learner Module

Provides easy-to-use wrapper around scikit-learn.

author:Michael Heilman (mheilman@ets.org)
author:Nitin Madnani (nmadnani@ets.org)
author:Dan Blanchard (dblanchard@ets.org)
author:Aoife Cahill (acahill@ets.org)
organization:ETS
class skll.learner.FilteredLeaveOneGroupOut(keep, example_ids)[source]

Bases: sklearn.model_selection._split.LeaveOneGroupOut

Version of LeaveOneGroupOut cross-validation iterator that only outputs indices of instances with IDs in a prespecified set.

Parameters:
  • keep (set of str) – A set of IDs to keep.
  • example_ids (list of str, of length n_samples) – A list of example IDs.
split(X, y, groups)[source]

Generate indices to split data into training and test set.

Parameters:
  • X (array-like, with shape (n_samples, n_features)) – Training data, where n_samples is the number of samples and n_features is the number of features.
  • y (array-like, of length n_samples) – The target variable for supervised learning problems.
  • groups (array-like, with shape (n_samples,)) – Group labels for the samples used while splitting the dataset into train/test set.
Yields:
  • train_index (np.array) – The training set indices for that split.
  • test_index (np.array) – The testing set indices for that split.
class skll.learner.Learner(model_type, probability=False, feature_scaling=u'none', model_kwargs=None, pos_label_str=None, min_feature_count=1, sampler=None, sampler_kwargs=None, custom_learner_path=None, logger=None)[source]

Bases: object

A simpler learner interface around many scikit-learn classification and regression functions.

Parameters:
  • model_type (str) – Name of estimator to create (e.g., 'LogisticRegression'). See the skll package documentation for valid options.
  • probability (bool, optional) – Should learner return probabilities of all labels (instead of just label with highest probability)? Defaults to False.
  • feature_scaling (str, optional) – How to scale the features, if at all. Options are - ‘with_std’: scale features using the standard deviation - ‘with_mean’: center features using the mean - ‘both’: do both scaling as well as centering - ‘none’: do neither scaling nor centering Defaults to ‘none’.
  • model_kwargs (dict, optional) – A dictionary of keyword arguments to pass to the initializer for the specified model. Defaults to None.
  • pos_label_str (str, optional) – The string for the positive label in the binary classification setting. Otherwise, an arbitrary label is picked. Defaults to None.
  • min_feature_count (int, optional) – The minimum number of examples a feature must have a nonzero value in to be included. Defaults to 1.
  • sampler (str, optional) – The sampler to use for kernel approximation, if desired. Valid values are - ‘AdditiveChi2Sampler’ - ‘Nystroem’ - ‘RBFSampler’ - ‘SkewedChi2Sampler’ Defaults to None.
  • sampler_kwargs (dict, optional) – A dictionary of keyword arguments to pass to the initializer for the specified sampler. Defaults to None.
  • custom_learner_path (str, optional) – Path to module where a custom classifier is defined. Defaults to None.
  • logger (logging object, optional) – A logging object. If None is passed, get logger from __name__. Defaults to None.
cross_validate(examples, stratified=True, cv_folds=10, grid_search=False, grid_search_folds=3, grid_jobs=None, grid_objective=u'f1_score_micro', output_metrics=[], prediction_prefix=None, param_grid=None, shuffle=False, save_cv_folds=False, use_custom_folds_for_grid_search=True)[source]

Cross-validates a given model on the training examples.

Parameters:
  • examples (skll.FeatureSet) – The FeatureSet instance to cross-validate learner performance on.
  • stratified (bool, optional) – Should we stratify the folds to ensure an even distribution of labels for each fold? Defaults to True.
  • cv_folds (int, optional) – The number of folds to use for cross-validation, or a mapping from example IDs to folds. Defaults to 10.
  • grid_search (bool, optional) – Should we do grid search when training each fold? Note: This will make this take much longer. Defaults to False.
  • grid_search_folds (int or dict, optional) – The number of folds to use when doing the grid search, or a mapping from example IDs to folds. Defaults to 3.
  • grid_jobs (int, optional) – The number of jobs to run in parallel when doing the grid search. If None or 0, the number of grid search folds will be used. Defaults to None.
  • grid_objective (str, optional) – The name of the objective function to use when doing the grid search. Defaults to 'f1_score_micro'.
  • output_metrics (list of str, optional) – List of additional metric names to compute in addition to the metric used for grid search. Empty by default. Defaults to an empty list.
  • prediction_prefix (str, optional) – If saving the predictions, this is the prefix that will be used for the filename. It will be followed by "_predictions.tsv" Defaults to None.
  • param_grid (list of dicts, optional) – The parameter grid to traverse. Defaults to None.
  • shuffle (bool, optional) – Shuffle examples before splitting into folds for CV. Defaults to False.
  • save_cv_folds (bool, optional) – Whether to save the cv fold ids or not? Defaults to False.
  • use_custom_folds_for_grid_search (bool, optional) – If cv_folds is a custom dictionary, but grid_search_folds is not, perhaps due to user oversight, should the same custom dictionary automatically be used for the inner grid-search cross-validation? Defaults to True.
Returns:

  • results (list of 6-tuples) – The confusion matrix, overall accuracy, per-label PRFs, model parameters, objective function score, and evaluation metrics (if any) for each fold.
  • grid_search_scores (list of floats) – The grid search scores for each fold.
  • skll_fold_ids (dict) – A dictionary containing the test-fold number for each id if save_cv_folds is True, otherwise None.

Raises:

ValueError – If labels are not encoded as strings.

evaluate(examples, prediction_prefix=None, append=False, grid_objective=None, output_metrics=[])[source]

Evaluates a given model on a given dev or test FeatureSet.

Parameters:
  • examples (skll.FeatureSet) – The FeatureSet instance to evaluate the performance of the model on.
  • prediction_prefix (str, optional) – If saving the predictions, this is the prefix that will be used for the filename. It will be followed by "_predictions.tsv" Defaults to None.
  • append (bool, optional) – Should we append the current predictions to the file if it exists? Defaults to False.
  • grid_objective (function, optional) – The objective function that was used when doing the grid search. Defaults to None.
  • output_metrics (list of str, optional) – List of additional metric names to compute in addition to grid objective. Empty by default. Defaults to an empty list.
Returns:

res – The confusion matrix, the overall accuracy, the per-label PRFs, the model parameters, the grid search objective function score, and the additional evaluation metrics, if any.

Return type:

6-tuple

classmethod from_file(learner_path)[source]

Load a saved Learner instance from a file path.

Parameters:

learner_path (str) – The path to a saved Learner instance file.

Returns:

learner – The Learner instance loaded from the file.

Return type:

skll.Learner

Raises:
  • ValueError – If the pickled object is not a Learner instance.
  • ValueError – If the pickled version of the Learner instance is out of date.
learning_curve(examples, cv_folds=10, train_sizes=array([ 0.1, 0.325, 0.55, 0.775, 1. ]), metric=u'f1_score_micro')[source]

Generates learning curves for a given model on the training examples via cross-validation. Adapted from the scikit-learn code for learning curve generation (cf.``sklearn.model_selection.learning_curve``).

Parameters:
  • examples (skll.FeatureSet) – The FeatureSet instance to generate the learning curve on.
  • cv_folds (int, optional) – The number of folds to use for cross-validation, or a mapping from example IDs to folds. Defaults to 10.
  • train_sizes (list of float or int, optional) – Relative or absolute numbers of training examples that will be used to generate the learning curve. If the type is float, it is regarded as a fraction of the maximum size of the training set (that is determined by the selected validation method), i.e. it has to be within (0, 1]. Otherwise it is interpreted as absolute sizes of the training sets. Note that for classification the number of samples usually have to be big enough to contain at least one sample from each class. Defaults to np.linspace(0.1, 1.0, 5).
  • metric (str, optional) – The name of the metric function to use when computing the train and test scores for the learning curve. (default: ‘f1_score_micro’) Defaults to 'f1_score_micro'.
Returns:

  • train_scores (list of float) – The scores for the training set.
  • test_scores (list of float) – The scores on the test set.
  • num_examples (list of int) – The numbers of training examples used to generate the curve

load(learner_path)[source]

Replace the current learner instance with a saved learner.

Parameters:learner_path (str) – The path to a saved learner object file to load.
model

The underlying scikit-learn model

model_kwargs

A dictionary of the underlying scikit-learn model’s keyword arguments

model_params

Model parameters (i.e., weights) for a LinearModel (e.g., Ridge) regression and liblinear models.

Returns:
  • res (dict) – A dictionary of labeled weights.
  • intercept (dict) – A dictionary of intercept(s).
Raises:ValueError – If the instance does not support model parameters.
model_type

The model type (i.e., the class)

predict(examples, prediction_prefix=None, append=False, class_labels=False)[source]

Uses a given model to generate predictions on a given FeatureSet.

Parameters:
  • examples (skll.FeatureSet) – The FeatureSet instance to predict labels for.
  • prediction_prefix (str, optional) – If saving the predictions, this is the prefix that will be used for the filename. It will be followed by "_predictions.tsv" Defaults to None.
  • append (bool, optional) – Should we append the current predictions to the file if it exists? Defaults to False.
  • class_labels (bool, optional) – For classifier, should we convert class indices to their (str) labels? Defaults to False.
Returns:

yhat – The predictions returned by the Learner instance.

Return type:

array-like

Raises:

MemoryError – If process runs out of memory when converting to dense.

probability

Should learner return probabilities of all labels (instead of just label with highest probability)?

save(learner_path)[source]

Save the Learner instance to a file.

Parameters:learner_path (str) – The path to save the Learner instance to.
train(examples, param_grid=None, grid_search_folds=3, grid_search=True, grid_objective=u'f1_score_micro', grid_jobs=None, shuffle=False, create_label_dict=True)[source]

Train a classification model and return the model, score, feature vectorizer, scaler, label dictionary, and inverse label dictionary.

Parameters:
  • examples (skll.FeatureSet) – The FeatureSet instance to use for training.
  • param_grid (list of dicts, optional) – The parameter grid to search through for grid search. If None, a default parameter grid will be used. Defaults to None.
  • grid_search_folds (int or dict, optional) – The number of folds to use when doing the grid search, or a mapping from example IDs to folds. Defaults to 3.
  • grid_search (bool, optional) – Should we do grid search? Defaults to True.
  • grid_objective (str, optional) – The name of the objective function to use when doing the grid search. Defaults to 'f1_score_micro'.
  • grid_jobs (int, optional) – The number of jobs to run in parallel when doing the grid search. If None or 0, the number of grid search folds will be used. Defaults to None.
  • shuffle (bool, optional) – Shuffle examples (e.g., for grid search CV.) Defaults to False.
  • create_label_dict (bool, optional) – Should we create the label dictionary? This dictionary is used to map between string labels and their corresponding numerical values. This should only be done once per experiment, so when cross_validate calls train, create_label_dict gets set to False. Defaults to True.
Returns:

grid_score – The best grid search objective function score, or 0 if we’re not doing grid search.

Return type:

float

Raises:
  • ValueError – If grid_objective is not a valid grid objective.
  • MemoryError – If process runs out of memory converting training data to dense.
  • ValueError – If FeatureHasher is used with MultinomialNB.
class skll.learner.SelectByMinCount(min_count=1)[source]

Bases: sklearn.feature_selection.univariate_selection.SelectKBest

Select features occurring in more (and/or fewer than) than a specified number of examples in the training data (or a CV training fold).

Parameters:min_count (int, optional) – The minimum feature count to select. Defaults to 1.
fit(X, y=None)[source]

Fit the SelectByMinCount model.

Parameters:
  • X (array-like, with shape (n_samples, n_features)) – The training data to fit.
  • y (Ignored) –
Returns:

Return type:

self

skll.learner.rescaled(cls)[source]

Decorator to create regressors that store a min and a max for the training data and make sure that predictions fall within that range. It also stores the means and SDs of the gold standard and the predictions on the training set to rescale the predictions (e.g., as in e-rater).

Parameters:cls (BaseEstimator) – An estimator class to add rescaling to.
Returns:cls – Modified version of estimator class with rescaled functions added.
Return type:BaseEstimator
Raises:ValueError – If classifier cannot be rescaled (i.e. is not a regressor).