sciedm.PredictNonlinear#

class sciedm.PredictNonlinear(columns=None, target=None, theta=[0.01, 0.1, 0.3, 0.5, 0.75, 1, 1.5, 2, 3, 4, 5, 6, 7, 8, 9], E=1, lib=None, pred=None, Tp=1, tau=-1, exclusionRadius=0, embedded=False, noTime=False, mpMethod=None, chunksize=1, n_jobs=10)#

Evaluate nonlinearity (state-dependence)

This class is a wrapper for SMap. The goal is to estimate the optimal SMap localization parameter theta.

Both time series columns & target must be present as named columns in X. columns and target can be the same representing a univariate observation, or can be different in which case the variables are cross mapped with columns as the shadow manifold source and target the variable to predict.

Parameters:

columns[str]: Vector of column names to create embedding library
targetstr: DataFrame column name of target feature to predict
theta[float]: Vector of theta values, theta >= 0.
lib[int]: Vector of pairs of 1-offset integer indices defining the embedding library The first index of a pair is the start index, the second the stop index. Default lib=None will assign lib=[1,N_obs] where N_obs is the number of observations. lib is _not_ 0-offset but ranges from 1 to N_obs.
pred[int]: Vector of pairs of 1-offset integer indices defining target prediction times The first index of a pair is the start index, the second the stop index. Default pred=None will assign pred=[1,N_obs] where N_obs is the number of observations. pred is _not_ 0-offset but ranges from 1 to N_obs.
tauint: Embedding time delay offset. Negative are delays, positive future values.
Tpint: Prediction horizon in units of time series row indices
exclusionRadiusint: Temporal exclusion radius for nearest neighbors. Neighbors closer than exclusionRadius indices from the target are ignored. Not applicable if lib and pred are disjoint.
embeddedbool: Is the input an embedding? If False (default) all columns will be time delay embedded with E and tau.
noTimebool: X is expected to be DataFrame with time index/values/strings in first column. If no time vector is provided set noTime=True.
mpMethodstr: Multiprocessing context start method See: docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods

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.
theta_rho_DataFrame: DataFrame of SMap rho(theta)

Returns:

Examples

>>> from sciedm import PredictNonlinear
>>> from pandas import read_csv
>>> df = read_csv('data/S12CD-S333-SumFlow_1980-2005.csv')
>>> pnl = PredictNonlinear(columns='SumFlow', target='SumFlow', E=3,
                          lib=[1,700], pred=[701,1379], Tp=3)
>>> thetaRho = pnl.fit_transform(df)
>>> from aux_func import PlotPredictNonlinear
>>> PlotPredictNonlinear(thetaRho, E=pnl.E, Tp=pnl.Tp)

SMapTheta(theta, data, args)#: SMap prediction with theta

fit(X, y=None)#

This method does no work. It copies mutable parameters and sets some feature objects for scikit-learn compatibility. It does not call validate_data() as it is called in transform and again in each Simplex object.

Parameters:

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

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 DataFrame with [theta, rho]

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='pandas')#

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)#

Use multiprocessing pool to evaluate theta with a SMap predictor.

A list of embedding dimensions Evals holds the E to be evaluated for simplex predictive fidelity. The list is used to create the _poolArgs iterable fed to a multiprocessing context which executes the SimplexE() function for each iterable item.

Parameters:

X{array-like}, shape (n_samples, n_features): Passed to instances of class Simplex: see simplex.py

Returns:

DataFrame: [theta, rho]