159 lines
6.2 KiB
Python
159 lines
6.2 KiB
Python
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# ruff: noqa
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"""
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=======================================
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Release Highlights for scikit-learn 1.3
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=======================================
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.. currentmodule:: sklearn
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We are pleased to announce the release of scikit-learn 1.3! Many bug fixes
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and improvements were added, as well as some new key features. We detail
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below a few of the major features of this release. **For an exhaustive list of
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all the changes**, please refer to the :ref:`release notes <release_notes_1_3>`.
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To install the latest version (with pip)::
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pip install --upgrade scikit-learn
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or with conda::
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conda install -c conda-forge scikit-learn
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"""
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# %%
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# Metadata Routing
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# ----------------
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# We are in the process of introducing a new way to route metadata such as
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# ``sample_weight`` throughout the codebase, which would affect how
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# meta-estimators such as :class:`pipeline.Pipeline` and
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# :class:`model_selection.GridSearchCV` route metadata. While the
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# infrastructure for this feature is already included in this release, the work
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# is ongoing and not all meta-estimators support this new feature. You can read
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# more about this feature in the :ref:`Metadata Routing User Guide
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# <metadata_routing>`. Note that this feature is still under development and
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# not implemented for most meta-estimators.
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#
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# Third party developers can already start incorporating this into their
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# meta-estimators. For more details, see
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# :ref:`metadata routing developer guide
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# <sphx_glr_auto_examples_miscellaneous_plot_metadata_routing.py>`.
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# %%
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# HDBSCAN: hierarchical density-based clustering
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# ----------------------------------------------
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# Originally hosted in the scikit-learn-contrib repository, :class:`cluster.HDBSCAN`
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# has been adpoted into scikit-learn. It's missing a few features from the original
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# implementation which will be added in future releases.
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# By performing a modified version of :class:`cluster.DBSCAN` over multiple epsilon
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# values simultaneously, :class:`cluster.HDBSCAN` finds clusters of varying densities
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# making it more robust to parameter selection than :class:`cluster.DBSCAN`.
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# More details in the :ref:`User Guide <hdbscan>`.
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import numpy as np
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from sklearn.cluster import HDBSCAN
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from sklearn.datasets import load_digits
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from sklearn.metrics import v_measure_score
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X, true_labels = load_digits(return_X_y=True)
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print(f"number of digits: {len(np.unique(true_labels))}")
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hdbscan = HDBSCAN(min_cluster_size=15).fit(X)
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non_noisy_labels = hdbscan.labels_[hdbscan.labels_ != -1]
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print(f"number of clusters found: {len(np.unique(non_noisy_labels))}")
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print(v_measure_score(true_labels[hdbscan.labels_ != -1], non_noisy_labels))
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# %%
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# TargetEncoder: a new category encoding strategy
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# -----------------------------------------------
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# Well suited for categorical features with high cardinality,
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# :class:`preprocessing.TargetEncoder` encodes the categories based on a shrunk
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# estimate of the average target values for observations belonging to that category.
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# More details in the :ref:`User Guide <target_encoder>`.
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import numpy as np
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from sklearn.preprocessing import TargetEncoder
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X = np.array([["cat"] * 30 + ["dog"] * 20 + ["snake"] * 38], dtype=object).T
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y = [90.3] * 30 + [20.4] * 20 + [21.2] * 38
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enc = TargetEncoder(random_state=0)
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X_trans = enc.fit_transform(X, y)
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enc.encodings_
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# %%
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# Missing values support in decision trees
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# ----------------------------------------
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# The classes :class:`tree.DecisionTreeClassifier` and
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# :class:`tree.DecisionTreeRegressor` now support missing values. For each potential
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# threshold on the non-missing data, the splitter will evaluate the split with all the
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# missing values going to the left node or the right node.
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# See more details in the :ref:`User Guide <tree_missing_value_support>` or see
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# :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` for a usecase
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# example of this feature in :class:`~ensemble.HistGradientBoostingRegressor`.
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import numpy as np
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from sklearn.tree import DecisionTreeClassifier
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X = np.array([0, 1, 6, np.nan]).reshape(-1, 1)
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y = [0, 0, 1, 1]
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tree = DecisionTreeClassifier(random_state=0).fit(X, y)
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tree.predict(X)
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# %%
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# New display :class:`~model_selection.ValidationCurveDisplay`
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# ------------------------------------------------------------
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# :class:`model_selection.ValidationCurveDisplay` is now available to plot results
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# from :func:`model_selection.validation_curve`.
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from sklearn.datasets import make_classification
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from sklearn.linear_model import LogisticRegression
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from sklearn.model_selection import ValidationCurveDisplay
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X, y = make_classification(1000, 10, random_state=0)
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_ = ValidationCurveDisplay.from_estimator(
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LogisticRegression(),
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X,
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y,
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param_name="C",
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param_range=np.geomspace(1e-5, 1e3, num=9),
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score_type="both",
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score_name="Accuracy",
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)
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# %%
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# Gamma loss for gradient boosting
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# --------------------------------
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# The class :class:`ensemble.HistGradientBoostingRegressor` supports the
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# Gamma deviance loss function via `loss="gamma"`. This loss function is useful for
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# modeling strictly positive targets with a right-skewed distribution.
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import numpy as np
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from sklearn.model_selection import cross_val_score
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from sklearn.datasets import make_low_rank_matrix
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from sklearn.ensemble import HistGradientBoostingRegressor
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n_samples, n_features = 500, 10
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rng = np.random.RandomState(0)
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X = make_low_rank_matrix(n_samples, n_features, random_state=rng)
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coef = rng.uniform(low=-10, high=20, size=n_features)
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y = rng.gamma(shape=2, scale=np.exp(X @ coef) / 2)
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gbdt = HistGradientBoostingRegressor(loss="gamma")
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cross_val_score(gbdt, X, y).mean()
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# %%
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# Grouping infrequent categories in :class:`~preprocessing.OrdinalEncoder`
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# ------------------------------------------------------------------------
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# Similarly to :class:`preprocessing.OneHotEncoder`, the class
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# :class:`preprocessing.OrdinalEncoder` now supports aggregating infrequent categories
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# into a single output for each feature. The parameters to enable the gathering of
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# infrequent categories are `min_frequency` and `max_categories`.
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# See the :ref:`User Guide <encoder_infrequent_categories>` for more details.
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from sklearn.preprocessing import OrdinalEncoder
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import numpy as np
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X = np.array(
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[["dog"] * 5 + ["cat"] * 20 + ["rabbit"] * 10 + ["snake"] * 3], dtype=object
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).T
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enc = OrdinalEncoder(min_frequency=6).fit(X)
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enc.infrequent_categories_
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