giotto.time_series.PearsonDissimilarity¶
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class
giotto.time_series.PearsonDissimilarity(absolute_value=False, n_jobs=None)¶ Pearson dissimilarities from collections of multivariate time series.
The sample Pearson correlation coefficients between pairs of components of an \(N\)-variate time series form an \(N \times N\) matrix \(R\) with entries
\[R_{ij} = \frac{ C_{ij} }{ \sqrt{ C_{ii} C_{jj} } },\]where \(C\) is the covariance matrix. Setting \(D_{ij} = (1 - R_{ij})/2\) or \(D_{ij} = 1 - |R_{ij}|\) we obtain a dissimilarity matrix with entries between 0 and 1.
This transformer computes one dissimilarity matrix per multivariate time series in a collection. Examples of such collections are the outputs of
SlidingWindow.- Parameters
- absolute_valuebool, default:
False Whether absolute values of the Pearson correlation coefficients should be taken. Doing so makes pairs of strongly anti-correlated variables as similar as pairs of strongly correlated ones.
- n_jobsint or None, optional, default:
None The number of jobs to use for the computation.
Nonemeans 1 unless in ajoblib.parallel_backendcontext.-1means using all processors.
- absolute_valuebool, default:
Methods
fit(self, X[, y])Do nothing and return the estimator unchanged.
fit_transform(self, X[, y])Fit to data, then transform it.
get_params(self[, deep])Get parameters for this estimator.
set_params(self, \*\*params)Set the parameters of this estimator.
transform(self, X[, y])Compute Pearson dissimilarities.
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__init__(self, absolute_value=False, n_jobs=None)¶ Initialize self. See help(type(self)) for accurate signature.
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fit(self, X, y=None)¶ Do nothing and return the estimator unchanged.
This method is there to implement the usual scikit-learn API and hence work in pipelines.
- Parameters
- Xndarray, shape (n_samples, n_observations, n_features)
Input data. Each entry along axis 0 is a sample of
n_featuresdifferent variables, of sizen_observations.- yNone
There is no need for a target in a transformer, yet the pipeline API requires this parameter.
- Returns
- selfobject
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fit_transform(self, X, y=None, **fit_params)¶ Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters
- Xnumpy array of shape [n_samples, n_features]
Training set.
- ynumpy array of shape [n_samples]
Target values.
- Returns
- X_newnumpy array of shape [n_samples, n_features_new]
Transformed array.
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get_params(self, deep=True)¶ Get parameters for this estimator.
- Parameters
- deepboolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsmapping of string to any
Parameter names mapped to their values.
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set_params(self, **params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>so that it’s possible to update each component of a nested object.- Returns
- self
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transform(self, X, y=None)¶ Compute Pearson dissimilarities.
- Parameters
- Xndarray, shape (n_samples, n_observations, n_features)
Input data. Each entry along axis 0 is a sample of
n_featuresdifferent variables, of sizen_observations.- yNone
There is no need for a target in a transformer, yet the pipeline API requires this parameter.
- Returns
- Xtndarray, shape (n_samples, n_features, n_features)
Array of Pearson dissimilarities.