Word gap prediction - solution

.gitignore

@@ -6,3 +6,4 @@
 *.o
 .DS_Store
 .token
+env

run.py

@@ -0,0 +1,65 @@
+import pandas as pd
+import csv
+from collections import Counter, defaultdict
+from nltk.tokenize import RegexpTokenizer
+from nltk import trigrams
+
+
+class WordGapPrediction:
+
+    def __init__(self):
+        self.tokenizer = RegexpTokenizer(r"\w+")
+        self.model = defaultdict(lambda: defaultdict(lambda: 0))
+
+    def read_train_data(self, file):
+        data = pd.read_csv(file, sep="\t", error_bad_lines=False, index_col=0, header=None)
+        for index, row in data[:90000].iterrows():
+            text = str(row[6]) + ' ' + str(row[7])
+            tokens = self.tokenizer.tokenize(text)
+            for w1, w2, w3 in trigrams(tokens, pad_right=True, pad_left=True):
+                if w1 and w2 and w3:
+                    self.model[(w2, w3)][w1] += 1
+                    self.model[(w1, w2)][w3] += 1
+        
+        for word_pair in self.model:
+            num_n_grams = float(sum(self.model[word_pair].values()))
+            for word in self.model[word_pair]:
+                self.model[word_pair][word] /= num_n_grams
+
+    def generate_outputs(self, input_file, output_file):
+        data = pd.read_csv(input_file, sep='\t', error_bad_lines=False, index_col=0, header=None, quoting=csv.QUOTE_NONE)
+        with open(output_file, 'w') as f:
+            for index, row in data.iterrows():
+                text = str(row[7])
+                tokens = self.tokenizer.tokenize(text)
+                if len(tokens) < 4:
+                    prediction = 'the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1'
+                else:
+                    prediction = word_gap_prediction.predict_probs(tokens[0], tokens[1])
+                f.write(prediction + '\n')
+
+    def predict_probs(self, word1, word2):
+        predictions = dict(self.model[word1, word2])
+        most_common = dict(Counter(predictions).most_common(6))
+        
+        total_prob = 0.0
+        str_prediction = ''
+
+        for word, prob in most_common.items():
+            total_prob += prob
+            str_prediction += f'{word}:{prob} '
+
+        if total_prob == 0.0:
+            return 'the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1'
+
+        if 1 - total_prob >= 0.01:
+            str_prediction += f":{1-total_prob}"
+        else:
+            str_prediction += f":0.01"
+        
+        return str_prediction
+
+word_gap_prediction = WordGapPrediction()
+word_gap_prediction.read_train_data('./train/in.tsv')
+word_gap_prediction.generate_outputs('dev-0/in.tsv', 'dev-0/out.tsv')
+word_gap_prediction.generate_outputs('test-A/in.tsv', 'test-A/out.tsv')