diff --git a/aeon/similarity_search/__init__.py b/aeon/similarity_search/__init__.py index f576c41f03..26b79c7da2 100644 --- a/aeon/similarity_search/__init__.py +++ b/aeon/similarity_search/__init__.py @@ -1,7 +1,5 @@ """Similarity search module.""" -__all__ = ["BaseSimilaritySearch", "QuerySearch", "SeriesSearch"] +__all__ = ["BaseSimilaritySearch"] -from aeon.similarity_search.base import BaseSimilaritySearch -from aeon.similarity_search.query_search import QuerySearch -from aeon.similarity_search.series_search import SeriesSearch +from aeon.similarity_search._base import BaseSimilaritySearch diff --git a/aeon/similarity_search/_base.py b/aeon/similarity_search/_base.py new file mode 100644 index 0000000000..a87487fde1 --- /dev/null +++ b/aeon/similarity_search/_base.py @@ -0,0 +1,103 @@ +"""Base class for similarity search.""" + +__maintainer__ = ["baraline"] +__all__ = [ + "BaseSimilaritySearch", +] + + +from abc import abstractmethod +from typing import Union + +import numpy as np +from numba.typed import List + +from aeon.base import BaseAeonEstimator + + +class BaseSimilaritySearch(BaseAeonEstimator): + """Base class for similarity search applications.""" + + _tags = { + "requires_y": False, + "fit_is_empty": False, + } + + @abstractmethod + def __init__(self): + super().__init__() + + @abstractmethod + def fit( + self, + X: Union[np.ndarray, List], + y=None, + ): + """ + Fit estimator to X. + + State change: + Changes state to "fitted". + + Writes to self: + _is_fitted : flag is set to True. + + Parameters + ---------- + X : Series or Collection, any supported type + Data to fit transform to, of python type as follows: + Series: 2D np.ndarray shape (n_channels, n_timepoints) + Collection: 3D np.ndarray shape (n_cases, n_channels, n_timepoints) + or list of 2D np.ndarray, case i has shape (n_channels, n_timepoints_i) + y: ignored, exists for API consistency reasons. + + Returns + ------- + self : a fitted instance of the estimator + """ + ... + + @abstractmethod + def predict( + self, + X: Union[np.ndarray, None] = None, + ): + """ + Predict method. + + Parameters + ---------- + X : 2D np.array of shape ``(n_cases, n_timepoints)`` + Optional data to use for predict. + """ + ... + + def _check_predict_series_format(self, X, length=None): + """ + Check wheter a series X in predict is correctly formated. + + Parameters + ---------- + X : np.ndarray, shape = (n_channels, n_timepoints) + A series to be used in predict. + """ + if isinstance(X, np.ndarray): + if X.ndim != 2: + raise TypeError( + "A np.ndarray given in predict must be 2D" + f"(n_channels, n_timepoints) but found {X.ndim}D." + ) + else: + raise TypeError( + "Expected a 2D np.ndarray in predict but found" f" {type(X)}." + ) + if self.n_channels_ != X.shape[0]: + raise ValueError( + f"Expected X to have {self.n_channels_} channels but" + f" got {X.shape[0]} channels." + ) + if length is not None and X.shape[1] != length: + raise ValueError( + f"Expected X to have {length} timepoints but" + f" got {X.shape[1]} timepoints." + ) diff --git a/aeon/similarity_search/_commons.py b/aeon/similarity_search/_commons.py deleted file mode 100644 index 1d20a6a5b0..0000000000 --- a/aeon/similarity_search/_commons.py +++ /dev/null @@ -1,504 +0,0 @@ -"""Helper and common function for similarity search estimators and functions.""" - -__maintainer__ = ["baraline"] - -import warnings - -import numpy as np -from numba import njit, prange -from numba.typed import List -from scipy.signal import convolve - -from aeon.utils.numba.general import ( - get_all_subsequences, - normalise_subsequences, - sliding_mean_std_one_series, - z_normalise_series_2d, -) - - -@njit(cache=True, fastmath=True) -def _compute_dist_profile(X_subs, q): - """ - Compute the distance profile between subsequences and a query. - - Parameters - ---------- - X_subs : array, shape=(n_samples, n_channels, query_length) - Input subsequences extracted from a time series. - q : array, shape=(n_channels, query_length) - Query used for the distance computation - - Returns - ------- - dist_profile : np.ndarray, 1D array of shape (n_samples) - The distance between the query all subsequences. - - """ - n_candidates, n_channels, q_length = X_subs.shape - dist_profile = np.zeros(n_candidates) - for i in range(n_candidates): - for j in range(n_channels): - for k in range(q_length): - dist_profile[i] += (X_subs[i, j, k] - q[j, k]) ** 2 - return dist_profile - - -@njit(cache=True, fastmath=True) -def naive_squared_distance_profile( - X, - q, - mask, - normalise=False, - X_means=None, - X_stds=None, -): - """ - Compute a squared euclidean distance profile. - - Parameters - ---------- - X : array, shape=(n_samples, n_channels, n_timepoints) - Input time series dataset to search in. - q : array, shape=(n_channels, query_length) - Query used during the search. - mask : array, shape=(n_samples, n_timepoints - query_length + 1) - Boolean mask indicating candidates for which the distance - profiles computed for each query should be set to infinity. - normalise : bool - Wheter to use a z-normalised distance. - X_means : array, shape=(n_samples, n_channels, n_timepoints - query_length + 1) - Mean of each candidate (subsequence) of length query_length in X. The - default is None, meaning that these values will be computed if normalise - is True. If provided, the computations will be skipped. - X_stds : array, shape=(n_samples, n_channels, n_timepoints - query_length + 1) - Standard deviation of each candidate (subsequence) of length query_length - in X. The default is None, meaning that these values will be computed if - normalise is True. If provided, the computations will be skipped. - - Returns - ------- - out : np.ndarray, 1D array of shape (n_samples, n_timepoints_t - query_length + 1) - The distance between the query and all candidates in X. - - """ - query_length = q.shape[1] - dist_profiles = List() - # Init distance profile array with unequal length support - for i in range(len(X)): - dist_profiles.append(np.zeros(X[i].shape[1] - query_length + 1)) - if normalise: - q = z_normalise_series_2d(q) - else: - q = q.astype(np.float64) - for i in range(len(X)): - # Numba don't support strides with integers ? - - X_subs = get_all_subsequences(X[i].astype(np.float64), query_length, 1) - if normalise: - if X_means is None and X_stds is None: - _X_means, _X_stds = sliding_mean_std_one_series(X[i], query_length, 1) - else: - _X_means, _X_stds = X_means[i], X_stds[i] - X_subs = normalise_subsequences(X_subs, _X_means, _X_stds) - dist_profile = _compute_dist_profile(X_subs, q) - dist_profile[~mask[i]] = np.inf - dist_profiles[i] = dist_profile - return dist_profiles - - -@njit(cache=True, fastmath=True) -def naive_squared_matrix_profile(X, T, query_length, mask, normalise=False): - """ - Compute a squared euclidean matrix profile. - - Parameters - ---------- - X : array, shape=(n_samples, n_channels, n_timepoints_x) - Input time series dataset to search in. - T : array, shape=(n_channels, n_timepoints_t) - Time series from which queries are extracted. - query_length : int - Length of the queries to extract from T. - mask : array, shape=(n_samples, n_timepoints_x - query_length + 1) - Boolean mask indicating candidates for which the distance - profiles computed for each query should be set to infinity. - normalise : bool - Wheter to use a z-normalised distance. - - Returns - ------- - out : np.ndarray, 1D array of shape (n_timepoints_t - query_length + 1) - The minimum distance between each query in T and all candidates in X. - """ - X_subs = List() - for i in range(len(X)): - i_subs = get_all_subsequences(X[i].astype(np.float64), query_length, 1) - if normalise: - X_means, X_stds = sliding_mean_std_one_series(X[i], query_length, 1) - i_subs = normalise_subsequences(i_subs, X_means, X_stds) - X_subs.append(i_subs) - - n_candidates = T.shape[1] - query_length + 1 - mp = np.full(n_candidates, np.inf) - - for i in range(n_candidates): - q = T[:, i : i + query_length] - if normalise: - q = z_normalise_series_2d(q) - for id_sample in range(len(X)): - dist_profile = _compute_dist_profile(X_subs[id_sample], q) - dist_profile[~mask[id_sample]] = np.inf - mp[i] = min(mp[i], dist_profile.min()) - return mp - - -def fft_sliding_dot_product(X, q): - """ - Use FFT convolution to calculate the sliding window dot product. - - This function applies the Fast Fourier Transform (FFT) to efficiently compute - the sliding dot product between the input time series `X` and the query `q`. - The dot product is computed for each channel individually. The sliding window - approach ensures that the dot product is calculated for every possible subsequence - of `X` that matches the length of `q` - - Parameters - ---------- - X : array, shape=(n_channels, n_timepoints) - Input time series - q : array, shape=(n_channels, query_length) - Input query - - Returns - ------- - out : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) - Sliding dot product between q and X. - """ - n_channels, n_timepoints = X.shape - query_length = q.shape[1] - out = np.zeros((n_channels, n_timepoints - query_length + 1)) - for i in range(n_channels): - out[i, :] = convolve(np.flipud(q[i, :]), X[i, :], mode="valid").real - return out - - -def get_ith_products(X, T, L, ith): - """ - Compute dot products between X and the i-th subsequence of size L in T. - - Parameters - ---------- - X : array, shape = (n_channels, n_timepoints_X) - Input data. - T : array, shape = (n_channels, n_timepoints_T) - Data containing the query. - L : int - Overall query length. - ith : int - Query starting index in T. - - Returns - ------- - np.ndarray, 2D array of shape (n_channels, n_timepoints_X - L + 1) - Sliding dot product between the i-th subsequence of size L in T and X. - - """ - return fft_sliding_dot_product(X, T[:, ith : ith + L]) - - -@njit(cache=True) -def numba_roll_1D_no_warparound(array, shift, warparound_value): - """ - Roll the rows of an array. - - Wheter to allow values at the end of the array to appear at the start after - being rolled out of the array length. - - Parameters - ---------- - array : np.ndarray of shape (n_columns) - Array to roll. - shift : int - The amount of indexes the values will be rolled on each row of the array. - Must be inferior or equal to n_columns. - warparound_value : any type - A value of the type of array to insert instead of the value that got rolled - over the array length - - Returns - ------- - rolled_array : np.ndarray of shape (n_rows, n_columns) - The rolled array. Can also be a TypedList in the case where n_columns changes - between rows. - - """ - length = array.shape[0] - _a1 = array[: length - shift] - array[shift:] = _a1 - array[:shift] = warparound_value - return array - - -@njit(cache=True) -def numba_roll_2D_no_warparound(array, shift, warparound_value): - """ - Roll the rows of an array. - - Wheter to allow values at the end of the array to appear at the start after - being rolled out of the array length. - - Parameters - ---------- - array : np.ndarray of shape (n_rows, n_columns) - Array to roll. Can also be a TypedList in the case where n_columns changes - between rows. - shift : int - The amount of indexes the values will be rolled on each row of the array. - Must be inferior or equal to n_columns. - warparound_value : any type - A value of the type of array to insert instead of the value that got rolled - over the array length - - Returns - ------- - rolled_array : np.ndarray of shape (n_rows, n_columns) - The rolled array. Can also be a TypedList in the case where n_columns changes - between rows. - - """ - for i in prange(len(array)): - length = len(array[i]) - _a1 = array[i][: length - shift] - array[i][shift:] = _a1 - array[i][:shift] = warparound_value - return array - - -@njit(cache=True) -def extract_top_k_and_threshold_from_distance_profiles_one_series( - distance_profiles, - id_x, - k=1, - threshold=np.inf, - exclusion_size=None, - inverse_distance=False, -): - """ - Extract the top-k smallest values from distance profiles and apply threshold. - - This function processes a distance profile and extracts the top-k smallest - distance values, optionally applying a threshold to exclude distances above - a given value. It also optionally handles exclusion zones to avoid selecting - neighboring timestamps. - - Parameters - ---------- - distance_profiles : np.ndarray, 2D array of shape (n_cases, n_candidates) - Precomputed distance profile. Can be a TypedList if n_candidates vary between - cases. - id_x : int - Identifier of the series or subsequence from which the distance profile - is computed. - k : int - Number of matches to returns - threshold : float - All matches below this threshold will be returned - exclusion_size : int or None, optional, default=None - Size of the exclusion zone around the current subsequence. This prevents - selecting neighboring subsequences within the specified range, useful for - avoiding trivial matches in time series data. If set to `None`, no - exclusion zone is applied. - inverse_distance : bool, optional - Wheter to return the worst matches instead of the bests. The default is False. - - Returns - ------- - top_k_dist : np.ndarray - Array of the top-k smallest distance values, potentially excluding values above - the threshold or those within the exclusion zone. - top_k : np.ndarray - Array of shape (k, 2) where each row contains the `id_x` identifier and the - index of the corresponding subsequence (or timestamp) with the top-k smallest - distances. - """ - if inverse_distance: - # To avoid div by 0 case - distance_profiles += 1e-8 - distance_profiles[distance_profiles != np.inf] = ( - 1 / distance_profiles[distance_profiles != np.inf] - ) - - if threshold != np.inf: - distance_profiles[distance_profiles > threshold] = np.inf - - _argsort = np.argsort(distance_profiles) - - if distance_profiles[distance_profiles <= threshold].shape[0] < k: - _k = distance_profiles[distance_profiles <= threshold].shape[0] - elif _argsort.shape[0] < k: - _k = _argsort.shape[0] - else: - _k = k - - if exclusion_size is None: - indexes = np.zeros((_k, 2), dtype=np.int_) - for i in range(_k): - indexes[i, 0] = id_x - indexes[i, 1] = _argsort[i] - return distance_profiles[_argsort[:_k]], indexes - else: - # Apply exclusion zone to avoid neighboring matches - top_k = np.zeros((_k, 2), dtype=np.int_) - exclusion_size - top_k_dist = np.zeros((_k), dtype=np.float64) - - top_k[0, 0] = id_x - top_k[0, 1] = _argsort[0] - - top_k_dist[0] = distance_profiles[_argsort[0]] - - n_inserted = 1 - i_current = 1 - - while n_inserted < _k and i_current < _argsort.shape[0]: - candidate_timestamp = _argsort[i_current] - - insert = True - LB = candidate_timestamp >= (top_k[:, 1] - exclusion_size) - UB = candidate_timestamp <= (top_k[:, 1] + exclusion_size) - if np.any(UB & LB): - insert = False - - if insert: - top_k[n_inserted, 0] = id_x - top_k[n_inserted, 1] = _argsort[i_current] - top_k_dist[n_inserted] = distance_profiles[_argsort[i_current]] - n_inserted += 1 - i_current += 1 - return top_k_dist[:n_inserted], top_k[:n_inserted] - - -def extract_top_k_and_threshold_from_distance_profiles( - distance_profiles, - k=1, - threshold=np.inf, - exclusion_size=None, - inverse_distance=False, -): - """ - Extract the best matches from a distance profile given k and threshold parameters. - - Parameters - ---------- - distance_profiles : np.ndarray, 2D array of shape (n_cases, n_candidates) - Precomputed distance profile. Can be a TypedList if n_candidates vary between - cases. - k : int - Number of matches to returns - threshold : float - All matches below this threshold will be returned - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestamp - exclusion_size` and - :math:`id_timestamp + exclusion_size` which cannot be returned - as best match if :math:`id_timestamp` was already selected. By default, - the value None means that this is not used. - inverse_distance : bool, optional - Wheter to return the worst matches instead of the bests. The default is False. - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(n_matches)``, contains the distance between - the query and its best matches in X_. The second array, of shape - ``(n_matches, 2)``, contains the indexes of these matches as - ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - # This whole function could be optimized and maybe made in numba to avoid stepping - # out of numba mode during distance computations - - n_cases_ = len(distance_profiles) - - id_timestamps = np.concatenate( - [np.arange(distance_profiles[i].shape[0]) for i in range(n_cases_)] - ) - id_samples = np.concatenate( - [[i] * distance_profiles[i].shape[0] for i in range(n_cases_)] - ) - - distance_profiles = np.concatenate(distance_profiles) - - if inverse_distance: - # To avoid div by 0 case - distance_profiles += 1e-8 - distance_profiles[distance_profiles != np.inf] = ( - 1 / distance_profiles[distance_profiles != np.inf] - ) - - if threshold != np.inf: - distance_profiles[distance_profiles > threshold] = np.inf - - _argsort_1d = np.argsort(distance_profiles) - _argsort = np.asarray( - [ - [id_samples[_argsort_1d[i]], id_timestamps[_argsort_1d[i]]] - for i in range(len(_argsort_1d)) - ], - dtype=int, - ) - - if distance_profiles[distance_profiles <= threshold].shape[0] < k: - _k = distance_profiles[distance_profiles <= threshold].shape[0] - warnings.warn( - f"Only {_k} matches are bellow the threshold of {threshold}, while" - f" k={k}. The number of returned match will be {_k}.", - stacklevel=2, - ) - elif _argsort.shape[0] < k: - _k = _argsort.shape[0] - warnings.warn( - f"The number of possible match is {_argsort.shape[0]}, but got" - f" k={k}. The number of returned match will be {_k}.", - stacklevel=2, - ) - else: - _k = k - - if exclusion_size is None: - return distance_profiles[_argsort_1d[:_k]], _argsort[:_k] - else: - # Apply exclusion zone to avoid neighboring matches - top_k = np.zeros((_k, 2), dtype=int) - top_k_dist = np.zeros((_k), dtype=float) - - top_k[0] = _argsort[0, :] - top_k_dist[0] = distance_profiles[_argsort_1d[0]] - - n_inserted = 1 - i_current = 1 - - while n_inserted < _k and i_current < _argsort.shape[0]: - candidate_sample, candidate_timestamp = _argsort[i_current] - - insert = True - is_from_same_sample = top_k[:, 0] == candidate_sample - if np.any(is_from_same_sample): - LB = candidate_timestamp >= ( - top_k[is_from_same_sample, 1] - exclusion_size - ) - UB = candidate_timestamp <= ( - top_k[is_from_same_sample, 1] + exclusion_size - ) - if np.any(UB & LB): - insert = False - - if insert: - top_k[n_inserted] = _argsort[i_current] - top_k_dist[n_inserted] = distance_profiles[_argsort_1d[i_current]] - n_inserted += 1 - i_current += 1 - return top_k_dist[:n_inserted], top_k[:n_inserted] diff --git a/aeon/similarity_search/base.py b/aeon/similarity_search/base.py deleted file mode 100644 index 5b0ce8c555..0000000000 --- a/aeon/similarity_search/base.py +++ /dev/null @@ -1,232 +0,0 @@ -"""Base class for similarity search.""" - -__maintainer__ = ["baraline"] - -from abc import abstractmethod -from collections.abc import Iterable -from typing import Optional, final - -import numpy as np -from numba import get_num_threads, set_num_threads -from numba.typed import List - -from aeon.base import BaseCollectionEstimator -from aeon.utils.numba.general import sliding_mean_std_one_series - - -class BaseSimilaritySearch(BaseCollectionEstimator): - """ - Base class for similarity search applications. - - Parameters - ---------- - distance : str, default="euclidean" - Name of the distance function to use. A list of valid strings can be found in - the documentation for :func:`aeon.distances.get_distance_function`. - If a callable is passed it must either be a python function or numba function - with nopython=True, that takes two 1d numpy arrays as input and returns a float. - distance_args : dict, default=None - Optional keyword arguments for the distance function. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - normalise : bool, default=False - Whether the distance function should be z-normalised. - speed_up : str, default='fastest' - Which speed up technique to use with for the selected distance - function. By default, the fastest algorithm is used. A list of available - algorithm for each distance can be obtained by calling the - `get_speedup_function_names` function of the child classes. - n_jobs : int, default=1 - Number of parallel jobs to use. - - Attributes - ---------- - X_ : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input time series stored during the fit method. - - Notes - ----- - For now, the multivariate case is only treated as independent. - Distances are computed for each channel independently and then - summed together. - """ - - _tags = { - "capability:multivariate": True, - "capability:unequal_length": True, - "capability:multithreading": True, - "fit_is_empty": False, - "X_inner_type": ["np-list", "numpy3D"], - } - - @abstractmethod - def __init__( - self, - distance: str = "euclidean", - distance_args: Optional[dict] = None, - inverse_distance: bool = False, - normalise: bool = False, - speed_up: str = "fastest", - n_jobs: int = 1, - ): - self.distance = distance - self.distance_args = distance_args - self.inverse_distance = inverse_distance - self.normalise = normalise - self.n_jobs = n_jobs - self.speed_up = speed_up - super().__init__() - - @final - def fit(self, X: np.ndarray, y=None): - """ - Fit method: data preprocessing and storage. - - Parameters - ---------- - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - Input array to be used as database for the similarity search - y : optional - Not used. - - Raises - ------ - TypeError - If the input X array is not 3D raise an error. - - Returns - ------- - self - """ - prev_threads = get_num_threads() - X = self._preprocess_collection(X) - # Store minimum number of n_timepoints for unequal length collections - self.min_timepoints_ = min([X[i].shape[-1] for i in range(len(X))]) - self.n_channels_ = X[0].shape[0] - self.n_cases_ = len(X) - if self.metadata_["unequal_length"]: - X = List(X) - set_num_threads(self._n_jobs) - self._fit(X, y) - set_num_threads(prev_threads) - self.is_fitted = True - return self - - def _store_mean_std_from_inputs(self, query_length: int) -> None: - """ - Store the mean and std of each subsequence of size query_length in X_. - - Parameters - ---------- - query_length : int - Length of the query. - - Returns - ------- - None - - """ - means = [] - stds = [] - - for i in range(len(self.X_)): - _mean, _std = sliding_mean_std_one_series(self.X_[i], query_length, 1) - - stds.append(_std) - means.append(_mean) - - self.X_means_ = List(means) - self.X_stds_ = List(stds) - - def _init_X_index_mask( - self, - X_index: Optional[Iterable[int]], - query_length: int, - exclusion_factor: Optional[float] = 2.0, - ) -> np.ndarray: - """ - Initiliaze the mask indicating the candidates to be evaluated in the search. - - Parameters - ---------- - X_index : Iterable - Any Iterable (tuple, list, array) of length two used to specify the index of - the query X if it was extracted from the input data X given during the fit - method. Given the tuple (id_sample, id_timestamp), the similarity search - will define an exclusion zone around the X_index in order to avoid matching - X with itself. If None, it is considered that the query is not extracted - from X_ (the training data). - query_length : int - Length of the queries. - exclusion_factor : float, optional - The exclusion factor is used to prevent candidates close or equal to the - query sample point to be returned as best matches. It is used to define a - region between :math:`id_timestamp - query_length//exclusion_factor` and - :math:`id_timestamp + query_length//exclusion_factor` which cannot be used - in the search. The default is 2.0. - - Raises - ------ - ValueError - If the length of the q_index iterable is not two, will raise a ValueError. - TypeError - If q_index is not an iterable, will raise a TypeError. - - Returns - ------- - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean array which indicates the candidates that should be evaluated in the - similarity search. - - """ - if self.metadata_["unequal_length"]: - mask = List( - [ - np.ones(self.X_[i].shape[1] - query_length + 1, dtype=bool) - for i in range(self.n_cases_) - ] - ) - else: - mask = np.ones( - (self.n_cases_, self.min_timepoints_ - query_length + 1), - dtype=bool, - ) - if X_index is not None: - if isinstance(X_index, Iterable): - if len(X_index) != 2: - raise ValueError( - "The X_index should contain an interable of size 2 such as " - "(id_sample, id_timestamp), but got an iterable of " - "size {}".format(len(X_index)) - ) - else: - raise TypeError( - "If not None, the X_index parameter should be an iterable, here " - "X_index is of type {}".format(type(X_index)) - ) - - if exclusion_factor <= 0: - raise ValueError( - "The value of exclusion_factor should be superior to 0, but got " - "{}".format(len(exclusion_factor)) - ) - - i_instance, i_timestamp = X_index - profile_length = self.X_[i_instance].shape[1] - query_length + 1 - exclusion_LB = max(0, int(i_timestamp - query_length // exclusion_factor)) - exclusion_UB = min( - profile_length, - int(i_timestamp + query_length // exclusion_factor), - ) - mask[i_instance][exclusion_LB:exclusion_UB] = False - - return mask - - @abstractmethod - def _fit(self, X, y=None): ... - - @abstractmethod - def get_speedup_function_names(self): - """Return a dictionnary containing the name of the speedup functions.""" - ... diff --git a/aeon/similarity_search/collection/__init__.py b/aeon/similarity_search/collection/__init__.py new file mode 100644 index 0000000000..3a08ed22d6 --- /dev/null +++ b/aeon/similarity_search/collection/__init__.py @@ -0,0 +1,8 @@ +"""Similarity search for time series collection.""" + +__all__ = ["BaseCollectionSimilaritySearch", "RandomProjectionIndexANN"] + +from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch +from aeon.similarity_search.collection.neighbors._rp_cosine_lsh import ( + RandomProjectionIndexANN, +) diff --git a/aeon/similarity_search/collection/_base.py b/aeon/similarity_search/collection/_base.py new file mode 100644 index 0000000000..cbbf8de1e9 --- /dev/null +++ b/aeon/similarity_search/collection/_base.py @@ -0,0 +1,89 @@ +"""Base similiarity search for collections.""" + +__maintainer__ = ["baraline"] +__all__ = [ + "BaseCollectionSimilaritySearch", +] + +from abc import abstractmethod +from typing import Union, final + +import numpy as np + +from aeon.base import BaseCollectionEstimator +from aeon.similarity_search._base import BaseSimilaritySearch + + +class BaseCollectionSimilaritySearch(BaseCollectionEstimator, BaseSimilaritySearch): + """Similarity search base class for collections.""" + + # tag values specific to CollectionTransformers + _tags = { + "input_data_type": "Collection", + "capability:multivariate": True, + "X_inner_type": ["numpy3D"], + } + + @final + def fit( + self, + X: np.ndarray, + y=None, + ): + """ + Fit method: data preprocessing and storage. + + Parameters + ---------- + X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) + Input array to be used as database for the similarity search. If it is an + unequal length collection, it should be a list of 2d numpy arrays. + y : optional + Not used. + + Raises + ------ + TypeError + If the input X array is not 3D raise an error. + + Returns + ------- + self + """ + self.reset() + X = self._preprocess_collection(X) + # Store minimum number of n_timepoints for unequal length collections + self.n_channels_ = X[0].shape[0] + self.n_cases_ = len(X) + self._fit(X, y=y) + self.is_fitted = True + return self + + @abstractmethod + def _fit( + self, + X: np.ndarray, + y=None, + ): ... + + def _pre_predict( + self, + X: Union[np.ndarray, None] = None, + length: int = None, + ): + """ + Predict method. + + Parameters + ---------- + X : Union[np.ndarray, None], optional + Optional data to use for predict.. The default is None. + length: int, optional + If not None, the number of timepoint of X should be equal to length. + + """ + self._check_is_fitted() + if X is not None: + # Could we call somehow _preprocess_series from a BaseCollectionEstimator ? + self._check_predict_series_format(X, length=length) + return X diff --git a/aeon/similarity_search/collection/motifs/__init__.py b/aeon/similarity_search/collection/motifs/__init__.py new file mode 100644 index 0000000000..fc014bcced --- /dev/null +++ b/aeon/similarity_search/collection/motifs/__init__.py @@ -0,0 +1 @@ +"""Motif search for time series collection.""" diff --git a/aeon/similarity_search/collection/neighbors/__init__.py b/aeon/similarity_search/collection/neighbors/__init__.py new file mode 100644 index 0000000000..f5cf0d925b --- /dev/null +++ b/aeon/similarity_search/collection/neighbors/__init__.py @@ -0,0 +1,7 @@ +"""Neighbors search for time series collection.""" + +__all__ = ["RandomProjectionIndexANN"] + +from aeon.similarity_search.collection.neighbors._rp_cosine_lsh import ( + RandomProjectionIndexANN, +) diff --git a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py new file mode 100644 index 0000000000..61142d6f83 --- /dev/null +++ b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py @@ -0,0 +1,262 @@ +"""Random projection LSH index.""" + +import numpy as np +from numba import get_num_threads, njit, prange, set_num_threads + +from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch +from aeon.utils.numba.general import z_normalise_series_2d, z_normalise_series_3d + + +@njit(cache=True) +def _hamming_dist(X, Y): + d = 0 + for i in prange(X.shape[0]): + d += X[i] ^ Y[i] + return d + + +@njit(cache=True, parallel=True) +def _hamming_dist_series_to_collection(X_bool, collection_bool): + n_buckets = collection_bool.shape[0] + res = np.zeros(n_buckets, dtype=np.int64) + for i in prange(n_buckets): + res[i] = _hamming_dist(collection_bool[i], X_bool) + return res + + +@njit(cache=True, fastmath=True, parallel=True) +def _series_to_bool(X, hash_funcs, start_points, length): + n_hash_funcs = hash_funcs.shape[0] + res = np.empty(n_hash_funcs, dtype=np.bool_) + for j in prange(n_hash_funcs): + res[j] = _nb_flat_dot( + X[:, start_points[j] : start_points[j] + length], hash_funcs[j] + ) + return res + + +@njit(cache=True, fastmath=True) +def _nb_flat_dot(X, Y): + n_channels, n_timepoints = X.shape + out = 0 + for i in prange(n_channels): + for j in prange(n_timepoints): + out += X[i, j] * Y[i, j] + return out >= 0 + + +@njit(cache=True, parallel=True) +def _collection_to_bool(X, hash_funcs, start_points, length): + n_hash_funcs = hash_funcs.shape[0] + n_samples = X.shape[0] + res = np.empty((n_samples, n_hash_funcs), dtype=np.bool_) + for j in prange(n_hash_funcs): + for i in range(n_samples): + res[i, j] = _nb_flat_dot( + X[i, :, start_points[j] : start_points[j] + length], hash_funcs[j] + ) + return res + + +class RandomProjectionIndexANN(BaseCollectionSimilaritySearch): + """ + Random Projection Locality Sensitive Hashing index with cosine similarity. + + In this method based on SimHash, we define a hash function as a boolean operation + such as, given a random vector ``V`` of shape ``(n_channels, L)`` and a time series + ``X`` of shape ``(n_channels, n_timeponts)`` (with ``L<=n_timepoints``), we compute + ``X.V > 0`` to obtain the boolean result. + In the case where ``L>> from aeon.datasets import load_classification + >>> from aeon.similarity_search.collection.neighbors import RandomProjectionIndexANN + >>> index = RandomProjectionIndexANN() + >>> X, y = load_classification("ArrowHead") + >>> index.fit(X[:200]) + >>> r = index.predict(X[201]) + """ + + _tags = { + "capability:unequal_length": False, + "capability:multithreading": True, + } + + def __init__( + self, + n_hash_funcs=128, + hash_func_coverage=0.25, + use_discrete_vectors=True, + random_state=None, + normalize=True, + n_jobs=1, + ): + self.n_hash_funcs = n_hash_funcs + self.hash_func_coverage = hash_func_coverage + self.use_discrete_vectors = use_discrete_vectors + self.random_state = random_state + self.normalize = normalize + self.n_jobs = n_jobs + super().__init__() + + def _fit(self, X, y=None): + """ + Build the index based on the X. + + Parameters + ---------- + X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) + Input array to be used to build the index. + y : optional + Not used. + + Returns + ------- + self + + """ + prev_threads = get_num_threads() + set_num_threads(self._n_jobs) + rng = np.random.default_rng(self.random_state) + if self.normalize: + X = z_normalise_series_3d(X) + self.n_timepoints_ = X.shape[2] + self.window_length_ = max(1, int(self.n_timepoints_ * self.hash_func_coverage)) + + if self.use_discrete_vectors: + self.hash_funcs_ = rng.choice( + [-1, 1], size=(self.n_hash_funcs, self.n_channels_, self.window_length_) + ) + else: + self.hash_funcs_ = rng.uniform( + low=-1, + high=1.0, + size=(self.n_hash_funcs, self.n_channels_, self.window_length_), + ) + self.start_points_ = rng.choice( + self.n_timepoints_ - self.window_length_ + 1, + size=self.n_hash_funcs, + replace=True, + ) + bool_hashes = _collection_to_bool( + X, self.hash_funcs_, self.start_points_, self.window_length_ + ) + + str_hashes = [hash(bool_hashes[i].tobytes()) for i in range(len(bool_hashes))] + self.dict_X_index_ = {} + self.dict_bool_hashes_ = {} + for i in range(len(str_hashes)): + if str_hashes[i] in self.dict_X_index_: + self.dict_X_index_[str_hashes[i]].append(i) + else: + self.dict_X_index_[str_hashes[i]] = [i] + self.dict_bool_hashes_[str_hashes[i]] = bool_hashes[i] + + self.bool_hashes_value_list_ = np.asarray(list(self.dict_bool_hashes_.values())) + self.bool_hashes_key_list_ = np.asarray(list(self.dict_bool_hashes_.keys())) + set_num_threads(prev_threads) + return self + + def _get_bucket_content(self, key): + return self.dict_X_index_[key] + + def _get_bucket_sizes(self): + return {key: len(self.dict_X_index_[key]) for key in self.dict_X_index_} + + def _get_series_bucket(self, X): + bool_hash = _series_to_bool( + X, self.hash_funcs_, self.start_points_, self.window_length_ + ) + str_hash = hash(bool_hash.tobytes()) + if str_hash in self.dict_X_index_: + return str_hash + else: + return None + + def predict( + self, + X, + k=1, + threshold=np.inf, + inverse_distance=False, + ): + """ + Find approximate nearest neighbors of a collection in the index. + + Parameters + ---------- + X : np.ndarray, shape = (n_channels, n_tiempoints) + Series for which we want to find neighbors. + k : int, optional + Number of neighbors to return for each series. The default is 1. + threshold : int, optional + A threshold on the distance to determine which candidates will be returned. + inverse_distance : bool, optional + Wheter to inverse the computed distance, meaning that the method will return + the k most dissimilar neighbors instead of the k most similar. + + Returns + ------- + top_k : np.ndarray, shape = (n_cases, k) + Indexes of k series in the index that are similar to X. + top_k_dist : np.ndarray, shape = (n_cases, k) + Distance of k series in the index to X. The distance + is the hamming distance between the result of each hash function. + """ + X = self._pre_predict(X, length=self.n_timepoints_) + + if self.normalize: + X = z_normalise_series_2d(X) + + X_bool = _series_to_bool( + X, self.hash_funcs_, self.start_points_, self.window_length_ + ) + top_k = np.zeros(k, dtype=int) + top_k_dist = np.zeros(k, dtype=float) + dists = _hamming_dist_series_to_collection(X_bool, self.bool_hashes_value_list_) + if inverse_distance: + dists = 1 / (dists + 1e-8) + # Get top k buckets + ids = np.argpartition(dists, kth=k)[:k] + # and reoder them + ids = ids[np.argsort(dists[ids])] + + _i_bucket = 0 + current_k = 0 + while current_k < k: + if dists[ids[_i_bucket]] <= threshold: + candidates = self.dict_X_index_[ + self.bool_hashes_key_list_[ids[_i_bucket]] + ] + # Can do exact search by computing distances here + if len(candidates) > k - current_k: + candidates = candidates[: k - current_k] + top_k[current_k : current_k + len(candidates)] = candidates + top_k_dist[current_k : current_k + len(candidates)] = dists[ + ids[_i_bucket] + ] + current_k += len(candidates) + else: + break + _i_bucket += 1 + return top_k[:current_k], top_k_dist[:current_k] diff --git a/aeon/similarity_search/collection/neighbors/tests/__init__.py b/aeon/similarity_search/collection/neighbors/tests/__init__.py new file mode 100644 index 0000000000..89bc3412fb --- /dev/null +++ b/aeon/similarity_search/collection/neighbors/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for similarity search for time series collection neighbors module.""" diff --git a/aeon/similarity_search/collection/tests/__init__.py b/aeon/similarity_search/collection/tests/__init__.py new file mode 100644 index 0000000000..d136a8571e --- /dev/null +++ b/aeon/similarity_search/collection/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for similarity search for time series collection base class and commons.""" diff --git a/aeon/similarity_search/collection/tests/test_base.py b/aeon/similarity_search/collection/tests/test_base.py new file mode 100644 index 0000000000..c1efaa30f0 --- /dev/null +++ b/aeon/similarity_search/collection/tests/test_base.py @@ -0,0 +1,54 @@ +"""Test for collection similarity search base class.""" + +__maintainer__ = ["baraline"] + +import pytest + +from aeon.testing.mock_estimators._mock_similarity_searchers import ( + MockCollectionSimilaritySearch, +) +from aeon.testing.testing_data import ( + make_example_1d_numpy, + make_example_2d_numpy_series, + make_example_3d_numpy, +) + + +def test_input_shape_fit_predict_collection(): + """Test input shapes.""" + estimator = MockCollectionSimilaritySearch() + # dummy data to pass to fit when testing predict/predict_proba + X_3D_uni = make_example_3d_numpy(n_channels=1, return_y=False) + X_3D_multi = make_example_3d_numpy(n_channels=2, return_y=False) + X_2D_uni = make_example_2d_numpy_series(n_channels=1) + X_2D_multi = make_example_2d_numpy_series(n_channels=2) + X_1D = make_example_1d_numpy() + + # 2D are converted to 3D + valid_inputs_fit = [ + X_3D_uni, + X_3D_multi, + X_2D_uni, + X_2D_multi, + ] + # Valid inputs + for _input in valid_inputs_fit: + estimator.fit(_input) + + with pytest.raises(ValueError): + estimator.fit(X_1D) + + estimator_multi = MockCollectionSimilaritySearch().fit(X_3D_multi) + estimator_uni = MockCollectionSimilaritySearch().fit(X_3D_uni) + + estimator_uni.predict(X_2D_uni) + estimator_multi.predict(X_2D_multi) + + with pytest.raises(ValueError): + estimator_uni.predict(X_2D_multi) + with pytest.raises(ValueError): + estimator_multi.predict(X_2D_uni) + with pytest.raises(TypeError): + estimator_uni.predict(X_3D_uni) + with pytest.raises(TypeError): + estimator_multi.predict(X_3D_multi) diff --git a/aeon/similarity_search/distance_profiles/__init__.py b/aeon/similarity_search/distance_profiles/__init__.py deleted file mode 100644 index 4be73f9d8e..0000000000 --- a/aeon/similarity_search/distance_profiles/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -"""Distance profiles.""" - -__all__ = [ - "euclidean_distance_profile", - "normalised_euclidean_distance_profile", - "squared_distance_profile", - "normalised_squared_distance_profile", -] - - -from aeon.similarity_search.distance_profiles.euclidean_distance_profile import ( - euclidean_distance_profile, - normalised_euclidean_distance_profile, -) -from aeon.similarity_search.distance_profiles.squared_distance_profile import ( - normalised_squared_distance_profile, - squared_distance_profile, -) diff --git a/aeon/similarity_search/distance_profiles/euclidean_distance_profile.py b/aeon/similarity_search/distance_profiles/euclidean_distance_profile.py deleted file mode 100644 index 1dd781e467..0000000000 --- a/aeon/similarity_search/distance_profiles/euclidean_distance_profile.py +++ /dev/null @@ -1,102 +0,0 @@ -"""Optimized distance profile for euclidean distance.""" - -__maintainer__ = ["baraline"] - - -from typing import Union - -import numpy as np -from numba.typed import List - -from aeon.similarity_search.distance_profiles.squared_distance_profile import ( - normalised_squared_distance_profile, - squared_distance_profile, -) - - -def euclidean_distance_profile( - X: Union[np.ndarray, List], q: np.ndarray, mask: np.ndarray -) -> np.ndarray: - """ - Compute a distance profile using the squared Euclidean distance. - - It computes the distance profiles between the input time series and the query using - the squared Euclidean distance. The distance between the query and a candidate is - comptued using a dot product and a rolling sum to avoid recomputing parts of the - operation. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a numba TypedList - of 2D arrays of shape (n_channels, n_timepoints) - q : np.ndarray, 2D array of shape (n_channels, query_length) - The query used for similarity search. - mask : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - - Returns - ------- - distance_profiles : np.ndarray - 3D array of shape (n_cases, n_timepoints - query_length + 1) - The distance profile between q and the input time series X. - - """ - distance_profiles = squared_distance_profile(X, q, mask) - # Need loop as we can return a list of np array in the unequal length case - for i in range(len(distance_profiles)): - distance_profiles[i] = distance_profiles[i] ** 0.5 - return distance_profiles - - -def normalised_euclidean_distance_profile( - X: Union[np.ndarray, List], - q: np.ndarray, - mask: np.ndarray, - X_means: Union[np.ndarray, List], - X_stds: Union[np.ndarray, List], - q_means: np.ndarray, - q_stds: np.ndarray, -) -> np.ndarray: - """ - Compute a distance profile in a brute force way. - - It computes the distance profiles between the input time series and the query using - the specified distance. The search is made in a brute force way without any - optimizations and can thus be slow. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a numba TypedList - of 2D arrays of shape (n_channels, n_timepoints) - q : np.ndarray, 2D array of shape (n_channels, query_length) - The query used for similarity search. - mask : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Means of each subsequences of X of size query_length. Should be a numba - TypedList if X is unequal length. - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Stds of each subsequences of X of size query_length. Should be a numba - TypedList if X is unequal length. - q_means : np.ndarray, 1D array of shape (n_channels) - Means of the query q - q_stds : np.ndarray, 1D array of shape (n_channels) - - Returns - ------- - distance_profiles : np.ndarray - 3D array of shape (n_cases, n_timepoints - query_length + 1) - The distance profile between q and the input time series X. - - """ - distance_profiles = normalised_squared_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - # Need loop as we can return a list of np array in the unequal length case - for i in range(len(distance_profiles)): - distance_profiles[i] = distance_profiles[i] ** 0.5 - return distance_profiles diff --git a/aeon/similarity_search/distance_profiles/squared_distance_profile.py b/aeon/similarity_search/distance_profiles/squared_distance_profile.py deleted file mode 100644 index a42beeac2f..0000000000 --- a/aeon/similarity_search/distance_profiles/squared_distance_profile.py +++ /dev/null @@ -1,319 +0,0 @@ -"""Optimized distance profile for euclidean distance.""" - -__maintainer__ = ["baraline"] - - -from typing import Union - -import numpy as np -from numba import njit, prange -from numba.typed import List - -from aeon.similarity_search._commons import fft_sliding_dot_product -from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD - - -def squared_distance_profile( - X: Union[np.ndarray, List], q: np.ndarray, mask: np.ndarray -) -> np.ndarray: - """ - Compute a distance profile using the squared Euclidean distance. - - It computes the distance profiles between the input time series and the query using - the squared Euclidean distance. The distance between the query and a candidate is - comptued using a dot product and a rolling sum to avoid recomputing parts of the - operation. - - Parameters - ---------- - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a numba TypedList - 2D array of shape (n_channels, n_timepoints) - q : np.ndarray, 2D array of shape (n_channels, query_length) - The query used for similarity search. - mask : np.ndarray, 3D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - - Returns - ------- - distance_profile : np.ndarray - 3D array of shape (n_cases, n_timepoints - query_length + 1) - The distance profile between q and the input time series X. - - """ - QX = [fft_sliding_dot_product(X[i], q) for i in range(len(X))] - if isinstance(X, np.ndarray): - QX = np.asarray(QX) - elif isinstance(X, List): - QX = List(QX) - distance_profiles = _squared_distance_profile(QX, X, q, mask) - if isinstance(X, np.ndarray): - distance_profiles = np.asarray(distance_profiles) - return distance_profiles - - -def normalised_squared_distance_profile( - X: Union[np.ndarray, List], - q: np.ndarray, - mask: np.ndarray, - X_means: np.ndarray, - X_stds: np.ndarray, - q_means: np.ndarray, - q_stds: np.ndarray, -) -> np.ndarray: - """ - Compute a distance profile in a brute force way. - - It computes the distance profiles between the input time series and the query using - the specified distance. The search is made in a brute force way without any - optimizations and can thus be slow. - - Parameters - ---------- - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a numba TypedList - 2D array of shape (n_channels, n_timepoints) - q : np.ndarray, 2D array of shape (n_channels, query_length) - The query used for similarity search. - mask : np.ndarray, 3D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Means of each subsequences of X of size query_length - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Stds of each subsequences of X of size query_length - q_means : np.ndarray, 1D array of shape (n_channels) - Means of the query q - q_stds : np.ndarray, 1D array of shape (n_channels) - Stds of the query q - - Returns - ------- - distance_profiles : np.ndarray - 3D array of shape (n_cases, n_timepoints - query_length + 1) - The distance profile between q and the input time series X. - - """ - query_length = q.shape[1] - QX = [fft_sliding_dot_product(X[i], q) for i in range(len(X))] - if isinstance(X, np.ndarray): - QX = np.asarray(QX) - elif isinstance(X, List): - QX = List(QX) - - distance_profiles = _normalised_squared_distance_profile( - QX, mask, X_means, X_stds, q_means, q_stds, query_length - ) - if isinstance(X, np.ndarray): - distance_profiles = np.asarray(distance_profiles) - return distance_profiles - - -@njit(cache=True, fastmath=True, parallel=True) -def _squared_distance_profile(QX, X, q, mask): - """ - Compute squared distance profiles between query subsequence and time series. - - Parameters - ---------- - QX : List of np.ndarray - List of precomputed dot products between queries and time series, with each - element corresponding to a different time series. - Shape of each array is (n_channels, n_timepoints - query_length + 1). - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a numba TypedList - 2D array of shape (n_channels, n_timepoints) - q : np.ndarray, 2D array of shape (n_channels, query_length) - The query used for similarity search. - mask : np.ndarray, 3D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - - Returns - ------- - distance_profiles : np.ndarray - 3D array of shape (n_cases, n_timepoints - query_length + 1) - The distance profile between q and the input time series X. - - """ - distance_profiles = List() - query_length = q.shape[1] - - # Init distance profile array with unequal length support - for i_instance in range(len(X)): - profile_length = X[i_instance].shape[1] - query_length + 1 - distance_profiles.append(np.full((profile_length), np.inf)) - - for _i_instance in prange(len(QX)): - # prange cast iterator to unit64 with parallel=True - i_instance = np.int_(_i_instance) - - distance_profiles[i_instance][mask[i_instance]] = ( - _squared_dist_profile_one_series(QX[i_instance], X[i_instance], q)[ - mask[i_instance] - ] - ) - return distance_profiles - - -@njit(cache=True, fastmath=True) -def _squared_dist_profile_one_series(QT, T, Q): - """ - Compute squared distance profile between query subsequence and a single time series. - - This function calculates the squared distance profile for a single time series by - leveraging the dot product of the query and time series as well as precomputed sums - of squares to efficiently compute the squared distances. - - Parameters - ---------- - QT : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) - The dot product between the query and the time series. - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - Q : np.ndarray - 2D array of shape (n_channels, query_length) representing query subsequence. - - Returns - ------- - distance_profile : np.ndarray - 2D array of shape (n_channels, n_timepoints - query_length + 1) - The squared distance profile between the query and the input time series. - """ - n_channels, profile_length = QT.shape - query_length = Q.shape[1] - _QT = -2 * QT - distance_profile = np.zeros(profile_length) - for k in prange(n_channels): - _sum = 0 - _qsum = 0 - for j in prange(query_length): - _sum += T[k, j] ** 2 - _qsum += Q[k, j] ** 2 - - distance_profile += _qsum + _QT[k] - distance_profile[0] += _sum - for i in prange(1, profile_length): - _sum += T[k, i + (query_length - 1)] ** 2 - T[k, i - 1] ** 2 - distance_profile[i] += _sum - return distance_profile - - -@njit(cache=True, fastmath=True, parallel=True) -def _normalised_squared_distance_profile( - QX, mask, X_means, X_stds, q_means, q_stds, query_length -): - """ - Compute the normalised squared distance profiles between query subsequence and input time series. - - Parameters - ---------- - QX : List of np.ndarray - List of precomputed dot products between queries and time series, with each element - corresponding to a different time series. - Shape of each array is (n_channels, n_timepoints - query_length + 1). - mask : np.ndarray, 3D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Means of each subsequences of X of size query_length - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Stds of each subsequences of X of size query_length - q_means : np.ndarray, 1D array of shape (n_channels) - Means of the query q - q_stds : np.ndarray, 1D array of shape (n_channels) - Stds of the query q - query_length : int - The length of the query subsequence used for the distance profile computation. - - Returns - ------- - List of np.ndarray - List of 2D arrays, each of shape (n_channels, n_timepoints - query_length + 1). - Each array contains the normalised squared distance profile between the query subsequence and the corresponding time series. - Entries in the array are set to infinity where the mask is False. - """ - distance_profiles = List() - Q_is_constant = q_stds <= AEON_NUMBA_STD_THRESHOLD - # Init distance profile array with unequal length support - for i_instance in range(len(QX)): - profile_length = QX[i_instance].shape[1] - distance_profiles.append(np.full((profile_length), np.inf)) - - for _i_instance in prange(len(QX)): - # prange cast iterator to unit64 with parallel=True - i_instance = np.int_(_i_instance) - - distance_profiles[i_instance][mask[i_instance]] = ( - _normalised_squared_dist_profile_one_series( - QX[i_instance], - X_means[i_instance], - X_stds[i_instance], - q_means, - q_stds, - query_length, - Q_is_constant, - )[mask[i_instance]] - ) - return distance_profiles - - -@njit(cache=True, fastmath=True) -def _normalised_squared_dist_profile_one_series( - QT, T_means, T_stds, Q_means, Q_stds, query_length, Q_is_constant -): - """ - Compute the z-normalised squared Euclidean distance profile for one time series. - - Parameters - ---------- - QT : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) - The dot product between the query and the time series. - T_means : np.ndarray, 1D array of length n_channels - The mean values of the time series for each channel. - - T_stds : np.ndarray, 2D array of shape (n_channels, profile_length) - The standard deviations of the time series for each channel and position. - Q_means : np.ndarray, 1D array of shape (n_channels) - Means of the query q - Q_stds : np.ndarray, 1D array of shape (n_channels) - Stds of the query q - query_length : int - The length of the query subsequence used for the distance profile computation. - Q_is_constant : np.ndarray - 1D array of shape (n_channels,) where each element is a Boolean indicating - whether the query standard deviation for that channel is less than or equal - to a specified threshold. - - Returns - ------- - np.ndarray - 2D array of shape (n_channels, n_timepoints - query_length + 1) containing the - z-normalised squared distance profile between the query subsequence and the time - series. Entries are computed based on the z-normalised values, with special - handling for constant values. - """ - n_channels, profile_length = QT.shape - distance_profile = np.zeros(profile_length) - - for i in prange(profile_length): - Sub_is_constant = T_stds[:, i] <= AEON_NUMBA_STD_THRESHOLD - for k in prange(n_channels): - # Two Constant case - if Q_is_constant[k] and Sub_is_constant[k]: - _val = 0 - # One Constant case - elif Q_is_constant[k] or Sub_is_constant[k]: - _val = query_length - else: - denom = query_length * Q_stds[k] * T_stds[k, i] - - p = (QT[k, i] - query_length * (Q_means[k] * T_means[k, i])) / denom - p = min(p, 1.0) - - _val = abs(2 * query_length * (1.0 - p)) - distance_profile[i] += _val - - return distance_profile diff --git a/aeon/similarity_search/distance_profiles/tests/__init__.py b/aeon/similarity_search/distance_profiles/tests/__init__.py deleted file mode 100644 index 566dda7367..0000000000 --- a/aeon/similarity_search/distance_profiles/tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Tests for distance profiles.""" diff --git a/aeon/similarity_search/distance_profiles/tests/test_euclidean_distance.py b/aeon/similarity_search/distance_profiles/tests/test_euclidean_distance.py deleted file mode 100644 index 2eafff78bb..0000000000 --- a/aeon/similarity_search/distance_profiles/tests/test_euclidean_distance.py +++ /dev/null @@ -1,208 +0,0 @@ -"""Tests for naive Euclidean distance profile.""" - -__maintainer__ = [] - - -import numpy as np -import pytest -from numba.typed import List -from numpy.testing import assert_array_almost_equal, assert_array_equal - -from aeon.similarity_search._commons import naive_squared_distance_profile -from aeon.similarity_search.distance_profiles.euclidean_distance_profile import ( - euclidean_distance_profile, - normalised_euclidean_distance_profile, -) -from aeon.utils.numba.general import sliding_mean_std_one_series - -DATATYPES = ["float64", "int64"] - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_distance(dtype): - """Test Euclidean distance.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - expected = [T**0.5 for T in naive_squared_distance_profile(X, q, mask)] - dist_profile = euclidean_distance_profile(X, q, mask) - - assert_array_almost_equal(dist_profile, expected) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_constant_case(dtype): - """Test Euclidean distance profile calculation.""" - X = np.ones((2, 1, 10), dtype=dtype) - q = np.zeros((1, 3), dtype=dtype) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - expected = [T**0.5 for T in naive_squared_distance_profile(X, q, mask)] - dist_profile = euclidean_distance_profile(X, q, mask) - - assert_array_almost_equal(dist_profile, expected) - - -def test_non_alteration_of_inputs_euclidean(): - """Test if input is altered during Euclidean distance profile.""" - X = np.asarray([[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]]) - X_copy = np.copy(X) - q = np.asarray([[3, 4, 5]]) - q_copy = np.copy(q) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - _ = euclidean_distance_profile(X, q, mask) - assert_array_equal(q, q_copy) - assert_array_equal(X, X_copy) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_distance(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - - dist_profile = normalised_euclidean_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = [ - T**0.5 for T in naive_squared_distance_profile(X, q, mask, normalise=True) - ] - - assert_array_almost_equal(dist_profile, expected) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_distance_unequal_length(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = List( - [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6]], dtype=dtype), - ] - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - X_means = List() - X_stds = List() - - for i in range(len(X)): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds.append(_std) - X_means.append(_mean) - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - mask = List( - [np.ones(X[i].shape[1] - q.shape[1] + 1, dtype=bool) for i in range(len(X))] - ) - - dist_profile = normalised_euclidean_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = [ - T**0.5 - for T in naive_squared_distance_profile( - X, q, mask, normalise=True, X_means=X_means, X_stds=X_stds - ) - ] - for i in range(len(X)): - assert_array_almost_equal(dist_profile[i], expected[i]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_distance_unequal_length(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = List( - [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6]], dtype=dtype), - ] - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - mask = List( - [np.ones(X[i].shape[1] - q.shape[1] + 1, dtype=bool) for i in range(len(X))] - ) - expected = [T**0.5 for T in naive_squared_distance_profile(X, q, mask)] - dist_profile = euclidean_distance_profile(X, q, mask) - for i in range(len(X)): - assert_array_almost_equal(dist_profile[i], expected[i]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_constant_case(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = np.ones((2, 2, 10), dtype=dtype) - q = np.zeros((2, 3), dtype=dtype) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - - dist_profile = normalised_euclidean_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = [ - T**0.5 for T in naive_squared_distance_profile(X, q, mask, normalise=True) - ] - - assert_array_almost_equal(dist_profile, expected) - - -def test_non_alteration_of_inputs_normalised_euclidean(): - """Test if input is altered during normalised Euclidean distance profile.""" - X = np.asarray([[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]]) - X_copy = np.copy(X) - q = np.asarray([[3, 4, 5]]) - q_copy = np.copy(q) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - _ = normalised_euclidean_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - - assert_array_equal(q, q_copy) - assert_array_equal(X, X_copy) diff --git a/aeon/similarity_search/distance_profiles/tests/test_squared_distance.py b/aeon/similarity_search/distance_profiles/tests/test_squared_distance.py deleted file mode 100644 index cdb7b35cbc..0000000000 --- a/aeon/similarity_search/distance_profiles/tests/test_squared_distance.py +++ /dev/null @@ -1,200 +0,0 @@ -"""Tests for naive Euclidean distance profile.""" - -__maintainer__ = [] - - -import numpy as np -import pytest -from numba.typed import List -from numpy.testing import assert_array_almost_equal, assert_array_equal - -from aeon.similarity_search._commons import naive_squared_distance_profile -from aeon.similarity_search.distance_profiles.squared_distance_profile import ( - normalised_squared_distance_profile, - squared_distance_profile, -) -from aeon.utils.numba.general import sliding_mean_std_one_series - -DATATYPES = ["float64", "int64"] - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_distance(dtype): - """Test Euclidean distance.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - expected = naive_squared_distance_profile(X, q, mask) - dist_profile = squared_distance_profile(X, q, mask) - - assert_array_almost_equal(dist_profile, expected) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_constant_case(dtype): - """Test Euclidean distance profile calculation.""" - X = np.ones((2, 1, 10), dtype=dtype) - q = np.zeros((1, 3), dtype=dtype) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - expected = naive_squared_distance_profile(X, q, mask) - dist_profile = squared_distance_profile(X, q, mask) - - assert_array_almost_equal(dist_profile, expected) - - -def test_non_alteration_of_inputs_euclidean(): - """Test if input is altered during Euclidean distance profile.""" - X = np.asarray([[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]]) - X_copy = np.copy(X) - q = np.asarray([[3, 4, 5]]) - q_copy = np.copy(q) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - _ = squared_distance_profile(X, q, mask) - assert_array_equal(q, q_copy) - assert_array_equal(X, X_copy) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_distance(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - - dist_profile = normalised_squared_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = naive_squared_distance_profile(X, q, mask, normalise=True) - - assert_array_almost_equal(dist_profile, expected) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_distance_unequal_length(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = List( - [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6]], dtype=dtype), - ] - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - X_means = List() - X_stds = List() - - for i in range(len(X)): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds.append(_std) - X_means.append(_mean) - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - mask = List( - [np.ones(X[i].shape[1] - q.shape[1] + 1, dtype=bool) for i in range(len(X))] - ) - - dist_profile = normalised_squared_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = naive_squared_distance_profile(X, q, mask, normalise=True) - for i in range(len(X)): - assert_array_almost_equal(dist_profile[i], expected[i]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_distance_unequal_length(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = List( - [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6]], dtype=dtype), - ] - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - mask = List( - [np.ones(X[i].shape[1] - q.shape[1] + 1, dtype=bool) for i in range(len(X))] - ) - - expected = naive_squared_distance_profile(X, q, mask) - dist_profile = squared_distance_profile(X, q, mask) - for i in range(len(X)): - assert_array_almost_equal(dist_profile[i], expected[i]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_constant_case(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = np.ones((2, 2, 10), dtype=dtype) - q = np.zeros((2, 3), dtype=dtype) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - - dist_profile = normalised_squared_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = naive_squared_distance_profile(X, q, mask, normalise=True) - - assert_array_almost_equal(dist_profile, expected) - - -def test_non_alteration_of_inputs_normalised_euclidean(): - """Test if input is altered during normalised Euclidean distance profile.""" - X = np.asarray([[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]]) - X_copy = np.copy(X) - q = np.asarray([[3, 4, 5]]) - q_copy = np.copy(q) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - _ = normalised_squared_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - - assert_array_equal(q, q_copy) - assert_array_equal(X, X_copy) diff --git a/aeon/similarity_search/matrix_profiles/__init__.py b/aeon/similarity_search/matrix_profiles/__init__.py deleted file mode 100644 index d04f1cbfd3..0000000000 --- a/aeon/similarity_search/matrix_profiles/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -"""Distance profiles.""" - -__all__ = [ - "stomp_normalised_euclidean_matrix_profile", - "stomp_euclidean_matrix_profile", - "stomp_normalised_squared_matrix_profile", - "stomp_squared_matrix_profile", -] -from aeon.similarity_search.matrix_profiles.stomp import ( - stomp_euclidean_matrix_profile, - stomp_normalised_euclidean_matrix_profile, - stomp_normalised_squared_matrix_profile, - stomp_squared_matrix_profile, -) diff --git a/aeon/similarity_search/matrix_profiles/stomp.py b/aeon/similarity_search/matrix_profiles/stomp.py deleted file mode 100644 index 509e68ad49..0000000000 --- a/aeon/similarity_search/matrix_profiles/stomp.py +++ /dev/null @@ -1,633 +0,0 @@ -"""Implementation of stomp for euclidean and squared euclidean distance profile.""" - -from typing import Optional - -__maintainer__ = ["baraline"] - - -from typing import Union - -import numpy as np -from numba import njit -from numba.typed import List - -from aeon.similarity_search._commons import ( - extract_top_k_and_threshold_from_distance_profiles_one_series, - get_ith_products, - numba_roll_1D_no_warparound, -) -from aeon.similarity_search.distance_profiles.squared_distance_profile import ( - _normalised_squared_dist_profile_one_series, - _squared_dist_profile_one_series, -) -from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD - - -def stomp_euclidean_matrix_profile( - X: Union[np.ndarray, List], - T: np.ndarray, - L: int, - mask: np.ndarray, - k: int = 1, - threshold: float = np.inf, - inverse_distance: bool = False, - exclusion_size: Optional[int] = None, -): - """ - Compute a euclidean euclidean matrix profile using STOMP [1]_. - - This improves on the naive matrix profile by updating the dot products for each - sucessive query in T instead of recomputing them. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - The length of the subsequences considered during the search. This parameter - cannot be larger than n_timepoints and series_length. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for the - first query of size L in T. This mask will be updated during the algorithm. - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, - the value None means that this is not used. - - References - ---------- - .. [1] Matrix Profile II: Exploiting a Novel Algorithm and GPUs to break the one - Hundred Million Barrier for Time Series Motifs and Joins. Yan Zhu, Zachary - Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth Funning, Abdullah - Mueen, Philip Berisk and Eamonn Keogh. IEEE ICDM 2016 - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - MP, IP = stomp_squared_matrix_profile( - X, - T, - L, - mask, - k=k, - threshold=threshold, - exclusion_size=exclusion_size, - inverse_distance=inverse_distance, - ) - for i in range(len(MP)): - MP[i] = MP[i] ** 0.5 - return MP, IP - - -def stomp_squared_matrix_profile( - X: Union[np.ndarray, List], - T: np.ndarray, - L: int, - mask: np.ndarray, - k: int = 1, - threshold: float = np.inf, - inverse_distance: bool = False, - exclusion_size: Optional[int] = None, -): - """ - Compute a squared euclidean matrix profile using STOMP [1]_. - - This improves on the naive matrix profile by updating the dot products for each - sucessive query in T instead of recomputing them. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - The length of the subsequences considered during the search. This parameter - cannot be larger than n_timepoints and series_length. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for the - first query of size L in T. This mask will be updated during the algorithm. - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, - the value None means that this is not used. - - References - ---------- - .. [1] Matrix Profile II: Exploiting a Novel Algorithm and GPUs to break the one - Hundred Million Barrier for Time Series Motifs and Joins. Yan Zhu, Zachary - Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth Funning, Abdullah - Mueen, Philip Berisk and Eamonn Keogh. IEEE ICDM 2016 - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - XdotT = [get_ith_products(X[i], T, L, 0) for i in range(len(X))] - if isinstance(X, np.ndarray): - XdotT = np.asarray(XdotT) - elif isinstance(X, List): - XdotT = List(XdotT) - - MP, IP = _stomp( - X, - T, - XdotT, - L, - mask, - k, - threshold, - exclusion_size, - inverse_distance, - ) - return MP, IP - - -def stomp_normalised_euclidean_matrix_profile( - X: Union[np.ndarray, List], - T: np.ndarray, - L: int, - X_means: Union[np.ndarray, List], - X_stds: Union[np.ndarray, List], - T_means: np.ndarray, - T_stds: np.ndarray, - mask: np.ndarray, - k: int = 1, - threshold: float = np.inf, - inverse_distance: bool = False, - exclusion_size: Optional[int] = None, -): - """ - Compute a euclidean matrix profile using STOMP [1]_. - - This improves on the naive matrix profile by updating the dot products for each - sucessive query in T instead of recomputing them. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - The length of the subsequences considered during the search. This parameter - cannot be larger than n_timepoints and series_length. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Means of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Stds of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - T_means : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Means of each subsequences of T of size L. - T_stds : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Stds of each subsequences of T of size L. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for the - first query of size L in T. This mask will be updated during the algorithm. - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, - the value None means that this is not used. - - References - ---------- - .. [1] Matrix Profile II: Exploiting a Novel Algorithm and GPUs to break the one - Hundred Million Barrier for Time Series Motifs and Joins. Yan Zhu, Zachary - Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth Funning, Abdullah - Mueen, Philip Berisk and Eamonn Keogh. IEEE ICDM 2016 - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - MP, IP = stomp_normalised_squared_matrix_profile( - X, - T, - L, - X_means, - X_stds, - T_means, - T_stds, - mask, - k=k, - threshold=threshold, - exclusion_size=exclusion_size, - inverse_distance=inverse_distance, - ) - for i in range(len(MP)): - MP[i] = MP[i] ** 0.5 - return MP, IP - - -def stomp_normalised_squared_matrix_profile( - X: Union[np.ndarray, List], - T: np.ndarray, - L: int, - X_means: Union[np.ndarray, List], - X_stds: Union[np.ndarray, List], - T_means: np.ndarray, - T_stds: np.ndarray, - mask: np.ndarray, - k: int = 1, - threshold: float = np.inf, - inverse_distance: bool = False, - exclusion_size: Optional[int] = None, -): - """ - Compute a squared euclidean matrix profile using STOMP [1]_. - - This improves on the naive matrix profile by updating the dot products for each - sucessive query in T instead of recomputing them. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - The length of the subsequences considered during the search. This parameter - cannot be larger than n_timepoints and series_length. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Means of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Stds of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - T_means : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Means of each subsequences of T of size L. - T_stds : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Stds of each subsequences of T of size L. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for the - first query of size L in T. This mask will be updated during the algorithm. - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, - the value None means that this is not used. - - References - ---------- - .. [1] Matrix Profile II: Exploiting a Novel Algorithm and GPUs to break the one - Hundred Million Barrier for Time Series Motifs and Joins. Yan Zhu, Zachary - Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth Funning, Abdullah - Mueen, Philip Berisk and Eamonn Keogh. IEEE ICDM 2016 - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - XdotT = [get_ith_products(X[i], T, L, 0) for i in range(len(X))] - if isinstance(X, np.ndarray): - XdotT = np.asarray(XdotT) - elif isinstance(X, List): - XdotT = List(XdotT) - - MP, IP = _stomp_normalised( - X, - T, - XdotT, - X_means, - X_stds, - T_means, - T_stds, - L, - mask, - k, - threshold, - exclusion_size, - inverse_distance, - ) - return MP, IP - - -def _stomp_normalised( - X, - T, - XdotT, - X_means, - X_stds, - T_means, - T_stds, - L, - mask, - k, - threshold, - exclusion_size, - inverse_distance, -): - """ - Compute the Matrix Profile using the STOMP algorithm with normalised distances. - - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - Length of the subsequences used for the distance computation. - XdotT : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Precomputed dot products between each time series in X and the query series T. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Means of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Stds of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - T_means : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Means of each subsequences of T of size L. - T_stds : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Stds of each subsequences of T of size L. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for the - first query of size L in T. This mask will be updated during the algorithm. - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, - the value None means that this is not used. - - Returns - ------- - tuple of np.ndarray - - MP : array of shape (n_queries,) - Matrix profile distances for each query subsequence. - - IP : array of shape (n_queries,) - Indexes of the top matches for each query subsequence. - """ - n_queries = T.shape[1] - L + 1 - MP = np.empty(n_queries, dtype=object) - IP = np.empty(n_queries, dtype=object) - for i_x in range(len(X)): - for i in range(n_queries): - dist_profiles = _normalised_squared_dist_profile_one_series( - XdotT[i_x], - X_means[i_x], - X_stds[i_x], - T_means[:, i], - T_stds[:, i], - L, - T_stds[:, i] <= AEON_NUMBA_STD_THRESHOLD, - ) - dist_profiles[~mask[i_x]] = np.inf - if i + 1 < n_queries: - XdotT[i_x] = _update_dot_products_one_series( - X[i_x], T, XdotT[i_x], L, i + 1 - ) - - mask[i_x] = numba_roll_1D_no_warparound(mask[i_x], 1, True) - ( - top_dists, - top_indexes, - ) = extract_top_k_and_threshold_from_distance_profiles_one_series( - dist_profiles, - i_x, - k=k, - threshold=threshold, - exclusion_size=exclusion_size, - inverse_distance=inverse_distance, - ) - if i_x > 0: - top_dists, top_indexes = _sort_out_tops( - top_dists, MP[i], top_indexes, IP[i], k - ) - MP[i] = top_dists - IP[i] = top_indexes - else: - MP[i] = top_dists - IP[i] = top_indexes - - return MP, IP - - -def _stomp( - X, - T, - XdotT, - L, - mask, - k, - threshold, - exclusion_size, - inverse_distance, -): - n_queries = T.shape[1] - L + 1 - MP = np.empty(n_queries, dtype=object) - IP = np.empty(n_queries, dtype=object) - for i_x in range(len(X)): - for i in range(n_queries): - Q = T[:, i : i + L] - dist_profiles = _squared_dist_profile_one_series(XdotT[i_x], X[i_x], Q) - dist_profiles[~mask[i_x]] = np.inf - if i + 1 < n_queries: - XdotT[i_x] = _update_dot_products_one_series( - X[i_x], T, XdotT[i_x], L, i + 1 - ) - - mask[i_x] = numba_roll_1D_no_warparound(mask[i_x], 1, True) - ( - top_dists, - top_indexes, - ) = extract_top_k_and_threshold_from_distance_profiles_one_series( - dist_profiles, - i_x, - k=k, - threshold=threshold, - exclusion_size=exclusion_size, - inverse_distance=inverse_distance, - ) - if i_x > 0: - top_dists, top_indexes = _sort_out_tops( - top_dists, MP[i], top_indexes, IP[i], k - ) - MP[i] = top_dists - IP[i] = top_indexes - else: - MP[i] = top_dists - IP[i] = top_indexes - - return MP, IP - - -def _sort_out_tops(top_dists, prev_top_dists, top_indexes, prev_to_indexes, k): - """ - Sort and combine top distance results from previous and current computations. - - Parameters - ---------- - top_dists : np.ndarray - Array of distances from the current computation. Shape should be (n,). - prev_top_dists : np.ndarray - Array of distances from previous computations. Shape should be (n,). - top_indexes : np.ndarray - Array of indexes corresponding to the top distances from current computation. - Shape should be (n,). - prev_to_indexes : np.ndarray - Array of indexes corresponding to the top distances from previous computations. - Shape should be (n,). - k : int, default=1 - The number of best matches to return during predict for each subsequence. - - Returns - ------- - tuple - A tuple containing two elements: - - A 1D numpy array of sorted distances, of length min(k, - total number of distances). - - A 1D numpy array of indexes corresponding to the sorted distances, - of length min(k, total number of distances). - """ - all_dists = np.concatenate((prev_top_dists, top_dists)) - all_indexes = np.concatenate((prev_to_indexes, top_indexes)) - if k == np.inf: - return all_dists, all_indexes - else: - idx = np.argsort(all_dists)[:k] - return all_dists[idx], all_indexes[idx] - - -@njit(cache=True, fastmath=True) -def _update_dot_products_one_series( - X, - T, - XT_products, - L, - i_query, -): - """ - Update dot products of the i-th query of size L in T from the dot products of i-1. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - Input time series on which the sliding dot product is computed. - T: np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - The length of the subsequences considered during the search. This parameter - cannot be larger than n_timepoints and series_length. - i_query : int - Query starting index in T. - - Returns - ------- - XT_products : np.ndarray of shape (n_cases, n_channels, n_timepoints - L + 1) - Sliding dot product between the i-th subsequence of size L in T and X. - - """ - n_channels = T.shape[0] - Q = T[:, i_query : i_query + L] - n_candidates = X.shape[1] - L + 1 - - for i_ft in range(n_channels): - # first element of all 0 to n-1 candidates * first element of previous query - _a1 = X[i_ft, : n_candidates - 1] * T[i_ft, i_query - 1] - # last element of all 1 to n candidates * last element of current query - _a2 = X[i_ft, L : L - 1 + n_candidates] * T[i_ft, i_query + L - 1] - - XT_products[i_ft, 1:] = XT_products[i_ft, :-1] - _a1 + _a2 - - # Compute first dot product - XT_products[i_ft, 0] = np.sum(Q[i_ft] * X[i_ft, :L]) - return XT_products diff --git a/aeon/similarity_search/matrix_profiles/tests/__init__.py b/aeon/similarity_search/matrix_profiles/tests/__init__.py deleted file mode 100644 index 3feb8d4ca5..0000000000 --- a/aeon/similarity_search/matrix_profiles/tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Tests for series methods.""" diff --git a/aeon/similarity_search/matrix_profiles/tests/test_stomp.py b/aeon/similarity_search/matrix_profiles/tests/test_stomp.py deleted file mode 100644 index ffcf7d0b6a..0000000000 --- a/aeon/similarity_search/matrix_profiles/tests/test_stomp.py +++ /dev/null @@ -1,205 +0,0 @@ -"""Tests for stomp algorithm.""" - -__maintainer__ = ["baraline"] - -import numpy as np -import pytest -from numba.typed import List -from numpy.testing import assert_almost_equal, assert_array_almost_equal, assert_equal - -from aeon.distances import get_distance_function -from aeon.similarity_search._commons import get_ith_products -from aeon.similarity_search.matrix_profiles.stomp import ( - _update_dot_products_one_series, - stomp_normalised_squared_matrix_profile, - stomp_squared_matrix_profile, -) -from aeon.utils.numba.general import sliding_mean_std_one_series - -DATATYPES = ["int64", "float64"] -K_VALUES = [1] - - -def test__update_dot_products_one_series(): - """Test the _update_dot_product function.""" - X = np.random.rand(1, 50) - T = np.random.rand(1, 25) - L = 10 - current_product = get_ith_products(X, T, L, 0) - for i_query in range(1, T.shape[1] - L + 1): - new_product = get_ith_products( - X, - T, - L, - i_query, - ) - current_product = _update_dot_products_one_series( - X, - T, - current_product, - L, - i_query, - ) - assert_array_almost_equal(new_product, current_product) - - -@pytest.mark.parametrize("dtype", DATATYPES) -@pytest.mark.parametrize("k", K_VALUES) -def test_stomp_squared_matrix_profile(dtype, k): - """Test stomp series search.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - - S = np.asarray([[3, 4, 5, 4, 3, 4, 5, 3, 2, 4, 5]], dtype=dtype) - L = 3 - mask = np.ones((X.shape[0], X.shape[2] - L + 1), dtype=bool) - distance = get_distance_function("squared") - mp, ip = stomp_squared_matrix_profile(X, S, L, mask, k=k) - for i in range(S.shape[-1] - L + 1): - q = S[:, i : i + L] - - expected = np.array( - [ - [distance(q, X[j, :, _i : _i + L]) for _i in range(X.shape[-1] - L + 1)] - for j in range(X.shape[0]) - ] - ) - id_bests = np.vstack( - np.unravel_index( - np.argsort(expected.ravel(), kind="stable"), expected.shape - ) - ).T - - for j in range(k): - assert_almost_equal(mp[i][j], expected[id_bests[j, 0], id_bests[j, 1]]) - assert_equal(ip[i][j], id_bests[j]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -@pytest.mark.parametrize("k", K_VALUES) -def test_stomp_normalised_squared_matrix_profile(dtype, k): - """Test stomp series search.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - - S = np.asarray([[3, 4, 5, 4, 3, 4, 5, 3, 2, 4, 5]], dtype=dtype) - L = 3 - mask = np.ones((X.shape[0], X.shape[2] - L + 1), dtype=bool) - distance = get_distance_function("squared") - X_means = [] - X_stds = [] - - for i in range(len(X)): - _mean, _std = sliding_mean_std_one_series(X[i], L, 1) - - X_stds.append(_std) - X_means.append(_mean) - X_means = np.asarray(X_means) - X_stds = np.asarray(X_stds) - - S_means, S_stds = sliding_mean_std_one_series(S, L, 1) - - mp, ip = stomp_normalised_squared_matrix_profile( - X, S, L, X_means, X_stds, S_means, S_stds, mask, k=k - ) - - for i in range(S.shape[-1] - L + 1): - q = (S[:, i : i + L] - S_means[:, i]) / S_stds[:, i] - - expected = np.array( - [ - [ - distance( - q, - (X[j, :, _i : _i + L] - X_means[j, :, _i]) / X_stds[j, :, _i], - ) - for _i in range(X.shape[-1] - L + 1) - ] - for j in range(X.shape[0]) - ] - ) - id_bests = np.vstack( - np.unravel_index(np.argsort(expected.ravel()), expected.shape) - ).T - - for j in range(k): - assert_almost_equal(mp[i][j], expected[id_bests[j, 0], id_bests[j, 1]]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_stomp_squared_matrix_profile_unequal_length(dtype): - """Test stomp with unequal length.""" - X = List( - [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6]], dtype=dtype), - ] - ) - L = 3 - mask = List( - [ - np.ones(X[0].shape[1] - L + 1, dtype=bool), - np.ones(X[1].shape[1] - L + 1, dtype=bool), - ] - ) - S = np.asarray([[3, 4, 5, 4, 3, 4, 5, 3, 2, 4, 5]], dtype=dtype) - - distance = get_distance_function("squared") - mp, ip = stomp_squared_matrix_profile(X, S, L, mask) - - for i in range(S.shape[-1] - L + 1): - q = S[:, i : i + L] - - expected = [ - [ - distance(q, X[j][:, _i : _i + q.shape[-1]]) - for _i in range(X[j].shape[-1] - q.shape[-1] + 1) - ] - for j in range(len(X)) - ] - assert_almost_equal(mp[i][0], np.concatenate(expected).min()) - - -@pytest.mark.parametrize("dtype", DATATYPES) -@pytest.mark.parametrize("k", K_VALUES) -def test_stomp_squared_matrix_profile_inverse(dtype, k): - """Test stomp series search for inverse distance.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - S = np.asarray([[3, 4, 5, 4, 3, 4, 5, 3, 2, 4, 5]], dtype=dtype) - L = 3 - mask = np.ones((X.shape[0], X.shape[2] - L + 1), dtype=bool) - distance = get_distance_function("squared") - mp, ip = stomp_squared_matrix_profile( - X, - S, - L, - mask, - k=k, - inverse_distance=True, - ) - - for i in range(S.shape[-1] - L + 1): - q = S[:, i : i + L] - - expected = np.array( - [ - [ - distance(q, X[j, :, _i : _i + q.shape[-1]]) - for _i in range(X.shape[-1] - q.shape[-1] + 1) - ] - for j in range(X.shape[0]) - ] - ) - expected += 1e-8 - expected = 1 / expected - id_bests = np.vstack( - np.unravel_index(np.argsort(expected.ravel()), expected.shape) - ).T - - for j in range(k): - assert_almost_equal(mp[i][j], expected[id_bests[j, 0], id_bests[j, 1]]) - assert_equal(ip[i][j], id_bests[j]) diff --git a/aeon/similarity_search/query_search.py b/aeon/similarity_search/query_search.py deleted file mode 100644 index 393439148d..0000000000 --- a/aeon/similarity_search/query_search.py +++ /dev/null @@ -1,428 +0,0 @@ -"""Base class for query search.""" - -__maintainer__ = ["baraline"] - -from typing import Optional, final - -import numpy as np -from numba import get_num_threads, set_num_threads - -from aeon.similarity_search._commons import ( - extract_top_k_and_threshold_from_distance_profiles, -) -from aeon.similarity_search.base import BaseSimilaritySearch -from aeon.similarity_search.distance_profiles.euclidean_distance_profile import ( - euclidean_distance_profile, - normalised_euclidean_distance_profile, -) -from aeon.similarity_search.distance_profiles.squared_distance_profile import ( - normalised_squared_distance_profile, - squared_distance_profile, -) - - -class QuerySearch(BaseSimilaritySearch): - """ - Query search estimator. - - The query search estimator will return a set of matches of a query in a search space - , which is defined by a time series dataset given during fit. Depending on the `k` - and/or `threshold` parameters, which condition what is considered a valid match - during the search, the number of matches will vary. If `k` is used, at most `k` - matches (the `k` best) will be returned, if `threshold` is used and `k` is set to - `np.inf`, all the candidates which distance to the query is inferior or equal to - `threshold` will be returned. If both are used, the `k` best matches to the query - with distance inferior to `threshold` will be returned. - - - Parameters - ---------- - k : int, default=1 - The number of best matches to return during predict for a given query. - threshold : float, default=np.inf - The number of best matches to return during predict for a given query. - distance : str, default="euclidean" - Name of the distance function to use. A list of valid strings can be found in - the documentation for :func:`aeon.distances.get_distance_function`. - If a callable is passed it must either be a python function or numba function - with nopython=True, that takes two 1d numpy arrays as input and returns a float. - distance_args : dict, default=None - Optional keyword arguments for the distance function. - normalise : bool, default=False - Whether the distance function should be z-normalised. - speed_up : str, default='fastest' - Which speed up technique to use with for the selected distance - function. By default, the fastest algorithm is used. A list of available - algorithm for each distance can be obtained by calling the - `get_speedup_function_names` function. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - n_jobs : int, default=1 - Number of parallel jobs to use. - store_distance_profiles : bool, default=False. - Whether to store the computed distance profiles in the attribute - "distance_profiles_" after calling the predict method. It will store the raw - distance profile, meaning without potential inversion or thresholding applied. - - Attributes - ---------- - X_ : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input time series stored during the fit method. This is the - database we search in when given a query. - distance_profile_function : function - The function used to compute the distance profile. This is determined - during the fit method based on the distance and normalise - parameters. - - Notes - ----- - For now, the multivariate case is only treated as independent. - Distances are computed for each channel independently and then - summed together. - """ - - def __init__( - self, - k: int = 1, - threshold: float = np.inf, - distance: str = "euclidean", - distance_args: Optional[dict] = None, - inverse_distance: bool = False, - normalise: bool = False, - speed_up: str = "fastest", - n_jobs: int = 1, - store_distance_profiles: bool = False, - ): - self.k = k - self.threshold = threshold - self.store_distance_profiles = store_distance_profiles - self._previous_query_length = -1 - self.axis = 1 - - super().__init__( - distance=distance, - distance_args=distance_args, - inverse_distance=inverse_distance, - normalise=normalise, - speed_up=speed_up, - n_jobs=n_jobs, - ) - - def _fit(self, X: np.ndarray, y=None): - """ - Check input format and store it to be used as search space during predict. - - Parameters - ---------- - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - Input array to used as database for the similarity search - y : optional - Not used. - - Raises - ------ - TypeError - If the input X array is not 3D raise an error. - - Returns - ------- - self - - """ - self.X_ = X - self.distance_profile_function_ = self._get_distance_profile_function() - return self - - @final - def predict( - self, - X: np.ndarray, - axis=1, - X_index=None, - exclusion_factor=2.0, - apply_exclusion_to_result=False, - ) -> np.ndarray: - """ - Predict method : Check the shape of X and call _predict to perform the search. - - If the distance profile function is normalised, it stores the mean and stds - from X and X_, with X_ the training data. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, query_length) - Input query used for similarity search. - axis : int - The time point axis of the input series if it is 2D. If ``axis==0``, it is - assumed each column is a time series and each row is a time point. i.e. the - shape of the data is ``(n_timepoints,n_channels)``. ``axis==1`` indicates - the time series are in rows, i.e. the shape of the data is - ``(n_channels,n_timepoints)``. - X_index : Iterable - An Interable (tuple, list, array) of length two used to specify the index of - the query X if it was extracted from the input data X given during the fit - method. Given the tuple (id_sample, id_timestamp), the similarity search - will define an exclusion zone around the X_index in order to avoid matching - X with itself. If None, it is considered that the query is not extracted - from X_. - exclusion_factor : float, default=2. - The factor to apply to the query length to define the exclusion zone. The - exclusion zone is define from - :math:`id_timestamp - query_length//exclusion_factor` to - :math:`id_timestamp + query_length//exclusion_factor`. This also applies to - the matching conditions defined by child classes. For example, with - TopKSimilaritySearch, the k best matches are also subject to the exclusion - zone, but with :math:`id_timestamp` the index of one of the k matches. - apply_exclusion_to_result : bool, default=False - Wheter to apply the exclusion factor to the output of the similarity search. - This means that two matches of the query from the same sample must be at - least spaced by +/- :math:`query_length//exclusion_factor`. - This can avoid pathological matching where, for example if we extract the - best two matches, there is a high chance that if the best match is located - at :math:`id_timestamp`, the second best match will be located at - :math:`id_timestamp` +/- 1, as they both share all their values except one. - - Raises - ------ - TypeError - If the input X array is not 2D raise an error. - ValueError - If the length of the query is greater - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(n_matches)``, contains the distance between - the query and its best matches in X_. The second array, of shape - ``(n_matches, 2)``, contains the indexes of these matches as - ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - self._check_is_fitted() - prev_threads = get_num_threads() - set_num_threads(self._n_jobs) - - query_dim, query_length = self._check_query_format(X, axis) - - mask = self._init_X_index_mask( - X_index, - query_length, - exclusion_factor=exclusion_factor, - ) - - if self.normalise: - self.query_means_ = np.mean(X, axis=-1) - self.query_stds_ = np.std(X, axis=-1) - if self._previous_query_length != query_length: - self._store_mean_std_from_inputs(query_length) - - if apply_exclusion_to_result: - exclusion_size = query_length // exclusion_factor - else: - exclusion_size = None - - self._previous_query_length = query_length - - X_preds = self._predict( - self._call_distance_profile(X, mask), - exclusion_size=exclusion_size, - ) - set_num_threads(prev_threads) - return X_preds - - def _predict( - self, distance_profiles: np.ndarray, exclusion_size: Optional[int] = None - ) -> np.ndarray: - """ - Private predict method for QuerySearch. - - It takes the distance profiles and apply the `k` and `threshold` conditions to - return the set of best matches. - - Parameters - ---------- - distance_profiles : np.ndarray, 2D array of shape (n_cases, n_timepoints - query_length + 1) # noqa: E501 - Precomputed distance profile. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestamp - exclusion_size` and - :math:`id_timestamp + exclusion_size` which cannot be returned - as best match if :math:`id_timestamp` was already selected. By default, - the value None means that this is not used. - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(n_matches)``, contains the distance between - the query and its best matches in X_. The second array, of shape - ``(n_matches, 2)``, contains the indexes of these matches as - ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - - """ - if self.store_distance_profiles: - self.distance_profiles_ = distance_profiles - # Define id sample and timestamp to not "loose" them due to concatenation - return extract_top_k_and_threshold_from_distance_profiles( - distance_profiles, - k=self.k, - threshold=self.threshold, - exclusion_size=exclusion_size, - inverse_distance=self.inverse_distance, - ) - - def _check_query_format(self, X, axis): - if axis not in [0, 1]: - raise ValueError("The axis argument is expected to be either 1 or 0") - if self.axis != axis: - X = X.T - if not isinstance(X, np.ndarray) or X.ndim != 2: - raise TypeError( - "Error, only supports 2D numpy for now. If the query X is univariate " - "do X = X[np.newaxis, :]." - ) - - query_dim, query_length = X.shape - if query_length >= self.min_timepoints_: - raise ValueError( - "The length of the query should be inferior or equal to the length of " - "data (X_) provided during fit, but got {} for X and {} for X_".format( - query_length, self.min_timepoints_ - ) - ) - - if query_dim != self.n_channels_: - raise ValueError( - "The number of feature should be the same for the query X and the data " - "(X_) provided during fit, but got {} for X and {} for X_".format( - query_dim, self.n_channels_ - ) - ) - return query_dim, query_length - - def _get_distance_profile_function(self): - """ - Given distance and speed_up parameters, return the distance profile function. - - Raises - ------ - ValueError - If the distance parameter given at initialization is not a string nor a - numba function or a callable, or if the speedup parameter is unknow or - unsupported, raisea ValueError. - - Returns - ------- - function - The distance profile function matching the distance argument. - - """ - if isinstance(self.distance, str): - distance_dict = _QUERY_SEARCH_SPEED_UP_DICT.get(self.distance) - if distance_dict is None: - raise NotImplementedError( - f"No distance profile have been implemented for {self.distance}." - ) - else: - speed_up_profile = distance_dict.get(self.normalise).get(self.speed_up) - - if speed_up_profile is None: - raise ValueError( - f"Unknown or unsupported speed up {self.speed_up} for " - f"{self.distance} distance function with" - ) - self.speed_up_ = self.speed_up - return speed_up_profile - else: - raise ValueError( - f"Expected distance argument to be str but got {type(self.distance)}" - ) - - def _call_distance_profile(self, X: np.ndarray, mask: np.ndarray) -> np.ndarray: - """ - Obtain the distance profile function and call it with the query and the mask. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, query_length) - Input query used for similarity search. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean array which indicates the candidates that should be evaluated in - the similarity search. - - Returns - ------- - distance_profiles : np.ndarray, 2D array of shape (n_cases, n_timepoints - query_length + 1) # noqa: E501 - The distance profiles between the input time series and the query. - - """ - if self.normalise: - distance_profiles = self.distance_profile_function_( - self.X_, - X, - mask, - self.X_means_, - self.X_stds_, - self.query_means_, - self.query_stds_, - ) - else: - distance_profiles = self.distance_profile_function_(self.X_, X, mask) - - return distance_profiles - - @classmethod - def get_speedup_function_names(self) -> dict: - """ - Get available speedup for query search in aeon. - - The returned structure is a dictionnary that contains the names of all - avaialble speedups for normalised and non-normalised distance functions. - - Returns - ------- - dict - The available speedups name that can be used as parameters in - similarity search classes. - - """ - speedups = {} - for dist_name in _QUERY_SEARCH_SPEED_UP_DICT.keys(): - for normalise in _QUERY_SEARCH_SPEED_UP_DICT[dist_name].keys(): - speedups_names = list( - _QUERY_SEARCH_SPEED_UP_DICT[dist_name][normalise].keys() - ) - if normalise: - speedups.update({f"normalised {dist_name}": speedups_names}) - else: - speedups.update({f"{dist_name}": speedups_names}) - return speedups - - -_QUERY_SEARCH_SPEED_UP_DICT = { - "euclidean": { - True: { - "fastest": normalised_euclidean_distance_profile, - "Mueen": normalised_euclidean_distance_profile, - }, - False: { - "fastest": euclidean_distance_profile, - "Mueen": euclidean_distance_profile, - }, - }, - "squared": { - True: { - "fastest": normalised_squared_distance_profile, - "Mueen": normalised_squared_distance_profile, - }, - False: { - "fastest": squared_distance_profile, - "Mueen": squared_distance_profile, - }, - }, -} diff --git a/aeon/similarity_search/series/__init__.py b/aeon/similarity_search/series/__init__.py new file mode 100644 index 0000000000..d1b5494c13 --- /dev/null +++ b/aeon/similarity_search/series/__init__.py @@ -0,0 +1,7 @@ +"""Similarity search for series.""" + +__all__ = ["BaseSeriesSimilaritySearch", "MassSNN", "StompMotif"] + +from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch +from aeon.similarity_search.series.motifs._stomp import StompMotif +from aeon.similarity_search.series.neighbors._mass import MassSNN diff --git a/aeon/similarity_search/series/_base.py b/aeon/similarity_search/series/_base.py new file mode 100644 index 0000000000..6ee1f27270 --- /dev/null +++ b/aeon/similarity_search/series/_base.py @@ -0,0 +1,112 @@ +"""Base similiarity search for series.""" + +from abc import abstractmethod +from typing import Union, final + +import numpy as np + +from aeon.base import BaseSeriesEstimator +from aeon.similarity_search._base import BaseSimilaritySearch +from aeon.utils.validation import check_n_jobs + + +class BaseSeriesSimilaritySearch(BaseSeriesEstimator, BaseSimilaritySearch): + """Base class for similarity search applications on single series.""" + + _tags = { + "input_data_type": "Series", + "capability:multivariate": True, + } + + @abstractmethod + def __init__(self, axis=1): + super().__init__(axis=axis) + + @final + def fit( + self, + X: np.ndarray, + y=None, + ): + """ + Fit method: data preprocessing and storage. + + Parameters + ---------- + X : np.ndarray, 2D array of shape (n_channels, n_timepoints) + Input series to be used for the similarity search operations. + y : optional + Not used. + + Raises + ------ + TypeError + If the input X array is not 2D raise an error. + + Returns + ------- + self + """ + self.reset() + self._n_jobs = check_n_jobs(self.n_jobs) + X = self._preprocess_series(X, self.axis, True) + # Store minimum number of n_timepoints for unequal length collections + self.n_channels_ = X.shape[0] + self.n_timepoints_ = X.shape[1] + self.X_ = X + self._fit(X, y=y) + self.is_fitted = True + return self + + @abstractmethod + def _fit( + self, + X: np.ndarray, + y=None, + ): ... + + def _pre_predict( + self, + X: Union[np.ndarray, None] = None, + length: int = None, + ): + """ + Predict method. + + Parameters + ---------- + X : Union[np.ndarray, None], optional + Optional data to use for predict.. The default is None. + length: int, optional + If not None, the number of timepoint of X should be equal to length. + + """ + self._check_is_fitted() + if X is not None: + X = self._preprocess_series(X, self.axis, False) + self._check_predict_series_format(X, length=length) + return X + + def _check_X_index(self, X_index: int): + """ + Check wheter a X_index parameter is correctly formated and is admissible. + + Parameters + ---------- + X_index : int + Index of a timestamp in X_. + + """ + if X_index is not None: + if not isinstance(X_index, int): + raise TypeError("Expected an integer for X_index but got {X_index}") + + max_timepoints = self.n_timepoints_ + if hasattr(self, "length"): + max_timepoints -= self.length + if X_index >= max_timepoints or X_index < 0: + raise ValueError( + "The value of X_index cannot exced the number " + "of timepoint in series given during fit. Expected a value " + f"between [0, {max_timepoints - 1}] but got {X_index}" + ) diff --git a/aeon/similarity_search/series/_commons.py b/aeon/similarity_search/series/_commons.py new file mode 100644 index 0000000000..8b309bb6b2 --- /dev/null +++ b/aeon/similarity_search/series/_commons.py @@ -0,0 +1,221 @@ +"""Helper and common function for similarity search series estimators.""" + +__maintainer__ = ["baraline"] + +import numpy as np +from numba import njit +from scipy.signal import convolve + + +def fft_sliding_dot_product(X, q): + """ + Use FFT convolution to calculate the sliding window dot product. + + This function applies the Fast Fourier Transform (FFT) to efficiently compute + the sliding dot product between the input time series `X` and the query `q`. + The dot product is computed for each channel individually. The sliding window + approach ensures that the dot product is calculated for every possible subsequence + of `X` that matches the length of `q` + + Parameters + ---------- + X : array, shape=(n_channels, n_timepoints) + Input time series + q : array, shape=(n_channels, query_length) + Input query + + Returns + ------- + out : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) + Sliding dot product between q and X. + """ + n_channels, n_timepoints = X.shape + query_length = q.shape[1] + out = np.zeros((n_channels, n_timepoints - query_length + 1)) + for i in range(n_channels): + out[i, :] = convolve(np.flipud(q[i, :]), X[i, :], mode="valid").real + return out + + +def get_ith_products(X, T, L, ith): + """ + Compute dot products between X and the i-th subsequence of size L in T. + + Parameters + ---------- + X : array, shape = (n_channels, n_timepoints_X) + Input data. + T : array, shape = (n_channels, n_timepoints_T) + Data containing the query. + L : int + Overall query length. + ith : int + Query starting index in T. + + Returns + ------- + np.ndarray, 2D array of shape (n_channels, n_timepoints_X - L + 1) + Sliding dot product between the i-th subsequence of size L in T and X. + + """ + return fft_sliding_dot_product(X, T[:, ith : ith + L]) + + +@njit(cache=True, fastmath=True) +def _inverse_distance_profile(dist_profile): + return 1 / (dist_profile + 1e-8) + + +@njit(cache=True) +def _extract_top_k_from_dist_profile( + dist_profile, + k, + threshold, + allow_trivial_matches, + exclusion_size, +): + """ + Given a distance profiles, extract the top k lower distances. + + Parameters + ---------- + dist_profile : np.ndarray, shape = (n_timepoints - length + 1) + A distance profile of length ``n_timepoints - length + 1``, with + ``length`` the size of the query used to compute the distance profiles. + k : int + Number of best matches to return + threshold : float + A threshold on the distances of the best matches. To be returned, a candidate + must have a distance bellow this threshold. This can reduce the number of + returned matches to be bellow ``k`` + allow_trivial_matches : bool + Wheter to allow returning matches that are in the same neighborhood. + exclusion_size : int + The size of the exlusion size to apply when ``allow_trivial_matches`` is + False. It is applied on both side of existing matches (+/- their indexes). + + Returns + ------- + top_k_indexes : np.ndarray, shape = (k) + The indexes of the best matches in ``distance_profile``. + top_k_distances : np.ndarray, shape = (k) + The distances of the best matches. + + """ + top_k_indexes = np.zeros(k, dtype=np.int64) - 1 + top_k_distances = np.full(k, np.inf, dtype=np.float64) + ub = np.full(k, np.inf) + lb = np.full(k, -1.0) + # Could be optimized by using argpartition + sorted_indexes = np.argsort(dist_profile) + _current_k = 0 + if not allow_trivial_matches: + _current_j = 0 + # Until we extract k value or explore all the array or until dist is > threshold + while _current_k < k and _current_j < len(sorted_indexes): + # if we didn't insert anything or there is a conflict in lb/ub + if _current_k > 0 and np.any( + (sorted_indexes[_current_j] >= lb[:_current_k]) + & (sorted_indexes[_current_j] <= ub[:_current_k]) + ): + pass + else: + _idx = sorted_indexes[_current_j] + if dist_profile[_idx] <= threshold: + top_k_indexes[_current_k] = _idx + top_k_distances[_current_k] = dist_profile[_idx] + ub[_current_k] = min( + top_k_indexes[_current_k] + exclusion_size, + len(dist_profile), + ) + lb[_current_k] = max(top_k_indexes[_current_k] - exclusion_size, 0) + _current_k += 1 + else: + break + _current_j += 1 + else: + _current_k += min(k, len(dist_profile)) + dist_profile = dist_profile[sorted_indexes[:_current_k]] + dist_profile = dist_profile[dist_profile <= threshold] + _current_k = len(dist_profile) + + top_k_indexes[:_current_k] = sorted_indexes[:_current_k] + top_k_distances[:_current_k] = dist_profile[:_current_k] + + return top_k_indexes[:_current_k], top_k_distances[:_current_k] + + +# Could add aggregation function as parameter instead of just max +def _extract_top_k_motifs(MP, IP, k, allow_trivial_matches, exclusion_size): + criterion = np.zeros(len(MP)) + for i in range(len(MP)): + if len(MP[i]) > 0: + criterion[i] = max(MP[i]) + else: + criterion[i] = np.inf + + idx, _ = _extract_top_k_from_dist_profile( + criterion, k, np.inf, allow_trivial_matches, exclusion_size + ) + return [MP[i] for i in idx], [IP[i] for i in idx] + + +def _extract_top_r_motifs(MP, IP, k, allow_trivial_matches, exclusion_size): + criterion = np.zeros(len(MP)) + for i in range(len(MP)): + criterion[i] = len(MP[i]) + idx, _ = _extract_top_k_from_dist_profile( + _inverse_distance_profile(criterion), + k, + np.inf, + allow_trivial_matches, + exclusion_size, + ) + return [MP[i] for i in idx], [IP[i] for i in idx] + + +@njit(cache=True, fastmath=True) +def _update_dot_products( + X, + T, + XT_products, + L, + i_query, +): + """ + Update dot products of the i-th query of size L in T from the dot products of i-1. + + Parameters + ---------- + X: np.ndarray, 2D array of shape (n_channels, n_timepoints) + Input time series on which the sliding dot product is computed. + T: np.ndarray, 2D array of shape (n_channels, series_length) + The series used for similarity search. Note that series_length can be equal, + superior or inferior to n_timepoints, it doesn't matter. + L : int + The length of the subsequences considered during the search. This parameter + cannot be larger than n_timepoints and series_length. + i_query : int + Query starting index in T. + + Returns + ------- + XT_products : np.ndarray of shape (n_channels, n_timepoints - L + 1) + Sliding dot product between the i-th subsequence of size L in T and X. + + """ + n_channels = T.shape[0] + Q = T[:, i_query : i_query + L] + n_candidates = X.shape[1] - L + 1 + + for i_ft in range(n_channels): + # first element of all 0 to n-1 candidates * first element of previous query + _a1 = X[i_ft, : n_candidates - 1] * T[i_ft, i_query - 1] + # last element of all 1 to n candidates * last element of current query + _a2 = X[i_ft, L : L - 1 + n_candidates] * T[i_ft, i_query + L - 1] + + XT_products[i_ft, 1:] = XT_products[i_ft, :-1] - _a1 + _a2 + + # Compute first dot product + XT_products[i_ft, 0] = np.sum(Q[i_ft] * X[i_ft, :L]) + return XT_products diff --git a/aeon/similarity_search/series/motifs/__init__.py b/aeon/similarity_search/series/motifs/__init__.py new file mode 100644 index 0000000000..d4853a68fe --- /dev/null +++ b/aeon/similarity_search/series/motifs/__init__.py @@ -0,0 +1,7 @@ +"""Subsequence Neighbor search for series.""" + +__all__ = [ + "StompMotif", +] + +from aeon.similarity_search.series.motifs._stomp import StompMotif diff --git a/aeon/similarity_search/series/motifs/_stomp.py b/aeon/similarity_search/series/motifs/_stomp.py new file mode 100644 index 0000000000..43bc76f049 --- /dev/null +++ b/aeon/similarity_search/series/motifs/_stomp.py @@ -0,0 +1,495 @@ +"""Implementation of STOMP with squared euclidean distance.""" + +__maintainer__ = ["baraline"] +__all__ = ["StompMotif"] + +from typing import Optional + +import numpy as np +from numba import njit +from numba.typed import List + +from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch +from aeon.similarity_search.series._commons import ( + _extract_top_k_from_dist_profile, + _extract_top_k_motifs, + _extract_top_r_motifs, + _inverse_distance_profile, + _update_dot_products, + get_ith_products, +) +from aeon.similarity_search.series.neighbors._mass import ( + _normalized_squared_distance_profile, + _squared_distance_profile, +) +from aeon.utils.numba.general import sliding_mean_std_one_series + + +class StompMotif(BaseSeriesSimilaritySearch): + """ + Estimator to extract top k motifs using STOMP, descibed in [1]_. + + This estimators allows to perform multiple type of motif search operations by using + different parameterization. We base oursleves on Figure 3 of [2]_ to establish the + following list, we do not yet support "Learning" and "Valmod" motifs : + + - for "Pair Motifs" : This is the default configuration + + - for "k-Motiflets" : { + "motif_size": k, + } + + - for "k-motifs" (naming is confusing here, it is a range based motif): { + "motif_size":np.inf, + "dist_threshold":r, + "motif_extraction_method":"r_motifs" + } + + Parameters + ---------- + length : int + The length of the motifs to extract. This is the length of the subsequence + that will be used in the computations. + normalize : bool + Wheter the computations between subsequences should use a z-normalied distance. + + Notes + ----- + This estimator only provide exact computation method, faster approximate methods + also exists in the litterature. We use a squared euclidean distance instead of the + euclidean distance, if you want euclidean distance results, you should square root + the obtained results. + + References + ---------- + .. [1] Yan Zhu, Zachary Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael + Yeh, Gareth Funning, Abdullah Mueen, Philip Brisk, and Eamonn Keogh. 2016. + Matrix profile II: Exploiting a novel algorithm and GPUs to break the one hundred + million barrier for time series motifs and joins. In 2016 IEEE 16th international + conference on data mining (ICDM). IEEE, 739–748. + .. [2] Patrick Schäfer and Ulf Leser. 2022. Motiflets: Simple and Accurate Detection + of Motifs in Time Series. Proc. VLDB Endow. 16, 4 (December 2022), 725–737. + https://doi.org/10.14778/3574245.3574257 + """ + + def __init__( + self, + length: int, + normalize: Optional[bool] = False, + ): + self.length = length + self.normalize = normalize + super().__init__() + + def _fit( + self, + X: np.ndarray, + y=None, + ): + if self.normalize: + self.X_means_, self.X_stds_ = sliding_mean_std_one_series(X, self.length, 1) + return self + + def predict( + self, + X: np.ndarray = None, + k: Optional[int] = 1, + motif_size: Optional[int] = 1, + dist_threshold: Optional[float] = np.inf, + allow_trivial_matches: Optional[bool] = False, + exclusion_factor: Optional[float] = 2, + inverse_distance: Optional[bool] = False, + motif_extraction_method: Optional[str] = "k_motifs", + ): + """ + Exctract the motifs of X_ relative to a series X using STOMP matrix prfoile. + + To compute self-motifs, X is set to None. + + Parameters + ---------- + X : np.ndarray, shape=(n_channels, n_timepoint) + Series to use to compute the matrix profile against X_. If None, will + compute the self matrix profile of X_. Motifs will then be extracted from + the matrix profile. + k : int + The number of motifs to return. The default is 1, meaning we return only + the motif set with the minimal sum of distances to its query. + motif_size : int + The number of subsequences in a motif. Default is 1, meaning we extract + motif pairs (the query and its best match) + dist_threshold : float + The maximum allowed distance of a candidate subsequence of X to a query + subsequence from X_ for the candidate to be considered as a neighbor. + allow_trivial_matches: bool, optional + Wheter a neighbors of a match to a query can be also considered as matches + (True), or if an exclusion zone is applied around each match to avoid + trivial matches with their direct neighbors (False). + exclusion_factor : float, default=1. + A factor of the query length used to define the exclusion zone when + ``allow_trivial_matches`` is set to False. For a given timestamp, + the exclusion zone starts from + :math:`id_timestamp - length//exclusion_factor` and end at + :math:`id_timestamp + length//exclusion_factor`. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, + the farther neighbors will be returned instead of the closest ones. + motif_extraction_method : str + A string indicating the methodology to use to extract the top motifs. + Available methods are "r_motifs" and "k_motifs". "r_motifs" means we rank + motif set by their cardinality, with higher is better. "k_motifs" means + we rank motif set by their maximum distance to their query + + Returns + ------- + np.ndarray, shape = (k, motif_size) + The indexes of the best matches in ``distance_profile``. + np.ndarray, shape = (k, motif_size) + The distances of the best matches. + + """ + X = self._pre_predict(X) + if motif_extraction_method not in ["k_motifs", "r_motifs"]: + raise ValueError( + "Expected motif_extraction_method to be either 'k_motifs' or 'r_motifs'" + f"but got {motif_extraction_method}" + ) + + MP, IP = self.compute_matrix_profile( + X, + motif_size=motif_size, + dist_threshold=dist_threshold, + allow_trivial_matches=allow_trivial_matches, + exclusion_factor=exclusion_factor, + inverse_distance=inverse_distance, + ) + if motif_extraction_method == "k_motifs": + return _extract_top_k_motifs( + MP, IP, k, allow_trivial_matches, self.length // exclusion_factor + ) + elif motif_extraction_method == "r_motifs": + return _extract_top_r_motifs( + MP, IP, k, allow_trivial_matches, self.length // exclusion_factor + ) + + def compute_matrix_profile( + self, + X: np.ndarray = None, + motif_size: Optional[int] = 1, + dist_threshold: Optional[float] = np.inf, + allow_trivial_matches: Optional[bool] = False, + exclusion_factor: Optional[float] = 2, + inverse_distance: Optional[bool] = False, + ): + """ + Compute matrix profile. + + The matrix profile is computed on the series given in fit (X_). If X is + not given, computes the self matrix profile of X_. Otherwise, compute the matrix + profile of X_ relative to X. + + Parameters + ---------- + X : np.ndarray, shape = (n_channels, n_timepoints) + A 2D array time series on against which the matrix profile of X_ will be + computed. + motif_size : int + The number of subsequences in a motif. Default is 1, meaning we extract + motif pairs (the query and its best match). + dist_threshold : float + The maximum allowed distance of a candidate subsequence of X to a query + subsequence from X_ for the candidate to be considered as a neighbor. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, + the worst matches to the query will be returned instead of the best ones. + exclusion_factor : float, default=1. + A factor of the query length used to define the exclusion zone when + ``allow_trivial_matches`` is set to False. For a given timestamp, + the exclusion zone starts from + :math:`id_timestamp - length//exclusion_factor` and end at + :math:`id_timestamp + length//exclusion_factor`. + + Returns + ------- + MP : TypedList of np.ndarray (n_timepoints - L + 1) + Matrix profile distances for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + IP : TypedList of np.ndarray (n_timepoints - L + 1) + Indexes of the top matches for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + """ + if X is None: + is_self_mp = True + X = self.X_ + if self.normalize: + X_means, X_stds = self.X_means_, self.X_stds_ + else: + is_self_mp = False + if self.normalize: + X_means, X_stds = sliding_mean_std_one_series(X, self.length, 1) + X_dotX = get_ith_products(X, self.X_, self.length, 0) + exclusion_size = self.length // exclusion_factor + + if motif_size == np.inf: + # convert infs here as numba seem to not be able to do == np.inf ? + motif_size = X.shape[1] - self.length + 1 + + if self.normalize: + MP, IP = _stomp_normalized( + self.X_, + X, + X_dotX, + self.X_means_, + self.X_stds_, + X_means, + X_stds, + self.length, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + is_self_mp, + ) + else: + MP, IP = _stomp( + self.X_, + X, + X_dotX, + self.length, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + is_self_mp, + ) + return MP, IP + + @classmethod + def _get_test_params(cls, parameter_set: str = "default"): + """Return testing parameter settings for the estimator. + + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return, for use in tests. If no + special parameters are defined for a value, will return `"default"` set. + There are currently no reserved values for transformers. + + Returns + ------- + params : dict or list of dict, default = {} + Parameters to create testing instances of the class + Each dict are parameters to construct an "interesting" test instance, i.e., + `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + """ + if parameter_set == "default": + params = {"length": 3} + else: + raise NotImplementedError( + f"The parameter set {parameter_set} is not yet implemented" + ) + return params + + +@njit(cache=True, fastmath=True) +def _stomp_normalized( + X_A, + X_B, + AdotB, + X_A_means, + X_A_stds, + X_B_means, + X_B_stds, + L, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + is_self_mp, +): + """ + Compute the Matrix Profile using the STOMP algorithm with normalized distances. + + X_A : np.ndarray, 2D array of shape (n_channels, n_timepoints) + The series from which the queries will be extracted. + X_B : np.ndarray, 2D array of shape (n_channels, series_length) + The time series on which the distance profile of each query will be computed. + AdotB : np.ndarray, 2D array of shape (n_channels, series_length - L + 1) + Precomputed dot products between the first query of size L of X_A and X_B. + X_A_means : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) + Means of each subsequences of X_A of size L. + X_A_stds : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) + Stds of each subsequences of X of size L. + X_B_means : np.ndarray, 2D array of shape (n_channels, series_length - L + 1) + Means of each subsequences of X_B of size L. + X_B_stds : np.ndarray, 2D array of shape (n_channels, series_length - L + 1) + Stds of each subsequences of X_B of size L. + L : int + Length of the subsequences used for the distance computation. + motif_size : int + The number of subsequences to extract from each distance profile. + dist_threshold : float + The maximum allowed distance of a candidate subsequence of X to a query + subsequence from X_ for the candidate to be considered as a neighbor. + allow_trivial_matches : bool + Wheter the top-k candidates can be neighboring subsequences. + exclusion_size : int + The size of the exclusion zone used to prevent returning as top k candidates + the ones that are close to each other (for example i and i+1). + It is used to define a region between + :math:`id_timestamp - exclusion_size` and + :math:`id_timestamp + exclusion_size` which cannot be returned + as best match if :math:`id_timestamp` was already selected. By default, + the value None means that this is not used. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, the + worst matches to the query will be returned instead of the best ones. + is_self_mp : bool + Wheter X_A == X_B. + + Returns + ------- + MP : TypedList of np.ndarray (n_timepoints - L + 1) + Matrix profile distances for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + IP : TypedList of np.ndarray (n_timepoints - L + 1) + Indexes of the top matches for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + """ + n_queries = X_A.shape[1] - L + 1 + _max_timestamp = X_B.shape[1] - L + 1 + MP = List() + IP = List() + + for i_q in range(n_queries): + # size T.shape[1] - L + 1 + dist_profile = _normalized_squared_distance_profile( + AdotB, X_B_means, X_B_stds, X_A_means[:, i_q], X_A_stds[:, i_q], L + ) + + if i_q + 1 < n_queries: + AdotB = _update_dot_products(X_B, X_A, AdotB, L, i_q + 1) + + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + + if is_self_mp: + ub = min(i_q + exclusion_size, _max_timestamp + 1) + lb = max(0, i_q - exclusion_size) + dist_profile[lb:ub] = np.inf + + _top_indexes, top_dists = _extract_top_k_from_dist_profile( + dist_profile, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + ) + top_indexes = np.zeros((len(_top_indexes), 2), dtype=np.int64) + for i_idx in range(len(_top_indexes)): + top_indexes[i_idx, 0] = i_q + top_indexes[i_idx, 1] = _top_indexes[i_idx] + MP.append(top_dists) + IP.append(top_indexes) + + return MP, IP + + +@njit(cache=True, fastmath=True) +def _stomp( + X_A, + X_B, + AdotB, + L, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + is_self_mp, +): + """ + Compute the Matrix Profile using the STOMP algorithm with non-normalized distances. + + X_A : np.ndarray, 2D array of shape (n_channels, n_timepoints) + The series from which the queries will be extracted. + X_B : np.ndarray, 2D array of shape (n_channels, series_length) + The time series on which the distance profile of each query will be computed. + AdotB : np.ndarray, 2D array of shape (n_channels, series_length - L + 1) + Precomputed dot products between the first query of size L of X_A and X_B. + L : int + Length of the subsequences used for the distance computation. + motif_size : int + The number of subsequences to extract from each distance profile. + dist_threshold : float + The maximum allowed distance of a candidate subsequence of X to a query + subsequence from X_ for the candidate to be considered as a neighbor. + allow_trivial_matches : bool + Wheter the top-k candidates can be neighboring subsequences. + exclusion_size : int + The size of the exclusion zone used to prevent returning as top k candidates + the ones that are close to each other (for example i and i+1). + It is used to define a region between + :math:`id_timestamp - exclusion_size` and + :math:`id_timestamp + exclusion_size` which cannot be returned + as best match if :math:`id_timestamp` was already selected. By default, + the value None means that this is not used. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, the + worst matches to the query will be returned instead of the best ones. + is_self_mp : bool + Wheter X_A == X_B. + + Returns + ------- + MP : TypedList of np.ndarray (n_timepoints - L + 1) + Matrix profile distances for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + IP : TypedList of np.ndarray (n_timepoints - L + 1) + Indexes of the top matches for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + """ + n_queries = X_A.shape[1] - L + 1 + _max_timestamp = X_B.shape[1] - L + 1 + MP = List() + IP = List() + + # For each query of size L in X_A + for i_q in range(n_queries): + Q = X_A[:, i_q : i_q + L] + dist_profile = _squared_distance_profile(AdotB, X_B, Q) + if i_q + 1 < n_queries: + AdotB = _update_dot_products(X_B, X_A, AdotB, L, i_q + 1) + + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + + if is_self_mp: + ub = min(i_q + exclusion_size, _max_timestamp + 1) + lb = max(0, i_q - exclusion_size) + dist_profile[lb:ub] = np.inf + + _top_indexes, top_dists = _extract_top_k_from_dist_profile( + dist_profile, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + ) + top_indexes = np.zeros((len(_top_indexes), 2), dtype=np.int64) + for i_idx in range(len(_top_indexes)): + top_indexes[i_idx, 0] = i_q + top_indexes[i_idx, 1] = _top_indexes[i_idx] + MP.append(top_dists) + IP.append(top_indexes) + + return MP, IP diff --git a/aeon/similarity_search/series/motifs/tests/__init__.py b/aeon/similarity_search/series/motifs/tests/__init__.py new file mode 100644 index 0000000000..d0d8f2c42c --- /dev/null +++ b/aeon/similarity_search/series/motifs/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for series motif search methods.""" diff --git a/aeon/similarity_search/series/motifs/tests/test_stomp.py b/aeon/similarity_search/series/motifs/tests/test_stomp.py new file mode 100644 index 0000000000..67ff930de1 --- /dev/null +++ b/aeon/similarity_search/series/motifs/tests/test_stomp.py @@ -0,0 +1,149 @@ +""" +Tests for stomp algorithm. + +We do not test equality for returned indexes due to the unstable nature of argsort +and the fact that the "kind=stable" parameter is not yet supported in numba. We instead +test that the returned index match the expected distance value. +""" + +__maintainer__ = ["baraline"] + +import numpy as np +import pytest +from numpy.testing import assert_almost_equal, assert_array_almost_equal + +from aeon.similarity_search.series._commons import ( + _extract_top_k_from_dist_profile, + _inverse_distance_profile, + get_ith_products, +) +from aeon.similarity_search.series.motifs._stomp import _stomp, _stomp_normalized +from aeon.similarity_search.series.neighbors._dummy import ( + _naive_squared_distance_profile, +) +from aeon.testing.data_generation import make_example_2d_numpy_series +from aeon.utils.numba.general import ( + get_all_subsequences, + sliding_mean_std_one_series, + z_normalise_series_3d, +) + +MOTIFS_SIZE_VALUES = [1, 3] +THRESHOLD = [np.inf, 0.75] +THRESHOLD_NORM = [np.inf, 4.5] +NN_MATCHES = [True, False] +INVERSE = [True, False] + + +@pytest.mark.parametrize("motif_size", MOTIFS_SIZE_VALUES) +@pytest.mark.parametrize("threshold", THRESHOLD) +@pytest.mark.parametrize("allow_trivial_matches", NN_MATCHES) +@pytest.mark.parametrize("inverse_distance", INVERSE) +def test__stomp(motif_size, threshold, allow_trivial_matches, inverse_distance): + """Test STOMP method.""" + L = 3 + + X_A = make_example_2d_numpy_series( + n_channels=2, + n_timepoints=10, + ) + X_B = make_example_2d_numpy_series(n_channels=2, n_timepoints=10) + AdotB = get_ith_products(X_B, X_A, L, 0) + + exclusion_size = L + # MP : distances to best matches for each query + # IP : Indexes of best matches for each query + MP, IP = _stomp( + X_A, + X_B, + AdotB, + L, + motif_size, + threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + False, + ) + # For each query of size L in T + X_B_subs = get_all_subsequences(X_B, L, 1) + X_A_subs = get_all_subsequences(X_A, L, 1) + for i in range(X_A.shape[1] - L + 1): + dist_profile = _naive_squared_distance_profile(X_B_subs, X_A_subs[i]) + # Check that the top matches extracted have the same value that the + # top matches in the distance profile + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + + top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( + dist_profile, motif_size, threshold, allow_trivial_matches, exclusion_size + ) + # Check that the top matches extracted have the same value that the + # top matches in the distance profile + assert_array_almost_equal(MP[i], top_k_distances) + + # Check that the index in IP correspond to a distance profile point + # with value equal to the corresponding MP point. + for j, index in enumerate(top_k_indexes): + assert_almost_equal(MP[i][j], dist_profile[index]) + + +@pytest.mark.parametrize("motif_size", MOTIFS_SIZE_VALUES) +@pytest.mark.parametrize("threshold", THRESHOLD_NORM) +@pytest.mark.parametrize("allow_trivial_matches", NN_MATCHES) +@pytest.mark.parametrize("inverse_distance", INVERSE) +def test__stomp_normalised( + motif_size, threshold, allow_trivial_matches, inverse_distance +): + """Test STOMP normalised method.""" + L = 3 + + X_A = make_example_2d_numpy_series( + n_channels=2, + n_timepoints=10, + ) + X_B = make_example_2d_numpy_series(n_channels=2, n_timepoints=10) + X_A_means, X_A_stds = sliding_mean_std_one_series(X_A, L, 1) + X_B_means, X_B_stds = sliding_mean_std_one_series(X_B, L, 1) + AdotB = get_ith_products(X_B, X_A, L, 0) + + exclusion_size = L + # MP : distances to best matches for each query + # IP : Indexes of best matches for each query + MP, IP = _stomp_normalized( + X_A, + X_B, + AdotB, + X_A_means, + X_A_stds, + X_B_means, + X_B_stds, + L, + motif_size, + threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + False, + ) + # For each query of size L in T + X_B_subs = z_normalise_series_3d(get_all_subsequences(X_B, L, 1)) + X_A_subs = z_normalise_series_3d(get_all_subsequences(X_A, L, 1)) + for i in range(X_A.shape[1] - L + 1): + dist_profile = _naive_squared_distance_profile(X_B_subs, X_A_subs[i]) + # Check that the top matches extracted have the same value that the + # top matches in the distance profile + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( + dist_profile, motif_size, threshold, allow_trivial_matches, exclusion_size + ) + + # Check that the top matches extracted have the same value that the + # top matches in the distance profile + assert_array_almost_equal(MP[i], top_k_distances) + + # Check that the index in IP correspond to a distance profile point + # with value equal to the corresponding MP point. + for j, index in enumerate(top_k_indexes): + assert_almost_equal(MP[i][j], dist_profile[index]) diff --git a/aeon/similarity_search/series/neighbors/__init__.py b/aeon/similarity_search/series/neighbors/__init__.py new file mode 100644 index 0000000000..047bfbe9c4 --- /dev/null +++ b/aeon/similarity_search/series/neighbors/__init__.py @@ -0,0 +1,9 @@ +"""Subsequence Neighbor search for series.""" + +__all__ = [ + "DummySNN", + "MassSNN", +] + +from aeon.similarity_search.series.neighbors._dummy import DummySNN +from aeon.similarity_search.series.neighbors._mass import MassSNN diff --git a/aeon/similarity_search/series/neighbors/_dummy.py b/aeon/similarity_search/series/neighbors/_dummy.py new file mode 100644 index 0000000000..12bb7a1035 --- /dev/null +++ b/aeon/similarity_search/series/neighbors/_dummy.py @@ -0,0 +1,200 @@ +"""Implementation of NN with brute force.""" + +from typing import Optional + +__maintainer__ = ["baraline"] +__all__ = ["DummySNN"] + +import numpy as np +from numba import get_num_threads, njit, prange, set_num_threads + +from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch +from aeon.similarity_search.series._commons import ( + _extract_top_k_from_dist_profile, + _inverse_distance_profile, +) +from aeon.utils.numba.general import ( + get_all_subsequences, + z_normalise_series_2d, + z_normalise_series_3d, +) + + +class DummySNN(BaseSeriesSimilaritySearch): + """Estimator to compute the on profile and distance profile using brute force.""" + + _tags = {"capability:multithreading": True} + + def __init__( + self, + length: int, + normalize: Optional[bool] = False, + n_jobs: Optional[int] = 1, + ): + self.length = length + self.normalize = normalize + self.n_jobs = n_jobs + super().__init__() + + def _fit( + self, + X: np.ndarray, + y=None, + ): + prev_threads = get_num_threads() + set_num_threads(self._n_jobs) + + self.X_subs = get_all_subsequences(self.X_, self.length, 1) + if self.normalize: + self.X_subs = z_normalise_series_3d(self.X_subs) + set_num_threads(prev_threads) + return self + + def predict( + self, + X: np.ndarray, + k: Optional[int] = 1, + dist_threshold: Optional[float] = np.inf, + exclusion_factor: Optional[float] = 2, + inverse_distance: Optional[bool] = False, + allow_neighboring_matches: Optional[bool] = False, + X_index: Optional[int] = None, + ): + """ + Compute nearest neighbors to X in subsequences of X_. + + Parameters + ---------- + X : np.ndarray, shape=(n_channels, length) + Subsequence we want to find neighbors for. + k : int + The number of neighbors to return. + dist_threshold : float + The maximum distance of neighbors to X. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, + the farther neighbors will be returned instead of the closest ones. + exclusion_factor : float, default=1. + A factor of the query length used to define the exclusion zone when + ``allow_neighboring_matches`` is set to False. For a given timestamp, + the exclusion zone starts from + :math:`id_timestamp - length//exclusion_factor` and end at + :math:`id_timestamp + length//exclusion_factor`. + X_index : int, optional + If ``X`` is a subsequence of X_, specify its starting timestamp in ``X_``. + If specified, neighboring subsequences of X won't be able to match as + neighbors. + + Returns + ------- + np.ndarray, shape = (k) + The indexes of the best matches in ``distance_profile``. + np.ndarray, shape = (k) + The distances of the best matches. + + """ + X = self._pre_predict(X, length=self.length) + X_index = self._check_X_index(X_index) + dist_profile = self.compute_distance_profile(X) + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + + exclusion_size = self.length // exclusion_factor + if X_index is not None: + exclusion_size = self.length // exclusion_factor + _max_timestamp = self.n_timepoints_ - self.length + ub = min(X_index + exclusion_size, _max_timestamp) + lb = max(0, X_index - exclusion_size) + dist_profile[lb:ub] = np.inf + + if k == np.inf: + k = len(dist_profile) + + return _extract_top_k_from_dist_profile( + dist_profile, + k, + dist_threshold, + allow_neighboring_matches, + exclusion_size, + ) + + def compute_distance_profile(self, X: np.ndarray): + """ + Compute the distance profile of X to all samples in X_. + + Parameters + ---------- + X : np.ndarray, 2D array of shape (n_channels, length) + The query to use to compute the distance profiles. + + Returns + ------- + distance_profile : np.ndarray, 1D array of shape (n_candidates) + The distance profile of X to X_. The ``n_candidates`` value + is equal to ``n_timepoins - length + 1``, with ``n_timepoints`` the + length of X_. + + """ + prev_threads = get_num_threads() + set_num_threads(self._n_jobs) + if self.normalize: + X = z_normalise_series_2d(X) + distance_profile = _naive_squared_distance_profile(self.X_subs, X) + set_num_threads(prev_threads) + return distance_profile + + @classmethod + def _get_test_params(cls, parameter_set: str = "default"): + """Return testing parameter settings for the estimator. + + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return, for use in tests. If no + special parameters are defined for a value, will return `"default"` set. + There are currently no reserved values for transformers. + + Returns + ------- + params : dict or list of dict, default = {} + Parameters to create testing instances of the class + Each dict are parameters to construct an "interesting" test instance, i.e., + `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + """ + if parameter_set == "default": + params = {"length": 3} + else: + raise NotImplementedError( + f"The parameter set {parameter_set} is not yet implemented" + ) + return params + + +@njit(cache=True, fastmath=True, parallel=True) +def _naive_squared_distance_profile( + X_subs, + Q, +): + """ + Compute a squared euclidean distance profile. + + Parameters + ---------- + X_subs : array, shape=(n_subsequences, n_channels, length) + Subsequences of size length of the input time series to search in. + Q : array, shape=(n_channels, query_length) + Query used during the search. + + Returns + ------- + out : np.ndarray, 1D array of shape (n_samples, n_timepoints_t - query_length + 1) + The distance between the query and all candidates in X. + + """ + n_subs, n_channels, length = X_subs.shape + dist_profile = np.zeros(n_subs) + for i in prange(n_subs): + for j in range(n_channels): + for k in range(length): + dist_profile[i] += (X_subs[i, j, k] - Q[j, k]) ** 2 + return dist_profile diff --git a/aeon/similarity_search/series/neighbors/_mass.py b/aeon/similarity_search/series/neighbors/_mass.py new file mode 100644 index 0000000000..befc8b33fd --- /dev/null +++ b/aeon/similarity_search/series/neighbors/_mass.py @@ -0,0 +1,291 @@ +"""Implementation of NN with MASS.""" + +from typing import Optional + +__maintainer__ = ["baraline"] +__all__ = ["MassSNN"] + +import numpy as np +from numba import njit, prange + +from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch +from aeon.similarity_search.series._commons import ( + _extract_top_k_from_dist_profile, + _inverse_distance_profile, + fft_sliding_dot_product, +) +from aeon.utils.numba.general import ( + AEON_NUMBA_STD_THRESHOLD, + sliding_mean_std_one_series, +) + + +class MassSNN(BaseSeriesSimilaritySearch): + """ + Estimator to compute the subsequences nearest neighbors using MASS _[1]. + + Parameters + ---------- + length : int + The length of the subsequences to use for the search. + normalize : bool + Wheter the subsequences should be z-normalized. + + References + ---------- + .. [1] Abdullah Mueen, Yan Zhu, Michael Yeh, Kaveh Kamgar, Krishnamurthy + Viswanathan, Chetan Kumar Gupta and Eamonn Keogh (2015), The Fastest Similarity + Search Algorithm for Time Series Subsequences under Euclidean Distance. + """ + + def __init__( + self, + length: int, + normalize: Optional[bool] = False, + ): + self.length = length + self.normalize = normalize + super().__init__() + + def _fit( + self, + X: np.ndarray, + y=None, + ): + if self.normalize: + self.X_means_, self.X_stds_ = sliding_mean_std_one_series(X, self.length, 1) + return self + + def predict( + self, + X: np.ndarray, + k: Optional[int] = 1, + dist_threshold: Optional[float] = np.inf, + allow_trivial_matches: Optional[bool] = False, + exclusion_factor: Optional[float] = 2, + inverse_distance: Optional[bool] = False, + X_index: Optional[int] = None, + ): + """ + Compute nearest neighbors to X in subsequences of X_. + + Parameters + ---------- + X : np.ndarray, shape=(n_channels, length) + Subsequence we want to find neighbors for. + k : int + The number of neighbors to return. + dist_threshold : float + The maximum allowed distance of a candidate subsequence of X_ to X + for the candidate to be considered as a neighbor. + allow_trivial_matches: bool, optional + Wheter a neighbors of a match to a query can be also considered as matches + (True), or if an exclusion zone is applied around each match to avoid + trivial matches with their direct neighbors (False). + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, + the farther neighbors will be returned instead of the closest ones. + exclusion_factor : float, default=1. + A factor of the query length used to define the exclusion zone when + ``allow_trivial_matches`` is set to False. For a given timestamp, + the exclusion zone starts from + :math:`id_timestamp - length//exclusion_factor` and end at + :math:`id_timestamp + length//exclusion_factor`. + X_index : int, optional + If ``X`` is a subsequence of X_, specify its starting timestamp in ``X_``. + If specified, neighboring subsequences of X won't be able to match as + neighbors. + + Returns + ------- + np.ndarray, shape = (k) + The indexes of the best matches in ``distance_profile``. + np.ndarray, shape = (k) + The distances of the best matches. + + """ + X = self._pre_predict(X, length=self.length) + X_index = self._check_X_index(X_index) + dist_profile = self.compute_distance_profile(X) + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + + exclusion_size = self.length // exclusion_factor + if X_index is not None: + _max_timestamp = self.n_timepoints_ - self.length + ub = min(X_index + exclusion_size, _max_timestamp) + lb = max(0, X_index - exclusion_size) + dist_profile[lb:ub] = np.inf + + if k == np.inf: + k = len(dist_profile) + + return _extract_top_k_from_dist_profile( + dist_profile, + k, + dist_threshold, + allow_trivial_matches, + exclusion_size, + ) + + def compute_distance_profile(self, X: np.ndarray): + """ + Compute the distance profile of X to all samples in X_. + + Parameters + ---------- + X : np.ndarray, 2D array of shape (n_channels, length) + The query to use to compute the distance profiles. + + Returns + ------- + distance_profiles : np.ndarray, 2D array of shape (n_cases, n_candidates) + The distance profile of X to all samples in X_. The ``n_candidates`` value + is equal to ``n_timepoins - length + 1``. If X_ is an unequal length + collection, returns a numba typed list instead of an ndarray. + + """ + QT = fft_sliding_dot_product(self.X_, X) + + if self.normalize: + distance_profile = _normalized_squared_distance_profile( + QT, + self.X_means_, + self.X_stds_, + X.mean(axis=1), + X.std(axis=1), + self.length, + ) + else: + distance_profile = _squared_distance_profile( + QT, + self.X_, # T + X, # Q + ) + + return distance_profile + + @classmethod + def _get_test_params(cls, parameter_set: str = "default"): + """Return testing parameter settings for the estimator. + + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return, for use in tests. If no + special parameters are defined for a value, will return `"default"` set. + There are currently no reserved values for transformers. + + Returns + ------- + params : dict or list of dict, default = {} + Parameters to create testing instances of the class + Each dict are parameters to construct an "interesting" test instance, i.e., + `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + """ + if parameter_set == "default": + params = {"length": 3} + else: + raise NotImplementedError( + f"The parameter set {parameter_set} is not yet implemented" + ) + return params + + +@njit(cache=True, fastmath=True) +def _squared_distance_profile(QT, T, Q): + """ + Compute squared distance profile between query subsequence and a single time series. + + This function calculates the squared distance profile for a single time series by + leveraging the dot product of the query and time series as well as precomputed sums + of squares to efficiently compute the squared distances. + + Parameters + ---------- + QT : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) + The dot product between the query and the time series. + T : np.ndarray, 2D array of shape (n_channels, series_length) + The series used for similarity search. Note that series_length can be equal, + superior or inferior to n_timepoints, it doesn't matter. + Q : np.ndarray + 2D array of shape (n_channels, query_length) representing query subsequence. + + Returns + ------- + distance_profile : np.ndarray + 2D array of shape (n_channels, n_timepoints - query_length + 1) + The squared distance profile between the query and the input time series. + """ + n_channels, profile_length = QT.shape + query_length = Q.shape[1] + _QT = -2 * QT + distance_profile = np.zeros(profile_length) + for k in prange(n_channels): + _sum = 0 + _qsum = 0 + for j in prange(query_length): + _sum += T[k, j] ** 2 + _qsum += Q[k, j] ** 2 + + distance_profile += _qsum + _QT[k] + distance_profile[0] += _sum + for i in prange(1, profile_length): + _sum += T[k, i + (query_length - 1)] ** 2 - T[k, i - 1] ** 2 + distance_profile[i] += _sum + return distance_profile + + +@njit(cache=True, fastmath=True) +def _normalized_squared_distance_profile( + QT, T_means, T_stds, Q_means, Q_stds, query_length +): + """ + Compute the z-normalized squared Euclidean distance profile for one time series. + + Parameters + ---------- + QT : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) + The dot product between the query and the time series. + T_means : np.ndarray, 1D array of length n_channels + The mean values of the time series for each channel. + T_stds : np.ndarray, 2D array of shape (n_channels, profile_length) + The standard deviations of the time series for each channel and position. + Q_means : np.ndarray, 1D array of shape (n_channels) + Means of the query q + Q_stds : np.ndarray, 1D array of shape (n_channels) + Stds of the query q + query_length : int + The length of the query subsequence used for the distance profile computation. + + + Returns + ------- + np.ndarray + 2D array of shape (n_channels, n_timepoints - query_length + 1) containing the + z-normalized squared distance profile between the query subsequence and the time + series. Entries are computed based on the z-normalized values, with special + handling for constant values. + """ + n_channels, profile_length = QT.shape + distance_profile = np.zeros(profile_length) + Q_is_constant = Q_stds <= AEON_NUMBA_STD_THRESHOLD + for i in prange(profile_length): + Sub_is_constant = T_stds[:, i] <= AEON_NUMBA_STD_THRESHOLD + for k in prange(n_channels): + # Two Constant case + if Q_is_constant[k] and Sub_is_constant[k]: + _val = 0 + # One Constant case + elif Q_is_constant[k] or Sub_is_constant[k]: + _val = query_length + else: + denom = query_length * Q_stds[k] * T_stds[k, i] + + p = (QT[k, i] - query_length * (Q_means[k] * T_means[k, i])) / denom + p = min(p, 1.0) + + _val = abs(2 * query_length * (1.0 - p)) + distance_profile[i] += _val + + return distance_profile diff --git a/aeon/similarity_search/series/neighbors/tests/__init__.py b/aeon/similarity_search/series/neighbors/tests/__init__.py new file mode 100644 index 0000000000..00ef2e73ec --- /dev/null +++ b/aeon/similarity_search/series/neighbors/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for series neighbors search methods.""" diff --git a/aeon/similarity_search/series/neighbors/tests/test_dummy.py b/aeon/similarity_search/series/neighbors/tests/test_dummy.py new file mode 100644 index 0000000000..df8ff72655 --- /dev/null +++ b/aeon/similarity_search/series/neighbors/tests/test_dummy.py @@ -0,0 +1,40 @@ +""" +Tests for stomp algorithm. + +We do not test equality for returned indexes due to the unstable nature of argsort +and the fact that the "kind=stable" parameter is not yet supported in numba. We instead +test that the returned index match the expected distance value. +""" + +__maintainer__ = ["baraline"] + +import numpy as np +import pytest +from numpy.testing import assert_almost_equal + +from aeon.similarity_search.series.neighbors._brute_force import ( + _naive_squared_distance_profile, +) +from aeon.testing.data_generation import make_example_2d_numpy_series +from aeon.utils.numba.general import get_all_subsequences, z_normalise_series_2d + +NORMALIZE = [True, False] + + +@pytest.mark.parametrize("normalize", NORMALIZE) +def test__naive_squared_distance_profile(normalize): + """Test Euclidean distance with brute force.""" + L = 3 + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) + dist_profile = _naive_squared_distance_profile( + get_all_subsequences(X, L, 1), Q, normalize=normalize + ) + + if normalize: + Q = z_normalise_series_2d(Q) + for i_t in range(X.shape[1] - L + 1): + S = X[:, i_t : i_t + L] + if normalize: + S = z_normalise_series_2d(X[:, i_t : i_t + L]) + assert_almost_equal(dist_profile[i_t], np.sum((S - Q) ** 2)) diff --git a/aeon/similarity_search/series/neighbors/tests/test_mass.py b/aeon/similarity_search/series/neighbors/tests/test_mass.py new file mode 100644 index 0000000000..b6bf1953ea --- /dev/null +++ b/aeon/similarity_search/series/neighbors/tests/test_mass.py @@ -0,0 +1,44 @@ +"""Tests for MASS algorithm.""" + +__maintainer__ = ["baraline"] + +import numpy as np +from numpy.testing import assert_almost_equal + +from aeon.similarity_search.series._commons import fft_sliding_dot_product +from aeon.similarity_search.series.neighbors._mass import ( + _normalized_squared_distance_profile, + _squared_distance_profile, +) +from aeon.testing.data_generation import make_example_2d_numpy_series +from aeon.utils.numba.general import sliding_mean_std_one_series, z_normalise_series_2d + + +def test__squared_distance_profile(): + """Test squared distance profile.""" + L = 3 + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) + QX = fft_sliding_dot_product(X, Q) + dist_profile = _squared_distance_profile(QX, X, Q) + for i_t in range(X.shape[1] - L + 1): + assert_almost_equal(dist_profile[i_t], np.sum((X[:, i_t : i_t + L] - Q) ** 2)) + + +def test__normalized_squared_distance_profile(): + """Test Euclidean distance.""" + L = 3 + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) + QX = fft_sliding_dot_product(X, Q) + X_mean, X_std = sliding_mean_std_one_series(X, L, 1) + Q_mean = Q.mean(axis=1) + Q_std = Q.std(axis=1) + + dist_profile = _normalized_squared_distance_profile( + QX, X_mean, X_std, Q_mean, Q_std, L + ) + Q = z_normalise_series_2d(Q) + for i_t in range(X.shape[1] - L + 1): + S = z_normalise_series_2d(X[:, i_t : i_t + L]) + assert_almost_equal(dist_profile[i_t], np.sum((S - Q) ** 2)) diff --git a/aeon/similarity_search/series/tests/__init__.py b/aeon/similarity_search/series/tests/__init__.py new file mode 100644 index 0000000000..4762fe16ce --- /dev/null +++ b/aeon/similarity_search/series/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for base class and commons functions.""" diff --git a/aeon/similarity_search/series/tests/test_base.py b/aeon/similarity_search/series/tests/test_base.py new file mode 100644 index 0000000000..1b4d17b991 --- /dev/null +++ b/aeon/similarity_search/series/tests/test_base.py @@ -0,0 +1,64 @@ +"""Test for series similarity search base class.""" + +__maintainer__ = ["baraline"] + +import pytest + +from aeon.testing.mock_estimators._mock_similarity_searchers import ( + MockSeriesSimilaritySearch, +) +from aeon.testing.testing_data import ( + make_example_1d_numpy, + make_example_2d_numpy_series, + make_example_3d_numpy, + make_example_3d_numpy_list, +) + + +def test_input_shape_fit_predict_series(): + """Test input shapes.""" + estimator = MockSeriesSimilaritySearch() + # dummy data to pass to fit when testing predict/predict_proba + X_3D_uni = make_example_3d_numpy(n_channels=1, return_y=False) + X_3D_multi = make_example_3d_numpy(n_channels=2, return_y=False) + X_3D_uni_list = make_example_3d_numpy_list(n_channels=1, return_y=False) + X_3D_multi_list = make_example_3d_numpy_list(n_channels=2, return_y=False) + X_2D_uni = make_example_2d_numpy_series(n_channels=1) + X_2D_multi = make_example_2d_numpy_series(n_channels=2) + X_1D = make_example_1d_numpy() + + valid_inputs_fit = [X_1D, X_2D_uni, X_2D_multi] + # 1D is converted to 2D univariate + for _input in valid_inputs_fit: + estimator.fit(_input) + + invalid_inputs_fit = [ + X_3D_multi, + X_3D_uni, + X_3D_multi_list, + X_3D_uni_list, + ] + for _input in invalid_inputs_fit: + with pytest.raises(ValueError): + estimator.fit(_input) + + estimator_multi = MockSeriesSimilaritySearch().fit(X_2D_multi) + estimator_uni = MockSeriesSimilaritySearch().fit(X_2D_uni) + + estimator_uni.predict(X_2D_uni) + # 1D is converted to 2D univariate + estimator_uni.predict(X_1D) + estimator_multi.predict(X_2D_multi) + + with pytest.raises(ValueError): + estimator_uni.predict(X_2D_multi) + with pytest.raises(ValueError): + estimator_multi.predict(X_2D_uni) + + for _input in [X_3D_uni, X_3D_uni_list]: + with pytest.raises(ValueError): + estimator_uni.predict(_input) + + for _input in [X_3D_multi, X_3D_multi_list]: + with pytest.raises(ValueError): + estimator_multi.predict(_input) diff --git a/aeon/similarity_search/series/tests/test_commons.py b/aeon/similarity_search/series/tests/test_commons.py new file mode 100644 index 0000000000..6f2c816193 --- /dev/null +++ b/aeon/similarity_search/series/tests/test_commons.py @@ -0,0 +1,174 @@ +"""Test _commons.py functions.""" + +__maintainer__ = ["baraline"] +import numpy as np +import pytest +from numba.typed import List +from numpy.testing import assert_, assert_array_almost_equal + +from aeon.similarity_search.series._commons import ( + _extract_top_k_from_dist_profile, + _extract_top_k_motifs, + _extract_top_r_motifs, + _inverse_distance_profile, + _update_dot_products, + fft_sliding_dot_product, + get_ith_products, +) +from aeon.testing.data_generation import ( + make_example_1d_numpy, + make_example_2d_numpy_series, +) + +K_VALUES = [1, 3, 5] +THRESHOLDS = [np.inf, 1.5] +NN_MATCHES = [False, True] +EXCLUSION_SIZE = [3, 5] + + +def test_fft_sliding_dot_product(): + """Test the fft_sliding_dot_product function.""" + L = 4 + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) + + values = fft_sliding_dot_product(X, Q) + # Compare values[0] only as input is univariate + assert_array_almost_equal( + values[0], + [np.dot(Q[0], X[0, i : i + L]) for i in range(X.shape[1] - L + 1)], + ) + + +def test__update_dot_products(): + """Test the _update_dot_product function.""" + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=20) + T = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + L = 7 + current_product = get_ith_products(X, T, L, 0) + for i_query in range(1, T.shape[1] - L + 1): + new_product = get_ith_products( + X, + T, + L, + i_query, + ) + current_product = _update_dot_products( + X, + T, + current_product, + L, + i_query, + ) + assert_array_almost_equal(new_product, current_product) + + +def test_get_ith_products(): + """Test i-th dot product of a subsequence of size L.""" + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + L = 5 + + values = get_ith_products(X, Q, L, 0) + # Compare values[0] only as input is univariate + assert_array_almost_equal( + values[0], + [np.dot(Q[0, 0:L], X[0, i : i + L]) for i in range(X.shape[1] - L + 1)], + ) + + values = get_ith_products(X, Q, L, 4) + # Compare values[0] only as input is univariate + assert_array_almost_equal( + values[0], + [np.dot(Q[0, 4 : 4 + L], X[0, i : i + L]) for i in range(X.shape[1] - L + 1)], + ) + + +def test__inverse_distance_profile(): + """Test method to inverse a TypedList of distance profiles.""" + X = make_example_1d_numpy() + X_inv = _inverse_distance_profile(X) + assert_array_almost_equal(1 / (X + 1e-8), X_inv) + + +def test__extract_top_k_motifs(): + """Test motif extraction based on max distance.""" + MP = List( + [ + [1.0, 2.0], + [1.0, 4.0], + [0.5, 0.9], + [0.6, 0.7], + ] + ) + IP = List( + [ + [1, 2], + [1, 4], + [0, 3], + [0, 7], + ] + ) + MP_k, IP_k = _extract_top_k_motifs(MP, IP, 2, True, 0) + assert_(len(MP_k) == 2) + assert_(MP_k[0] == [0.6, 0.7]) + assert_(IP_k[0] == [0, 7]) + assert_(MP_k[1] == [0.5, 0.9]) + assert_(IP_k[1] == [0, 3]) + + +def test__extract_top_r_motifs(): + """Test motif extraction based on motif set cardinality.""" + MP = List( + [ + [1.0, 1.5, 2.0, 1.5], + [1.0, 4.0], + [0.5, 0.9, 1.0], + [0.6, 0.7], + ] + ) + IP = List( + [ + [1, 2, 3, 4], + [1, 4], + [0, 3, 6], + [0, 7], + ] + ) + MP_k, IP_k = _extract_top_r_motifs(MP, IP, 2, True, 0) + assert_(len(MP_k) == 2) + assert_(MP_k[0] == [1.0, 1.5, 2.0, 1.5]) + assert_(IP_k[0] == [1, 2, 3, 4]) + assert_(MP_k[1] == [0.5, 0.9, 1.0]) + assert_(IP_k[1] == [0, 3, 6]) + + +@pytest.mark.parametrize("k", K_VALUES) +@pytest.mark.parametrize("threshold", THRESHOLDS) +@pytest.mark.parametrize("allow_nn_matches", NN_MATCHES) +@pytest.mark.parametrize("exclusion_size", EXCLUSION_SIZE) +def test__extract_top_k_from_dist_profile( + k, threshold, allow_nn_matches, exclusion_size +): + """Test method to esxtract the top k candidates from a list of distance profiles.""" + X = make_example_1d_numpy(n_timepoints=30) + X_sort = np.argsort(X) + exclusion_size = 3 + top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( + X, k, threshold, allow_nn_matches, exclusion_size + ) + + if len(top_k_indexes) == 0 or len(top_k_distances) == 0: + raise AssertionError("_extract_top_k_from_dist_profile returned empty list") + for i, index in enumerate(top_k_indexes): + assert_(X[index] == top_k_distances[i]) + + assert_(np.all(top_k_distances <= threshold)) + + if allow_nn_matches: + assert_(np.all(top_k_distances <= X_sort[len(top_k_indexes) - 1])) + + if not allow_nn_matches: + same_X = np.sort(top_k_indexes) + if len(same_X) > 1: + assert_(np.all(np.diff(same_X) >= exclusion_size)) diff --git a/aeon/similarity_search/series_search.py b/aeon/similarity_search/series_search.py deleted file mode 100644 index 3c36cf9c4a..0000000000 --- a/aeon/similarity_search/series_search.py +++ /dev/null @@ -1,436 +0,0 @@ -"""Base class for series search.""" - -__maintainer__ = ["baraline"] - -from typing import Union, final - -import numpy as np -from numba import get_num_threads, set_num_threads - -from aeon.similarity_search.base import BaseSimilaritySearch -from aeon.similarity_search.matrix_profiles.stomp import ( - stomp_euclidean_matrix_profile, - stomp_normalised_euclidean_matrix_profile, - stomp_normalised_squared_matrix_profile, - stomp_squared_matrix_profile, -) -from aeon.utils.numba.general import sliding_mean_std_one_series - - -class SeriesSearch(BaseSimilaritySearch): - """ - Series search estimator. - - The series search estimator will return a set of matches for each subsequence of - size L in a time series given during predict. The matching of each subsequence will - be made against all subsequence of size L inside the time series given during fit, - which will represent the search space. - - Depending on the `k` and/or `threshold` parameters, which condition what is - considered a valid match during the search, the number of matches will vary. If `k` - is used, at most `k` matches (the `k` best) will be returned, if `threshold` is used - and `k` is set to `np.inf`, all the candidates which distance to the query is - inferior or equal to `threshold` will be returned. If both are used, the `k` best - matches to the query with distance inferior to `threshold` will be returned. - - - Parameters - ---------- - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - distance : str, default="euclidean" - Name of the distance function to use. A list of valid strings can be found in - the documentation for :func:`aeon.distances.get_distance_function`. - If a callable is passed it must either be a python function or numba function - with nopython=True, that takes two 1d numpy arrays as input and returns a float. - distance_args : dict, default=None - Optional keyword arguments for the distance function. - normalise : bool, default=False - Whether the distance function should be z-normalised. - speed_up : str, default='fastest' - Which speed up technique to use with for the selected distance - function. By default, the fastest algorithm is used. A list of available - algorithm for each distance can be obtained by calling the - `get_speedup_function_names` function. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - n_jobs : int, default=1 - Number of parallel jobs to use. - - Attributes - ---------- - X_ : array, shape (n_cases, n_channels, n_timepoints) - The input time series stored during the fit method. This is the - database we search in when given a query. - distance_profile_function : function - The function used to compute the distance profile. This is determined - during the fit method based on the distance and normalise - parameters. - - Notes - ----- - For now, the multivariate case is only treated as independent. - Distances are computed for each channel independently and then - summed together. - """ - - def __init__( - self, - k: int = 1, - threshold: float = np.inf, - distance: str = "euclidean", - distance_args: Union[None, dict] = None, - inverse_distance: bool = False, - normalise: bool = False, - speed_up: str = "fastest", - n_jobs: int = 1, - ): - self.k = k - self.threshold = threshold - self._previous_query_length = -1 - self.axis = 1 - - super().__init__( - distance=distance, - distance_args=distance_args, - inverse_distance=inverse_distance, - normalise=normalise, - speed_up=speed_up, - n_jobs=n_jobs, - ) - - def _fit(self, X, y=None): - """ - Check input format and store it to be used as search space during predict. - - Parameters - ---------- - X : array, shape (n_cases, n_channels, n_timepoints) - Input array to used as database for the similarity search - y : optional - Not used. - - Raises - ------ - TypeError - If the input X array is not 3D raise an error. - - Returns - ------- - self - - """ - self.X_ = X - self.matrix_profile_function_ = self._get_series_method_function() - return self - - @final - def predict( - self, - X: np.ndarray, - length: int, - axis: int = 1, - X_index=None, - exclusion_factor=2.0, - apply_exclusion_to_result=False, - ): - """ - Predict method : Check the shape of X and call _predict to perform the search. - - If the distance profile function is normalised, it stores the mean and stds - from X and X_, with X_ the training data. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, series_length) - Input time series used for the search. - length : int - The length parameter that will be used to extract queries from X. - axis : int - The time point axis of the input series if it is 2D. If ``axis==0``, it is - assumed each column is a time series and each row is a time point. i.e. the - shape of the data is ``(n_timepoints,n_channels)``. ``axis==1`` indicates - the time series are in rows, i.e. the shape of the data is - ``(n_channels,n_timepoints)``. - X_index : int - An integer indicating if X was extracted is part of the dataset that was - given during the fit method. If so, this integer should be the sample id. - The search will define an exclusion zone for the queries extarcted from X - in order to avoid matching with themself. If None, it is considered that - the query is not extracted from X_. - exclusion_factor : float, default=2. - The factor to apply to the query length to define the exclusion zone. The - exclusion zone is define from - ``id_timestamp - query_length//exclusion_factor`` to - ``id_timestamp + query_length//exclusion_factor``. This also applies to - the matching conditions defined by child classes. For example, with - TopKSimilaritySearch, the k best matches are also subject to the exclusion - zone, but with :math:`id_timestamp` the index of one of the k matches. - apply_exclusion_to_result : bool, default=False - Wheter to apply the exclusion factor to the output of the similarity search. - This means that two matches of the query from the same sample must be at - least spaced by +/- ``query_length//exclusion_factor``. - This can avoid pathological matching where, for example if we extract the - best two matches, there is a high chance that if the best match is located - at ``id_timestamp``, the second best match will be located at - ``id_timestamp`` +/- 1, as they both share all their values except one. - - Raises - ------ - TypeError - If the input X array is not 2D raise an error. - ValueError - If the length of the query is greater - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - self._check_is_fitted() - prev_threads = get_num_threads() - set_num_threads(self._n_jobs) - series_dim, series_length = self._check_series_format(X, length, axis) - - mask = self._init_X_index_mask( - None if X_index is None else [X_index, 0], - length, - exclusion_factor=exclusion_factor, - ) - - if self.normalise: - _mean, _std = sliding_mean_std_one_series(X, length, 1) - self.T_means_ = _mean - self.T_stds_ = _std - if self._previous_query_length != length: - self._store_mean_std_from_inputs(length) - - if apply_exclusion_to_result: - exclusion_size = length // exclusion_factor - else: - exclusion_size = None - - self._previous_query_length = length - - X_preds = self._predict( - X, - length, - mask, - exclusion_size, - X_index, - exclusion_factor, - apply_exclusion_to_result, - ) - set_num_threads(prev_threads) - return X_preds - - def _predict( - self, - X, - length, - mask, - exclusion_size, - X_index, - exclusion_factor, - apply_exclusion_to_result, - ): - """ - Private predict method for SeriesSearch. - - This method calculates the matrix profile for a given time series dataset by - comparing all possible subsequences of a specified length against a reference - time series. It handles exclusion zones to prevent nearby matches from being - selected and supports normalization. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, series_length) - Input time series used for the search. - length : int - The length parameter that will be used to extract queries from X. - axis : int - The time point axis of the input series if it is 2D. If ``axis==0``, it is - assumed each column is a time series and each row is a time point. i.e. the - shape of the data is ``(n_timepoints,n_channels)``. ``axis==1`` indicates - the time series are in rows, i.e. the shape of the data is - ``(n_channels,n_timepoints)``. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for - the first query of size L in T. This mask will be updated during the - algorithm. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestamp - exclusion_size` and - :math:`id_timestamp + exclusion_size` which cannot be returned - as best match if :math:`id_timestamp` was already selected. By default, - the value None means that this is not used. - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - if self.normalise: - return self.matrix_profile_function_( - self.X_, - X, - length, - self.X_means_, - self.X_stds_, - self.T_means_, - self.T_stds_, - mask, - k=self.k, - threshold=self.threshold, - inverse_distance=self.inverse_distance, - exclusion_size=exclusion_size, - ) - else: - return self.matrix_profile_function_( - self.X_, - X, - length, - mask, - k=self.k, - threshold=self.threshold, - inverse_distance=self.inverse_distance, - exclusion_size=exclusion_size, - ) - - def _check_series_format(self, X, length, axis): - if axis not in [0, 1]: - raise ValueError("The axis argument is expected to be either 1 or 0") - if self.axis != axis: - X = X.T - if not isinstance(X, np.ndarray) or X.ndim != 2: - raise TypeError( - "Error, only supports 2D numpy for now. If the series X is univariate " - "do X = X[np.newaxis, :]." - ) - - series_dim, series_length = X.shape - if series_length < length: - raise ValueError( - "The length of the series should be superior or equal to the length " - "parameter given during predict, but got {} < {}".format( - series_length, length - ) - ) - - if series_dim != self.n_channels_: - raise ValueError( - "The number of feature should be the same for the series X and the data" - " (X_) provided during fit, but got {} for X and {} for X_".format( - series_dim, self.n_channels_ - ) - ) - return series_dim, series_length - - def _get_series_method_function(self): - """ - Given distance and speed_up parameters, return the series method function. - - Raises - ------ - ValueError - If the distance parameter given at initialization is not a string nor a - numba function or a callable, or if the speedup parameter is unknow or - unsupported, raisea ValueError. - - Returns - ------- - function - The series method function matching the distance argument. - - """ - if isinstance(self.distance, str): - distance_dict = _SERIES_SEARCH_SPEED_UP_DICT.get(self.distance) - if distance_dict is None: - raise NotImplementedError( - f"No distance profile have been implemented for {self.distance}." - ) - else: - speed_up_series_method = distance_dict.get(self.normalise).get( - self.speed_up - ) - - if speed_up_series_method is None: - raise ValueError( - f"Unknown or unsupported speed up {self.speed_up} for " - f"{self.distance} distance function with" - ) - self.speed_up_ = self.speed_up - return speed_up_series_method - else: - raise ValueError( - f"Expected distance argument to be str but got {type(self.distance)}" - ) - - @classmethod - def get_speedup_function_names(self): - """ - Get available speedup for series search in aeon. - - The returned structure is a dictionnary that contains the names of all - avaialble speedups for normalised and non-normalised distance functions. - - Returns - ------- - dict - The available speedups name that can be used as parameters in - similarity search classes. - - """ - speedups = {} - for dist_name in _SERIES_SEARCH_SPEED_UP_DICT.keys(): - for normalise in _SERIES_SEARCH_SPEED_UP_DICT[dist_name].keys(): - speedups_names = list( - _SERIES_SEARCH_SPEED_UP_DICT[dist_name][normalise].keys() - ) - if normalise: - speedups.update({f"normalised {dist_name}": speedups_names}) - else: - speedups.update({f"{dist_name}": speedups_names}) - return speedups - - -_SERIES_SEARCH_SPEED_UP_DICT = { - "euclidean": { - True: { - "fastest": stomp_normalised_euclidean_matrix_profile, - "STOMP": stomp_normalised_euclidean_matrix_profile, - }, - False: { - "fastest": stomp_euclidean_matrix_profile, - "STOMP": stomp_euclidean_matrix_profile, - }, - }, - "squared": { - True: { - "fastest": stomp_normalised_squared_matrix_profile, - "STOMP": stomp_normalised_squared_matrix_profile, - }, - False: { - "fastest": stomp_squared_matrix_profile, - "STOMP": stomp_squared_matrix_profile, - }, - }, -} diff --git a/aeon/similarity_search/tests/test__commons.py b/aeon/similarity_search/tests/test__commons.py deleted file mode 100644 index a97519ad31..0000000000 --- a/aeon/similarity_search/tests/test__commons.py +++ /dev/null @@ -1,49 +0,0 @@ -"""Test _commons.py functions.""" - -__maintainer__ = ["baraline"] - -import numpy as np -from numpy.testing import assert_array_almost_equal - -from aeon.similarity_search._commons import ( - fft_sliding_dot_product, - naive_squared_distance_profile, - naive_squared_matrix_profile, -) - - -def test_fft_sliding_dot_product(): - """Test the fft_sliding_dot_product function.""" - X = np.random.rand(1, 10) - q = np.random.rand(1, 5) - - values = fft_sliding_dot_product(X, q) - - assert_array_almost_equal( - values[0], - [np.dot(q[0], X[0, i : i + 5]) for i in range(X.shape[1] - 5 + 1)], - ) - - -def test_naive_squared_distance_profile(): - """Test naive squared distance profile computation is correct.""" - X = np.zeros((1, 1, 6)) - X[0, 0] = np.arange(6) - Q = np.array([[1, 2, 3]]) - query_length = Q.shape[1] - mask = np.ones((X.shape[0], X.shape[2] - query_length + 1), dtype=bool) - dist_profile = naive_squared_distance_profile(X, Q, mask) - assert_array_almost_equal(dist_profile[0], np.array([3.0, 0.0, 3.0, 12.0])) - - -def test_naive_squared_matrix_profile(): - """Test naive squared matrix profile computation is correct.""" - X = np.zeros((1, 1, 6)) - X[0, 0] = np.arange(6) - Q = np.zeros((1, 6)) - - Q[0] = np.arange(6, 12) - query_length = 3 - mask = np.ones((X.shape[0], X.shape[2] - query_length + 1), dtype=bool) - matrix_profile = naive_squared_matrix_profile(X, Q, query_length, mask) - assert_array_almost_equal(matrix_profile, np.array([27.0, 48.0, 75.0, 108.0])) diff --git a/aeon/similarity_search/tests/test_base.py b/aeon/similarity_search/tests/test_base.py new file mode 100644 index 0000000000..e066e14680 --- /dev/null +++ b/aeon/similarity_search/tests/test_base.py @@ -0,0 +1 @@ +"""Tests for base similarity search.""" diff --git a/aeon/similarity_search/tests/test_query_search.py b/aeon/similarity_search/tests/test_query_search.py deleted file mode 100644 index f97f6a50bf..0000000000 --- a/aeon/similarity_search/tests/test_query_search.py +++ /dev/null @@ -1,176 +0,0 @@ -"""Tests for QuerySearch.""" - -__maintainer__ = ["baraline"] - -import numpy as np -import pytest -from numpy.testing import assert_almost_equal, assert_array_equal - -from aeon.similarity_search.query_search import QuerySearch - -DATATYPES = ["int64", "float64"] - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_mean_std_equal_length(dtype): - """Test the mean and std computation of QuerySearch.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(normalise=True) - search.fit(X) - _ = search.predict(q, X_index=(1, 2)) - for i in range(len(X)): - for j in range(X[i].shape[1] - q.shape[1] + 1): - subsequence = X[i, :, j : j + q.shape[1]] - assert_almost_equal(search.X_means_[i][:, j], subsequence.mean(axis=-1)) - assert_almost_equal(search.X_stds_[i][:, j], subsequence.std(axis=-1)) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_mean_std_unequal_length(dtype): - """Test the mean and std computation of QuerySearch on unequal length data.""" - X = [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6, 5]], dtype=dtype), - ] - - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(normalise=True) - search.fit(X) - _ = search.predict(q, X_index=(1, 2)) - for i in range(len(X)): - for j in range(X[i].shape[1] - q.shape[1] + 1): - subsequence = X[i][:, j : j + q.shape[1]] - assert_almost_equal(search.X_means_[i][:, j], subsequence.mean(axis=-1)) - assert_almost_equal(search.X_stds_[i][:, j], subsequence.std(axis=-1)) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_threshold_and_k(dtype): - """Test the k and threshold combination of QuerySearch.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(k=3, threshold=1) - search.fit(X) - dist, idx = search.predict(q) - assert_array_equal(idx, [(0, 2), (1, 2)]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_inverse_distance(dtype): - """Test the inverse distance parameter of QuerySearch.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(k=1, inverse_distance=True) - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 5)]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_euclidean(dtype): - """Test the functionality of QuerySearch with Euclidean distance.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(k=1) - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 2)]) - - search = QuerySearch(k=3) - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 2), (1, 2), (1, 1)]) - - _, idx = search.predict(q, apply_exclusion_to_result=True) - assert_array_equal(idx, [(0, 2), (1, 2), (1, 4)]) - - search = QuerySearch(k=1, normalise=True) - search.fit(X) - q = np.asarray([[8, 8, 10]], dtype=dtype) - _, idx = search.predict(q) - assert_array_equal(idx, [(1, 2)]) - - _, idx = search.predict(q, apply_exclusion_to_result=True) - assert_array_equal(idx, [(1, 2)]) - - search = QuerySearch(k=1, normalise=True) - search.fit(X) - _, idx = search.predict(q, X_index=(1, 2)) - assert_array_equal(idx, [(1, 0)]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_euclidean_unequal_length(dtype): - """Test the functionality of QuerySearch on unequal length data.""" - X = [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6, 5]], dtype=dtype), - ] - - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(k=1) - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 2)]) - - search = QuerySearch(k=3) - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 2), (1, 2), (1, 1)]) - - _, idx = search.predict(q, apply_exclusion_to_result=True) - assert_array_equal(idx, [(0, 2), (1, 2), (1, 4)]) - - search = QuerySearch(k=1, normalise=True) - search.fit(X) - q = np.asarray([[8, 8, 10]], dtype=dtype) - _, idx = search.predict(q) - assert_array_equal(idx, [(1, 2)]) - - _, idx = search.predict(q, apply_exclusion_to_result=True) - assert_array_equal(idx, [(1, 2)]) - - search = QuerySearch(k=1, normalise=True) - search.fit(X) - _, idx = search.predict(q, X_index=(1, 2)) - assert_array_equal(idx, [(1, 0)]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_speedup(dtype): - """Test the speedup functionality of QuerySearch.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(k=1, speed_up="fastest") - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 2)]) - - search = QuerySearch( - k=1, - distance="euclidean", - speed_up="fastest", - normalise=True, - ) - search.fit(X) - q = np.asarray([[8, 8, 10]], dtype=dtype) - _, idx = search.predict(q) - assert_array_equal(idx, [(1, 2)]) diff --git a/aeon/similarity_search/tests/test_series_search.py b/aeon/similarity_search/tests/test_series_search.py deleted file mode 100644 index a10109359c..0000000000 --- a/aeon/similarity_search/tests/test_series_search.py +++ /dev/null @@ -1,74 +0,0 @@ -"""Tests for SeriesSearch similarity search algorithm.""" - -__maintainer__ = ["baraline"] - - -import numpy as np -import pytest - -from aeon.similarity_search.series_search import SeriesSearch - -DATATYPES = ["int64", "float64"] -K_VALUES = [1, 3] -normalise = [True, False] - -# See #2236 -# @pytest.mark.parametrize("k", K_VALUES) -# @pytest.mark.parametrize("normalise", normalise) -# def test_SeriesSearch_k(k, normalise): -# """Test the k and threshold combination of SeriesSearch.""" -# X = np.asarray([[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]]) -# S = np.asarray([[3, 4, 5, 4, 3, 4]]) -# L = 3 -# -# search = SeriesSearch(k=k, normalise=normalise) -# search.fit(X) -# mp, ip = search.predict(S, L) -# -# assert mp[0].shape[0] == ip[0].shape[0] == k -# assert len(mp) == len(ip) == S.shape[1] - L + 1 -# assert ip[0].shape[1] == 2 - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_SeriesSearch_error_predict(dtype): - """Test the functionality of SeriesSearch with Euclidean distance.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - S = np.asarray([[3, 4, 5, 4, 3, 4, 5]], dtype=dtype) - L = 100 - - search = SeriesSearch() - search.fit(X) - with pytest.raises(ValueError): - mp, ip = search.predict(S, L) - L = 3 - S = np.asarray( - [ - [3, 4, 5, 4, 3, 4], - [6, 5, 3, 2, 4, 5], - ], - dtype=dtype, - ) - with pytest.raises(ValueError): - mp, ip = search.predict(S, L) - - S = [6, 5, 3, 2, 4, 5] - with pytest.raises(TypeError): - mp, ip = search.predict(S, L) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_SeriesSearch_process_unequal_length(dtype): - """Test the functionality of SeriesSearch on unequal length data.""" - X = [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6, 5]], dtype=dtype), - ] - S = np.asarray([[3, 4, 5, 4, 3, 4]], dtype=dtype) - L = 3 - - search = SeriesSearch() - search.fit(X) - mp, ip = search.predict(S, L) diff --git a/aeon/testing/mock_estimators/__init__.py b/aeon/testing/mock_estimators/__init__.py index 219fc3e987..e517e07ca0 100644 --- a/aeon/testing/mock_estimators/__init__.py +++ b/aeon/testing/mock_estimators/__init__.py @@ -29,8 +29,6 @@ "MockUnivariateSeriesTransformer", "MockMultivariateSeriesTransformer", "MockSeriesTransformerNoFit", - # similarity search - "MockSimilaritySearch", ] from aeon.testing.mock_estimators._mock_anomaly_detectors import ( @@ -64,4 +62,3 @@ MockSeriesTransformerNoFit, MockUnivariateSeriesTransformer, ) -from aeon.testing.mock_estimators._mock_similarity_search import MockSimilaritySearch diff --git a/aeon/testing/mock_estimators/_mock_similarity_search.py b/aeon/testing/mock_estimators/_mock_similarity_search.py deleted file mode 100644 index 55c9c435c7..0000000000 --- a/aeon/testing/mock_estimators/_mock_similarity_search.py +++ /dev/null @@ -1,21 +0,0 @@ -"""Mock similarity searchers useful for testing and debugging.""" - -__maintainer__ = ["baraline"] -__all__ = [ - "MockSimilaritySearch", -] - -from aeon.similarity_search.base import BaseSimilaritySearch - - -class MockSimilaritySearch(BaseSimilaritySearch): - """Mock similarity search for testing base class predict.""" - - def _fit(self, X, y=None): - """_fit dummy.""" - self.X_ = X - return self - - def predict(self, X): - """Predict dummy.""" - return [(0, 0)] diff --git a/aeon/testing/mock_estimators/_mock_similarity_searchers.py b/aeon/testing/mock_estimators/_mock_similarity_searchers.py new file mode 100644 index 0000000000..a2919c939d --- /dev/null +++ b/aeon/testing/mock_estimators/_mock_similarity_searchers.py @@ -0,0 +1,37 @@ +"""Mock series transformers useful for testing and debugging.""" + +__maintainer__ = ["baraline"] +__all__ = ["MockSeriesSimilaritySearch", "MockCollectionSimilaritySearch"] + +from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch +from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch + + +class MockSeriesSimilaritySearch(BaseSeriesSimilaritySearch): + """Mock estimator for BaseMatrixProfile.""" + + def __init__(self): + super().__init__() + + def _fit(self, X, y=None): + return self + + def predict(self, X): + """Compute matrix profiles between X_ and X or between all series in X_.""" + X = self._pre_predict(X) + return [0], [0.1] + + +class MockCollectionSimilaritySearch(BaseCollectionSimilaritySearch): + """Mock estimator for BaseMatrixProfile.""" + + def __init__(self): + super().__init__() + + def _fit(self, X, y=None): + return self + + def predict(self, X): + """Compute matrix profiles between X_ and X or between all series in X_.""" + X = self._pre_predict(X) + return [0], [0.1] diff --git a/aeon/testing/testing_config.py b/aeon/testing/testing_config.py index 4c46058318..61ff90cdd1 100644 --- a/aeon/testing/testing_config.py +++ b/aeon/testing/testing_config.py @@ -57,10 +57,6 @@ "ClaSPSegmenter": ["check_non_state_changing_method"], "HMMSegmenter": ["check_non_state_changing_method"], "RSTSF": ["check_non_state_changing_method"], - # Keeps length during predict to avoid recomputing means and std of data in fit - # if the next predict calls uses the same query length parameter. - "QuerySearch": ["check_non_state_changing_method"], - "SeriesSearch": ["check_non_state_changing_method"], # Unknown issue not producing the same results "RDSTRegressor": ["check_regressor_against_expected_results"], "RISTRegressor": ["check_regressor_against_expected_results"], diff --git a/aeon/testing/testing_data.py b/aeon/testing/testing_data.py index eb134cddda..e6730c9958 100644 --- a/aeon/testing/testing_data.py +++ b/aeon/testing/testing_data.py @@ -10,7 +10,8 @@ from aeon.forecasting import BaseForecaster from aeon.regression import BaseRegressor from aeon.segmentation import BaseSegmenter -from aeon.similarity_search import BaseSimilaritySearch +from aeon.similarity_search.collection import BaseCollectionSimilaritySearch +from aeon.similarity_search.series import BaseSeriesSimilaritySearch from aeon.testing.data_generation import ( make_example_1d_numpy, make_example_2d_dataframe_collection, @@ -219,7 +220,7 @@ }, } -EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH = { +EQUAL_LENGTH_UNIVARIATE_COLLETION_SIMILARITY_SEARCH = { "numpy3D": { "train": ( make_example_3d_numpy( @@ -401,7 +402,7 @@ }, } -EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH = { +EQUAL_LENGTH_MULTIVARIATE_COLLETION_SIMILARITY_SEARCH = { "numpy3D": { "train": ( make_example_3d_numpy( @@ -553,7 +554,7 @@ }, } -UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH = { +UNEQUAL_LENGTH_UNIVARIATE_COLLETION_SIMILARITY_SEARCH = { "np-list": { "train": ( make_example_3d_numpy_list( @@ -685,30 +686,6 @@ }, } -UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH = { - "np-list": { - "train": ( - make_example_3d_numpy_list( - n_cases=10, - n_channels=2, - min_n_timepoints=10, - max_n_timepoints=20, - random_state=data_rng.randint(np.iinfo(np.int32).max), - return_y=False, - ), - None, - ), - "test": ( - make_example_2d_numpy_series( - n_timepoints=10, - n_channels=2, - random_state=data_rng.randint(np.iinfo(np.int32).max), - ), - None, - ), - }, -} - X_classification_missing_train, y_classification_missing_train = make_example_3d_numpy( n_cases=10, n_channels=1, @@ -828,7 +805,7 @@ FULL_TEST_DATA_DICT.update( { f"EqualLengthUnivariate-SimilaritySearch-{k}": v - for k, v in EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH.items() + for k, v in EQUAL_LENGTH_UNIVARIATE_COLLETION_SIMILARITY_SEARCH.items() } ) FULL_TEST_DATA_DICT.update( @@ -846,7 +823,7 @@ FULL_TEST_DATA_DICT.update( { f"EqualLengthMultivariate-SimilaritySearch-{k}": v - for k, v in EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH.items() + for k, v in EQUAL_LENGTH_MULTIVARIATE_COLLETION_SIMILARITY_SEARCH.items() } ) FULL_TEST_DATA_DICT.update( @@ -863,8 +840,8 @@ ) FULL_TEST_DATA_DICT.update( { - f"UnequalLengthUnivariate-SimilaritySearch-{k}": v - for k, v in UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH.items() + f"UnequalLengthUnivariate-CollectionSimilaritySearch-{k}": v + for k, v in UNEQUAL_LENGTH_UNIVARIATE_COLLETION_SIMILARITY_SEARCH.items() } ) FULL_TEST_DATA_DICT.update( @@ -879,12 +856,6 @@ for k, v in UNEQUAL_LENGTH_MULTIVARIATE_REGRESSION.items() } ) -FULL_TEST_DATA_DICT.update( - { - f"UnequalLengthMultivariate-SimilaritySearch-{k}": v - for k, v in UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH.items() - } -) FULL_TEST_DATA_DICT.update( { f"MissingValues-Classification-{k}": v @@ -1017,14 +988,15 @@ def _get_task_for_estimator(estimator): # collection data with continuous target labels elif isinstance(estimator, BaseRegressor): data_label = "Regression" - elif isinstance(estimator, BaseSimilaritySearch): - data_label = "SimilaritySearch" + elif isinstance(estimator, BaseCollectionSimilaritySearch): + data_label = "CollectionSimilaritySearch" # series data with no secondary input elif ( isinstance(estimator, BaseAnomalyDetector) or isinstance(estimator, BaseSegmenter) or isinstance(estimator, BaseSeriesTransformer) or isinstance(estimator, BaseForecaster) + or isinstance(estimator, BaseSeriesSimilaritySearch) ): data_label = "None" else: diff --git a/aeon/testing/utils/estimator_checks.py b/aeon/testing/utils/estimator_checks.py index b2e0973dbf..d556ff0249 100644 --- a/aeon/testing/utils/estimator_checks.py +++ b/aeon/testing/utils/estimator_checks.py @@ -7,7 +7,7 @@ import numpy as np -from aeon.similarity_search.base import BaseSimilaritySearch +from aeon.similarity_search import BaseSimilaritySearch from aeon.testing.testing_data import FULL_TEST_DATA_DICT from aeon.utils.validation import get_n_cases diff --git a/aeon/utils/base/_identifier.py b/aeon/utils/base/_identifier.py index cf2722cfcb..03e8d8beaf 100644 --- a/aeon/utils/base/_identifier.py +++ b/aeon/utils/base/_identifier.py @@ -55,6 +55,8 @@ def get_identifier(estimator): identifiers.remove("collection-estimator") if len(identifiers) > 1 and "transformer" in identifiers: identifiers.remove("transformer") + if len(identifiers) > 1 and "similarity-search" in identifiers: + identifiers.remove("similarity-search") if len(identifiers) > 1: TypeError( diff --git a/aeon/utils/base/_register.py b/aeon/utils/base/_register.py index 1d81c2512c..5e81e29b33 100644 --- a/aeon/utils/base/_register.py +++ b/aeon/utils/base/_register.py @@ -24,7 +24,9 @@ from aeon.forecasting.base import BaseForecaster from aeon.regression.base import BaseRegressor from aeon.segmentation.base import BaseSegmenter -from aeon.similarity_search.base import BaseSimilaritySearch +from aeon.similarity_search._base import BaseSimilaritySearch +from aeon.similarity_search.collection import BaseCollectionSimilaritySearch +from aeon.similarity_search.series import BaseSeriesSimilaritySearch from aeon.transformations.base import BaseTransformer from aeon.transformations.collection import BaseCollectionTransformer from aeon.transformations.series import BaseSeriesTransformer @@ -36,6 +38,7 @@ "estimator": BaseAeonEstimator, "series-estimator": BaseSeriesEstimator, "transformer": BaseTransformer, + "similarity-search": BaseSimilaritySearch, # estimator types "anomaly-detector": BaseAnomalyDetector, "collection-transformer": BaseCollectionTransformer, @@ -44,14 +47,21 @@ "early_classifier": BaseEarlyClassifier, "regressor": BaseRegressor, "segmenter": BaseSegmenter, - "similarity_searcher": BaseSimilaritySearch, "series-transformer": BaseSeriesTransformer, "forecaster": BaseForecaster, + "series-similarity-search": BaseSeriesSimilaritySearch, + "collection-similarity-search": BaseCollectionSimilaritySearch, } # base classes which are valid for estimator to directly inherit from VALID_ESTIMATOR_BASES = { k: BASE_CLASS_REGISTER[k] for k in BASE_CLASS_REGISTER.keys() - - {"estimator", "collection-estimator", "series-estimator", "transformer"} + - { + "estimator", + "collection-estimator", + "series-estimator", + "transformer", + "similarity-search", + } } diff --git a/aeon/utils/discovery.py b/aeon/utils/discovery.py index 8fd4a05efe..d6e5ce61fc 100644 --- a/aeon/utils/discovery.py +++ b/aeon/utils/discovery.py @@ -92,6 +92,7 @@ def all_estimators( # ignore test modules and base classes "base", "tests", + "similarity_search" # ignore these submodules "benchmarking", "datasets", diff --git a/aeon/utils/numba/general.py b/aeon/utils/numba/general.py index 10e96abde6..958c584459 100644 --- a/aeon/utils/numba/general.py +++ b/aeon/utils/numba/general.py @@ -8,7 +8,9 @@ "first_order_differences_3d", "z_normalise_series_with_mean", "z_normalise_series", + "z_normalise_series_with_mean_std", "z_normalise_series_2d", + "z_normalise_series_2d_with_mean_std", "z_normalise_series_3d", "set_numba_random_seed", "choice_log", @@ -20,6 +22,7 @@ "slope_derivative_2d", "slope_derivative_3d", "generate_combinations", + "get_all_subsequences", ] @@ -273,7 +276,7 @@ def z_normalise_series_2d_with_mean_std( Parameters ---------- - X : array, shape = (n_channels, n_timestamps) + X : array, shape = (n_channels, n_timepoints) Input array to normalise. mean : array, shape = (n_channels) Mean of each channel of X. @@ -282,7 +285,7 @@ def z_normalise_series_2d_with_mean_std( Returns ------- - arr : array, shape = (n_channels, n_timestamps) + arr : array, shape = (n_channels, n_timepoints) The normalised array """ arr = np.zeros(X.shape) @@ -376,10 +379,10 @@ def get_subsequence( Parameters ---------- - X : array, shape (n_channels, n_timestamps) + X : array, shape (n_channels, n_timepoints) Input time series. i_start : int - A starting index between [0, n_timestamps - (length-1)*dilation] + A starting index between [0, n_timepoints - (length-1)*dilation] length : int Length parameter of the subsequence. dilation : int @@ -408,10 +411,10 @@ def get_subsequence_with_mean_std( Parameters ---------- - X : array, shape (n_channels, n_timestamps) + X : array, shape (n_channels, n_timepoints) Input time series. i_start : int - A starting index between [0, n_timestamps - (length-1)*dilation] + A starting index between [0, n_timepoints - (length-1)*dilation] length : int Length parameter of the subsequence. dilation : int @@ -451,15 +454,56 @@ def get_subsequence_with_mean_std( return values, means, stds +@njit(cache=True, fastmath=True, parallel=True) +def compute_mean_stds_collection_parallel(X): + """ + Return the mean and standard deviation for each channel of all series in X. + + Parameters + ---------- + X : array, shape (n_cases, n_channels, n_timepoints) + A time series collection + + Returns + ------- + means : array, shape (n_cases, n_channels) + The mean of each channel of each time series in X. + stds : array, shape (n_cases, n_channels) + The std of each channel of each time series in X. + + """ + n_channels = X[0].shape[0] + n_cases = len(X) + means = np.zeros((n_cases, n_channels)) + stds = np.zeros((n_cases, n_channels)) + for i_x in prange(n_cases): + n_timepoints = X[i_x].shape[1] + _s = np.zeros(n_channels) + _s2 = np.zeros(n_channels) + for i_t in range(n_timepoints): + for i_c in range(n_channels): + _s += X[i_x][i_c, i_t] + _s2 += X[i_x][i_c, i_t] ** 2 + + for i_c in range(n_channels): + means[i_x, i_c] = _s / n_timepoints + _std = _s2 / n_timepoints - means[i_x, i_c] ** 2 + if _s > AEON_NUMBA_STD_THRESHOLD: + stds[i_x, i_c] = _std**0.5 + + return means, stds + + @njit(fastmath=True, cache=True) def sliding_mean_std_one_series( X: np.ndarray, length: int, dilation: int ) -> tuple[np.ndarray, np.ndarray]: - """Return the mean and standard deviation for all subsequence (l,d) in X. + """ + Return the mean and standard deviation for all subsequence (l,d) in X. Parameters ---------- - X : array, shape (n_channels, n_timestamps) + X : array, shape (n_channels, n_timepoints) An input time series length : int Length of the subsequence @@ -468,14 +512,14 @@ def sliding_mean_std_one_series( Returns ------- - mean : array, shape (n_channels, n_timestamps - (length-1) * dilation) + mean : array, shape (n_channels, n_timepoints - (length-1) * dilation) The mean of each subsequence with parameter length and dilation in X. - std : array, shape (n_channels, n_timestamps - (length-1) * dilation) + std : array, shape (n_channels, n_timepoints - (length-1) * dilation) The standard deviation of each subsequence with parameter length and dilation in X. """ - n_channels, n_timestamps = X.shape - n_subs = n_timestamps - (length - 1) * dilation + n_channels, n_timepoints = X.shape + n_subs = n_timepoints - (length - 1) * dilation if n_subs <= 0: raise ValueError( "Invalid input parameter for sliding mean and std computations" @@ -493,7 +537,7 @@ def sliding_mean_std_one_series( _sum2 = np.zeros(n_channels) # Initialize first subsequence if it is valid - if np.all(_idx_sub < n_timestamps): + if np.all(_idx_sub < n_timepoints): for i_length in prange(length): _idx_sub[i_length] = (i_length * dilation) + i_mod_dil for i_channel in prange(n_channels): @@ -510,7 +554,7 @@ def sliding_mean_std_one_series( _idx_sub += dilation # As long as subsequences further subsequences are valid - while np.all(_idx_sub < n_timestamps): + while np.all(_idx_sub < n_timepoints): # Update sums and mean stds arrays for i_channel in prange(n_channels): _v_new = X[i_channel, _idx_sub[-1]] @@ -534,17 +578,17 @@ def normalise_subsequences(X_subs: np.ndarray, X_means: np.ndarray, X_stds: np.n Parameters ---------- - X_subs : array, shape (n_timestamps-(length-1)*dilation, n_channels, length) - The subsequences of an input time series of size n_timestamps given the + X_subs : array, shape (n_timepoints-(length-1)*dilation, n_channels, length) + The subsequences of an input time series of size n_timepoints given the length and dilation parameter. - X_means : array, shape (n_channels, n_timestamps-(length-1)*dilation) + X_means : array, shape (n_channels, n_timepoints-(length-1)*dilation) Mean of the subsequences to normalise. - X_stds : array, shape (n_channels, n_timestamps-(length-1)*dilation) + X_stds : array, shape (n_channels, n_timepoints-(length-1)*dilation) Stds of the subsequences to normalise. Returns ------- - array, shape = (n_timestamps-(length-1)*dilation, n_channels, length) + array, shape = (n_timepoints-(length-1)*dilation, n_channels, length) Z-normalised subsequences. """ n_subsequences, n_channels, length = X_subs.shape @@ -755,8 +799,8 @@ def get_all_subsequences(X: np.ndarray, length: int, dilation: int) -> np.ndarra Parameters ---------- - X : array, shape = (n_channels, n_timestamps) - An input time series as (n_channels, n_timestamps). + X : array, shape = (n_channels, n_timepoints) + An input time series as (n_channels, n_timepoints). length : int Length of the subsequences to generate. dilation : int @@ -764,11 +808,11 @@ def get_all_subsequences(X: np.ndarray, length: int, dilation: int) -> np.ndarra Returns ------- - array, shape = (n_timestamps-(length-1)*dilation, n_channels, length) + array, shape = (n_timepoints-(length-1)*dilation, n_channels, length) The view of the subsequences of the input time series. """ - n_features, n_timestamps = X.shape + n_features, n_timepoints = X.shape s0, s1 = X.strides - out_shape = (n_timestamps - (length - 1) * dilation, n_features, np.int64(length)) + out_shape = (n_timepoints - (length - 1) * dilation, n_features, np.int64(length)) strides = (s1, s0, s1 * dilation) return np.lib.stride_tricks.as_strided(X, shape=out_shape, strides=strides) diff --git a/aeon/utils/tags/_tags.py b/aeon/utils/tags/_tags.py index e1bacdd5ad..d85ba87caa 100644 --- a/aeon/utils/tags/_tags.py +++ b/aeon/utils/tags/_tags.py @@ -138,7 +138,7 @@ class : identifier for the base class of objects this tag applies to "point belongs to.", }, "requires_y": { - "class": ["transformer", "anomaly-detector", "segmenter"], + "class": ["transformer", "anomaly-detector", "segmenter", "similarity-search"], "type": "bool", "description": "Does this estimator require y to be passed in its methods?", }, diff --git a/docs/api_reference/similarity_search.rst b/docs/api_reference/similarity_search.rst index eb13cafd23..7212179953 100644 --- a/docs/api_reference/similarity_search.rst +++ b/docs/api_reference/similarity_search.rst @@ -4,51 +4,47 @@ Similarity search ================= The :mod:`aeon.similarity_search` module contains algorithms and tools for similarity -search tasks. +search tasks. First, we distinguish between `series` estimator and `collection` +estimators, similarly to the `aeon.transformer` module. Secondly, we distinguish between +estimators used `neighbors` (with sufix SNN for subsequence nearest neighbors, or ANN +for approximate nearest neighbors) search and estimators used for `motifs` search. -Similarity search estimators ----------------------------- +Series Similarity search estimators +----------------------------------- -.. currentmodule:: aeon.similarity_search +.. currentmodule:: aeon.similarity_search.series.neighbors .. autosummary:: :toctree: auto_generated/ :template: class.rst - QuerySearch - SeriesSearch + DummySNN + MassSNN -Distance profile functions --------------------------- - -.. currentmodule:: aeon.similarity_search.distance_profiles +.. currentmodule:: aeon.similarity_search.series.motifs .. autosummary:: :toctree: auto_generated/ - :template: function.rst + :template: class.rst + + StompMotif - euclidean_distance_profile - normalised_euclidean_distance_profile - squared_distance_profile - normalised_squared_distance_profile -Matrix profile functions --------------------------- +Collection Similarity search estimators +----------------------------------- -.. currentmodule:: aeon.similarity_search.matrix_profiles +.. currentmodule:: aeon.similarity_search.collection.neighbors .. autosummary:: :toctree: auto_generated/ - :template: function.rst + :template: class.rst + + RandomProjectionIndexANN - stomp_normalised_euclidean_matrix_profile - stomp_euclidean_matrix_profile - stomp_normalised_squared_matrix_profile - stomp_squared_matrix_profile -Base ----- +Base Estimators +--------------- .. currentmodule:: aeon.similarity_search.base @@ -57,3 +53,20 @@ Base :template: class.rst BaseSimilaritySearch + + +.. currentmodule:: aeon.similarity_search.series.base + +.. autosummary:: + :toctree: auto_generated/ + :template: class.rst + + BaseSeriesSimilaritySearch + +.. currentmodule:: aeon.similarity_search.collection.base + +.. autosummary:: + :toctree: auto_generated/ + :template: class.rst + + BaseCollectionSimilaritySearch diff --git a/docs/api_reference/utils.rst b/docs/api_reference/utils.rst index 40dea9f67c..6f43398a44 100644 --- a/docs/api_reference/utils.rst +++ b/docs/api_reference/utils.rst @@ -87,7 +87,6 @@ Mock Estimators MockUnivariateSeriesTransformer MockMultivariateSeriesTransformer MockSeriesTransformerNoFit - MockSimilaritySearch Utilities ^^^^^^^^^ diff --git a/docs/getting_started.md b/docs/getting_started.md index 36f18583cb..ce519359f2 100644 --- a/docs/getting_started.md +++ b/docs/getting_started.md @@ -21,8 +21,9 @@ classical techniques for the following learning tasks: - [**Clustering**](api_reference/clustering), where a collection of time series without any labels are used to train a model to label cases ([more details](examples/clustering/clustering.ipynb)). -- [**Similarity search**](api_reference/similarity_search), where the goal is to evaluate - the similarity between a query time series and a collection of other longer time series +- [**Similarity search**](api_reference/similarity_search), where the goal is to find + time series motifs or nearest neighbors in an efficient way for either single series + or collections. ([more details](examples/similarity_search/similarity_search.ipynb)). - [**Anomaly detection**](api_reference/anomaly_detection), where the goal is to find values or areas of a single time series that are not representative of the whole series. diff --git a/examples/similarity_search/code_speed.ipynb b/examples/similarity_search/code_speed.ipynb index f31155333d..9b4c08acf3 100644 --- a/examples/similarity_search/code_speed.ipynb +++ b/examples/similarity_search/code_speed.ipynb @@ -554,7 +554,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/examples/similarity_search/distance_profiles.ipynb b/examples/similarity_search/distance_profiles.ipynb index ec56fcc6bf..d5ea595ff5 100644 --- a/examples/similarity_search/distance_profiles.ipynb +++ b/examples/similarity_search/distance_profiles.ipynb @@ -146,7 +146,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/examples/similarity_search/similarity_search.ipynb b/examples/similarity_search/similarity_search.ipynb index cdbaa86948..91024292ef 100644 --- a/examples/similarity_search/similarity_search.ipynb +++ b/examples/similarity_search/similarity_search.ipynb @@ -7,12 +7,27 @@ "source": [ "# Time Series Similarity Search with aeon\n", "\n", - "The goal of Time Series Similarity Search is to asses the similarities between a time\n", - " series, denoted as a query `q` of length `l`, and a collection of time series,\n", - " denoted as `X`, with lengths greater than or equal to `l`. In this\n", - " context, the notion of similiarity between `q` and the other series in `X` is quantified by similarity functions. Those functions are most of the time defined as distance function, such as the Euclidean distance. Knowing the similarity between `q` and other admissible candidates, we can then perform many other tasks for \"free\", such as anomaly or motif detection.\n", + "\"time\n", "\n", - "\"time" + "The objectives of the similarity search module in aeon is to provide estimators with a `fit`/`predict` interface to solve the following use cases :\n", + "\n", + "- Nearest neighbors search on time series subesequences or whole series\n", + "- Motifs search on time series subsequences\n", + "\n", + "Similarly to the `transformer` module, the `similarity_search` module split estimators between `series` estimators and `collection` estimators, such as :\n", + "\n", + "- `series` estimators take as input a single time series of shape `(n_channels, n_timepoints)` during fit and predict.\n", + "- `collection` estimators take as input a time series collection of shape `(n_cases, n_channels, n_timepoints)` during fit, and a single series of shape `(n_channels, n_timepoints)` during predict.\n", + "\n", + "Note that the above is a general guideline, and that some estimators can also take `None` as input during predict, or series of length different to `n_timepoints`. We'll explore the different estimators in the next sections.\n", + "\n", + "### Other similarity search notebooks\n", + "\n", + "This notebook gives an overview of similarity search module and the available estimators. The following notebooks are also avaiable to go more in depth with specific subject of similarity search in aeon:\n", + "\n", + "- [The theory and math behind the similarity search estimators in aeon](distance_profiles.ipynb)\n", + "- [Analysis of the performance of the estimators provided by similarity search module](code_speed.ipynb)\n", + "\n" ] }, { @@ -22,25 +37,34 @@ "metadata": {}, "outputs": [], "source": [ - "def plot_best_matches(top_k_search, best_matches):\n", + "# Define some plotting functions we'll use later !\n", + "def plot_best_matches(\n", + " X_fit, X_predict, idx_predict, idx_matches, length, normalize=False\n", + "):\n", " \"\"\"Plot the top best matches of a query in a dataset.\"\"\"\n", - " fig, ax = plt.subplots(figsize=(20, 5), ncols=3)\n", - " for i_k, (id_sample, id_timestamp) in enumerate(best_matches):\n", + " fig, ax = plt.subplots(figsize=(20, 5), ncols=len(idx_matches))\n", + " if len(idx_matches) == 1:\n", + " ax = [ax]\n", + " for i_k, id_timestamp in enumerate(idx_matches):\n", " # plot the sample of the best match\n", - " ax[i_k].plot(top_k_search.X_[id_sample, 0], linewidth=2)\n", + " ax[i_k].plot(X_fit[0], linewidth=2)\n", " # plot the location of the best match on it\n", + " match = X_fit[0, id_timestamp : id_timestamp + length]\n", " ax[i_k].plot(\n", - " range(id_timestamp, id_timestamp + q.shape[1]),\n", - " top_k_search.X_[id_sample, 0, id_timestamp : id_timestamp + q.shape[1]],\n", + " range(id_timestamp, id_timestamp + length),\n", + " match,\n", " linewidth=7,\n", " alpha=0.5,\n", " color=\"green\",\n", " label=\"best match location\",\n", " )\n", " # plot the query on the location of the best match\n", + " Q = X_predict[0, idx_predict : idx_predict + length]\n", + " if normalize:\n", + " Q = Q * np.std(match) + np.mean(match)\n", " ax[i_k].plot(\n", - " range(id_timestamp, id_timestamp + q.shape[1]),\n", - " q[0],\n", + " range(id_timestamp, id_timestamp + length),\n", + " Q,\n", " linewidth=5,\n", " alpha=0.5,\n", " color=\"red\",\n", @@ -66,73 +90,32 @@ " plt.show()" ] }, - { - "cell_type": "markdown", - "id": "7e06b213-6038-4901-b98e-2433625115c4", - "metadata": {}, - "source": [ - "## Similarity search Notebooks\n", - "\n", - "This notebook gives an overview of similarity search module and the available estimators. The following notebooks are avaiable to go more in depth with specific subject of similarity search in aeon:\n", - "\n", - "- [Deep dive in the distance profiles](distance_profiles.ipynb)\n", - "- [Analysis of the speedups provided by similarity search module](code_speed.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "ca967c08-9a05-411a-a09a-ad8a13c0adb9", - "metadata": {}, - "source": [ - "## Expected inputs and format\n", - "For both `QuerySearch` and `SeriesSearch`, the `fit` method expects a time series dataset of shape `(n_cases, n_channels, n_timepoints)`. This can be 3D numpy array or a list of 2D numpy arrays if `n_timepoints` varies between cases (i.e. unequal length dataset).\n", - "\n", - "The `predict` method expects a 2D numpy array of shape `(n_channels, query_length)` for `QuerySearch`. In `SeriesSearch`, the predict methods also expects a 2D numpy array, but of shape `(n_channels, n_timepoints)` (`n_timepoints` doesn't have to be the same as in fit) and a `query_length` parameter." - ] - }, { "cell_type": "markdown", "id": "d1fd75ae-84c2-40be-95f6-bd7de409317d", "metadata": {}, "source": [ - "## Available estimators\n", + "### A word on base clases\n", "\n", - "All estimators of the similarity search module in aeon inherit from the `BaseSimilaritySearch` class, which requires the following arguments:\n", - "- `distance` : a string indicating which distance function to use as similarity function. By default this is `\"euclidean\"`, which means that the Euclidean distance is used.\n", - "- `normalise` : a boolean indicating whether this similarity function should be z-normalised. This means that the scale of the two series being compared will be ignored, and that, loosely speaking, we will only focus on their shape during the comparison. By default, this parameter is set `False`.\n", + "All estimators of the similarity search module in aeon inherit from the `BaseSimilaritySearch` class, which define the some abstract methods that estimator must implement, such as `fit` and `predict` and some private function used to validate the format of the time series you will provide. Then, the two submodules `series` and `collection` also define a base class (`BaseSeriesSimilaritySearch` and `BaseCollectionSeriesSearch`) that their respective estimator will inherit from. If you ever want to extend the module or create your own estimators, these are the classes you'll want to use to define the base structure of your estimator.\n", "\n", - "Another parameter, which has no effect on the output of the estimators, is a boolean named `store_distance_profile`, set to `False` by default. If set to `True`, the estimators will expose an attribute named `_distance_profile` after the `predict` function is called. This attribute will contain the computed distance profile for query given as input to the `predict` function.\n", + "### Load a dataset\n", + "In the following, we'll use an easy dataset (`GunPoint`) to help build intuition. Don't hesitate to swap it with other datasets to explore ! We load it using the `load_classification` function.\n", "\n", - "To illustrate how to work with similarity search estimators in aeon, we will now present some example use cases." - ] - }, - { - "cell_type": "markdown", - "id": "01fa67c2-0126-4152-98a9-fa0df84c4629", - "metadata": {}, - "source": [ - "### Query search" - ] - }, - { - "cell_type": "markdown", - "id": "8e99b251-d156-4989-b5a0-3a2c79cb75d4", - "metadata": {}, - "source": [ - "We will use the GunPoint dataset for this example, which can be loaded using the `load_classification` function." + "The GunPoint dataset is composed of two classes which are discriminated by the \"bumps\" located before and after the central peak. These bumps correspond to an actor drawing a fake gun from a holster before pointing it (hence the name \"GunPoint\" !). In the second class, the actor simply points his fingers without making the motion of taking the gun out of the holster." ] }, { "cell_type": "code", "execution_count": 2, - "id": "f8a6bb7e-b219-41f1-b508-b849c45672eb", + "id": "20d3b591-f275-4548-a7d2-75b16380b055", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -162,99 +145,97 @@ }, { "cell_type": "markdown", - "id": "5392f7f4-1825-4b15-9248-27eeecb1af3c", + "id": "01fa67c2-0126-4152-98a9-fa0df84c4629", "metadata": {}, "source": [ - "The GunPoint dataset is composed of two classes which are discriminated by the \"bumps\" located before and after the central peak. These bumps correspond to an actor drawing a fake gun from a holster before pointing it (hence the name \"GunPoint\" !). In the second class, the actor simply points his fingers without making the motion of taking the gun out of the holster.\n", + "## 1. Series estimators\n", "\n", - "Suppose that we define our input query for the similarity search task as one of these bumps:" + "First, we'll explore estimators of the `series` module, where you must provide single series of shape `(n_channels, n_timepoints)` during fit." ] }, { - "cell_type": "code", - "execution_count": 3, - "id": "a494a0be-4459-414d-9fc2-1400feefd171", + "cell_type": "markdown", + "id": "78f17f93-28b3-49c0-be5f-1d430a273b0c", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ - "# We will use the fourth sample an testing data\n", - "X_test = X[3]\n", - "mask = np.ones(X.shape[0], dtype=bool)\n", - "mask[3] = False\n", - "# Use this mask to exluce the sample from which we will extract the query\n", - "X_train = X[mask]\n", - "\n", - "q = X_test[:, 20:55]\n", - "plt.plot(q[0])\n", - "plt.show()" + "### 1.1 Subsequence nearest neighbors with MASS\n", + "\n", + "To perform nearest neighbors search on subsequences on a series, we can use the `MassSNN` estimator.\n", + "\n", + "It takes as parameter during initialisation :\n", + "- `length` : an integer giving the length of the subsequences to extract from the series. It is also the expected length of the series given in `predict`\n", + "- `normalize`: a boolean indicating wheter the subsequences should be independently z-normalized (`(X-mean(X))/std(X)`) before the distance computations. This results in a scale-independent matching.\n", + " \n", + "To parameterize the search, additional parameters are available when calling the `predict` method:\n", + "\n", + "- `k` (int) : the number of nearest neighbors to return.\n", + "- `dist_threshold` (float) : the maximum allowed distance for a candidate subsequence to be considered as a neighbor.\n", + "- `allow_trivial_matches` (bool) : wheter a neighbors of a match to a query can be also considered as matches (True), or if an exclusion zone is applied around each match to avoid trivial matches with their direct neighbors (False).\n", + "- `inverse_distance` (bool) : if True, the matching will be made on the inverse of the distance, and thus, the farther neighbors will be returned instead of the closest ones.\n", + "- `exclusion_factor` (float): A factor of the `length` used to define the exclusion zone when `allow_trivial_matches` is set to False. For a given timestamp, the exclusion zone starts from `id_timestamp - length//exclusion_factor` and end at `id_timestamp + length//exclusion_factor`.\n", + "- `X_index` (int): If series given during predict is a subsequence of series given during fit, specify its starting timestamp. If specified, neighboring subsequences of X won't be able to match as neighbors." ] }, { "cell_type": "markdown", - "id": "fcf10a34-930a-4fce-86f8-4dfa207cad11", + "id": "33105406-fc83-4143-9345-af589a06a00a", "metadata": {}, "source": [ - "Then, we can use the `QuerySearch` class to search for the top `k` matches of this query in a collection of series. The training data for `QuerySearch` can be seen as the database in which want to search for the query on." + "First, we'll select a series from the dataset to use during fit. This is the series we want our neighbors to come from." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "80eaab8f-204f-439f-84c8-ad3462f1575e", + "execution_count": 3, + "id": "a494a0be-4459-414d-9fc2-1400feefd171", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "match 0 : [195 26] with distance 0.1973741999473598 to q\n", - "match 1 : [92 23] with distance 0.20753669049486048 to q\n", - "match 2 : [154 22] with distance 0.21538593730366784 to q\n" + "(1, 150)\n" ] } ], "source": [ - "from aeon.similarity_search import QuerySearch\n", - "\n", - "# Here, the distance function (distance and normalise arguments)\n", - "top_k_search = QuerySearch(k=3, distance=\"euclidean\")\n", - "# Call fit to store X_train as the database to search in\n", - "top_k_search.fit(X_train)\n", - "distances_to_matches, best_matches = top_k_search.predict(q)\n", - "for i in range(len(best_matches)):\n", - " print(f\"match {i} : {best_matches[i]} with distance {distances_to_matches[i]} to q\")" + "from aeon.similarity_search.series import MassSNN\n", + "\n", + "length = 35\n", + "# We'll take a sample of the class with a \"bump\".\n", + "series_fit = X[2]\n", + "print(series_fit.shape)\n", + "snn = MassSNN(length=length, normalize=False).fit(series_fit)" ] }, { "cell_type": "markdown", - "id": "3dc402cf-80b7-4d0c-b07c-2f8e7822ac97", + "id": "320ef728-ca92-4fd5-9686-2b9739fcab83", "metadata": {}, "source": [ - "The similarity search estimators return a list of size `k`, which contains a tuple containing the location of the best matches as `(id_sample, id_timestamp)`. We can then plot the results as:" + "Then we'll take a subsequence of size `length` in another series of the same class to use in `predict` :" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "23efe48e-8257-4ecc-93a2-d72f19024ab5", + "execution_count": 4, + "id": "98560db4-4289-4072-8662-2cde2ad5c44a", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "match 0 : 27 with distance 0.3020071566139322\n", + "match 1 : 28 with distance 0.48913603040398357\n", + "match 2 : 26 with distance 0.889697094966067\n" + ] + }, { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -262,244 +243,221 @@ } ], "source": [ - "plot_best_matches(top_k_search, best_matches)" + "series_predict = X[3]\n", + "starting_timestep_predict = 25\n", + "indexes, distances = snn.predict(\n", + " series_predict[:, starting_timestep_predict : starting_timestep_predict + length],\n", + " k=3,\n", + " allow_trivial_matches=True,\n", + ")\n", + "for i in range(len(indexes)):\n", + " print(f\"match {i} : {indexes[i]} with distance {distances[i]}\")\n", + "plot_best_matches(\n", + " series_fit, series_predict, starting_timestep_predict, indexes, length\n", + ")" ] }, { "cell_type": "markdown", - "id": "877b1b32-d978-4c54-a4e7-b475496f710a", + "id": "fcf10a34-930a-4fce-86f8-4dfa207cad11", "metadata": {}, "source": [ - "You may also want to search not for the top-k matches, but for all matches below a threshold on the distance from the query to a candidate. To do so, you can use the `threshold` parameter of `QuerySearch` :" + "The `predict` method returns two lists, containing the starting timesteps of the matches in `series_fit` and the squared euclidean distance of these matches to the subsequence we gave in `predict`. Now, you can then play with the different parameters of `predict` to customize your search results to your needs!\n", + "\n", + "It is also possible to get the distance profile which is used to extract the best matches :" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "23ad7adb-2b01-4425-a2e8-c393f3721a0f", + "execution_count": 5, + "id": "7d2bd3f7-7eb9-4406-be1c-b6fcd9c76730", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "match 0 : [195 26] with distance 0.1973741999473598 to q\n", - "match 1 : [92 23] with distance 0.20753669049486048 to q\n", - "match 2 : [154 22] with distance 0.21538593730366784 to q\n", - "match 3 : [176 25] with distance 0.21889484294879047 to q\n", - "match 4 : [23 20] with distance 0.22668346183441293 to q\n", - "match 5 : [167 23] with distance 0.24774491003815066 to q\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\antoine\\Documents\\aeon\\aeon\\similarity_search\\query_search.py:270: UserWarning: Only 6 matches are bellow the threshold of 0.25, while k=inf. The number of returned match will be 6.\n", - " return extract_top_k_and_threshold_from_distance_profiles(\n" - ] + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "# Here, the distance function (distance and normalise arguments)\n", - "top_k_search = QuerySearch(k=np.inf, threshold=0.25, distance=\"euclidean\")\n", - "top_k_search.fit(X_train)\n", - "distances_to_matches, best_matches = top_k_search.predict(q)\n", - "for i in range(len(best_matches)):\n", - " print(f\"match {i} : {best_matches[i]} with distance {distances_to_matches[i]} to q\")" + "distance_profile = snn.compute_distance_profile(\n", + " series_predict[:, starting_timestep_predict : starting_timestep_predict + length],\n", + ")\n", + "plt.figure(figsize=(7, 2))\n", + "plt.plot(distance_profile)\n", + "plt.show()" ] }, { "cell_type": "markdown", - "id": "0efd83a5-b36f-4809-be96-94de734d931c", + "id": "b5240535-5123-4ac5-a5e0-e0502ef80b3e", "metadata": {}, "source": [ - "You may also combine the `k` and `threshold` parameter :" + "### 1.2 Motif search with STOMP" ] }, { - "cell_type": "code", - "execution_count": 7, - "id": "65db1593-3873-4a47-9e2a-d8dfcf42dd1a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "match 0 : [195 26] with distance 0.1973741999473598 to q\n", - "match 1 : [92 23] with distance 0.20753669049486048 to q\n", - "match 2 : [154 22] with distance 0.21538593730366784 to q\n" - ] + "attachments": { + "f492cb89-5bf3-4641-8be2-a77805f20b88.png": { + "image/png": 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" } - ], - "source": [ - "# Here, the distance function (distance and normalise arguments)\n", - "top_k_search = QuerySearch(k=3, threshold=0.25, distance=\"euclidean\")\n", - "top_k_search.fit(X_train)\n", - "distances_to_matches, best_matches = top_k_search.predict(q)\n", - "for i in range(len(best_matches)):\n", - " print(f\"match {i} : {best_matches[i]} with distance {distances_to_matches[i]} to q\")" - ] - }, - { + }, "cell_type": "markdown", - "id": "ff62a385-d58e-4fb1-95dd-eb0474711531", + "id": "6aecb58e-9de9-4264-959e-4180ab3fa27a", "metadata": {}, "source": [ - "It is also possible to return the **worst** matches (not that the title of the plots are not accurate here) to the query, by using the `inverse_distance` parameter :" + "When doing motif search, it's important to define the type of motif you want to extract from a series. We'll use the figure and definitions given by [1] :\n", + "\n", + "![image.png](attachment:f492cb89-5bf3-4641-8be2-a77805f20b88.png)\n", + "\n", + "For now, the `StompMotif` estimators supports only \"Pair motifs\", \"k-Motiflets\", and \"k-motifs\". Note that the naming \"k-motifs\" is a bit confusing, it extract motifs based on a range parameter and not by number of closests neihbors. To choose the type of motifs you want to extract, you will have to use the parameters of the `predict` method :\n", + "\n", + "- for **\"Pair Motifs\"** : This is the default configuration\n", + "\n", + "- for **\"k-Motiflets\"** : ```{\"motif_size\": k}```\n", + "\n", + "- for **\"k-motifs\"** : ```{\"motif_size\": np.inf, \"dist_threshold\": r, \"motif_extraction_method\": \"r_motifs\"}```\n", + "\n", + "These configuration will extract the best motif only, if you want to extract more than one motifs, you can use the `k` parameter to extract the `top-k` motifs. \n", + "\n", + "**The term `k` of `top-k` motifs, while also used in `k-Motiflets`, is not the same. We use `motif_size` as the `k` in `k-Motiflets`. This is to avoid \"extraction the `top-k` `k-motiflets`\", which can lead to confusions. Rather, we extract the `top-k` `motif_size-motiflets`**.\n", + "\n", + "The `top-k` using `motif_extraction_method=\"r_motifs\"` will be the motif with the highest cardinality (i.e. the more matches in range `r`), while for `motif_extraction_method=\"k_motifs\"`,which is the default value, the best motifs will be those who minimize the maximum pairwise distance." ] }, { "cell_type": "code", - "execution_count": 8, - "id": "6d6078ab-9104-462e-9856-1d0fc9594b24", + "execution_count": 6, + "id": "ff23faf5-2941-441a-8c4c-0cf66eaca121", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "([array([2.16605047]),\n", + " array([3.23155459]),\n", + " array([8.15076681]),\n", + " array([8.15076681]),\n", + " array([26.42906254])],\n", + " [array([[13, 30]], dtype=int64),\n", + " array([[31, 13]], dtype=int64),\n", + " array([[108, 77]], dtype=int64),\n", + " array([[ 77, 108]], dtype=int64),\n", + " array([[59, 76]], dtype=int64)])" ] }, + "execution_count": 6, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "# Here, the distance function (distance and normalise arguments)\n", - "top_k_search = QuerySearch(k=3, inverse_distance=True, distance=\"euclidean\")\n", - "top_k_search.fit(X_train)\n", - "distances_to_matches, best_matches = top_k_search.predict(q)\n", - "plot_best_matches(top_k_search, best_matches)" - ] - }, - { - "cell_type": "markdown", - "id": "b5240535-5123-4ac5-a5e0-e0502ef80b3e", - "metadata": {}, - "source": [ - "## Using the speed_up option for similarity search" + "from aeon.similarity_search.series import StompMotif\n", + "\n", + "motif = StompMotif(length=length, normalize=True).fit(series_fit)\n", + "motif.predict(\n", + " k=5,\n", + " motif_size=1,\n", + ")" ] }, { "cell_type": "markdown", - "id": "b5e13c31-2aa3-4987-8d44-8a296c81a318", + "id": "d16036a3-f5b9-41d2-ae23-a1bcf0737c93", "metadata": {}, "source": [ - "In the similarity search module, we implement different kind of optimization to decrease the time necessary to extract the best matches to a query. You can find more information about these optimization in the other notebooks of the similarity search module. An utility function is available to list the optimizations currently implemented in aeon :" + "\n", + "Note that we also support giving another series in `predict`, which will use this series to search for the motifs matching subsequences in the series given during `fit`. For those familiar with the matrix profile notations, this is the case of using `MP(A,B)`, while not using a series in `predict` is doing a self matrix profile `MP(A,A)`." ] }, { "cell_type": "code", - "execution_count": 9, - "id": "d22e2d74-f44d-4c81-ba1b-72d618bd5862", + "execution_count": 7, + "id": "59117ea7-2cbf-49d6-829a-792805b4aaf7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'normalised euclidean': ['fastest', 'Mueen'],\n", - " 'euclidean': ['fastest', 'Mueen'],\n", - " 'normalised squared': ['fastest', 'Mueen'],\n", - " 'squared': ['fastest', 'Mueen']}" + "([array([0.01197907]),\n", + " array([0.0622802]),\n", + " array([0.14565364]),\n", + " array([0.70546699]),\n", + " array([1.19303001])],\n", + " [array([[83, 78]], dtype=int64),\n", + " array([[50, 49]], dtype=int64),\n", + " array([[32, 30]], dtype=int64),\n", + " array([[9, 4]], dtype=int64),\n", + " array([[101, 95]], dtype=int64)])" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "QuerySearch.get_speedup_function_names()" - ] - }, - { - "cell_type": "markdown", - "id": "bf12616c-6ace-478b-806f-5419c2c19f2b", - "metadata": {}, - "source": [ - "By default, the `fastest` option is used, which use the best optimisation available. You can change this behavior by using the values of t with the corresponding distance function and normalization options in the estimators, for example with a `QuerySearch` using the `normalised euclidean` distance:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "6313f26a-5788-42dc-881a-40746458414c", - "metadata": {}, - "outputs": [], - "source": [ - "top_k_search = QuerySearch(distance=\"euclidean\", normalise=True, speed_up=\"Mueen\")" - ] - }, - { - "cell_type": "markdown", - "id": "6ab51d84-7220-4333-b50e-2db695eaf45d", - "metadata": {}, - "source": [ - "For more information on these optimizations you can refer to the [distance profile notebook](distance_profiles.ipynb) for the theory, and to the [analysis of the speedups provided by similarity search module](code_speed.ipynb) for a comparison of their performance." + "from aeon.similarity_search.series import StompMotif\n", + "\n", + "motif.predict(\n", + " series_predict,\n", + " k=5,\n", + " motif_size=1,\n", + ")" ] }, { "cell_type": "markdown", - "id": "4149c40f", + "id": "9190fdf4-db3d-4d51-b2c8-41b88a9f6f74", "metadata": {}, "source": [ - "# Series search\n", - "For series search, we are not interest in exploring the relationship of the input dataset `X` (given in `fit`) and a single query, but to all queries of size `query_length` that exists in another time series `T`. For example, with using again our simple GunPoint dataset:" + "You can also return the matrix profile with the same parameterization as `predict` (minus `motif_extraction_method` parameter) using :" ] }, { "cell_type": "code", - "execution_count": 11, - "id": "d510c4cc", + "execution_count": 8, + "id": "4c36738a-e6a0-4452-aee2-ccbad99d6d8b", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index of the 20-th query best matches : [[195 26]]\n" - ] } ], "source": [ - "from aeon.similarity_search import SeriesSearch\n", + "MP, IP = motif.compute_matrix_profile()\n", "\n", - "query_length = 35\n", - "estimator = SeriesSearch(distance=\"euclidean\").fit(X_train) # X_test is a 3D array\n", - "mp, ip = estimator.predict(X_test, query_length) # X_test is a 2D array\n", - "plot_matrix_profile(X_test, mp, 0)\n", - "print(f\"Index of the 20-th query best matches : {ip[20]}\")" + "plt.figure(figsize=(7, 2))\n", + "plt.plot([MP[i][0] for i in range(len(MP))])\n", + "plt.show()" ] }, { "cell_type": "markdown", - "id": "0dca5122", + "id": "1610adf3-5cb1-466e-9cad-fb248148fd5a", "metadata": {}, "source": [ - "Notice that we find the same best match for the 20-ith query, which was the query that we used for `QuerySearch` !\n", - "\n", - "`SeriesSearch` returns two lists, `mp` and `ip`, which respectively contain the distances to the best matches of all queries of size `query_length` in `X_test` (the `i-th` query being `X_test[:, i : i + query_length]`) and the indexes of these best matches in `X_train` in the `(ix_case, ix_timepoint)` format, such as `X_train[ix_case, :, ix_timepoint : ix_timepoint + query_length]` will be the matching subsquence.\n", - "\n", - "Most of the options (`k`, `threshold`, `inverse_distance`, etc.) from `QuerySearch` are also available for `SeriesSearch`." + "## References\n", + "[1] Patrick Schäfer and Ulf Leser. 2022. Motiflets: Simple and Accurate Detection\n", + " of Motifs in Time Series. Proc. VLDB Endow. 16, 4 (December 2022), 725–737." ] }, { "cell_type": "code", "execution_count": null, - "id": "ff23faf5-2941-441a-8c4c-0cf66eaca121", + "id": "989ba9f2-6dd8-4db7-9dfc-783aac5e6fcb", "metadata": {}, "outputs": [], "source": [] @@ -521,7 +479,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/examples/similarity_search/similarity_search_tasks.ipynb b/examples/similarity_search/similarity_search_tasks.ipynb new file mode 100644 index 0000000000..86fe5b5274 --- /dev/null +++ b/examples/similarity_search/similarity_search_tasks.ipynb @@ -0,0 +1,136 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2347de94-27a7-486e-a900-e80db5c7f427", + "metadata": {}, + "source": [ + "# Similarity search tasks\n", + "\n", + "To discuss : the term subsequences appear more often than subseries in similarity search papers, so maybe stick to subsequences ?\n", + "\n", + "## Notations\n", + "- A single time point $x \\in \\mathbb{R}^{d}$ representing a vector of size $d$, with $d$ the number of channels\n", + "- A single time series $X \\in \\mathbb{R}^{d,m}$ of $d$ channels and $m$ timepoints\n", + "- A collection ${\\cal X} \\in \\mathbb{R}^{n,d,m}$ of $n$ time series \n", + "- $l$ a length parameter for subseries extracted using a sliding window on a time series $X$ over its timepoints\n", + "- $W_{i,j} \\in \\mathbb{R}^{d,l}$ a subseries extracted from a collection ${\\cal X}$, with $i$ the sample id and $j$ the starting timepoint, such as $W_{i,j} = X_{i,[j:j+l[}$. Denoted $W_{j}$ if used outside of the context of a collection. ${\\cal W}$ denotes the set of all admissible subseries.\n", + " \n", + "## Series tasks\n", + "Given a single series $X$, we want to be able to do the following tasks :\n", + "\n", + "#### Subseries Neighbor search:\n", + "$K$-nn based and/or range ($r$) based search (radius only for now, extent necessary for [k-Motiflefts](https://www.vldb.org/pvldb/vol16/p725-schafer.pdf) ?). Given a series $X$ and a subseries $W_i$, find the other subseries in $X$ that are the most similar to $W_i$. In terms of parameterization, we want to be able to toggle on/off :\n", + "- ignore neighboring matches. Given $W_j$ a neighboring subseries of $W_i$, the subseries $W_{j-l//\\epsilon}, ..., W_{j+l//\\epsilon}$ cannot be in the returned set.\n", + "- inverse distance. Return the worst matches instead of the best ones.\n", + "- normalize. Wheter subseries should be normalized prior to distance computations\n", + "\n", + "#### Subseries Motif search :\n", + "Extract $k$-motifs or range motifs or $r$-motifs.\n", + "\n", + "The $k^{th}$ motif is the $k^{th}$ most similar pair of subseries in $X$. Given $\\forall a,b,i,j$ the pair ${W_i, W_j}$ is the motif if $dist(W_i, W_j) ≤ dist(W_a, W_b), i \\neq j$ and $a \\neq b$\n", + "\n", + "For the $r$-motif,: $S$ is a maximal set of subseries with range $r$ if $\\forall\\ W_i,W_j \\in S,\\ dist(W_i, W_j) \\leq 2r$ and $\\forall\\ W_a \\in {\\cal W}-S,\\ dist(W_a, W_i) > 2r$\n", + "\n", + "\n", + "#### Compute self distance profile\n", + "Given a subseries $W_i$, compute the self distance profile to $X$. Returns a vector of size $m-l+1$ containing the distance to all subseries. \n", + "\n", + "In terms of parameterization, we want to be able to toggle on/off :\n", + "- ignore neighboring matches. Given $W_j$ a neighboring subseries of $W_i$, the subseries $W_{j-l//\\epsilon}, ..., W_{j+l//\\epsilon}$ cannot be in the returned set.\n", + "- inverse distance. Return the worst matches instead of the best ones.\n", + "- normalize. Wheter subseries should be normalized prior to distance computations\n", + "\n", + "\n", + "#### Compute self matrix profile\n", + "Given a series $X$ and a length parameter $l$, compute its self matrix profile. Returns a vector of size $m-l+1$ containing the distances to the best matches of each subseries $W_i$, and another vector of size $m-l+1$ containg the timestamp of the best matches in $X$ for each subseries. Implement it as A/B matrix profile with B=A.\n", + "\n", + "In terms of parameterization, we want to be able to toggle on/off :\n", + "- $k$ : number of best matches to return for each subseries in $X$\n", + "- $r$ : maximal distance of the best matches to be in the returned set for each subseries in $X$\n", + "- ignore neighboring matches. Given $W_j$ a neighboring subseries of $W_i$, the subseries $W_{j-l//\\epsilon}, ..., W_{j+l//\\epsilon}$ cannot be in the returned set.\n", + "- inverse distance. Return the worst matches instead of the best ones.\n", + "- normalize. Wheter subseries should be normalized prior to distance computations\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "7313599d-66e2-4d03-959e-bd0abe05baed", + "metadata": {}, + "source": [ + "\n", + "## Collection tasks\n", + "Given a time series collection $\\cal X$, we want to be able to do the following tasks :\n", + "(we consider all subseries $W_{i,j}$ part its of $\\cal X$ due to notation but doesn't have to be when given as inputs for example in neighbor search).\n", + "\n", + "#### Subseries Neighbor search :\n", + "$K$-nn based and/or range ($r$) based search (radius only for now, extent necessary for [k-Motiflefts](https://www.vldb.org/pvldb/vol16/p725-schafer.pdf) ?). Given a subseries $W_{i,j}$, find the other subseries in $\\cal X$ that are the most similar to $W_{i,j}$. In terms of parameterization, we want to be able to toggle on/off :\n", + "- ignore neighboring matches. Given ${W_a,b}$ a neighboring subseries of $W_{i,j}$, the subseries $W_{a, b-l//\\epsilon}, ..., W_{a,b+l//\\epsilon}$ cannot be in the returned set.\n", + "- inverse distance. Return the worst matches instead of the best ones.\n", + "- normalize. Wheter subseries should be normalized prior to distance computations\n", + "\n", + "#### Series Neighbor search :\n", + "$K$-nn based and/or range ($r$) based search. Given a series $X_i$, find the other series in $\\cal X$ that are the most similar to $X_i$. In terms of parameterization, we want to be able to toggle on/off :\n", + "- inverse distance. Return the worst matches instead of the best ones.\n", + "- normalize. Wheter subseries should be normalized prior to distance computations\n", + "\n", + "#### Subseries Motif search :\n", + "Extract $k$-motifs or range $r$-motifs.\n", + "\n", + "The $k^{th}$ motif is the $k^{th}$ most similar pair of subseries in $X$. Given $\\forall a,b,a^\\prime,b^\\prime,i,j,i^\\prime,j^\\prime$ the pair $(W_{i,j}, W_{i^\\prime,j^\\prime})$ is the motif if $dist(W_{i,j}, W_{i^\\prime,j^\\prime}) ≤ dist(W_{a,b}, W_{a^\\prime,b^\\prime}), i \\neq i^\\prime, j \\neq j^\\prime, a \\neq a^\\prime, b \\neq b^\\prime$.\n", + "\n", + "For the $r$-motif,: $S$ is a maximal set of subseries with range $r$ if $\\forall\\ (W_{i,j},W_{i^\\prime,j^\\prime}) \\in S,\\ dist(W_{i,j}, W_{i^\\prime,j^\\prime}) \\leq 2r$ and $\\forall\\ W_{a,b} \\in {\\cal W}-S,\\ dist(W_{i,j}, W_{a,b}) > 2r$\n", + "\n", + "\n", + "#### Compute distance profiles :\n", + "Given a subseries $W_{i,j}$, compute the distance profiles to all series in $\\cal X$. Returns a vector of size $n, m-l+1$ containing the distance to all subseries. \n", + "\n", + "In terms of parameterization, we want to be able to toggle on/off :\n", + "- ignore neighboring matches. Given $W_{i,b}$ a neighboring subseries of $W_{i,j}$ the subseries $W_{i,b-l//\\epsilon}, ..., W_{i,b+l//\\epsilon}$ cannot be in the returned set.\n", + "- inverse distance. Return the worst matches instead of the best ones.\n", + "- normalize. Wheter subseries should be normalized prior to distance computations.\n", + "\n", + "\n", + "#### Compute matrix profiles :\n", + "Given a series $X_i \\in {\\cal X}$ and a length parameter $l$, compute its matrix profile over the collection. Returns a vector of size $m-l+1$ containing the distances to the best matches of each subseries $W_{i,j}$, and another vector of size $m-l+1$ containg the timestamp of the best matches in ${\\cal X}$ for each subseries.\n", + "\n", + "In terms of parameterization, we want to be able to toggle on/off :\n", + "- $k$ : number of best matches to return for each subseries in $X$\n", + "- $r$ : maximal distance of the best matches to be in the returned set for each subseries in $X$\n", + "- ignore neighboring matches. Given $W_{a,b}$ a neighbor of subseries $W_{i,j}$ the subseries $W_{a,b-l//\\epsilon}, ..., W_{a,b+l//\\epsilon}$ cannot be in the returned set.\n", + "- inverse distance. Return the worst matches instead of the best ones.\n", + "- normalize. Wheter subseries should be normalized prior to distance computations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be1430f0-dce0-4de4-b702-11ee5e33f462", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (Spyder)", + "language": "python3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}