sciedm.CCM_Matrix#

class sciedm.CCM_Matrix(E, libSizes=[], pLibSizes=[10, 20, 80, 90], Tp=0, tau=-1, exclusionRadius=0, sample=30, seed=None, noTime=False, parallel=True, mpMethod=None, sharedMB=0.01, targetBatchSize=None, expConverge=False, progressLog=None, progressInterval=5)#

Compute the full M×M×L convergent cross mapping tensor.

Parameters:

Eint or array-like of int: Embedding dimension. Scalar or per-column vector of length M.
Tpint: Prediction horizon. Default 0.
tauint: Embedding delay. Default -1.
exclusionRadiusint: Temporal exclusion radius. Default 0.
libSizeslist of int: Explicit library sizes. If non-empty, used directly and pLibSizes is ignored. Default [].
pLibSizeslist of float: Percentiles of N to generate library sizes. Used only when libSizes is empty. Default [10, 20, 80, 90].
sampleint: Subsamples per library size. Default 100.
seedint or None: RNG seed.
noTimebool: If True, all columns are data. If False, first column is time (stripped). Default False.
parallelbool or int: Worker count. Default True.
mpMethodstr or None: Multiprocessing start method: ‘forkserver’ or ‘spawn’ only. Default None.
sharedMBfloat: Data size threshold for shared memory vs pickle. Default 5.
targetBatchSizeint or None: Max target columns per batch within each worker. Default None.
expConvergebool: If True, fit exponential convergence curve. Default False.
progressLogNone, True, or str: None: no logging. True: log to stderr. str: log to file path. Default None.
progressIntervalint: Percentage increment for progress log lines. Default 5.
The slope of CCM rho(libSizes) is computed based on a [0,1]
normalization of libSizes.
if expConverge = True a nonlinear convergence function is fit
to rho(libSizes)y0 + b * ( 1 - exp(-a * x) ) with fit coefficient
a returned in the (M, M) matrix self.exp_a

Attributes:

is_fitted_bool: A boolean indicating whether the estimator has been fitted.
n_features_in_int: Number of features seen during fit.
feature_names_in_ndarray of shape (n_features_in_,): Names of features seen during fit. Defined only when X has feature names that are all strings.
tensor_ndarray, float16: (M, M, L) array of MxM CCM matrices at the L libSizes
slope_ndarray, float32: (M, M) array of CCM rho ~ LibSizes
exp_a_ndarray, float32: (M, M) array of exponential fit coefficient or None
columns_[str]: List of columns/rows of the CCM and slope matrices
lib_sizes_arr_ndarray: Integer libSizes
lib_sizes_norm_ndarray: Normalized [0,1] libSizes for slope regression

Returns:

tuple (MxMxL tensor, columns, libSizes)

Examples

>>> from sciedm import CCM_Matrix, PlotMatrix
>>> from pandas import DataFrame
>>> X = np.random.rand(10,10)
>>> df = DataFrame(X,columns=[f"x{i+1}" for i in range(10)])
>>> cmat = CCM_Matrix(E=4,noTime=True)
>>> tensor,columns = cmat.fit_transform(df)
>>> PlotMatrix(tensor[:,:,2],columns)

Run()#

Compute the M×M×L CCM tensor, linear convergence slope, and (optionally) exponential convergence rate.

Returns:

tensorndarray (M, M, |L|), float16

Validate()#: Parse DataFrame, extract data, resolve E and library sizes.

fit(X, y=None)#

Initialize lib & pred indices, embed data, find neighbors

Parameters:

X{array-like}, shape (n_samples, n_features): Observed data to be embedded, or used as embedding.
yaccepted but silently ignored — embedding is target-independent

Returns:

selfobject: Returns self.

fit_transform(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:

Xarray-like of shape (n_samples, n_features): Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None: Target values (None for unsupervised transformations).
**fit_paramsdict: Additional fit parameters. Pass only if the estimator accepts additional params in its fit method.

Returns:

X_newndarray array of shape (n_samples, n_features_new): Transformed array.

get_feature_names_out(input_features=None)#

set_output for downstream pipeline compatibility

The ‘output’ is a MxMxL tensor with M columns_ names

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

set_output(*, transform=None)#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

"default": Default output format of a transformer
"pandas": DataFrame output
"polars": Polars output
None: Transform configuration is unchanged

Added in version 1.4: "polars" option was added.

Returns:

selfestimator instance: Estimator instance.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

transform(X)#

Call CrossMap() for forward and reverse mappings.

The output is independent of the specific X passed here (CCM was fit on the training X); this method exists so the estimator participates correctly in sklearn Pipelines.

Parameters:

X{array-like}, shape (n_samples, n_features): Not used but accepted for sklearn convention.

Returns:

tuple: MxMxL tensor, columns, libSizes