Implements LinkedMLPRegressor

_unittests/ut_mlmodel/test_linked_mlpregression.py

@@ -0,0 +1,126 @@
+# -*- coding: utf-8 -*-
+"""
+@brief      test log(time=2s)
+"""
+import unittest
+import numpy
+from numpy.random import random
+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 LinkedMLPRegressor
+from mlinsights.mlmodel import (
+    run_test_sklearn_pickle,
+    run_test_sklearn_clone,
+    run_test_sklearn_grid_search_cv,
+)
+
+
+class TestLinkedMLPRegression(ExtTestCase):
+    @ignore_warnings(ConvergenceWarning)
+    def test_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 = LinkedMLPRegressor(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_regression_linked_int(self):
+        X = numpy.array(
+            [[0.1, 0.11], [0.2, 0.21], [0.3, 0.31], [0.4, 0.41], [0.5, 0.51]]
+        )
+        Y = numpy.array([1.0, 1.1, 1.2, 10, 1.4])
+        clr = MLPRegressor(hidden_layer_sizes=(3,))
+        clr.fit(X, Y)
+        clq = LinkedMLPRegressor(hidden_layer_sizes=(3,), linked=2)
+        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_regression_linked(self):
+        linked = [
+            ((0, "c", 1, 2), (0, "i", 0)),
+            ((1, "c", 0, 0), (1, "c", 2, 0)),
+            ((0, "c", 1, 1), (0, "c", 0, 2)),
+            ((0, "i", 2), (0, "c", 0, 0)),
+            ((1, "i", 0), (1, "c", 1, 0)),
+            ((0, "i", 1), (0, "c", 0, 1)),
+        ]
+        X = numpy.array(
+            [[0.1, 0.11], [0.2, 0.21], [0.3, 0.31], [0.4, 0.41], [0.5, 0.51]]
+        )
+        Y = numpy.array([1.0, 1.1, 1.2, 10, 1.4])
+        clr = MLPRegressor(hidden_layer_sizes=(3,))
+        clr.fit(X, Y)
+        clq = LinkedMLPRegressor(hidden_layer_sizes=(3,), linked=linked)
+        clq.fit(X, Y)
+        self.assertEqual(clq.linked_, linked)
+        self.assertEqual(clq.coefs_[0][1, 2], clq.intercepts_[0][0])
+        self.assertEqual(clq.coefs_[1][0, 0], clq.coefs_[1][2, 0])
+        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_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))  # pylint: disable=E1101
+        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: LinkedMLPRegressor(hidden_layer_sizes=(3,)), X, Y
+        )
+
+    @ignore_warnings(ConvergenceWarning)
+    def test_regression_clone(self):
+        run_test_sklearn_clone(lambda: LinkedMLPRegressor())
+
+    @ignore_warnings(ConvergenceWarning)
+    def test_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))  # pylint: disable=E1101
+        Y = X.ravel() * 3.4 + 5.6 + eps
+        self.assertRaise(
+            lambda: run_test_sklearn_grid_search_cv(
+                lambda: LinkedMLPRegressor(hidden_layer_sizes=(3,)), X, Y
+            ),
+            ValueError,
+        )
+        res = run_test_sklearn_grid_search_cv(
+            lambda: LinkedMLPRegressor(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"], -1)
+        self.assertLesser(res["score"], 11)
+
+
+if __name__ == "__main__":
+    # TestLinkedMLPRegression().test_regression_linked()
+    unittest.main()

appveyor.yml

@@ -0,0 +1,34 @@
+image:
+  - Visual Studio 2019
+environment:
+  matrix:
+    - PYTHON: "C:\\Python310-x64"
+      PYTHON_VERSION: "3.10.x"
+      PYTHON_ARCH: "64"
+      SKL: '>=1.0'
+init:
+  - "ECHO %PYTHON% %PYTHON_VERSION% %PYTHON_ARCH%"
+
+install:
+  - "%PYTHON%\\python -m pip install --upgrade pip"
+  # for many packages
+  - "%PYTHON%\\Scripts\\pip install llvmlite numba"
+  # install precompiled versions not available on pypi
+  - "%PYTHON%\\Scripts\\pip install torch torchvision torchaudio"
+  # other dependencies
+  - "%PYTHON%\\Scripts\\pip install -r requirements.txt"
+  - "%PYTHON%\\Scripts\\pip install scikit-learn%SKL%"
+build: off
+
+before_test:
+  - "%PYTHON%\\python -u setup.py build_ext --inplace --verbose"
+
+test_script:
+  - "%PYTHON%\\python -u setup.py unittests"
+
+after_test:
+  - "%PYTHON%\\python -u setup.py bdist_wheel"
+
+artifacts:
+  - path: dist
+    name: mlinsights

mlinsights/mlmodel/__init__.py

@@ -4,6 +4,7 @@
 from .decision_tree_logreg import DecisionTreeLogisticRegression
 from .extended_features import ExtendedFeatures
 from .interval_regressor import IntervalRegressor
+from .linked_mlpregressor import LinkedMLPRegressor
 from .kmeans_constraint import ConstraintKMeans
 from .kmeans_l1 import KMeansL1L2
 from .ml_featurizer import model_featurizer