SFA

SFA

class SFA(word_length=8, alphabet_size=4, window_size=12, norm=False, binning_method='equi-depth', anova=False, bigrams=False, skip_grams=False, remove_repeat_words=False, levels=1, lower_bounding=True, save_words=False, keep_binning_dft=False, return_pandas_data_series=False, use_fallback_dft=False, typed_dict=False, n_jobs=1)[source]

Symbolic Fourier Approximation (SFA) Transformer.

Overview: for each series:

run a sliding window across the series for each window

shorten the series with DFT discretise the shortened series into bins set by MFC form a word from these discrete values

by default SFA produces a single word per series (window_size=0) if a window is used, it forms a histogram of counts of words.

Parameters:

word_length: int, default = 8

length of word to shorten window to (using PAA)

alphabet_size: int, default = 4

number of values to discretise each value to

window_size: int, default = 12

size of window for sliding. Input series length for whole series transform

norm: boolean, default = False

mean normalise words by dropping first fourier coefficient

binning_method: {“equi-depth”, “equi-width”, “information-gain”, “kmeans”},

default=”equi-depth”

the binning method used to derive the breakpoints.

anova: boolean, default = False

If True, the Fourier coefficient selection is done via a one-way ANOVA test. If False, the first Fourier coefficients are selected. Only applicable if labels are given

bigrams: boolean, default = False

whether to create bigrams of SFA words

skip_grams: boolean, default = False

whether to create skip-grams of SFA words

remove_repeat_words: boolean, default = False

whether to use numerosity reduction (default False)

levels: int, default = 1

Number of spatial pyramid levels

save_words: boolean, default = False

whether to save the words generated for each series (default False)

return_pandas_data_series: boolean, default = False

set to true to return Pandas Series as a result of transform. setting to true reduces speed significantly but is required for automatic test.

n_jobs: int, optional, default = 1

The number of jobs to run in parallel for both transform. -1 means using all processors.

Attributes:

words: []

breakpoints: = []

num_insts = 0

num_atts = 0

References

[1]

Schäfer, Patrick, and Mikael Högqvist. “SFA: a symbolic fourier approximation

and index for similarity search in high dimensional datasets.” Proceedings of the 15th international conference on extending database technology. 2012.

Methods

bag_to_string(bag)	Convert a bag of SFA words into a string.
check_is_fitted([method_name])	Check if the estimator has been fitted.
clone()	Obtain a clone of the object with same hyper-parameters and config.
clone_tags(estimator[, tag_names])	Clone tags from another object as dynamic override.
create_test_instance([parameter_set])	Construct an instance of the class, using first test parameter set.
create_test_instances_and_names([parameter_set])	Create list of all test instances and a list of names for them.
fit(X[, y])	Calculate word breakpoints using MCB or IGB.
fit_transform(X[, y])	Fit to data, then transform it.
get_class_tag(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
get_class_tags()	Get class tags from class, with tag level inheritance from parent classes.
get_config()	Get config flags for self.
get_fitted_params([deep])	Get fitted parameters.
get_param_defaults()	Get object's parameter defaults.
get_param_names([sort])	Get object's parameter names.
get_params([deep])	Get a dict of parameters values for this object.
get_tag(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
get_tags()	Get tags from instance, with tag level inheritance and overrides.
get_test_params([parameter_set])	Return testing parameter settings for the estimator.
inverse_transform(X[, y])	Inverse transform X and return an inverse transformed version.
is_composite()	Check if the object is composed of other BaseObjects.
load_from_path(serial)	Load object from file location.
load_from_serial(serial)	Load object from serialized memory container.
reset()	Reset the object to a clean post-init state.
save([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
set_config(**config_dict)	Set config flags to given values.
set_params(**params)	Set the parameters of this object.
set_random_state([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
set_tags(**tag_dict)	Set instance level tag overrides to given values.
transform(X[, y])	Transform X and return a transformed version.
update(X[, y, update_params])	Update transformer with X, optionally y.
word_list(word)	Find list of integers to obtain input word.
word_list_typed(word)	Find list of integers to obtain input word.