mlinsights/_unittests/ut_mlmodel/test_quantile_mlpregression.py at 4145ed76faaee4ea9132b28cfb31fa1aba8456e7 · sdpython/mlinsights · GitHub

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import unittest
import numpy
from numpy.random import random
import pandas
from sklearn.neural_network import MLPRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.exceptions import ConvergenceWarning
from mlinsights.ext_test_case import ExtTestCase, ignore_warnings
from mlinsights.mlmodel import QuantileMLPRegressor
from mlinsights.mlmodel import (
    run_test_sklearn_pickle,
    run_test_sklearn_clone,
    run_test_sklearn_grid_search_cv,
)


class TestQuantileMLPRegression(ExtTestCase):
    @ignore_warnings(ConvergenceWarning)
    def test_quantile_regression_diff(self):
        X = numpy.array([[0.1], [0.2], [0.3], [0.4], [0.5]])
        Y = numpy.array([1.0, 1.1, 1.2, 10, 1.4])
        clr = MLPRegressor(hidden_layer_sizes=(3,))
        clr.fit(X, Y)
        clq = QuantileMLPRegressor(hidden_layer_sizes=(3,))
        clq.fit(X, Y)
        self.assertGreater(clr.n_iter_, 10)
        self.assertGreater(clq.n_iter_, 10)
        err1 = mean_absolute_error(Y, clr.predict(X))
        err2 = mean_absolute_error(Y, clq.predict(X))
        self.assertLesser(err1, 5)
        self.assertLesser(err2, 5)

    @ignore_warnings(ConvergenceWarning)
    def test_quantile_mlpregression_pandas(self):
        X = pandas.DataFrame(numpy.array([[0.1, 0.2], [0.2, 0.3]]))
        Y = numpy.array([1.0, 1.1])
        clr = MLPRegressor(hidden_layer_sizes=(3,))
        clr.fit(X, Y)
        clq = QuantileMLPRegressor(hidden_layer_sizes=(3,))
        clq.fit(X, Y)
        self.assertGreater(clr.n_iter_, 10)
        self.assertGreater(clq.n_iter_, 10)
        err1 = mean_absolute_error(Y, clr.predict(X))
        err2 = mean_absolute_error(Y, clq.predict(X))
        self.assertLesser(err1, 3.2)
        self.assertLesser(err2, 3.2)

    @ignore_warnings(ConvergenceWarning)
    def test_quantile_regression_pickle(self):
        X = random(100)
        eps1 = (random(90) - 0.5) * 0.1
        eps2 = random(10) * 2
        eps = numpy.hstack([eps1, eps2])
        X = X.reshape((100, 1))
        Y = X.ravel() * 3.4 + 5.6 + eps
        run_test_sklearn_pickle(lambda: MLPRegressor(hidden_layer_sizes=(3,)), X, Y)
        run_test_sklearn_pickle(
            lambda: QuantileMLPRegressor(hidden_layer_sizes=(3,)), X, Y
        )

    @ignore_warnings(ConvergenceWarning)
    def test_quantile_regression_clone(self):
        run_test_sklearn_clone(lambda: QuantileMLPRegressor())

    @ignore_warnings(ConvergenceWarning)
    def test_quantile_regression_grid_search(self):
        X = random(100)
        eps1 = (random(90) - 0.5) * 0.1
        eps2 = random(10) * 2
        eps = numpy.hstack([eps1, eps2])
        X = X.reshape((100, 1))
        Y = X.ravel() * 3.4 + 5.6 + eps
        self.assertRaise(
            lambda: run_test_sklearn_grid_search_cv(
                lambda: QuantileMLPRegressor(hidden_layer_sizes=(3,)), X, Y
            ),
            AssertionError,
        )
        res = run_test_sklearn_grid_search_cv(
            lambda: QuantileMLPRegressor(hidden_layer_sizes=(3,)),
            X,
            Y,
            learning_rate_init=[0.001, 0.0001],
        )
        self.assertIn("model", res)
        self.assertIn("score", res)
        self.assertGreater(res["score"], 0)
        self.assertLesser(res["score"], 11)


if __name__ == "__main__":
    unittest.main()