.. _common_pitfalls:

=========================================
Common pitfalls and recommended practices
=========================================

The purpose of this chapter is to illustrate some common pitfalls and
anti-patterns that occur when using scikit-learn. It provides
examples of what **not** to do, along with a corresponding correct
example.

Inconsistent preprocessing
==========================

scikit-learn provides a library of :ref:`data-transforms`, which
may clean (see :ref:`preprocessing`), reduce
(see :ref:`data_reduction`), expand (see :ref:`kernel_approximation`)
or generate (see :ref:`feature_extraction`) feature representations.
If these data transforms are used when training a model, they also
must be used on subsequent datasets, whether it's test data or
data in a production system. Otherwise, the feature space will change,
and the model will not be able to perform effectively.

For the following example, let's create a synthetic dataset with a
single feature::

    >>> from sklearn.datasets import make_regression
    >>> from sklearn.model_selection import train_test_split

    >>> random_state = 42
    >>> X, y = make_regression(random_state=random_state, n_features=1, noise=1)
    >>> X_train, X_test, y_train, y_test = train_test_split(
    ...     X, y, test_size=0.4, random_state=random_state)

**Wrong**

The train dataset is scaled, but not the test dataset, so model
performance on the test dataset is worse than expected::

    >>> from sklearn.metrics import mean_squared_error
    >>> from sklearn.linear_model import LinearRegression
    >>> from sklearn.preprocessing import StandardScaler

    >>> scaler = StandardScaler()
    >>> X_train_transformed = scaler.fit_transform(X_train)
    >>> model = LinearRegression().fit(X_train_transformed, y_train)
    >>> mean_squared_error(y_test, model.predict(X_test))
    62.80...

**Right**

Instead of passing the non-transformed `X_test` to `predict`, we should
transform the test data, the same way we transformed the training data::

    >>> X_test_transformed = scaler.transform(X_test)
    >>> mean_squared_error(y_test, model.predict(X_test_transformed))
    0.90...

Alternatively, we recommend using a :class:`Pipeline
<sklearn.pipeline.Pipeline>`, which makes it easier to chain transformations
with estimators, and reduces the possibility of forgetting a transformation::

    >>> from sklearn.pipeline import make_pipeline

    >>> model = make_pipeline(StandardScaler(), LinearRegression())
    >>> model.fit(X_train, y_train)
    Pipeline(steps=[('standardscaler', StandardScaler()),
                    ('linearregression', LinearRegression())])
    >>> mean_squared_error(y_test, model.predict(X_test))
    0.90...

Pipelines also help avoiding another common pitfall: leaking the test data
into the training data.

.. _data_leakage:

Data leakage
============

Data leakage occurs when information that would not be available at prediction
time is used when building the model. This results in overly optimistic
performance estimates, for example from :ref:`cross-validation
<cross_validation>`, and thus poorer performance when the model is used
on actually novel data, for example during production.

A common cause is not keeping the test and train data subsets separate.
Test data should never be used to make choices about the model.
**The general rule is to never call** `fit` **on the test data**. While this
may sound obvious, this is easy to miss in some cases, for example when
applying certain pre-processing steps.

Although both train and test data subsets should receive the same
preprocessing transformation (as described in the previous section), it is
important that these transformations are only learnt from the training data.
For example, if you have a
normalization step where you divide by the average value, the average should
be the average of the train subset, **not** the average of all the data. If the
test subset is included in the average calculation, information from the test
subset is influencing the model.

How to avoid data leakage
-------------------------

Below are some tips on avoiding data leakage:

* Always split the data into train and test subsets first, particularly
  before any preprocessing steps.
* Never include test data when using the `fit` and `fit_transform`
  methods. Using all the data, e.g., `fit(X)`, can result in overly optimistic
  scores.

  Conversely, the `transform` method should be used on both train and test
  subsets as the same preprocessing should be applied to all the data.
  This can be achieved by using `fit_transform` on the train subset and
  `transform` on the test subset.
* The scikit-learn :ref:`pipeline <pipeline>` is a great way to prevent data
  leakage as it ensures that the appropriate method is performed on the
  correct data subset. The pipeline is ideal for use in cross-validation
  and hyper-parameter tuning functions.

An example of data leakage during preprocessing is detailed below.

Data leakage during pre-processing
----------------------------------

.. note::
    We here choose to illustrate data leakage with a feature selection step.
    This risk of leakage is however relevant with almost all transformations
    in scikit-learn, including (but not limited to)
    :class:`~sklearn.preprocessing.StandardScaler`,
    :class:`~sklearn.impute.SimpleImputer`, and
    :class:`~sklearn.decomposition.PCA`.

A number of :ref:`feature_selection` functions are available in scikit-learn.
They can help remove irrelevant, redundant and noisy features as well as
improve your model build time and performance. As with any other type of
preprocessing, feature selection should **only** use the training data.
Including the test data in feature selection will optimistically bias your
model.

To demonstrate we will create this binary classification problem with
10,000 randomly generated features::

    >>> import numpy as np
    >>> n_samples, n_features, n_classes = 200, 10000, 2
    >>> rng = np.random.RandomState(42)
    >>> X = rng.standard_normal((n_samples, n_features))
    >>> y = rng.choice(n_classes, n_samples)

**Wrong**

Using all the data to perform feature selection results in an accuracy score
much higher than chance, even though our targets are completely random.
This randomness means that our `X` and `y` are independent and we thus expect
the accuracy to be around 0.5. However, since the feature selection step
'sees' the test data, the model has an unfair advantage. In the incorrect
example below we first use all the data for feature selection and then split
the data into training and test subsets for model fitting. The result is a
much higher than expected accuracy score::

    >>> from sklearn.model_selection import train_test_split
    >>> from sklearn.feature_selection import SelectKBest
    >>> from sklearn.ensemble import GradientBoostingClassifier
    >>> from sklearn.metrics import accuracy_score

    >>> # Incorrect preprocessing: the entire data is transformed
    >>> X_selected = SelectKBest(k=25).fit_transform(X, y)

    >>> X_train, X_test, y_train, y_test = train_test_split(
    ...     X_selected, y, random_state=42)
    >>> gbc = GradientBoostingClassifier(random_state=1)
    >>> gbc.fit(X_train, y_train)
    GradientBoostingClassifier(random_state=1)

    >>> y_pred = gbc.predict(X_test)
    >>> accuracy_score(y_test, y_pred)
    0.76

**Right**

To prevent data leakage, it is good practice to split your data into train
and test subsets **first**. Feature selection can then be formed using just
the train dataset. Notice that whenever we use `fit` or `fit_transform`, we
only use the train dataset. The score is now what we would expect for the
data, close to chance::

    >>> X_train, X_test, y_train, y_test = train_test_split(
    ...     X, y, random_state=42)
    >>> select = SelectKBest(k=25)
    >>> X_train_selected = select.fit_transform(X_train, y_train)

    >>> gbc = GradientBoostingClassifier(random_state=1)
    >>> gbc.fit(X_train_selected, y_train)
    GradientBoostingClassifier(random_state=1)

    >>> X_test_selected = select.transform(X_test)
    >>> y_pred = gbc.predict(X_test_selected)
    >>> accuracy_score(y_test, y_pred)
    0.46

Here again, we recommend using a :class:`~sklearn.pipeline.Pipeline` to chain
together the feature selection and model estimators. The pipeline ensures
that only the training data is used when performing `fit` and the test data
is used only for calculating the accuracy score::

    >>> from sklearn.pipeline import make_pipeline
    >>> X_train, X_test, y_train, y_test = train_test_split(
    ...     X, y, random_state=42)
    >>> pipeline = make_pipeline(SelectKBest(k=25),
    ...                          GradientBoostingClassifier(random_state=1))
    >>> pipeline.fit(X_train, y_train)
    Pipeline(steps=[('selectkbest', SelectKBest(k=25)),
                    ('gradientboostingclassifier',
                    GradientBoostingClassifier(random_state=1))])

    >>> y_pred = pipeline.predict(X_test)
    >>> accuracy_score(y_test, y_pred)
    0.46

The pipeline can also be fed into a cross-validation
function such as :func:`~sklearn.model_selection.cross_val_score`.
Again, the pipeline ensures that the correct data subset and estimator
method is used during fitting and predicting::

    >>> from sklearn.model_selection import cross_val_score
    >>> scores = cross_val_score(pipeline, X, y)
    >>> print(f"Mean accuracy: {scores.mean():.2f}+/-{scores.std():.2f}")
    Mean accuracy: 0.46+/-0.07


.. _randomness:

Controlling randomness
======================

Some scikit-learn objects are inherently random. These are usually estimators
(e.g. :class:`~sklearn.ensemble.RandomForestClassifier`) and cross-validation
splitters (e.g. :class:`~sklearn.model_selection.KFold`). The randomness of
these objects is controlled via their `random_state` parameter, as described
in the :term:`Glossary <random_state>`. This section expands on the glossary
entry, and describes good practices and common pitfalls w.r.t. this
subtle parameter.

.. note:: Recommendation summary

    For an optimal robustness of cross-validation (CV) results, pass
    `RandomState` instances when creating estimators, or leave `random_state`
    to `None`. Passing integers to CV splitters is usually the safest option
    and is preferable; passing `RandomState` instances to splitters may
    sometimes be useful to achieve very specific use-cases.
    For both estimators and splitters, passing an integer vs passing an
    instance (or `None`) leads to subtle but significant differences,
    especially for CV procedures. These differences are important to
    understand when reporting results.

    For reproducible results across executions, remove any use of
    `random_state=None`.

Using `None` or `RandomState` instances, and repeated calls to `fit` and `split`
--------------------------------------------------------------------------------

The `random_state` parameter determines whether multiple calls to :term:`fit`
(for estimators) or to :term:`split` (for CV splitters) will produce the same
results, according to these rules:

- If an integer is passed, calling `fit` or `split` multiple times always
  yields the same results.
- If `None` or a `RandomState` instance is passed: `fit` and `split` will
  yield different results each time they are called, and the succession of
  calls explores all sources of entropy. `None` is the default value for all
  `random_state` parameters.

We here illustrate these rules for both estimators and CV splitters.

.. note::
    Since passing `random_state=None` is equivalent to passing the global
    `RandomState` instance from `numpy`
    (`random_state=np.random.mtrand._rand`), we will not explicitly mention
    `None` here. Everything that applies to instances also applies to using
    `None`.

Estimators
..........

Passing instances means that calling `fit` multiple times will not yield the
same results, even if the estimator is fitted on the same data and with the
same hyper-parameters::

    >>> from sklearn.linear_model import SGDClassifier
    >>> from sklearn.datasets import make_classification
    >>> import numpy as np

    >>> rng = np.random.RandomState(0)
    >>> X, y = make_classification(n_features=5, random_state=rng)
    >>> sgd = SGDClassifier(random_state=rng)

    >>> sgd.fit(X, y).coef_
    array([[ 8.85418642,  4.79084103, -3.13077794,  8.11915045, -0.56479934]])

    >>> sgd.fit(X, y).coef_
    array([[ 6.70814003,  5.25291366, -7.55212743,  5.18197458,  1.37845099]])

We can see from the snippet above that repeatedly calling `sgd.fit` has
produced different models, even if the data was the same. This is because the
Random Number Generator (RNG) of the estimator is consumed (i.e. mutated)
when `fit` is called, and this mutated RNG will be used in the subsequent
calls to `fit`. In addition, the `rng` object is shared across all objects
that use it, and as a consequence, these objects become somewhat
inter-dependent. For example, two estimators that share the same
`RandomState` instance will influence each other, as we will see later when
we discuss cloning. This point is important to keep in mind when debugging.

If we had passed an integer to the `random_state` parameter of the
:class:`~sklearn.linear_model.SGDClassifier`, we would have obtained the
same models, and thus the same scores each time. When we pass an integer, the
same RNG is used across all calls to `fit`. What internally happens is that
even though the RNG is consumed when `fit` is called, it is always reset to
its original state at the beginning of `fit`.

CV splitters
............

Randomized CV splitters have a similar behavior when a `RandomState`
instance is passed; calling `split` multiple times yields different data
splits::

    >>> from sklearn.model_selection import KFold
    >>> import numpy as np

    >>> X = y = np.arange(10)
    >>> rng = np.random.RandomState(0)
    >>> cv = KFold(n_splits=2, shuffle=True, random_state=rng)

    >>> for train, test in cv.split(X, y):
    ...     print(train, test)
    [0 3 5 6 7] [1 2 4 8 9]
    [1 2 4 8 9] [0 3 5 6 7]

    >>> for train, test in cv.split(X, y):
    ...     print(train, test)
    [0 4 6 7 8] [1 2 3 5 9]
    [1 2 3 5 9] [0 4 6 7 8]

We can see that the splits are different from the second time `split` is
called. This may lead to unexpected results if you compare the performance of
multiple estimators by calling `split` many times, as we will see in the next
section.

Common pitfalls and subtleties
------------------------------

While the rules that govern the `random_state` parameter are seemingly simple,
they do however have some subtle implications. In some cases, this can even
lead to wrong conclusions.

Estimators
..........

**Different `random_state` types lead to different cross-validation
procedures**

Depending on the type of the `random_state` parameter, estimators will behave
differently, especially in cross-validation procedures. Consider the
following snippet::

    >>> from sklearn.ensemble import RandomForestClassifier
    >>> from sklearn.datasets import make_classification
    >>> from sklearn.model_selection import cross_val_score
    >>> import numpy as np

    >>> X, y = make_classification(random_state=0)

    >>> rf_123 = RandomForestClassifier(random_state=123)
    >>> cross_val_score(rf_123, X, y)
    array([0.85, 0.95, 0.95, 0.9 , 0.9 ])

    >>> rf_inst = RandomForestClassifier(random_state=np.random.RandomState(0))
    >>> cross_val_score(rf_inst, X, y)
    array([0.9 , 0.95, 0.95, 0.9 , 0.9 ])

We see that the cross-validated scores of `rf_123` and `rf_inst` are
different, as should be expected since we didn't pass the same `random_state`
parameter. However, the difference between these scores is more subtle than
it looks, and **the cross-validation procedures that were performed by**
:func:`~sklearn.model_selection.cross_val_score` **significantly differ in
each case**:

- Since `rf_123` was passed an integer, every call to `fit` uses the same RNG:
  this means that all random characteristics of the random forest estimator
  will be the same for each of the 5 folds of the CV procedure. In
  particular, the (randomly chosen) subset of features of the estimator will
  be the same across all folds.
- Since `rf_inst` was passed a `RandomState` instance, each call to `fit`
  starts from a different RNG. As a result, the random subset of features
  will be different for each folds.

While having a constant estimator RNG across folds isn't inherently wrong, we
usually want CV results that are robust w.r.t. the estimator's randomness. As
a result, passing an instance instead of an integer may be preferable, since
it will allow the estimator RNG to vary for each fold.

.. note::
    Here, :func:`~sklearn.model_selection.cross_val_score` will use a
    non-randomized CV splitter (as is the default), so both estimators will
    be evaluated on the same splits. This section is not about variability in
    the splits. Also, whether we pass an integer or an instance to
    :func:`~sklearn.datasets.make_classification` isn't relevant for our
    illustration purpose: what matters is what we pass to the
    :class:`~sklearn.ensemble.RandomForestClassifier` estimator.

.. dropdown:: Cloning

    Another subtle side effect of passing `RandomState` instances is how
    :func:`~sklearn.base.clone` will work::

        >>> from sklearn import clone
        >>> from sklearn.ensemble import RandomForestClassifier
        >>> import numpy as np

        >>> rng = np.random.RandomState(0)
        >>> a = RandomForestClassifier(random_state=rng)
        >>> b = clone(a)

    Since a `RandomState` instance was passed to `a`, `a` and `b` are not clones
    in the strict sense, but rather clones in the statistical sense: `a` and `b`
    will still be different models, even when calling `fit(X, y)` on the same
    data. Moreover, `a` and `b` will influence each-other since they share the
    same internal RNG: calling `a.fit` will consume `b`'s RNG, and calling
    `b.fit` will consume `a`'s RNG, since they are the same. This bit is true for
    any estimators that share a `random_state` parameter; it is not specific to
    clones.

    If an integer were passed, `a` and `b` would be exact clones and they would not
    influence each other.

    .. warning::
        Even though :func:`~sklearn.base.clone` is rarely used in user code, it is
        called pervasively throughout scikit-learn codebase: in particular, most
        meta-estimators that accept non-fitted estimators call
        :func:`~sklearn.base.clone` internally
        (:class:`~sklearn.model_selection.GridSearchCV`,
        :class:`~sklearn.ensemble.StackingClassifier`,
        :class:`~sklearn.calibration.CalibratedClassifierCV`, etc.).


CV splitters
............

When passed a `RandomState` instance, CV splitters yield different splits
each time `split` is called. When comparing different estimators, this can
lead to overestimating the variance of the difference in performance between
the estimators::

    >>> from sklearn.naive_bayes import GaussianNB
    >>> from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
    >>> from sklearn.datasets import make_classification
    >>> from sklearn.model_selection import KFold
    >>> from sklearn.model_selection import cross_val_score
    >>> import numpy as np

    >>> rng = np.random.RandomState(0)
    >>> X, y = make_classification(random_state=rng)
    >>> cv = KFold(shuffle=True, random_state=rng)
    >>> lda = LinearDiscriminantAnalysis()
    >>> nb = GaussianNB()

    >>> for est in (lda, nb):
    ...     print(cross_val_score(est, X, y, cv=cv))
    [0.8  0.75 0.75 0.7  0.85]
    [0.85 0.95 0.95 0.85 0.95]


Directly comparing the performance of the
:class:`~sklearn.discriminant_analysis.LinearDiscriminantAnalysis` estimator
vs the :class:`~sklearn.naive_bayes.GaussianNB` estimator **on each fold** would
be a mistake: **the splits on which the estimators are evaluated are
different**. Indeed, :func:`~sklearn.model_selection.cross_val_score` will
internally call `cv.split` on the same
:class:`~sklearn.model_selection.KFold` instance, but the splits will be
different each time. This is also true for any tool that performs model
selection via cross-validation, e.g.
:class:`~sklearn.model_selection.GridSearchCV` and
:class:`~sklearn.model_selection.RandomizedSearchCV`: scores are not
comparable fold-to-fold across different calls to `search.fit`, since
`cv.split` would have been called multiple times. Within a single call to
`search.fit`, however, fold-to-fold comparison is possible since the search
estimator only calls `cv.split` once.

For comparable fold-to-fold results in all scenarios, one should pass an
integer to the CV splitter: `cv = KFold(shuffle=True, random_state=0)`.

.. note::
    While fold-to-fold comparison is not advisable with `RandomState`
    instances, one can however expect that average scores allow to conclude
    whether one estimator is better than another, as long as enough folds and
    data are used.

.. note::
    What matters in this example is what was passed to
    :class:`~sklearn.model_selection.KFold`. Whether we pass a `RandomState`
    instance or an integer to :func:`~sklearn.datasets.make_classification`
    is not relevant for our illustration purpose. Also, neither
    :class:`~sklearn.discriminant_analysis.LinearDiscriminantAnalysis` nor
    :class:`~sklearn.naive_bayes.GaussianNB` are randomized estimators.

General recommendations
-----------------------

Getting reproducible results across multiple executions
.......................................................

In order to obtain reproducible (i.e. constant) results across multiple
*program executions*, we need to remove all uses of `random_state=None`, which
is the default. The recommended way is to declare a `rng` variable at the top
of the program, and pass it down to any object that accepts a `random_state`
parameter::

    >>> from sklearn.ensemble import RandomForestClassifier
    >>> from sklearn.datasets import make_classification
    >>> from sklearn.model_selection import train_test_split
    >>> import numpy as np

    >>> rng = np.random.RandomState(0)
    >>> X, y = make_classification(random_state=rng)
    >>> rf = RandomForestClassifier(random_state=rng)
    >>> X_train, X_test, y_train, y_test = train_test_split(X, y,
    ...                                                     random_state=rng)
    >>> rf.fit(X_train, y_train).score(X_test, y_test)
    0.84

We are now guaranteed that the result of this script will always be 0.84, no
matter how many times we run it. Changing the global `rng` variable to a
different value should affect the results, as expected.

It is also possible to declare the `rng` variable as an integer. This may
however lead to less robust cross-validation results, as we will see in the
next section.

.. note::
    We do not recommend setting the global `numpy` seed by calling
    `np.random.seed(0)`. See `here
    <https://stackoverflow.com/questions/5836335/consistently-create-same-random-numpy-array/5837352#comment6712034_5837352>`_
    for a discussion.

Robustness of cross-validation results
......................................

When we evaluate a randomized estimator performance by cross-validation, we
want to make sure that the estimator can yield accurate predictions for new
data, but we also want to make sure that the estimator is robust w.r.t. its
random initialization. For example, we would like the random weights
initialization of a :class:`~sklearn.linear_model.SGDClassifier` to be
consistently good across all folds: otherwise, when we train that estimator
on new data, we might get unlucky and the random initialization may lead to
bad performance. Similarly, we want a random forest to be robust w.r.t the
set of randomly selected features that each tree will be using.

For these reasons, it is preferable to evaluate the cross-validation
performance by letting the estimator use a different RNG on each fold. This
is done by passing a `RandomState` instance (or `None`) to the estimator
initialization.

When we pass an integer, the estimator will use the same RNG on each fold:
if the estimator performs well (or bad), as evaluated by CV, it might just be
because we got lucky (or unlucky) with that specific seed. Passing instances
leads to more robust CV results, and makes the comparison between various
algorithms fairer. It also helps limiting the temptation to treat the
estimator's RNG as a hyper-parameter that can be tuned.

Whether we pass `RandomState` instances or integers to CV splitters has no
impact on robustness, as long as `split` is only called once. When `split`
is called multiple times, fold-to-fold comparison isn't possible anymore. As
a result, passing integer to CV splitters is usually safer and covers most
use-cases.
