From 1c987158d5301d89725d2fb633841f77ae07a146 Mon Sep 17 00:00:00 2001 From: gruppi Date: Sat, 4 Apr 2020 16:35:58 -0400 Subject: [PATCH] Set up --- README.md | 27 +- features/english/cosine.csv | 11627 +++++ features/english/freq_diff.csv | 11627 +++++ features/english/map.csv | 11627 +++++ features/german/cosine.csv | 47764 ++++++++++++++++++ features/german/freq_diff.csv | 47764 ++++++++++++++++++ features/german/map.csv | 47764 ++++++++++++++++++ features/latin/cosine.csv | 7750 +++ features/latin/freq_diff.csv | 7750 +++ features/latin/map.csv | 7750 +++ features/swedish/cosine.csv | 53577 +++++++++++++++++++++ features/swedish/freq_diff.csv | 53577 +++++++++++++++++++++ features/swedish/map.csv | 53577 +++++++++++++++++++++ generate_answers.py | 205 + WordVectors.py => include/WordVectors.py | 6 +- include/extract_features.py | 136 + include/utils.py | 14 + include/word2vec_utils.py | 117 + precompute_features.py | 118 + requirements.txt | 32 + 20 files changed, 362805 insertions(+), 4 deletions(-) create mode 100644 features/english/cosine.csv create mode 100644 features/english/freq_diff.csv create mode 100644 features/english/map.csv create mode 100644 features/german/cosine.csv create mode 100644 features/german/freq_diff.csv create mode 100644 features/german/map.csv create mode 100644 features/latin/cosine.csv create mode 100644 features/latin/freq_diff.csv create mode 100644 features/latin/map.csv create mode 100644 features/swedish/cosine.csv create mode 100644 features/swedish/freq_diff.csv create mode 100644 features/swedish/map.csv create mode 100644 generate_answers.py rename WordVectors.py => include/WordVectors.py (97%) create mode 100644 include/extract_features.py create mode 100644 include/utils.py create mode 100644 include/word2vec_utils.py create mode 100644 precompute_features.py create mode 100644 requirements.txt diff --git a/README.md b/README.md index 1270304..9f944f7 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,25 @@ -# schme -Semantic Change Detection with Model Ensemble +# SChME - Semantic Change with Model Ensemble + + +### What is SChME +This is the semantic change detection model as seen at SemEval-2020 Task 1: +Unsupervised Detection of Lexical Semantic Change. + +It combines alignment of word embeddings with voting mechanisms to quantify the semantic change between two given corpora. + +### How to use + +You need to provide data from a pair of sources, which can be done in two ways. + +1. A pair of text files, each containing one corpora relative to different time periods or domains. +2. A pair of word embeddings in [Word2Vec format](https://radimrehurek.com/gensim/scripts/glove2word2vec.html). + +Word2Vec format: + +``` +n (no. of words) d (dimension) + x11 x12 x13 ... x1d + x21 x22 x23 ... x2d +... + xn1 xn2 xn3 ... xnd +``` diff --git a/features/english/cosine.csv b/features/english/cosine.csv new file mode 100644 index 0000000..b1ccb33 --- /dev/null +++ b/features/english/cosine.csv @@ -0,0 +1,11627 @@ +he 0.066300 +may 0.186400 +find 0.126300 +himself 0.105100 +unexpectedly 0.592800 +throw 0.272500 +into 0.058300 +new 0.174500 +and 0.149600 +strange 0.223700 +position 0.173100 +where 0.129400 +after 0.161300 +look 0.104500 +carefully 0.209800 +around 0.243900 +him 0.075400 +fear 0.152800 +to 0.110400 +move 0.228100 +lest 0.521100 +by 0.153000 +a 0.105600 +mistake 0.321600 +step 0.205500 +should 0.239700 +do 0.069800 +an 0.061600 +injury 0.497900 +the 0.182000 +cause 0.280000 +which 0.127800 +have 0.115200 +it 0.114000 +in 0.113600 +his 0.065900 +heart 0.282300 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0.814500 +Spinnstoff 0.655400 +Camillo 0.530100 +Metallfabrik 0.556600 +Monarchist 0.568600 +Schäfers 0.730400 +wurzelnd 0.496000 +zufrieren 0.456100 +Ute 0.713700 +Weck 0.795300 +Silo 0.725000 +gestrickt 0.390600 +aufgestapelt 0.679100 +Weltereignis 0.703000 +Yu 0.552900 +zählbar 0.758400 +Off 0.645200 +tupfen 0.521800 +Inferno 0.809200 +Rauchtabak 0.522600 +47,3 0.753500 +Herrschaftsgebiet 0.787900 +per. 0.532200 +Kettenhund 0.553200 +unersetzbar 0.690800 +sportlich 0.665900 +Stellvertreterin 0.865100 +marokkanisch 0.554300 +Köpenicker 0.782500 +Waffenschmied 0.760400 +Volksstück 0.500500 +Führerschaft 0.684000 +Holzrahmen 0.501100 +Erschießen 0.369100 +Aquarell 0.459400 +Zahler 0.737400 +Erziehungssystem 0.691100 +stolzieren 0.437200 +Ger. 0.617600 +1466 0.663700 +Zündstoff 0.822200 +Haarnadel 0.439200 +Knochenrest 0.548100 +millionenfach 0.816000 +hasel- 0.357200 +Hook 0.734300 +geölt 0.579800 +Gesundbrunnen 0.748400 +Verbietene 0.716100 +Gert 0.656500 +Gegenspieler 0.709800 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+kägelformig 0.227103 +namda 0.373900 +klassifikation 0.719291 +bergling 0.256617 +romar 0.218610 +holsteins 0.449219 +vagnmakare 0.453736 +konklusion 0.571596 +lovisin 0.144845 +nämnare 0.229385 +fagerlind 0.358670 +tryserums 0.641833 +gyberg 0.266741 +örbom 0.156134 +underlätes 0.673689 +l1 0.691576 +ilända 0.409113 +ridau 0.332229 +skeppsholmsbron 0.464239 +thorsell 0.316419 +liktydig 0.804499 +liskar 0.295328 +holter 0.199253 +öuskar 0.253166 +gräsgrön 0.388439 +oinflränkt 0.382088 +eunuck 0.690714 +bröde 0.300790 +skärstads 0.361068 +grinda 0.441142 +fluktuation 0.689848 +pristäflan 0.731961 +återförena 0.705287 +trift 0.520122 +förtrollande 0.452318 +tenar 0.278553 +brecher 0.382898 +trianon 0.338891 +jäfwig 0.316616 +patriotifla 0.486358 +östlings 0.164899 +underbarn 0.513223 +stadsgata 0.538612 +landstigningsförsök 0.284989 +cronhamn 0.173954 diff --git a/generate_answers.py b/generate_answers.py new file mode 100644 index 0000000..0d2d77d --- /dev/null +++ b/generate_answers.py @@ -0,0 +1,205 @@ +import os +import numpy as np +from scipy.stats import spearmanr + + + +# Write scores/classes as a tab separated text file +def write_answer(path, targets, scores): + with open(path, 'w', encoding='utf-8') as f_out: + for target in targets: + f_out.write('\t'.join((target, str(scores[target])+'\n'))) + + +def read_feature_file(path): + with open(path, encoding="utf-8") as fin: + data = map(lambda s: s.strip().split("\t"), fin.readlines()) + words, x = zip(*data) + return words, np.array(x, dtype=float) + + +# Read ground truth values +def read_answers(path="test_data_public/"): + languages = ["english", "german", "latin", "swedish"] + + ans_bin = dict() + ans_rank = dict() + + for language in languages: + with open(os.path.join(path, language, "truth", "binary.txt"), encoding="utf-8") as fin: + # Read truth files + ans_bin[language] = {w: int(y) for w, y in map(lambda s: s.strip().split("\t", 1), fin.readlines())} + with open(os.path.join(path, language, "truth", "graded.txt"), encoding="utf-8") as fin: + ans_rank[language] = {w: float(y) for w, y in map(lambda s: s.strip().split("\t", 1), fin.readlines())} + return ans_bin, ans_rank + + +# Load list of target words and return it as a list +def load_targets(path): + with open(path, 'r', encoding='utf-8') as f_in: + targets = [line.strip() for line in f_in] + return targets + + +# Given a dict of answers and a dict of truth, compute evaluation +def eval_binary(answer, truth): + acc = 0 + + # Compute accuracy + # Also compute true positives, false positives and true negatives + # These values will be used to compute precision and recall + tp = 0; fp = 0; fn =0; tn=0 + for word in answer: + if answer[word] == truth[word]: + acc += 1 + # Decide if it's true positive + if answer[word] == 1: + tp += 1 + else: + tn += 1 + else: # got a false, decide if it's positive or negative + if answer[word] == 1: # false positive + fp += 1 + else: + fn += 1 + + acc = acc/len(answer) + precision = tp/(tp+fp+1e-10) + recall = tp/(tp+fn+1e-10) + fallout = fp/(fp+tn+1e-10) + f1 = 2*(precision*recall)/(precision+recall+1e-10) + + return acc, precision, recall, f1, fallout + +# Evaluate ranking with spearman's rho +def eval_ranking(answer, truth): + words = sorted(list(answer.keys())) + x_ans = [answer[w] for w in words] + x_tru = [truth[w] for w in words] + r, p = spearmanr(x_ans, x_tru) + + return r + + + +# p_features: dictionary of str-> array of feature columns +# target_ids: id (index) of words to be evaluated +# use: columns to use, if None then use all columns +def soft_voting(p_features, targets, target_ids, use=None, threshold=0.5): + voting = dict() + labels = dict() + if not use: + use = list(p_features.keys()) + for t, tid in zip(targets, target_ids): + voting[t] = sum(p_features[f][tid] for f in use)/len(use) + labels[t] = int(voting[t] > threshold) + return voting, labels + + +# Estimate a CDF for feature distribution x +# One way this can be done is via sorting arguments according to values, +# getting a sorted array of positions (low to high) +# then normalize this by len(x) +def get_feature_cdf(x): + y = np.argsort(x) + p = np.zeros(len(x)) + for i, v in enumerate(y): + p[v] = i + p = p/len(x) + return p + + +# Load features from feature/ directory and produce answers +def main(): + languages = ["english", "german", "latin", "swedish"] + + # Read ground truth + truth_bin, truth_rank = read_answers() + + # Defines the model parameters for each language + + # Features to use in final model + lang_features = \ + { + "english": ["cosine"], + "german": ["cosine", "freq_diff"], + "latin": ["cosine", "freq_diff"], + "swedish": ["map"] + } + + # Set up thresholds for each language + lang_t = \ + { + "english": 0.75, + "german": 0.75, + "latin": 0.5, + "swedish": 0.75 + } + + overall_acc = 0 + overall_rank = 0 + + for language in languages: + + # Get features and threshold for current language + use_features = lang_features[language] + t = lang_t[language] + + feature_path = "features/%s/" % language + features = dict() + + for root, dirs, files in os.walk(feature_path): + for f in files: + feature_name = f.split(".", 1)[0] + words, x = read_feature_file(os.path.join(feature_path, f)) + features[feature_name] = x + + # Read target words + word_id = {w: i for i, w in enumerate(words)} + target_path = "test_data_public/%s/targets.txt" % language + targets = load_targets(target_path) + target_ids = [word_id[t] for t in targets] + + # Compute all CDFs + p_features = {f: get_feature_cdf(features[f]) for f in features} + + + # Get votes and aggregate votes into final class decision + # based on threshold t + voting, classes = soft_voting(p_features, targets, target_ids, + use=use_features, threshold=t) + + + # Write answer files + out_task1 = "answer/task1/%s.txt" % language + out_task2 = "answer/task2/%s.txt" % language + + if not os.path.exists("answer"): + os.mkdir("answer") + if not os.path.exists("answer/task1"): + os.mkdir("answer/task1") + if not os.path.exists("answer/task2"): + os.mkdir("answer/task2") + + # Get accuracy, precision, and recall + acc_bin, prec_bin, rec_bin, f1_bin, fo_bin = eval_binary(classes, truth_bin[language]) + # Get spearman rho for ranking + rank_r = eval_ranking(voting, truth_rank[language]) + + overall_acc += acc_bin + overall_rank += rank_r + + + print("%s\t| t=%.2f\t| acc=%.3f\t| rank=%.3f" + % (language, t, acc_bin, rank_r)) + + write_answer(out_task1, targets, classes) + write_answer(out_task2, targets, voting) + + print("-"*30) + print("Overall\t| acc=%.3f\t| rank=%.3f" % (overall_acc/4, overall_rank/4)) + + + +if __name__ == "__main__": + main() diff --git a/WordVectors.py b/include/WordVectors.py similarity index 97% rename from WordVectors.py rename to include/WordVectors.py index d2e6bfd..5637df6 100644 --- a/WordVectors.py +++ b/include/WordVectors.py @@ -1,6 +1,5 @@ import numpy as np from collections import OrderedDict -from sklearn import preprocessing # This file contains the WordVectors class used to load and handle word embeddings @@ -76,8 +75,11 @@ def __init__(self, words=None, vectors=None, counts=None, zipped=None, def center(self): self.vectors = self.vectors - self.vectors.mean(axis=1, keepdims=True) + def normalize(self): - self.vectors = preprocessing.normalize(self.vectors, norm="l2") + self.vectors = self.vectors/np.linalg.norm(self.vectors, axis=1) + for i, v in enumerate(self.vectors): + self.vectors[i] = v/np.linalg.norm(v) def get_words(self): return self.word_id.keys() diff --git a/include/extract_features.py b/include/extract_features.py new file mode 100644 index 0000000..1520bc0 --- /dev/null +++ b/include/extract_features.py @@ -0,0 +1,136 @@ +import os +from collections import defaultdict +from scipy.spatial.distance import cosine +from sklearn.neighbors import NearestNeighbors +import numpy as np + + +def save_feature_file(path, dico, words): + with open(path, "w", encoding="utf-8") as fout: + for w in words: + fout.write("%s\t%f\n" % (w, dico[w])) + + +def read_feature_file(path): + with open(path, encoding="utf-8") as fin: + data = map(lambda s: s.strip().split("\t"), fin.readlines()) + words, x = zip(*data) + return np.array(x, dtype=float) + +# Extract all features from input corpora/word vectors +# Files are saved in features//.csv +# The format of the output file is , +# Those values already measure the "shift" or change between corpora 1 and 2 +# Input: +# - corpora corpus1 and corpus2 +# - wordvectors wv1 and wv2 +# - output_path (where to save files) +# Returns dict of features feature_name->values(array) +def extract_features(corpus1, corpus2, wv1, wv2): + + # wv1 and wv2 now contain the same words in the same order + words = wv1.words + + # Initialize dictionary of features + features = dict() + + features["freq_diff"] = frequency_differential_scores(corpus1, corpus2, words) + features["cosine"] = get_metric(wv1, wv2, words, "cosine") + feature["map"] = map_distance(wv1, wv2) + +# Get relative frequency for all words in corpus +# Corpus must be tokenized +# Returns a dictionary of word->frequency +def get_frequency(corpus): + print('Counting',) + count = defaultdict(int) + total = 0 + for line in corpus: + for token in line: + count[token] += 1 + total += 1 + # Normalize count + for word in count: + count[word] = count[word]/total + + return count + + +# Get the frequency differential between words in dico1 and dico2 +# words - contains the list of words to compute this feature for +# Returns list of freqquency diferentials, follows order of +def frequency_differential_scores(corpus1, corpus2, words): + x = dict() + dico1 = get_frequency(corpus1) + dico2 = get_frequency(corpus2) + + for i, t in enumerate(words): + x[t] = (dico1[t] - dico2[t])/(dico1[t] + dico2[t]) + return x + + +# Compute any metric between wv_a and wv_b, returning a vector +def get_metric(wv_a, wv_b, words, metric, m=None): + scores = dict() + if metric == "cosine": + def eval(x, y): + return cosine(x, y) + # For precomputed metrics, the input is a n x n matrix with pairwise + # distances already given, we use the index of a word to obtain + # its values + elif metric == "precomputed": + if not m: + print("Error: matrix m not given for metric 'precomputed'") + + def eval(i): + return m[i] + + else: + def eval(x, y): + return 'nan' + + for target in words: + try: + if metric != "precomputed": + scores[target] = round(eval(wv_a[target], wv_b[target]), 4) + else: + scores[target] = round(eval(wv_a.word_id[target])) + except KeyError as e: # if words not found, report nan + scores[target] = 'nan' + + return scores + + +# Generate the inverse neighborhood distance vector for given sources +# Input: wordvectors a and b, and knn neighbors (int) +# Output: vector d of inverse neighborhood distance +# *** input word vectors must have the same words in the same order +# For neighbors of w x, y, z in a and n, o, p in b, the inverse neighborhood +# distance is d = || mean(x, y, z) - mean(n, o, p) || +def mapped_neighborhood_distance(wv_a, wv_b, list_a, list_b, knn): + d = np.zeros((len(wv_a.words)), dtype=float) + + for i in range(len(wv_a.words)): + # Compute mean neighborhood vectors for A and B + mean_a = sum(wv_a[u] for u in list_a[i])/knn + mean_b = sum(wv_b[v] for v in list_b[i])/knn + d[i] += cosine(mean_a, mean_b) + + return d + + +def map_distance(wv_a, wv_b, words, knn=10): + nbrs_a = NearestNeighbors(n_neighbors=knn, n_jobs=-1).fit(wv_a.vectors) + nbrs_b = NearestNeighbors(n_neighbors=knn, n_jobs=-1).fit(wv_b.vectors) + + # Find nearest neighbors of a word in its own source + dist_a, list_a = nbrs_a.kneighbors() + dist_b, list_b = nbrs_b.kneighbors() + + map_neighborhood = mapped_neighborhood_distance(wv_a, wv_b, list_a, + list_b, knn) + dico = dict() + for w in words: + dico[w] = map_neighborhood[wv_a.word_id[w]] + + return dico diff --git a/include/utils.py b/include/utils.py new file mode 100644 index 0000000..b1ca4ad --- /dev/null +++ b/include/utils.py @@ -0,0 +1,14 @@ +import time +import sys + + +# Print time diff to destination (defaults to stdout) +def log_time(t0, dest=sys.stdout): + print("t = %.3f sec" % (time.time()-t0)) + + +# Load list of target words and return it as a list +def load_targets(path): + with open(path, 'r', encoding='utf-8') as f_in: + targets = [line.strip() for line in f_in] + return targets diff --git a/include/word2vec_utils.py b/include/word2vec_utils.py new file mode 100644 index 0000000..dc0d633 --- /dev/null +++ b/include/word2vec_utils.py @@ -0,0 +1,117 @@ +from gensim.models import Word2Vec +from gensim.models.word2vec import PathLineSentences +from scipy.linalg import orthogonal_procrustes +from include.WordVectors import WordVectors +import numpy as np +import time +from include.utils import log_time + + +# Functions to handle word embedding, learning, loading, etc + + +# Converts Word2Vec model into WordVectors class +# We do not need the whole trainable Word2Vec model +# We'll only use the learned vectors +def w2v_to_wv(model): + words = [w for w in model.wv.vocab] + vectors = [model.wv[w] for w in words] + wv = WordVectors(words=words, vectors=vectors) + + return wv + + + +# Train word2vec model from input path +def train_word2vec(sentences, + dim=300, window=10, workers=12, min_count=10, + sorted_vocab=1): + + t0 = time.time() + + print("Training Word2Vec") + model = Word2Vec(sentences, size=dim, window=window, + min_count=min_count, + sorted_vocab=sorted_vocab, + workers=12) + + log_time(t0) + + # Convert Word2Vec model to WordVectors and return + return w2v_to_wv(model) + + +# Input: wv - list of WordVectors +# landmarks - Number of landmarks to use. If none, then the every +# word is used +# random_landmarks - toggle use of random words for landmarks +# target - (int) index of WordVector to be used as target +# default 0: align on the first source +# if -1 or "mean" +# use the mean point for every word +# Return - list of aligned WordVectors [, landmarks (optionally)] +def align_wordvectors(*wv, landmarks=None, random_landmarks=False, target=0, + return_intersect=False, least_freq=False, + return_loss=False, + exclude=set()): + if len(wv) < 2: + print("! Error: not enough WordVectors for alignment (2 required)") + return None + + # Computes the mean vectors for every intersecting word in the input + # The resulting WordVector will become the target for the alignment + if target == -1 or target == "mean": + tgt_words = list(set.intersection(*[set(s.words) for s in wv])) + tgt_vecs = list() + for w_ in wv: + tgt_vecs.append([w_[word] for word in tgt_words]) + tgt_vecs = np.sum(tgt_vecs, 0)/len(wv) + tgt_wv = WordVectors(words=tgt_words, vectors=tgt_vecs) + else: + tgt_wv = wv[target] # otherwise set target from input + + aligned_wv = list() + isect_list = list() + + # If landmarks is not provided, use all possible words + if not landmarks: + landmarks = tgt_wv.words + # If int, then select first n landmarks + elif isinstance(landmarks, int): + if least_freq: + landmarks = tgt_wv.words[-landmarks:] + else: + landmarks = tgt_wv.words[:landmarks] + elif isinstance(landmarks, list): + landmarks = [tgt_wv.words[i] for i in landmarks] + + + # We can align multiple wordvectors at once (not just 2) + for src_wv in wv[0:]: + if not random_landmarks: + # Get the intersection between current source and + # the landmark set of words + isect = [w for w in src_wv.words if w in landmarks] + + else: # WARNING: for random_landmarks, landmakrs must be int + isect = set.intersection(set(src_wv.words), + set(tgt_wv.words)) + isect = isect - exclude + isect = np.random.choice(list(isect), size=len(landmarks)) + + # Get target vectors for selected words + isect_tgt = np.array([tgt_wv[w] for w in isect]) + isect_src = np.array([src_wv[w] for w in isect]) + + # Perform procrustes alignment + r, scale = orthogonal_procrustes(isect_src, isect_tgt) + + # Given transformation matrix r, we apply it to src_wv via matrix mult + a_wv = WordVectors(src_wv.words, np.dot(src_wv.vectors, r)) + aligned_wv.append(a_wv) + isect_list.append(isect) + + if return_intersect: + return aligned_wv, isect + else: + return aligned_wv diff --git a/precompute_features.py b/precompute_features.py new file mode 100644 index 0000000..12ecd5e --- /dev/null +++ b/precompute_features.py @@ -0,0 +1,118 @@ +import argparse +import os +import time +from gensim.models.word2vec import PathLineSentences +from include import extract_features +from include.WordVectors import WordVectors, intersection +from include.utils import log_time +from include import word2vec_utils +from include.word2vec_utils import align_wordvectors + + +# Run on input data (raw corpora or word vector pair) + + + +# Reproduce experiments from SemEval-2020 Task 1 +def main(): + + path = "test_data_public" + + if not os.path.exists(path): + print("Could not find path", path) + print("Please run download-semeval-data.sh to download the data") + return 0 + + languages = ["english", "german", "latin", "swedish"] + + + for lang in languages: + + print("Language:", lang) + + print(" - Loading corpora") + path_corpus1 = "test_data_public/%s/corpus1/lemma/" % lang + path_corpus2 = "test_data_public/%s/corpus2/lemma/" % lang + corpus1 = PathLineSentences(path_corpus1) + corpus2 = PathLineSentences(path_corpus2) + + # First, check if we can find the word vectors for each language + # (if it has already been computed, we don't have to do it again) + if not os.path.exists("wordvectors"): + os.mkdir("wordvectors") + if not os.path.exists("wordvectors/%s" % lang): + os.mkdir("wordvectors/%s" % lang) + + # Check word vectors for corpus1 + # Save word vectors for later use + print(" - Checking wordvectors") + # Check if we need to train word2vec + if not os.path.exists("wordvectors/%s/1.vec" % lang): + wv1 = word2vec_utils.train_word2vec(corpus1) + wv1.save_txt("wordvectors/%s/1.vec" % lang) + else: # already trained, load word vectors + wv1 = WordVectors(input_file="wordvectors/%s/1.vec" % lang) + if not os.path.exists("wordvectors/%s/2.vec" % lang): + wv2 = word2vec_utils.train_word2vec(corpus2) + wv2.save_txt("wordvectors/%s/2.vec" % lang) + else: + wv2 = WordVectors(input_file="wordvectors/%s/2.vec" % lang) + + # Compute intersecting words and align wordvectors + wv1, wv2 = intersection(wv1, wv2) + wv1, wv2 = align_wordvectors(wv1, wv2) + + words = wv1.words # both wv1 and wv2 now have the same words + + # Extract all features + if not os.path.exists("features"): + os.mkdir("features") + # Create file directory for current language, if needed + if not os.path.exists("features/%s/" % lang): + os.mkdir("features/%s/" % lang) + + features = dict() # store features here + t0 = time.time() + + # Searches for pre-computed features to avoid computing them again + # Only re-compute if feature file is not found + # In particular, search for file features//.csv + + # Check for cosine + f_path = "features/%s/cosine.csv" % lang + if not os.path.exists(f_path): + print("Computing cosine") + features["cosine"] = extract_features.get_metric(wv1, wv2, + words, "cosine") + extract_features.save_feature_file(f_path, + features["cosine"], + words) + # load cosine.csv file + print("Loading feature file", f_path) + features["cosine"] = extract_features.read_feature_file(f_path) + + # Check frequency differential + f_path = "features/%s/freq_diff.csv" % lang + if not os.path.exists(f_path): + print("Computing freq diff") + dico = extract_features.frequency_differential_scores(corpus1, corpus2, words) + extract_features.save_feature_file(f_path,dico, words) + # load freq_diff.csv + print("Loading feature file", f_path) + features["freq_diff"] = extract_features.read_feature_file(f_path) + + # Check for MAP feature + f_path = "features/%s/map.csv" % lang + if not os.path.exists(f_path): + print("Computing map") + dico = extract_features.map_distance(wv1, wv2, words) + extract_features.save_feature_file(f_path, dico, words) +# load map.csv + print("Loading feature file", f_path) + features["map"] = extract_features.read_feature_file(f_path) + + log_time(t0) + + +if __name__ == "__main__": + main() diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..3cebad1 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,32 @@ +boto3==1.12.36 +botocore==1.15.36 +cachetools==4.0.0 +certifi==2019.11.28 +chardet==3.0.4 +docutils==0.15.2 +gensim==3.8.1 +google-api-core==1.16.0 +google-auth==1.13.1 +google-cloud-core==1.3.0 +google-cloud-storage==1.27.0 +google-resumable-media==0.5.0 +googleapis-common-protos==1.51.0 +idna==2.9 +jmespath==0.9.5 +joblib==0.14.1 +numpy==1.18.2 +pkg-resources==0.0.0 +protobuf==3.11.3 +pyasn1==0.4.8 +pyasn1-modules==0.2.8 +python-dateutil==2.8.1 +pytz==2019.3 +requests==2.23.0 +rsa==4.0 +s3transfer==0.3.3 +scikit-learn==0.22.2.post1 +scipy==1.4.1 +six==1.14.0 +sklearn==0.0 +smart-open==1.10.0 +urllib3==1.25.8