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test_metric.py
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test_metric.py
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import unittest
import numpy as np
import pandas as pd
from numpy.testing import assert_almost_equal
from supervised.utils.metric import Metric
from supervised.utils.metric import UserDefinedEvalMetric
class MetricTest(unittest.TestCase):
def test_create(self):
params = {"name": "logloss"}
m = Metric(params)
y_true = np.array([0, 0, 1, 1])
y_predicted = np.array([0, 0, 1, 1])
score = m(y_true, y_predicted)
self.assertTrue(score < 0.1)
y_true = np.array([0, 0, 1, 1])
y_predicted = np.array([1, 1, 0, 0])
score = m(y_true, y_predicted)
self.assertTrue(score > 1.0)
def test_metric_improvement(self):
params = {"name": "logloss"}
m = Metric(params)
y_true = np.array([0, 0, 1, 1])
y_predicted = np.array([0, 0, 0, 1])
score_1 = m(y_true, y_predicted)
y_true = np.array([0, 0, 1, 1])
y_predicted = np.array([0, 0, 1, 1])
score_2 = m(y_true, y_predicted)
self.assertTrue(m.improvement(score_1, score_2))
def test_sample_weight(self):
metrics = ["logloss", "auc", "acc", "rmse", "mse", "mae", "r2", "mape"]
for m in metrics:
metric = Metric({"name": m})
y_true = np.array([0, 0, 1, 1])
y_predicted = np.array([0, 0, 0, 1])
sample_weight = np.array([1, 1, 1, 1])
score_1 = metric(y_true, y_predicted)
score_2 = metric(y_true, y_predicted, sample_weight)
assert_almost_equal(score_1, score_2)
def test_r2_metric(self):
params = {"name": "r2"}
m = Metric(params)
y_true = np.array([0, 0, 1, 1])
y_predicted = np.array([0, 0, 1, 1])
score = m(y_true, y_predicted)
self.assertEqual(score, -1.0) # negative r2
def test_mape_metric(self):
params = {"name": "mape"}
m = Metric(params)
y_true = np.array([0, 0, 1, 1])
y_predicted = np.array([0, 0, 1, 1])
score = m(y_true, y_predicted)
self.assertEqual(score, 0.0)
def test_user_defined_metric(self):
def custom(x, y, sample_weight=None):
return np.sum(x + y)
UserDefinedEvalMetric().set_metric(custom)
params = {"name": "user_defined_metric"}
m = Metric(params)
a = np.array([1, 1, 1])
score = m(a, a)
self.assertEqual(score, 6)