437622 alpha=0.1

run.py

@@ -4,62 +4,70 @@ import pandas as pd
 import csv
 import regex as re
 
+default_pred = 'to:0.02 be:0.02 the:0.02 or:0.01 not:0.01 and:0.01 a:0.01 :0.9'
+
 
 def preprocess(text):
     text = text.lower().replace('-\\n', '').replace('\\n', ' ')
     return re.sub(r'\p{P}', '', text)
 
 
-def predict(before, after):
+class Model():
-    prediction = dict(Counter(dict(trigram[before, after])).most_common(5))
+    def __init__(self, alpha, test_file_name):
-    result = ''
+        train_data = pd.read_csv('train/in.tsv.xz', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE,
-    prob = 0.0
+                                 nrows=20000)
-    for key, value in prediction.items():
+        train_labels = pd.read_csv('train/expected.tsv', sep='\t', on_bad_lines='skip', header=None,
-        prob += value
+                                   quoting=csv.QUOTE_NONE, nrows=20000)
-        result += f'{key}:{value} '
+        train_data = train_data[[6, 7]]
-    if prob == 0.0:
+        train_data = pd.concat([train_data, train_labels], axis=1)
-        return 'to:0.02 be:0.02 the:0.02 or:0.01 not:0.01 and:0.01 a:0.01 :0.9'
+        train_data['line'] = train_data[6] + train_data[0] + train_data[7]
-    result += f':{max(1 - prob, 0.01)}'
-    return result
 
+        self.file = train_data[['line']]
+        self.test_file_name = test_file_name
+        self.alpha = alpha;
+        self.model = defaultdict(lambda: defaultdict(lambda: 0))
 
-def make_prediction(file):
+    def train(self):
-    data = pd.read_csv(f'{file}/in.tsv.xz', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE)
+        rows = self.file.iterrows()
-    with open(f'{file}/out.tsv', 'w', encoding='utf-8') as file_out:
+        rows_len = len(self.file)
-        for _, row in data.iterrows():
+        for index, (_, row) in enumerate(rows):
-            before, after = word_tokenize(preprocess(str(row[6]))), word_tokenize(preprocess(str(row[7])))
-            if len(before) < 3 or len(after) < 3:
-                prediction = 'to:0.02 be:0.02 the:0.02 or:0.01 not:0.01 and:0.01 a:0.01 :0.9'
-            else:
-                prediction = predict(before[-1], after[0])
-            file_out.write(prediction + '\n')
-
-
-train_data = pd.read_csv('train/in.tsv.xz', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE, nrows=20000)
-train_labels = pd.read_csv('train/expected.tsv', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE, nrows=20000)
-train_data = train_data[[6, 7]]
-train_data = pd.concat([train_data, train_labels], axis=1)
-train_data['line'] = train_data[6] + train_data[0] + train_data[7]
-
-
-trigram = defaultdict(lambda: defaultdict(lambda: 0))
-
-rows = train_data.iterrows()
-rows_len = len(train_data)
-for index, (_, row) in enumerate(rows):
             text = preprocess(str(row['line']))
             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:
-            trigram[(word_1, word_3)][word_2] += 1
+                    self.model[(word_1, word_3)][word_2] += 1
+        model_len = len(self.model)
+        for index, words_1_3 in enumerate(self.model):
+            count = sum(self.model[words_1_3].values())
+            for word_2 in self.model[words_1_3]:
+                self.model[words_1_3][word_2] += self.alpha
+                self.model[words_1_3][word_2] /= float(count + self.alpha + len(word_2))
 
-model_len = len(trigram)
+    def predict(self, before, after):
-for index, words_1_3 in enumerate(trigram):
+        prediction = dict(Counter(dict(self.model[before, after])).most_common(5))
-    count = sum(trigram[words_1_3].values())
+        result = []
-    for word_2 in trigram[words_1_3]:
+        prob = 0.0
-        trigram[words_1_3][word_2] += 0.25
+        for key, value in prediction.items():
-        trigram[words_1_3][word_2] /= float(count + 0.25 + len(word_2))
+            prob += value
+            result.append(f'{key}:{value} ')
+        if prob == 0.0:
+            return default_pred
+        result.append(f':{max(1 - prob, 0.01)}')
+        return ''.join(result)
+
+    def make_prediction(self):
+        data = pd.read_csv(f'{self.test_file_name}/in.tsv.xz', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE)
+        with open(f'{self.test_file_name}/out.tsv', 'w', encoding='utf-8') as file_out:
+            for _, row in data.iterrows():
+                before, after = word_tokenize(preprocess(str(row[6]))), word_tokenize(preprocess(str(row[7])))
+                if len(before) < 3 or len(after) < 3:
+                    prediction = default_pred
+                else:
+                    prediction = self.predict(before[-1], after[0])
+                file_out.write(prediction + '\n')
 
 
-make_prediction('test-A')
+alpha = 0.1
-make_prediction('dev-0')
+model = Model(alpha, 'test-A')
+model.train()
+model.make_prediction()