8000 ENH Add verbose to classifier regressor chains (#23977) · kasmith11/scikit-learn@be00837 · GitHub
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ENH Add verbose to classifier regressor chains (scikit-learn#23977)
Co-authored-by: ericfiegel <efiegel01@gmail.com> Co-authored-by: Eric Fiegel <fiegel@usc.edu> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Chiara Marmo <cmarmo@users.noreply.github.com> Co-authored-by: Julien Jerphanion <git@jjerphan.xyz> Co-authored-by: jeremiedbb <jeremiedbb@yahoo.fr>
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‎doc/whats_new/v1.2.rst

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@@ -220,6 +220,16 @@ Changelog
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- |Fix| Fixed error message of :class:`metrics.coverage_error` for 1D array input.
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:pr:`23548` by :user:`Hao Chun Chang <haochunchang>`.
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:mod:`sklearn.multioutput`
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..........................
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- |Feature| Added boolean `verbose` flag to classes:
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:class:`multioutput.ClassifierChain` and :class:`multioutput.RegressorChain`.
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:pr:`23977` by :user:`Eric Fiegel <efiegel>`,
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:user:`Chiara Marmo <cmarmo>`,
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:user:`Lucy Liu <lucyleeow>`, and
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:user:`Guillaume Lemaitre <glemaitre>`.
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:mod:`sklearn.naive_bayes`
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..........................
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‎sklearn/multioutput.py

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@@ -23,10 +23,14 @@
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from .base import BaseEstimator, clone, MetaEstimatorMixin
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from .base import RegressorMixin, ClassifierMixin, is_classifier
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from .model_selection import cross_val_predict
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from .utils import check_random_state, _print_elapsed_time
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from .utils.metaestimators import available_if
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from .utils import check_random_state
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from .utils.validation import check_is_fitted, has_fit_parameter, _check_fit_params
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from .utils.multiclass import check_classification_targets
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from .utils.validation import (
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check_is_fitted,
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has_fit_parameter,
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_check_fit_params,
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)
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from .utils.fixes import delayed
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from .utils._param_validation import HasMethods
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@@ -538,11 +542,19 @@ def _check(self):
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class _BaseChain(BaseEstimator, metaclass=ABCMeta):
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def __init__(self, base_estimator, *, order=None, cv=None, random_state=None):
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def __init__(
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self, base_estimator, *, order=None, cv=None, random_state=None, verbose=False
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):
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self.base_estimator = base_estimator
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self.order = order
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self.cv = cv
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self.random_state = random_state
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self.verbose = verbose
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def _log_message(self, *, estimator_idx, n_estimators, processing_msg):
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if not self.verbose:
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return None
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return f"({estimator_idx} of {n_estimators}) {processing_msg}"
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@abstractmethod
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def fit(self, X, Y, **fit_params):
@@ -602,8 +614,14 @@ def fit(self, X, Y, **fit_params):
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del Y_pred_chain
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for chain_idx, estimator in enumerate(self.estimators_):
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message = self._log_message(
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estimator_idx=chain_idx + 1,
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n_estimators=len(self.estimators_),
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processing_msg=f"Processing order {self.order_[chain_idx]}",
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)
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y = Y[:, self.order_[chain_idx]]
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estimator.fit(X_aug[:, : (X.shape[1] + chain_idx)], y, **fit_params)
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with _print_elapsed_time("Chain", message):
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estimator.fit(X_aug[:, : (X.shape[1] + chain_idx)], y, **fit_params)
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if self.cv is not None and chain_idx < len(self.estimators_) - 1:
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col_idx = X.shape[1] + chain_idx
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cv_result = cross_val_predict(
@@ -702,6 +720,11 @@ class ClassifierChain(MetaEstimatorMixin, ClassifierMixin, _BaseChain):
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Pass an int for reproducible output across multiple function calls.
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See :term:`Glossary <random_state>`.
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verbose : bool, default=False
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If True, chain progress is output as each model is completed.
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.. versionadded:: 1.2
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Attributes
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----------
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classes_ : list
@@ -903,6 +926,11 @@ class RegressorChain(MetaEstimatorMixin, RegressorMixin, _BaseChain):
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Pass an int for reproducible output across multiple function calls.
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See :term:`Glossary <random_state>`.
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verbose : bool, default=False
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If True, chain progress is output as each model is completed.
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.. versionadded:: 1.2
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Attributes
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----------
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estimators_ : list

‎sklearn/tests/test_multioutput.py

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@@ -2,6 +2,7 @@
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import numpy as np
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import scipy.sparse as sp
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from joblib import cpu_count
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import re
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from sklearn.utils._testing import assert_almost_equal
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from sklearn.utils._testing import assert_array_equal
@@ -10,6 +11,8 @@
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from sklearn.base import clone
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from sklearn.datasets import make_classification
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from sklearn.datasets import load_linnerud
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from sklearn.datasets import make_multilabel_classification
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from sklearn.datasets import make_regression
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from sklearn.ensemble import GradientBoostingRegressor, RandomForestClassifier
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from sklearn.exceptions import NotFittedError
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from sklearn.linear_model import Lasso
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from sklearn.linear_model import Ridge
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from sklearn.linear_model import SGDClassifier
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from sklearn.linear_model import SGDRegressor
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from sklearn.linear_model import LinearRegression
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from sklearn.metrics import jaccard_score, mean_squared_error
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from sklearn.multiclass import OneVsRestClassifier
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from sklearn.multioutput import ClassifierChain, RegressorChain
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from sklearn.multioutput import MultiOutputClassifier
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from sklearn.multioutput import MultiOutputRegressor
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from sklearn.svm import LinearSVC
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.base import ClassifierMixin
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from sklearn.utils import shuffle
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from sklearn.model_selection import GridSearchCV
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from sklearn.model_selection import GridSearchCV, train_test_split
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from sklearn.dummy import DummyRegressor, DummyClassifier
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from sklearn.pipeline import make_pipeline
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from sklearn.impute import SimpleImputer
@@ -702,6 +707,47 @@ def test_classifier_chain_tuple_invalid_order():
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chain.fit(X, y)
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def test_classifier_chain_verbose(capsys):
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X, y = make_multilabel_classification(
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n_samples=100, n_features=5, n_classes=3, n_labels=3, random_state=0
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)
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
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pattern = (
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r"\[Chain\].*\(1 of 3\) Processing order 0, total=.*\n"
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r"\[Chain\].*\(2 of 3\) Processing order 1, total=.*\n"
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r"\[Chain\].*\(3 of 3\) Processing order 2, total=.*\n$"
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)
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classifier = ClassifierChain(
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DecisionTreeClassifier(),
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order=[0, 1, 2],
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random_state=0,
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verbose=True,
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)
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classifier.fit(X_train, y_train)
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assert re.match(pattern, capsys.readouterr()[0])
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def test_regressor_chain_verbose(capsys):
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X, y = make_regression(n_samples=125, n_targets=3, random_state=0)
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
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pattern = (
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r"\[Chain\].*\(1 of 3\) Processing order 1, total=.*\n"
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r"\[Chain\].*\(2 of 3\) Processing order 0, total=.*\n"
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r"\[Chain\].*\(3 of 3\) Processing order 2, total=.*\n$"
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)
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regressor = RegressorChain(
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LinearRegression(),
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order=[1, 0, 2],
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random_state=0,
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verbose=True,
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)
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regressor.fit(X_train, y_train)
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assert re.match(pattern, capsys.readouterr()[0])
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def test_multioutputregressor_ducktypes_fitted_estimator():
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"""Test that MultiOutputRegressor checks the fitted estimator for
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predict. Non-regression test for #16549."""

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