From 0492836be3acb7b787aad13fd21c36eb4ea45e7b Mon Sep 17 00:00:00 2001
From: adrinjalali <adrin.jalali@gmail.com>
Date: Wed, 11 Dec 2019 11:22:18 -0800
Subject: [PATCH 01/34] support a scalar fit param

---
 sklearn/model_selection/_search.py           | 10 ++++++++--
 sklearn/model_selection/tests/test_search.py | 21 +++++++++++++++++++-
 2 files changed, 28 insertions(+), 3 deletions(-)

diff --git a/sklearn/model_selection/_search.py b/sklearn/model_selection/_search.py
index e6a8493ef6250..2890a784ed5b3 100644
--- a/sklearn/model_selection/_search.py
+++ b/sklearn/model_selection/_search.py
@@ -649,8 +649,14 @@ def fit(self, X, y=None, groups=None, **fit_params):
 
         X, y, groups = indexable(X, y, groups)
         # make sure fit_params are sliceable
-        fit_params_values = indexable(*fit_params.values())
-        fit_params = dict(zip(fit_params.keys(), fit_params_values))
+        # TODO: remove in 0.24
+        try:
+            fit_params_values = indexable(*fit_params.values())
+            fit_params = dict(zip(fit_params.keys(), fit_params_values))
+        except TypeError:
+            warnings.warn("Support for scaler fit params is deprecated. "
+                          "Passing scalar values as a fit parameter will "
+                          "raise an error in v0.24", FutureWarning)
 
         n_splits = cv.get_n_splits(X, y, groups)
 
diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py
index 056927bee75d0..429215fe09df0 100644
--- a/sklearn/model_selection/tests/test_search.py
+++ b/sklearn/model_selection/tests/test_search.py
@@ -27,7 +27,7 @@
 
 from scipy.stats import bernoulli, expon, uniform
 
-from sklearn.base import BaseEstimator
+from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.base import clone
 from sklearn.exceptions import NotFittedError
 from sklearn.datasets import make_classification
@@ -1846,3 +1846,22 @@ def test_search_cv__pairwise_property_equivalence_of_precomputed():
 
     attr_message = "GridSearchCV not identical with precomputed metric"
     assert (preds_original == preds_precomputed).all(), attr_message
+
+
+def test_scalar_fit_param():
+    # test that a scalar fit param is supported with a warning.
+    # TODO: it should raise an error from v0.24. Issue #15805
+    class TestEstimator(BaseEstimator, ClassifierMixin):
+        def __init__(self, a=None):
+            self.a = a
+
+        def fit(self, X, y, r):
+            assert r == 42
+
+        def predict(self, X):
+            return np.zeros(shape=(len(X)))
+
+    cv = GridSearchCV(TestEstimator(), param_grid = {'a': [1, 2]})
+    X, y = make_classification()
+    with pytest.warns(FutureWarning, match="Support for scaler fit params"):
+        cv.fit(X, y, r=42)

From 1ae9d033d7f109fe67cb5e1fa8107de81470dd6f Mon Sep 17 00:00:00 2001
From: adrinjalali <adrin.jalali@gmail.com>
Date: Wed, 11 Dec 2019 11:25:14 -0800
Subject: [PATCH 02/34] pep8

---
 sklearn/model_selection/tests/test_search.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py
index 429215fe09df0..c34490a848ba7 100644
--- a/sklearn/model_selection/tests/test_search.py
+++ b/sklearn/model_selection/tests/test_search.py
@@ -1861,7 +1861,7 @@ def fit(self, X, y, r):
         def predict(self, X):
             return np.zeros(shape=(len(X)))
 
-    cv = GridSearchCV(TestEstimator(), param_grid = {'a': [1, 2]})
+    cv = GridSearchCV(TestEstimator(), param_grid={'a': [1, 2]})
     X, y = make_classification()
     with pytest.warns(FutureWarning, match="Support for scaler fit params"):
         cv.fit(X, y, r=42)

From 341f8e0b081000e3569087ce2192d62d981642bc Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 14:05:10 +0100
Subject: [PATCH 03/34] TST add test for desired behavior

---
 sklearn/model_selection/tests/test_search.py | 43 +++++++++++++++++---
 1 file changed, 37 insertions(+), 6 deletions(-)

diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py
index c34490a848ba7..cf766ed15327a 100644
--- a/sklearn/model_selection/tests/test_search.py
+++ b/sklearn/model_selection/tests/test_search.py
@@ -36,6 +36,7 @@
 
 from sklearn.model_selection import fit_grid_point
 from sklearn.model_selection import cross_val_score
+from sklearn.model_selection import train_test_split
 from sklearn.model_selection import KFold
 from sklearn.model_selection import StratifiedKFold
 from sklearn.model_selection import StratifiedShuffleSplit
@@ -1849,8 +1850,9 @@ def test_search_cv__pairwise_property_equivalence_of_precomputed():
 
 
 def test_scalar_fit_param():
-    # test that a scalar fit param is supported with a warning.
-    # TODO: it should raise an error from v0.24. Issue #15805
+    # check general support for scalar in fit_params
+    # non-regression test for:
+    # https://github.com/scikit-learn/scikit-learn/issues/15805
     class TestEstimator(BaseEstimator, ClassifierMixin):
         def __init__(self, a=None):
             self.a = a
@@ -1861,7 +1863,36 @@ def fit(self, X, y, r):
         def predict(self, X):
             return np.zeros(shape=(len(X)))
 
-    cv = GridSearchCV(TestEstimator(), param_grid={'a': [1, 2]})
-    X, y = make_classification()
-    with pytest.warns(FutureWarning, match="Support for scaler fit params"):
-        cv.fit(X, y, r=42)
+    model = GridSearchCV(TestEstimator(), param_grid={'a': [1, 2]})
+    X, y = make_classification(random_state=42)
+    model.fit(X, y, r=42)
+
+
+def _custom_lgbm_metric(y_test, y_pred):
+    # y_pred are probablities which need to be thresholded
+    y_pred = (y_pred > 0.5).astype(int)
+    acc = accuracy_score(y_test, y_pred)
+    # required output of format: (eval_name, eval_result, is_higher_better)
+    return ('accuracy', acc, True)
+
+
+@pytest.mark.parametrize("metric", ['auc', _custom_lgbm_metric])
+def test_scalar_fit_param_lgbm(metric):
+    # check support for scalar in fit_params in LightGBM
+    # non-regression test for:
+    # https://github.com/scikit-learn/scikit-learn/issues/15805
+    lgbm = pytest.importorskip("lightgbm")
+    X_train, X_valid, y_train, y_valid = train_test_split(
+        *make_classification(random_state=42), random_state=42
+    )
+    model = GridSearchCV(
+        lgbm.LGBMClassifier(n_estimators=5),
+        param_grid={'learning_rate': [0.1, 0.01]}
+    )
+    fit_params = {
+        'eval_set': [(X_valid, y_valid)],
+        'eval_metric': metric,
+        'early_stopping_rounds': 5,
+        'verbose': False
+    }
+    model.fit(X_train, y_train, **fit_params)

From 53d7a91c05f396e8d8ab5c867fc58d0cb72ceb51 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 14:06:03 +0100
Subject: [PATCH 04/34] FIX introduce _check_fit_params to validate parameters

---
 sklearn/model_selection/_search.py | 12 +-----
 sklearn/utils/validation.py        | 63 ++++++++++++++++++++++++------
 2 files changed, 54 insertions(+), 21 deletions(-)

diff --git a/sklearn/model_selection/_search.py b/sklearn/model_selection/_search.py
index 2890a784ed5b3..31a6b607d3591 100644
--- a/sklearn/model_selection/_search.py
+++ b/sklearn/model_selection/_search.py
@@ -33,7 +33,7 @@
 from ..utils import check_random_state
 from ..utils.fixes import MaskedArray
 from ..utils.random import sample_without_replacement
-from ..utils.validation import indexable, check_is_fitted
+from ..utils.validation import indexable, check_is_fitted, _check_fit_params
 from ..utils.metaestimators import if_delegate_has_method
 from ..metrics._scorer import _check_multimetric_scoring
 from ..metrics import check_scoring
@@ -648,15 +648,7 @@ def fit(self, X, y=None, groups=None, **fit_params):
             refit_metric = 'score'
 
         X, y, groups = indexable(X, y, groups)
-        # make sure fit_params are sliceable
-        # TODO: remove in 0.24
-        try:
-            fit_params_values = indexable(*fit_params.values())
-            fit_params = dict(zip(fit_params.keys(), fit_params_values))
-        except TypeError:
-            warnings.warn("Support for scaler fit params is deprecated. "
-                          "Passing scalar values as a fit parameter will "
-                          "raise an error in v0.24", FutureWarning)
+        fit_params = _check_fit_params(fit_params)
 
         n_splits = cv.get_n_splits(X, y, groups)
 
diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py
index 5502fdd534965..655a6b819952f 100644
--- a/sklearn/utils/validation.py
+++ b/sklearn/utils/validation.py
@@ -16,7 +16,8 @@
 import numpy as np
 import scipy.sparse as sp
 from distutils.version import LooseVersion
-from inspect import signature, isclass, Parameter
+from inspect import signature, Parameter
+from inspect import isclass, isfunction, ismethod, ismodule
 
 from numpy.core.numeric import ComplexWarning
 import joblib
@@ -212,6 +213,26 @@ def check_consistent_length(*arrays):
                          " samples: %r" % [int(l) for l in lengths])
 
 
+def _convert_iterable(iterable):
+    """Helper convert iterable to arrays of sparse matrices.
+
+    Convert sparse matrices to csr and non-interable objects to arrays.
+    Let passes `None`.
+
+    Parameters
+    ----------
+    iterable : {list, dataframe, array, sparse} or None
+        Object to be converted to a sliceable iterable.
+    """
+    if sp.issparse(iterable):
+        return iterable.tocsr()
+    elif hasattr(iterable, "__getitem__") or hasattr(iterable, "iloc"):
+        return iterable
+    elif iterable is None:
+        return iterable
+    return np.array(iterable)
+
+
 def indexable(*iterables):
     """Make arrays indexable for cross-validation.
 
@@ -224,16 +245,7 @@ def indexable(*iterables):
     *iterables : lists, dataframes, arrays, sparse matrices
         List of objects to ensure sliceability.
     """
-    result = []
-    for X in iterables:
-        if sp.issparse(X):
-            result.append(X.tocsr())
-        elif hasattr(X, "__getitem__") or hasattr(X, "iloc"):
-            result.append(X)
-        elif X is None:
-            result.append(X)
-        else:
-            result.append(np.array(X))
+    result = [_convert_iterable(X) for X in iterables]
     check_consistent_length(*result)
     return result
 
@@ -1257,3 +1269,32 @@ def inner_f(*args, **kwargs):
         kwargs.update({k: arg for k, arg in zip(all_args, args)})
         return f(**kwargs)
     return inner_f
+
+
+def _check_fit_params(fit_params):
+    """Check and validate the parameters passed during `fit`.
+
+    Parameters
+    ----------
+    fit_params : dict
+        Dictionary containing the parameters passed at fit.
+
+    Returns
+    -------
+    fit_params_validated : dict
+        Validated parameters. We ensure that the values are iterable.
+    """
+    fit_params_validated = {}
+    for param_key, param_value in fit_params.items():
+        is_scalar = [
+            check(param_value)
+            for check in [np.isscalar, ismodule, isclass, ismethod, isfunction]
+        ]
+        if any(is_scalar):
+            # keep scalar as is for backward-compatibility
+            # https://github.com/scikit-learn/scikit-learn/issues/15805
+            fit_params_validated[param_key] = param_value
+        else:
+            # ensure iterable will be sliceable
+            fit_params_validated[param_key] = _convert_iterable(param_value)
+    return fit_params_validated
\ No newline at end of file

From ef64f0bb0afe12e32b299f0c664d47392e58e476 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 14:06:12 +0100
Subject: [PATCH 05/34] DOC update whats new

---
 doc/whats_new/v0.22.rst | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/doc/whats_new/v0.22.rst b/doc/whats_new/v0.22.rst
index af08b832e9f6f..d7572334b2113 100644
--- a/doc/whats_new/v0.22.rst
+++ b/doc/whats_new/v0.22.rst
@@ -21,6 +21,15 @@ Changelog
 - |Fix| :func:`utils.check_array` now correctly converts pandas DataFrame with
   boolean columns to floats. :pr:`15797` by `Thomas Fan`_.
 
+:mod:`sklearn.model_selection`
+..............................
+
+- |Fix| :class:`model_selection.GridSearchCV` and
+  :class:`model_selection.RandomizedSearchCV` will accept scalar provided in
+  `fit_params`. Change in 0.22 was breaking backward compatibility.
+  :pr:`15863` by :user:`Adrin Jalali <adrinjalali>` and
+  :user:`Guillaume Lemaitre <glemaitre>`.
+
 .. _changes_0_22:
 
 Version 0.22.0

From 9567b4431d5427986515314de43ea7fc7f04b6b9 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 14:14:32 +0100
Subject: [PATCH 06/34] TST tests both grid-search and randomize-search

---
 sklearn/model_selection/tests/test_search.py | 21 ++++++++++++++------
 1 file changed, 15 insertions(+), 6 deletions(-)

diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py
index cf766ed15327a..6b06c9f4c7fa3 100644
--- a/sklearn/model_selection/tests/test_search.py
+++ b/sklearn/model_selection/tests/test_search.py
@@ -1849,7 +1849,12 @@ def test_search_cv__pairwise_property_equivalence_of_precomputed():
     assert (preds_original == preds_precomputed).all(), attr_message
 
 
-def test_scalar_fit_param():
+@pytest.mark.parametrize(
+    "SearchCV, param_search",
+    [(GridSearchCV, {'a': [0.1, 0.01]}),
+     (RandomizedSearchCV, {'a': np.random.randint(1, 3, size=2)})]
+)
+def test_scalar_fit_param(SearchCV, param_search):
     # check general support for scalar in fit_params
     # non-regression test for:
     # https://github.com/scikit-learn/scikit-learn/issues/15805
@@ -1863,7 +1868,7 @@ def fit(self, X, y, r):
         def predict(self, X):
             return np.zeros(shape=(len(X)))
 
-    model = GridSearchCV(TestEstimator(), param_grid={'a': [1, 2]})
+    model = SearchCV(TestEstimator(), param_search)
     X, y = make_classification(random_state=42)
     model.fit(X, y, r=42)
 
@@ -1877,7 +1882,12 @@ def _custom_lgbm_metric(y_test, y_pred):
 
 
 @pytest.mark.parametrize("metric", ['auc', _custom_lgbm_metric])
-def test_scalar_fit_param_lgbm(metric):
+@pytest.mark.parametrize(
+    "SearchCV, param_search",
+    [(GridSearchCV, {'learning_rate': [0.1, 0.01]}),
+     (RandomizedSearchCV, {'learning_rate': uniform(0.01, 0.1)})]
+)
+def test_scalar_fit_param_lgbm(metric, SearchCV, param_search):
     # check support for scalar in fit_params in LightGBM
     # non-regression test for:
     # https://github.com/scikit-learn/scikit-learn/issues/15805
@@ -1885,9 +1895,8 @@ def test_scalar_fit_param_lgbm(metric):
     X_train, X_valid, y_train, y_valid = train_test_split(
         *make_classification(random_state=42), random_state=42
     )
-    model = GridSearchCV(
-        lgbm.LGBMClassifier(n_estimators=5),
-        param_grid={'learning_rate': [0.1, 0.01]}
+    model = SearchCV(
+        lgbm.LGBMClassifier(n_estimators=5), param_search
     )
     fit_params = {
         'eval_set': [(X_valid, y_valid)],

From f340ab6f13a7fdd2a20129ad806f608c407448c6 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 14:15:08 +0100
Subject: [PATCH 07/34] PEP8

---
 sklearn/utils/validation.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py
index 1213790c6eba7..e0205b46060c3 100644
--- a/sklearn/utils/validation.py
+++ b/sklearn/utils/validation.py
@@ -1299,4 +1299,5 @@ def _check_fit_params(fit_params):
         else:
             # ensure iterable will be sliceable
             fit_params_validated[param_key] = _convert_iterable(param_value)
-    return fit_params_validated
\ No newline at end of file
+
+    return fit_params_validated

From ffb7ce569a641f878c7834d7d91de36587943f46 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 14:35:46 +0100
Subject: [PATCH 08/34] DOC revert unecessary change

---
 doc/whats_new/v0.22.rst | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/doc/whats_new/v0.22.rst b/doc/whats_new/v0.22.rst
index 3efc455be0ca4..21ff085d33346 100644
--- a/doc/whats_new/v0.22.rst
+++ b/doc/whats_new/v0.22.rst
@@ -63,6 +63,13 @@ Changelog
   :pr:`15863` by :user:`Adrin Jalali <adrinjalali>` and
   :user:`Guillaume Lemaitre <glemaitre>`.
 
+:mod:`sklearn.inspection`
+.........................
+
+- |Fix| :func:`inspection.plot_partial_dependence` and
+  :meth:`inspection.PartialDependenceDisplay.plot` now consistently checks
+  the number of axes passed in. :pr:`15760` by `Thomas Fan`_.
+
 :mod:`sklearn.utils`
 ....................
 

From 2b5b1db0205291ac69abe3d6c90e3057b83eabcd Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 14:35:58 +0100
Subject: [PATCH 09/34] TST add test for _check_fit_params

---
 sklearn/model_selection/tests/test_search.py |  2 +-
 sklearn/utils/tests/test_validation.py       | 28 ++++++++++++++++++++
 2 files changed, 29 insertions(+), 1 deletion(-)

diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py
index 6b06c9f4c7fa3..c082e4ef6096e 100644
--- a/sklearn/model_selection/tests/test_search.py
+++ b/sklearn/model_selection/tests/test_search.py
@@ -1862,7 +1862,7 @@ class TestEstimator(BaseEstimator, ClassifierMixin):
         def __init__(self, a=None):
             self.a = a
 
-        def fit(self, X, y, r):
+        def fit(self, X, y, r=None):
             assert r == 42
 
         def predict(self, X):
diff --git a/sklearn/utils/tests/test_validation.py b/sklearn/utils/tests/test_validation.py
index f121f11658051..4cc1db29ce012 100644
--- a/sklearn/utils/tests/test_validation.py
+++ b/sklearn/utils/tests/test_validation.py
@@ -4,6 +4,7 @@
 import os
 
 from tempfile import NamedTemporaryFile
+from inspect import isclass, isfunction
 from itertools import product
 
 import pytest
@@ -28,6 +29,7 @@
 from sklearn.utils.estimator_checks import _NotAnArray
 from sklearn.random_projection import _sparse_random_matrix
 from sklearn.linear_model import ARDRegression
+from sklearn.metrics import accuracy_score
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.svm import SVR
@@ -46,6 +48,7 @@
     _check_sample_weight,
     _allclose_dense_sparse,
     FLOAT_DTYPES)
+from sklearn.utils.validation import _check_fit_params
 
 import sklearn
 
@@ -1053,3 +1056,28 @@ def __init__(self, a=1, b=1, *, c=1, d=1):
     with pytest.warns(FutureWarning,
                       match=r"Pass c=3, d=4 as keyword args"):
         A2(1, 2, 3, 4)
+
+
+def test_check_fit_params():
+    fit_params = {
+        'list': [1, 2, 3, 4],
+        'tuple': (1, 2, 3, 4),
+        'array': np.array([1, 2, 3, 4]),
+        'sparse': sp.csc_matrix([1, 2, 3, 4]),
+        'scalar-func': accuracy_score,
+        'scalar-class': KNeighborsClassifier,
+        'scalar-int': 1,
+        'scalar-str': 'xxx',
+        'None': None,
+    }
+    result = _check_fit_params(fit_params)
+
+    assert isinstance(fit_params['list'], list)
+    assert isinstance(fit_params['tuple'], tuple)
+    assert isinstance(fit_params['array'], np.ndarray)
+    assert isinstance(fit_params['sparse'], sp.csc_matrix)
+    assert isfunction(fit_params['scalar-func'])
+    assert isclass(fit_params['scalar-class'])
+    assert fit_params['scalar-int'] == 1
+    assert fit_params['scalar-str'] == 'xxx'
+    assert fit_params['None'] is None

From ffbac6f1cc0553589aac500b2a2c13c6d18ad96f Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 14:36:57 +0100
Subject: [PATCH 10/34] olivier comments

---
 doc/whats_new/v0.22.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/whats_new/v0.22.rst b/doc/whats_new/v0.22.rst
index 21ff085d33346..75057c244318b 100644
--- a/doc/whats_new/v0.22.rst
+++ b/doc/whats_new/v0.22.rst
@@ -58,7 +58,7 @@ Changelog
 ..............................
 
 - |Fix| :class:`model_selection.GridSearchCV` and
-  :class:`model_selection.RandomizedSearchCV` will accept scalar provided in
+  :class:`model_selection.RandomizedSearchCV` accept scalar values provided in
   `fit_params`. Change in 0.22 was breaking backward compatibility.
   :pr:`15863` by :user:`Adrin Jalali <adrinjalali>` and
   :user:`Guillaume Lemaitre <glemaitre>`.

From c0216dc1e849b3496741a72b55cf684594b7669f Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 14:38:26 +0100
Subject: [PATCH 11/34] TST fixes

---
 sklearn/utils/tests/test_validation.py | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/sklearn/utils/tests/test_validation.py b/sklearn/utils/tests/test_validation.py
index 4cc1db29ce012..706e642e29c05 100644
--- a/sklearn/utils/tests/test_validation.py
+++ b/sklearn/utils/tests/test_validation.py
@@ -1072,12 +1072,12 @@ def test_check_fit_params():
     }
     result = _check_fit_params(fit_params)
 
-    assert isinstance(fit_params['list'], list)
-    assert isinstance(fit_params['tuple'], tuple)
-    assert isinstance(fit_params['array'], np.ndarray)
-    assert isinstance(fit_params['sparse'], sp.csc_matrix)
-    assert isfunction(fit_params['scalar-func'])
-    assert isclass(fit_params['scalar-class'])
-    assert fit_params['scalar-int'] == 1
-    assert fit_params['scalar-str'] == 'xxx'
-    assert fit_params['None'] is None
+    assert isinstance(result['list'], list)
+    assert isinstance(result['tuple'], tuple)
+    assert isinstance(result['array'], np.ndarray)
+    assert isinstance(result['sparse'], sp.csr_matrix)
+    assert isfunction(result['scalar-func'])
+    assert isclass(result['scalar-class'])
+    assert result['scalar-int'] == 1
+    assert result['scalar-str'] == 'xxx'
+    assert result['None'] is None

From 46937298c89d5741f5b946b3b5d270fcb9311e94 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 14:40:44 +0100
Subject: [PATCH 12/34] DOC whats new

---
 doc/whats_new/v0.22.rst | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/doc/whats_new/v0.22.rst b/doc/whats_new/v0.22.rst
index 75057c244318b..ced7e9936e89c 100644
--- a/doc/whats_new/v0.22.rst
+++ b/doc/whats_new/v0.22.rst
@@ -63,6 +63,12 @@ Changelog
   :pr:`15863` by :user:`Adrin Jalali <adrinjalali>` and
   :user:`Guillaume Lemaitre <glemaitre>`.
 
+:mod:`sklearn.utils`
+....................
+
+- |Fix| :func:`utils.check_array` now correctly converts pandas DataFrame with
+  boolean columns to floats. :pr:`15797` by `Thomas Fan`_.
+
 :mod:`sklearn.inspection`
 .........................
 
@@ -70,12 +76,6 @@ Changelog
   :meth:`inspection.PartialDependenceDisplay.plot` now consistently checks
   the number of axes passed in. :pr:`15760` by `Thomas Fan`_.
 
-:mod:`sklearn.utils`
-....................
-
-- |Fix| :func:`utils.check_array` now correctly converts pandas DataFrame with
-  boolean columns to floats. :pr:`15797` by `Thomas Fan`_.
-
 .. _changes_0_22:
 
 Version 0.22.0

From d7a2c198fc15e8c178e3a714a8a7e946b51a2008 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 14:41:07 +0100
Subject: [PATCH 13/34] DOC whats new

---
 doc/whats_new/v0.22.rst | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/doc/whats_new/v0.22.rst b/doc/whats_new/v0.22.rst
index ced7e9936e89c..44c03adc8d649 100644
--- a/doc/whats_new/v0.22.rst
+++ b/doc/whats_new/v0.22.rst
@@ -35,10 +35,6 @@ Changelog
   Follow-up of :pr:`15898` by :user:`Shivam Gargsya <shivamgargsya>`.
   :pr:`15933` by :user:`Guillaume Lemaitre <glemaitre>` and `Olivier Grisel`_.
 
-- |Fix| :func:`inspection.plot_partial_dependence` and
-  :meth:`inspection.PartialDependenceDisplay.plot` now consistently checks
-  the number of axes passed in. :pr:`15760` by `Thomas Fan`_.
-
 :mod:`sklearn.metrics`
 ......................
 

From 52ecee46fc83cef35ae5fcc3e10aec075c720e0d Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 15:33:08 +0100
Subject: [PATCH 14/34] TST revert type of error

---
 sklearn/model_selection/tests/test_search.py | 32 ++++++++------------
 1 file changed, 12 insertions(+), 20 deletions(-)

diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py
index c082e4ef6096e..011cb227028f9 100644
--- a/sklearn/model_selection/tests/test_search.py
+++ b/sklearn/model_selection/tests/test_search.py
@@ -219,33 +219,25 @@ def test_grid_search_pipeline_steps():
     assert not hasattr(param_grid['regressor'][1], 'coef_')
 
 
-def check_hyperparameter_searcher_with_fit_params(klass, **klass_kwargs):
+@pytest.mark.parametrize("SearchCV", [GridSearchCV, RandomizedSearchCV])
+def test_SearchCV_with_fit_params(SearchCV):
     X = np.arange(100).reshape(10, 10)
     y = np.array([0] * 5 + [1] * 5)
     clf = CheckingClassifier(expected_fit_params=['spam', 'eggs'])
-    searcher = klass(clf, {'foo_param': [1, 2, 3]}, cv=2, **klass_kwargs)
+    searcher = SearchCV(
+        clf, {'foo_param': [1, 2, 3]}, cv=2, error_score="raise"
+    )
 
     # The CheckingClassifier generates an assertion error if
     # a parameter is missing or has length != len(X).
-    assert_raise_message(AssertionError,
-                         "Expected fit parameter(s) ['eggs'] not seen.",
-                         searcher.fit, X, y, spam=np.ones(10))
-    assert_raise_message(
-        ValueError,
-        "Found input variables with inconsistent numbers of samples: [",
-        searcher.fit, X, y, spam=np.ones(1),
-        eggs=np.zeros(10))
-    searcher.fit(X, y, spam=np.ones(10), eggs=np.zeros(10))
+    err_msg = r"Expected fit parameter\(s\) \['eggs'\] not seen."
+    with pytest.raises(AssertionError, match=err_msg):
+        searcher.fit(X, y, spam=np.ones(10))
 
-
-def test_grid_search_with_fit_params():
-    check_hyperparameter_searcher_with_fit_params(GridSearchCV,
-                                                  error_score='raise')
-
-
-def test_random_search_with_fit_params():
-    check_hyperparameter_searcher_with_fit_params(RandomizedSearchCV, n_iter=1,
-                                                  error_score='raise')
+    err_msg = "Fit parameter spam has length 1; expected"
+    with pytest.raises(AssertionError, match=err_msg):
+        searcher.fit(X, y, spam=np.ones(1), eggs=np.zeros(10))
+    searcher.fit(X, y, spam=np.ones(10), eggs=np.zeros(10))
 
 
 @ignore_warnings

From be69ce046c3e4895b74e0b9ac73859abf316151c Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 15:38:51 +0100
Subject: [PATCH 15/34] add olivier suggestions

---
 sklearn/utils/validation.py | 19 ++++++++++---------
 1 file changed, 10 insertions(+), 9 deletions(-)

diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py
index e0205b46060c3..a5d1528574803 100644
--- a/sklearn/utils/validation.py
+++ b/sklearn/utils/validation.py
@@ -213,16 +213,16 @@ def check_consistent_length(*arrays):
                          " samples: %r" % [int(l) for l in lengths])
 
 
-def _convert_iterable(iterable):
-    """Helper convert iterable to arrays of sparse matrices.
+def _make_indexable(iterable):
+    """Ensure iterable supports indexing or convert to an indexable variant.
 
-    Convert sparse matrices to csr and non-interable objects to arrays.
-    Let passes `None`.
+    Convert sparse matrices to csr and other non-indexable iterable to arrays.
+    Let `None` and indexable objects (e.g. pandas dataframes) pass unchanged.
 
     Parameters
     ----------
     iterable : {list, dataframe, array, sparse} or None
-        Object to be converted to a sliceable iterable.
+        Object to be converted to an indexable iterable.
     """
     if sp.issparse(iterable):
         return iterable.tocsr()
@@ -245,7 +245,7 @@ def indexable(*iterables):
     *iterables : lists, dataframes, arrays, sparse matrices
         List of objects to ensure sliceability.
     """
-    result = [_convert_iterable(X) for X in iterables]
+    result = [_make_indexable(X) for X in iterables]
     check_consistent_length(*result)
     return result
 
@@ -1284,7 +1284,7 @@ def _check_fit_params(fit_params):
     Returns
     -------
     fit_params_validated : dict
-        Validated parameters. We ensure that the values are iterable.
+        Validated parameters. We ensure that the values support indexing.
     """
     fit_params_validated = {}
     for param_key, param_value in fit_params.items():
@@ -1297,7 +1297,8 @@ def _check_fit_params(fit_params):
             # https://github.com/scikit-learn/scikit-learn/issues/15805
             fit_params_validated[param_key] = param_value
         else:
-            # ensure iterable will be sliceable
-            fit_params_validated[param_key] = _convert_iterable(param_value)
+            # Any other fit_params should support indexing
+            # (e.g. for cross-validation).
+            fit_params_validated[param_key] = _make_indexable(param_value)
 
     return fit_params_validated

From 9b71a9c5e05ee39e1c5161650cda45435908278b Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 16:54:38 +0100
Subject: [PATCH 16/34] address olivier comments

---
 sklearn/model_selection/tests/test_search.py | 10 ++++++++--
 sklearn/utils/validation.py                  | 11 +++--------
 2 files changed, 11 insertions(+), 10 deletions(-)

diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py
index 011cb227028f9..1970318808424 100644
--- a/sklearn/model_selection/tests/test_search.py
+++ b/sklearn/model_selection/tests/test_search.py
@@ -1847,7 +1847,7 @@ def test_search_cv__pairwise_property_equivalence_of_precomputed():
      (RandomizedSearchCV, {'a': np.random.randint(1, 3, size=2)})]
 )
 def test_scalar_fit_param(SearchCV, param_search):
-    # check general support for scalar in fit_params
+    # unofficially sanctioned tolerance for scalar values in fit_params
     # non-regression test for:
     # https://github.com/scikit-learn/scikit-learn/issues/15805
     class TestEstimator(BaseEstimator, ClassifierMixin):
@@ -1880,7 +1880,7 @@ def _custom_lgbm_metric(y_test, y_pred):
      (RandomizedSearchCV, {'learning_rate': uniform(0.01, 0.1)})]
 )
 def test_scalar_fit_param_lgbm(metric, SearchCV, param_search):
-    # check support for scalar in fit_params in LightGBM
+    # check support for scalar values in fit_params in LightGBM
     # non-regression test for:
     # https://github.com/scikit-learn/scikit-learn/issues/15805
     lgbm = pytest.importorskip("lightgbm")
@@ -1890,6 +1890,12 @@ def test_scalar_fit_param_lgbm(metric, SearchCV, param_search):
     model = SearchCV(
         lgbm.LGBMClassifier(n_estimators=5), param_search
     )
+
+    # NOTE: `fit_params` should be data dependent (e.g. `sample_weight`) which
+    # is not the case for the following parameters. But this abuse is common in
+    # popular third-party libraries and we should tolerate this behavior for
+    # now and be careful not to break support for those without following
+    # proper deprecation cycle.
     fit_params = {
         'eval_set': [(X_valid, y_valid)],
         'eval_metric': metric,
diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py
index a5d1528574803..abde1b66f01bc 100644
--- a/sklearn/utils/validation.py
+++ b/sklearn/utils/validation.py
@@ -16,8 +16,7 @@
 import numpy as np
 import scipy.sparse as sp
 from distutils.version import LooseVersion
-from inspect import signature, Parameter
-from inspect import isclass, isfunction, ismethod, ismodule
+from inspect import signature, isclass, Parameter
 
 from numpy.core.numeric import ComplexWarning
 import joblib
@@ -1288,12 +1287,8 @@ def _check_fit_params(fit_params):
     """
     fit_params_validated = {}
     for param_key, param_value in fit_params.items():
-        is_scalar = [
-            check(param_value)
-            for check in [np.isscalar, ismodule, isclass, ismethod, isfunction]
-        ]
-        if any(is_scalar):
-            # keep scalar as is for backward-compatibility
+        if not _is_arraylike(param_value):
+            # Non-indexable pass-through (for now for backward-compatibility).
             # https://github.com/scikit-learn/scikit-learn/issues/15805
             fit_params_validated[param_key] = param_value
         else:

From 46c4b9f162842856e029f8489977b85b004a660b Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 17:12:23 +0100
Subject: [PATCH 17/34] address thomas comments

---
 sklearn/model_selection/tests/test_search.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py
index 1970318808424..8bd79ace67e52 100644
--- a/sklearn/model_selection/tests/test_search.py
+++ b/sklearn/model_selection/tests/test_search.py
@@ -1855,7 +1855,7 @@ def __init__(self, a=None):
             self.a = a
 
         def fit(self, X, y, r=None):
-            assert r == 42
+            self.r_ = r
 
         def predict(self, X):
             return np.zeros(shape=(len(X)))
@@ -1863,7 +1863,7 @@ def predict(self, X):
     model = SearchCV(TestEstimator(), param_search)
     X, y = make_classification(random_state=42)
     model.fit(X, y, r=42)
-
+    assert model.best_estimator_.r_ == 42
 
 def _custom_lgbm_metric(y_test, y_pred):
     # y_pred are probablities which need to be thresholded

From 71fab3f90a2f3874d59aa80b40c098d561953031 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 17:13:52 +0100
Subject: [PATCH 18/34] PEP8

---
 sklearn/model_selection/tests/test_search.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py
index 8bd79ace67e52..c9fd2035da328 100644
--- a/sklearn/model_selection/tests/test_search.py
+++ b/sklearn/model_selection/tests/test_search.py
@@ -1865,6 +1865,7 @@ def predict(self, X):
     model.fit(X, y, r=42)
     assert model.best_estimator_.r_ == 42
 
+
 def _custom_lgbm_metric(y_test, y_pred):
     # y_pred are probablities which need to be thresholded
     y_pred = (y_pred > 0.5).astype(int)

From 9a85162f712b85815b9cc86ef3dbc6f626c8591d Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 17:55:07 +0100
Subject: [PATCH 19/34] comments olivier

---
 sklearn/model_selection/_search.py           | 2 +-
 sklearn/model_selection/tests/test_search.py | 2 +-
 sklearn/utils/validation.py                  | 8 ++++++--
 3 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/sklearn/model_selection/_search.py b/sklearn/model_selection/_search.py
index 31a6b607d3591..a70bdd7a2f9dc 100644
--- a/sklearn/model_selection/_search.py
+++ b/sklearn/model_selection/_search.py
@@ -648,7 +648,7 @@ def fit(self, X, y=None, groups=None, **fit_params):
             refit_metric = 'score'
 
         X, y, groups = indexable(X, y, groups)
-        fit_params = _check_fit_params(fit_params)
+        fit_params = _check_fit_params(X, fit_params)
 
         n_splits = cv.get_n_splits(X, y, groups)
 
diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py
index c9fd2035da328..44b44ba8788ab 100644
--- a/sklearn/model_selection/tests/test_search.py
+++ b/sklearn/model_selection/tests/test_search.py
@@ -1880,7 +1880,7 @@ def _custom_lgbm_metric(y_test, y_pred):
     [(GridSearchCV, {'learning_rate': [0.1, 0.01]}),
      (RandomizedSearchCV, {'learning_rate': uniform(0.01, 0.1)})]
 )
-def test_scalar_fit_param_lgbm(metric, SearchCV, param_search):
+def test_scalar_fit_param_lightgbm(metric, SearchCV, param_search):
     # check support for scalar values in fit_params in LightGBM
     # non-regression test for:
     # https://github.com/scikit-learn/scikit-learn/issues/15805
diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py
index abde1b66f01bc..d15a7d7d3da12 100644
--- a/sklearn/utils/validation.py
+++ b/sklearn/utils/validation.py
@@ -1272,11 +1272,14 @@ def inner_f(*args, **kwargs):
     return inner_f
 
 
-def _check_fit_params(fit_params):
+def _check_fit_params(X, fit_params):
     """Check and validate the parameters passed during `fit`.
 
     Parameters
     ----------
+    X : array-like of shape (n_samples, n_features)
+        Data array.
+
     fit_params : dict
         Dictionary containing the parameters passed at fit.
 
@@ -1287,7 +1290,8 @@ def _check_fit_params(fit_params):
     """
     fit_params_validated = {}
     for param_key, param_value in fit_params.items():
-        if not _is_arraylike(param_value):
+        if (not _is_arraylike(param_value) or
+                _num_samples(param_value) != _num_samples(X)):
             # Non-indexable pass-through (for now for backward-compatibility).
             # https://github.com/scikit-learn/scikit-learn/issues/15805
             fit_params_validated[param_key] = param_value

From f41c808f4f3c5603d005c8d8e2da1c0055b63827 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 18:47:58 +0100
Subject: [PATCH 20/34] TST fix test by passing X

---
 sklearn/utils/tests/test_validation.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/sklearn/utils/tests/test_validation.py b/sklearn/utils/tests/test_validation.py
index 706e642e29c05..445390a6e0a32 100644
--- a/sklearn/utils/tests/test_validation.py
+++ b/sklearn/utils/tests/test_validation.py
@@ -1059,6 +1059,7 @@ def __init__(self, a=1, b=1, *, c=1, d=1):
 
 
 def test_check_fit_params():
+    X = np.random.randn(4, 2)
     fit_params = {
         'list': [1, 2, 3, 4],
         'tuple': (1, 2, 3, 4),
@@ -1070,12 +1071,12 @@ def test_check_fit_params():
         'scalar-str': 'xxx',
         'None': None,
     }
-    result = _check_fit_params(fit_params)
+    result = _check_fit_params(X, fit_params)
 
     assert isinstance(result['list'], list)
     assert isinstance(result['tuple'], tuple)
     assert isinstance(result['array'], np.ndarray)
-    assert isinstance(result['sparse'], sp.csr_matrix)
+    assert result['sparse'].format == 'csc'
     assert isfunction(result['scalar-func'])
     assert isclass(result['scalar-class'])
     assert result['scalar-int'] == 1

From c989c7075cd1d43d6fe35071fd6603ed32607744 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 20:10:45 +0100
Subject: [PATCH 21/34] avoid to call twice tocsr

---
 sklearn/model_selection/_validation.py | 2 --
 sklearn/utils/tests/test_validation.py | 4 ++--
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/sklearn/model_selection/_validation.py b/sklearn/model_selection/_validation.py
index 67a30c6416031..f778f0ee4583e 100644
--- a/sklearn/model_selection/_validation.py
+++ b/sklearn/model_selection/_validation.py
@@ -942,8 +942,6 @@ def _index_param_value(X, v, indices):
     if not _is_arraylike(v) or _num_samples(v) != _num_samples(X):
         # pass through: skip indexing
         return v
-    if sp.issparse(v):
-        v = v.tocsr()
     return _safe_indexing(v, indices)
 
 
diff --git a/sklearn/utils/tests/test_validation.py b/sklearn/utils/tests/test_validation.py
index 445390a6e0a32..5de4b0cbf4e67 100644
--- a/sklearn/utils/tests/test_validation.py
+++ b/sklearn/utils/tests/test_validation.py
@@ -1064,7 +1064,7 @@ def test_check_fit_params():
         'list': [1, 2, 3, 4],
         'tuple': (1, 2, 3, 4),
         'array': np.array([1, 2, 3, 4]),
-        'sparse': sp.csc_matrix([1, 2, 3, 4]),
+        'sparse': sp.csc_matrix([1, 2, 3, 4]).T,
         'scalar-func': accuracy_score,
         'scalar-class': KNeighborsClassifier,
         'scalar-int': 1,
@@ -1076,7 +1076,7 @@ def test_check_fit_params():
     assert isinstance(result['list'], list)
     assert isinstance(result['tuple'], tuple)
     assert isinstance(result['array'], np.ndarray)
-    assert result['sparse'].format == 'csc'
+    assert result['sparse'].format == 'csr'
     assert isfunction(result['scalar-func'])
     assert isclass(result['scalar-class'])
     assert result['scalar-int'] == 1

From 570dfa895779fe1dbaad79d85c7da7112ac57da5 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 20:13:00 +0100
Subject: [PATCH 22/34] add case column/row sparse in check_fit_param

---
 sklearn/utils/tests/test_validation.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/sklearn/utils/tests/test_validation.py b/sklearn/utils/tests/test_validation.py
index 5de4b0cbf4e67..054eb0e79c31c 100644
--- a/sklearn/utils/tests/test_validation.py
+++ b/sklearn/utils/tests/test_validation.py
@@ -1064,7 +1064,8 @@ def test_check_fit_params():
         'list': [1, 2, 3, 4],
         'tuple': (1, 2, 3, 4),
         'array': np.array([1, 2, 3, 4]),
-        'sparse': sp.csc_matrix([1, 2, 3, 4]).T,
+        'sparse-col': sp.csc_matrix([1, 2, 3, 4]).T,
+        'sparse-row': sp.csc_matrix([1, 2, 3, 4]),
         'scalar-func': accuracy_score,
         'scalar-class': KNeighborsClassifier,
         'scalar-int': 1,
@@ -1076,7 +1077,8 @@ def test_check_fit_params():
     assert isinstance(result['list'], list)
     assert isinstance(result['tuple'], tuple)
     assert isinstance(result['array'], np.ndarray)
-    assert result['sparse'].format == 'csr'
+    assert result['sparse-col'].format == 'csr'
+    assert result['sparse-row'].format == 'csc'
     assert isfunction(result['scalar-func'])
     assert isclass(result['scalar-class'])
     assert result['scalar-int'] == 1

From 444c9472d693a9ebfe4da50ceaa60f2a3fbb6f1a Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 22:34:50 +0100
Subject: [PATCH 23/34] provide optional indices

---
 sklearn/model_selection/_validation.py | 15 +++------------
 sklearn/utils/validation.py            | 10 +++++++++-
 2 files changed, 12 insertions(+), 13 deletions(-)

diff --git a/sklearn/model_selection/_validation.py b/sklearn/model_selection/_validation.py
index f778f0ee4583e..ea009a8d636fb 100644
--- a/sklearn/model_selection/_validation.py
+++ b/sklearn/model_selection/_validation.py
@@ -23,6 +23,7 @@
 from ..base import is_classifier, clone
 from ..utils import (indexable, check_random_state, _safe_indexing,
                      _message_with_time)
+from ..utils.validation import _check_fit_params
 from ..utils.validation import _is_arraylike, _num_samples
 from ..utils.metaestimators import _safe_split
 from ..metrics import check_scoring
@@ -489,8 +490,7 @@ def _fit_and_score(estimator, X, y, scorer, train, test, verbose,
 
     # Adjust length of sample weights
     fit_params = fit_params if fit_params is not None else {}
-    fit_params = {k: _index_param_value(X, v, train)
-                  for k, v in fit_params.items()}
+    fit_params = _check_fit_params(X, fit_params, train)
 
     train_scores = {}
     if parameters is not None:
@@ -830,8 +830,7 @@ def _fit_and_predict(estimator, X, y, train, test, verbose, fit_params,
     """
     # Adjust length of sample weights
     fit_params = fit_params if fit_params is not None else {}
-    fit_params = {k: _index_param_value(X, v, train)
-                  for k, v in fit_params.items()}
+    fit_params = _check_fit_params(X, _check_fit_params, train)
 
     X_train, y_train = _safe_split(estimator, X, y, train)
     X_test, _ = _safe_split(estimator, X, y, test, train)
@@ -937,14 +936,6 @@ def _check_is_permutation(indices, n_samples):
     return True
 
 
-def _index_param_value(X, v, indices):
-    """Private helper function for parameter value indexing."""
-    if not _is_arraylike(v) or _num_samples(v) != _num_samples(X):
-        # pass through: skip indexing
-        return v
-    return _safe_indexing(v, indices)
-
-
 def permutation_test_score(estimator, X, y, groups=None, cv=None,
                            n_permutations=100, n_jobs=None, random_state=0,
                            verbose=0, scoring=None):
diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py
index d15a7d7d3da12..693a74f984aea 100644
--- a/sklearn/utils/validation.py
+++ b/sklearn/utils/validation.py
@@ -1272,7 +1272,7 @@ def inner_f(*args, **kwargs):
     return inner_f
 
 
-def _check_fit_params(X, fit_params):
+def _check_fit_params(X, fit_params, indices=None):
     """Check and validate the parameters passed during `fit`.
 
     Parameters
@@ -1283,11 +1283,15 @@ def _check_fit_params(X, fit_params):
     fit_params : dict
         Dictionary containing the parameters passed at fit.
 
+    indices : array-like of shape (n_samples,), default=None
+        Indices to be selected if the parameter has the same size than `X`.
+
     Returns
     -------
     fit_params_validated : dict
         Validated parameters. We ensure that the values support indexing.
     """
+    from . import _safe_indexing
     fit_params_validated = {}
     for param_key, param_value in fit_params.items():
         if (not _is_arraylike(param_value) or
@@ -1299,5 +1303,9 @@ def _check_fit_params(X, fit_params):
             # Any other fit_params should support indexing
             # (e.g. for cross-validation).
             fit_params_validated[param_key] = _make_indexable(param_value)
+            if indices is not None:
+                fit_params_validated[param_key] = _safe_indexing(
+                    fit_params_validated[param_key], indices
+                )
 
     return fit_params_validated

From 9f47b585cf619b6073514d5548c9d57d1d755746 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 22:56:25 +0100
Subject: [PATCH 24/34] TST check content when indexing params

---
 sklearn/utils/tests/test_validation.py | 26 +++++++++++++++++---------
 1 file changed, 17 insertions(+), 9 deletions(-)

diff --git a/sklearn/utils/tests/test_validation.py b/sklearn/utils/tests/test_validation.py
index 054eb0e79c31c..5df2047df9ceb 100644
--- a/sklearn/utils/tests/test_validation.py
+++ b/sklearn/utils/tests/test_validation.py
@@ -4,7 +4,6 @@
 import os
 
 from tempfile import NamedTemporaryFile
-from inspect import isclass, isfunction
 from itertools import product
 
 import pytest
@@ -34,6 +33,7 @@
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.svm import SVR
 from sklearn.datasets import make_blobs
+from sklearn.utils import _safe_indexing
 from sklearn.utils.validation import (
     has_fit_parameter,
     check_is_fitted,
@@ -1058,29 +1058,37 @@ def __init__(self, a=1, b=1, *, c=1, d=1):
         A2(1, 2, 3, 4)
 
 
-def test_check_fit_params():
+@pytest.mark.parametrize("indices", [None, [1, 3]])
+def test_check_fit_params(indices):
     X = np.random.randn(4, 2)
     fit_params = {
         'list': [1, 2, 3, 4],
-        'tuple': (1, 2, 3, 4),
         'array': np.array([1, 2, 3, 4]),
         'sparse-col': sp.csc_matrix([1, 2, 3, 4]).T,
         'sparse-row': sp.csc_matrix([1, 2, 3, 4]),
-        'scalar-func': accuracy_score,
-        'scalar-class': KNeighborsClassifier,
         'scalar-int': 1,
         'scalar-str': 'xxx',
         'None': None,
     }
-    result = _check_fit_params(X, fit_params)
+    result = _check_fit_params(X, fit_params, indices)
+    indices_ = indices if indices is not None else list(range(X.shape[0]))
 
     assert isinstance(result['list'], list)
-    assert isinstance(result['tuple'], tuple)
     assert isinstance(result['array'], np.ndarray)
     assert result['sparse-col'].format == 'csr'
     assert result['sparse-row'].format == 'csc'
-    assert isfunction(result['scalar-func'])
-    assert isclass(result['scalar-class'])
     assert result['scalar-int'] == 1
     assert result['scalar-str'] == 'xxx'
     assert result['None'] is None
+
+    assert result['list'] == _safe_indexing(fit_params['list'], indices_)
+    assert_array_equal(
+        result['array'], _safe_indexing(fit_params['array'], indices_)
+    )
+    assert_allclose_dense_sparse(
+        result['sparse-col'],
+        _safe_indexing(fit_params['sparse-col'], indices_)
+    )
+    assert_allclose_dense_sparse(
+        result['sparse-row'], fit_params['sparse-row']
+    )

From 75bd0a949d2015ab0fe4010252ad49a4c9070878 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 23:17:47 +0100
Subject: [PATCH 25/34] PEP8

---
 sklearn/utils/tests/test_validation.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/sklearn/utils/tests/test_validation.py b/sklearn/utils/tests/test_validation.py
index 5df2047df9ceb..adb44f2571863 100644
--- a/sklearn/utils/tests/test_validation.py
+++ b/sklearn/utils/tests/test_validation.py
@@ -28,7 +28,6 @@
 from sklearn.utils.estimator_checks import _NotAnArray
 from sklearn.random_projection import _sparse_random_matrix
 from sklearn.linear_model import ARDRegression
-from sklearn.metrics import accuracy_score
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.svm import SVR

From c24f39d4e82ca1f172328a522bfcb100831da35a Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 23:31:19 +0100
Subject: [PATCH 26/34] TST update tests to check identity

---
 sklearn/utils/tests/test_validation.py | 12 ++----------
 1 file changed, 2 insertions(+), 10 deletions(-)

diff --git a/sklearn/utils/tests/test_validation.py b/sklearn/utils/tests/test_validation.py
index adb44f2571863..a3bd7b566fbbf 100644
--- a/sklearn/utils/tests/test_validation.py
+++ b/sklearn/utils/tests/test_validation.py
@@ -1072,13 +1072,8 @@ def test_check_fit_params(indices):
     result = _check_fit_params(X, fit_params, indices)
     indices_ = indices if indices is not None else list(range(X.shape[0]))
 
-    assert isinstance(result['list'], list)
-    assert isinstance(result['array'], np.ndarray)
-    assert result['sparse-col'].format == 'csr'
-    assert result['sparse-row'].format == 'csc'
-    assert result['scalar-int'] == 1
-    assert result['scalar-str'] == 'xxx'
-    assert result['None'] is None
+    for key in ['sparse-row', 'scalar-int', 'scalar-str', 'None']:
+        assert result[key] is fit_params[key]
 
     assert result['list'] == _safe_indexing(fit_params['list'], indices_)
     assert_array_equal(
@@ -1088,6 +1083,3 @@ def test_check_fit_params(indices):
         result['sparse-col'],
         _safe_indexing(fit_params['sparse-col'], indices_)
     )
-    assert_allclose_dense_sparse(
-        result['sparse-row'], fit_params['sparse-row']
-    )

From 63679fdc9f35171bfd34147c40f80b67038a6cf2 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 23 Dec 2019 23:52:13 +0100
Subject: [PATCH 27/34] stupid fix

---
 sklearn/model_selection/_validation.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sklearn/model_selection/_validation.py b/sklearn/model_selection/_validation.py
index ea009a8d636fb..1e48a5a610e3c 100644
--- a/sklearn/model_selection/_validation.py
+++ b/sklearn/model_selection/_validation.py
@@ -830,7 +830,7 @@ def _fit_and_predict(estimator, X, y, train, test, verbose, fit_params,
     """
     # Adjust length of sample weights
     fit_params = fit_params if fit_params is not None else {}
-    fit_params = _check_fit_params(X, _check_fit_params, train)
+    fit_params = _check_fit_params(X, fit_params, train)
 
     X_train, y_train = _safe_split(estimator, X, y, train)
     X_test, _ = _safe_split(estimator, X, y, test, train)

From 849615bec7784ba1567aadbb2c7de607a3601172 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Tue, 24 Dec 2019 09:15:07 +0100
Subject: [PATCH 28/34] use a distribution in RandomizedSearchCV

---
 sklearn/model_selection/tests/test_search.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py
index 44b44ba8788ab..6231652ffe446 100644
--- a/sklearn/model_selection/tests/test_search.py
+++ b/sklearn/model_selection/tests/test_search.py
@@ -1844,7 +1844,7 @@ def test_search_cv__pairwise_property_equivalence_of_precomputed():
 @pytest.mark.parametrize(
     "SearchCV, param_search",
     [(GridSearchCV, {'a': [0.1, 0.01]}),
-     (RandomizedSearchCV, {'a': np.random.randint(1, 3, size=2)})]
+     (RandomizedSearchCV, {'a': uniform(1, 3)})]
 )
 def test_scalar_fit_param(SearchCV, param_search):
     # unofficially sanctioned tolerance for scalar values in fit_params

From 7837cdf751036ddc1e8400aff73760e12d01c3bd Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Tue, 24 Dec 2019 10:00:03 +0100
Subject: [PATCH 29/34] MNT add lightgbm to one of the CI build

---
 azure-pipelines.yml          | 6 +++---
 build_tools/azure/install.sh | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index e2ff71802ce72..bd44777e11186 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -71,11 +71,11 @@ jobs:
         JOBLIB_VERSION: '0.12.3'
         COVERAGE: 'true'
       # Linux environment to test the latest available dependencies and MKL.
-      # It runs tests requiring pandas and PyAMG.
+      # It runs tests requiring lightgbm, pandas and PyAMG.
       pylatest_pip_openblas_pandas:
         DISTRIB: 'conda-pip-latest'
-        # FIXME: pinned until SciPy wheels are available for Python 3.8
-        PYTHON_VERSION: '3.8'
+        # Pin the python version to 3.7 since lightgbm is not available for 3.8
+        PYTHON_VERSION: '3.7'
         PYTEST_VERSION: '4.6.2'
         COVERAGE: 'true'
         CHECK_PYTEST_SOFT_DEPENDENCY: 'true'
diff --git a/build_tools/azure/install.sh b/build_tools/azure/install.sh
index aa2707bb03837..2dac02165d52f 100755
--- a/build_tools/azure/install.sh
+++ b/build_tools/azure/install.sh
@@ -91,7 +91,7 @@ elif [[ "$DISTRIB" == "conda-pip-latest" ]]; then
     python -m pip install -U pip
     python -m pip install numpy scipy cython joblib
     python -m pip install pytest==$PYTEST_VERSION pytest-cov pytest-xdist
-    python -m pip install pandas matplotlib pyamg
+    python -m pip install pandas matplotlib pyamg lightgbm
 fi
 
 if [[ "$COVERAGE" == "true" ]]; then

From b98e194280dc1e19d50305499a3de9d1c476101f Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Tue, 24 Dec 2019 10:24:13 +0100
Subject: [PATCH 30/34] move to another build

---
 azure-pipelines.yml          | 6 +++---
 build_tools/azure/install.sh | 6 +++++-
 2 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index bd44777e11186..e3052cd9ab3d6 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -36,6 +36,7 @@ jobs:
         NUMPY_VERSION: '*'
         SCIPY_VERSION: '*'
         CYTHON_VERSION: '*'
+        LIGHTGBM_VERSION: '*'
         PILLOW_VERSION: '*'
         PYTEST_VERSION: '*'
         JOBLIB_VERSION: '*'
@@ -71,11 +72,10 @@ jobs:
         JOBLIB_VERSION: '0.12.3'
         COVERAGE: 'true'
       # Linux environment to test the latest available dependencies and MKL.
-      # It runs tests requiring lightgbm, pandas and PyAMG.
+      # It runs tests requiring pandas and PyAMG.
       pylatest_pip_openblas_pandas:
         DISTRIB: 'conda-pip-latest'
-        # Pin the python version to 3.7 since lightgbm is not available for 3.8
-        PYTHON_VERSION: '3.7'
+        PYTHON_VERSION: '3.8'
         PYTEST_VERSION: '4.6.2'
         COVERAGE: 'true'
         CHECK_PYTEST_SOFT_DEPENDENCY: 'true'
diff --git a/build_tools/azure/install.sh b/build_tools/azure/install.sh
index 2dac02165d52f..245662cd0603a 100755
--- a/build_tools/azure/install.sh
+++ b/build_tools/azure/install.sh
@@ -41,6 +41,10 @@ if [[ "$DISTRIB" == "conda" ]]; then
         TO_INSTALL="$TO_INSTALL matplotlib=$MATPLOTLIB_VERSION"
     fi
 
+    if [[ -n "$LIGHTGBM_VERSION" ]]; then
+        TO_INSTALL="$TO_INSTALL lightgbm=$LIGHTGBM_VERSION"
+    fi
+
     if [[ "$UNAMESTR" == "Darwin" ]]; then
         if [[ "$SKLEARN_TEST_NO_OPENMP" != "true" ]]; then
             # on macOS, install an OpenMP-enabled clang/llvm from conda-forge.
@@ -91,7 +95,7 @@ elif [[ "$DISTRIB" == "conda-pip-latest" ]]; then
     python -m pip install -U pip
     python -m pip install numpy scipy cython joblib
     python -m pip install pytest==$PYTEST_VERSION pytest-cov pytest-xdist
-    python -m pip install pandas matplotlib pyamg lightgbm
+    python -m pip install pandas matplotlib pyamg
 fi
 
 if [[ "$COVERAGE" == "true" ]]; then

From 3127d2bf20f4a31bf9fd17b0f2bc737cd7826f52 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Tue, 24 Dec 2019 11:32:20 +0100
Subject: [PATCH 31/34] do not install dependencies lightgbm

---
 build_tools/azure/install.sh | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/build_tools/azure/install.sh b/build_tools/azure/install.sh
index 245662cd0603a..d4c3f5d8fbd44 100755
--- a/build_tools/azure/install.sh
+++ b/build_tools/azure/install.sh
@@ -41,10 +41,6 @@ if [[ "$DISTRIB" == "conda" ]]; then
         TO_INSTALL="$TO_INSTALL matplotlib=$MATPLOTLIB_VERSION"
     fi
 
-    if [[ -n "$LIGHTGBM_VERSION" ]]; then
-        TO_INSTALL="$TO_INSTALL lightgbm=$LIGHTGBM_VERSION"
-    fi
-
     if [[ "$UNAMESTR" == "Darwin" ]]; then
         if [[ "$SKLEARN_TEST_NO_OPENMP" != "true" ]]; then
             # on macOS, install an OpenMP-enabled clang/llvm from conda-forge.
@@ -96,6 +92,7 @@ elif [[ "$DISTRIB" == "conda-pip-latest" ]]; then
     python -m pip install numpy scipy cython joblib
     python -m pip install pytest==$PYTEST_VERSION pytest-cov pytest-xdist
     python -m pip install pandas matplotlib pyamg
+    python -m pip install lightgbm --no-deps
 fi
 
 if [[ "$COVERAGE" == "true" ]]; then

From a096a7d600d2bbd97f311c63b862d235aece92bd Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Tue, 24 Dec 2019 11:55:57 +0100
Subject: [PATCH 32/34] MNT comments on the CI setup

---
 azure-pipelines.yml          | 3 +--
 build_tools/azure/install.sh | 1 +
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index e3052cd9ab3d6..b029a2fd18574 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -36,7 +36,6 @@ jobs:
         NUMPY_VERSION: '*'
         SCIPY_VERSION: '*'
         CYTHON_VERSION: '*'
-        LIGHTGBM_VERSION: '*'
         PILLOW_VERSION: '*'
         PYTEST_VERSION: '*'
         JOBLIB_VERSION: '*'
@@ -72,7 +71,7 @@ jobs:
         JOBLIB_VERSION: '0.12.3'
         COVERAGE: 'true'
       # Linux environment to test the latest available dependencies and MKL.
-      # It runs tests requiring pandas and PyAMG.
+      # It runs tests requiring lightgbm, pandas and PyAMG.
       pylatest_pip_openblas_pandas:
         DISTRIB: 'conda-pip-latest'
         PYTHON_VERSION: '3.8'
diff --git a/build_tools/azure/install.sh b/build_tools/azure/install.sh
index d4c3f5d8fbd44..1ef981b5dd6e8 100755
--- a/build_tools/azure/install.sh
+++ b/build_tools/azure/install.sh
@@ -92,6 +92,7 @@ elif [[ "$DISTRIB" == "conda-pip-latest" ]]; then
     python -m pip install numpy scipy cython joblib
     python -m pip install pytest==$PYTEST_VERSION pytest-cov pytest-xdist
     python -m pip install pandas matplotlib pyamg
+    # do not install dependencies for lightgbm since it requires scikit-learn
     python -m pip install lightgbm --no-deps
 fi
 

From 18b1207441999841b97d32a91e6d5ae32ad2bdba Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 27 Dec 2019 09:36:11 +0100
Subject: [PATCH 33/34] address some comments

---
 sklearn/utils/validation.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py
index 693a74f984aea..810ae44a5d60e 100644
--- a/sklearn/utils/validation.py
+++ b/sklearn/utils/validation.py
@@ -1284,7 +1284,7 @@ def _check_fit_params(X, fit_params, indices=None):
         Dictionary containing the parameters passed at fit.
 
     indices : array-like of shape (n_samples,), default=None
-        Indices to be selected if the parameter has the same size than `X`.
+        Indices to be selected if the parameter has the same size as `X`.
 
     Returns
     -------
@@ -1303,9 +1303,8 @@ def _check_fit_params(X, fit_params, indices=None):
             # Any other fit_params should support indexing
             # (e.g. for cross-validation).
             fit_params_validated[param_key] = _make_indexable(param_value)
-            if indices is not None:
-                fit_params_validated[param_key] = _safe_indexing(
-                    fit_params_validated[param_key], indices
-                )
+            fit_params_validated[param_key] = _safe_indexing(
+                fit_params_validated[param_key], indices
+            )
 
     return fit_params_validated

From 74d70e724a76e69640b5ca77d32d0b564bb5cf52 Mon Sep 17 00:00:00 2001
From: Olivier Grisel <olivier.grisel@ensta.org>
Date: Tue, 31 Dec 2019 12:02:49 +0100
Subject: [PATCH 34/34] Test fit_params compat without dependency on lightgbm

---
 sklearn/model_selection/tests/test_search.py | 50 ++++++++++++--------
 1 file changed, 30 insertions(+), 20 deletions(-)

diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py
index 6231652ffe446..bacfc20eb1fc1 100644
--- a/sklearn/model_selection/tests/test_search.py
+++ b/sklearn/model_selection/tests/test_search.py
@@ -1866,30 +1866,41 @@ def predict(self, X):
     assert model.best_estimator_.r_ == 42
 
 
-def _custom_lgbm_metric(y_test, y_pred):
-    # y_pred are probablities which need to be thresholded
-    y_pred = (y_pred > 0.5).astype(int)
-    acc = accuracy_score(y_test, y_pred)
-    # required output of format: (eval_name, eval_result, is_higher_better)
-    return ('accuracy', acc, True)
-
-
-@pytest.mark.parametrize("metric", ['auc', _custom_lgbm_metric])
 @pytest.mark.parametrize(
     "SearchCV, param_search",
-    [(GridSearchCV, {'learning_rate': [0.1, 0.01]}),
-     (RandomizedSearchCV, {'learning_rate': uniform(0.01, 0.1)})]
+    [(GridSearchCV, {'alpha': [0.1, 0.01]}),
+     (RandomizedSearchCV, {'alpha': uniform(0.01, 0.1)})]
 )
-def test_scalar_fit_param_lightgbm(metric, SearchCV, param_search):
-    # check support for scalar values in fit_params in LightGBM
-    # non-regression test for:
+def test_scalar_fit_param_compat(SearchCV, param_search):
+    # check support for scalar values in fit_params, for instance in LightGBM
+    # that do not exactly respect the scikit-learn API contract but that we do
+    # not want to break without an explicit deprecation cycle and API
+    # recommendations for implementing early stopping with a user provided
+    # validation set. non-regression test for:
     # https://github.com/scikit-learn/scikit-learn/issues/15805
-    lgbm = pytest.importorskip("lightgbm")
     X_train, X_valid, y_train, y_valid = train_test_split(
         *make_classification(random_state=42), random_state=42
     )
+
+    class _FitParamClassifier(SGDClassifier):
+
+        def fit(self, X, y, sample_weight=None, tuple_of_arrays=None,
+                scalar_param=None, callable_param=None):
+            super().fit(X, y, sample_weight=sample_weight)
+            assert scalar_param > 0
+            assert callable(callable_param)
+
+            # The tuple of arrays should be preserved as tuple.
+            assert isinstance(tuple_of_arrays, tuple)
+            assert tuple_of_arrays[0].ndim == 2
+            assert tuple_of_arrays[1].ndim == 1
+            return self
+
+    def _fit_param_callable():
+        pass
+
     model = SearchCV(
-        lgbm.LGBMClassifier(n_estimators=5), param_search
+        _FitParamClassifier(), param_search
     )
 
     # NOTE: `fit_params` should be data dependent (e.g. `sample_weight`) which
@@ -1898,9 +1909,8 @@ def test_scalar_fit_param_lightgbm(metric, SearchCV, param_search):
     # now and be careful not to break support for those without following
     # proper deprecation cycle.
     fit_params = {
-        'eval_set': [(X_valid, y_valid)],
-        'eval_metric': metric,
-        'early_stopping_rounds': 5,
-        'verbose': False
+        'tuple_of_arrays': (X_valid, y_valid),
+        'callable_param': _fit_param_callable,
+        'scalar_param': 42,
     }
     model.fit(X_train, y_train, **fit_params)