artlib.common.BaseART

Base class for all ART objects.

Classes

BaseART

Generic implementation of Adaptive Resonance Theory (ART)

Module Contents

class artlib.common.BaseART.BaseART(params: Dict)

Bases: sklearn.base.BaseEstimator, sklearn.base.ClusterMixin

Generic implementation of Adaptive Resonance Theory (ART)

data_format = 'default'
params
sample_counter_ = 0
weight_sample_counter_: List[int] = []
d_min_ = None
d_max_ = None
is_fitted_ = False
labels_
__getattr__(key)
__setattr__(key, value)
get_params(deep: bool = True) → Dict
Parameters:

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

Returns:

Parameter names mapped to their values.

Return type:

dict

set_params(**params)

Set the parameters of this estimator.

Specific redefinition of sklearn.BaseEstimator.set_params for ART classes.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

object

set_data_bounds(lower_bounds: numpy.ndarray, upper_bounds: numpy.ndarray)

Manually set the data bounds for normalization.

Parameters:

  • lower_bounds (np.ndarray) – The lower bounds for each column.

  • upper_bounds (np.ndarray) – The upper bounds for each column.

find_data_bounds(*data_batches: list[numpy.ndarray]) → Tuple[numpy.ndarray, numpy.ndarray]

Automatically find the data bounds for normalization from a list of data batches.

Parameters:

*data_batches (list[np.ndarray]) – Batches of data to be presented to the model

Returns:

Lower and upper bounds for data.

Return type:

tuple[np.ndarray, np.ndarray]

prepare_data(X: numpy.ndarray) → numpy.ndarray

Prepare data for clustering.

Parameters:

X (np.ndarray) – The dataset.

Returns:

Normalized data.

Return type:

np.ndarray

restore_data(X: numpy.ndarray) → numpy.ndarray

Restore data to state prior to preparation.

Parameters:

X (np.ndarray) – The dataset.

Returns:

Restored data.

Return type:

np.ndarray

property n_clusters: int

Get the current number of clusters.

Returns:

The number of clusters.

Return type:

int

static validate_params(params: Dict)
Abstractmethod:

Validate clustering parameters.

Parameters:

params (dict) – Dictionary containing parameters for the algorithm.

check_dimensions(X: numpy.ndarray)

Check the data has the correct dimensions.

Parameters:

X (np.ndarray) – The dataset.

validate_data(X: numpy.ndarray)

Validates the data prior to clustering.

Parameters: - X: data set

abstract category_choice(i: numpy.ndarray, w: numpy.ndarray, params: Dict) → Tuple[float, Dict | None]

Get the activation of the cluster.

Parameters:

  • i (np.ndarray) – Data sample.

  • w (np.ndarray) – Cluster weight or information.

  • params (dict) – Dictionary containing parameters for the algorithm.

Returns:

Cluster activation and cache used for later processing.

Return type:

tuple

abstract match_criterion(i: numpy.ndarray, w: numpy.ndarray, params: Dict, cache: Dict | None = None) → Tuple[float, Dict | None]

Get the match criterion of the cluster.

Parameters:

  • i (np.ndarray) – Data sample.

  • w (np.ndarray) – Cluster weight or information.

  • params (dict) – Dictionary containing parameters for the algorithm.

  • cache (dict, optional) – Cache containing values from previous calculations.

Returns:

Cluster match criterion and cache used for later processing.

Return type:

tuple

match_criterion_bin(i: numpy.ndarray, w: numpy.ndarray, params: Dict, cache: Dict | None = None, op: Callable = operator.ge) → Tuple[bool, Dict]

Get the binary match criterion of the cluster.

Parameters:

  • i (np.ndarray) – Data sample.

  • w (np.ndarray) – Cluster weight or information.

  • params (dict) – Dictionary containing parameters for the algorithm.

  • cache (dict, optional) – Cache containing values from previous calculations.

Returns:

Binary match criterion and cache used for later processing.

Return type:

tuple

abstract update(i: numpy.ndarray, w: numpy.ndarray, params: Dict, cache: Dict | None = None) → numpy.ndarray

Get the updated cluster weight.

Parameters:

  • i (np.ndarray) – Data sample.

  • w (np.ndarray) – Cluster weight or information.

  • params (dict) – Dictionary containing parameters for the algorithm.

  • cache (dict, optional) – Cache containing values from previous calculations.

Returns:

Updated cluster weight.

Return type:

np.ndarray

abstract new_weight(i: numpy.ndarray, params: Dict) → numpy.ndarray

Generate a new cluster weight.

Parameters:

  • i (np.ndarray) – Data sample.

  • params (dict) – Dictionary containing parameters for the algorithm.

Returns:

Updated cluster weight.

Return type:

np.ndarray

add_weight(new_w: numpy.ndarray)

Add a new cluster weight.

Parameters:

new_w (np.ndarray) – New cluster weight to add.

set_weight(idx: int, new_w: numpy.ndarray)

Set the value of a cluster weight.

Parameters:

  • idx (int) – Index of cluster to update.

  • new_w (np.ndarray) – New cluster weight.

_match_tracking(cache: List[Dict] | Dict, epsilon: float, params: List[Dict] | Dict, method: Literal['MT+', 'MT-', 'MT0', 'MT1', 'MT~']) → bool

Perform match tracking using the specified method.

Parameters:

  • cache (dict) – Cached match criterion value.

  • epsilon (float) – Small adjustment factor for match tracking.

  • params (dict) – Parameters

  • method (Literal["MT+", "MT-", "MT0", "MT1", "MT~"]) – Match tracking method to apply.

Returns:

Whether to continue searching for a match.

Return type:

bool

static _match_tracking_operator(method: Literal['MT+', 'MT-', 'MT0', 'MT1', 'MT~']) → Callable
_set_params(new_params)
_deep_copy_params() → Dict
step_fit(x: numpy.ndarray, match_reset_func: Callable | None = None, match_tracking: Literal['MT+', 'MT-', 'MT0', 'MT1', 'MT~'] = 'MT+', epsilon: float = 0.0) → int

Fit the model to a single sample.

Parameters:

  • x (np.ndarray) – Data sample.

  • match_reset_func (callable, optional) – A callable that influences cluster creation.

  • match_tracking ({"MT+", "MT-", "MT0", "MT1", "MT~"}, default="MT+") – Method for resetting match criterion.

  • epsilon (float, default=0.0) – Epsilon value used for adjusting match criterion.

Returns:

Cluster label of the input sample.

Return type:

int

step_pred(x) → int

Predict the label for a single sample.

Parameters:

x (np.ndarray) – Data sample.

Returns:

Cluster label of the input sample.

Return type:

int

pre_step_fit(X: numpy.ndarray)

Undefined function called prior to each sample fit. Useful for cluster pruning.

Parameters:

X (np.ndarray) – The dataset.

post_step_fit(X: numpy.ndarray)

Undefined function called after each sample fit. Useful for cluster pruning.

Parameters:

X (np.ndarray) – The dataset.

post_fit(X: numpy.ndarray)

Undefined function called after fit. Useful for cluster pruning.

Parameters:

X (np.ndarray) – The dataset.

fit(X: numpy.ndarray, y: numpy.ndarray | None = None, match_reset_func: Callable | None = None, max_iter=1, match_tracking: Literal['MT+', 'MT-', 'MT0', 'MT1', 'MT~'] = 'MT+', epsilon: float = 0.0, verbose: bool = False, leave_progress_bar: bool = True)

Fit the model to the data.

Parameters:

  • X (np.ndarray) – The dataset.

  • y (np.ndarray, optional) – Not used. For compatibility.

  • match_reset_func (callable, optional) – A callable that influences cluster creation.

  • max_iter (int, default=1) – Number of iterations to fit the model on the same dataset.

  • match_tracking ({"MT+", "MT-", "MT0", "MT1", "MT~"}, default="MT+") – Method for resetting match criterion.

  • epsilon (float, default=0.0) – Epsilon value used for adjusting match criterion.

  • verbose (bool, default=False) – If True, displays progress of the fitting process.

  • leave_progress_bar (bool, default=True) – If True, leaves thge progress of the fitting process. Only used when verbose=True

partial_fit(X: numpy.ndarray, match_reset_func: Callable | None = None, match_tracking: Literal['MT+', 'MT-', 'MT0', 'MT1', 'MT~'] = 'MT+', epsilon: float = 0.0)

Iteratively fit the model to the data.

Parameters:

  • X (np.ndarray) – The dataset.

  • match_reset_func (callable, optional) – A callable that influences cluster creation.

  • match_tracking ({"MT+", "MT-", "MT0", "MT1", "MT~"}, default="MT+") – Method for resetting match criterion.

  • epsilon (float, default=0.0) – Epsilon value used for adjusting match criterion.

fit_gif(X: numpy.ndarray, y: numpy.ndarray | None = None, match_reset_func: Callable | None = None, max_iter=1, match_tracking: Literal['MT+', 'MT-', 'MT0', 'MT1', 'MT~'] = 'MT+', epsilon: float = 0.0, verbose: bool = False, leave_progress_bar: bool = True, ax: matplotlib.axes.Axes | None = None, filename: str | None = None, colors: artlib.common.utils.IndexableOrKeyable | None = None, n_cluster_estimate: int = 20, fps: int = 5, final_hold_secs: float = 0.0, **kwargs)

Fit the model to the data and make a gif of the process.

Parameters:

  • X (np.ndarray) – The dataset.

  • y (np.ndarray, optional) – Not used. For compatibility.

  • match_reset_func (callable, optional) – A callable that influences cluster creation.

  • max_iter (int, default=1) – Number of iterations to fit the model on the same dataset.

  • match_tracking ({"MT+", "MT-", "MT0", "MT1", "MT~"}, default="MT+") – Method for resetting match criterion.

  • epsilon (float, default=0.0) – Epsilon value used for adjusting match criterion.

  • verbose (bool, default=False) – If True, displays progress of the fitting process.

  • leave_progress_bar (bool, default=True) – If True, leaves thge progress of the fitting process. Only used when verbose=True

  • ax (matplotlib.axes.Axes, optional) – Figure axes.

  • colors (IndexableOrKeyable, optional) – Colors to use for each cluster.

  • n_cluster_estimate (int, default=20) – estimate of number of clusters. Used for coloring plot.

  • fps (int, default=5) – gif frames per second

  • final_hold_secs (float, default=0.0) – seconds to hold the final frame (n_final_frames=ceil(final_hold_secs * fps))

  • **kwargs (dict) – see :func: artlib.common.BaseART.visualize

predict(X: numpy.ndarray, clip: bool = False) → numpy.ndarray

Predict labels for the data.

Parameters:

  • X (np.ndarray) – The dataset.

  • clip (bool) – clip the input values to be between the previously seen data limits

Returns:

Labels for the data.

Return type:

np.ndarray

shrink_clusters(shrink_ratio: float = 0.1)

Shrink the clusters by a specified ratio.

Parameters:

shrink_ratio (float, optional) – The ratio by which to shrink the clusters. Must be between 0 and 1. Default is 0.1.

Returns:

self – Returns the instance with shrunken clusters.

Return type:

object

abstract plot_cluster_bounds(ax: matplotlib.axes.Axes, colors: artlib.common.utils.IndexableOrKeyable, linewidth: int = 1)

Undefined function for visualizing the bounds of each cluster.

Parameters:

  • ax (matplotlib.axes.Axes) – Figure axes.

  • colors (IndexableOrKeyable) – Colors to use for each cluster.

  • linewidth (int, default=1) – Width of boundary line.

abstract get_cluster_centers() → List[numpy.ndarray]

Undefined function for getting centers of each cluster. Used for regression.

Returns:

Cluster centroids.

Return type:

list of np.ndarray

visualize(X: numpy.ndarray, y: numpy.ndarray, ax: matplotlib.axes.Axes | None = None, marker_size: int = 10, linewidth: int = 1, colors: artlib.common.utils.IndexableOrKeyable | None = None)

Visualize the clustering of the data.

Parameters:

  • X (np.ndarray) – The dataset.

  • y (np.ndarray) – Sample labels.

  • ax (matplotlib.axes.Axes, optional) – Figure axes.

  • marker_size (int, default=10) – Size used for data points.

  • linewidth (int, default=1) – Width of boundary line.

  • colors (IndexableOrKeyable, optional) – Colors to use for each cluster.