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from nltk.corpus import LazyCorpusLoader |
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from nltk.corpus.reader import WordNetCorpusReader, CorpusReader |
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from nltk.corpus import wordnet as wn30, wordnet_ic |
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import numpy as np |
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import matplotlib.pyplot as plt |
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from sklearn.preprocessing import minmax_scale |
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wn31 = LazyCorpusLoader( |
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"wordnet31", |
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WordNetCorpusReader, |
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LazyCorpusLoader("omw", CorpusReader, r".*/wn-data-.*\.tab", encoding="utf8"), |
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) |
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metrics = ['Path', 'Leacock-Chodorow', 'Wu-Palmer', 'Resnik', 'Jiang-Conrath', 'Lin'] |
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def write_similarities(): |
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brown_ic = wordnet_ic.ic('ic-brown.dat') |
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places_classes = [] |
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places_synsets = [] |
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with open('places365_labels.txt', "r") as f: |
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for line in f: |
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fields = line.split() |
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_class = { |
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'full_name': fields[0], |
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'cleaned_name': fields[0].split('/')[2].replace("_", " "), |
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'index': int(fields[1]), |
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'synsets': [wn31.synset(f) for f in fields[2:]] |
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} |
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places_classes.append(_class) |
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places_synsets += _class['synsets'] |
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scores = [] |
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with open("imagenet_places_similarities.txt", "w") as o: |
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o.write("in_synset;in_label;Path;Path_synset;Leacock-Chodorow;\ |
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LC_synset;Wu-Palmer;WP_synset;Resnik;Resnik_synset;Jiang-Conrath;JC_synset;Lin;Lin_synset\n") |
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with open('LOC_synset_mapping.txt', "r") as f: |
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for line in f: |
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fields = line.split() |
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synset_id = fields[0] |
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pos = synset_id[0] |
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offset = int(synset_id[1:]) |
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synset = wn30.synset_from_pos_and_offset(pos, offset) |
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similarities = { |
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'Path': [s.path_similarity(synset) for s in places_synsets], |
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'Leacock-Chodorow': [s.lch_similarity(synset) for s in places_synsets], |
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'Wu-Palmer': [s.wup_similarity(synset) for s in places_synsets], |
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'Resnik': [s.res_similarity(synset, brown_ic) for s in places_synsets], |
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'Jiang-Conrath': [s.jcn_similarity(synset, brown_ic) for s in places_synsets], |
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'Lin': [s.lin_similarity(synset, brown_ic) for s in places_synsets], |
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} |
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most_similar = {} |
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for metric in similarities: |
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val = max(similarities[metric]) |
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idx = similarities[metric].index(val) |
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most_similar[metric] = { |
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'value': val, |
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'synset': places_synsets[idx] |
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} |
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scores.append((fields[1:], most_similar)) |
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sims = ";".join([";".join([str(most_similar[metric]['value']), |
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most_similar[metric]['synset'].name()]) for metric in metrics]) |
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label = " ".join(fields[1:]) |
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output_line = ";".join([fields[0], label, sims]) |
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o.write(output_line + "\n") |
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def read_similarities(): |
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res = [] |
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with open('imagenet_places_similarities.txt', "r") as f: |
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for line in f.readlines()[1:]: |
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fields = line.split(";") |
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obj = {} |
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cnt = 2 |
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for m in metrics: |
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obj[m] = { |
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'value': float(fields[cnt]), |
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'synset': wn31.synset(fields[cnt + 1]) |
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} |
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cnt += 2 |
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res.append((fields[1], obj)) |
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return res |
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def draw_histogram(data, labels, name): |
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fig, axs = plt.subplots(len(labels), 1, figsize=(6.4, 10)) |
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for i, l in enumerate(labels): |
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axs[i].set_title(l) |
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axs[i].hist(data[:, i], bins=30) |
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fig.show() |
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fig.savefig(name) |
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def analysis(data): |
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similarity_arr = np.array([[c[1][m]['value'] for m in metrics] for c in data]) |
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draw_histogram(similarity_arr, metrics, "histogram") |
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scaled_data = minmax_scale(similarity_arr, axis=0, feature_range=(0, 1)) |
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columns = metrics + ["Average", "Average (no ic)"] |
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avg = np.average(scaled_data, axis=1).reshape(-1, 1) |
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avg_no_ic = np.average(scaled_data[:, 0:3], axis=1).reshape(-1, 1) |
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data_and_average = np.concatenate((scaled_data, avg, avg_no_ic), axis=1) |
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draw_histogram(data_and_average, columns, "scaled_histogram") |
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fig, ax = plt.subplots(figsize=(20, 180)) |
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im = ax.pcolormesh(data_and_average) |
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fig.colorbar(im, ax=ax) |
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ax.set_xticks(0.5 + np.arange(len(columns))) |
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ax.set_yticks(0.5 + np.arange(len(data))) |
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ax.set_xticklabels(labels=columns, rotation=90) |
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ax.set_yticklabels(labels=[c[0].split(",")[0] for c in data]) |
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for y in range(len(data)): |
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for x in range(len(metrics)): |
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ax.text(x+0.1, y+0.2, data[y][1][metrics[x]]['synset'].name(), color="grey") |
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ax.text(len(columns) - 1 + 0.1, y + 0.2, "%.2f" % data_and_average[y, -1], color="grey") |
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fig.show() |
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fig.savefig("heatmap") |
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return data_and_average |
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def write_output(similarities): |
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with open("in_wordnet_oodness.txt", "w") as f: |
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for s in similarities: |
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f.write("%.2f\n" % (1 - s)) |
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def main(): |
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scores = read_similarities() |
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res = analysis(scores) |
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write_output(res[:, -1]) |
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main() |
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