pykoop.DataRegressor

class DataRegressor(coef=None)

Bases: KoopmanRegressor

Create a KoopmanRegressor object from a NumPy array.

This is useful for interoperability with other packages, where you may already have a Koopman (or discrete-time state-space) matirx, but you want to test it with pykoop.

Parameters:

coef (ndarray | None)

n_features_in_

Number of features input, including episode feature.

Type:

int

n_states_in_

Number of states input.

Type:

int

n_inputs_in_

Number of inputs input.

Type:

int

episode_feature_

Indicates if episode feature was present during fit().

Type:

bool

feature_names_in_

Array of input feature name strings.

Type:

np.ndarray

coef_

Fit coefficient matrix.

Type:

np.ndarray

Examples

Create a Koopman pipeline with the Koopman matrix forced to be:

 0.0  1.0 0.0
-0.5 -0.5 1.0

You may want to do this because you have a Koopman matrix from another library and you want to use pykoop to evaluate its performance.

>>> kp = pykoop.KoopmanPipeline(regressor=pykoop.DataRegressor(
...     coef=np.array([[0.0, 1.0, 0.0], [-0.5, -0.5, 1.0]]).T)
... )
>>> kp.fit(X_msd, n_inputs=1, episode_feature=True)
KoopmanPipeline(regressor=DataRegressor(coef=array(...
__init__(coef=None)

Instantiate DataRegressor.

Parameters:

coef (Optional[np.ndarray]) – Coefficient matrix to copy to coef_. If None, an appropriately-sized zero matrix is used.

Return type:

None

Methods

__init__([coef])

Instantiate DataRegressor.

fit(X[, y, n_inputs, episode_feature])

Fit the regressor.

frequency_response(t_step[, f_min, f_max, ...])

Compute frequency response of Koopman system.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

plot_bode(t_step[, f_min, f_max, n_points, ...])

Plot frequency response of Koopman system.

plot_eigenvalues([unit_circle, figure_kw, ...])

Plot eigenvalues of Koopman A matrix.

plot_koopman_matrix([subplots_kw, plot_kw])

Plot heatmap of Koopman matrices.

plot_svd([subplots_kw, plot_kw])

Plot singular values of Koopman matrices.

predict(X)

Perform a single-step prediction for each state in each episode.

score(X, y[, sample_weight])

Return the coefficient of determination of the prediction.

set_fit_request(*[, episode_feature, n_inputs])

Request metadata passed to the fit method.

set_params(**params)

Set the parameters of this estimator.

set_score_request(*[, sample_weight])

Request metadata passed to the score method.
fit(X, y=None, n_inputs=0, episode_feature=False)

Fit the regressor.

If only X is specified, the regressor will compute its unshifted and shifted versions. If X and y are specified, X is treated as the unshifted data matrix, while y is treated as the shifted data matrix.

Parameters:

  • X (np.ndarray) – Full data matrix if y=None. Unshifted data matrix if y is specified.

  • y (Optional[np.ndarray]) – Optional shifted data matrix. If None, shifted data matrix is computed using X.

  • n_inputs (int) – Number of input features at the end of X.

  • episode_feature (bool) – True if first feature indicates which episode a timestep is from.

Returns:

Instance of itself.

Return type:

KoopmanRegressor

Raises:

ValueError – If constructor or fit parameters are incorrect.

frequency_response(t_step, f_min=0, f_max=None, n_points=1000, decibels=True)

Compute frequency response of Koopman system.

Parameters:

  • t_step (float) – Sampling timestep.

  • f_min (float) – Minimum frequency to plot.

  • f_max (Optional[float]) – Maximum frequency to plot. If None, uses Nyquist frequency.

  • n_points (int) – Number of frequecy points to plot.

  • decibels (bool) – Plot gain in dB (default is true).

Returns:

Frequency (Hz) and frequency response (gain or dB).

Return type:

Tuple[np.ndarray, np.ndarray]

Raises:

ValueError – If f_min is less than zero or f_max is greater than the Nyquist frequency.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params – Parameter names mapped to their values.

Return type:

dict

plot_bode(t_step, f_min=0, f_max=None, n_points=1000, decibels=True, subplots_kw=None, plot_kw=None)

Plot frequency response of Koopman system.

Parameters:

  • t_step (float) – Sampling timestep.

  • f_min (float) – Minimum frequency to plot.

  • f_max (Optional[float]) – Maximum frequency to plot. If None, uses Nyquist frequency.

  • n_points (int) – Number of frequecy points to plot.

  • decibels (bool) – Plot gain in dB (default is true).

  • subplots_kw (Optional[Dict[str, Any]]) – Keyword arguments for plt.subplots().

  • plot_kw (Optional[Dict[str, Any]]) – Keyword arguments for Matplotlib plt.Axes.plot().

Returns:

Matplotlib plt.Figure and plt.Axes objects.

Return type:

Tuple[plt.Figure, plt.Axes]

Raises:

ValueError – If f_min is less than zero or f_max is greater than the Nyquist frequency.

plot_eigenvalues(unit_circle=True, figure_kw=None, subplot_kw=None, plot_kw=None)

Plot eigenvalues of Koopman A matrix.

Parameters:

  • figure_kw (Optional[Dict[str, Any]]) – Keyword arguments for plt.figure().

  • subplot_kw (Optional[Dict[str, Any]]) – Keyword arguments for plt.subplot().

  • plot_kw (Optional[Dict[str, Any]]) – Keyword arguments for Matplotlib plt.Axes.plot().

  • unit_circle (bool)

Returns:

Matplotlib plt.Figure and plt.Axes objects.

Return type:

Tuple[plt.Figure, plt.Axes]

plot_koopman_matrix(subplots_kw=None, plot_kw=None)

Plot heatmap of Koopman matrices.

Parameters:

  • subplots_kw (Optional[Dict[str, Any]]) – Keyword arguments for plt.subplots().

  • plot_kw (Optional[Dict[str, Any]]) – Keyword arguments for Matplotlib plt.Axes.plot().

Returns:

Matplotlib plt.Figure and plt.Axes objects.

Return type:

Tuple[plt.Figure, plt.Axes]

plot_svd(subplots_kw=None, plot_kw=None)

Plot singular values of Koopman matrices.

Parameters:

  • subplots_kw (Optional[Dict[str, Any]]) – Keyword arguments for plt.subplots().

  • plot_kw (Optional[Dict[str, Any]]) – Keyword arguments for Matplotlib plt.Axes.plot().

Returns:

Matplotlib plt.Figure and plt.Axes objects.

Return type:

Tuple[plt.Figure, plt.Axes]

predict(X)

Perform a single-step prediction for each state in each episode.

Parameters:

X (np.ndarray) – Data matrix.

Returns:

Predicted data matrix.

Return type:

np.ndarray

score(X, y, sample_weight=None)

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters:

  • X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

  • y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.

  • sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns:

score – \(R^2\) of self.predict(X) w.r.t. y.

Return type:

float

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_fit_request(*, episode_feature='$UNCHANGED$', n_inputs='$UNCHANGED$')

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

  • episode_feature (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for episode_feature parameter in fit.

  • n_inputs (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for n_inputs parameter in fit.

  • self (DataRegressor)

Returns:

self – The updated object.

Return type:

object

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:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance

set_score_request(*, sample_weight='$UNCHANGED$')

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

  • sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

  • self (DataRegressor)

Returns:

self – The updated object.

Return type:

object