Quillot Mathias / volia

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Commit e7d811503f88129fb1d8eb28dd6af09f681a771e

Authored by Quillot Mathias
1 parent 15e16ec460
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New file architecture. Now scripts are on volia's directory and the library is o… 

…n the core directory.

Showing 14 changed files with 419 additions and 670 deletions Side-by-side Diff

scripts/data-management/convert-old.py
View file @ e7d8115
1   -import argparse
2   -from os.path import isfile
3   -
4   -
5   -if __name__ == "__main__":
6   -
7   - parser = argparse.ArgumentParser(
8   - description="Convert old files with wrong id to new one. Masseffect.")
9   -
10   - parser.add_argument("file", type=str, help="feature, x2x, or list file")
11   - parser.add_argument("--outfile", type=str, default="out.txt", help="output file")
12   -
13   - args = parser.parse_args()
14   -
15   - assert isfile(args.file), "The given file does not exist."
16   -
17   - with open(args.file, "r") as f, open(args.outfile, "w") as of:
18   - for line in f:
19   - splited = line.replace("\n", "").split(" ")
20   - metas = splited[0].split(",")
21   - metas.pop(2)
22   - splited[0] = ",".join(metas)
23   - of.write(" ".join(splited) + "\n")
scripts/data-management/filter_ids.py
View file @ e7d8115
1   -import argparse
2   -from os.path import isfile
3   -from volia.data_io import read_lst
4   -
5   -if __name__ == "__main__":
6   - parser = argparse.ArgumentParser(description="Filter ids of the given file to only keep a subset")
7   - parser.add_argument("file", type=str, help="")
8   - parser.add_argument("--filter", default=None, type=str, help="")
9   - parser.add_argument("--outfile", default="out.txt", type=str, help="")
10   -
11   - args = parser.parse_args()
12   -
13   - assert args.filter is not None
14   - assert isfile(args.file)
15   -
16   - list_ = read_lst(args.file)
17   - filter_ = read_lst(args.filter)
18   -
19   - with open(args.outfile, "w") as of:
20   - for key in filter_.keys():
21   - of.write(key + " " + " ".join(list_[key]) + "\n")
22   -
23   - print("File filtered and written in: ", args.outfile)
scripts/dim-reduction/tsne.py
View file @ e7d8115
1   -'''
2   -The goal of this script is to display calculate tsne of pvectors.
3   -'''
4   -
5   -import os
6   -from os.path import isfile
7   -import argparse
8   -import numpy as np
9   -from sklearn.manifold import TSNE
10   -
11   -from volia.data_io import read_features
12   -
13   -if __name__ == "__main__":
14   - # Defining argparse
15   - parser = argparse.ArgumentParser(prog='pvector tsne', description='Calculate the tsne representation of pvector in 3 or 2d')
16   - parser.add_argument('features', type=str,
17   - help='the path of the file you want to calculate tsne')
18   - parser.add_argument('-o', '--outfile', type=str,
19   - default='.',
20   - help='the path of the output file.')
21   - parser.add_argument('-n', '--n-comp', type=int, choices=[2, 3],
22   - default='2',
23   - help='number of components output of tsne')
24   -
25   - args = parser.parse_args()
26   -
27   - assert isfile(args.features)
28   -
29   - features_list = read_features(args.features)
30   - tuples_key_feat = np.vstack([ (key, feats) for key, feats in features_list.items()])
31   - keys, features = zip(*tuples_key_feat)
32   - feat_tsne = TSNE(n_components=args.n_comp).fit_transform(features)
33   -
34   - with open(args.outfile, "w") as of:
35   - for i in range(len(keys)):
36   - of.write(keys[i] + " " + " ".join([str(feat) for feat in feat_tsne[i]]) + "\n")
37   - print("TSNE finished. Check if everything has been done well.")
scripts/evaluations/clustering.py
View file @ e7d8115
1   -'''
2   -This script allows the user to evaluate a classification system on new labels using clustering methods.
3   -The algorithms are applied on the given latent space (embedding).
4   -'''
5   -import argparse
6   -import numpy as np
7   -import pandas as pd
8   -import os
9   -import time
10   -import pickle
11   -import csv
12   -import json
13   -
14   -from sklearn.preprocessing import LabelEncoder
15   -from sklearn.metrics.pairwise import pairwise_distances
16   -from sklearn.cluster import KMeans
17   -from sklearn.manifold import TSNE
18   -from sklearn.metrics import f1_score, homogeneity_score, completeness_score, v_measure_score
19   -import matplotlib.pyplot as plt
20   -
21   -from volia.data_io import read_features,read_lst
22   -from volia.measures import entropy_score, purity_score
23   -
24   -'''
25   -TODO:
26   -- Add an option allowing the user to choose the number of
27   -clustering to train in order to compute the average and the
28   -'''
29   -
30   -
31   -def train_clustering(label_encoder, feats, classes, outdir):
32   - num_classes = len(label_encoder.classes_)
33   - estimator = None
34   - kmeans_filepath = os.path.join(outdir, f"{args.prefix}kmeans.pkl")
35   - if args.onlymeasures:
36   - print(f"Loading model: {kmeans_filepath}")
37   - with open(kmeans_filepath, "rb") as f:
38   - estimator = pickle.load(f)
39   - else:
40   - # Compute KMEANS clustering on data
41   - print("Saving parameters")
42   - kmeans_parameters = {
43   - "n_clusters": num_classes,
44   - "n_init": 100,
45   - "tol": 10-6,
46   - "algorithm": "elkan"
47   - }
48   - with open(os.path.join(outdir, f"{args.prefix}kmeans_parameters.json"), "w") as f:
49   - json.dump(kmeans_parameters, f)
50   -
51   - # Fit the model and Save parameters
52   - print(f"Fit the model: {kmeans_filepath}")
53   - estimator = KMeans(
54   - **kmeans_parameters
55   - )
56   - estimator.fit(feats)
57   - print(f"Kmeans: processed {estimator.n_iter_} iterations - intertia={estimator.inertia_}")
58   -
59   - with open(kmeans_filepath, "wb") as f:
60   - pickle.dump(estimator, f)
61   -
62   - # contains distance to each cluster for each sample
63   - dist_space = estimator.transform(feats)
64   - predictions = np.argmin(dist_space, axis=1)
65   -
66   - # gives each cluster a name (considering most represented character)
67   - dataframe = pd.DataFrame({
68   - "label": pd.Series(list(map(lambda x: le.classes_[x], labels))),
69   - "prediction": pd.Series(predictions)
70   - })
71   -
72   - def find_cluster_name_fn(c):
73   - mask = dataframe["prediction"] == c
74   - return dataframe[mask]["label"].value_counts(sort=False).idxmax()
75   -
76   - cluster_names = list(map(find_cluster_name_fn, range(num_classes)))
77   - predicted_labels = le.transform(
78   - [cluster_names[pred] for pred in predictions])
79   -
80   - # F-measure
81   - fscores = f1_score(labels, predicted_labels, average=None)
82   - fscores_str = "\n".join(map(lambda i: "{0:25s}: {1:.4f}".format(le.classes_[i], fscores[i]), range(len(fscores))))
83   -
84   - # Entropy
85   - _, _, entropy = entropy_score(labels, predicted_labels)
86   -
87   - # Homogenity
88   - homogeneity = homogeneity_score(labels, predicted_labels)
89   -
90   - # Completeness
91   - completeness = completeness_score(labels, predicted_labels)
92   -
93   - # V-Measure
94   - v_measure = v_measure_score(labels, predicted_labels)
95   -
96   - # Purity
97   - purity_scores = purity_score(labels, predicted_labels)
98   - purity_class_score = purity_scores["purity_class_score"]
99   - purity_cluster_score = purity_scores["purity_cluster_score"]
100   - K = purity_scores["K"]
101   -
102   - # Write results
103   - with open(os.path.join(outdir, args.prefix + "eval_clustering.log"), "w") as fd:
104   - print(f"F1-scores for each classes:\n{fscores_str}", file=fd)
105   - print(f"Entropy: {entropy}", file=fd)
106   - print(f"Global score : {np.mean(fscores)}", file=fd)
107   - print(f"Homogeneity: {homogeneity}", file=fd)
108   - print(f"completeness: {completeness}", file=fd)
109   - print(f"v-measure: {v_measure}", file=fd)
110   - print(f"purity class score: {purity_class_score}", file=fd)
111   - print(f"purity cluster score: {purity_cluster_score}", file=fd)
112   - print(f"purity overall evaluation criterion (K): {K}", file=fd)
113   -
114   - # Process t-SNE and plot
115   - tsne_estimator = TSNE()
116   - embeddings = tsne_estimator.fit_transform(feats)
117   - print("t-SNE: processed {0} iterations - KL_divergence={1:.4f}".format(
118   - tsne_estimator.n_iter_, tsne_estimator.kl_divergence_))
119   -
120   - fig, [axe1, axe2] = plt.subplots(1, 2, figsize=(10, 5))
121   - for c, name in enumerate(le.classes_):
122   - c_mask = np.where(labels == c)
123   - axe1.scatter(embeddings[c_mask][:, 0], embeddings[c_mask][:, 1], label=name, alpha=0.2, edgecolors=None)
124   -
125   - try:
126   - id_cluster = cluster_names.index(name)
127   - except ValueError:
128   - print("WARNING: no cluster found for {}".format(name))
129   - continue
130   - c_mask = np.where(predictions == id_cluster)
131   - axe2.scatter(embeddings[c_mask][:, 0], embeddings[c_mask][:, 1], label=name, alpha=0.2, edgecolors=None)
132   -
133   - axe1.legend(loc="lower center", bbox_to_anchor=(0.5, -0.35))
134   - axe1.set_title("true labels")
135   - axe2.legend(loc="lower center", bbox_to_anchor=(0.5, -0.35))
136   - axe2.set_title("predicted cluster label")
137   -
138   - plt.suptitle("Kmeans Clustering")
139   -
140   - loc = os.path.join(
141   - outdir,
142   - args.prefix + "kmeans.pdf"
143   - )
144   - plt.savefig(loc, bbox_inches="tight")
145   - plt.close()
146   -
147   - print("INFO: figure saved at {}".format(loc))
148   -
149   - end = time.time()
150   - print("program ended in {0:.2f} seconds".format(end-start))
151   - return {
152   - "f1": np.mean(fscores),
153   - "entropy": entropy,
154   - "homogeneity": homogeneity,
155   - "completeness": completeness,
156   - "v-measure": v_measure,
157   - "purity_class_score": purity_class_score,
158   - "purity_cluster score": purity_cluster_score,
159   - "K": K
160   - }
161   -
162   -
163   -if __name__ == "__main__":
164   - # Argparse
165   - parser = argparse.ArgumentParser("Compute clustering on a latent space")
166   - parser.add_argument("features")
167   - parser.add_argument("utt2",
168   - type=str,
169   - help="file with [utt] [value]")
170   - parser.add_argument("--idsfrom",
171   - type=str,
172   - default="utt2",
173   - choices=[
174   - "features",
175   - "utt2"
176   - ],
177   - help="from features or from utt2?")
178   - parser.add_argument("--prefix",
179   - default="",
180   - type=str,
181   - help="prefix of saved files")
182   - parser.add_argument("--outdir",
183   - default=None,
184   - type=str,
185   - help="Output directory")
186   - parser.add_argument("--nmodels",
187   - type=int,
188   - default=1,
189   - help="specifies the number of models to train")
190   - parser.add_argument("--onlymeasures",
191   - action='store_true',
192   - help="Don't compute the clustering, compute only the measures")
193   - args = parser.parse_args()
194   -
195   - assert args.outdir
196   -
197   - start = time.time()
198   -
199   - # Load features and utt2
200   - features = read_features(args.features)
201   - utt2 = read_lst(args.utt2)
202   -
203   - # Take id list
204   - if args.idsfrom == "features":
205   - ids = list(features.keys())
206   - elif args.idsfrom == "utt2":
207   - ids = list(utt2.keys())
208   - else:
209   - print(f"idsfrom is not good: {args.idsfrom}")
210   - exit(1)
211   -
212   - feats = np.vstack([ features[id_] for id_ in ids ])
213   - classes = [ utt2[id_] for id_ in ids ]
214   -
215   - # Encode labels
216   - le = LabelEncoder()
217   - labels = le.fit_transform(classes)
218   -
219   - measures = {}
220   - for i in range(1, args.nmodels+1):
221   - subdir = os.path.join(args.outdir, str(i))
222   - if not os.path.exists(subdir):
223   - os.mkdir(subdir)
224   - print(f"[{i}/{args.nmodels}] => {subdir}")
225   - results = train_clustering(le, feats, classes, subdir)
226   -
227   - for key, value in results.items():
228   - if key not in measures:
229   - measures[key] = []
230   - measures[key].append(results[key])
231   -
232   -
233   - # File with results
234   - file_results = os.path.join(args.outdir, args.prefix + "clustering_measures.txt")
235   -
236   - with open(file_results, "w") as f:
237   - f.write(f"[nmodels: {args.nmodels}]\n")
238   - for key in measures.keys():
239   - values = np.asarray(measures[key], dtype=float)
240   - mean = np.mean(values)
241   - std = np.std(values)
242   - f.write(f"[{key} => mean: {mean}, std: {std}] \n")
243   -
244   - # CSV File with all the values
245   - file_csv_measures = os.path.join(args.outdir, args.prefix + "clustering_measures.csv")
246   -
247   - with open(file_csv_measures, "w", newline="") as f:
248   - writer = csv.writer(f, delimiter=",")
249   - writer.writerow(["measure"] + list(range(1, args.nmodels+1)) + ["mean"] + ["std"])
250   - for key in measures.keys():
251   - values = np.asarray(measures[key], dtype=float)
252   - mean = np.mean(values)
253   - std = np.std(values)
254   - writer.writerow([key] + list(values) + [mean] + [std])
scripts/plot/plot-character.py
View file @ e7d8115
1   -
2   -import matplotlib.pyplot as plt
3   -import numpy as np
4   -import pandas as pd
5   -import argparse
6   -from os.path import isfile
7   -from volia.data_io import read_features, read_lst
8   -
9   -
10   -if __name__ == "__main__":
11   - # Argparse
12   - parser = argparse.ArgumentParser(description="Plot points with color for each character")
13   - parser.add_argument("--features", type=str, help="features file path")
14   - parser.add_argument("--utt2char", type=str, help="char2utt file path")
15   - parser.add_argument("--sublist", type=str, default=None, help="white list of ids to take into account")
16   - parser.add_argument("--outfile", default="out.pdf", type=str, help="")
17   - parser.add_argument("--title", default="Example of plot", type=str, help="Specify the title")
18   - args = parser.parse_args()
19   -
20   - # List of assertions
21   - assert args.features, "Need to specify features option"
22   - assert args.utt2char, "Need to specify char2utt option file"
23   - assert isfile(args.features), "Features path should point to a file"
24   - assert isfile(args.utt2char), "char2utt path should point to a file"
25   - if args.sublist is not None:
26   - assert isfile(args.sublist), "sublist path should point to a file"
27   -
28   -
29   - id_to_features = read_features(args.features)
30   -
31   - ids = []
32   - if args.sublist is not None:
33   - print("Using sublist")
34   - list_ids = read_lst(args.sublist)
35   - ids = [ key for key in list_ids.keys() ]
36   - else:
37   - ids = [ key for key in id_to_features.keys() ]
38   -
39   - utt2char = read_lst(args.utt2char)
40   -
41   - features = [ id_to_features[id_] for id_ in ids ]
42   - features = np.vstack(features)
43   -
44   - characters_list = [ utt2char[id_][0] for id_ in ids ]
45   -
46   - features_T = features.transpose()
47   - print("Number of characters: ", len(np.unique(characters_list)))
48   - df = pd.DataFrame(dict(
49   - x=features_T[0],
50   - y=features_T[1],
51   - character=characters_list))
52   -
53   - groups = df.groupby('character')
54   -
55   - # Plot
56   - fig, ax = plt.subplots()
57   -
58   - for character, group in groups:
59   - p = ax.plot(group.x, group.y, marker='o', linestyle='', ms=1, label=character)
60   - ax.legend()
61   - plt.savefig(args.outfile)
62   - print("Your plot is saved well (no check of this affirmation)")
volia/convert-old.py
Diff comments   View file @ e7d8115
  1 +import argparse
  2 +from os.path import isfile
  3 +
  4 +
  5 +if __name__ == "__main__":
  6 +
  7 + parser = argparse.ArgumentParser(
  8 + description="Convert old files with wrong id to new one. Masseffect.")
  9 +
  10 + parser.add_argument("file", type=str, help="feature, x2x, or list file")
  11 + parser.add_argument("--outfile", type=str, default="out.txt", help="output file")
  12 +
  13 + args = parser.parse_args()
  14 +
  15 + assert isfile(args.file), "The given file does not exist."
  16 +
  17 + with open(args.file, "r") as f, open(args.outfile, "w") as of:
  18 + for line in f:
  19 + splited = line.replace("\n", "").split(" ")
  20 + metas = splited[0].split(",")
  21 + metas.pop(2)
  22 + splited[0] = ",".join(metas)
  23 + of.write(" ".join(splited) + "\n")
  1 +'''
  2 +Data management input/output
  3 +'''
  4 +
  5 +# Import packages and modules
  6 +import numpy as np
  7 +
  8 +# Defining some types
  9 +from typing import List, Dict
  10 +KeyToList = Dict[str, List[str]]
  11 +KeyToFeatures = Dict[str, List[float]]
  12 +
  13 +
  14 +def read_lst(file_path: str) -> KeyToList:
  15 + '''
  16 + Read lst file with this structure:
  17 + [id] [value1] [value2] ... [value n]
  18 +
  19 + This is a basic function reused by others like read_features.
  20 + returns a dictionary with id as key and a list of value as corresponding values
  21 + '''
  22 + # KeyToList type variable
  23 + key_to_list = dict()
  24 + with open(file_path, "r") as f:
  25 + for line in f:
  26 + splited = line.replace("\n", "").split(" ")
  27 + id = splited[0]
  28 + values = splited[1:]
  29 + key_to_list[id] = values
  30 + return key_to_list
  31 +
  32 +
  33 +def read_features(file_path: str) -> KeyToFeatures:
  34 + '''
  35 + '''
  36 + # KeyToFeatures type variable
  37 + key_to_features = dict()
  38 + # and the KeyToList
  39 + key_to_list = read_lst(file_path)
  40 +
  41 + for key_, list_ in key_to_list.items():
  42 + key_to_features[key_] = np.asarray(list_, dtype=float)
  43 +
  44 + return key_to_features
volia/core/measures.py
Diff comments   View file @ e7d8115
  1 +'''
  2 +This module is a part of my library.
  3 +It aims to compute some measures for clustering.
  4 +'''
  5 +
  6 +import numpy as np
  7 +
  8 +def disequilibrium_(matrix1, matrix2, isGlobal=False, mod=None):
  9 + '''
  10 + Compute disequilibrium for all the clusters.
  11 + The disequilibrium is compute from the difference
  12 + between two clustering sets.
  13 + isGlobal permet ร  l'utilisateur de choisir le dรฉnominateur de
  14 + la fonction :
  15 + - True : divise la valeur par le nombre d'รฉlรฉment du cluster
  16 + - False : divise la valeur par le nombre d'รฉlรฉment total
  17 +
  18 + withPower permet ร  l'utilisateur de dรฉcider d'appliquer un carrรฉ 2 ou
  19 + une valeur absolue.
  20 + '''
  21 +
  22 + def divide_line(a, divider):
  23 + '''
  24 + Sub function used for dividing matrix by a vector line by line.
  25 + '''
  26 + return np.divide(a, divider, out=np.zeros_like(a), where=divider!=0)
  27 +
  28 + dividers1 = 0
  29 + dividers2 = 0
  30 +
  31 + if isGlobal:
  32 + dividers1 = matrix1.sum()
  33 + dividers2 = matrix2.sum()
  34 + else:
  35 + dividers1 = matrix1.sum(axis=1)
  36 + dividers2 = matrix2.sum(axis=1)
  37 +
  38 + matrix1_divided = np.apply_along_axis(divide_line, 0, np.asarray(matrix1, dtype=np.float), dividers1)
  39 +
  40 + matrix2_divided = np.apply_along_axis(divide_line, 0, np.asarray(matrix2, dtype=np.float), dividers2)
  41 +
  42 + diff = matrix1_divided - matrix2_divided
  43 +
  44 + mask = np.logical_not(np.logical_and(matrix2==0, matrix1==0))
  45 +
  46 + result = diff
  47 +
  48 + if mod != None or mod == "":
  49 + for word in mod.split(" "):
  50 + if word == "power":
  51 + result = np.power(result,2)
  52 + elif word == "human":
  53 + result = result * 100
  54 + elif word == "abs":
  55 + result = np.absolute(result)
  56 + else:
  57 + raise Exception("Need to specify an accepted mod of the disequilibrium (\"power\", \"human\" or \"abs\"")
  58 + return (mask, result)
  59 +
  60 +
  61 +
  62 +def disequilibrium_mean_by_cluster(mask, matrix):
  63 + '''
  64 + Mean of disequilibrium
  65 + matrix is the disequilibrium calculated
  66 + from number of occurences belonging to a class,
  67 + for each cluster.
  68 + '''
  69 + nb_k = len(matrix)
  70 + results = np.zeros((nb_k))
  71 +
  72 + for i in range(nb_k):
  73 + results[i] = matrix[i].sum() / mask[i].sum()
  74 + return results
  75 +
  76 +
  77 +def disequilibrium(matrix1, matrix2, isGlobal=False):
  78 + '''
  79 + Disequilibrium matrix
  80 + And Disequilibrium value
  81 + '''
  82 + mask, result = disequilibrium_(matrix1, matrix2, isGlobal)
  83 + result_human = result * 100
  84 + result_power = np.power(result, 2)
  85 +
  86 + return (
  87 + mask,
  88 + result_human,
  89 + disequilibrium_mean_by_cluster(mask, result_power).sum()/matrix1.shape[0]
  90 + )
  91 +
  92 +
  93 +def compute_count_matrix(y_truth, y_hat):
  94 + '''
  95 + Check the size of the lists with assertion
  96 + '''
  97 + # Check size of the lists
  98 + assert len(y_hat) == len(y_truth), f"Matrices should have the same length y_hat: {len(y_hat)}, y_truth: {len(y_truth)}"
  99 +
  100 + # Build count matrix
  101 + count_matrix = np.zeros((max(y_hat+1), max(y_truth+1)))
  102 + for i in range(len(y_hat)):
  103 + count_matrix[y_hat[i]][y_truth[i]] += 1
  104 + return count_matrix
  105 +
  106 +
  107 +def entropy_score(y_truth, y_hat):
  108 + '''
  109 + Need to use label encoder before givin y_hat and y_truth
  110 + Don't use one hot labels
  111 +
  112 + Return a tuple with:
  113 + - result_matrix : the matrix with the log multiplied probabilities (P(x) * log(P(x)))
  114 + - result_vector : the vector avec summing entropy of each class. Each value corresponds to a cluster.
  115 + - result : the final entropy measure of the clustering
  116 + '''
  117 + def divide_line(a, divider):
  118 + '''
  119 + Sub function used for dividing matrix by a vector line by line.
  120 + '''
  121 + return np.divide(a, divider, out=np.zeros_like(a), where=divider!=0)
  122 +
  123 + # Build count matrix
  124 + count_matrix = compute_count_matrix(y_truth, y_hat)
  125 +
  126 + # Build dividers vector
  127 + dividers = count_matrix.sum(axis=1)
  128 +
  129 + matrix_divided = np.apply_along_axis(divide_line, 0, np.asarray(count_matrix, dtype=np.float), dividers)
  130 +
  131 + log_matrix = np.zeros(matrix_divided.shape)
  132 + np.log2(matrix_divided, out=log_matrix, where=count_matrix != 0)
  133 + result_matrix = -1 * np.multiply(matrix_divided, log_matrix)
  134 + result_vector = result_matrix.sum(axis=1)
  135 + result_vector.sum()
  136 +
  137 + if np.isnan(np.sum(result_vector)):
  138 + print("COUNT MATRIX")
  139 + print(count_matrix)
  140 + print("MATRIX DIVIDED")
  141 + print(matrix_divided)
  142 + print("RESULT MATRIX")
  143 + print(result_matrix)
  144 + print("VECTOR MATRIX")
  145 + print(result_vector)
  146 + print("An error occured due to nan value, some values are printed before")
  147 + exit(1)
  148 +
  149 + result = result_vector * dividers / dividers.sum()
  150 + result = result.sum()
  151 + return (result_matrix, result_vector, result)
  152 +
  153 +
  154 +def purity_score(y_truth, y_hat):
  155 + '''
  156 + Return three values in a dictionary:
  157 + - purity_class_score: the purity score of the class (asp)
  158 + - purity_cluster_score: the purity score of the cluster (acp)
  159 + - K: the overall evaluation criterion (sqrt(asp * acp))
  160 +
  161 + This function is based on the following article:
  162 + Unknown-multiple speaker clustering using HMM, J. Ajmera, H. Bourlard, I. Lapidot, I. McCowan
  163 + '''
  164 +
  165 + def divide_line(a, divider):
  166 + '''
  167 + Sub function used for dividing matrix by a vector line by line.
  168 + '''
  169 + return np.divide(a, divider, out=np.zeros_like(a), where=divider!=0)
  170 +
  171 + def compute_purity_score(count_matrix, axis=0):
  172 + if axis==0:
  173 + other_axis = 1
  174 + else:
  175 + other_axis = 0
  176 + count_per_row = count_matrix.sum(axis=axis)
  177 + dividers = np.square(count_per_row)
  178 +
  179 + count_matrix_squared = np.square(count_matrix)
  180 + matrix_divided = np.apply_along_axis(divide_line, other_axis, np.asarray(count_matrix_squared, dtype=np.float), dividers)
  181 + vector_purity = np.sum(matrix_divided, axis=axis)
  182 +
  183 + scalar_purity = np.average(vector_purity, weights=count_per_row)
  184 + return (vector_purity, scalar_purity)
  185 +
  186 +
  187 + count_matrix = compute_count_matrix(y_truth, y_hat)
  188 + _, purity_cluster_score = compute_purity_score(count_matrix, 1)
  189 + _, purity_class_score = cluster_purity = compute_purity_score(count_matrix, 0)
  190 +
  191 + K = np.sqrt(purity_cluster_score * purity_class_score)
  192 +
  193 + for i in range(count_matrix.shape[0]):
  194 + for j in range(count_matrix.shape[1]):
  195 + count_matrix[i][j]
  196 + count_matrix[i]
  197 + return {
  198 + "purity_class_score": purity_class_score,
  199 + "purity_cluster_score": purity_cluster_score,
  200 + "K": K
  201 + }
  202 +
  203 +
  204 +if __name__ == "__main__":
  205 + print("Purity test #1")
  206 + # Hypothesis
  207 + y_hat = np.asarray([0, 1, 2, 0, 1, 0, 3, 2, 2, 3, 3, 0])
  208 + # Truth
  209 + y = np.asarray([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3])
  210 +
  211 + (result_matrix, result_vector, result) = entropy_score(y, y_hat)
  212 + print(purity_score(y, y_hat))
  213 +
  214 + exit(1)
  215 + print("Purity test #2")
  216 + # Hypothesis
  217 + y_hat = np.asarray([0, 1, 2, 0, 1, 0, 3, 2, 2, 3, 3, 0, 4, 4, 4])
  218 + # Truth
  219 + y = np.asarray([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 0, 3, 3, 3])
  220 +
  221 + (result_matrix, result_vector, result) = entropy_score(y, y_hat)
  222 + exit(1)
  223 + print("Result matrix: ")
  224 + print(result_matrix)
  225 + print("Result vector: ")
  226 + print(result_vector)
  227 + print("Result: ", result)
volia/data_io.py
View file @ e7d8115
1   -'''
2   -Data management input/output
3   -'''
4   -
5   -# Import packages and modules
6   -import numpy as np
7   -
8   -# Defining some types
9   -from typing import List, Dict
10   -KeyToList = Dict[str, List[str]]
11   -KeyToFeatures = Dict[str, List[float]]
12   -
13   -
14   -def read_lst(file_path: str) -> KeyToList:
15   - '''
16   - Read lst file with this structure:
17   - [id] [value1] [value2] ... [value n]
18   -
19   - This is a basic function reused by others like read_features.
20   - returns a dictionary with id as key and a list of value as corresponding values
21   - '''
22   - # KeyToList type variable
23   - key_to_list = dict()
24   - with open(file_path, "r") as f:
25   - for line in f:
26   - splited = line.replace("\n", "").split(" ")
27   - id = splited[0]
28   - values = splited[1:]
29   - key_to_list[id] = values
30   - return key_to_list
31   -
32   -
33   -def read_features(file_path: str) -> KeyToFeatures:
34   - '''
35   - '''
36   - # KeyToFeatures type variable
37   - key_to_features = dict()
38   - # and the KeyToList
39   - key_to_list = read_lst(file_path)
40   -
41   - for key_, list_ in key_to_list.items():
42   - key_to_features[key_] = np.asarray(list_, dtype=float)
43   -
44   - return key_to_features
  1 +import argparse
  2 +from os.path import isfile
  3 +#from volia.data_io import read_lst
  4 +
  5 +import volia
  6 +if __name__ == "__main__":
  7 + parser = argparse.ArgumentParser(description="Filter ids of the given file to only keep a subset")
  8 + parser.add_argument("file", type=str, help="")
  9 + parser.add_argument("--filter", default=None, type=str, help="")
  10 + parser.add_argument("--outfile", default="out.txt", type=str, help="")
  11 +
  12 + args = parser.parse_args()
  13 +
  14 + assert args.filter is not None
  15 + assert isfile(args.file)
  16 +
  17 + list_ = read_lst(args.file)
  18 + filter_ = read_lst(args.filter)
  19 +
  20 + with open(args.outfile, "w") as of:
  21 + for key in filter_.keys():
  22 + of.write(key + " " + " ".join(list_[key]) + "\n")
  23 +
  24 + print("File filtered and written in: ", args.outfile)
volia/measures.py
View file @ e7d8115
1   -'''
2   -This module is a part of my library.
3   -It aims to compute some measures for clustering.
4   -'''
5   -
6   -import numpy as np
7   -
8   -def disequilibrium_(matrix1, matrix2, isGlobal=False, mod=None):
9   - '''
10   - Compute disequilibrium for all the clusters.
11   - The disequilibrium is compute from the difference
12   - between two clustering sets.
13   - isGlobal permet ร  l'utilisateur de choisir le dรฉnominateur de
14   - la fonction :
15   - - True : divise la valeur par le nombre d'รฉlรฉment du cluster
16   - - False : divise la valeur par le nombre d'รฉlรฉment total
17   -
18   - withPower permet ร  l'utilisateur de dรฉcider d'appliquer un carrรฉ 2 ou
19   - une valeur absolue.
20   - '''
21   -
22   - def divide_line(a, divider):
23   - '''
24   - Sub function used for dividing matrix by a vector line by line.
25   - '''
26   - return np.divide(a, divider, out=np.zeros_like(a), where=divider!=0)
27   -
28   - dividers1 = 0
29   - dividers2 = 0
30   -
31   - if isGlobal:
32   - dividers1 = matrix1.sum()
33   - dividers2 = matrix2.sum()
34   - else:
35   - dividers1 = matrix1.sum(axis=1)
36   - dividers2 = matrix2.sum(axis=1)
37   -
38   - matrix1_divided = np.apply_along_axis(divide_line, 0, np.asarray(matrix1, dtype=np.float), dividers1)
39   -
40   - matrix2_divided = np.apply_along_axis(divide_line, 0, np.asarray(matrix2, dtype=np.float), dividers2)
41   -
42   - diff = matrix1_divided - matrix2_divided
43   -
44   - mask = np.logical_not(np.logical_and(matrix2==0, matrix1==0))
45   -
46   - result = diff
47   -
48   - if mod != None or mod == "":
49   - for word in mod.split(" "):
50   - if word == "power":
51   - result = np.power(result,2)
52   - elif word == "human":
53   - result = result * 100
54   - elif word == "abs":
55   - result = np.absolute(result)
56   - else:
57   - raise Exception("Need to specify an accepted mod of the disequilibrium (\"power\", \"human\" or \"abs\"")
58   - return (mask, result)
59   -
60   -
61   -
62   -def disequilibrium_mean_by_cluster(mask, matrix):
63   - '''
64   - Mean of disequilibrium
65   - matrix is the disequilibrium calculated
66   - from number of occurences belonging to a class,
67   - for each cluster.
68   - '''
69   - nb_k = len(matrix)
70   - results = np.zeros((nb_k))
71   -
72   - for i in range(nb_k):
73   - results[i] = matrix[i].sum() / mask[i].sum()
74   - return results
75   -
76   -
77   -def disequilibrium(matrix1, matrix2, isGlobal=False):
78   - '''
79   - Disequilibrium matrix
80   - And Disequilibrium value
81   - '''
82   - mask, result = disequilibrium_(matrix1, matrix2, isGlobal)
83   - result_human = result * 100
84   - result_power = np.power(result, 2)
85   -
86   - return (
87   - mask,
88   - result_human,
89   - disequilibrium_mean_by_cluster(mask, result_power).sum()/matrix1.shape[0]
90   - )
91   -
92   -
93   -def compute_count_matrix(y_truth, y_hat):
94   - '''
95   - Check the size of the lists with assertion
96   - '''
97   - # Check size of the lists
98   - assert len(y_hat) == len(y_truth), f"Matrices should have the same length y_hat: {len(y_hat)}, y_truth: {len(y_truth)}"
99   -
100   - # Build count matrix
101   - count_matrix = np.zeros((max(y_hat+1), max(y_truth+1)))
102   - for i in range(len(y_hat)):
103   - count_matrix[y_hat[i]][y_truth[i]] += 1
104   - return count_matrix
105   -
106   -
107   -def entropy_score(y_truth, y_hat):
108   - '''
109   - Need to use label encoder before givin y_hat and y_truth
110   - Don't use one hot labels
111   -
112   - Return a tuple with:
113   - - result_matrix : the matrix with the log multiplied probabilities (P(x) * log(P(x)))
114   - - result_vector : the vector avec summing entropy of each class. Each value corresponds to a cluster.
115   - - result : the final entropy measure of the clustering
116   - '''
117   - def divide_line(a, divider):
118   - '''
119   - Sub function used for dividing matrix by a vector line by line.
120   - '''
121   - return np.divide(a, divider, out=np.zeros_like(a), where=divider!=0)
122   -
123   - # Build count matrix
124   - count_matrix = compute_count_matrix(y_truth, y_hat)
125   -
126   - # Build dividers vector
127   - dividers = count_matrix.sum(axis=1)
128   -
129   - matrix_divided = np.apply_along_axis(divide_line, 0, np.asarray(count_matrix, dtype=np.float), dividers)
130   -
131   - log_matrix = np.zeros(matrix_divided.shape)
132   - np.log2(matrix_divided, out=log_matrix, where=count_matrix != 0)
133   - result_matrix = -1 * np.multiply(matrix_divided, log_matrix)
134   - result_vector = result_matrix.sum(axis=1)
135   - result_vector.sum()
136   -
137   - if np.isnan(np.sum(result_vector)):
138   - print("COUNT MATRIX")
139   - print(count_matrix)
140   - print("MATRIX DIVIDED")
141   - print(matrix_divided)
142   - print("RESULT MATRIX")
143   - print(result_matrix)
144   - print("VECTOR MATRIX")
145   - print(result_vector)
146   - print("An error occured due to nan value, some values are printed before")
147   - exit(1)
148   -
149   - result = result_vector * dividers / dividers.sum()
150   - result = result.sum()
151   - return (result_matrix, result_vector, result)
152   -
153   -
154   -def purity_score(y_truth, y_hat):
155   - '''
156   - Return three values in a dictionary:
157   - - purity_class_score: the purity score of the class (asp)
158   - - purity_cluster_score: the purity score of the cluster (acp)
159   - - K: the overall evaluation criterion (sqrt(asp * acp))
160   -
161   - This function is based on the following article:
162   - Unknown-multiple speaker clustering using HMM, J. Ajmera, H. Bourlard, I. Lapidot, I. McCowan
163   - '''
164   -
165   - def divide_line(a, divider):
166   - '''
167   - Sub function used for dividing matrix by a vector line by line.
168   - '''
169   - return np.divide(a, divider, out=np.zeros_like(a), where=divider!=0)
170   -
171   - def compute_purity_score(count_matrix, axis=0):
172   - if axis==0:
173   - other_axis = 1
174   - else:
175   - other_axis = 0
176   - count_per_row = count_matrix.sum(axis=axis)
177   - dividers = np.square(count_per_row)
178   -
179   - count_matrix_squared = np.square(count_matrix)
180   - matrix_divided = np.apply_along_axis(divide_line, other_axis, np.asarray(count_matrix_squared, dtype=np.float), dividers)
181   - vector_purity = np.sum(matrix_divided, axis=axis)
182   -
183   - scalar_purity = np.average(vector_purity, weights=count_per_row)
184   - return (vector_purity, scalar_purity)
185   -
186   -
187   - count_matrix = compute_count_matrix(y_truth, y_hat)
188   - _, purity_cluster_score = compute_purity_score(count_matrix, 1)
189   - _, purity_class_score = cluster_purity = compute_purity_score(count_matrix, 0)
190   -
191   - K = np.sqrt(purity_cluster_score * purity_class_score)
192   -
193   - for i in range(count_matrix.shape[0]):
194   - for j in range(count_matrix.shape[1]):
195   - count_matrix[i][j]
196   - count_matrix[i]
197   - return {
198   - "purity_class_score": purity_class_score,
199   - "purity_cluster_score": purity_cluster_score,
200   - "K": K
201   - }
202   -
203   -
204   -if __name__ == "__main__":
205   - print("Purity test #1")
206   - # Hypothesis
207   - y_hat = np.asarray([0, 1, 2, 0, 1, 0, 3, 2, 2, 3, 3, 0])
208   - # Truth
209   - y = np.asarray([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3])
210   -
211   - (result_matrix, result_vector, result) = entropy_score(y, y_hat)
212   - print(purity_score(y, y_hat))
213   -
214   - exit(1)
215   - print("Purity test #2")
216   - # Hypothesis
217   - y_hat = np.asarray([0, 1, 2, 0, 1, 0, 3, 2, 2, 3, 3, 0, 4, 4, 4])
218   - # Truth
219   - y = np.asarray([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 0, 3, 3, 3])
220   -
221   - (result_matrix, result_vector, result) = entropy_score(y, y_hat)
222   - exit(1)
223   - print("Result matrix: ")
224   - print(result_matrix)
225   - print("Result vector: ")
226   - print(result_vector)
227   - print("Result: ", result)
volia/plot-character.py
Diff comments   View file @ e7d8115
  1 +
  2 +import matplotlib.pyplot as plt
  3 +import numpy as np
  4 +import pandas as pd
  5 +import argparse
  6 +from os.path import isfile
  7 +from volia.data_io import read_features, read_lst
  8 +
  9 +
  10 +if __name__ == "__main__":
  11 + # Argparse
  12 + parser = argparse.ArgumentParser(description="Plot points with color for each character")
  13 + parser.add_argument("--features", type=str, help="features file path")
  14 + parser.add_argument("--utt2char", type=str, help="char2utt file path")
  15 + parser.add_argument("--sublist", type=str, default=None, help="white list of ids to take into account")
  16 + parser.add_argument("--outfile", default="out.pdf", type=str, help="")
  17 + parser.add_argument("--title", default="Example of plot", type=str, help="Specify the title")
  18 + args = parser.parse_args()
  19 +
  20 + # List of assertions
  21 + assert args.features, "Need to specify features option"
  22 + assert args.utt2char, "Need to specify char2utt option file"
  23 + assert isfile(args.features), "Features path should point to a file"
  24 + assert isfile(args.utt2char), "char2utt path should point to a file"
  25 + if args.sublist is not None:
  26 + assert isfile(args.sublist), "sublist path should point to a file"
  27 +
  28 +
  29 + id_to_features = read_features(args.features)
  30 +
  31 + ids = []
  32 + if args.sublist is not None:
  33 + print("Using sublist")
  34 + list_ids = read_lst(args.sublist)
  35 + ids = [ key for key in list_ids.keys() ]
  36 + else:
  37 + ids = [ key for key in id_to_features.keys() ]
  38 +
  39 + utt2char = read_lst(args.utt2char)
  40 +
  41 + features = [ id_to_features[id_] for id_ in ids ]
  42 + features = np.vstack(features)
  43 +
  44 + characters_list = [ utt2char[id_][0] for id_ in ids ]
  45 +
  46 + features_T = features.transpose()
  47 + print("Number of characters: ", len(np.unique(characters_list)))
  48 + df = pd.DataFrame(dict(
  49 + x=features_T[0],
  50 + y=features_T[1],
  51 + character=characters_list))
  52 +
  53 + groups = df.groupby('character')
  54 +
  55 + # Plot
  56 + fig, ax = plt.subplots()
  57 +
  58 + for character, group in groups:
  59 + p = ax.plot(group.x, group.y, marker='o', linestyle='', ms=1, label=character)
  60 + ax.legend()
  61 + plt.savefig(args.outfile)
  62 + print("Your plot is saved well (no check of this affirmation)")
  1 +if __name__ == "__main__":
  2 + print("Volia is well installed.")
  1 +'''
  2 +The goal of this script is to display calculate tsne of pvectors.
  3 +'''
  4 +
  5 +import os
  6 +from os.path import isfile
  7 +import argparse
  8 +import numpy as np
  9 +from sklearn.manifold import TSNE
  10 +
  11 +from volia.data_io import read_features
  12 +
  13 +if __name__ == "__main__":
  14 + # Defining argparse
  15 + parser = argparse.ArgumentParser(prog='pvector tsne', description='Calculate the tsne representation of pvector in 3 or 2d')
  16 + parser.add_argument('features', type=str,
  17 + help='the path of the file you want to calculate tsne')
  18 + parser.add_argument('-o', '--outfile', type=str,
  19 + default='.',
  20 + help='the path of the output file.')
  21 + parser.add_argument('-n', '--n-comp', type=int, choices=[2, 3],
  22 + default='2',
  23 + help='number of components output of tsne')
  24 +
  25 + args = parser.parse_args()
  26 +
  27 + assert isfile(args.features)
  28 +
  29 + features_list = read_features(args.features)
  30 + tuples_key_feat = np.vstack([ (key, feats) for key, feats in features_list.items()])
  31 + keys, features = zip(*tuples_key_feat)
  32 + feat_tsne = TSNE(n_components=args.n_comp).fit_transform(features)
  33 +
  34 + with open(args.outfile, "w") as of:
  35 + for i in range(len(keys)):
  36 + of.write(keys[i] + " " + " ".join([str(feat) for feat in feat_tsne[i]]) + "\n")
  37 + print("TSNE finished. Check if everything has been done well.")