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‎_unittests/ut_metrics/test_scoring_metrics.py‎

Lines changed: 48 additions & 48 deletions
Original file line numberDiff line numberDiff line change
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# -*- coding: utf-8 -*-
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"""
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@brief test log(time=12s)
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"""
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import unittest
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import pandas
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import numpy
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from sklearn import datasets
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from sklearn.linear_model import LinearRegression
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from sklearn.metrics import r2_score
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from pyquickhelper.pycode import ExtTestCase
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from mlinsights.metrics import r2_score_comparable
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class TestScoringMetrics(ExtTestCase):
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def test_r2_score_comparable(self):
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iris = datasets.load_iris()
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X = iris.data[:, :4]
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y = iris.target + 1
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df = pandas.DataFrame(X)
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df.columns = ["X1", "X2", "X3", "X4"]
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model1 = LinearRegression().fit(X, y)
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model2 = LinearRegression().fit(X, numpy.log(y))
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r2a = r2_score(y, model1.predict(X))
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r2b = r2_score(numpy.log(y), model2.predict(X))
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r2c = r2_score_comparable(y, model2.predict(X), tr='log')
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r2d = r2_score_comparable(y, model2.predict(X), inv_tr='exp')
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self.assertEqual(r2b, r2c)
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self.assertGreater(r2c, r2a)
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self.assertLesser(r2a, r2d)
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r2e = r2_score_comparable(y, model2.predict(X), inv_tr='exp', tr='exp')
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self.assertLesser(r2e, 0)
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def test_r2_score_comparable_exception(self):
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iris = datasets.load_iris()
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y = iris.target + 1
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self.assertRaise(lambda: r2_score_comparable(y, y), ValueError)
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self.assertRaise(
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lambda: r2_score_comparable(y, y, tr="log2"),
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TypeError)
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self.assertRaise(
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lambda: r2_score_comparable(y, y, inv_tr="log2"),
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TypeError)
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if __name__ == "__main__":
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unittest.main()
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# -*- coding: utf-8 -*-
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"""
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@brief test log(time=12s)
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"""
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import unittest
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import pandas
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import numpy
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from sklearn import datasets
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from sklearn.linear_model import LinearRegression
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from sklearn.metrics import r2_score
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from pyquickhelper.pycode import ExtTestCase
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from mlinsights.metrics import r2_score_comparable
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class TestScoringMetrics(ExtTestCase):
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def test_r2_score_comparable(self):
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iris = datasets.load_iris()
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X = iris.data[:, :4]
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y = iris.target + 1
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df = pandas.DataFrame(X)
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df.columns = ["X1", "X2", "X3", "X4"]
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model1 = LinearRegression().fit(X, y)
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model2 = LinearRegression().fit(X, numpy.log(y))
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r2a = r2_score(y, model1.predict(X))
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r2b = r2_score(numpy.log(y), model2.predict(X))
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r2c = r2_score_comparable(y, model2.predict(X), tr='log')
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r2d = r2_score_comparable(y, model2.predict(X), inv_tr='exp')
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self.assertEqual(r2b, r2c)
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self.assertGreater(r2c, r2a)
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self.assertLesser(r2a, r2d)
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r2e = r2_score_comparable(y, model2.predict(X), inv_tr='exp', tr='exp')
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self.assertLesser(r2e, 0)
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def test_r2_score_comparable_exception(self):
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iris = datasets.load_iris()
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y = iris.target + 1
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self.assertRaise(lambda: r2_score_comparable(y, y), ValueError)
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self.assertRaise(
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lambda: r2_score_comparable(y, y, tr="log2"),
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TypeError)
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self.assertRaise(
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lambda: r2_score_comparable(y, y, inv_tr="log2"),
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TypeError)
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if __name__ == "__main__":
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unittest.main()

‎mlinsights/metrics/scoring_metrics.py‎

Lines changed: 93 additions & 93 deletions
Original file line numberDiff line numberDiff line change
@@ -1,93 +1,93 @@
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"""
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@file
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@brief Metrics to compare machine learning.
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"""
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import numpy
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from sklearn.metrics import r2_score
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_known_functions = {
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'exp': numpy.exp,
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'log': numpy.log
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}
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def comparable_metric(metric_function, y_true, y_pred,
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tr="log", inv_tr='exp', **kwargs):
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"""
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Applies function on either the true target or/and the predictions
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before computing r2 score.
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:param metric_function: metric to compute
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:param y_true: expected targets
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:param y_pred: predictions
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:param sample_weight: weights
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:param multioutput: see :epkg:`sklearn:metrics:r2_score`
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:param tr: transformation applied on the target
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:param inv_tr: transformation applied on the predictions
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:return: results
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"""
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tr = _known_functions.get(tr, tr)
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inv_tr = _known_functions.get(inv_tr, inv_tr)
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if tr is not None and not callable(tr):
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raise TypeError("Argument tr must be callable.")
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if inv_tr is not None and not callable(inv_tr):
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raise TypeError("Argument inv_tr must be callable.")
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if tr is None and inv_tr is None:
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raise ValueError(
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"tr and inv_tr cannot be both None at the same time.")
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if tr is None:
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return metric_function(y_true, inv_tr(y_pred), **kwargs)
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if inv_tr is None:
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return metric_function(tr(y_true), y_pred, **kwargs)
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return metric_function(tr(y_true), inv_tr(y_pred), **kwargs)
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def r2_score_comparable(y_true, y_pred, *, sample_weight=None,
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multioutput='uniform_average',
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tr=None, inv_tr=None):
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"""
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Applies function on either the true target or/and the predictions
50-
before computing r2 score.
51-
52-
:param y_true: expected targets
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:param y_pred: predictions
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:param sample_weight: weights
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:param multioutput: see :epkg:`sklearn:metrics:r2_score`
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:param tr: transformation applied on the target
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:param inv_tr: transformation applied on the predictions
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:return: results
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Example:
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.. runpython::
63-
:showcode:
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65-
import numpy
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from sklearn import datasets
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LinearRegression
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from sklearn.metrics import r2_score
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from mlinsights.metrics import r2_score_comparable
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iris = datasets.load_iris()
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X = iris.data[:, :4]
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y = iris.target + 1
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X_train, X_test, y_train, y_test = train_test_split(X, y)
77-
78-
model1 = LinearRegression().fit(X_train, y_train)
79-
print('r2', r2_score(y_test, model1.predict(X_test)))
80-
print('r2 log', r2_score(numpy.log(y_test), numpy.log(model1.predict(X_test))))
81-
print('r2 log comparable', r2_score_comparable(
82-
y_test, model1.predict(X_test), tr="log", inv_tr="log"))
83-
84-
model2 = LinearRegression().fit(X_train, numpy.log(y_train))
85-
print('r2', r2_score(numpy.log(y_test), model2.predict(X_test)))
86-
print('r2 log', r2_score(y_test, numpy.exp(model2.predict(X_test))))
87-
print('r2 log comparable', r2_score_comparable(
88-
y_test, model2.predict(X_test), inv_tr="exp"))
89-
"""
90-
return comparable_metric(r2_score, y_true, y_pred,
91-
sample_weight=sample_weight,
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multioutput=multioutput,
93-
tr=tr, inv_tr=inv_tr)
1+
"""
2+
@file
3+
@brief Metrics to compare machine learning.
4+
"""
5+
import numpy
6+
from sklearn.metrics import r2_score
7+
8+
_known_functions = {
9+
'exp': numpy.exp,
10+
'log': numpy.log
11+
}
12+
13+
14+
def comparable_metric(metric_function, y_true, y_pred,
15+
tr="log", inv_tr='exp', **kwargs):
16+
"""
17+
Applies function on either the true target or/and the predictions
18+
before computing r2 score.
19+
20+
:param metric_function: metric to compute
21+
:param y_true: expected targets
22+
:param y_pred: predictions
23+
:param sample_weight: weights
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:param multioutput: see :epkg:`sklearn:metrics:r2_score`
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:param tr: transformation applied on the target
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:param inv_tr: transformation applied on the predictions
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:return: results
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"""
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tr = _known_functions.get(tr, tr)
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inv_tr = _known_functions.get(inv_tr, inv_tr)
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if tr is not None and not callable(tr):
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raise TypeError("Argument tr must be callable.")
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if inv_tr is not None and not callable(inv_tr):
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raise TypeError("Argument inv_tr must be callable.")
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if tr is None and inv_tr is None:
36+
raise ValueError(
37+
"tr and inv_tr cannot be both None at the same time.")
38+
if tr is None:
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return metric_function(y_true, inv_tr(y_pred), **kwargs)
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if inv_tr is None:
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return metric_function(tr(y_true), y_pred, **kwargs)
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return metric_function(tr(y_true), inv_tr(y_pred), **kwargs)
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44+
45+
def r2_score_comparable(y_true, y_pred, *, sample_weight=None,
46+
multioutput='uniform_average',
47+
tr=None, inv_tr=None):
48+
"""
49+
Applies function on either the true target or/and the predictions
50+
before computing r2 score.
51+
52+
:param y_true: expected targets
53+
:param y_pred: predictions
54+
:param sample_weight: weights
55+
:param multioutput: see :epkg:`sklearn:metrics:r2_score`
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:param tr: transformation applied on the target
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:param inv_tr: transformation applied on the predictions
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:return: results
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60+
Example:
61+
62+
.. runpython::
63+
:showcode:
64+
65+
import numpy
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from sklearn import datasets
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LinearRegression
69+
from sklearn.metrics import r2_score
70+
from mlinsights.metrics import r2_score_comparable
71+
72+
iris = datasets.load_iris()
73+
X = iris.data[:, :4]
74+
y = iris.target + 1
75+
76+
X_train, X_test, y_train, y_test = train_test_split(X, y)
77+
78+
model1 = LinearRegression().fit(X_train, y_train)
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print('r2', r2_score(y_test, model1.predict(X_test)))
80+
print('r2 log', r2_score(numpy.log(y_test), numpy.log(model1.predict(X_test))))
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print('r2 log comparable', r2_score_comparable(
82+
y_test, model1.predict(X_test), tr="log", inv_tr="log"))
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84+
model2 = LinearRegression().fit(X_train, numpy.log(y_train))
85+
print('r2', r2_score(numpy.log(y_test), model2.predict(X_test)))
86+
print('r2 log', r2_score(y_test, numpy.exp(model2.predict(X_test))))
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print('r2 log comparable', r2_score_comparable(
88+
y_test, model2.predict(X_test), inv_tr="exp"))
89+
"""
90+
return comparable_metric(r2_score, y_true, y_pred,
91+
sample_weight=sample_weight,
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multioutput=multioutput,
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tr=tr, inv_tr=inv_tr)

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