434766

run.py

@@ -0,0 +1,64 @@
+from collections import defaultdict, Counter
+from nltk import trigrams, word_tokenize
+import csv
+import regex as re
+import pandas as pd
+
+in_file = 'train/in.tsv.xz'
+out_file = 'train/expected.tsv'
+
+X_train = pd.read_csv(in_file, sep='\t',  header=None, quoting=csv.QUOTE_NONE, nrows=10000, error_bad_lines=False)
+Y_train = pd.read_csv(out_file, sep='\t', header=None, quoting=csv.QUOTE_NONE, nrows=10000, error_bad_lines=False)
+
+X_train = X_train[[6, 7]]
+X_train = pd.concat([X_train, Y_train], axis=1)
+X_train['row'] = X_train[6] + X_train[0] + X_train[7]
+
+def train(X_train, Y_train):
+    model = defaultdict(lambda: defaultdict(lambda: 0))
+    for _, (_, row) in enumerate(X_train.iterrows()):
+        text = preprocess(str(row['row']))
+        words = word_tokenize(text)
+        for w1, w2, w3 in trigrams(words, pad_right=True, pad_left=True):
+            if w1 and w2 and w3:
+                model[(w1, w3)][w2] += 1
+
+    for _, w13 in enumerate(model):
+        count = sum(model[w13].values())
+        for w2 in model[w13]:
+            model[w13][w2] += 0.25
+            model[w13][w2] /= float(count + 0.25 + len(w2))
+            
+    return model
+
+def preprocess(row):
+    row = re.sub(r'\p{P}', '', row.lower().replace('-\\n', '').replace('\\n', ' '))
+    return row
+
+def predict_word(before, after):
+    output = ''
+    p = 0.0
+    Y_pred = dict(Counter(dict(model[before, after])).most_common(7))
+    for key, value in Y_pred.items():
+        p += value
+        output += f'{key}:{value} '
+    if p == 0.0:
+        output = 'the:0.04 be:0.04 to:0.04 and:0.02 not:0.02 or:0.02 a:0.02 :0.8'
+        return output
+    output += f':{max(1 - p, 0.01)}'
+    return output
+
+def word_gap_prediction(file):
+    X_test = pd.read_csv(f'{file}/in.tsv.xz', sep='\t', header=None, quoting=csv.QUOTE_NONE,  error_bad_lines=False)
+    with open(f'{file}/out.tsv', 'w', encoding='utf-8') as output_file:
+        for _, row in X_test.iterrows():
+            before, after = word_tokenize(preprocess(str(row[6]))), word_tokenize(preprocess(str(row[7])))
+            if len(before) < 3 or len(after) < 3:
+                output = 'the:0.04 be:0.04 to:0.04 and:0.02 not:0.02 or:0.02 a:0.02 :0.8'
+            else:
+                output = predict_word(before[-1], after[0])
+            output_file.write(output + '\n')
+
+model = train(X_train, Y_train)
+word_gap_prediction('dev-0')
+word_gap_prediction('test-A')