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How to use distinct_labels method in hypothesis

Best Python code snippet using hypothesis

latent_plot_utils.py

Source:latent_plot_utils.py

1import os2import sys3import numpy as np4import matplotlib.pyplot as plt5import torch6from sklearn.manifold import TSNE7from sklearn.decomposition import PCA8from matplotlib import cm9from matplotlib.legend_handler import HandlerLine2D, HandlerTuple10import tikzplotlib11from os.path import join as pjoin12BASEPATH = os.path.dirname(os.path.abspath(__file__))13sys.path.insert(0, BASEPATH)14sys.path.insert(0, pjoin(BASEPATH, '..'))15from py_utils import ensure_dirs16def distinct_labels_and_indices(labels):17    distinct_labels = list(set(labels))18    distinct_labels.sort()19    num_labels = len(distinct_labels)20    indices_i = {label: [] for label in distinct_labels}21    for i, label in enumerate(labels):22        indices_i[label].append(i)23    indices_i = {label: np.array(indices) for label, indices in indices_i.items()}24    return num_labels, distinct_labels, indices_i25def plot2D(data, labels, title):26    x_min, x_max = np.min(data, axis=0), np.max(data, axis=0)27    data = (data - x_min) / (x_max - x_min)28    fig, ax = plt.subplots(figsize=(8, 8))29    cjet = cm.get_cmap("jet")30    num_labels, distinct_labels, indices = distinct_labels_and_indices(labels)31    for i, label in enumerate(distinct_labels):32        index = indices[label]33        ax.scatter(data[index, 0], data[index, 1], label=label, c=[cjet(1.0 * i / num_labels)], linewidths=0.)34    ax.legend(loc="center left", bbox_to_anchor=(1, 0, 1, 1),35              title=title.split('/')[-1])36    fig.tight_layout()37    tikzplotlib.save("%s.tex" % title, figure=fig, strict=True)38    plt.savefig("%s.png" % title)39    return fig40def plot2D_overlay(data_list, labels_list, alpha_list, title):41    x_min, x_max = np.array((1e9, 1e9)), np.array((-1e9, -1e9))42    for data in data_list:43        x_min = np.minimum(x_min, np.min(data, axis=0))44        x_max = np.maximum(x_max, np.max(data, axis=0))45    for i in range(len(data_list)):46        data_list[i] = (data_list[i] - x_min) / (x_max - x_min)47    fig, ax = plt.subplots(figsize=(8, 8))48    cjet = cm.get_cmap("jet")49    indices_list = []50    distinct_labels = []51    for labels in labels_list:52        _, cur_labels, indices = distinct_labels_and_indices(labels)53        indices_list.append(indices)54        for label in cur_labels:55            if label not in distinct_labels:56                distinct_labels.append(label)57    num_labels = len(distinct_labels)58    for i, label in enumerate(distinct_labels):59        res = 0.060        for data, labels, indices, alpha in zip(data_list, labels_list, indices_list, alpha_list):61            if label in indices.keys():62                index = indices[label]63            else:64                index = np.array([])65            c = cjet((1.0 * i + res) / (num_labels + 1))66            ax.scatter(data[index, 0], data[index, 1], label=label, c=[c], alpha=alpha, linewidths=0.)67            res += 0.368    handles, labels = ax.get_legend_handles_labels()69    paired_handles = []70    handles_tot = len(handles) // 271    for i in range(handles_tot):72        paired_handles.append((handles[i * 2], handles[i * 2 + 1]))73    ax.legend(handles=paired_handles, labels=distinct_labels, numpoints=1,74              handler_map={tuple: HandlerTuple(ndivide=None)},75              loc="center left", bbox_to_anchor=(1, 0, 1, 1),76              title=title.split('/')[-1])77    fig.tight_layout()78    tikzplotlib.save("%s.tex" % title, figure=fig, strict=True)79    plt.savefig("%s.png" % title)80    return fig81def plot2D_phase(data, labels, title):82    x_min, x_max = np.min(data, axis=0), np.max(data, axis=0)83    data = (data - x_min) / (x_max - x_min)84    figsize = (8, 8)85    add_width = 286    new_width = figsize[0] + add_width87    fig = plt.figure(figsize=(new_width, figsize[1]))88    fac_l, fac_r = figsize[0] / new_width, add_width / new_width89    rect_l = [0.1, 0.1, 0.8, 0.8]90    rect_r = [0., 0.1, 0.2, 0.8]91    ax = fig.add_axes(np.array(rect_l) * np.array([fac_l, 1, fac_l, 1]))92    cax = fig.add_axes(np.array(rect_r) * np.array([fac_r, 1, fac_r, 1]) + np.array([fac_l, 0, 0, 0]))93    sin_labels = list(map(lambda l: np.sin(float(l)), labels))94    bla = ax.scatter(data[:, 0], data[:, 1], c=sin_labels, cmap="jet", alpha=1.0)95    # plt.colorbar(bla, cax=cax) <- some problem with the color bar..96    # fig.tight_layout()97    tikzplotlib.save("%s.tex" % title, figure=fig, strict=True)98    plt.savefig("%s.png" % title)99    return fig100tsne = None101def calc_tsne(raw):102    global tsne103    if tsne is None:104        tsne = TSNE(n_components=2, init='pca', random_state=7)  # n_iter = xxx105    result = tsne.fit_transform(raw)106    return result107pca = None108def calc_pca(raw):109    global pca110    if pca is None:111        pca = PCA(n_components=2)112    return pca.fit_transform(raw)113def calc_pca_curve(raw):114    pcan = PCA()115    pcan.fit_transform(raw)116    pct = pcan.explained_variance_ratio_117    prefix = np.cumsum(pct / np.sum(pct))118    fig = plt.figure(figsize=(4, 4))119    ax = fig.add_axes([0.2, 0.2, 0.6, 0.6])120    ax.plot(list(range(1, 6)), prefix[:5])121    ax.plot(2, prefix[1], "ro")122    ax.annotate("{:.3f}% of variation".format(prefix[1] * 100),123                (2, prefix[1]),124                textcoords="offset points",125                xytext=(60, -20),126                ha="center")127    ax.set_xticks(list(range(1, 6)))128    ax.set_yticks(list(np.arange(0.5, 1.01, 0.1)))129    ax.set_xlabel("number of components")130    ax.set_ylabel("explained variance ratio")131    name = "pca_curve"132    tikzplotlib.save(name + ".tex", figure=fig, strict=True)133    plt.savefig("pca_curve.png")134    return pct135def plot_tsne(raw, labels, title):136    result = calc_tsne(raw)137    return plot2D(result, labels, title)138def plot_content_tsne(raw, slabels, clabels, title):139    name = title + "_tsne"140    path = name + ".npz"141    if os.path.exists(path):142        print("%s already exists" % path)143        result = np.load(path, allow_pickle=True)["result"]144    else:145        print("start to produce %s" % path)146        result = calc_tsne(raw)147        np.savez_compressed(name, result=result)148    plot2D(result, slabels, title + "_style_labels")149    plot2D(result, clabels, title + "_content_labels")150def calc_many_blas(raws, calc_single):151    lens = list(map(lambda x: len(x), raws))152    whole = np.concatenate(raws, axis=0)153    proj = calc_single(whole)154    ret = ()155    suml = 0156    for l in lens:157        ret += (proj[suml: suml + l],)158        suml += l159    return ret160def get_all_plots(data, output_path, writers, iter, summary=True,161                  style_cluster_protocols=('pca'),162                  separate_compute=False):163    """164    data: {"train": dict_train, "test": dict_test}165    dict_train: {"style2d_code": blabla, etc.}166    separate_compute: compute t-SNE for 2D & 3D separately167    """168    ensure_dirs(output_path)169    def fig_title(title):170        return pjoin(output_path, title)171    def add_fig(fig, title, phase):172        if summary:173            writers[phase].add_figure(title, fig, global_step=iter)174    keys = data["train"].keys()175    has2d = "style2d_code" in keys176    has3d = "style3d_code" in keys177    # style codes & adain params178    for suffix in ["_code", "_adain"]:179        codes_raw = []180        titles = []181        phases = []182        data_keys = []183        if has2d: data_keys.append("style2d" + suffix)184        if has3d: data_keys.append("style3d" + suffix)185        for key in data_keys:186            for phase in ["train", "test"]:187                codes_raw.append(data[phase][key])188                titles.append(f'{phase}_{key}')189                phases.append(phase)190        # calc tsne with style2/3d, train/test altogether191        for name, protocol in zip(['pca', 'tsne'], [calc_pca, calc_tsne]):192            if name not in style_cluster_protocols:193                continue194            style_codes = calc_many_blas(codes_raw, protocol)195            fig = plot2D_overlay([style_codes[0], style_codes[2]],196                                 [data["train"]["meta"]["style"], data["train"]["meta"]["style"]],197                                 [1.0, 0.5],198                                 fig_title(f'joint_embedding_{name}{suffix}'))199            add_fig(fig, f'joint_embedding_{name}{suffix}', "train")200            for i, (code, phase, title) in enumerate(zip(style_codes, phases, titles)):201                if separate_compute:202                    code = protocol(codes_raw[i])203                for label_type in ["style", "content"]:204                    fig = plot2D(code, data[phase]["meta"][label_type], fig_title(f'{title}_{name}_{label_type}'))205                    add_fig(fig, f'{title}_{name}_{label_type}', phase)206    # content codes (train only)207    content_code_pca = calc_pca(data["train"]["content_code"])208    for label in ["style", "content", "phase"]:209        if label == "phase":210            indices = [i for i in range(len(data["train"]["meta"]["content"])) if data["train"]["meta"]["content"][i] == "walk"]211            walk_code = content_code_pca[np.array(indices)]212            phase_labels = [data["train"]["meta"]["phase"][i] for i in indices]213            fig = plot2D_phase(walk_code, phase_labels, fig_title(f'content_by_{label}'))214        else:215            fig = plot2D(content_code_pca, data["train"]["meta"][label], fig_title(f'content_by_{label}'))216        add_fig(fig, f'content_by_{label}', "train")217    """218    fig = show_images_from_disk("", all_titles, 2, output_path + "all_codes")219    if summary:220        writers["train"].add_figure("all codes", fig, global_step=iter)221    """222def get_demo_plots(data, output_path):223    """224    data: {"train": dict_train, "test": dict_test}225    dict_train: {"style2d_code": blabla, etc.}226    """227    ensure_dirs(output_path)228    def fig_title(title):229        return pjoin(output_path, title)230    style_labels = data["train"]["meta"]["style"]231    adain_raw = []232    for key in ["style2d_adain", "style3d_adain"]:233        for phase in ["train", "test"]:234            adain_raw.append(data[phase][key])235    adain_tsne = calc_many_blas(adain_raw, calc_tsne)236    plot2D_overlay([adain_tsne[0], adain_tsne[2]],237                   [style_labels, style_labels],238                   [1.0, 0.5],239                   fig_title(f'joint_embedding_adain_tsne'))240    for key in ["style3d_code", "style3d_adain"]:241        tsne_code = calc_tsne(data["train"][key])242        plot2D(tsne_code, style_labels, fig_title(f'{key}_tsne'))243    content_code_pca = calc_pca(data["train"]["content_code"])244    indices = [i for i in range(len(data["train"]["meta"]["content"])) if data["train"]["meta"]["content"][i] == "walk"]245    walk_code = content_code_pca[np.array(indices)]246    phase_labels = [data["train"]["meta"]["phase"][i] for i in indices]247    plot2D_phase(walk_code, phase_labels, fig_title(f'content_by_phase'))248    plot2D(content_code_pca, style_labels, fig_title(f'content_by_style'))249def show_images_from_disk(path, titles, rows, this_title):250    images = []251    for title in titles:252        name = "%s.png" % title253        input_path = os.path.join(path, name)254        images.append(plt.imread(input_path))255    this_title = os.path.join(path, this_title)256    return show_images(images, titles, this_title, rows)257def show_images(images, titles, this_title, rows=1):258    """Display a list of images in a single figure with matplotlib.259    Parameters260    ---------261    images: List of np.arrays compatible with plt.imshow.262    cols (Default = 1): Number of columns in figure (number of rows is263                        set to np.ceil(n_images/float(cols))).264    titles: List of titles corresponding to each image. Must have265            the same length as titles.266    """267    assert (len(images) == len(titles))268    n_images = len(images)269    cols = np.ceil(n_images / float(rows))270    # if titles is None: titles = ['Image (%d)' % i for i in range(1,n_images + 1)]271    size = np.array((8, 8)) * np.array(rows, cols)272    fig = plt.figure(figsize=size)273    for n, (image, title) in enumerate(zip(images, titles)):274        a = fig.add_subplot(rows, cols, n + 1)275        if image.ndim == 2:276            plt.gray()277        a.set_axis_off()278        plt.imshow(image)279        a.set_title(title)280    fig.tight_layout(pad=0, w_pad=0, h_pad=0)281    plt.subplots_adjust(wspace=0, hspace=0)282    # plt.show()283    plt.savefig("%s.png" % this_title, dpi=150, bbox_inches='tight', pad_inches=0)...

MyKNeighborsClassifier.py

Source:MyKNeighborsClassifier.py

1#!/usr/bin/env python2# -*- coding: utf-8 -*-3from scipy.spatial import distance4class MyKNeighborsClassifier:5	"""Classifier implementing the k-nearest neighbors vote similar to sklearn 6	library but different.7	https://goo.gl/Cmji3U8	But still same.9	10	Parameters11	----------12	n_neighbors : int, optional (default = 5)13		Number of neighbors to use by default.14	method : string, optional (default = 'classical')15		method for voting. Possible values:16		- 'classical' : uniform weights.  All points in each neighborhood17		  are weighted equally.18		- 'weighted' : weight points by the inverse of their distance.19		  in this case, closer neighbors of a query point will have a20		  greater influence than neighbors which are further away.21		- 'validity' weights are calculated with distance and multiplied22		  of validity for each voter.  23		Note: implementing kd_tree is bonus.24	norm : {'l1', 'l2'}, optional (default = 'l2')25		Distance norm. 'l1' is manhattan distance. 'l2' is euclidean distance.26	Examples27	--------28	"""29	def __init__(self, n_neighbors=5, method='classical', norm='l2'):30		self.n_neighbors = n_neighbors31		self.method = method32		self.norm = norm33		self.labels = []34		self.distinct_labels = []35		self.data = []36	def fit(self, X, y):37		"""Fit the model using X as training data and y as target values38		Parameters39		----------40		X : array-like, shape (n_query, n_features),41			Training data. 42		y : array-like, shape = [n_samples] 43			Target values.44		"""45		self.data = X46		self.labels = y47		self.distinct_labels = sorted(list(set(y)))					# get distinct classes48		if self.method == "validity":49			self.validities = []50			# calculate validities for each sample			51			for sample_i, sample in enumerate(self.data):52				distances = []53				if self.norm == "l1":54					# apply Manhattan distance55					for i, x in enumerate(self.data):56						if x != sample:57							print x, sample58							distances.append([distance.cityblock(x, sample), self.labels[i]])			# store [distance, label] pairs in distances list59				elif self.norm == "l2":60					# apply Euclidean distance61					for i, x in enumerate(self.data):62						if x != sample:63							print x, sample64							distances.append([distance.euclidean(x, sample), self.labels[i]])			# store [distance, label] pairs in distances list65				# get n nearest neighbors66				nearest_neighbors = sorted(distances, key = lambda x: x[0])[:self.n_neighbors]		# sort wrt distance67				label_weights = [0.0] * len(self.distinct_labels)						# store label weights wrt neighbors for each label68				for i, neighbor in enumerate(nearest_neighbors):69					label_index = self.distinct_labels.index(neighbor[1])70					label_weights[label_index] += (1.0 / (neighbor[0] + 1e-15))71				sample_label = self.labels[sample_i]72				validity = label_weights[self.distinct_labels.index(sample_label)] / sum(label_weights)73				self.validities.append(validity)74	def predict(self, X):75		"""Predict the class labels for the provided data76		Parameters77		----------78		X : array-like, shape (n_query, n_features),79			Test samples.80		Returns81		-------82		y : array of shape [n_samples]83			Class labels for each data sample.84		"""85		if len(self.labels) == 0:86			raise ValueError("You should fit first!")87			88		y = []			# store labels for each data89		for x_i, x in enumerate(X):90			# find distance of x to all training data91			distances = []92			if self.norm == "l1":				93				for i, data in enumerate(self.data):94					distances.append([distance.cityblock(x, data), self.labels[i], i])			# store [distance, label, data_index] pairs in distances list95			elif self.norm == "l2":96				for i, data in enumerate(self.data):97					distances.append([distance.euclidean(x, data), self.labels[i], i])			# store [distance, label, data_index] pairs in distances list98			# get n nearest neighbors99			nearest_neighbors = sorted(distances, key = lambda x: x[0])[:self.n_neighbors]		# sort wrt distance100				101			votes = [0] * self.n_neighbors 							# store vote(label) of each nearest neighbor102			if self.method == "classical":103				for i, neighbor in enumerate(nearest_neighbors):104					votes[i] = neighbor[1]105				y.append(max(votes, key = votes.count))106			elif self.method == "weighted":107					108				weights = []										# store weight of each neighbor (1/(distance + 1))109				for i, neighbor in enumerate(nearest_neighbors):110					weights.append(1.0 / (neighbor[0] + 1e-15))111				total_weights = [0] * len(self.distinct_labels)					# store total weights for each label wrt indexes in self.distinct_labels112				for i, neighbor in enumerate(nearest_neighbors):113					label_index = self.distinct_labels.index(neighbor[1])114					total_weights[label_index] += weights[i]115				y.append(self.distinct_labels[total_weights.index(max(total_weights))])116			elif self.method == "validity":117				validities = []										# store weight of each neighbor (1/(distance + 1))118				for i, neighbor in enumerate(nearest_neighbors):119					validities.append((1.0 / (neighbor[0] + 1e-15)) * self.validities[neighbor[2]])						120				121				total_validities = [0] * len(self.distinct_labels)					# store total validity*weight values for each label wrt indexes in self.distinct_labels122				for i, neighbor in enumerate(nearest_neighbors):123					label_index = self.distinct_labels.index(neighbor[1])124					total_validities[label_index] += validities[i]125				y.append(self.distinct_labels[total_validities.index(max(total_validities))])126		return y127		128	def predict_proba(self, X, method=None):129		"""Return probability estimates for the test data X.130		Parameters131		----------132		X : array-like, shape (n_query, n_features),133			Test samples.134		method : string, if None uses self.method.135		Returns136		-------137		p : array of shape = [n_samples, n_classes]138			The class probabilities of the input samples. Classes are ordered139			by lexicographic order.140		"""141		if method == None:142			method = self.method143		p = [[0] * len(self.distinct_labels)] * len(X)					# store probabilities of each class for each sample144		145		for x_i, x in enumerate(X):			146			# find distance of x to all training data147			distances = []148			if self.norm == "l1":				149				for i, data in enumerate(self.data):150					distances.append([distance.cityblock(x, data), self.labels[i], i])			# store [distance, label, data_index] pairs in distances list151			elif self.norm == "l2":152				for i, data in enumerate(self.data):153					distances.append([distance.euclidean(x, data), self.labels[i], i])			# store [distance, label, data_index] pairs in distances list154			# get n nearest neighbors155			nearest_neighbors = sorted(distances, key = lambda x: x[0])[:self.n_neighbors]		# sort wrt distance156				157			classes = [0] * len(self.distinct_labels) 					# store how many neighbors there are in each class158			159			if method == "classical":160				for i, neighbor in enumerate(nearest_neighbors):161					label_index = self.distinct_labels.index(neighbor[1])162					classes[label_index] += 1163				164				for class_i in range(len(classes)):165					p[x_i][class_i] = float(classes[class_i]) / self.n_neighbors166				167			elif method == "weighted":168				weights = []										# store weight of each neighbor (1/(distance + 1))169				for i, neighbor in enumerate(nearest_neighbors):170					weights.append(1.0 / (neighbor[0] + 1e-15))171				for i, neighbor in enumerate(nearest_neighbors):172					label_index = self.distinct_labels.index(neighbor[1])173					classes[label_index] += weights[i]174				for class_i in range(len(classes)):175					p[x_i][class_i] = float(classes[class_i]) / self.n_neighbors176				177			elif method == "validity":178				validities = []										# store validity*weight of each neighbor (1/(distance + 1))*validity179				for i, neighbor in enumerate(nearest_neighbors):180					validities.append((1.0 / (neighbor[0] + 1e-15)) * self.validities[neighbor[2]])						181				class_validities = [0] * len(self.distinct_labels)					# store total validity*weight values for each label wrt indexes in self.distinct_labels182				for i, neighbor in enumerate(nearest_neighbors):183					label_index = self.distinct_labels.index(neighbor[1])184					class_validities[label_index] += validities[i]185				for valid_i in range(len(class_validities)):186					p[x_i][valid_i] = float(class_validities[valid_i]) / self.n_neighbors187			# normalize probability list for each sample188			norm = sum(p[x_i])			189			p[x_i] = [round(j / norm, 8) for j in p[x_i]]190		return p191if __name__=='__main__':192	X = [[0], [1], [2], [3]]193	y = [0, 0, 1, 1]194	neigh = MyKNeighborsClassifier(n_neighbors=3, method="validity")195	neigh.fit(X, y)196	197	print neigh.predict([[1.1]]) #, [6.7], [5], [1.9], [0]])198	n = 0.9199	print(neigh.predict_proba([[n]], method='classical'))200	# [[0.66666667 0.33333333]]201	print(neigh.predict_proba([[n]], method='weighted'))202	# [[0.92436975 0.07563025]]203	print(neigh.predict_proba([[n]], method='validity'))...

aknn_alg.py

Source:aknn_alg.py

1"""2Code for the AKNN classification rule from:3An adaptive nearest neighbor rule for classification4Akshay Balsubramani, Sanjoy Dasgupta, Yoav Freund, Shay Moran5https://arxiv.org/abs/1905.127176Author: Akshay Balsubramani7"""8import numpy as np, sklearn, time9import sklearn.metrics10from sklearn.neighbors import NearestNeighbors11import pynndescent12def aknn_predict(13    ref_data, 14    labels, 15    margin=1.0, 16    query_data=None, 17    max_k=100, 18    use_nndescent=False19):20    # itime = time.time()21    self_is_data = False22    if query_data is None:23        query_data = ref_data24        self_is_data = True25    26    itime = time.time()27    nbrs_list = _calc_nbrs_exact(ref_data, k=max_k, use_nndescent=False)28    print('Neighbor indices computed. Time:\t {}'.format(time.time() - itime))29    aknn_predictions = predict_nn_rule(nbrs_list, labels)30    print('AKNN predictions made. Time:\t {}'.format(time.time() - itime))31    return aknn_predictions32    """33    nbrs = sklearn.neighbors.NearestNeighbors(n_neighbors=max_k).fit(ref_data)34    if use_nndescent:35        index = pynndescent.NNDescent(raw_data, n_neighbors=k)36        indices, distances = index.neighbor_graph37    else:38        distances, indices = nbrs.kneighbors(query_data)39    if self_is_data:40        indices = indices[:, 1:]41    distinct_labels = np.unique(labels)42    rngarr = np.arange(indices.shape[1])+143    query_nbrs = labels[indices]44    fracs_labels = [np.cumsum(query_nbrs == i, axis=1)/rngarr for i in distinct_labels]45    46    thresholds = margin/np.sqrt(np.arange(indices.shape[1]) + 1)47    numlabels_predicted = np.add.reduce([f > (thresholds + 1.0/len(distinct_labels)) for f in fracs_labels])48    adaptive_k = np.argmax(numlabels_predicted > 0, axis=1)49    pred_labels = np.zeros(fracs_labels[0].shape[0]).astype(str)50    for i in range(fracs_labels[0].shape[0]):51        if adaptive_k[i] == 0:52            pred_labels[i] = '?'53        else:54            lst = [f[i, adaptive_k[i]] for f in fracs_labels]55            pred_labels[i] = distinct_labels[np.argmax(lst)]56    return np.array(pred_labels), np.array(adaptive_k)57    """58def predict_nn_rule(nbr_list_sorted, labels, margin=1.0):59    """60    Given matrix of ordered nearest neighbors for each point, returns AKNN's label predictions and adaptive neighborhood sizes.61    62    Parameters63    ----------64    nbr_list_sorted: array of shape (n_samples, n_neighbors)65        Indices of the `n_neighbors` nearest neighbors in the dataset, for each data point.66    labels: array of shape (n_samples)67        Dataset labels.68    69    margin: float70        The confidence parameter "A" from the AKNN paper.71    Returns72    -------73    pred_labels: array of shape (n_samples)74        AKNN label predictions on dataset.75    adaptive_ks: array of shape (n_samples)76        AKNN neighborhood sizes on dataset.77    78    emp_margins: array of shape (n_samples)79        Empirically calculated "advantage" of each point.80    """81    pred_labels = []82    adaptive_ks = []83    thresholds = margin/np.sqrt(np.arange(nbr_list_sorted.shape[1])+1)84    distinct_labels = np.unique(labels)85    for i in range(nbr_list_sorted.shape[0]):86        (pred_label, adaptive_k_ndx, _, emp_margin) = aknn(nbr_list_sorted[i,:], labels, thresholds)87        pred_labels.append(pred_label)88        adaptive_ks.append(adaptive_k_ndx + 1)89        emp_margins.append(emp_margin)90    return np.array(pred_labels), np.array(adaptive_ks), np.array(emp_margins)91def aknn(nbrs_arr, labels, thresholds, distinct_labels=['A','B','C','D','E','F','G','H','I','J']):92    """93    Apply AKNN rule for a query point, given its list of nearest neighbors.94    95    Parameters96    ----------97    nbrs_arr: array of shape (n_neighbors)98        Indices of the `n_neighbors` nearest neighbors in the dataset.99    labels: array of shape (n_samples)100        Dataset labels.101    102    thresholds: array of shape (n_neighbors)103        Bias thresholds at different neighborhood sizes.104    Returns105    -------106    pred_label: string107        AKNN label prediction.108    first_admissible_ndx: int109        n-1, where AKNN chooses neighborhood size n.110    111    fracs_labels: array of shape (n_labels, n_neighbors)112        Fraction of each label in balls of different neighborhood sizes.113        114    emp_margin: float115        Empirical "advantage" of the point, as specific by the AKNN paper.116    """117    query_nbrs = labels[nbrs_arr]118    mtr = np.stack([query_nbrs == i for i in distinct_labels])119    rngarr = np.arange(len(nbrs_arr))+1120    fracs_labels = np.cumsum(mtr, axis=1)/rngarr121    biases = fracs_labels - 1.0/len(distinct_labels)122    emp_margin = np.max(rngarr*biases*biases)123    numlabels_predicted = np.sum(biases > thresholds, axis=0)124    admissible_ndces = np.where(numlabels_predicted > 0)[0]125    first_admissible_ndx = admissible_ndces[0] if len(admissible_ndces) > 0 else len(nbrs_arr)126    # Break any ties between labels at stopping radius, by taking the most biased label127    pred_label = '?' if first_admissible_ndx == len(nbrs_arr) else distinct_labels[np.argmax(biases[:, first_admissible_ndx])]128    return (pred_label, first_admissible_ndx, fracs_labels, emp_margin)129def knn_rule(nbr_list_sorted, labels, k=10):130    """131    For benchmarking: given matrix of ordered nearest neighbors for each point, returns kNN rule's label predictions.132    133    Parameters134    ----------135    nbr_list_sorted: array of shape (n_samples, n_neighbors)136        Indices of the `n_neighbors` nearest neighbors in the dataset, for each data point.137    Returns138    -------139    array of shape (n_samples)140        Predictions of the k-NN rule for each data point.141    """142    toret = []143    for i in range(nbr_list_sorted.shape[0]):144        uq = np.unique(labels[nbr_list_sorted[i,:k]], return_counts=True)145        toret.append(uq[0][np.argmax(uq[1])])146    return np.array(toret)147def _calc_nbrs_exact(raw_data, k=1000, brute_force=False, use_nndescent=False, query_is_ref=True):148    """149    Calculate list of `k` exact Euclidean nearest neighbors for each point.150    151    Parameters152    ----------153    raw_data: array of shape (n_samples, n_features)154        Input dataset.155    Returns156    -------157    nbr_list_sorted: array of shape (n_samples, n_neighbors)158        Indices of the `n_neighbors` nearest neighbors in the dataset, for each data point.159    """160    if use_nndescent:161        index = pynndescent.NNDescent(raw_data, n_neighbors=k)162        indices, distances = index.neighbor_graph163        if query_is_ref:164            return indices[:, 1:]165        else:166            return indices167    if brute_force:168        a = sklearn.metrics.pairwise_distances(raw_data)169        nbr_list_sorted = np.argsort(a, axis=1)170        if query_is_ref:171            nbr_list_sorted = nbr_list_sorted[:, 1:]172        return nbr_list_sorted[:, :k]173    else:174        distances, indices = NearestNeighbors(n_neighbors=k+1).fit(raw_data).kneighbors(raw_data)175        if query_is_ref:176            return indices[:, 1:]177        else:...

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