128 lines
3.4 KiB
Python
128 lines
3.4 KiB
Python
#!/usr/bin/env python
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# coding: utf-8
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# In[136]:
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import nltk
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from nltk import trigrams
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from nltk.tokenize import word_tokenize
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from collections import defaultdict, Counter
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from nltk import ngrams
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import pandas as pd
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import csv
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import re
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model = defaultdict(lambda: defaultdict(lambda: 0))
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# In[173]:
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train_file_in = pd.read_csv("train/in.tsv.xz", sep="\t", error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE, nrows=200000)
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train_file_out = pd.read_csv("train/expected.tsv", sep="\t", error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE, nrows=200000)
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print("read train file")
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# In[174]:
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stop_words= nltk.corpus.stopwords.words('english')
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def get_20common_2grams(text, n):
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outputTrigrams = []
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n_grams = ngrams(nltk.tokenize.word_tokenize(text), n)
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for grams in n_grams:
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outputTrigrams.append(grams)
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return outputTrigrams
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def get_20common_2grams_no_stop(text, n):
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tokenized_world = nltk.tokenize.word_tokenize(text)
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stop_words= nltk.corpus.stopwords.words('english')
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tokenized_no_stop = [i for i in tokenized_world if i not in stop_words]
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n_grams = ngrams(tokenized_no_stop, n)
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return n_grams
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def predict(word_before, word_after):
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prob_list = dict(Counter(model[(word_before, word_after)]).most_common(6)).items()
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predictions = []
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prob_sum = 0.0
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for key, value in prob_list:
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prob_sum += value
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predictions.append(f'{key}:{value}')
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if prob_sum == 0.0:
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return 'the:0:2 be:0.2 to:0.2 of:0.15 and:0.15 :0.1'
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remaining_prob = 1 - prob_sum
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if remaining_prob < 0.01:
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predictions.append(f':{0.01}')
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return ' '.join(predictions)
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# In[175]:
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train = train_file_in[[6, 7]]
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train = pd.concat([train, train_file_out], axis=1)
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train["result"] = train[6] + train[0] + train[7]
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# In[ ]:
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for index, row in train.iterrows():
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text = str(row["result"]).lower().replace('-\\n', '').replace('\\n', ' ')
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words = word_tokenize(text)
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for w1, w2, w3 in trigrams(words, pad_right=True, pad_left=True):
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if w1 and w2 and w3:
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model[(w2, w3)][w1] += 1
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# In[ ]:
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print("train model")
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for key in model:
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total_count = float(sum(model[key].values()))
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for value in model[key]:
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model[key][value] /= total_count
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# In[ ]:
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dev_data = pd.read_csv('dev-0/in.tsv.xz', sep='\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)
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test_a_data = pd.read_csv('test-A/in.tsv.xz', sep='\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)
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# In[ ]:
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print("dev_0");
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with open('dev-0/out.tsv', 'w', encoding="utf-8") as file:
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for index, row in dev_data.iterrows():
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text = str(row[7]).lower().replace('-\\n', '').replace('\\n', ' ')
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words = word_tokenize(text)
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if len(words) < 4:
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print(words)
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prediction = 'the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1'
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else:
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prediction = predict(words[0], words[1])
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file.write(prediction + '\n')
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print("test_A");
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with open('test-A/out.tsv', 'w', encoding="utf-8") as file:
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for index, row in test_a_data.iterrows():
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text = str(row[7]).lower().replace('-\\n', '').replace('\\n', ' ')
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words = word_tokenize(text)
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if len(words) < 4:
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print(words)
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prediction = 'the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1'
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else:
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prediction = predict(words[0], words[1])
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file.write(prediction + '\n')
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# In[ ]:
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