ----------------------------------------------------------------------------
# Copyright (c) 2017-2023, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ----------------------------------------------------------------------------
from warnings import filterwarnings
import pandas as pd
import numpy as np
import skbio
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import RFECV
import pandas.testing as pdt
import biom
import qiime2
from qiime2.plugins import sample_classifier
from q2_sample_classifier.visuals import (
_custom_palettes, _roc_palette, _roc_per_class, _roc_micro_average,
_roc_macro_average, _binarize_labels, _generate_roc_plots)
from q2_sample_classifier.utilities import _extract_rfe_scores
from q2_sample_classifier.tests.test_base_class import \
SampleClassifierTestPluginBase
filterwarnings("ignore", category=UserWarning)
filterwarnings("ignore", category=Warning)
class TestRFEExtractor(SampleClassifierTestPluginBase):
def setUp(self):
super().setUp()
np.random.seed(0)
self.X = np.random.rand(50, 20)
self.y = np.random.randint(0, 2, 50)
self.exp1 = pd.Series([
0.4999999999999999, 0.52, 0.52, 0.5399999999999999,
0.44000000000000006, 0.52, 0.4600000000000001,
0.5599999999999998, 0.52, 0.52, 0.5, 0.5399999999999999, 0.54,
0.5599999999999999, 0.47999999999999987, 0.6199999999999999,
0.5399999999999999, 0.5, 0.4999999999999999, 0.45999999999999996],
index=pd.Index(range(1, 21)), name='Accuracy')
self.exp2 = pd.Series([
0.5000000000000001, 0.52, 0.48, 0.5599999999999998, 0.5,
0.5799999999999998, 0.54, 0.4600000000000001, 0.6,
0.45999999999999996, 0.45999999999999996],
index=pd.Index([1] + [i for i in range(2, 21, 2)]),
name='Accuracy')
self.exp3 = pd.Series({1: 0.4600000000000001, 20: 0.45999999999999996},
name='Accuracy')
def extract_rfe_scores_template(self, steps, expected):
selector = RFECV(RandomForestClassifier(
random_state=123, n_estimators=2), step=steps, cv=10)
selector = selector.fit(self.X, self.y.ravel())
pdt.assert_series_equal(
_extract_rfe_scores(selector), expected)
def test_extract_rfe_scores_step_int_one(self):
self.extract_rfe_scores_template(1, self.exp1)
def test_extract_rfe_scores_step_float_one(self):
self.extract_rfe_scores_template(0.05, self.exp1)
def test_extract_rfe_scores_step_int_two(self):
self.extract_rfe_scores_template(2, self.exp2)
def test_extract_rfe_scores_step_float_two(self):
self.extract_rfe_scores_template(0.1, self.exp2)
def test_extract_rfe_scores_step_full_range(self):
self.extract_rfe_scores_template(20, self.exp3)
def test_extract_rfe_scores_step_out_of_range(self):
# should be equal to full_range
self.extract_rfe_scores_template(21, self.exp3)
# test classifier pipelines succeed on binary data
class TestBinaryClassification(SampleClassifierTestPluginBase):
def setUp(self):
super().setUp()
self.md = qiime2.CategoricalMetadataColumn(pd.Series(
['a', 'a', 'a', 'b', 'b', 'b'],
index=pd.Index([c for c in 'abcdef'], name='id'), name='foo'))
tab = biom.Table(np.array(
[[13, 26, 37, 3, 6, 1], [33, 24, 23, 5, 6, 2],
[38, 26, 33, 4, 1, 0], [3, 2, 1, 22, 25, 31],
[2, 1, 3, 44, 46, 42]]),
observation_ids=[c for c in 'vwxyz'],
sample_ids=[c for c in 'abcdef'])
self.tab = qiime2.Artifact.import_data('FeatureTable[Frequency]', tab)
dist = skbio.DistanceMatrix.from_iterable(
iterable=[1, 16, 2, 1, 16, 17],
metric=lambda x, y: abs(y-x), keys=[c for c in 'abcdef']
)
self.dist = qiime2.Artifact.import_data('DistanceMatrix', dist)
# we will make sure predictions are correct, but no need to validate
# other outputs, which are tested elsewhere.
def test_classify_samples_binary(self):
res = sample_classifier.actions.classify_samples(
table=self.tab, metadata=self.md,
test_size=0.3, cv=1, n_estimators=2, n_jobs=1, random_state=123,
parameter_tuning=False, optimize_feature_selection=False)
exp = pd.Series(['a', 'b'], name='prediction',
index=pd.Index(['c', 'f'], name='id'))
pdt.assert_series_equal(exp, res[2].view(pd.Series))
def test_classify_samples_ncv_binary(self):
res = sample_classifier.actions.classify_samples_ncv(
table=self.tab, metadata=self.md, cv=3, n_estimators=2, n_jobs=1,
random_state=123, parameter_tuning=False)
exp = pd.Series([c for c in 'ababab'], name='prediction',
index=pd.Index([i for i in 'aebdcf'], name='id'))
pdt.assert_series_equal(exp, res[0].view(pd.Series))
def test_classify_samples_dist_binary(self):
res = sample_classifier.actions.classify_samples_from_dist(
distance_matrix=self.dist, metadata=self.md, k=2, cv=3,
n_jobs=1, random_state=123)
exp = pd.Series([c for c in 'abaaaa'], name='0',
index=pd.Index([i for i in 'abcdef'], name='id'))
pdt.assert_series_equal(
exp.sort_index(), res[0].view(pd.Series).sort_index()
)
class TestROC(SampleClassifierTestPluginBase):
def setUp(self):
super().setUp()
self.md = np.array(
[[1, 0, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0],
[1, 0, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0],
[0, 1, 0], [0, 1, 0], [0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1],
[0, 0, 1], [0, 0, 1]])
np.random.seed(0)
probs = np.random.rand(20, 3)
# probabilities should sum to 1 for each sample
self.probs = np.apply_along_axis(
lambda x: x / x.sum(), axis=1, arr=probs)
self.exp_fpr = {0: [0., 0.07692308, 0.46153846, 0.46153846, 0.76923077,
0.76923077, 0.84615385, 0.84615385, 1., 1.],
1: [0., 0., 0.15384615, 0.15384615, 0.61538462,
0.61538462, 0.69230769, 0.69230769, 1., 1.],
2: [0., 0.07142857, 0.07142857, 0.14285714, 0.14285714,
0.78571429, 0.78571429, 0.92857143, 0.92857143,
1.]}
self.exp_tdr = {0: [0., 0., 0., 0.57142857, 0.57142857, 0.71428571,
0.71428571, 0.85714286, 0.85714286, 1.],
1: [0., 0.14285714, 0.14285714, 0.28571429, 0.28571429,
0.57142857, 0.57142857, 0.85714286, 0.85714286,
1.],
2: [0., 0., 0.16666667, 0.16666667, 0.5, 0.5,
0.66666667, 0.66666667, 1., 1.]}
self.exp_roc_auc = {0: 0.3626373626373626, 1: 0.4615384615384615,
2: 0.49999999999999994}
# this test confirms that all palettes load properly.
def test_roc_palette(self):
[_roc_palette(p, 3) for p in _custom_palettes().keys()]
def test_roc_per_class(self):
fpr, tdr, roc_auc = _roc_per_class(self.md, self.probs, [0, 1, 2])
for d, e in zip([fpr, tdr, roc_auc],
[self.exp_fpr, self.exp_tdr, self.exp_roc_auc]):
for c in [0, 1, 2]:
np.testing.assert_array_almost_equal(d[c], e[c])
def test_roc_micro_average(self):
fpr, tdr, roc_auc = _roc_micro_average(
self.md, self.probs, self.exp_fpr, self.exp_tdr, self.exp_roc_auc)
np.testing.assert_array_almost_equal(fpr['micro'], np.array(
[0., 0.025, 0.025, 0.075, 0.075, 0.1, 0.1, 0.225, 0.225, 0.275,
0.275, 0.475, 0.475, 0.575, 0.575, 0.6, 0.6, 0.65, 0.65, 0.675,
0.675, 0.725, 0.725, 0.75, 0.75, 0.825, 0.825, 0.925, 0.925, 1.,
1.]))
np.testing.assert_array_almost_equal(tdr['micro'], np.array(
[0., 0., 0.05, 0.05, 0.1, 0.1, 0.15, 0.15, 0.2, 0.2, 0.25, 0.25,
0.35, 0.35, 0.4, 0.4, 0.45, 0.45, 0.5, 0.5, 0.55, 0.55, 0.6, 0.6,
0.75, 0.75, 0.8, 0.8, 0.95, 0.95, 1.]))
self.assertAlmostEqual(roc_auc['micro'], 0.41374999999999995)
def test_roc_macro_average(self):
fpr, tdr, roc_auc = _roc_macro_average(
self.exp_fpr, self.exp_tdr, self.exp_roc_auc, [0, 1, 2])
np.testing.assert_array_almost_equal(fpr['macro'], np.array(
[0., 0.07142857, 0.07692308, 0.14285714, 0.15384615, 0.46153846,
0.61538462, 0.69230769, 0.76923077, 0.78571429, 0.84615385,
0.92857143, 1.]))
np.testing.assert_array_almost_equal(tdr['macro'], np.array(
[0.04761905, 0.1031746, 0.1031746, 0.21428571, 0.26190476,
0.45238095, 0.54761905, 0.64285714, 0.69047619, 0.74603175,
0.7936508, 0.90476191, 1.]))
self.assertAlmostEqual(roc_auc['macro'], 0.49930228548098726)
# Proves that the ROC nuts + bolts work if predictions does not have all
# the classes present in probabilities. This will occur if there are many
# classes or few samples and the data are not stratified:
# https://github.com/qiime2/q2-sample-classifier/issues/171
def test_binarize_and_roc_on_missing_classes(self):
# seven samples with only 4 classes (adeh) of 8 possible classes
# (abcdefgh) represented
md = pd.Series([i for i in 'hedhadd'])
# array of 7 samples X 8 classes
# the values do not matter, only the labels
probs = pd.DataFrame(np.random.rand(7, 8),
columns=[i for i in 'abcdefgh'])
_generate_roc_plots(md, probs, 'GreenBlue')
class TestBinarize(SampleClassifierTestPluginBase):
def setUp(self):
super().setUp()
def test_binarize_labels_binary(self):
md = pd.Series([c for c in 'aabbaa'])
labels = _binarize_labels(md, ['a', 'b'])
exp = np.array([[1, 0], [1, 0], [0, 1], [0, 1], [1, 0], [1, 0]])
np.testing.assert_array_equal(exp, labels)
def test_binarize_labels_multiclass(self):
md = pd.Series([c for c in 'abcabc'])
labels = _binarize_labels(md, ['a', 'b', 'c'])
exp = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1],
[1, 0, 0], [0, 1, 0], [0, 0, 1]])
np.testing.assert_array_equal(exp, labels)
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