xklearn 0.0.3

Handy machine learning tools in the spirit of scikit-learn.

extrakit-learn

Machine learnings components built to extend scikit-learn. All components use scikit's object API to work interchangably with scikit components. It is mostly a collection of tools that have been useful for Kaggle competitions. extrakit-learn is in no way affiliated with scikit-learn in anyway, just inspired by it.

Installation

pip install xklearn

Components

• CategoryEncoder - Like scikit's LabelEncoder but supports NaNs and missing values.
• CountEncoder - Categorical feature engineering based on value counts.
• TargetEncoder - Categorical feature engineering based on target means.
• MultiColumnEncoder - Apply a column encoder to multiple columns
• FoldEstimator - K-fold on scikit estimator wrapped into an estimator.
• FoldLGBM - K-fold on LGBM wrapped into an estimator.
• StackingClassifier - Stack an ensemble of classifiers with a meta classifier.
• StackingRegressor - Stack an ensemble of regressors with a meta regressor.

Hierachy

xklearn
|
├── preprocessing
│   ├── CountEncoder      
│   └── TargetEncoder
|
└── models
    ├── FoldEstimator
    ├── FoldLGBM
    ├── StackingClassifier
    └── StackingRegressor

Example

from xklearn.models import FoldEstimator

CategoryEncoder

Wraps scikit's LabelEncoder, allowing missing and unseen values to be handled.

Arguments

unseen - Strategy for handling unseen values. See replacement strategies below for options.

missing - Strategy for handling missing values. See replacement strategies below for options.

Replacement strategies

'encode' - Replace value with -1.

'nan' - Replace value with np.nan.

'error' - Raise ValueError.

Example:

from xklearn.preprocessing import CategoryEncoder
...

ce = CategoryEncoder(unseen='nan', missing='nan')
X[:, 0] = ce.fit_transform(X[:, 0])

CountEncoder

Replaces categorical values with their respective value count during training. Classes with a count of one and previously unseen classes during prediction are encoded as either one or NaN.

Arguments

unseen - Strategy for handling unseen values. See replacement strategies below for options.

missing - Strategy for handling missing values. See replacement strategies below for options.

Replacement strategies

'one' - Replace value with 1.

'nan' - Replace value with np.nan.

'error' - Raise ValueError.

Example:

from xklearn.preprocessing import CountEncoder
...

ce = CountEncoder(unseen='one')
X[:, 0] = ce.fit_transform(X[:, 0])

TargetEncoder

Performs target mean encoding of categorical features with optional smoothing.

Arguments

smoothing - Smoothing weight.

unseen - Strategy for handling unseen values. See replacement strategies below for options.

missing - Strategy for handling missing values. See replacement strategies below for options.

Replacement strategies

'one' - Replace value with 1.

'nan' - Replace value with np.nan.

'error' - Raise ValueError.

Example:

from xklearn.preprocessing import TargetEncoder
...

te = TargetEncoder(smoothing=10)
X[:, 0] = te.fit_transform(X[:, 0], y)

MultiColumnEncoder

Applies a column encoder over multiple columns.

Arguments

enc - Base encoder that will be applied to selected columns

columns - Column selection, either bool-mask, indices or None (default=None).

Example:

from xklearn.preprocessing import CountEncoder
from xklearn.preprocessing import MultiColumnEncoder
...

columns = [1, 3, 4]
enc = CountEncoder()

mce = MultiColumnEncoder(enc, columns)
X = mce.fit_transform(X)

FoldEstimator

K-fold wrapped into an estimator that performs cross validation over a selected folding method automatically when fit. Can optionally be used as a stacked ensemble of k estimators after fit.

Arguments

est - Base estimator.

fold - Folding cross validation object, i.e KFold and StratifedKfold.

metric - Evaluation metric.

ensemble - Flag indicting post fit behaviour. True will make it a stacked ensemble, False will do a full refit on the full data.

verbose - Flag for printing intermediate scores during fit.

Example:

from xklearn.models import FoldEstimator
...

base = RandomForestRegressor(n_estimators=10)
fold = KFold(n_splits=5)

est = FoldEstimator(base, fold=fold, metric=mean_squared_error, verbose=1)

est.fit(X_train, y_train)
est.predict(X_test)

FoldLGBM

K-fold wrapped into an estimator that performs cross validation on a LGBM over a selected folding method automatically when fit. Can optionally be used as a stacked ensemble of k estimators after fit.

Arguments

lgbm - Base estimator.

fold - Folding cross validation object, i.e KFold and StratifedKfold.

metric - Evaluation metric.

fit_params - Dictionary of parameter that should be fed to the fit method.

ensemble - Flag indicting post fit behaviour. True will make it a stacked ensemble, False will do a full refit on the full data.

refit_params - Dictionary of parameter that should be fed to the refit if ensemble=False.

verbose - Flag for printing intermediate scores during fit.

Example:

from xklearn.models import FoldLGBM
...

base = LGBMClassifier(n_estimators=1000)
fold = KFold(n_splits=5)
fit_params = {'eval_metric': 'auc',
              'early_stopping_rounds': 50,
              'verbose': 0}
              
fold_lgbm = FoldLGBM(base, 
                     fold=fold, 
                     metric=roc_auc_score,
                     fit_params=fit_params,
                     verbose=1)
               
fold_lgbm.fit(X_train, y_train)
fold_lgbm.predict(X_test)

StackingClassifier

Ensemble classifier that stacks an ensemble of classifiers by using their outputs as input features.

Arguments

clfs - List of ensemble of classifiers.

meta_clf - Meta classifier that stacks the predictions of the ensemble.

keep_features - Flag to train the meta classifier on the original features too.

refit - Flag to retrain the ensemble of classifiers.

Example:

from xklearn.models import StackingClassifier
...

meta_clf = RidgeClassifier()
ensemble = [RandomForestClassifier(), KNeighborsClassifier(), SVC()]

stack_clf = StackingClassifier(clfs=ensemble, meta_clf=meta_clf, refit=True)

stack_clf.fit(X_train, y_train)
y_ = stack_clf.predict(X_test)

StackingRegressor

Ensemble regressor that stacks an ensemble of regressors by using their outputs as input features.

Arguments

regs - List of ensemble of regressors.

meta_reg - Meta regressor that stacks the predictions of the ensemble.

keep_features - Flag to train the meta regressor on the original features too.

refit - Flag to retrain the ensemble of regressors.

Example:

from xklearn.models import StackingRegressor
...

meta_reg = RidgeRegressor()
ensemble = [RandomForestRegressor(), KNeighborsRegressor(), SVR()]

stack_reg = StackingRegressor(regs=ensemble, meta_reg=meta_reg, refit=True)

stack_reg.fit(X_train, y_train)
y_ = stack_reg.predict(X_test)