dump put emoji counts
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8300ed4510
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@ -24,6 +24,8 @@ import pickle
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import operator
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from sklearn.pipeline import Pipeline
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import json
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import datetime
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nltk.download('punkt')
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nltk.download('averaged_perceptron_tagger')
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nltk.download('wordnet')
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@ -227,6 +229,7 @@ class sample_data_manager(object):
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"""
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assert np.min(file_index_range) >= 0 and np.max(file_index_range) < self.n_files
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n = len(file_index_range)
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for i in file_index_range:
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print("reading file: " + self.json_files[i] + "...")
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raw_data_i = pd.read_json(self.json_files[i], encoding="utf-8")
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@ -259,7 +262,7 @@ class sample_data_manager(object):
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tmp = [np.nanmean(emoji2sent(e, only_emoticons=only_emoticons), axis=0, dtype=float) for e in emojis_i]
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c = 0
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for t in tmp:
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# only to find and debug wrong formatted data
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if str(type(t)) != "<class 'numpy.ndarray'>":
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print(t, type(t))
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print(emojis_i[c])
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@ -358,6 +361,12 @@ class sample_data_manager(object):
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self.emoji_weights['X'] = 0 # dummy values
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self.emoji_count['X'] = 0
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# dump count data to json:
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f = open("count_from_read_progress_" + str(datetime.datetime.now()) + ".json", 'w')
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f.write(json.dumps(self.emoji_count, ensure_ascii=False))
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f.close()
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def get_emoji_count(self):
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"""
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@ -371,7 +380,7 @@ class sample_data_manager(object):
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def filter_by_top_emojis(self,n_top = 20):
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"""
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filgter out messages not containing one of the `n_top` emojis
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filter out messages not containing one of the `n_top` emojis
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@param n_top: number of top emojis used for filtering
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"""
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