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fix doc
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‎_doc/api/mlmodel.rst

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@@ -37,6 +37,12 @@ ClassifierAfterKMeans
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.. autoclass:: mlinsights.mlmodel.classification_kmeans.ClassifierAfterKMeans
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:members:
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CustomizedMultilayerPerceptron
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++++++++++++++++++++++++++++++
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.. autoclass:: mlinsights.mlmodel.quantile_mlpregressor.CustomizedMultilayerPerceptron
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:members:
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IntervalRegressor
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+++++++++++++++++
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Transforms
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==========
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NGramsMixin
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+++++++++++
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.. autoclass:: mlinsights.mlmodel.sklearn_text.NGramsMixin
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:members:
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BaseReciprocalTransformer
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+++++++++++++++++++++++++
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.. autoclass:: mlinsights.mlmodel.sklearn_transform_inv.BaseReciprocalTransformer
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:members:
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CategoriesToIntegers
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++++++++++++++++++++
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++++++++++++++++++++++++
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.. autoclass:: mlinsights.mlmodel.piecewise_tree_regression_criterion.SimpleRegressorCriterion
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:members:
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SimpleRegressorCriterionFast
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++++++++++++++++++++++++++++
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:epkg:`scikit-learn` implements.
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.. autoclass:: mlinsights.mlmodel.piecewise_tree_regression_criterion_fast.SimpleRegressorCriterionFast
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:members:
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LinearRegressorCriterion
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++++++++++++++++++++++++
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The next one implements a criterion which optimizes the mean square error
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assuming the points falling into one node of the tree are approximated by
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a line. The mean square error is the error made with a linear regressor
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and not a constant anymore.
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and not a constant anymore. The documentation will be completed later.
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`mlinsights.mlmodel.piecewise_tree_regression_criterion_linear.LinearRegressorCriterion`
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`mlinsights.mlmodel.piecewise_tree_regression_criterion_linear_fast.SimpleRegressorCriterionFast`
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Losses
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++++++
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.. autofunction:: mlinsights.mlmodel.quantile_mlpregressor.absolute_loss
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Hidden API
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==========
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_switch_clusters
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++++++++++++++++
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.. autoclass:: mlinsights.mlmodel.piecewise_tree_regression_criterion_linear.LinearRegressorCriterion
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.. autofunction:: mlinsights.mlmodel._kmeans_constraint_._switch_clusters

‎_doc/api/search_rank.rst

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search_rank
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===========
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SearchEngineVectors
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+++++++++++++++++++
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.. autoclass:: mlinsights.search_rank.search_engine_vectors.SearchEngineVectors
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:members:
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SearchEnginePredictions
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+++++++++++++++++++++++
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.. autoclass:: mlinsights.search_rank.search_engine_predictions.SearchEnginePredictions
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:members:
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SearchEnginePredictionImages
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++++++++++++++++++++++++++++
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.. autoclass:: mlinsights.search_rank.search_engine_predictions_images.SearchEnginePredictionImages
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:members:

‎_doc/api/timeseries.rst

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==========
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Timeseries
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==========
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.. contents::
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:local:
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Datasets
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++++++++
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========
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.. autofunction:: mlinsights.timeseries.datasets.artificial_data
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Experimentation
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+++++++++++++++
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===============
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.. autofunction:: mlinsights.timeseries.patterns.find_ts_group_pattern
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Manipulation
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++++++++++++
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============
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.. autofunction:: mlinsights.timeseries.agg.aggregate_timeseries
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Plotting
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++++++++
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========
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.. autofunction:: mlinsights.timeseries.plotting.plot_week_timeseries
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Prediction
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++++++++++
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==========
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BaseReciprocalTimeSeriesTransformer
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+++++++++++++++++++++++++++++++++++
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The following function builds a regular dataset from
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a timeseries so that it can be used by machine learning models.
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.. autoclass:: mlinsights.timeseries.base.BaseReciprocalTimeSeriesTransformer
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:members:
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build_ts_X_y
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++++++++++++
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.. autofunction:: mlinsights.timeseries.utils.build_ts_X_y
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BaseTimeSeries
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++++++++++++++
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The first class defined the template for all timeseries
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estimators. It deals with a timeseries ine one dimension
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and additional features.
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.. autoclass:: mlinsights.timeseries.base.BaseTimeSeries
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:members:
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the first predictor is a dummy one: it uses the current value to
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DummyTimeSeriesRegressor
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++++++++++++++++++++++++
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The first predictor is a dummy one: it uses the current value to
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predict the future.
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.. autoclass:: mlinsights.timeseries.dummies.DummyTimeSeriesRegressor
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:members:
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ARTimeSeriesRegressor
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+++++++++++++++++++++
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The first regressor is an auto-regressor. It can be estimated
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with any regressor implemented in :epkg:`scikit-learn`.
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.. autoclass:: mlinsights.timeseries.ar.ARTimeSeriesRegressor
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:members:
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ts_mape
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+++++++
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The library implements one scoring function which compares
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the prediction to what a dummy predictor would do
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by using the previous day as a prediction.

‎_doc/conf.py

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"pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None),
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"python": (f"https://docs.python.org/{sys.version_info.major}", None),
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"scipy": ("https://docs.scipy.org/doc/scipy/reference", None),
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"sklearn": ("https://scikit-learn.org/stable/", None),
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"torch": ("https://pytorch.org/docs/stable/", None),
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}
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("py:func", ".*numpy[.].*"),
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("py:func", ".*scipy[.].*"),
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("py:func", ".*sklearn[.].*"),
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("py:func", ".*metadata_routing.*"),
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("py:func", ".*<locals>.*"),
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]
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sphinx_gallery_conf = {

‎_doc/examples/plot_kmeans_l1.py

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"""
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.. _l-kmeans-l1-example:
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KMeans with norm L1
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===================
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‎_doc/examples/plot_predictable_tsne.py

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"""
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.. _l-predictable-tsne-example:
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Predictable t-SNE
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=================
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‎_doc/examples/plot_quantile_regression.py

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"""
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.. _l-quantile-regression-example:
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Quantile Regression
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===================
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‎_doc/examples/plot_search_images_torch.py

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"""
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.. _l-search-images-torch-example:
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Search images with deep learning (torch)
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========================================
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‎_doc/examples/plot_visualize_pipeline.py

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"""
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.. _l-visualize-pipeline-example:
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Visualize a scikit-learn pipeline
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=================================
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‎mlinsights/helpers/pipeline.py

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:param vs: subset of variables for the model, None for all
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:return: iterator on models ``tuple(coordinate, model)``
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See notebook :ref:`visualizepipelinerst`.
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See example :ref:`l-visualize-pipeline-example`.
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"""
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if coor is None:
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coor = (0,)
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The object *pipe* is modified, it should be copied
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before calling this function if you need the object
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untouched after that. The prediction is slower.
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See notebook :ref:`visualizepipelinerst`.
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See notebook :ref:`l-visualize-pipeline-example`.
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"""
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def transform(self, X, *args, **kwargs):

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