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‎HISTORY.rst

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History
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=======
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current - 2022-07-22 - 0.00Mb
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current - 2023-07-03 - 0.00Mb
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=============================
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* #115: Updates tree decision criterion for scikit-learn 1.2 (2023-07-02)
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* #113: Removes normalize attributes (deprecated) (2022-11-29)
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* #110: Fixes perplexity issue with PredictableTSNE (2022-08-06)
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* #109: Use f strings in more places (2022-07-22)
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0.3.649 - 2022-07-22 - 2.35Mb
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=============================
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* #105: Update for python 3.10 (2022-07-22)
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* #108: Uses f strings (2022-07-19)
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0.3.631 - 2022-05-19 - 0.52Mb
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0.3.631 - 2022-05-19 - 2.21Mb
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=============================
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* #107: Updates CI for scikit-learn==1.1 (2022-05-18)
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* #106: Fixes failing import _joblib_parallel_args (2022-02-18)
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* #99: LICENSE file missing in PyPI release (2021-11-20)
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0.3.614 - 2021-10-02 - 0.52Mb
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0.3.614 - 2021-10-02 - 1.73Mb
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=============================
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* #103: Updates for scikit-learn>=1.0 (2021-10-02)
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* #94: Fixed Numpy boolean array indexing issue for 2dim arrays. (2021-09-27)
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0.3.606 - 2021-08-22 - 0.52Mb
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0.3.606 - 2021-08-22 - 2.35Mb
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=============================
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* #102: Implements numpy.digitalize with a DecisionTreeRegressor (2021-08-22)
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* #98: Fixes #97, fix issue with deepcopy and criterion (2021-05-03)
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* #97: piecewise_decision_tree does not compile with the latest version of scikit-learn (2021-05-03)
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* #85: Fixes #70, implements DecisionTreeLogisticRegression (2021-05-02)
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0.3.549 - 2021-01-09 - 0.67Mb
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=============================
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* #93: Include build wheel for all platforms in CI (2021-01-09)
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0.3.543 - 2021-01-03 - 0.67Mb
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=============================
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* #89: Install fails: ModuleNotFoundError: No module named 'sklearn' (2021-01-03)
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* #92: QuantileMLPRegressor does not work with scikit-learn 0.24 (2021-01-01)
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* #91: Fixes regression criterion for scikit-learn 0.24 (2021-01-01)
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* #90: Fixes PipelineCache for scikit-learn 0.24 (2021-01-01)
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0.2.508 - 2020-09-02 - 0.43Mb
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=============================
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* #88: Change for scikit-learn 0.24 (2020-09-02)
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* #87: Set up CI with Azure Pipelines (2020-09-02)
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* #86: Update CI, use python 3.8 (2020-09-02)
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* #71: update kmeans l1 to the latest kmeans (signatures changed) (2020-08-31)
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* #84: style (2020-08-30)
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0.2.491 - 2020-08-06 - 0.83Mb
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=============================
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* #83: Upgrade version (2020-08-06)
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* #82: Fixes #81, skl 0.22, 0.23 together (2020-08-06)
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* #81: Make mlinsights work with scikit-learn 0.22 and 0.23 (2020-08-06)

‎MANIFEST.in

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recursive-include onnx_extended *.c *.cpp *.h *.pyx *.pxd *.pxi *.py
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include pyproject.toml
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include MANIFEST.in
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include setup.cfg
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prune _doc
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prune _unittests
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prune bin
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prune .circleci
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exclude *.bat
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exclude *.yml
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exclude *.git*

‎_unittests/ut_mlmodel/test_categories_to_integers.py

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@@ -105,7 +105,8 @@ def test_categories_to_integers_pickle(self):
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data = os.path.join(os.path.abspath(
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os.path.dirname(__file__)), "data", "adult_set.txt")
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df = pandas.read_csv(data, sep="\t")
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run_test_sklearn_pickle(lambda: CategoriesToIntegers(skip_errors=True), df)
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run_test_sklearn_pickle(
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lambda: CategoriesToIntegers(skip_errors=True), df)
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@ignore_warnings(skipped_warnings)
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def test_categories_to_integers_clone(self):

‎mlinsights/__init__.py

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@brief Module *mlinsights*.
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Look for insights for machine learned models.
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"""
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__version__ = "0.4.649"
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__version__ = "0.4.664"
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__author__ = "Xavier Dupré"
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__github__ = "https://github.com/sdpython/mlinsights"
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__url__ = "http://www.xavierdupre.fr/app/mlinsights/helpsphinx/index.html"

‎mlinsights/mlmodel/piecewise_tree_regression.py

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if len(ys.shape) == 1:
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ys = ys[:, numpy.newaxis]
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ys = ys.copy()
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ws = sample_weight[ind].copy() if sample_weight is not None else None
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ws = sample_weight[ind].copy(
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) if sample_weight is not None else None
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dec = LinearRegressorCriterion.create(xs, ys, ws)
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dec.node_beta(self.betas_[i, :])
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‎mlinsights/mltree/tree_digitize.py

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is_left = False
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is_leaf = False
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threshold = bins[index]
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n = tree_add_node(tree, parent, is_left, is_leaf, 0, threshold, 0, 1, 1., 0)
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n = tree_add_node(tree, parent, is_left, is_leaf,
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0, threshold, 0, 1, 1., 0)
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values.append(UNUSED)
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n_nodes.append(n)
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return n
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# it means j is the parent split
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if i == j:
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# leaf
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n = tree_add_node(tree, parent, is_left, True, 0, 0, 0, 1, 1., 0)
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n = tree_add_node(tree, parent, is_left,
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True, 0, 0, 0, 1, 1., 0)
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n_nodes.append(n)
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values.append(i)
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return n
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if i + 1 == j:
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# split
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values.append(UNUSED)
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th = bins[i]
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n = tree_add_node(tree, parent, is_left, False, 0, th, 0, 1, 1., 0)
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n = tree_add_node(tree, parent, is_left,
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False, 0, th, 0, 1, 1., 0)
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n_nodes.append(n)
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add_nodes(n, i, i, True)
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add_nodes(n, i, j, False)
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values.append(UNUSED)
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index = (i + j) // 2
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th = bins[index]
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n = tree_add_node(tree, parent, is_left, False, 0, th, 0, 1, 1., 0)
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n = tree_add_node(tree, parent, is_left,
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False, 0, th, 0, 1, 1., 0)
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n_nodes.append(n)
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add_nodes(n, i, index, True)
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add_nodes(n, index, j, False)
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if i + 1 == j:
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# leaf
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values.append(j)
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n = tree_add_node(tree, parent, is_left, True, 0, 0, 0, 1, 1., 0)
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n = tree_add_node(tree, parent, is_left,
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True, 0, 0, 0, 1, 1., 0)
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n_nodes.append(n)
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return n
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if i + 1 < j:
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# split
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values.append(UNUSED)
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index = (i + j) // 2
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th = bins[index]
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n = tree_add_node(tree, parent, is_left, False, 0, th, 0, 1, 1., 0)
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n = tree_add_node(tree, parent, is_left,
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False, 0, th, 0, 1, 1., 0)
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n_nodes.append(n)
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add_nodes(n, i, index, True)
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add_nodes(n, index, j, False)

‎mlinsights/plotting/visualize.py

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info = _pipeline_info(
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model, new_data, context, former_data=new_data)
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#new_outputs = []
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# new_outputs = []
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# for o in info[-1]['outputs']:
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# add = _get_name(context, prefix=o, info=info)
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# outputs.append(add)
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# new_outputs.append(add)
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#info[-1]['outputs'] = new_outputs
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# info[-1]['outputs'] = new_outputs
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outputs.extend(info[-1]['outputs'])
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infos.extend(info)
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