440058 plusalpha

run.py

@@ -1,97 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[1]:
-
-
-from collections import defaultdict, Counter
-import csv
-import regex as re
-import pandas as pd
-from nltk import trigrams, word_tokenize
-
-
-# In[26]:
-
-def prepare_data():
-    x_train = pd.read_csv('train/in.tsv.xz', sep='\t',  header=None,
-                          quoting=csv.QUOTE_NONE, nrows=10000, error_bad_lines=False)
-    y_train = pd.read_csv('train/expected.tsv', 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['l'] = x_train[6] + x_train[0] + x_train[7]
-
-    return x_train, y_train
-
-
-x_train, y_train = prepare_data()
-
-
-# In[39]:
-
-
-def train(x_train):
-    model = defaultdict(lambda: defaultdict(lambda: 0))
-    count = x_train.iterrows()
-    for i, (_, row) in enumerate(count):
-        text = re.sub(r'\p{P}', '', str(row['l']).lower().replace(
-            '-\\n', '').replace('\\n', ' '))
-        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:
-                model[(word_1, word_3)][word_2] += 1
-
-    for i, words_1_3 in enumerate(model):
-        count = sum(model[words_1_3].values())
-        for word_2 in model[words_1_3]:
-            model[words_1_3][word_2] += 0.25
-            model[words_1_3][word_2] /= float(count + 0.25 + len(word_2))
-
-    return model
-
-
-# In[41]:
-
-
-def predict(before, after):
-    result = ''
-    p = 0.0
-    pred = dict(Counter(dict(model[before, after])).most_common(7))
-    for key, value in pred.items():
-        p += value
-        result += f'{key}:{value} '
-    if p == 0.0:
-        result = 'to:0.02 the:0.02 be:0.02 and:0.01 or:0.01 and:0.01 a:0.01 :0.9'
-        return result
-    result += f':{max(1 - p, 0.01)}'
-
-    return result
-
-
-# In[42]:
-def preprocess(text):
-    text = text.lower().replace('-\\n', '').replace('\\n', ' ')
-    return re.sub(r'\p{P}', '', text)
-
-
-def gap_predict(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 result_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:
-                result = 'to:0.02 the:0.02 be:0.02 and:0.01 or:0.01 and:0.01 a:0.01 :0.9'
-            else:
-                result = predict(before[-1], after[0])
-            result_file.write(result + '\n')
-
-
-# In[43]:
-
-
-model = train(x_train)
-gap_predict('dev-0')
-gap_predict('test-A')