From a10876653f8809965d2626d4686d88dcaebfa82d Mon Sep 17 00:00:00 2001 From: Bela <66631119+belaboe97@users.noreply.github.com> Date: Thu, 30 Nov 2023 23:43:46 +0100 Subject: [PATCH 1/7] init sets --- .../counterfactual/SETS/class_shapelets.py | 165 +++ .../counterfactual/SETS/sets.py | 319 +++++ .../counterfactual/SETS/shapelets.py | 1191 +++++++++++++++++ .../counterfactual/SETS/sktime_convert.py | 985 ++++++++++++++ .../counterfactual/SETS/st.py | 120 ++ .../counterfactual/SETS/utils.py | 323 +++++ .../counterfactual/SETSCF.py | 132 ++ playground.ipynb | 78 ++ 8 files changed, 3313 insertions(+) create mode 100644 TSInterpret/InterpretabilityModels/counterfactual/SETS/class_shapelets.py create mode 100644 TSInterpret/InterpretabilityModels/counterfactual/SETS/sets.py create mode 100644 TSInterpret/InterpretabilityModels/counterfactual/SETS/shapelets.py create mode 100644 TSInterpret/InterpretabilityModels/counterfactual/SETS/sktime_convert.py create mode 100644 TSInterpret/InterpretabilityModels/counterfactual/SETS/st.py create mode 100644 TSInterpret/InterpretabilityModels/counterfactual/SETS/utils.py create mode 100644 TSInterpret/InterpretabilityModels/counterfactual/SETSCF.py create mode 100644 playground.ipynb diff --git a/TSInterpret/InterpretabilityModels/counterfactual/SETS/class_shapelets.py b/TSInterpret/InterpretabilityModels/counterfactual/SETS/class_shapelets.py new file mode 100644 index 0000000..e56d355 --- /dev/null +++ b/TSInterpret/InterpretabilityModels/counterfactual/SETS/class_shapelets.py @@ -0,0 +1,165 @@ +# Author: Omar Bahri + +import os +import random +import sys + +import numpy as np +from sklearn.preprocessing import LabelEncoder +from utils import ( + get_all_shapelet_locations_scaled_threshold, + get_all_shapelet_locations_scaled_threshold_test, +) + + +def main(): + random.seed(42) + + dataset_name = sys.argv[1] + time_contract_in_mins = int(sys.argv[2]) + max_perc = float(sys.argv[3]) + + # name of current run (dataset + parameters combination) + run_name = "_".join([dataset_name, str(time_contract_in_mins), str(max_perc)]) + + # path of intermediary results directory + inter_results = os.path.abspath(os.path.join("results", "util_data", run_name)) + + X_train = np.load( + os.path.abspath(os.path.join("data", dataset_name, "X_train.npy")) + ) + y_train = np.load( + os.path.abspath(os.path.join("data", dataset_name, "y_train.npy")) + ) + X_test = np.load(os.path.abspath(os.path.join("data", dataset_name, "X_test.npy"))) + y_test = np.load(os.path.abspath(os.path.join("data", dataset_name, "y_test.npy"))) + + le = LabelEncoder() + y_train = le.fit_transform(y_train) + y_test = le.transform(y_test) + + st_shapelets = np.load( + os.path.join(inter_results, "shapelets.pkl"), allow_pickle=True + ) + shapelets_distances = np.load( + os.path.join(inter_results, "shapelets_distances.pkl"), allow_pickle=True + ) + shapelets_distances_test = np.load( + os.path.join(inter_results, "shapelets_distances_test.pkl"), allow_pickle=True + ) + + # length of time series in dataset + ts_length = X_train.shape[2] + + # the percentage of shapelet occurrences to keep + occ_threshold = 1e-1 + + # get the shapelets locations in the training and testing sets + ( + all_shapelet_locations, + all_no_occurences, + threshold, + ) = get_all_shapelet_locations_scaled_threshold( + shapelets_distances, ts_length, occ_threshold / 100.0 + ) + all_shapelet_locations_test, _ = get_all_shapelet_locations_scaled_threshold_test( + shapelets_distances_test, ts_length, threshold + ) + + del shapelets_distances + + # initialize a dictionary that stores lists of class-shapelets + all_shapelets_class = {} + # initialize a dictionary that stores lists of class-shapelets heatmaps + all_heat_maps = {} + for c in np.unique(y_train): + all_shapelets_class[c] = [] + all_heat_maps[c] = [] + + # get the shapelet classes and their heatmaps at each dimension + for dim in range(X_test.shape[1]): + for index in sorted(all_no_occurences[dim], reverse=True): + del st_shapelets[dim][index] + + # Get shapelets class occurences + shapelets_classes = [] + for shapelet_locations in all_shapelet_locations[dim]: + shapelet_classes = [] + for sl in shapelet_locations: + shapelet_classes.append(y_train[sl[0]]) + shapelets_classes.append(shapelet_classes) + + not_one_class = [] + + # Find shapelets that happen exclusively under one class + for i, shapelet_class in enumerate(shapelets_classes): + if len(np.unique(shapelet_class)) > 1: + not_one_class.append(i) + + for index in sorted(not_one_class, reverse=True): + del st_shapelets[dim][index] + del all_shapelet_locations[dim][index] + try: + del all_shapelet_locations_test[dim][index] + except Exception: + pass + del shapelets_classes[index] + + # initialize a dictionary that stores lists of class-shapelets + # for current dimension + shapelets_class = {} + # initialize a dictionary that stores class-shapelets + # heatmaps for current dimension + heat_maps = {} + for c in np.unique(y_train): + shapelets_class[c] = [] + heat_maps[c] = {} + + # keep shapelets that occur in one single class only + for i, shapelet_classes in enumerate(shapelets_classes): + for c in np.unique(y_train): + if np.all(np.asarray(shapelet_classes) == c): + shapelets_class[c].append(i) + + for c in np.unique(y_train): + all_shapelets_class[c].append(shapelets_class[c]) + + print(all_shapelets_class) + + ###Get shapelet_locations distributions per exclusive class + + for s in shapelets_class[c]: + heat_map = np.zeros(ts_length) + num_occurences = 0 + + for sl in all_shapelet_locations[dim][s]: + for idx in range(sl[1], sl[2]): + heat_map[idx] += 1 + num_occurences += 1 + + heat_map = heat_map / num_occurences + heat_maps[c][s] = heat_map + + all_heat_maps[c].append(heat_maps[c]) + all_heat_maps[c].append(heat_maps[c]) + all_heat_maps[c].append(heat_maps[c]) + all_heat_maps[c].append(heat_maps[c]) + + # save intermediate results + np.save(os.path.join(inter_results, "all_heat_maps.npy"), all_heat_maps) + print(all_shapelets_class) + np.save(os.path.join(inter_results, "all_shapelets_class.npy"), all_shapelets_class) + all_shapelet_locations = np.array(all_shapelet_locations, dtype=object) + all_shapelet_locations_test = np.array(all_shapelet_locations_test, dtype=object) + np.save( + os.path.join(inter_results, "all_shapelet_locations.npy"), + all_shapelet_locations, + ) + np.save( + os.path.join(inter_results, "all_shapelet_locations_trth_test.npy"), + all_shapelet_locations_test, + ) + + +if __name__ == "__main__": + main() diff --git a/TSInterpret/InterpretabilityModels/counterfactual/SETS/sets.py b/TSInterpret/InterpretabilityModels/counterfactual/SETS/sets.py new file mode 100644 index 0000000..8a4f8a1 --- /dev/null +++ b/TSInterpret/InterpretabilityModels/counterfactual/SETS/sets.py @@ -0,0 +1,319 @@ +# Author: Omar Bahri + +import itertools +import os +import random +import sys + +import numpy as np +import tensorflow.keras as keras +from sklearn.preprocessing import LabelEncoder +from tslearn.neighbors import KNeighborsTimeSeries +from utils import get_nearest_neighbor, get_shapelets_locations_test + + +def main(): + random.seed(42) + + dataset_name = sys.argv[1] + time_contract_in_mins = int(sys.argv[2]) + max_perc = float(sys.argv[3]) + + # name of current run (dataset + parameters combination) + run_name = "_".join([dataset_name, str(time_contract_in_mins), str(max_perc)]) + + # path of intermediary results directory + inter_results = os.path.abspath(os.path.join("results", "util_data", run_name)) + + # path of CFs results directory + results = os.path.abspath(os.path.join("results", "cfs", run_name)) + if not os.path.exists(results): + os.makedirs(results) + + X_train = np.load( + os.path.abspath(os.path.join("data", dataset_name, "X_train.npy")) + ) + y_train = np.load( + os.path.abspath(os.path.join("data", dataset_name, "y_train.npy")) + ) + X_test = np.load(os.path.abspath(os.path.join("data", dataset_name, "X_test.npy"))) + y_test = np.load(os.path.abspath(os.path.join("data", dataset_name, "y_test.npy"))) + + le = LabelEncoder() + y_train = le.fit_transform(y_train) + y_test = le.transform(y_test) + + st_shapelets = np.load( + os.path.join(inter_results, "shapelets.pkl"), allow_pickle=True + ) + all_shapelet_locations = np.load( + os.path.join(inter_results, "all_shapelet_locations.npy"), allow_pickle=True + ) + all_shapelet_locations_test = np.load( + os.path.join(inter_results, "all_shapelet_locations_trth_test.npy"), + allow_pickle=True, + ) + all_shapelets_class = np.load( + os.path.join(inter_results, "all_shapelets_class.npy"), allow_pickle=True + ).item() + + # print("ALL", all_shapelets_class) + all_heat_maps = np.load( + os.path.join(inter_results, "all_heat_maps.npy"), allow_pickle=True + ).item() + + ts_length = X_train.shape[2] + + # Sort dimensions by their highest shapelet scores + all_shapelets_scores = np.load( + os.path.join(inter_results, "scores.npy"), allow_pickle=True + ) + shapelets_best_scores = [] + for dim in range(len(st_shapelets)): + shapelets_best_scores.append(max(all_shapelets_scores[dim])) + + shapelets_best_scores = np.argsort(shapelets_best_scores)[::-1] + + from sklearn.neural_network import MLPClassifier + + # train black-box model and get predictions + model = keras.models.load_model("./best_model.hdf5") + """ + + model = MLPClassifier( + random_state=42, max_iter=100, hidden_layer_sizes=240, learning_rate_init=1e-3 + ).fit(X_train.reshape((X_train.shape[0], -1)), y_train) + print(X_test.shape) + y_pred = model.predict(X_test.reshape((X_test.shape[0], -1))) + """ + + y_pred = model.predict(np.swapaxes(X_test, 1, 2)) + y_pred = np.argmax(y_pred, axis=1) + + # value ranges of shapelet-classes (used later for scaling) + all_shapelets_ranges = {} + for c in np.unique(y_train): + all_shapelets_ranges[c] = [] + + for dim in range(X_test.shape[1]): + shapelets_ranges = {} + for c in np.unique(y_train): + print(c) + shapelets_ranges[c] = {} + + # Get [min,max] of each shapelet occurences + for label, (all_shapelets_class, shapelets_ranges) in enumerate( + zip(list(all_shapelets_class.values()), list(shapelets_ranges.values())) + ): + for j, sls in enumerate( + [all_shapelet_locations[dim][i] for i in all_shapelets_class[dim]] + ): + s_mins = 0 + s_maxs = 0 + n_s = 0 + + for sl in sls: + ts = X_train[sl[0]][dim][sl[1] : sl[2]] + + s_mins += ts.min() + s_maxs += ts.max() + n_s += 1 + + # print(all_shapelets_class[dim][j]) + + shapelets_ranges[all_shapelets_class[dim][j]] = ( + s_mins / n_s, + s_maxs / n_s, + ) + + for c in np.unique(y_train): + all_shapelets_ranges[c] = shapelets_ranges + + # dictionary to store class KNNs + knns = {} + + # fit a KNN for each class + for c in np.unique(y_train): + knns[c] = KNeighborsTimeSeries(n_neighbors=1) + X_train_knn = X_train[np.argwhere(y_train == c)].reshape( + np.argwhere(y_train == c).shape[0], X_train.shape[1], X_train.shape[2] + ) + X_train_knn = np.swapaxes(X_train_knn, 1, 2) + knns[c].fit(X_train_knn) + + # generate CFs for each time series in the test set, for all possible target classes + for instance_idx in range(0, X_test.shape[0]): + orig_c = y_pred[instance_idx] + + for target_c in set(np.unique(y_train)) - set([orig_c]): + # print("instance_idx: " + str(instance_idx)) + # print("from: " + str(orig_c) + " to: " + str(target_c)) + + # get the original class shapelets and ranges + print(all_shapelets_class) + original_all_shapelets_class = all_shapelets_class[dim][orig_c] + original_shapelets_ranges = all_shapelets_ranges[c] + + # get the target class shapelets and ranges + all_target_shapelets_class = all_shapelets_class[dim][target_c] + all_target_heat_maps = all_heat_maps[target_c] + target_knn = knns[target_c] + target_shapelets_ranges = all_shapelets_ranges[target_c] + + nn_idx = get_nearest_neighbor( + target_knn, + X_test, + y_test, + y_train, + # X_test[y_test == target_c], + # y_test[y_test == target_c], + # y_train[y_train == target_c], + instance_idx, + ) + cf_dims = np.zeros((len(shapelets_best_scores), ts_length)) + + # starting the with the most important dimension, start CF generation + for dim in shapelets_best_scores: + cf = X_test[instance_idx].copy() + print("CF", np.expand_dims(np.swapaxes(cf, 0, 1), axis=0).shape) + cf_pred = model.predict(np.expand_dims(np.swapaxes(cf, 0, 1), axis=0)) + cf_pred = np.argmax(cf_pred) + if y_pred[instance_idx] == cf_pred: + # Get the locations where the original class shapelets occur + all_locs = get_shapelets_locations_test( + instance_idx, + all_shapelet_locations_test, + dim, + original_all_shapelets_class, + ) + + # Replace the original class shapelets with nn values + for c_i in all_locs: + for loc in all_locs.get(c_i): + cf_pred = model.predict( + np.expand_dims(np.swapaxes(cf, 0, 1), axis=0) + ) + cf_pred = np.argmax(cf_pred) + if y_pred[instance_idx] == cf_pred: + # print('Removing original shapelet') + nn = X_test[nn_idx].reshape(-1) + + target_shapelet = nn[loc[0] : loc[1]] + + s_min = target_shapelet.min() + s_max = target_shapelet.max() + t_min = cf[dim][loc[0] : loc[1]].min() + t_max = cf[dim][loc[0] : loc[1]].max() + + if s_max - s_min == 0: + target_shapelet = ( + (t_max + t_min) + / 2 + * np.ones(len(target_shapelet)) + ) + else: + target_shapelet = (t_max - t_min) * ( + target_shapelet - s_min + ) / (s_max - s_min) + t_min + + start = loc[0] + end = loc[1] + + cf[dim][start:end] = target_shapelet + + # Introduce new shapelets from the target class + for _, target_shapelet_idx in enumerate(all_target_heat_maps[dim]): + cf_pred = model.predict( + np.expand_dims(np.swapaxes(cf, 0, 1), axis=0) + ) + cf_pred = np.argmax(cf_pred) + if y_pred[instance_idx] == cf_pred: + # print('Introducing new shapelet') + h_m = all_target_heat_maps[dim].get(target_shapelet_idx) + + center = ( + np.argwhere(h_m > 0)[-1][0] - np.argwhere(h_m > 0)[0][0] + ) // 2 + np.argwhere(h_m > 0)[0][0] + + target_shapelet = st_shapelets[dim][target_shapelet_idx][0] + target_shapelet_length = target_shapelet.shape[0] + + start = center - target_shapelet_length // 2 + end = center + ( + target_shapelet_length - target_shapelet_length // 2 + ) + + if start < 0: + end = end - start + start = 0 + + if end > ts_length: + start = start - (end - ts_length + 1) + end = ts_length - 1 + + s_min = target_shapelet.min() + s_max = target_shapelet.max() + t_min = cf[dim][start:end].min() + t_max = cf[dim][start:end].max() + + if s_max - s_min == 0: + target_shapelet = ( + (t_max + t_min) / 2 * np.ones(len(target_shapelet)) + ) + else: + target_shapelet = (t_max - t_min) * ( + target_shapelet - s_min + ) / (s_max - s_min) + t_min + + cf[dim][start:end] = target_shapelet + + # Save the perturbed dimension + cf_dims[dim] = cf[dim] + + cf_pred = model.predict(np.expand_dims(np.swapaxes(cf, 0, 1), axis=0)) + cf_pred = np.argmax(cf_pred) + # if a CF is found, save it and move to next time series instance + # print(y_pred[instance_idx], cf_pred) + if y_pred[instance_idx] != cf_pred: + # print('cf found') + np.save( + os.path.join( + results, str(instance_idx) + "_to_" + str(target_c) + ".npy" + ), + cf, + ) + break + + else: + # print("Trying dims combinations") + # Try all combinations of dimensions + for L in range(0, len(shapelets_best_scores) + 1): + for subset in itertools.combinations(shapelets_best_scores, L): + if len(subset) >= 2: + cf = X_test[instance_idx].copy() + for dim_ in subset: + cf[dim_] = cf_dims[dim_] + + cf_pred = model.predict( + np.expand_dims(np.swapaxes(cf, 0, 1), axis=0) + ) + cf_pred = np.argmax(cf_pred) + if y_pred[instance_idx] != cf_pred: + # print('cf found') + # print('final dims: ', subset) + break + + # if a CF is found, save it and move to next time series instance + if y_pred[instance_idx] != cf_pred: + # print('cf found') + np.save( + os.path.join( + results, str(instance_idx) + "_to_" + str(target_c) + ".npy" + ), + cf, + ) + break + + +if __name__ == "__main__": + main() diff --git a/TSInterpret/InterpretabilityModels/counterfactual/SETS/shapelets.py b/TSInterpret/InterpretabilityModels/counterfactual/SETS/shapelets.py new file mode 100644 index 0000000..4be4c78 --- /dev/null +++ b/TSInterpret/InterpretabilityModels/counterfactual/SETS/shapelets.py @@ -0,0 +1,1191 @@ +"""The bulk of this file was written by the authors of sktime (DOI10.5281/zenodo.3749000).""" + +import heapq +import os +import time +import warnings +from itertools import zip_longest +from operator import itemgetter + +import numpy as np +import pandas as pd +from sklearn.utils import check_random_state +from sklearn.utils.multiclass import class_distribution +from sktime.transformations.base import _PanelToTabularTransformer +from sktime.utils.validation.panel import check_X, check_X_y + +warnings.filterwarnings("ignore", category=FutureWarning) + + +# TO-DO: thorough testing (some initial testing completed, but passing the +# code to David to develop +# before everything has been fully verified) + +# TO-DO: in case of unequal length time series, we have two options: +# 1) fix the maximum length for the shapelets to the minimum length time +# series (of train dataset, +# which can also fail if there is a smaller time series in the test set). +# 2) use another time series distance measure such as DTW to avoid this, +# since you can compare unequal +# time series (we lose the early abandon in the distance measurement). +# +# TO-DO: could cythonise important methods, eg the distance and info gain +# calculations +# +# TO-DO: add CI tests, comments, documentation, etc. + + +class ShapeletTransform(_PanelToTabularTransformer): + """ + Parameters + ---------- + min_shapelet_length : int, lower bound on candidate + shapelet lengths (default = 3) + max_shapelet_length : int, upper bound on candidate + shapelet lengths (default = inf or series length) + max_shapelets_to_store_per_class : int, upper bound on number of + shapelets to retain from each distinct class (default = 200) + random_state : RandomState, int, or none: to + control random state objects for deterministic results (default = None) + verbose : int, level of output printed to + the console (for information only) (default = 0) + remove_self_similar : boolean, remove overlapping + "self-similar" shapelets from the final transform (default = True) + + Attributes + ---------- + + predefined_ig_rejection_level : float, minimum information gain + required to keep a shapelet (default = 0.05) + self.shapelets : list of Shapelet objects, + the stored shapelets after a dataset has been processed + """ + + _tags = {"univariate-only": True} + + def __init__( + self, + min_shapelet_length=3, + max_shapelet_length=np.inf, + shapelets_lengths=None, + max_shapelets_to_store_per_class=200, + random_state=None, + verbose=0, + remove_self_similar=True, + predefined_ig_rejection_level=0.05, + ): + self.min_shapelet_length = min_shapelet_length + self.max_shapelet_length = max_shapelet_length + self.shapelets_lengths = shapelets_lengths + self.max_shapelets_to_store_per_class = max_shapelets_to_store_per_class + self.random_state = random_state + self.verbose = verbose + self.remove_self_similar = remove_self_similar + self.predefined_ig_rejection_level = predefined_ig_rejection_level + self.shapelets = None + self.is_fitted_ = False + super(ShapeletTransform, self).__init__() + + def fit(self, X, y=None): + print(X.shape, y.shape) + + # range = np.arange + """A method to fit the shapelet transform to a specified X and y + + Parameters + ---------- + X: pandas DataFrame + The training input samples. + y: array-like or list + The class values for X + + Returns + ------- + self : FullShapeletTransform + This estimator + """ + X, y = check_X_y(X, y, enforce_univariate=True, coerce_to_numpy=True) + + print(">> shape", X.shape, y.shape) + + if ( + type(self) is ContractedShapeletTransform + and self.time_contract_in_mins <= 0 + ): + raise ValueError("Error: time limit cannot be equal to or less than 0") + + X_lens = np.repeat(X.shape[-1], X.shape[0]) + # note, assumes all dimensions of a case are the same + # length. A shapelet would not be well defined if indices do not match! + # may need to pad with nans here for uneq length, + # look at later + + num_ins = len(y) + distinct_class_vals = class_distribution(np.asarray(y).reshape(-1, 1))[0][0] + + candidates_evaluated = 0 + + if type(self) is _RandomEnumerationShapeletTransform: + num_series_to_visit = min(self.num_cases_to_sample, len(y)) + else: + num_series_to_visit = num_ins + + shapelet_heaps_by_class = {i: ShapeletPQ() for i in distinct_class_vals} + + self._random_state = check_random_state(self.random_state) + + # Here we establish the order of cases to sample. We need to sample + # x cases and y shapelets from each (where x = num_cases_to_sample + # and y = num_shapelets_to_sample_per_case). We could simply sample + # x cases without replacement and y shapelets from each case, but + # the idea is that if we are using a time contract we may extract + # all y shapelets from each x candidate and still have time remaining. + # Therefore, if ,we get a list of the indices of the series and + # shuffle them appropriately, we can go through the list again and + # extract + # another y shapelets from each series (if we have time). + + # We also want to ensure that we visit all classes so we will visit + # in round-robin order. Therefore, the code below extracts the indices + # of all series by class, shuffles the indices for each class + # independently, and then combines them in alternating order. This + # results in + # a shuffled list of indices that are in alternating class order ( + # e.g. 1,2,3,1,2,3,1,2,3,1...) + + def _round_robin(*iterables): + sentinel = object() + return ( + a + for x in zip_longest(*iterables, fillvalue=sentinel) + for a in x + if a != sentinel + ) + + case_ids_by_class = {i: np.where(y == i)[0] for i in distinct_class_vals} + print(case_ids_by_class) + # if transform is random/contract then shuffle the data initially + # when determining which cases to visit + if ( + type(self) is _RandomEnumerationShapeletTransform + or type(self) is ContractedShapeletTransform + ): + for i in range(len(distinct_class_vals)): + self._random_state.shuffle(case_ids_by_class[distinct_class_vals[i]]) + + num_train_per_class = {i: len(case_ids_by_class[i]) for i in case_ids_by_class} + round_robin_case_order = _round_robin( + *[list(v) for k, v in case_ids_by_class.items()] + ) + cases_to_visit = [(i, y[i]) for i in round_robin_case_order] + # this dictionary will be used to store all possible starting + # positions and shapelet lengths for a give series length. This + # is because we enumerate all possible candidates and sample without + # replacement when assessing a series. If we have two series + # of the same length then they will obviously have the same valid + # shapelet starting positions and lengths (especially in standard + # datasets where all series are equal length) so it makes sense to + # store the possible candidates and reuse, rather than + # recalculating each time + + # Initially the dictionary will be empty, and each time a new series + # length is seen the dict will be updated. Next time that length + # is used the dict will have an entry so can simply reuse + possible_candidates_per_series_length = {} + + # a flag to indicate if extraction should stop (contract has ended) + time_finished = False + + # max time calculating a shapelet + # for timing the extraction when contracting + start_time = time.time() + + def time_taken(): + return time.time() - start_time + + max_time_calc_shapelet = -1 + time_last_shapelet = time_taken() + + # for every series + case_idx = 0 + while case_idx < len(cases_to_visit): + series_id = cases_to_visit[case_idx][0] + this_class_val = cases_to_visit[case_idx][1] + + # minus 1 to remove this candidate from sums + binary_ig_this_class_count = num_train_per_class[this_class_val] - 1 + binary_ig_other_class_count = num_ins - binary_ig_this_class_count - 1 + + if self.verbose: + if type(self) == _RandomEnumerationShapeletTransform: + print( # noqa + "visiting series: " + + str(series_id) + + " (#" + + str(case_idx + 1) + + "/" + + str(num_series_to_visit) + + ")" + ) + else: + pass + """ + + print( # noqa + "visiting series: " + + str(series_id) + + " (#" + + str(case_idx + 1) + + ")" + ) + """ + print("LEM", len(X[series_id][0])) + this_series_len = len(X[series_id][0]) + + print("SELF", self.shapelets_lengths) + + # check whether all shapelets in range min-max should be extracted, + # or just a few of them + if True: # self.shapelets_lengths == None: + # The bound on possible shapelet lengths will differ + # series-to-series if using unequal length data. + # However, shapelets cannot be longer than the series, so set to + # the minimum of the series length + # and max shapelet length (which is inf by default) + print("MAX", self.max_shapelet_length) + if self.max_shapelet_length == -1: + this_shapelet_length_upper_bound = this_series_len + else: + this_shapelet_length_upper_bound = min( + this_series_len, self.max_shapelet_length + ) + + print( + "SHPLEN", + self.min_shapelet_length, + this_shapelet_length_upper_bound + 1, + ) + self.shapelets_lengths = range( + self.min_shapelet_length, this_shapelet_length_upper_bound + 1 + ) + + else: + print(">>PRINT", self.shapelets_lengths, type(self.shapelets_lengths)) + + self.min_shapelet_length = [self.shapelets_lengths][0] + self.max_shapelet_length = [self.shapelets_lengths][-1] + print( + ">>PRINT2", + this_series_len, + self.shapelets_lengths, + self.min_shapelet_length, + [self.shapelets_lengths][0], + [self.shapelets_lengths][-1], + type(self.max_shapelet_length), + ) + this_shapelet_length_upper_bound = min( + this_series_len, self.max_shapelet_length + ) + + # all possible start and lengths for shapelets within this + # series (calculates if series length is new, a simple look-up + # if not) + # enumerate all possible candidate starting positions and lengths. + + # First, try to reuse if they have been calculated for a series + # of the same length before. + candidate_starts_and_lens = possible_candidates_per_series_length.get( + this_series_len + ) + # else calculate them for this series length and store for + # possible use again + if candidate_starts_and_lens is None: + candidate_starts_and_lens = [ + [start, length] + for start in range( + 0, this_series_len - self.min_shapelet_length + 1 + ) + for length in self.shapelets_lengths + if start + length <= this_series_len + ] + possible_candidates_per_series_length[ + this_series_len + ] = candidate_starts_and_lens + + # default for full transform + candidates_to_visit = candidate_starts_and_lens + num_candidates_per_case = len(candidate_starts_and_lens) + + # limit search otherwise: + if hasattr(self, "num_candidates_to_sample_per_case"): + num_candidates_per_case = min( + self.num_candidates_to_sample_per_case, num_candidates_per_case + ) + cand_idx = list( + self._random_state.choice( + list(range(0, len(candidate_starts_and_lens))), + num_candidates_per_case, + replace=False, + ) + ) + candidates_to_visit = [candidate_starts_and_lens[x] for x in cand_idx] + + for candidate_idx in range(num_candidates_per_case): + # if shapelet heap for this class is not full yet, set entry + # criteria to be the predetermined IG threshold + ig_cutoff = self.predefined_ig_rejection_level + # otherwise if we have max shapelets already, set the + # threshold as the IG of the current 'worst' shapelet we have + if ( + shapelet_heaps_by_class[this_class_val].get_size() + >= self.max_shapelets_to_store_per_class + ): + ig_cutoff = max( + shapelet_heaps_by_class[this_class_val].peek()[0], ig_cutoff + ) + + cand_start_pos = candidates_to_visit[candidate_idx][0] + cand_len = candidates_to_visit[candidate_idx][1] + + candidate = ShapeletTransform.zscore( + X[series_id][:, cand_start_pos : cand_start_pos + cand_len] + ) + + # now go through all other series and get a distance from + # the candidate to each + orderline = [] + + # initialise here as copy, decrease the new val each time we + # evaluate a comparison series + num_visited_this_class = 0 + num_visited_other_class = 0 + + candidate_rejected = False + + for comparison_series_idx in range(len(cases_to_visit)): + i = cases_to_visit[comparison_series_idx][0] + + if y[i] != cases_to_visit[comparison_series_idx][1]: + raise ValueError("class match sanity test broken") + + if i == series_id: + # don't evaluate candidate against own series + continue + + if y[i] == this_class_val: + num_visited_this_class += 1 + binary_class_identifier = 1 # positive for this class + else: + num_visited_other_class += 1 + binary_class_identifier = -1 # negative for any + # other class + + bsf_dist = np.inf + + start_left = cand_start_pos + start_right = cand_start_pos + 1 + + if X_lens[i] == cand_len: + start_left = 0 + start_right = 0 + + for _ in range( + max(1, int(np.ceil((X_lens[i] - cand_len) / 2))) + ): # max + # used to force iteration where series len == + # candidate len + if start_left < 0: + start_left = X_lens[i] - 1 - cand_len + + comparison = ShapeletTransform.zscore( + X[i][:, start_left : start_left + cand_len] + ) + dist_left = np.linalg.norm(candidate - comparison) + bsf_dist = min(dist_left * dist_left, bsf_dist) + + # for odd lengths + if start_left == start_right: + continue + + # right + if start_right == X_lens[i] - cand_len + 1: + start_right = 0 + comparison = ShapeletTransform.zscore( + X[i][:, start_right : start_right + cand_len] + ) + dist_right = np.linalg.norm(candidate - comparison) + bsf_dist = min(dist_right * dist_right, bsf_dist) + + start_left -= 1 + start_right += 1 + + orderline.append((bsf_dist, binary_class_identifier)) + # sorting required after each add for early IG abandon. + # timsort should be efficient as array is almost in + # order - insertion-sort like behaviour in this case. + # Can't use heap as need to traverse in order multiple + # times, not just access root + orderline.sort() + + if len(orderline) > 2: + ig_upper_bound = ShapeletTransform.calc_early_binary_ig( + orderline, + num_visited_this_class, + num_visited_other_class, + binary_ig_this_class_count - num_visited_this_class, + binary_ig_other_class_count - num_visited_other_class, + ) + # print("upper: "+str(ig_upper_bound)) + if ig_upper_bound <= ig_cutoff: + candidate_rejected = True + break + + candidates_evaluated += 1 + if self.verbose > 3 and candidates_evaluated % 100 == 0: + print("candidates evaluated: " + str(candidates_evaluated)) # noqa + + # only do if candidate was not rejected + if candidate_rejected is False: + final_ig = ShapeletTransform.calc_binary_ig( + orderline, + binary_ig_this_class_count, + binary_ig_other_class_count, + ) + accepted_candidate = Shapelet( + series_id, cand_start_pos, cand_len, final_ig, candidate + ) + + # add to min heap to store shapelets for this class + shapelet_heaps_by_class[this_class_val].push(accepted_candidate) + + # informal, but extra 10% allowance for self similar later + if ( + shapelet_heaps_by_class[this_class_val].get_size() + > self.max_shapelets_to_store_per_class * 3 + ): + shapelet_heaps_by_class[this_class_val].pop() + + # Takes into account the use of the MAX shapelet calculation + # time to not exceed the time_limit (not exact, but likely a + # good guess). + if ( + hasattr(self, "time_contract_in_mins") + and self.time_contract_in_mins > 0 + ): + time_now = time_taken() + time_this_shapelet = time_now - time_last_shapelet + if time_this_shapelet > max_time_calc_shapelet: + max_time_calc_shapelet = time_this_shapelet + if self.verbose > 0: + print(max_time_calc_shapelet) # noqa + time_last_shapelet = time_now + + # add a little 1% leeway to the timing incase one run was + # slightly faster than + # another based on the CPU. + time_in_seconds = self.time_contract_in_mins * 60 + max_shapelet_time_percentage = ( + max_time_calc_shapelet / 100.0 + ) * 0.75 + if (time_now + max_shapelet_time_percentage) > time_in_seconds: + if self.verbose > 0: + print( # noqa + "No more time available! It's been {0:02d}:{" + "1:02}".format( + int(round(time_now / 60, 3)), + int( + ( + round(time_now / 60, 3) + - int(round(time_now / 60, 3)) + ) + * 60 + ), + ) + ) + time_finished = True + break + else: + if self.verbose > 0: + if candidate_rejected is False: + print( # noqa + "Candidate finished. {0:02d}:{1:02} " + "remaining".format( + int( + round( + self.time_contract_in_mins + - time_now / 60, + 3, + ) + ), + int( + ( + round( + self.time_contract_in_mins + - time_now / 60, + 3, + ) + - int( + round( + ( + self.time_contract_in_mins + - time_now + ) + / 60, + 3, + ) + ) + ) + * 60 + ), + ) + ) + else: + print( # noqa + "Candidate rejected. {0:02d}:{1:02} " + "remaining".format( + int( + round( + (self.time_contract_in_mins - time_now) + / 60, + 3, + ) + ), + int( + ( + round( + ( + self.time_contract_in_mins + - time_now + ) + / 60, + 3, + ) + - int( + round( + ( + self.time_contract_in_mins + - time_now + ) + / 60, + 3, + ) + ) + ) + * 60 + ), + ) + ) + + # stopping condition: in case of iterative transform (i.e. + # num_cases_to_sample have been visited) + # in case of contracted transform (i.e. time + # limit has been reached) + case_idx += 1 + + if case_idx >= num_series_to_visit: + if hasattr(self, "time_contract_in_mins") and time_finished is not True: + case_idx = 0 + elif case_idx >= num_series_to_visit or time_finished: + if self.verbose > 0: + print("Stopping search") # noqa + break + + # remove self similar here + # for each class value + # get list of shapelets + # sort by quality + # remove self similar + + self.shapelets = [] + for class_val in distinct_class_vals: + by_class_descending_ig = sorted( + shapelet_heaps_by_class[class_val].get_array(), + key=itemgetter(0), + reverse=True, + ) + + if self.remove_self_similar and len(by_class_descending_ig) > 0: + by_class_descending_ig = ( + ShapeletTransform.remove_self_similar_shapelets( + by_class_descending_ig + ) + ) + else: + # need to extract shapelets from tuples + by_class_descending_ig = [x[2] for x in by_class_descending_ig] + + # if we have more than max_shapelet_per_class, trim to that + # amount here + if len(by_class_descending_ig) > self.max_shapelets_to_store_per_class: + max_n = self.max_shapelets_to_store_per_class + by_class_descending_ig = by_class_descending_ig[:max_n] + + self.shapelets.extend(by_class_descending_ig) + + # final sort so that all shapelets from all classes are in + # descending order of information gain + self.shapelets.sort(key=lambda x: x.info_gain, reverse=True) + self.is_fitted_ = True + + # warn the user if fit did not produce any valid shapelets + if len(self.shapelets) == 0: + warnings.warn( + "No valid shapelets were extracted from this dataset and " + "calling the transform method " + "will raise an Exception. Please re-fit the transform with " + "other data and/or " + "parameter options." + ) + + self._is_fitted = True + return self + + @staticmethod + def remove_self_similar_shapelets(shapelet_list): + """Remove self-similar shapelets from an input list. Note: this + method assumes + that shapelets are pre-sorted in descending order of quality (i.e. + if two candidates + are self-similar, the one with the later index will be removed) + + Parameters + ---------- + shapelet_list: list of Shapelet objects + + Returns + ------- + shapelet_list: list of Shapelet objects + """ + + # IMPORTANT: it is assumed that shapelets are already in descending + # order of quality. This is preferable in the fit method as removing + # self-similar + # shapelets may be False so the sort needs to happen there in those + # cases, and avoids a second redundant sort here if it is set to True + + def is_self_similar(shapelet_one, shapelet_two): + # not self similar if from different series + if shapelet_one.series_id != shapelet_two.series_id: + return False + + if (shapelet_one.start_pos >= shapelet_two.start_pos) and ( + shapelet_one.start_pos <= shapelet_two.start_pos + shapelet_two.length + ): + return True + if (shapelet_two.start_pos >= shapelet_one.start_pos) and ( + shapelet_two.start_pos <= shapelet_one.start_pos + shapelet_one.length + ): + return True + + # [s][2] will be a tuple with (info_gain,id,Shapelet), so we need to + # access [2] + to_return = [shapelet_list[0][2]] # first shapelet must be ok + for s in range(1, len(shapelet_list)): + can_add = True + for c in range(0, s): + if is_self_similar(shapelet_list[s][2], shapelet_list[c][2]): + can_add = False + break + if can_add: + to_return.append(shapelet_list[s][2]) + + return to_return + + # transform a set of data into distances to each extracted shapelet + def transform(self, X, y=None): + """Transforms X according to the extracted shapelets (self.shapelets) + + Parameters + ---------- + X : pandas DataFrame + The input dataframe to transform + + Returns + ------- + output : pandas DataFrame + The transformed dataframe in tabular format. + """ + self.check_is_fitted() + _X = check_X(X, enforce_univariate=True, coerce_to_numpy=True) + + if len(self.shapelets) == 0: + raise RuntimeError( + "No shapelets were extracted in fit that exceeded the " + "minimum information gain threshold. Please retry with other " + "data and/or parameter settings." + ) + + # may need to pad with nans here for uneq length, look at later + output = np.zeros( + [len(_X), len(self.shapelets)], + dtype=np.float32, + ) + + # initialize the arrays that will hold the distances between the + # shapelets and all their potential occurences + # would only work if all series are of the same size + for shapelet in self.shapelets: + # print(shapelet.length) + shapelet.distances = np.empty( + [_X.shape[0], len(_X[0][0]) - shapelet.length + 1], dtype=np.float64 + ) + + # for the i^th series to transform + for i in range(0, len(_X)): + this_series = _X[i] + + # get the s^th shapelet + for s in range(0, len(self.shapelets)): + # find distance between this series and each shapelet + min_dist = np.inf + this_shapelet_length = self.shapelets[s].length + + # array to hold the distances between the shapelet and + # its potential occurences in this time series + shapelet_distances = [] + + for start_pos in range( + 0, len(this_series[0]) - this_shapelet_length + 1 + ): + comparison = ShapeletTransform.zscore( + this_series[:, start_pos : (start_pos + this_shapelet_length)] + ) + + dist = np.linalg.norm(self.shapelets[s].data - comparison) + dist = dist * dist + dist = 1.0 / this_shapelet_length * dist + + shapelet_distances.append(dist) + + min_dist = min(min_dist, dist) + + output[i][s] = dist + + self.shapelets[s].distances[i] = np.asarray(shapelet_distances) + + return pd.DataFrame(output) + + def get_shapelets(self): + """An accessor method to return the extracted shapelets + + Returns + ------- + shapelets: a list of Shapelet objects + """ + return self.shapelets + + @staticmethod + def binary_entropy(num_this_class, num_other_class): + ent = 0 + if num_this_class != 0: + ent -= ( + num_this_class + / (num_this_class + num_other_class) + * np.log2(num_this_class / (num_this_class + num_other_class)) + ) + if num_other_class != 0: + ent -= ( + num_other_class + / (num_this_class + num_other_class) + * np.log2(num_other_class / (num_this_class + num_other_class)) + ) + return ent + + # could cythonise + @staticmethod + def calc_binary_ig(orderline, total_num_this_class, total_num_other_class): + # def entropy(ent_class_counts, all_class_count): + + initial_ent = ShapeletTransform.binary_entropy( + total_num_this_class, total_num_other_class + ) + bsf_ig = 0 + + count_this_class = 0 + count_other_class = 0 + + total_all = total_num_this_class + total_num_other_class + + # evaluate each split point + for split in range(0, len(orderline) - 1): + next_class = orderline[split][1] # +1 if this class, -1 if other + if next_class > 0: + count_this_class += 1 + else: + count_other_class += 1 + + # optimistically add this class to left side first and other to + # right + left_prop = (split + 1) / total_all + ent_left = ShapeletTransform.binary_entropy( + count_this_class, count_other_class + ) + + right_prop = 1 - left_prop # because right side must + # optimistically contain everything else + ent_right = ShapeletTransform.binary_entropy( + total_num_this_class - count_this_class, + total_num_other_class - count_other_class, + ) + + ig = initial_ent - left_prop * ent_left - right_prop * ent_right + bsf_ig = max(ig, bsf_ig) + + return bsf_ig + + # could cythonise + @staticmethod + def calc_early_binary_ig( + orderline, + num_this_class_in_orderline, + num_other_class_in_orderline, + num_to_add_this_class, + num_to_add_other_class, + ): + # def entropy(ent_class_counts, all_class_count): + + initial_ent = ShapeletTransform.binary_entropy( + num_this_class_in_orderline + num_to_add_this_class, + num_other_class_in_orderline + num_to_add_other_class, + ) + bsf_ig = 0 + + # actual observations in orderline + count_this_class = 0 + count_other_class = 0 + + total_all = ( + num_this_class_in_orderline + + num_other_class_in_orderline + + num_to_add_this_class + + num_to_add_other_class + ) + + # evaluate each split point + for split in range(0, len(orderline) - 1): + next_class = orderline[split][1] # +1 if this class, -1 if other + if next_class > 0: + count_this_class += 1 + else: + count_other_class += 1 + + # optimistically add this class to left side first and other to + # right + left_prop = (split + 1 + num_to_add_this_class) / total_all + ent_left = ShapeletTransform.binary_entropy( + count_this_class + num_to_add_this_class, count_other_class + ) + + right_prop = 1 - left_prop # because right side must + # optimistically contain everything else + ent_right = ShapeletTransform.binary_entropy( + num_this_class_in_orderline - count_this_class, + num_other_class_in_orderline + - count_other_class + + num_to_add_other_class, + ) + + ig = initial_ent - left_prop * ent_left - right_prop * ent_right + bsf_ig = max(ig, bsf_ig) + + # now optimistically add this class to right, other to left + left_prop = (split + 1 + num_to_add_other_class) / total_all + ent_left = ShapeletTransform.binary_entropy( + count_this_class, count_other_class + num_to_add_other_class + ) + + right_prop = 1 - left_prop # because right side must + # optimistically contain everything else + ent_right = ShapeletTransform.binary_entropy( + num_this_class_in_orderline - count_this_class + num_to_add_this_class, + num_other_class_in_orderline - count_other_class, + ) + ig = initial_ent - left_prop * ent_left - right_prop * ent_right + bsf_ig = max(ig, bsf_ig) + + return bsf_ig + + @staticmethod + def zscore(a, axis=0, ddof=0): + """A static method to return the normalised version of series. + This mirrors the scipy implementation + with a small difference - rather than allowing /0, the function + returns output = np.zeroes(len(input)). + This is to allow for sensible processing of candidate + shapelets/comparison subseries that are a straight + line. Original version: + https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats + .zscore.html + + Parameters + ---------- + a : array_like + An array like object containing the sample data. + + axis : int or None, optional + Axis along which to operate. Default is 0. If None, compute over + the whole array a. + + ddof : int, optional + Degrees of freedom correction in the calculation of the standard + deviation. Default is 0. + + Returns + ------- + zscore : array_like + The z-scores, standardized by mean and standard deviation of + input array a. + """ + zscored = np.empty(a.shape) + for i, j in enumerate(a): + # j = np.asanyarray(j) + sstd = j.std(axis=axis, ddof=ddof) + + # special case - if shapelet is a straight line (i.e. no + # variance), zscore ver should be np.zeros(len(a)) + if sstd == 0: + zscored[i] = np.zeros(len(j)) + else: + mns = j.mean(axis=axis) + if axis and mns.ndim < j.ndim: + zscored[i] = (j - np.expand_dims(mns, axis=axis)) / np.expand_dims( + sstd, axis=axis + ) + else: + zscored[i] = (j - mns) / sstd + return zscored + + @staticmethod + def euclidean_distance_early_abandon(u, v, min_dist): + sum_dist = 0 + for i in range(0, len(u[0])): + for j in range(0, len(u)): + u_v = u[j][i] - v[j][i] + sum_dist += np.dot(u_v, u_v) + if sum_dist >= min_dist: + # The distance is higher, so early abandon. + return min_dist + return sum_dist + + +class ContractedShapeletTransform(ShapeletTransform): + """Contracted Shapelet Transform. + @incollection{bostrom2017binary, + title={Binary shapelet transform for multiclass time series + classification}, + author={Bostrom, Aaron and Bagnall, Anthony}, + booktitle={Transactions on Large-Scale Data-and Knowledge-Centered + Systems XXXII}, + pages={24--46}, + year={2017}, + publisher={Springer} + } + + Parameters + ---------- + min_shapelet_length : int, lower bound on candidate + shapelet lengths (default = 3) + max_shapelet_length : int, upper bound on candidate + shapelet lengths (default = inf or series length) + max_shapelets_to_store_per_class : int, upper bound on number of + shapelets to retain from each distinct class (default = 200) + time_contract_in_mins : float, the number of minutes + allowed for shapelet extraction (default = 60) + num_candidates_to_sample_per_case : int, number of candidate shapelets + to assess per training series before moving on to + the next series (default = 20) + random_state : RandomState, int, or none: to + control random state objects for deterministic results (default = None) + verbose : int, level of output printed to + the console (for information only) (default = 0) + remove_self_similar : boolean, remove overlapping + "self-similar" shapelets from the final transform (default = True) + + Attributes + ---------- + + predefined_ig_rejection_level : float, minimum information gain + required to keep a shapelet (default = 0.05) + self.shapelets : list of Shapelet objects, + the stored shapelets after a dataset has been processed + """ + + def __init__( + self, + min_shapelet_length=3, + max_shapelet_length=np.inf, + shapelets_lengths=None, + max_shapelets_to_store_per_class=200, + time_contract_in_mins=0.1, + num_candidates_to_sample_per_case=20, + random_state=None, + verbose=0, + remove_self_similar=True, + predefined_ig_rejection_level=0.05, + ): + self.num_candidates_to_sample_per_case = num_candidates_to_sample_per_case + self.time_contract_in_mins = time_contract_in_mins + + self.predefined_ig_rejection_level = predefined_ig_rejection_level + self.shapelets = None + + super().__init__( + min_shapelet_length, + max_shapelet_length, + shapelets_lengths, + max_shapelets_to_store_per_class, + random_state, + verbose, + remove_self_similar, + predefined_ig_rejection_level, + ) + + +class _RandomEnumerationShapeletTransform(ShapeletTransform): + pass + # to follow + + +class Shapelet: + """A simple class to model a Shapelet with associated information + + Parameters + ---------- + series_id: int + The index of the series within the data (X) that was passed to fit. + start_pos: int + The starting position of the shapelet within the original series + length: int + The length of the shapelet + info_gain: flaot + The calculated information gain of this shapelet + data: array-like + The (z-normalised) data of this shapelet + """ + + def __init__(self, series_id, start_pos, length, info_gain, data): + self.series_id = series_id + self.start_pos = start_pos + self.length = length + self.info_gain = info_gain + self.data = data + self.distances = None + + def __str__(self): + return ( + "Series ID: {0}, start_pos: {1}, length: {2}, info_gain: {3}," + " ".format(self.series_id, self.start_pos, self.length, self.info_gain) + ) + + +class ShapeletPQ: + def __init__(self): + self._queue = [] + self._index = 0 + + def push(self, shapelet): + heapq.heappush(self._queue, (shapelet.info_gain, self._index, shapelet)) + self._index += 1 + + def pop(self): + return heapq.heappop(self._queue)[-1] + + def peek(self): + return self._queue[0] + + def get_size(self): + return len(self._queue) + + def get_array(self): + return self._queue + + +def write_transformed_data_to_arff(transform, labels, file_name): + """A simple function to save the transform obtained in arff format + + Parameters + ---------- + transform: array-like + The transform obtained for a dataset + labels: array-like + The labels of the dataset + file_name: string + The directory to save the transform + """ + # Create directory in case it doesn't exists + directory = "/".join(file_name.split("/")[:-1]) + if not os.path.exists(directory): + os.makedirs(directory) + + num_shapelets = transform.shape[1] + unique_labels = np.unique(labels).tolist() + + with open(file_name, "w+") as f: + # Headers + f.write("@Relation Shapelets" + file_name.split("/")[-1].split("_")[0] + "\n\n") + for i in range(0, num_shapelets): + f.write("@attribute Shapelet_" + str(i) + " numeric\n") + f.write("@attribute target {" + ",".join(unique_labels) + "}\n") + f.write("\n@data\n") + # Patterns + for i, j in enumerate(transform): + pattern = j.tolist() + [int(float(labels[i]))] + f.write(",".join(map(str, pattern)) + "\n") + f.close() + + +def write_shapelets_to_csv(shapelets, data, dim_to_use, time, file_name): + """A simple function to save the shapelets obtained in csv format + + Parameters + ---------- + shapelets: array-like + The shapelets obtained for a dataset + data: array-like + The original data + time: fload + The time spent obtaining shapelets + file_name: string + The directory to save the set of shapelets + """ + data = data.iloc[:, dim_to_use] + + data_aux = [[]] * len(data) + for i in range(0, len(data)): + data_aux[i] = np.array([np.asarray(x) for x in np.asarray(data.iloc[i, :])]) + data = data_aux.copy() + + # Create directory in case it doesn't exists + directory = "/".join(file_name.split("/")[:-1]) + if not os.path.exists(directory): + os.makedirs(directory) + + with open(file_name, "w+") as f: + # Number of shapelets and time extracting + f.write(str(len(shapelets)) + "," + str(time) + "\n") + for j in shapelets: + f.write( + str(j.info_gain) + + "," + + str(j.series_id) + + "," + + "".join(str(j.dims)).replace(", ", ":") + + "," + + str(j.start_pos) + + "," + + str(j.length) + + "\n" + ) + for k in range(0, len(dim_to_use)): + f.write( + ",".join( + map( + str, + data[j.series_id][ + k, j.start_pos : (j.start_pos + j.length) + ], + ) + ) + + "\n" + ) + f.close() diff --git a/TSInterpret/InterpretabilityModels/counterfactual/SETS/sktime_convert.py b/TSInterpret/InterpretabilityModels/counterfactual/SETS/sktime_convert.py new file mode 100644 index 0000000..d4f61d3 --- /dev/null +++ b/TSInterpret/InterpretabilityModels/counterfactual/SETS/sktime_convert.py @@ -0,0 +1,985 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Jul 29 15:33:17 2021 + +@author: omar +""" + +import numpy as np +import pandas as pd + +__all__ = [ + "convert_dict", +] + +__all__ = [ + "MTYPE_REGISTER_PANEL", + "MTYPE_LIST_PANEL", +] + + +MTYPE_REGISTER_PANEL = [ + ( + "nested_univ", + "Panel", + "pd.DataFrame with one column per variable, pd.Series in cells", + ), + ( + "numpy3D", + "Panel", + "3D np.array of format (n_instances, n_columns, n_timepoints)", + ), + ( + "numpyflat", + "Panel", + "2D np.array of format (n_instances, n_columns*n_timepoints)", + ), + ("pd-multiindex", "Panel", "pd.DataFrame with multi-index (instance, time point)"), + ("pd-wide", "Panel", "pd.DataFrame in wide format, cols = (instance*time point)"), + ( + "pd-long", + "Panel", + "pd.DataFrame in long format, cols = (index, time_index, column)", + ), + ("df-list", "Panel", "list of pd.DataFrame"), +] + +MTYPE_LIST_PANEL = pd.DataFrame(MTYPE_REGISTER_PANEL)[0].values + + +# dictionary indexed by triples of types +# 1st element = convert from - type +# 2nd element = convert to - type +# 3rd element = considered as this scitype - string +# elements are conversion functions of machine type (1st) -> 2nd + +convert_dict = dict() + + +def convert_identity(what, store=None): + + return what + + +# assign identity function to type conversion to self +for tp in MTYPE_LIST_PANEL: + convert_dict[(tp, tp, "Panel")] = convert_identity + + +def _cell_is_series_or_array(cell): + return isinstance(cell, (pd.Series, np.ndarray)) + + +def _nested_cell_mask(X): + return X.applymap(_cell_is_series_or_array) + + +def are_columns_nested(X): + """Check whether any cells have nested structure in each DataFrame column. + Parameters + ---------- + X : pd.DataFrame + DataFrame to check for nested data structures. + Returns + ------- + any_nested : bool + If True, at least one column is nested. + If False, no nested columns. + """ + any_nested = _nested_cell_mask(X).any().values + return any_nested + + +def _nested_cell_timepoints(cell): + if _cell_is_series_or_array(cell): + n_timepoints = cell.shape[0] + else: + n_timepoints = 0 + return n_timepoints + + +def _check_equal_index(X): + """Check if all time-series in nested pandas DataFrame have the same index. + Parameters + ---------- + X : nested pd.DataFrame + Input dataframe with time-series in cells. + Returns + ------- + indexes : list of indixes + List of indixes with one index for each column + """ + # TODO handle 1d series, not only 2d dataframes + # TODO assumes columns are typed (i.e. all rows for a given column have + # the same type) + # TODO only handles series columns, raises error for columns with + # primitives + + indexes = [] + # Check index for each column separately. + for c, col in enumerate(X.columns): + + # Get index from first row, can be either pd.Series or np.array. + first_index = ( + X.iloc[0, c].index + if hasattr(X.iloc[0, c], "index") + else np.arange(X.iloc[c, 0].shape[0]) + ) + + # Series must contain at least 2 observations, otherwise should be + # primitive. + if len(first_index) < 2: + raise ValueError( + f"Time series must contain at least 2 observations, " + f"but found: " + f"{len(first_index)} observations in column: {col}" + ) + + # Check index for all rows. + for i in range(1, X.shape[0]): + index = ( + X.iloc[i, c].index + if hasattr(X.iloc[i, c], "index") + else np.arange(X.iloc[c, 0].shape[0]) + ) + if not np.array_equal(first_index, index): + raise ValueError( + f"Found time series with unequal index in column {col}. " + f"Input time-series must have the same index." + ) + indexes.append(first_index) + + return indexes + + +def from_3d_numpy_to_2d_array(X): + """Convert 2D NumPy Panel to 2D numpy Panel. + Converts 3D numpy array (n_instances, n_columns, n_timepoints) to + a 2D numpy array with shape (n_instances, n_columns*n_timepoints) + Parameters + ---------- + X : np.ndarray + The input 3d-NumPy array with shape + (n_instances, n_columns, n_timepoints) + Returns + ------- + array_2d : np.ndarray + A 2d-NumPy array with shape (n_instances, n_columns*n_timepoints) + """ + array_2d = X.reshape(X.shape[0], -1) + return array_2d + + +def from_3d_numpy_to_2d_array_adp(what, store=None): + + return from_3d_numpy_to_2d_array(what) + + +convert_dict[("numpy3D", "numpyflat", "Panel")] = from_3d_numpy_to_2d_array_adp + + +def from_nested_to_2d_array(X, return_numpy=False): + """Convert nested Panel to 2D numpy Panel. + Convert nested pandas DataFrame or Series with NumPy arrays or + pandas Series in cells into tabular + pandas DataFrame with primitives in cells, i.e. a data frame with the + same number of rows as the input data and + as many columns as there are observations in the nested series. Requires + series to be have the same index. + Parameters + ---------- + X : nested pd.DataFrame or nested pd.Series + return_numpy : bool, default = False + - If True, returns a NumPy array of the tabular data. + - If False, returns a pandas DataFrame with row and column names. + Returns + ------- + Xt : pandas DataFrame + Transformed DataFrame in tabular format + """ + # TODO does not handle dataframes with nested series columns *and* + # standard columns containing only primitives + + # convert nested data into tabular data + if isinstance(X, pd.Series): + Xt = np.array(X.tolist()) + + elif isinstance(X, pd.DataFrame): + try: + Xt = np.hstack([X.iloc[:, i].tolist() for i in range(X.shape[1])]) + + # except strange key error for specific case + except KeyError: + if (X.shape == (1, 1)) and (X.iloc[0, 0].shape == (1,)): + # in fact only breaks when an additional condition is met, + # namely that the index of the time series of a single value + # does not start with 0, e.g. pd.RangeIndex(9, 10) as is the + # case in forecasting + Xt = X.iloc[0, 0].values + else: + raise + + if Xt.ndim != 2: + raise ValueError( + "Tabularization failed, it's possible that not " + "all series were of equal length" + ) + + else: + raise ValueError( + f"Expected input is pandas Series or pandas DataFrame, " + f"but found: {type(X)}" + ) + + if return_numpy: + return Xt + + Xt = pd.DataFrame(Xt) + + # create column names from time index + if X.ndim == 1: + time_index = ( + X.iloc[0].index + if hasattr(X.iloc[0], "index") + else np.arange(X.iloc[0].shape[0]) + ) + columns = [f"{X.name}__{i}" for i in time_index] + + else: + columns = [] + for colname, col in X.items(): + time_index = ( + col.iloc[0].index + if hasattr(col.iloc[0], "index") + else np.arange(col.iloc[0].shape[0]) + ) + columns.extend([f"{colname}__{i}" for i in time_index]) + + Xt.index = X.index + Xt.columns = columns + return Xt + + +def from_nested_to_pdwide(what, store=None): + + return from_nested_to_2d_array(X=what, return_numpy=False) + + +def from_nested_to_2d_np_array(what, store=None): + + return from_nested_to_2d_array(X=what, return_numpy=True) + + +convert_dict[("nested_univ", "pd-wide", "Panel")] = from_nested_to_pdwide + +convert_dict[("nested_univ", "numpyflat", "Panel")] = from_nested_to_2d_np_array + + +def from_2d_array_to_nested( + X, index=None, columns=None, time_index=None, cells_as_numpy=False +): + """Convert 2D dataframe to nested dataframe. + Convert tabular pandas DataFrame with only primitives in cells into + nested pandas DataFrame with a single column. + Parameters + ---------- + X : pd.DataFrame + cells_as_numpy : bool, default = False + If True, then nested cells contain NumPy array + If False, then nested cells contain pandas Series + index : array-like, shape=[n_samples], optional (default = None) + Sample (row) index of transformed DataFrame + time_index : array-like, shape=[n_obs], optional (default = None) + Time series index of transformed DataFrame + Returns + ------- + Xt : pd.DataFrame + Transformed DataFrame in nested format + """ + if (time_index is not None) and cells_as_numpy: + raise ValueError( + "`Time_index` cannot be specified when `return_arrays` is True, " + "time index can only be set to " + "pandas Series" + ) + if isinstance(X, pd.DataFrame): + X = X.to_numpy() + + container = np.array if cells_as_numpy else pd.Series + + # for 2d numpy array, rows represent instances, columns represent time points + n_instances, n_timepoints = X.shape + + if time_index is None: + time_index = np.arange(n_timepoints) + kwargs = {"index": time_index} + + Xt = pd.DataFrame( + pd.Series([container(X[i, :], **kwargs) for i in range(n_instances)]) + ) + if index is not None: + Xt.index = index + if columns is not None: + Xt.columns = columns + return Xt + + +def from_pd_wide_to_nested(what, store=None): + + return from_2d_array_to_nested(X=what) + + +convert_dict[("pd-wide", "nested_univ", "Panel")] = from_pd_wide_to_nested + + +def convert_from_dictionary(ts_dict): + """Conversion from dictionary to pandas. + Simple conversion from a dictionary of each series, e.g. univariate + x = { + "Series1": [1.0,2.0,3.0,1.0,2.0], + "Series2": [3.0,2.0,1.0,3.0,2.0], + } + or multivariate, e.g. + to sktime pandas format + TODO: Adapt for multivariate + """ + panda = pd.DataFrame(ts_dict) + panda = panda.transpose() + return from_2d_array_to_nested(panda) + + +def _concat_nested_arrays(arrs, cells_as_numpy=False): + """Nest tabular arrays from nested list. + Helper function to nest tabular arrays from nested list of arrays. + Parameters + ---------- + arrs : list of numpy arrays + Arrays must have the same number of rows, but can have varying + number of columns. + cells_as_numpy : bool, default = False + If True, then nested cells contain NumPy array + If False, then nested cells contain pandas Series + Returns + ------- + Xt : pandas DataFrame + Transformed dataframe with nested column for each input array. + """ + if cells_as_numpy: + Xt = pd.DataFrame( + np.column_stack( + [pd.Series([np.array(vals) for vals in interval]) for interval in arrs] + ) + ) + else: + Xt = pd.DataFrame( + np.column_stack( + [pd.Series([pd.Series(vals) for vals in interval]) for interval in arrs] + ) + ) + return Xt + + +def _get_index(x): + if hasattr(x, "index"): + return x.index + else: + # select last dimension for time index + return pd.RangeIndex(x.shape[-1]) + + +def _get_time_index(X): + """Get index of time series data, helper function. + Parameters + ---------- + X : pd.DataFrame + Returns + ------- + time_index : pandas Index + Index of time series + """ + # assumes that all samples share the same the time index, only looks at + # first row + if isinstance(X, pd.DataFrame): + return _get_index(X.iloc[0, 0]) + + elif isinstance(X, pd.Series): + return _get_index(X.iloc[0]) + + elif isinstance(X, np.ndarray): + return _get_index(X) + + else: + raise ValueError( + f"X must be a pandas DataFrame or Series, but found: {type(X)}" + ) + + +def _make_column_names(column_count): + return [f"var_{i}" for i in range(column_count)] + + +def _get_column_names(X): + if isinstance(X, pd.DataFrame): + return X.columns + else: + return _make_column_names(X.shape[1]) + + +def from_nested_to_long( + X, instance_column_name=None, time_column_name=None, dimension_column_name=None +): + """Convert nested DataFrame to long DataFrame. + Parameters + ---------- + X : pd.DataFrame + The nested DataFrame + instance_column_name : str + The name of column corresponding to the DataFrame's instances + time_column_name : str + The name of the column corresponding to the DataFrame's timepoints. + dimension_column_name : str + The name of the column corresponding to the DataFrame's dimensions. + Returns + ------- + long_df : pd.DataFrame + Long pandas DataFrame + """ + long_df = from_nested_to_multi_index( + X, instance_index="index", time_index="time_index" + ) + long_df.reset_index(inplace=True) + long_df = long_df.melt(id_vars=["index", "time_index"], var_name="column") + + col_rename_dict = {} + if instance_column_name is not None: + col_rename_dict["index"] = instance_column_name + + if time_column_name is not None: + col_rename_dict["time_index"] = time_column_name + + if dimension_column_name is not None: + col_rename_dict["column"] = dimension_column_name + + if len(col_rename_dict) > 0: + long_df = long_df.rename(columns=col_rename_dict) + + return long_df + + +def from_nested_to_long_adp(what, store=None): + + return from_nested_to_long( + X=what, + instance_column_name="case_id", + time_column_name="reading_id", + dimension_column_name="dim_id", + ) + + +convert_dict[("nested_univ", "pd-long", "Panel")] = from_nested_to_long_adp + + +def from_long_to_nested( + X_long, + instance_column_name="case_id", + time_column_name="reading_id", + dimension_column_name="dim_id", + value_column_name="value", + column_names=None, +): + """Convert long DataFrame to a nested DataFrame. + Parameters + ---------- + X_long : pd.DataFrame + The long DataFrame + instance_column_name : str, default = 'case_id' + The name of column corresponding to the DataFrame's instances. + time_column_name : str, default = 'reading_id' + The name of the column corresponding to the DataFrame's timepoints. + dimension_column_name : str, default = 'dim_id' + The name of the column corresponding to the DataFrame's dimensions. + value_column_name : str, default = 'value' + The name of the column corresponding to the DataFrame's values. + column_names : list, optional + Optional list of column names to use for nested DataFrame columns. + Returns + ------- + X_nested : pd.DataFrame + Nested pandas DataFrame + """ + X_nested = X_long.pivot( + index=[instance_column_name, time_column_name], + columns=dimension_column_name, + values=value_column_name, + ) + X_nested = from_multi_index_to_nested(X_nested, instance_index=instance_column_name) + + n_columns = X_nested.shape[1] + if column_names is None: + X_nested.columns = _make_column_names(n_columns) + + else: + X_nested.columns = column_names + + # # get distinct dimension ids + # unique_dim_ids = long_dataframe.iloc[:, 1].unique() + # num_dims = len(unique_dim_ids) + + # data_by_dim = [] + # indices = [] + + # # get number of distinct cases (note: a case may have 1 or many dimensions) + # unique_case_ids = long_dataframe.iloc[:, 0].unique() + # # assume series are indexed from 0 to m-1 (can map to non-linear indices + # # later if needed) + + # # init a list of size m for each d - to store the series data for m + # # cases over d dimensions + # # also, data may not be in order in long format so store index data for + # # aligning output later + # # (i.e. two stores required: one for reading id/timestamp and one for + # # value) + # for d in range(0, num_dims): + # data_by_dim.append([]) + # indices.append([]) + # for _c in range(0, len(unique_case_ids)): + # data_by_dim[d].append([]) + # indices[d].append([]) + + # # go through every row in the dataframe + # for i in range(0, len(long_dataframe)): + # # extract the relevant data, catch cases where the dim id is not an + # # int as it must be the class + + # row = long_dataframe.iloc[i] + # case_id = int(row[0]) + # dim_id = int(row[1]) + # reading_id = int(row[2]) + # value = row[3] + # data_by_dim[dim_id][case_id].append(value) + # indices[dim_id][case_id].append(reading_id) + + # x_data = {} + # for d in range(0, num_dims): + # key = "dim_" + str(d) + # dim_list = [] + # for i in range(0, len(unique_case_ids)): + # temp = pd.Series(data_by_dim[d][i], indices[d][i]) + # dim_list.append(temp) + # x_data[key] = pd.Series(dim_list) + + return X_nested + + +def from_long_to_nested_adp(what, store=None): + + return from_long_to_nested(X_long=what) + + +convert_dict[("pd-long", "nested_univ", "Panel")] = from_nested_to_long_adp + + +def from_multi_index_to_3d_numpy(X, instance_index=None, time_index=None): + """Convert pandas multi-index Panel to numpy 3D Panel. + Convert panel data stored as pandas multi-index DataFrame to + Numpy 3-dimensional NumPy array (n_instances, n_columns, n_timepoints). + Parameters + ---------- + X : pd.DataFrame + The multi-index pandas DataFrame + instance_index : str + Name of the multi-index level corresponding to the DataFrame's instances + time_index : str + Name of multi-index level corresponding to DataFrame's timepoints + Returns + ------- + X_3d : np.ndarray + 3-dimensional NumPy array (n_instances, n_columns, n_timepoints) + """ + if X.index.nlevels != 2: + raise ValueError("Multi-index DataFrame should have 2 levels.") + + if (instance_index is None) or (time_index is None): + msg = "Must supply parameters instance_index and time_index" + raise ValueError(msg) + + n_instances = len(X.groupby(level=instance_index)) + # Alternative approach is more verbose + # n_instances = (multi_ind_dataframe + # .index + # .get_level_values(instance_index) + # .unique()).shape[0] + n_timepoints = len(X.groupby(level=time_index)) + # Alternative approach is more verbose + # n_instances = (multi_ind_dataframe + # .index + # .get_level_values(time_index) + # .unique()).shape[0] + + n_columns = X.shape[1] + + X_3d = X.values.reshape(n_instances, n_timepoints, n_columns).swapaxes(1, 2) + + return X_3d + + +def from_multi_index_to_3d_numpy_adp(what, store=None): + + return from_multi_index_to_3d_numpy( + X=what, instance_index="instances", time_index="timepoints" + ) + + +convert_dict[("pd-multiindex", "numpy3D", "Panel")] = from_multi_index_to_3d_numpy_adp + + +def from_3d_numpy_to_multi_index( + X, instance_index=None, time_index=None, column_names=None +): + """Convert 3D numpy Panel to pandas multi-index Panel. + Convert 3-dimensional NumPy array (n_instances, n_columns, n_timepoints) + to panel data stored as pandas multi-indexed DataFrame. + Parameters + ---------- + X : np.ndarray + 3-dimensional NumPy array (n_instances, n_columns, n_timepoints) + instance_index : str + Name of the multi-index level corresponding to the DataFrame's instances + time_index : str + Name of multi-index level corresponding to DataFrame's timepoints + Returns + ------- + X_mi : pd.DataFrame + The multi-indexed pandas DataFrame + """ + if X.ndim != 3: + msg = " ".join( + [ + "Input should be 3-dimensional NumPy array with shape", + "(n_instances, n_columns, n_timepoints).", + ] + ) + raise TypeError(msg) + + n_instances, n_columns, n_timepoints = X.shape + multi_index = pd.MultiIndex.from_product( + [range(n_instances), range(n_columns), range(n_timepoints)], + names=["instances", "columns", "timepoints"], + ) + + X_mi = pd.DataFrame({"X": X.flatten()}, index=multi_index) + X_mi = X_mi.unstack(level="columns") + + # Assign column names + if column_names is None: + X_mi.columns = _make_column_names(n_columns) + + else: + X_mi.columns = column_names + + index_rename_dict = {} + if instance_index is not None: + index_rename_dict["instances"] = instance_index + + if time_index is not None: + index_rename_dict["timepoints"] = time_index + + if len(index_rename_dict) > 0: + X_mi = X_mi.rename_axis(index=index_rename_dict) + + return X_mi + + +def from_3d_numpy_to_multi_index_adp(what, store=None): + + return from_3d_numpy_to_multi_index(X=what) + + +convert_dict[("numpy3D", "pd-multiindex", "Panel")] = from_3d_numpy_to_multi_index_adp + + +def from_multi_index_to_nested( + multi_ind_dataframe, instance_index=None, cells_as_numpy=False +): + """Convert a pandas DataFrame witha multi-index to a nested DataFrame. + Parameters + ---------- + multi_ind_dataframe : pd.DataFrame + Input multi-indexed pandas DataFrame + instance_index_name : str + The name of multi-index level corresponding to the DataFrame's instances + cells_as_numpy : bool, default = False + If True, then nested cells contain NumPy array + If False, then nested cells contain pandas Series + Returns + ------- + x_nested : pd.DataFrame + The nested version of the DataFrame + """ + if instance_index is None: + raise ValueError("Supply a value for the instance_index parameter.") + + # get number of distinct cases (note: a case may have 1 or many dimensions) + instance_idxs = multi_ind_dataframe.index.get_level_values(instance_index).unique() + + x_nested = pd.DataFrame() + + # Loop the dimensions (columns) of multi-index DataFrame + for _label, _series in multi_ind_dataframe.iteritems(): # noqa + # for _label in multi_ind_dataframe.columns: + # _series = multi_ind_dataframe.loc[:, _label] + # Slice along the instance dimension to return list of series for each case + # Note: if you omit .rename_axis the returned DataFrame + # prints time axis dimension at the start of each cell, + # but this doesn't affect the values. + if cells_as_numpy: + dim_list = [ + _series.xs(instance_idx, level=instance_index).values + for instance_idx in instance_idxs + ] + else: + dim_list = [ + _series.xs(instance_idx, level=instance_index).rename_axis(None) + for instance_idx in instance_idxs + ] + + x_nested[_label] = pd.Series(dim_list) + x_nested = pd.DataFrame(x_nested) + + col_msg = "Multi-index and nested DataFrames should have same columns names" + assert (x_nested.columns == multi_ind_dataframe.columns).all(), col_msg + + return x_nested + + +def from_multi_index_to_nested_adp(what, store=None): + + return from_multi_index_to_nested(X=what, instance_index_name="instances") + + +convert_dict[("pd-multiindex", "nested_univ", "Panel")] = from_multi_index_to_nested_adp + + +def from_nested_to_multi_index(X, instance_index=None, time_index=None): + """Convert nested pandas Panel to multi-index pandas Panel. + Converts nested pandas DataFrame (with time series as pandas Series + or NumPy array in cells) into multi-indexed pandas DataFrame. + Can convert mixed nested and primitive DataFrame to multi-index DataFrame. + Parameters + ---------- + X : pd.DataFrame + The nested DataFrame to convert to a multi-indexed pandas DataFrame + instance_index : str + Name of the multi-index level corresponding to the DataFrame's instances + time_index : str + Name of multi-index level corresponding to DataFrame's timepoints + Returns + ------- + X_mi : pd.DataFrame + The multi-indexed pandas DataFrame + """ + if not is_nested_dataframe(X): + raise ValueError("Input DataFrame is not a nested DataFrame") + + if time_index is None: + time_index_name = "timepoints" + else: + time_index_name = time_index + + # n_columns = X.shape[1] + nested_col_mask = [*are_columns_nested(X)] + + if instance_index is None: + instance_idxs = X.index.get_level_values(-1).unique() + # n_instances = instance_idxs.shape[0] + instance_index_name = "instance" + + else: + if instance_index in X.index.names: + instance_idxs = X.index.get_level_values(instance_index).unique() + else: + instance_idxs = X.index.get_level_values(-1).unique() + # n_instances = instance_idxs.shape[0] + instance_index_name = instance_index + + instances = [] + for instance_idx in instance_idxs: + instance = [ + _val if isinstance(_val, pd.Series) else pd.Series(_val, name=_lab) + for _lab, _val in X.loc[instance_idx, :].iteritems() # noqa + ] + # instance = [ + # X.loc[instance_idx, _label] + # if isinstance(X.loc[instance_idx, _label], pd.Series) + # else pd.Series(X.loc[instance_idx, _label], name=_label) + # for _label in X.columns ] + + instance = pd.concat(instance, axis=1) + # For primitive (non-nested column) assume the same + # primitive value applies to every timepoint of the instance + for col_idx, is_nested in enumerate(nested_col_mask): + if not is_nested: + instance.iloc[:, col_idx] = instance.iloc[:, col_idx].ffill() + + # Correctly assign multi-index + multi_index = pd.MultiIndex.from_product( + [[instance_idx], instance.index], + names=[instance_index_name, time_index_name], + ) + instance.index = multi_index + instances.append(instance) + + X_mi = pd.concat(instances) + X_mi.columns = X.columns + + return X_mi + + +def from_nested_to_multi_index_adp(what, store=None): + + return from_nested_to_multi_index( + X=what, instance_index_name="instances", time_index="timepoints" + ) + + +convert_dict[("nested_univ", "pd-multiindex", "Panel")] = from_nested_to_multi_index_adp + + +def _convert_series_cell_to_numpy(cell): + if isinstance(cell, pd.Series): + return cell.to_numpy() + else: + return cell + + +def from_nested_to_3d_numpy(X): + """Convert nested Panel to 3D numpy Panel. + Convert nested pandas DataFrame (with time series as pandas Series + in cells) into NumPy ndarray with shape + (n_instances, n_columns, n_timepoints). + Parameters + ---------- + X : pd.DataFrame + Nested pandas DataFrame + Returns + ------- + X_3d : np.ndarrray + 3-dimensional NumPy array + """ + # n_instances, n_columns = X.shape + # n_timepoints = X.iloc[0, 0].shape[0] + # array = np.empty((n_instances, n_columns, n_timepoints)) + # for column in range(n_columns): + # array[:, column, :] = X.iloc[:, column].tolist() + # return array + if not is_nested_dataframe(X): + raise ValueError("Input DataFrame is not a nested DataFrame") + + # n_columns = X.shape[1] + nested_col_mask = [*are_columns_nested(X)] + + # If all the columns are nested in structure + if nested_col_mask.count(True) == len(nested_col_mask): + X_3d = np.stack( + X.applymap(_convert_series_cell_to_numpy) + .apply(lambda row: np.stack(row), axis=1) + .to_numpy() + ) + + # If some columns are primitive (non-nested) then first convert to + # multi-indexed DataFrame where the same value of these columns is + # repeated for each timepoint + # Then the multi-indexed DataFrame can be converted to 3d NumPy array + else: + X_mi = from_nested_to_multi_index(X) + X_3d = from_multi_index_to_3d_numpy( + X_mi, instance_index="instance", time_index="timepoints" + ) + + return X_3d + + +def from_nested_to_3d_numpy_adp(what, store=None): + + return from_nested_to_3d_numpy(X=what) + + +convert_dict[("nested_univ", "numpy3D", "Panel")] = from_nested_to_3d_numpy_adp + + +def from_3d_numpy_to_nested(X, column_names=None, cells_as_numpy=False): + """Convert numpy 3D Panel to nested pandas Panel. + Convert NumPy ndarray with shape (n_instances, n_columns, n_timepoints) + into nested pandas DataFrame (with time series as pandas Series in cells) + Parameters + ---------- + X : np.ndarray + 3-dimensional Numpy array to convert to nested pandas DataFrame format + column_names: list-like, default = None + Optional list of names to use for naming nested DataFrame's columns + cells_as_numpy : bool, default = False + If True, then nested cells contain NumPy array + If False, then nested cells contain pandas Series + Returns + ------- + df : pd.DataFrame + """ + df = pd.DataFrame() + # n_instances, n_variables, _ = X.shape + n_instances, n_columns, n_timepoints = X.shape + + container = np.array if cells_as_numpy else pd.Series + + if column_names is None: + column_names = _make_column_names(n_columns) + + else: + if len(column_names) != n_columns: + msg = " ".join( + [ + f"Input 3d Numpy array as {n_columns} columns,", + f"but only {len(column_names)} names supplied", + ] + ) + raise ValueError(msg) + + for j, column in enumerate(column_names): + df[column] = [container(X[instance, j, :]) for instance in range(n_instances)] + return df + + +def from_3d_numpy_to_nested_adp(what, store=None): + + return from_3d_numpy_to_nested(X=what) + + +convert_dict[("numpy3D", "nested_univ", "Panel")] = from_3d_numpy_to_nested_adp + + +def is_nested_dataframe(X): + """Check whether the input is a nested DataFrame. + To allow for a mixture of nested and primitive columns types the + the considers whether any column is a nested np.ndarray or pd.Series. + Column is consider nested if any cells in column have a nested structure. + Parameters + ---------- + X: Input that is checked to determine if it is a nested DataFrame. + Returns + ------- + bool: Whether the input is a nested DataFrame + """ + # return isinstance(X, pd.DataFrame) and isinstance( + # X.iloc[0, 0], (np.ndarray, pd.Series) + # ) + is_dataframe = isinstance(X, pd.DataFrame) + + # If not a DataFrame we know is_nested_dataframe is False + if not is_dataframe: + return is_dataframe + + # Otherwise we'll see if any column has a nested structure in first row + else: + is_nested = are_columns_nested(X).any() + + return is_dataframe and is_nested diff --git a/TSInterpret/InterpretabilityModels/counterfactual/SETS/st.py b/TSInterpret/InterpretabilityModels/counterfactual/SETS/st.py new file mode 100644 index 0000000..abd9b94 --- /dev/null +++ b/TSInterpret/InterpretabilityModels/counterfactual/SETS/st.py @@ -0,0 +1,120 @@ +# Author: Omar Bahri + +import os +import random +import sys + +import numpy as np +from shapelets import ContractedShapeletTransform +from sklearn.preprocessing import LabelEncoder +from sktime_convert import from_3d_numpy_to_nested +from utils import MultivariateTransformer, save_shapelets_distances, save_transformer + + +def main(): + random.seed(42) + + dataset_name = sys.argv[1] + + + # Load training set + print( + dataset_name, + np.load( + os.path.abspath(os.path.join("data", dataset_name, "X_train.npy")) + ).shape, + ) + X_train = np.load( + os.path.abspath(os.path.join("data", dataset_name, "X_train.npy")) + ) + y_train = np.load( + os.path.abspath(os.path.join("data", dataset_name, "y_train.npy")) + ) + X_test = np.load(os.path.abspath(os.path.join("data", dataset_name, "X_test.npy"))) + y_test = np.load(os.path.abspath(os.path.join("data", dataset_name, "y_test.npy"))) + + X_train = from_3d_numpy_to_nested(X_train) + y_train = np.asarray(y_train) + X_test = from_3d_numpy_to_nested(X_test) + y_test = np.asarray(y_test) + + le = LabelEncoder() + y_train = le.fit_transform(y_train) + y_test = le.transform(y_test) + + # How long (in minutes) to extract shapelets for. + # This is a simple lower-bound initially; + # once time is up, no further shapelets will be assessed + time_contract_in_mins = int(sys.argv[2]) + + time_contract_in_mins_per_dim = int(time_contract_in_mins / X_train.shape[1]) + + # Set lengths of shapelets to mine + max_perc = float(sys.argv[3]) + print("SHAPE", X_train.shape) + min_length, max_length = 3, 20 # int(max_perc / 100 * X_train.shape[1]) + + # If the time contract per dimension is less than one minute, sample + # time_contract_in_mins random dimensions and apply ST to them + seed = 10 + + if time_contract_in_mins_per_dim < 1: + random.seed(seed) + dims = [ + random.randint(0, X_train.shape[1] - 1) + for p in range(0, int(time_contract_in_mins)) + ] + + X_train = X_train.iloc[:, dims] + + # Spend one minute on each dimension + time_contract_in_mins_per_dim = 1 + + # The initial number of shapelet candidates to assess per training series. + # If all series are visited and time remains on the contract then another + # pass of the data will occur + initial_num_shapelets_per_case = 10 + + # Whether or not to print on-going information about shapelet extraction. + # Useful for demo/debugging + verbose = 2 + + st = ContractedShapeletTransform( + time_contract_in_mins=time_contract_in_mins_per_dim, + num_candidates_to_sample_per_case=initial_num_shapelets_per_case, + min_shapelet_length=min_length, + max_shapelet_length=max_length, + verbose=verbose, + predefined_ig_rejection_level=0.001, + max_shapelets_to_store_per_class=30, + ) + + print("MIN", min_length, "MAX", max_length) + + transformer = MultivariateTransformer(st) + + transformer.fit(X_train, y_train) + + X_new = transformer.transform(X_train) + + # name of current run (dataset + parameters combination) + run_name = "_".join([dataset_name, str(time_contract_in_mins), str(max_perc)]) + + # path of intermediary results directory + inter_results = os.path.abspath(os.path.join("results", "util_data", run_name)) + + if not os.path.exists(inter_results): + os.makedirs(inter_results) + + save_shapelets_distances(inter_results, transformer, test=False) + np.save(os.path.join(inter_results, "X_new.npy"), X_new) + save_transformer(inter_results, transformer) + + X_test_new = transformer.transform(X_test) + + np.save(os.path.join(inter_results, "X_test_new.npy"), X_test_new) + save_shapelets_distances(inter_results, transformer, test=True) + + +if __name__ == "__main__": + main() diff --git a/TSInterpret/InterpretabilityModels/counterfactual/SETS/utils.py b/TSInterpret/InterpretabilityModels/counterfactual/SETS/utils.py new file mode 100644 index 0000000..828b076 --- /dev/null +++ b/TSInterpret/InterpretabilityModels/counterfactual/SETS/utils.py @@ -0,0 +1,323 @@ +import os +import pickle +import random + +import numpy as np +import pandas as pd +from sklearn.base import clone + + +# Fit the transformer to all dimensions of the dataset +class MultivariateTransformer: + def __init__(self, st): + self.st = st + self.sts = None + + def fit(self, X, y=None): + self.n_dims = X.shape[1] + self.sts = [clone(self.st) for _ in range(self.n_dims)] + + for i, transformer in enumerate(self.sts): + transformer.fit(X.iloc[:, i].to_frame(), y) + return self + + def transform(self, X, y=None): + X_transformed = [] + for i, transformer in enumerate(self.sts): + try: + X_transformed.append(transformer.transform(X.iloc[:, i].to_frame())) + except RuntimeError: + continue + + X_new = pd.concat(X_transformed, axis=1) + return X_new + + +# write transformer to file +def save_transformer(parent_dir, transformer): + if not os.path.exists(parent_dir): + os.makedirs(parent_dir) + + with open(os.path.join(parent_dir, "shapelets.pkl"), "wb") as f: + pickle.dump(get_shapelets(transformer), f) + np.save(os.path.join(parent_dir, "indices.npy"), get_indices(transformer)) + np.save(os.path.join(parent_dir, "scores.npy"), get_scores(transformer)) + + +# save shapelets distances only (for test set) +def save_shapelets_distances(parent_dir, transformer, test=False): + if not os.path.exists(parent_dir): + os.makedirs(parent_dir) + + if test: + with open(os.path.join(parent_dir, "shapelets_distances_test.pkl"), "wb") as f: + pickle.dump(get_shapelets_distances(transformer), f) + else: + with open(os.path.join(parent_dir, "shapelets_distances.pkl"), "wb") as f: + pickle.dump(get_shapelets_distances(transformer), f) + + +# get the list of shapelets of a transformer +def get_shapelets(transformer): + all_shapelets = [] + + for st in transformer.sts: + dim_shapelets = [] + for shapelet in st.shapelets: + dim_shapelets.append(shapelet.data) + all_shapelets.append(dim_shapelets) + + return all_shapelets + + +# get the list of shapelet indices of a transformer +def get_indices(transformer): + all_indices = [] + + for st in transformer.sts: + dim_indices = [] + for shapelet in st.shapelets: + ind = np.empty(3, dtype=np.uint16) + ind[0] = shapelet.series_id + ind[1] = shapelet.start_pos + ind[2] = shapelet.start_pos + shapelet.length + dim_indices.append(ind) + all_indices.append(dim_indices) + + return np.asarray(np.asarray(all_indices)) + + +# get the list of shapelet scores of a transformer +def get_scores(transformer): + all_scores = [] + + for st in transformer.sts: + dim_scores = [] + for shapelet in st.shapelets: + dim_scores.append(shapelet.info_gain) + all_scores.append(dim_scores) + + return np.asarray(np.asarray(all_scores)) + + +# get the distance of shapelets from each other shapelet in the MTS +def get_shapelets_distances(transformer): + all_shapelets_distances = [] + + for st in transformer.sts: + shapelets_distances = [] + for shapelet in st.shapelets: + shapelets_distances.append(shapelet.distances) + + all_shapelets_distances.append(shapelets_distances) + return all_shapelets_distances + + +# get the distance of shapelets from each other shapelet in the MTS +def get_shapelets_distances_univariate(st): + shapelets_distances = [] + for shapelet in st.shapelets: + shapelets_distances.append(shapelet.distances) + + return [shapelets_distances] + + +# Given the shapelet_locations and shapelet_distances of one single shapelet, removes +# the overlapping shapelet locations except the closest to the original +def remove_similar_locations(shapelet_locations, shapelet_distances): + # List to keep indices to be discarded + to_discard = [] + + # Sort the shapelet_locations by sample index, then by start index + shapelet_locations = shapelet_locations[ + np.lexsort((shapelet_locations[:, 1], shapelet_locations[:, 0])) + ] + + # Variables to store the currently selected shapelet + current_dist = np.inf + current_idx = -1 # the sample index + current_start = -1 + current_end = -1 + current_i = -1 # the index in the shapelet_locations array + + i = 0 + for shapelet in shapelet_locations: + idx = shapelet[0] + start = shapelet[1] + end = shapelet[2] + # Check if this location overlaps the selected shapelet + if idx == current_idx and (not (start >= current_end or end <= current_start)): + dist = shapelet_distances[idx][start] + + # If the distance of this shapelet is smaller than the distance of the currently + # selected shapelet, discard the currently selected shapelet and select this one + if dist < current_dist: + to_discard.append(current_i) + current_i = i + current_dist = dist + # Widen shapelet l + current_start = np.minimum(current_start, start) + current_end = np.maximum(current_end, end) + # Else, discard this shapelet + else: + to_discard.append(i) + # If it doesn't overlap it, select this one + else: + current_idx = shapelet[0] + current_start = shapelet[1] + current_end = shapelet[2] + current_dist = shapelet_distances[idx][start] + current_i = i + i += 1 + + return np.delete(shapelet_locations, to_discard, axis=0) + + +# Given the shapelet_distances matrix of a given shapelet, get the locations of +# the closest shapelets from the entire dataset +def get_shapelet_locations_scaled_threshold(shapelet_distances, ts_length, threshold): + # Compute the length of the shapelet + shapelet_length = ts_length - shapelet_distances.shape[1] + 1 + + # Get the indices of the n closest shapelets to the original shapelet + s_indices = [] + for i in range(shapelet_distances.shape[0]): + for j in range(shapelet_distances.shape[1]): + # Compare to the threshold, scaled to shapelet length + if shapelet_distances[i][j] / shapelet_length <= threshold: + s_indices.append(np.array([i, j])) + + if len(s_indices) > 0: + s_indices = np.asarray(s_indices) + + # Create an array to store the locations of the closest n shapelets + shapelet_locations = np.empty( + (s_indices.shape[0], s_indices.shape[1] + 1), dtype=np.uint32 + ) + # Each shapelet is represented by (sample_index, start, end) + for i in range(shapelet_locations.shape[0]): + shapelet_locations[i] = np.append( + s_indices[i], s_indices[i][1] + shapelet_length + ) + + # Remove overlapping shapelets and keep the closest one to th original shapelet + shapelet_locations = remove_similar_locations( + shapelet_locations, shapelet_distances + ) + + return shapelet_locations + + else: + return np.array(np.array([[-1, -1, -1]]), dtype=np.uint32) + + +# Returns the threshold used to select shapelet occurences based on a given percentage +def get_occurences_threshold(shapelets_distances, ts_length, percentage): + # List to hold all distances values + sds = [] + + # Append the scaled distances + for dim in shapelets_distances: + for shapelet_distances in dim: + # Compute the length of the shapelet + shapelet_length = ts_length - shapelet_distances.shape[1] + 1 + for instance in shapelet_distances: + for distance in instance: + sds.append(distance / shapelet_length) + + # Sort the distances ascendingly + sds.sort() + + # Number of shapelet occurences to keep (per shapelet) + n = int(percentage * len(sds)) + + # Return the threshold distance to select the shapelet occurences to keep + return sds[n] + + +# Get the locations of the closest shapelets for each shapelet across the +# entire dataset based on a chosen percentage +def get_all_shapelet_locations_scaled_threshold( + shapelets_distances, ts_length, percentage +): + # Get the threshold to be used for selecting shapelet occurences + threshold = get_occurences_threshold(shapelets_distances, ts_length, percentage) + + all_shapelet_locations = [] + all_no_occurences = [] + + for dim in shapelets_distances: + dim_shapelet_locations = [] + no_occurences = [] + for i, shapelet in enumerate(dim): + sls = get_shapelet_locations_scaled_threshold( + shapelet, ts_length, threshold + ) + if sls[0][0] != 4294967295: + dim_shapelet_locations.append(sls) + else: + no_occurences.append(i) + all_shapelet_locations.append(dim_shapelet_locations) + all_no_occurences.append(no_occurences) + + return all_shapelet_locations, all_no_occurences, threshold + + +# Get the locations of the closest shapelets for each shapelet across the +# entire dataset based on the training threshold +def get_all_shapelet_locations_scaled_threshold_test( + shapelets_distances, ts_length, threshold +): + all_shapelet_locations = [] + all_no_occurences = [] + + for dim in shapelets_distances: + dim_shapelet_locations = [] + no_occurences = [] + for i, shapelet in enumerate(dim): + sls = get_shapelet_locations_scaled_threshold( + shapelet, ts_length, threshold + ) + if sls[0][0] != 4294967295: + dim_shapelet_locations.append(sls) + else: + no_occurences.append(i) + all_shapelet_locations.append(dim_shapelet_locations) + all_no_occurences.append(no_occurences) + + return all_shapelet_locations, all_no_occurences + + +def get_shapelets_locations_test(idx, all_sls, dim, all_shapelets_class): + all_locs = {} + try: + for i, s in enumerate([all_sls[dim][j] for j in all_shapelets_class[dim]]): + i_locs = [] + for loc in s: + if loc[0] == idx: + loc = (loc[1], loc[2]) + i_locs.append(loc) + all_locs[i] = i_locs + except Exception as ex: + print(ex) + return all_locs + + +##Optimize by fitting outside or returning a list of all nns at once +def get_nearest_neighbor(knn, X_test, y_test, y_train, idx): + # pred_label = y_pred[idx] + pred_label = y_test[idx] + target_labels = np.argwhere(y_train != pred_label) + + X_test_knn = X_test[idx].reshape(1, X_test.shape[1], X_test.shape[2]) + X_test_knn = np.swapaxes(X_test_knn, 1, 2) + + _, nn = knn.kneighbors(X_test_knn) + # print("TARGETLABELS", [t[0] for t in target_labels], [int(nn[0][0])]) + nn_idx = None + try: + nn_idx = [t[0] for t in target_labels][int(nn[0][0])] + except: + pass + # print("NN", nn_idx) + return nn_idx # None diff --git a/TSInterpret/InterpretabilityModels/counterfactual/SETSCF.py b/TSInterpret/InterpretabilityModels/counterfactual/SETSCF.py new file mode 100644 index 0000000..eff5719 --- /dev/null +++ b/TSInterpret/InterpretabilityModels/counterfactual/SETSCF.py @@ -0,0 +1,132 @@ +from typing import Tuple +import numpy as np +from TSInterpret.InterpretabilityModels.counterfactual.CF import CF +from TSInterpret.Models.PyTorchModel import PyTorchModel +from TSInterpret.Models.SklearnModel import SklearnModel +from TSInterpret.Models.TensorflowModel import TensorFlowModel +from TSInterpret.InterpretabilityModels.counterfactual.SETS.shapelets import ContractedShapeletTransform +from TSInterpret.InterpretabilityModels.counterfactual.SETS.utils import MultivariateTransformer, get_shapelets_distances + +class SETSCF(CF): + """Calculates and Visualizes Counterfactuals for Multivariate Time Series in accordance to the paper [1]. + + References + ---------- + [1] Ates, Emre, et al. + "Shapelet-based Temporal Association Rule Mining for Multivariate Time Series Classification" + 2021 International Conference on Applied Artificial Intelligence (ICAPAI). IEEE, 2021. + ---------- + """ + + def __init__( + self, + model, + data, + backend, + mode, + method="opt", + number_distractors=2, + max_attempts=1000, + max_iter=1000, + silent=False, + ) -> None: + """ + Arguments: + model [torch.nn.Module, Callable, tf.keras.model]: Model to be interpreted. + ref Tuple: Reference Dataset as Tuple (x,y). + backend str: desired Model Backend ('PYT', 'TF', 'SK'). + mode str: Name of second dimension: `time` -> `(-1, time, feature)` or `feat` -> `(-1, feature, time)` + method str : 'opt' if optimized calculation, 'brute' for Brute Force + number_distractors int: number of distractore to be used + silent bool: logging. + + """ + super().__init__(model, mode) + self.backend = backend + train_x, train_y, test_x, test_y = data + shape = test_x.shape + if mode == "time": + # Parse test data into (1, feat, time): + change = True + self.ts_length = shape[-2] + test_x = np.swapaxes( + test_x, 2, 1 + ) # test_x.reshape(test_x.shape[0], test_x.shape[2], test_x.shape[1]) + elif mode == "feat": + change = False + self.ts_length = shape[-1] + + if backend == "PYT": + self.predict = PyTorchModel(model, change).predict + elif backend == "TF": + self.predict = TensorFlowModel(model, change).predict + + elif backend == "SK": + self.predict = SklearnModel(model, change).predict + + self.referenceset = (test_x, test_y) + + self.min_shapelet_len = 3 + self.max_shapelet_len = 20 + self.time_contract_in_mins_per_dim = 1 + self.initial_num_shapelets_per_case = 10 + + # Required Shape (N,D,L) + + shapelet_transform = ContractedShapeletTransform( + time_contract_in_mins=self.time_contract_in_mins_per_dim, + num_candidates_to_sample_per_case=self.initial_num_shapelets_per_case, + min_shapelet_length=self.min_shapelet_len, + max_shapelet_length=self.max_shapelet_len, + verbose=0, + predefined_ig_rejection_level=0.05, + max_shapelets_to_store_per_class=30, + ) + + transformer = MultivariateTransformer(shapelet_transform) + transformer.fit(train_x, train_y) + train_x_new = transformer.transform(train_x) + test_x_new = transformer.transform(test_x) + self.train_distances = get_shapelets_distances(train_x_new) + self.test_distances = get_shapelets_distances(test_x_new) + + + + + def explain( + self, x: np.ndarray, orig_class: int = None, target: int = None + ) -> Tuple[np.ndarray, int]: + """ + Calculates the Counterfactual according to Ates. + Arguments: + x (np.array): The instance to explain. Shape : `mode = time` -> `(1,time, feat)` or `mode = time` -> `(1,feat, time)` + target int: target class. If no target class is given, the class with the secon heighest classification probability is selected. + + Returns: + ([np.array], int): Tuple of Counterfactual and Label. Shape of CF : `mode = time` -> `(time, feat)` or `mode = time` -> `(feat, time)` + + """ + org_shape = x.shape + if self.mode != "feat": + x = np.swapaxes(x, -1, -2) # x.reshape(-1, x.shape[-1], x.shape[-2]) + train_x, train_y = self.referenceset + if len(train_y.shape) > 1: + train_y = np.argmax(train_y, axis=1) + if self.method == "opt": + opt = OptimizedSearch( + self.predict, + train_x, + train_y, + silent=self.silent, + threads=1, + num_distractors=self.number_distractors, + max_attempts=self.max_attemps, + maxiter=self.max_iter, + ) + exp, label = opt.explain(x, to_maximize=target) + elif self.method == "brute": + opt = BruteForceSearch(self.predict, train_x, train_y, threads=1) + exp, label = opt.explain(x, to_maximize=target) + if self.mode != "feat": + exp = np.swapaxes(exp, -1, -2) + return exp.reshape(org_shape), label diff --git a/playground.ipynb b/playground.ipynb new file mode 100644 index 0000000..aa12685 --- /dev/null +++ b/playground.ipynb @@ -0,0 +1,78 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from tslearn.datasets import UCR_UEA_datasets\n", + "data = UCR_UEA_datasets().load_dataset(\"ECG200\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "module 'TSInterpret.InterpretabilityModels.counterfactual.SETSCF' has no attribute 'SETSCF'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32mc:\\Users\\ss281\\Desktop\\Projects\\TSInterpret\\playground.ipynb Cell 2\u001b[0m line \u001b[0;36m7\n\u001b[0;32m 3\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mTSInterpret\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mInterpretabilityModels\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mcounterfactual\u001b[39;00m \u001b[39mimport\u001b[39;00m SETSCF\n\u001b[0;32m 4\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtslearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mdatasets\u001b[39;00m \u001b[39mimport\u001b[39;00m UCR_UEA_datasets\n\u001b[1;32m----> 7\u001b[0m SETSCF\u001b[39m.\u001b[39;49mSETSCF(\u001b[39mNone\u001b[39;00m,data, \u001b[39m\"\u001b[39m\u001b[39mTF\u001b[39m\u001b[39m\"\u001b[39m,\u001b[39m\"\u001b[39m\u001b[39mtime\u001b[39m\u001b[39m\"\u001b[39m)\n", + "\u001b[1;31mAttributeError\u001b[0m: module 'TSInterpret.InterpretabilityModels.counterfactual.SETSCF' has no attribute 'SETSCF'" + ] + } + ], + "source": [ + "%load_ext autoreload \n", + "%autoreload 2\n", + "from TSInterpret.InterpretabilityModels.counterfactual.SETSCF import SETSCF\n", + "from tslearn.datasets import UCR_UEA_datasets\n", + "\n", + "\n", + "SETSCF.SETSCF(None,data, \"TF\",\"time\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "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": "3.8.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 2ba7ca2ee63a9231711c4f915ec9aa3cbd0b552b Mon Sep 17 00:00:00 2001 From: Bela <66631119+belaboe97@users.noreply.github.com> Date: Fri, 1 Dec 2023 16:20:36 +0100 Subject: [PATCH 2/7] initial version sets implementation --- .../counterfactual/ECG200_15_20.0/X_new.npy | Bin 0 -> 24128 bytes .../ECG200_15_20.0/X_test_new.npy | Bin 0 -> 24128 bytes .../ECG200_15_20.0/all_heat_maps.npy | Bin 0 -> 41163 bytes .../ECG200_15_20.0/all_shapelet_locations.npy | Bin 0 -> 3491 bytes .../all_shapelet_locations_trth_test.npy | Bin 0 -> 3762 bytes .../ECG200_15_20.0/all_shapelets_class.npy | Bin 0 -> 521 bytes .../counterfactual/ECG200_15_20.0/indices.npy | Bin 0 -> 488 bytes .../counterfactual/ECG200_15_20.0/scores.npy | Bin 0 -> 608 bytes .../ECG200_15_20.0/shapelets.pkl | Bin 0 -> 9132 bytes 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TSInterpret/InterpretabilityModels/counterfactual/ECG200_15_20.0/all_shapelet_locations_trth_test.npy create mode 100644 TSInterpret/InterpretabilityModels/counterfactual/ECG200_15_20.0/all_shapelets_class.npy create mode 100644 TSInterpret/InterpretabilityModels/counterfactual/ECG200_15_20.0/indices.npy create mode 100644 TSInterpret/InterpretabilityModels/counterfactual/ECG200_15_20.0/scores.npy create mode 100644 TSInterpret/InterpretabilityModels/counterfactual/ECG200_15_20.0/shapelets.pkl create mode 100644 TSInterpret/InterpretabilityModels/counterfactual/ECG200_15_20.0/shapelets_distances.pkl create mode 100644 TSInterpret/InterpretabilityModels/counterfactual/ECG200_15_20.0/shapelets_distances_test.pkl create mode 100644 best_model.hdf5 diff --git a/TSInterpret/InterpretabilityModels/counterfactual/ECG200_15_20.0/X_new.npy b/TSInterpret/InterpretabilityModels/counterfactual/ECG200_15_20.0/X_new.npy new file mode 100644 index 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zGV)IZbv`rMquv~w?EhE=^}90R?%&t3XV9mI(FyZ7DYCf-!n&EsfkgA2`&($x$GF4b zL>)Ep4eez5F1dp^apEJOfchluI1WoHaw(#T>gyVXxJ4G7;=_Ap8-@9Ixgs;sQBxZEw~T#E#-BMn(YO5F z%1`ZMCgzZ@IqY@C`O1s`a{K(xA@7a-;MRTk`s3b2Gq2&?zJ0@Rg!2jJ1?x4O!mxK! z+Le}868E7u{#qj*IM0Z&i}hfu!XAg5o2TL$N}%U@o@MG#8TdGOp0xf~3V+Yt>tr}n z2=~7`+g`7(1vM4NlzYg9{=Li3dDedzbUAka3OAjF;Gs9pQJepPlBxcsX5SU)%cB?X rHCu# 0)[-1][0] - np.argwhere(h_m > 0)[0][0] - ) // 2 + np.argwhere(h_m > 0)[0][0] - - target_shapelet = st_shapelets[dim][target_shapelet_idx][0] - target_shapelet_length = target_shapelet.shape[0] - - start = center - target_shapelet_length // 2 - end = center + ( - target_shapelet_length - target_shapelet_length // 2 - ) - - if start < 0: - end = end - start - start = 0 + # print('Removing original shapelet') + nn = X_test[nn_idx].reshape(-1) - if end > ts_length: - start = start - (end - ts_length + 1) - end = ts_length - 1 + target_shapelet = nn[loc[0] : loc[1]] s_min = target_shapelet.min() s_max = target_shapelet.max() - t_min = cf[dim][start:end].min() - t_max = cf[dim][start:end].max() + t_min = cf[dim][loc[0] : loc[1]].min() + t_max = cf[dim][loc[0] : loc[1]].max() if s_max - s_min == 0: target_shapelet = ( @@ -265,54 +171,95 @@ def main(): target_shapelet - s_min ) / (s_max - s_min) + t_min + start = loc[0] + end = loc[1] + cf[dim][start:end] = target_shapelet - # Save the perturbed dimension - cf_dims[dim] = cf[dim] - - cf_pred = model.predict(np.expand_dims(np.swapaxes(cf, 0, 1), axis=0)) - cf_pred = np.argmax(cf_pred) - # if a CF is found, save it and move to next time series instance - # print(y_pred[instance_idx], cf_pred) - if y_pred[instance_idx] != cf_pred: - # print('cf found') - np.save( - os.path.join( - results, str(instance_idx) + "_to_" + str(target_c) + ".npy" - ), - cf, + # Introduce new shapelets from the target class + for _, target_shapelet_idx in enumerate(all_target_heat_maps[dim]): + cf_pred = model.predict( + np.expand_dims(np.swapaxes(cf, 0, 1), axis=0) ) - break - - else: - # print("Trying dims combinations") - # Try all combinations of dimensions - for L in range(0, len(shapelets_best_scores) + 1): - for subset in itertools.combinations(shapelets_best_scores, L): - if len(subset) >= 2: - cf = X_test[instance_idx].copy() - for dim_ in subset: - cf[dim_] = cf_dims[dim_] - - cf_pred = model.predict( - np.expand_dims(np.swapaxes(cf, 0, 1), axis=0) - ) - cf_pred = np.argmax(cf_pred) - if y_pred[instance_idx] != cf_pred: - # print('cf found') - # print('final dims: ', subset) - break - - # if a CF is found, save it and move to next time series instance - if y_pred[instance_idx] != cf_pred: - # print('cf found') - np.save( - os.path.join( - results, str(instance_idx) + "_to_" + str(target_c) + ".npy" - ), - cf, - ) - break + cf_pred = np.argmax(cf_pred) + if y_pred[instance_idx] == cf_pred: + # print('Introducing new shapelet') + h_m = all_target_heat_maps[dim].get(target_shapelet_idx) + + center = ( + np.argwhere(h_m > 0)[-1][0] - np.argwhere(h_m > 0)[0][0] + ) // 2 + np.argwhere(h_m > 0)[0][0] + + target_shapelet = st_shapelets[dim][target_shapelet_idx][0] + target_shapelet_length = target_shapelet.shape[0] + + start = center - target_shapelet_length // 2 + end = center + ( + target_shapelet_length - target_shapelet_length // 2 + ) + + if start < 0: + end = end - start + start = 0 + + if end > ts_length: + start = start - (end - ts_length + 1) + end = ts_length - 1 + + s_min = target_shapelet.min() + s_max = target_shapelet.max() + t_min = cf[dim][start:end].min() + t_max = cf[dim][start:end].max() + + if s_max - s_min == 0: + target_shapelet = ( + (t_max + t_min) / 2 * np.ones(len(target_shapelet)) + ) + else: + target_shapelet = (t_max - t_min) * ( + target_shapelet - s_min + ) / (s_max - s_min) + t_min + + cf[dim][start:end] = target_shapelet + + # Save the perturbed dimension + cf_dims[dim] = cf[dim] + + cf_pred = model.predict(np.expand_dims(np.swapaxes(cf, 0, 1), axis=0)) + cf_pred = np.argmax(cf_pred) + # if a CF is found, save it and move to next time series instance + # print(y_pred[instance_idx], cf_pred) + if y_pred[instance_idx] != cf_pred: + # print('cf found') + return cf, y_pred[instance_idx] + + else: + # print("Trying dims combinations") + # Try all combinations of dimensions + for L in range(0, len(shapelets_best_scores) + 1): + for subset in itertools.combinations(shapelets_best_scores, L): + if len(subset) >= 2: + cf = X_test[instance_idx].copy() + for dim_ in subset: + cf[dim_] = cf_dims[dim_] + + cf_pred = model.predict( + np.expand_dims(np.swapaxes(cf, 0, 1), axis=0) + ) + cf_pred = np.argmax(cf_pred) + if y_pred[instance_idx] != cf_pred: + # print('cf found') + # print('final dims: ', subset) + break + + # if a CF is found, save it and move to next time series instance + if y_pred[instance_idx] != cf_pred: + # print('cf found') + return cf, y_pred[instance_idx] + + else: + print("No Counterfactual could be found this data instance") + return None if __name__ == "__main__": diff --git a/TSInterpret/InterpretabilityModels/counterfactual/SETS/shapelets.py b/TSInterpret/InterpretabilityModels/counterfactual/SETS/shapelets.py index 4be4c78..d194ea2 100644 --- a/TSInterpret/InterpretabilityModels/counterfactual/SETS/shapelets.py +++ b/TSInterpret/InterpretabilityModels/counterfactual/SETS/shapelets.py @@ -87,8 +87,6 @@ def __init__( super(ShapeletTransform, self).__init__() def fit(self, X, y=None): - print(X.shape, y.shape) - # range = np.arange """A method to fit the shapelet transform to a specified X and y @@ -106,7 +104,6 @@ def fit(self, X, y=None): """ X, y = check_X_y(X, y, enforce_univariate=True, coerce_to_numpy=True) - print(">> shape", X.shape, y.shape) if ( type(self) is ContractedShapeletTransform @@ -163,7 +160,6 @@ def _round_robin(*iterables): ) case_ids_by_class = {i: np.where(y == i)[0] for i in distinct_class_vals} - print(case_ids_by_class) # if transform is random/contract then shuffle the data initially # when determining which cases to visit if ( @@ -239,10 +235,8 @@ def time_taken(): + ")" ) """ - print("LEM", len(X[series_id][0])) - this_series_len = len(X[series_id][0]) - print("SELF", self.shapelets_lengths) + this_series_len = len(X[series_id][0]) # check whether all shapelets in range min-max should be extracted, # or just a few of them @@ -252,37 +246,19 @@ def time_taken(): # However, shapelets cannot be longer than the series, so set to # the minimum of the series length # and max shapelet length (which is inf by default) - print("MAX", self.max_shapelet_length) if self.max_shapelet_length == -1: this_shapelet_length_upper_bound = this_series_len else: this_shapelet_length_upper_bound = min( this_series_len, self.max_shapelet_length ) - - print( - "SHPLEN", - self.min_shapelet_length, - this_shapelet_length_upper_bound + 1, - ) self.shapelets_lengths = range( self.min_shapelet_length, this_shapelet_length_upper_bound + 1 ) else: - print(">>PRINT", self.shapelets_lengths, type(self.shapelets_lengths)) - self.min_shapelet_length = [self.shapelets_lengths][0] self.max_shapelet_length = [self.shapelets_lengths][-1] - print( - ">>PRINT2", - this_series_len, - self.shapelets_lengths, - self.min_shapelet_length, - [self.shapelets_lengths][0], - [self.shapelets_lengths][-1], - type(self.max_shapelet_length), - ) this_shapelet_length_upper_bound = min( this_series_len, self.max_shapelet_length ) @@ -1098,94 +1074,3 @@ def get_size(self): def get_array(self): return self._queue - - -def write_transformed_data_to_arff(transform, labels, file_name): - """A simple function to save the transform obtained in arff format - - Parameters - ---------- - transform: array-like - The transform obtained for a dataset - labels: array-like - The labels of the dataset - file_name: string - The directory to save the transform - """ - # Create directory in case it doesn't exists - directory = "/".join(file_name.split("/")[:-1]) - if not os.path.exists(directory): - os.makedirs(directory) - - num_shapelets = transform.shape[1] - unique_labels = np.unique(labels).tolist() - - with open(file_name, "w+") as f: - # Headers - f.write("@Relation Shapelets" + file_name.split("/")[-1].split("_")[0] + "\n\n") - for i in range(0, num_shapelets): - f.write("@attribute Shapelet_" + str(i) + " numeric\n") - f.write("@attribute target {" + ",".join(unique_labels) + "}\n") - f.write("\n@data\n") - # Patterns - for i, j in enumerate(transform): - pattern = j.tolist() + [int(float(labels[i]))] - f.write(",".join(map(str, pattern)) + "\n") - f.close() - - -def write_shapelets_to_csv(shapelets, data, dim_to_use, time, file_name): - """A simple function to save the shapelets obtained in csv format - - Parameters - ---------- - shapelets: array-like - The shapelets obtained for a dataset - data: array-like - The original data - time: fload - The time spent obtaining shapelets - file_name: string - The directory to save the set of shapelets - """ - data = data.iloc[:, dim_to_use] - - data_aux = [[]] * len(data) - for i in range(0, len(data)): - data_aux[i] = np.array([np.asarray(x) for x in np.asarray(data.iloc[i, :])]) - data = data_aux.copy() - - # Create directory in case it doesn't exists - directory = "/".join(file_name.split("/")[:-1]) - if not os.path.exists(directory): - os.makedirs(directory) - - with open(file_name, "w+") as f: - # Number of shapelets and time extracting - f.write(str(len(shapelets)) + "," + str(time) + "\n") - for j in shapelets: - f.write( - str(j.info_gain) - + "," - + str(j.series_id) - + "," - + "".join(str(j.dims)).replace(", ", ":") - + "," - + str(j.start_pos) - + "," - + str(j.length) - + "\n" - ) - for k in range(0, len(dim_to_use)): - f.write( - ",".join( - map( - str, - data[j.series_id][ - k, j.start_pos : (j.start_pos + j.length) - ], - ) - ) - + "\n" - ) - f.close() diff --git a/TSInterpret/InterpretabilityModels/counterfactual/SETS/utils.py b/TSInterpret/InterpretabilityModels/counterfactual/SETS/utils.py index 828b076..0b1c49b 100644 --- a/TSInterpret/InterpretabilityModels/counterfactual/SETS/utils.py +++ b/TSInterpret/InterpretabilityModels/counterfactual/SETS/utils.py @@ -213,6 +213,7 @@ def get_shapelet_locations_scaled_threshold(shapelet_distances, ts_length, thres # Returns the threshold used to select shapelet occurences based on a given percentage def get_occurences_threshold(shapelets_distances, ts_length, percentage): + print(shapelets_distances, ts_length, percentage) # List to hold all distances values sds = [] @@ -220,6 +221,7 @@ def get_occurences_threshold(shapelets_distances, ts_length, percentage): for dim in shapelets_distances: for shapelet_distances in dim: # Compute the length of the shapelet + print(type(ts_length), type(shapelet_distances.shape[1])) shapelet_length = ts_length - shapelet_distances.shape[1] + 1 for instance in shapelet_distances: for distance in instance: diff --git a/TSInterpret/InterpretabilityModels/counterfactual/SETSCF.py b/TSInterpret/InterpretabilityModels/counterfactual/SETSCF.py index eff5719..ea1d945 100644 --- a/TSInterpret/InterpretabilityModels/counterfactual/SETSCF.py +++ b/TSInterpret/InterpretabilityModels/counterfactual/SETSCF.py @@ -1,11 +1,30 @@ from typing import Tuple + import numpy as np +from sklearn.preprocessing import LabelEncoder + from TSInterpret.InterpretabilityModels.counterfactual.CF import CF +from TSInterpret.InterpretabilityModels.counterfactual.SETS.class_shapelets import ( + get_class_shapelets, +) +from TSInterpret.InterpretabilityModels.counterfactual.SETS.sets import sets_explain +from TSInterpret.InterpretabilityModels.counterfactual.SETS.shapelets import ( + ContractedShapeletTransform, +) +from TSInterpret.InterpretabilityModels.counterfactual.SETS.sktime_convert import ( + from_3d_numpy_to_nested, +) +from TSInterpret.InterpretabilityModels.counterfactual.SETS.utils import ( + MultivariateTransformer, + get_indices, + get_scores, + get_shapelets, + get_shapelets_distances, +) from TSInterpret.Models.PyTorchModel import PyTorchModel from TSInterpret.Models.SklearnModel import SklearnModel from TSInterpret.Models.TensorflowModel import TensorFlowModel -from TSInterpret.InterpretabilityModels.counterfactual.SETS.shapelets import ContractedShapeletTransform -from TSInterpret.InterpretabilityModels.counterfactual.SETS.utils import MultivariateTransformer, get_shapelets_distances + class SETSCF(CF): """Calculates and Visualizes Counterfactuals for Multivariate Time Series in accordance to the paper [1]. @@ -23,11 +42,8 @@ def __init__( model, data, backend, - mode, - method="opt", - number_distractors=2, - max_attempts=1000, - max_iter=1000, + mode, + time_contract_in_mins_per_dim, silent=False, ) -> None: """ @@ -43,12 +59,17 @@ def __init__( """ super().__init__(model, mode) self.backend = backend + train_x, train_y, test_x, test_y = data + le = LabelEncoder() + train_y = le.fit_transform(train_y) + test_y = le.transform(test_y) shape = test_x.shape if mode == "time": # Parse test data into (1, feat, time): change = True self.ts_length = shape[-2] + train_x = np.swapaxes(train_x, 2, 1) test_x = np.swapaxes( test_x, 2, 1 ) # test_x.reshape(test_x.shape[0], test_x.shape[2], test_x.shape[1]) @@ -56,6 +77,11 @@ def __init__( change = False self.ts_length = shape[-1] + self.data = [train_x, train_y, test_x, test_y] + self.ts_len = train_x.shape[2] + train_x_n = from_3d_numpy_to_nested(train_x) + test_x_n = from_3d_numpy_to_nested(test_x) + if backend == "PYT": self.predict = PyTorchModel(model, change).predict elif backend == "TF": @@ -68,9 +94,11 @@ def __init__( self.min_shapelet_len = 3 self.max_shapelet_len = 20 - self.time_contract_in_mins_per_dim = 1 + self.time_contract_in_mins_per_dim = time_contract_in_mins_per_dim self.initial_num_shapelets_per_case = 10 + self.trf_data = [train_x_n, train_y, test_x_n, test_y] + # Required Shape (N,D,L) shapelet_transform = ContractedShapeletTransform( @@ -78,23 +106,38 @@ def __init__( num_candidates_to_sample_per_case=self.initial_num_shapelets_per_case, min_shapelet_length=self.min_shapelet_len, max_shapelet_length=self.max_shapelet_len, - verbose=0, - predefined_ig_rejection_level=0.05, + verbose=2, + predefined_ig_rejection_level=0.001, max_shapelets_to_store_per_class=30, ) - + # Fit multivaraite transformer transformer = MultivariateTransformer(shapelet_transform) - transformer.fit(train_x, train_y) - train_x_new = transformer.transform(train_x) - test_x_new = transformer.transform(test_x) - self.train_distances = get_shapelets_distances(train_x_new) - self.test_distances = get_shapelets_distances(test_x_new) - - - + transformer.fit(train_x_n, train_y) + self.train_x_new = transformer.transform(train_x_n) + # Save shapelets, scores, indices, distances for train + self.transformer = transformer + self.train_distances = get_shapelets_distances(self.transformer) + self.shapelets = get_shapelets(self.transformer) + self.scores = get_scores(self.transformer) + self.indicies = get_indices(self.transformer) + # Save distances for test + self.test_x_new = transformer.transform(test_x_n) + self.test_distances = get_shapelets_distances(transformer) + ( + self.all_heat_maps, + self.all_shapelets_class, + self.all_shapelet_locations, + self.all_shapelet_locations_test, + ) = get_class_shapelets( + self.trf_data, + self.ts_len, + self.shapelets, + self.train_distances, + self.test_distances, + ) def explain( - self, x: np.ndarray, orig_class: int = None, target: int = None + self, ts_instance, x: np.ndarray, orig_class: int = None, target: int = None ) -> Tuple[np.ndarray, int]: """ Calculates the Counterfactual according to Ates. @@ -106,27 +149,18 @@ def explain( ([np.array], int): Tuple of Counterfactual and Label. Shape of CF : `mode = time` -> `(time, feat)` or `mode = time` -> `(feat, time)` """ + exp, label = sets_explain( + ts_instance, + self.model, + self.data, + self.ts_len, + self.shapelets, + self.all_shapelet_locations, + self.all_shapelet_locations_test, + self.all_shapelets_class, + self.all_heat_maps, + self.scores, + ) + org_shape = x.shape - if self.mode != "feat": - x = np.swapaxes(x, -1, -2) # x.reshape(-1, x.shape[-1], x.shape[-2]) - train_x, train_y = self.referenceset - if len(train_y.shape) > 1: - train_y = np.argmax(train_y, axis=1) - if self.method == "opt": - opt = OptimizedSearch( - self.predict, - train_x, - train_y, - silent=self.silent, - threads=1, - num_distractors=self.number_distractors, - max_attempts=self.max_attemps, - maxiter=self.max_iter, - ) - exp, label = opt.explain(x, to_maximize=target) - elif self.method == "brute": - opt = BruteForceSearch(self.predict, train_x, train_y, threads=1) - exp, label = opt.explain(x, to_maximize=target) - if self.mode != "feat": - exp = np.swapaxes(exp, -1, -2) return exp.reshape(org_shape), label diff --git a/best_model.hdf5 b/best_model.hdf5 new file mode 100644 index 0000000000000000000000000000000000000000..4a18af0ada384c6220087cad7fc8902fb206d6fe GIT binary patch literal 3255104 zcmeEv2V4}%()a=@s2DJyhyhUq1rU0q#0T~#wA4IezTS(6S;c+yKv zjn{zJQg+Mzd{SI=c|TbZm*?so6gNtWG*OY3D$8$_c#U|{y7r3lmJ0kjD)It5MZ*c> zh794ckQ{z4RW#%A4CHqlp5MxU-uzc^VE7O#JLy2DC`N0nNDnCFOp)bz`?|P#*@wD% zx_bl#vW32WL7v{8^IQYID3ii+@~RLkuKDj$4TZecmuq$2a2c!;uQ{&`PmMRnHNY{@ zKEyR3(9_q4t&7s=|?d9W^$iqCX4li`u`!`auz&C^}lffh@Ql&0^|^-PPcRN#{?(a-o=%J2mRIC}bc`ncOG zQ2r4c)4}V>4V`t*-G}3e5DUmzN-%mdduO>4_=N0*%$^R`5aQeVOAurDq!@E}{mjfkM%A{4CoN?>8y@edmoRX(XJe9BDIC*S8 zT3%l&Pk#IT9=u;4If#b>lA~{mBCl8iyt%5sp_1Jw$miL8UHm`JeV0D@_RZty<2o2- zJ;p{xgO0p}qerHn*rNV@`VHt~%;RxtR?b}>FUZv=&^N%%%QsZsPO+WyWcq_Gny>5Z z|2W9UAQ89K@vY*JX#{7-Z64iXza@F(6P1?B?hdDD6AI(cRtE<%yvNo>l4y%a6QsFWGs_H`ni0ECEudm&C@GL&ZmC;%~=xIJ;{gN z36in90z4H2$#~%B=;FevbGBYLP&!pCh>K&8qrIDNfVX3iwAk6h(T7D7 zXwS}PkgVR*%TYR&_5qGTGQ=`O?g75Re$s*VV^KJ=a~&cBl49`k^l^2RO;B*4tG$z_ zqZCXrwF(;9JtfG~(M!5fvvu=zS!oECNq}?^j)8r?s1KB}>nq_J=H(mU8#K|!Q%be0 zTDITGFa^5GW=}fx(wPcyb@vQpx#Y^uj9fIi8Yx|@!2Bz8OmhwJm6P{d|3B_iF?yD$ zU@yl2#pvZysw<=Cn>8}-dIp8rf3sGBla(HJuG!66I*DJR{9T2V#NW%@@8oy-bXKON z%c&pBVPo09`QbBXeoL$eNf)23RD#$Um5Tw}2ir0G5F(ott~>cE^{Ef}4HfV$d3T)a zDO*|nrGoA4>+R|j6zt97H|}F$!IrrC1$uh<`p625jiqAg%tFWnOd))M&W>NI87Eg( z@c(8BaCh|f{#QF49k^_Q zzqx4S)9}qIS)<=A8kXJiHovR+ayflRzQ47$SUhrV|Kp`)^;xriHRHdn;4f4!3-D)G z(r2u!z7BBp3jS(a`x@uBsQ>mre>@cHKi>Rgy5`Rg;`8>`-;~|r>u-OiQZ4aw`}?{5 z)!P@pT(9y)D3`i#iTPIx;m6Ybuk3+;N$h{_g7Oo_U)>CT?t;I2yV4&2F`}H~T z8~dS5>VCW*{t5a1mv+RzBKSXd#GgCjUpf%~6V8S9-(6&i9Z_2U;|=7m?1+D9J^kq& z@w;RA){gkc17huZPL5tytXIv^-PO+5mo+51f7e{P?f;V;fJ zQUkEBTadToT&XGPr=j+1>M26=q$tcEi18`)B^k!6~DCz zXYJQ474m2S)+6wl9EHJLItDIqfW@LH{Tx&9#d7@G-DMpd80711Y31Tr-ygw|%~i=e zw|8-Mb`1O6g|nBZpVTb=xkNFizps#%ggANyOI*4HD8 z5Ky>>jQg<0cVqcAz-)kp*#OgiCM?%wn{Ge0A#1MZvi;@Rdgl9nuDy-TSP)0=K=%O0 z-+Xc5;o&?snm~T3E7H#z&0gm5QO%?6A`p@*sE4Y3+Iu!KxYo=)TBl@KbXs&>3tw_HyPFYP0MLowa zc|Y=?~g* z&IMUpjz1lK&`v6eTt}J;IPD+6%giK-2JIAZ-M$ZR-|rhE;^amD&$RnuMAGma>D5u5 zuyI0CKlfK2G`7DlMDrgAPeop;Yh<^IycDNweJJu$og(W;k(cVKFX5$qeF-n6 z?WMX=rtcJaDJ|cRbNg{z+Lz;y>YmSWY2S;#`!T*R`6ks>^7vEUcjL`i zke}CoF$d&&vcYe9(pa$}a{5orP|lnwigHd*o~Q?RUJ)nuR3mEUmv8YuF^%{YL6cFO6=`uzI5o~3cC?RkpFV(HVeOi^m+fP(il zZ$MMtfKQ5mGbM$dtUv0X>$yHhe&)aA!1v-a+bh-~2g|K9JFehgOgZ_luLu6!^8dX! z&^(1c@lcH8=Q{ZpC-nb#^Y5(_S|LZ=@c)0UlluGM-?dKs6dP{oSL@{a?i_{ zx*S`6rvD`zsJ~8h6yx@&_gwak^STU@TR*=&=lw~(ar=qgcDW_SPx1bPx+2nFiTCa2 zsqenGqj;X;;{CZ)dgihlBPU9Fj+CA?xxBRJ@4at9@0HV)A(W>az4f14)p?0B`IEf)Yc^57r(#W)hwk5etRuZp85Iv zU&Dd=>z!7Ne2QWp;26Q#3OS2hecNWefN0JPDSIbLii0~xa`@!uQ0ZHO^6R|9F30JQ z`tLV!?S7_TbAWpfkz=%yg45i2GD-o?r7CQ_l*bx6UD$V|rI!w`of@xQr0ia5=Tei^ zD=RC@rb11DPesWN6d;@`Rb&$t=|3ak>>Hf*)^6C3>Eh(?E&4GWC%+EEe+*}!sP-H2 zW4M2M?bZHH@GB(4-CH4Gd&D2AZ0CUT=0tGZJqf1Vme2ZlqhgJ6$6h~`2)3L5p+v*g^GkAa7pxp19PJx>qs~pT{8pJ zA9_Q2UoX&{>j6U`6h;U5gI!(_gqtmZ=0gLZ(XtSjG&&UWjwiyX=aF!tEEX>GTmby6 zL}+1`2o8yHFlbFU9R4&1%fWnx-}l)SqNmBd4tkNH|Q@8hR&nrLeN=f2&qXm`~Q%FP2Hqcj>q-UdUNr4#6F zSqMgBwlK8WX~D?}!?Dpe4P5u6fEoIn>h%7q$A^(SML>=}Q zGGjrBU#E$%WaI|CYOabXet$L&T>O#r|J0i3HCsW}rA-mpd9}hDo|%Yzx@(YA{nC(q zb~91ToZbxcPKEU7c8shYp~cL;PLb449YoCxx{<+WLQz?ZeWYlBSh!O#iJ0tMg;i^I zFzBH^F5Z{K^fsh%qa&Gw*LETHH@HM8Sytgmb{m*?tFs7R;EknyPal;!cnAC}>B+=+Rvsgn5n;*VR(9%4n$s1M1=*25)PJ_!RWJeD6AAgmMYhOsB zn~ox#GnX(2uFoPxmLJf()i=n4S6hUaw|pRsjRP|2{Q~KwHOEK9G!A||h8!oEBsZy; 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