data Package

data.featureset Module

Classes related to storing/merging feature sets.

author:Dan Blanchard (dblanchard@ets.org) author:Nitin Madnani (nmadnani@ets.org) author:Jeremy Biggs (jbiggs@ets.org) organization:ETS

class skll.data.featureset.FeatureSet(name, ids, labels=None, features=None, vectorizer=None)

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)

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)

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)

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)

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.

data.readers Module

Handles loading data from various types of data files.

author:Dan Blanchard (dblanchard@ets.org) author:Michael Heilman (mheilman@ets.org) author:Nitin Madnani (nmadnani@ets.org) organization:ETS

class skll.data.readers.ARFFReader(path_or_list, **kwargs)

Bases: skll.data.readers.DelimitedReader

Reader for creating a FeatureSet instance from an ARFF file.

If example/instance IDs are included in the files, they must be specified in the id column.

Also, there must be a column with the name specified by label_col if the data is labeled, and this column must be the final one (as it is in Weka).

Parameters:

• path_or_list (str) – The path to the ARFF file.
• kwargs (dict, optional) – Other arguments to the Reader object.

static split_with_quotes(s, delimiter=' ', quote_char="'", escape_char='\\')

A replacement for string.split that won’t split delimiters enclosed in quotes.

Parameters:

• s (str) – The string with quotes to split
• delimiter (str, optional) – The delimiter to split on. Defaults to ' '.
• quote_char (str, optional) – The quote character to ignore. Defaults to "'".
• escape_char (str, optional) – The escape character. Defaults to '\'.

class skll.data.readers.CSVReader(path_or_list, **kwargs)

Bases: skll.data.readers.DelimitedReader

Reader for creating a FeatureSet instance from a CSV file.

If example/instance IDs are included in the files, they must be specified in the id column.

Also, there must be a column with the name specified by label_col if the data is labeled.

Parameters:

• path_or_list (str) – The path to a comma-delimited file.
• kwargs (dict, optional) – Other arguments to the Reader object.

class skll.data.readers.DelimitedReader(path_or_list, **kwargs)

Bases: skll.data.readers.Reader

Reader for creating a FeatureSet instance from a delimited (CSV/TSV) file.

If example/instance IDs are included in the files, they must be specified in the id column.

For ARFF, CSV, and TSV files, there must be a column with the name specified by label_col if the data is labeled. For ARFF files, this column must also be the final one (as it is in Weka).

Parameters:

• path_or_list (str) – The path to a delimited file.
• dialect (str) – The dialect of to pass on to the underlying CSV reader. Defaults to 'excel-tab'.
• kwargs (dict, optional) – Other arguments to the Reader object.

class skll.data.readers.DictListReader(path_or_list, quiet=True, ids_to_floats=False, label_col='y', id_col='id', class_map=None, sparse=True, feature_hasher=False, num_features=None, logger=None)

Bases: skll.data.readers.Reader

This class is to facilitate programmatic use of Learner.predict() and other methods that take FeatureSet objects as input. It iterates over examples in the same way as other Reader classes, but uses a list of example dictionaries instead of a path to a file.

read()

Read examples from list of dictionaries.

Returns:feature_set – FeatureSet representing the list of dictionaries we read in. Return type:skll.FeatureSet

class skll.data.readers.LibSVMReader(path_or_list, quiet=True, ids_to_floats=False, label_col='y', id_col='id', class_map=None, sparse=True, feature_hasher=False, num_features=None, logger=None)

Bases: skll.data.readers.Reader

Reader to create a FeatureSet instance from a LibSVM/LibLinear/SVMLight file.

We use a specially formatted comment for storing example IDs, class names, and feature names, which are normally not supported by the format. The comment is not mandatory, but without it, your labels and features will not have names. The comment is structured as follows:

ExampleID | 1=FirstClass | 1=FirstFeature 2=SecondFeature

class skll.data.readers.MegaMReader(path_or_list, quiet=True, ids_to_floats=False, label_col='y', id_col='id', class_map=None, sparse=True, feature_hasher=False, num_features=None, logger=None)

Bases: skll.data.readers.Reader

Reader to create a FeatureSet instance from a MegaM -fvals file.

If example/instance IDs are included in the files, they must be specified as a comment line directly preceding the line with feature values.

class skll.data.readers.NDJReader(path_or_list, quiet=True, ids_to_floats=False, label_col='y', id_col='id', class_map=None, sparse=True, feature_hasher=False, num_features=None, logger=None)

Bases: skll.data.readers.Reader

Reader to create a FeatureSet instance from a JSONlines/NDJ file.

If example/instance IDs are included in the files, they must be specified as the “id” key in each JSON dictionary.

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

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)

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()

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

Returns:

feature_set – FeatureSet 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.data.readers.TSVReader(path_or_list, **kwargs)

Bases: skll.data.readers.DelimitedReader

Reader for creating a FeatureSet instance from a TSV file.

If example/instance IDs are included in the files, they must be specified in the id column.

Also there must be a column with the name specified by label_col if the data is labeled.

Parameters:

• path_or_list (str) – The path to the TSV file.
• kwargs (dict, optional) – Other arguments to the Reader object.

skll.data.readers.safe_float(text, replace_dict=None, logger=None)

Attempts to convert a string to an int, and then a float, but if neither is possible, returns the original string value.

Parameters:

• text (str) – The text to convert.
• replace_dict (dict, optional) – Mapping from text to replacement text values. This is mainly used for collapsing multiple labels into a single class. Replacing happens before conversion to floats. Anything not in the mapping will be kept the same. Defaults to None.
• logger (logging.Logger) – The Logger instance to use to log messages. Used instead of creating a new Logger instance by default. Defaults to None.

Returns:

text – The text value converted to int or float, if possible

Return type:

int or float or str

data.writers Module

Handles loading data from various types of data files.

author:Dan Blanchard (dblanchard@ets.org) author:Michael Heilman (mheilman@ets.org) author:Nitin Madnani (nmadnani@ets.org) organization:ETS

class skll.data.writers.ARFFWriter(path, feature_set, **kwargs)

Bases: skll.data.writers.DelimitedFileWriter

Writer for writing out FeatureSets as ARFF files.

Parameters:

• path (str) – A path to the feature file we would like to create. If subsets is not None, this 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/.arff.
• feature_set (skll.FeatureSet) – The FeatureSet instance to dump to the output file.
• relation (str, optional) – The name of the relation in the ARFF file. Defaults to 'skll_relation'.
• regression (bool, optional) – Is this an ARFF file to be used for regression? Defaults to False.
• kwargs (dict, optional) – The arguments to the Writer object being instantiated.

class skll.data.writers.CSVWriter(path, feature_set, **kwargs)

Bases: skll.data.writers.DelimitedFileWriter

Writer for writing out FeatureSet instances as CSV files.

Parameters:

• path (str) – A path to the feature file we would like to create. If subsets is not None, this 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, optional) – The arguments to the Writer object being instantiated.

class skll.data.writers.DelimitedFileWriter(path, feature_set, **kwargs)

Bases: skll.data.writers.Writer

Writer for writing out FeatureSets as TSV/CSV files.

Parameters:

• path (str) – A path to the feature file we would like to create. If subsets is not None, this 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.
• quiet (bool) – Do not print “Writing…” status message to stderr. Defaults to True.
• label_col (str) – 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) – 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'.
• dialect (str) – Name of the column which contains the class labels for CSV/TSV files.
• logger (logging.Logger) – A logger instance to use to log messages instead of creating a new one by default. Defaults to None.
• kwargs (dict, optional) – The arguments to the Writer object being instantiated.

class skll.data.writers.LibSVMWriter(path, feature_set, **kwargs)

Bases: skll.data.writers.Writer

Writer for writing out FeatureSets as LibSVM/SVMLight files.

Parameters:

• path (str) – A path to the feature file we would like to create. If subsets is not None, this 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/.libsvm.
• feature_set (skll.FeatureSet) – The FeatureSet instance to dump to the output file.
• kwargs (dict, optional) – The arguments to the Writer object being instantiated.

class skll.data.writers.MegaMWriter(path, feature_set, **kwargs)

Bases: skll.data.writers.Writer

Writer for writing out FeatureSets as MegaM files.

class skll.data.writers.NDJWriter(path, feature_set, **kwargs)

Bases: skll.data.writers.Writer

Writer for writing out FeatureSets as .jsonlines/.ndj files.

Parameters:

• path (str) – A path to the feature file we would like to create. If subsets is not None, this 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/.ndj.
• feature_set (skll.FeatureSet) – The FeatureSet instance to dump to the output file.
• kwargs (dict, optional) – The arguments to the Writer object being instantiated.

class skll.data.writers.TSVWriter(path, feature_set, **kwargs)

Bases: skll.data.writers.DelimitedFileWriter

Writer for writing out FeatureSets as TSV files.

Parameters:

• path (str) – A path to the feature file we would like to create. If subsets is not None, this 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/.tsv.
• feature_set (skll.FeatureSet) – The FeatureSet instance to dump to the output file.
• kwargs (dict, optional) – The arguments to the Writer object being instantiated.

class skll.data.writers.Writer(path, feature_set, **kwargs)

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)

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()

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