skll Package

The most useful parts of our API are available at the package level in addition to the module level. They are documented in both places for convenience.

From data Package

class skll.FeatureSet(name, ids, labels=None, features=None, vectorizer=None)[source]

Bases: object

Encapsulation of all of the features, values, and metadata about a given set of data. This replaces ExamplesTuple from older versions of SKLL.

Parameters:
  • name (str) – The name of this feature set.
  • ids (np.array) – Example IDs for this set.
  • labels (np.array, optional) – labels for this set. Defaults to None.
  • feature (list of dict or array-like, optional) – The features for each instance represented as either a list of dictionaries or an array-like (if vectorizer is also specified). Defaults to None.
  • vectorizer (DictVectorizer or FeatureHasher, optional) – Vectorizer which will be used to generate the feature matrix. Defaults to None.

Warning

FeatureSets can only be equal if the order of the instances is identical because these are stored as lists/arrays. Since scikit-learn’s DictVectorizer automatically sorts the underlying feature matrix if it is sparse, we do not do any sorting before checking for equality. This is not a problem because we _always_ use sparse matrices with DictVectorizer when creating FeatureSets.

Notes

If ids, labels, and/or features are not None, the number of rows in each array must be equal.

filter(ids=None, labels=None, features=None, inverse=False)[source]

Removes or keeps features and/or examples from the Featureset depending on the parameters. Filtering is done in-place.

Parameters:
  • ids (list of str/float, optional) – Examples to keep in the FeatureSet. If None, no ID filtering takes place. Defaults to None.
  • labels (list of str/float, optional) – Labels that we want to retain examples for. If None, no label filtering takes place. Defaults to None.
  • features (list of str, optional) – Features to keep in the FeatureSet. To help with filtering string-valued features that were converted to sequences of boolean features when read in, any features in the FeatureSet that contain a = will be split on the first occurrence and the prefix will be checked to see if it is in features. If None, no feature filtering takes place. Cannot be used if FeatureSet uses a FeatureHasher for vectorization. Defaults to None.
  • inverse (bool, optional) – Instead of keeping features and/or examples in lists, remove them. Defaults to False.
Raises:

ValueError – If attempting to use features to filter a FeatureSet that uses a FeatureHasher vectorizer.

filtered_iter(ids=None, labels=None, features=None, inverse=False)[source]

A version of __iter__ that retains only the specified features and/or examples from the output.

Parameters:
  • ids (list of str/float, optional) – Examples to keep in the FeatureSet. If None, no ID filtering takes place. Defaults to None.
  • labels (list of str/float, optional) – Labels that we want to retain examples for. If None, no label filtering takes place. Defaults to None.
  • features (list of str, optional) – Features to keep in the FeatureSet. To help with filtering string-valued features that were converted to sequences of boolean features when read in, any features in the FeatureSet that contain a = will be split on the first occurrence and the prefix will be checked to see if it is in features. If None, no feature filtering takes place. Cannot be used if FeatureSet uses a FeatureHasher for vectorization. Defaults to None.
  • inverse (bool, optional) – Instead of keeping features and/or examples in lists, remove them. Defaults to False.
Yields:
  • id_ (str) – The ID of the example.
  • label_ (str) – The label of the example.
  • feat_dict (dict) – The feature dictionary, with feature name as the key and example value as the value.
Raises:

ValueError – If the vectorizer is not a DictVectorizer.

static from_data_frame(df, name, labels_column=None, vectorizer=None)[source]

Helper function to create a FeatureSet instance from a pandas.DataFrame. Will raise an Exception if pandas is not installed in your environment. The ids in the FeatureSet will be the index from the given frame.

Parameters:
  • df (pd.DataFrame) – The pandas.DataFrame object to use as a FeatureSet.
  • name (str) – The name of the output FeatureSet instance.
  • labels_column (str, optional) – The name of the column containing the labels (data to predict). Defaults to None.
  • vectorizer (DictVectorizer or FeatureHasher, optional) – Vectorizer which will be used to generate the feature matrix. Defaults to None.
Returns:

feature_set – A FeatureSet instance generated from from the given data frame.

Return type:

skll.FeatureSet

has_labels

Check if FeatureSet has finite labels.

Returns:has_labels – Whether or not this FeatureSet has any finite labels.
Return type:bool
static split_by_ids(fs, ids_for_split1, ids_for_split2=None)[source]

Split the FeatureSet into two new FeatureSet instances based on the given IDs for the two splits.

Parameters:
  • fs (skll.FeatureSet) – The FeatureSet instance to split.
  • ids_for_split1 (list of int) – A list of example IDs which will be split out into the first FeatureSet instance. Note that the FeatureSet instance will respect the order of the specified IDs.
  • ids_for_split2 (list of int, optional) – An optional ist of example IDs which will be split out into the second FeatureSet instance. Note that the FeatureSet instance will respect the order of the specified IDs. If this is not specified, then the second FeatureSet instance will contain the complement of the first set of IDs sorted in ascending order. Defaults to None.
Returns:

  • fs1 (skll.FeatureSet) – The first FeatureSet.
  • fs2 (skll.FeatureSet) – The second FeatureSet.

class skll.Reader(path_or_list, quiet=True, ids_to_floats=False, label_col=u'y', id_col=u'id', class_map=None, sparse=True, feature_hasher=False, num_features=None, logger=None)[source]

Bases: object

A helper class to make picklable iterators out of example dictionary generators.

Parameters:
  • path_or_list (str or list of dict) – Path or a list of example dictionaries.
  • quiet (bool, optional) – Do not print “Loading…” status message to stderr. Defaults to True.
  • ids_to_floats (bool, optional) – Convert IDs to float to save memory. Will raise error if we encounter an a non-numeric ID. Defaults to False.
  • label_col (str, optional) – Name of the column which contains the class labels for ARFF/CSV/TSV files. If no column with that name exists, or None is specified, the data is considered to be unlabelled. Defaults to 'y'.
  • id_col (str, optional) – Name of the column which contains the instance IDs. If no column with that name exists, or None is specified, example IDs will be automatically generated. Defaults to 'id'.
  • class_map (dict, optional) – Mapping from original class labels to new ones. This is mainly used for collapsing multiple labels into a single class. Anything not in the mapping will be kept the same. Defaults to None.
  • sparse (bool, optional) – Whether or not to store the features in a numpy CSR matrix when using a DictVectorizer to vectorize the features. Defaults to True.
  • feature_hasher (bool, optional) – Whether or not a FeatureHasher should be used to vectorize the features. Defaults to False.
  • num_features (int, optional) – If using a FeatureHasher, how many features should the resulting matrix have? You should set this to a power of 2 greater than the actual number of features to avoid collisions. Defaults to None.
  • logger (logging.Logger, optional) – A logger instance to use to log messages instead of creating a new one by default. Defaults to None.
classmethod for_path(path_or_list, **kwargs)[source]

Instantiate the appropriate Reader sub-class based on the file extension of the given path. Or use a dictionary reader if the input is a list of dictionaries.

Parameters:
  • path_or_list (str or list of dicts) – A path or list of example dictionaries.
  • kwargs (dict, optional) – The arguments to the Reader object being instantiated.
Returns:

reader – A new instance of the Reader sub-class that is appropriate for the given path.

Return type:

skll.Reader

Raises:

ValueError – If file does not have a valid extension.

read()[source]

Loads examples in the .arff, .csv, .jsonlines, .libsvm, .megam, .ndj, or .tsv formats.

Returns:

feature_setFeatureSet instance representing the input file.

Return type:

skll.FeatureSet

Raises:
  • ValueError – If ids_to_floats is True, but IDs cannot be converted.
  • ValueError – If no features are found.
  • ValueError – If the example IDs are not unique.
class skll.Writer(path, feature_set, **kwargs)[source]

Bases: object

Helper class for writing out FeatureSets to files on disk.

Parameters:
  • path (str) – A path to the feature file we would like to create. The suffix to this filename must be .arff, .csv, .jsonlines, .libsvm, .megam, .ndj, or .tsv. If subsets is not None, when calling the write() method, path is assumed to be a string containing the path to the directory to write the feature files with an additional file extension specifying the file type. For example /foo/.csv.
  • feature_set (skll.FeatureSet) – The FeatureSet instance to dump to the file.
  • quiet (bool) – Do not print “Writing…” status message to stderr. Defaults to True.
  • requires_binary (bool) – Whether or not the Writer must open the file in binary mode for writing with Python 2. Defaults to False.
  • subsets (dict (str to list of str)) – A mapping from subset names to lists of feature names that are included in those sets. If given, a feature file will be written for every subset (with the name containing the subset name as suffix to path). Note, since string- valued features are automatically converted into boolean features with names of the form FEATURE_NAME=STRING_VALUE, when doing the filtering, the portion before the = is all that’s used for matching. Therefore, you do not need to enumerate all of these boolean feature names in your mapping. Defaults to None.
  • logger (logging.Logger) – A logger instance to use to log messages instead of creating a new one by default. Defaults to None.
classmethod for_path(path, feature_set, **kwargs)[source]

Retrieve object of Writer sub-class that is appropriate for given path.

Parameters:
  • path (str) – A path to the feature file we would like to create. The suffix to this filename must be .arff, .csv, .jsonlines, .libsvm, .megam, .ndj, or .tsv. If subsets is not None, when calling the write() method, path is assumed to be a string containing the path to the directory to write the feature files with an additional file extension specifying the file type. For example /foo/.csv.
  • feature_set (skll.FeatureSet) – The FeatureSet instance to dump to the output file.
  • kwargs (dict) – The keyword arguments for for_path are the same as the initializer for the desired Writer subclass.
Returns:

writer – New instance of the Writer sub-class that is appropriate for the given path.

Return type:

skll.data.writers.Writer

write()[source]

Writes out this Writer’s FeatureSet to a file in its format.

From experiments Module

skll.run_configuration(config_file, local=False, overwrite=True, queue=u'all.q', hosts=None, write_summary=True, quiet=False, ablation=0, resume=False, log_level=20)[source]

Takes a configuration file and runs the specified jobs on the grid.

Parameters:
  • config_file (str) – Path to the configuration file we would like to use.
  • local (bool, optional) – Should this be run locally instead of on the cluster? Defaults to False.
  • overwrite (bool, optional) – If the model files already exist, should we overwrite them instead of re-using them? Defaults to True.
  • queue (str, optional) – The DRMAA queue to use if we’re running on the cluster. Defaults to 'all.q'.
  • hosts (list of str, optional) – If running on the cluster, these are the machines we should use. Defaults to None.
  • write_summary (bool, optional) – Write a TSV file with a summary of the results. Defaults to True.
  • quiet (bool, optional) – Suppress printing of “Loading…” messages. Defaults to False.
  • ablation (int, optional) – Number of features to remove when doing an ablation experiment. If positive, we will perform repeated ablation runs for all combinations of features removing the specified number at a time. If None, we will use all combinations of all lengths. If 0, the default, no ablation is performed. If negative, a ValueError is raised. Defaults to 0.
  • resume (bool, optional) – If result files already exist for an experiment, do not overwrite them. This is very useful when doing a large ablation experiment and part of it crashes. Defaults to False.
  • log_level (str, optional) – The level for logging messages. Defaults to logging.INFO.
Returns:

result_json_paths – A list of paths to .json results files for each variation in the experiment.

Return type:

list of str

Raises:
  • ValueError – If value for "ablation" is not a positive int or None.
  • OSError – If the lenth of the FeatureSet name > 210.

From learner Module

class skll.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, logger=None)[source]

Load a saved Learner instance from a file path.

Parameters:
  • learner_path (str) – The path to a saved Learner instance file.
  • logger (logging object, optional) – A logging object. If None is passed, get logger from __name__. Defaults to None.
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.

From metrics Module

skll.f1_score_least_frequent(y_true, y_pred)[source]

Calculate the F1 score of the least frequent label/class in y_true for y_pred.

Parameters:
  • y_true (array-like of float) – The true/actual/gold labels for the data.
  • y_pred (array-like of float) – The predicted/observed labels for the data.
Returns:

ret_score – F1 score of the least frequent label.

Return type:

float

skll.kappa(y_true, y_pred, weights=None, allow_off_by_one=False)[source]

Calculates the kappa inter-rater agreement between two the gold standard and the predicted ratings. Potential values range from -1 (representing complete disagreement) to 1 (representing complete agreement). A kappa value of 0 is expected if all agreement is due to chance.

In the course of calculating kappa, all items in y_true and y_pred will first be converted to floats and then rounded to integers.

It is assumed that y_true and y_pred contain the complete range of possible ratings.

This function contains a combination of code from yorchopolis’s kappa-stats and Ben Hamner’s Metrics projects on Github.

Parameters:
  • y_true (array-like of float) – The true/actual/gold labels for the data.
  • y_pred (array-like of float) – The predicted/observed labels for the data.
  • weights (str or np.array, optional) –

    Specifies the weight matrix for the calculation. Options are

    -  None = unweighted-kappa
    -  'quadratic' = quadratic-weighted kappa
    -  'linear' = linear-weighted kappa
    -  two-dimensional numpy array = a custom matrix of
    

    weights. Each weight corresponds to the \(w_{ij}\) values in the wikipedia description of how to calculate weighted Cohen’s kappa. Defaults to None.

  • allow_off_by_one (bool, optional) – If true, ratings that are off by one are counted as equal, and all other differences are reduced by one. For example, 1 and 2 will be considered to be equal, whereas 1 and 3 will have a difference of 1 for when building the weights matrix. Defaults to False.
Returns:

k – The kappa score, or weighted kappa score.

Return type:

float

Raises:
  • AssertionError – If y_true != y_pred.
  • ValueError – If labels cannot be converted to int.
  • ValueError – If invalid weight scheme.
skll.kendall_tau(y_true, y_pred)[source]

Calculate Kendall’s tau between y_true and y_pred.

Parameters:
  • y_true (array-like of float) – The true/actual/gold labels for the data.
  • y_pred (array-like of float) – The predicted/observed labels for the data.
Returns:

ret_score – Kendall’s tau if well-defined, else 0.0

Return type:

float

skll.spearman(y_true, y_pred)[source]

Calculate Spearman’s rank correlation coefficient between y_true and y_pred.

Parameters:
  • y_true (array-like of float) – The true/actual/gold labels for the data.
  • y_pred (array-like of float) – The predicted/observed labels for the data.
Returns:

ret_score – Spearman’s rank correlation coefficient if well-defined, else 0.0

Return type:

float

skll.pearson(y_true, y_pred)[source]

Calculate Pearson product-moment correlation coefficient between y_true and y_pred.

Parameters:
  • y_true (array-like of float) – The true/actual/gold labels for the data.
  • y_pred (array-like of float) – The predicted/observed labels for the data.
Returns:

ret_score – Pearson product-moment correlation coefficient if well-defined, else 0.0

Return type:

float