init solution

.gitignore

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+
+*~
+*.swp
+*.bak
+*.pyc
+*.o
+.DS_Store
+.token

README.md

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-# challenging-america-word-gap-prediction-alpha
+Challenging America word-gap prediction - s434708
+===================================
 
+Guess a word in a gap.
+
+Evaluation metric
+-----------------
+
+LikelihoodHashed is the metric

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

run_smooth.py

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+import pandas as pd
+import csv
+import regex as re
+from collections import Counter, defaultdict
+from nltk import trigrams, word_tokenize
+
+def clean_text(text):
+    text = text.lower().replace('-\\n', '').replace('\\n', ' ')
+    text = re.sub(r'\p{P}', '', text)
+
+    return text
+
+class Model():
+    def __init__(self, alpha):
+        self.alpha = alpha
+        self.probs = defaultdict(lambda: defaultdict(lambda: 0))
+        self.vocab = set()
+
+    def train(self, data):
+        for _, row in data.iterrows():
+            text = clean_text(str(row['text']))
+            words = word_tokenize(text)
+            for w1, w2, w3 in trigrams(words, pad_right=True, pad_left=True):
+                if w1 and w2 and w3:
+                    self.vocab.add(w1)
+                    self.vocab.add(w2)
+                    self.vocab.add(w3)
+                    self.probs[(w1, w3)][w2] += 1
+
+        for w1_w3 in self.probs:
+            total_count = float(sum(self.probs[w1_w3].values()))
+            denominator = total_count + self.alpha * len(self.vocab)
+            for w2 in self.probs[w1_w3]:
+                nominator = self.probs[w1_w3][w2] + self.alpha
+                self.probs[w1_w3][w2] = nominator / denominator
+
+    def predict(self, w1, w3):
+        raw_prediction = dict(self.probs[w1, w3])
+        prediction = dict(Counter(raw_prediction).most_common(6))
+
+        total_prob = 0.0
+        str_prediction = ''
+
+        for word, prob in prediction.items():
+            total_prob += prob
+            str_prediction += f'{word}:{prob} '
+
+        remaining_prob = 1 - total_prob
+
+        if remaining_prob == 0:
+            remaining_prob = 0.01
+            
+        str_prediction += f':{remaining_prob}'
+        
+        return str_prediction
+
+
+# load training data
+train_data = pd.read_csv('train/in.tsv.xz', sep='\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)
+train_labels = pd.read_csv('train/expected.tsv', sep='\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)
+
+train_data = train_data[[6, 7]]
+train_data = pd.concat([train_data, train_labels], axis=1)
+
+train_data['final'] = train_data[6] + train_data[0] + train_data[7]
+train_data = train_data[['final']]
+
+# init model with given aplha
+model = Model(0.01)
+
+# train model probs
+model.train(train_data)
+
+# make predictions
+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)
+test_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)
+
+with open('dev-0/out.tsv', 'w') as file:
+    for index, row in dev_data.iterrows():
+        left_text = clean_text(str(row[6]))
+        right_text = clean_text(str(row[7]))
+        left_words = word_tokenize(left_text)
+        right_words = word_tokenize(right_text)
+        if len(left_words) < 2 or len(right_words) < 2:
+            prediction = ':1.0'
+        else:
+            prediction = model.predict(left_words[len(left_words) - 1], right_words[0])
+        file.write(prediction + '\n')
+
+with open('test-A/out.tsv', 'w') as file:
+    for index, row in test_data.iterrows():
+        left_text = clean_text(str(row[6]))
+        right_text = clean_text(str(row[7]))
+        left_words = word_tokenize(left_text)
+        right_words = word_tokenize(right_text)
+        if len(left_words) < 2 or len(right_words) < 2:
+            prediction = ':1.0'
+        else:
+            prediction = model.predict(left_words[len(left_words) - 1], right_words[0])
+        file.write(prediction + '\n')