init

config.txt

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+--metric PerplexityHashed --precision 2  --in-header in-header.tsv  --out-header out-header.tsv

in-header.tsv

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+FileId	Year	LeftContext	RightContext

out-header.tsv

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+Word

readme.md

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+Challenging America word-gap prediction
+===================================
+
+Guess a word in a gap.
+
+Evaluation metric
+-----------------
+
+LikelihoodHashed is the metric

run.py

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+from nltk import trigrams, word_tokenize
+from collections import defaultdict, Counter
+import pandas as pd
+import csv
+import regex as re
+
+
+def preprocess(text):
+    text = text.lower().replace('-\\n', '').replace('\\n', ' ')
+    return re.sub(r'\p{P}', '', text)
+
+
+def predict(before, after):
+    prediction = dict(Counter(dict(trigram[before, after])).most_common(5))
+    result = ''
+    prob = 0.0
+    for key, value in prediction.items():
+        prob += value
+        result += f'{key}:{value} '
+    if prob == 0.0:
+        return 'to:0.02 be:0.02 the:0.02 or:0.01 not:0.01 and:0.01 a:0.01 :0.9'
+    result += f':{max(1 - prob, 0.01)}'
+    return result
+
+
+def make_prediction(file):
+    data = pd.read_csv(f'{file}/in.tsv.xz', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE)
+    with open(f'{file}/out.tsv', 'w', encoding='utf-8') as file_out:
+        for _, row in data.iterrows():
+            before, after = word_tokenize(preprocess(str(row[6]))), word_tokenize(preprocess(str(row[7])))
+            if len(before) < 3 or len(after) < 3:
+                prediction = 'to:0.02 be:0.02 the:0.02 or:0.01 not:0.01 and:0.01 a:0.01 :0.9'
+            else:
+                prediction = predict(before[-1], after[0])
+            file_out.write(prediction + '\n')
+
+
+train_data = pd.read_csv('train/in.tsv.xz', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE, nrows=20000)
+train_labels = pd.read_csv('train/expected.tsv', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE, nrows=20000)
+train_data = train_data[[6, 7]]
+train_data = pd.concat([train_data, train_labels], axis=1)
+train_data['line'] = train_data[6] + train_data[0] + train_data[7]
+
+
+trigram = defaultdict(lambda: defaultdict(lambda: 0))
+
+rows = train_data.iterrows()
+rows_len = len(train_data)
+for index, (_, row) in enumerate(rows):
+    text = preprocess(str(row['line']))
+    words = word_tokenize(text)
+    for word_1, word_2, word_3 in trigrams(words, pad_right=True, pad_left=True):
+        if word_1 and word_2 and word_3:
+            trigram[(word_1, word_3)][word_2] += 1
+
+model_len = len(trigram)
+for index, words_1_3 in enumerate(trigram):
+    count = sum(trigram[words_1_3].values())
+    for word_2 in trigram[words_1_3]:
+        trigram[words_1_3][word_2] += 0.25
+        trigram[words_1_3][word_2] /= float(count + 0.25 + len(word_2))
+
+
+make_prediction('test-A')
+make_prediction('dev-0')