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.
11.1. Inconsistent preprocessing#
scikit-learn provides a library of Dataset transformations, which may clean (see Preprocessing data), reduce (see Unsupervised dimensionality reduction), expand (see Kernel Approximation) or generate (see 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 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.
11.2. 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 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.
11.2.1. 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
fitandfit_transformmethods. Using all the data, e.g.,fit(X), can result in overly optimistic scores.Conversely, the
transformmethod 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 usingfit_transformon the train subset andtransformon the test subset.The scikit-learn 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.
11.2.2. 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)
StandardScaler,
SimpleImputer, and
PCA.
A number of 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 HistGradientBoostingClassifier >>> 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 = HistGradientBoostingClassifier(random_state=1) >>> gbc.fit(X_train, y_train) HistGradientBoostingClassifier(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 = HistGradientBoostingClassifier(random_state=1) >>> gbc.fit(X_train_selected, y_train) HistGradientBoostingClassifier(random_state=1) >>> X_test_selected = select.transform(X_test) >>> y_pred = gbc.predict(X_test_selected) >>> accuracy_score(y_test, y_pred) 0.5
Here again, we recommend using a 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), ... HistGradientBoostingClassifier(random_state=1)) >>> pipeline.fit(X_train, y_train) Pipeline(steps=[('selectkbest', SelectKBest(k=25)), ('histgradientboostingclassifier', HistGradientBoostingClassifier(random_state=1))]) >>> y_pred = pipeline.predict(X_test) >>> accuracy_score(y_test, y_pred) 0.5
The pipeline can also be fed into a cross-validation
function such as 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: 0.43+/-0.05
11.3. Controlling randomness#
Some scikit-learn objects are inherently random. These are usually estimators
(e.g. RandomForestClassifier) and cross-validation
splitters (e.g. KFold). The randomness of
these objects is controlled via their random_state parameter, as described
in the Glossary. 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.
11.3.1. Using None or RandomState instances, and repeated calls to fit and split#
The random_state parameter determines whether multiple calls to fit
(for estimators) or to split (for CV splitters) will produce the same
results, according to these rules:
If an integer is passed, calling
fitorsplitmultiple times always yields the same results.If
Noneor aRandomStateinstance is passed:fitandsplitwill yield different results each time they are called, and the succession of calls explores all sources of entropy.Noneis the default value for allrandom_stateparameters.
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.
11.3.1.1. 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
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.
11.3.1.2. 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.
11.3.2. 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.
11.3.2.1. 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
cross_val_score significantly differ in
each case:
Since
rf_123was passed an integer, every call tofituses 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_instwas passed aRandomStateinstance, each call tofitstarts from a different RNG. As a result, the random subset of features will be different for each fold.
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, 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
make_classification isn’t relevant for our
illustration purpose: what matters is what we pass to the
RandomForestClassifier estimator.
Another subtle side effect of passing RandomState instances is how
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.
11.3.2.2. 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
LinearDiscriminantAnalysis estimator
vs the GaussianNB estimator on each fold would
be a mistake: the splits on which the estimators are evaluated are
different. Indeed, cross_val_score will
internally call cv.split on the same
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.
GridSearchCV and
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.
11.3.3. General recommendations#
11.3.3.1. 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
for a discussion.
11.3.3.2. 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 an 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.