From 7743dd2472cda3078111d3411cd1d13b89aab885 Mon Sep 17 00:00:00 2001 From: s452662 Date: Tue, 23 Apr 2024 20:46:13 +0200 Subject: [PATCH] fourgram 100k corpus --- dev-0/out.tsv | 20804 +++++++++++++++++++++++------------------------ run.py | 178 + test-A/out.tsv | 14464 ++++++++++++++++---------------- 3 files changed, 17812 insertions(+), 17634 deletions(-) create mode 100644 run.py diff --git a/dev-0/out.tsv b/dev-0/out.tsv index 5d408b8..d7a8538 100644 --- a/dev-0/out.tsv +++ b/dev-0/out.tsv @@ -1,10519 +1,10519 @@ the:0.30000 of:0.20000 and:0.10000 to:0.10000 in:0.10000 a:0.10000 :0.10000 -,:0.00045 washing:0.00015 .:0.00011 washing¬:8e-05 ben:6e-05 is:6e-05 -,:0.00217 the:0.00112 .:0.00054 ;:0.00035 last:0.00029 present:0.00018 -of:0.00059 and:0.00028 .:0.00019 to:0.00017 ,:0.00016 in:0.00011 -the:0.30000 of:0.20000 and:0.10000 to:0.10000 in:0.10000 a:0.10000 :0.10000 -.:0.00066 ,:0.00027 w.:0.00019 j.:0.00016 a.:0.00012 c.:0.00011 -the:0.00171 ,:0.00159 .:0.00046 law:0.00018 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data = [] + with open(file_path, 'r', encoding='utf-8') as file: + for line in file: + line = line.strip().split('\t') # Rozdziel linie na elementy za pomocą tabulatora + data.append(line) # Dodaj elementy do listy danych + return data + + +# In[3]: + + +file_path = "train\\in.tsv.xz" + + +# In[4]: + + +data = read_xz_file(file_path) + + +# In[5]: + + +expected = read_tsv_file("train\\expected.tsv") + + +# In[6]: + + +corpus_before=[] +corpus_after=[] +for i in range(len(data)): + corpus_before.append(str(data[i].split("\t")[6])) + corpus_after.append(str(data[i].split("\t")[7])) + + +# In[7]: + + +for i in range(len(expected)): + expected[i] = str(expected[i]).lower() + + +# In[8]: + + +corpus = [] +for i in range(len(expected)): + corpus.append(corpus_before[i] + " " + expected[i] + " " + corpus_after[i]) + + +# In[9]: + + +from collections import defaultdict +from nltk import ngrams +from nltk.tokenize import word_tokenize + +model = defaultdict(lambda: defaultdict(float)) +dictionary = set() +for line in corpus[:100000]: + tokens = word_tokenize(line) + for word1, word2, word3, word4 in ngrams(tokens, n=4, pad_right=True, pad_left=True): + if word1 and word2 and word3 and word4: + model[(word2, word3, word4)][word1] += 1 + model[(word1, word2, word3)][word4] += 1 + dictionary.update([word1, word2, word3, word4]) + + +# In[10]: + + +model2 = model.copy() + + +# In[ ]: + + +len(model) + + +# In[11]: + + +smoothing = 0.0001 +for trio in model: + count_sum = sum(model[trio].values()) + smoothing * len(dictionary) + for token in model[trio]: + model[trio][token] = (model[trio][token] + smoothing) / count_sum + + +# In[12]: + + +from collections import Counter + +default = "the:0.30000 of:0.20000 and:0.10000 to:0.10000 in:0.10000 a:0.10000 :0.10000" + +data = read_xz_file("dev-0\\in.tsv.xz") +corpus_before=[] +corpus_after=[] +for i in range(len(data)): + corpus_before.append(str(data[i].split("\t")[6])) + corpus_after.append(str(data[i].split("\t")[7])) + +with open("dev-0\\out.tsv", "w", encoding="utf-8") as output: + for text in corpus_before: + tokens = word_tokenize(text) + if len(tokens) < 4: + prediction = default + + results = dict(model[(tokens[0], tokens[1], tokens[2])]) + if not results: + prediction = default + + prediction = ' '.join( + f"{term}:{round(prob, 5)}" for term, prob in Counter(results).most_common(6)) + if prediction == "": + prediction = default + output.write(str(prediction.replace("\n", "").strip() + "\n")) + + +# In[13]: + + +from collections import Counter + +default = "the:0.30000 of:0.20000 and:0.10000 to:0.10000 in:0.10000 a:0.10000 :0.10000" + +data = read_xz_file("test-A\\in.tsv.xz") +corpus_before=[] +corpus_after=[] +for i in range(len(data)): + corpus_before.append(str(data[i].split("\t")[6])) + corpus_after.append(str(data[i].split("\t")[7])) + +with open("test-A\\out.tsv", "w", encoding="utf-8") as output: + for text in corpus_before: + tokens = word_tokenize(text) + if len(tokens) < 4: + prediction = default + + results = dict(model[(tokens[0], tokens[1], tokens[2])]) + if not results: + prediction = default + + prediction = ' '.join( + f"{term}:{round(prob, 5)}" for term, prob in Counter(results).most_common(6)) + if prediction == "": + prediction = default + output.write(str(prediction.replace("\n", "").strip() + "\n")) + + +# In[ ]: + + + + diff --git a/test-A/out.tsv b/test-A/out.tsv index a651be3..ac5d81f 100644 --- a/test-A/out.tsv +++ b/test-A/out.tsv @@ -1,7414 +1,7414 @@ -the:0.07588 ``:0.02093 a:0.01047 beautifully:0.00523 's:0.00523 very:0.00523 -he:0.08756 i:0.04199 they:0.0319 right:0.02389 have:0.02375 and:0.01939 -the:0.01444 have:0.01156 .:0.00867 ,:0.00578 madeconcerning:0.00289 which:0.00289 -.:0.17608 ,:0.04609 that:0.04161 many:0.03845 no:0.02883 and:0.02649 -.:0.11672 ::0.09053 ,:0.06295 have:0.0386 am:0.03538 '':0.02803 -you:0.08013 .:0.0641 ,:0.05917 we:0.05548 they:0.04191 been:0.02835 -the:0.30000 of:0.20000 and:0.10000 to:0.10000 in:0.10000 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