custom alpha

run.py

@@ -4,72 +4,77 @@ from collections import defaultdict, Counter
 import pandas as pd
 import csv
 import regex as re
+import sys
 
 
 DEFAULT_PREDICTION = 'the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1'
 
-
 def preprocess(text):
     text = text.lower().replace('-\\n', '').replace('\\n', ' ')
     return re.sub(r'\p{P}', '', text)
 
 
-def predict(word_before, word_after):
-    prediction = dict(Counter(dict(model[word_before, word_after])).most_common(6))
-    result = []
-    prob = 0.0
-    for key, value in prediction.items():
-        prob += value
-        result.append(f'{key}:{value}')
-    if prob == 0.0:
-        return DEFAULT_PREDICTION
-    result.append(f':{max(1 - prob, 0.01)}')
-    return ' '.join(result)
+
+class Model():
+    def __init__(self, alpha, train_file_name, test_file_name):
+        
+        file_expected = pd.read_csv(f'{train_file_name}/expected.tsv', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE, nrows=200000)
+        file_in = pd.read_csv(f'{train_file_name}/in.tsv.xz', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE, nrows=200000)
+        file_in = file_in[[6, 7]]
+        file_concat = pd.concat([file_in, file_expected], axis=1)
+        file_concat['text'] = file_concat[6] + file_concat[0] + file_concat[7]
+        
+        self.file = file_concat[['text']]
+        self.test_file_name = test_file_name
+        self.alpha = alpha;
+        self.model = defaultdict(lambda: defaultdict(lambda: 0))
+    
+    def train(self):
+        rows = self.file.iterrows()
+        rows_len = len(self.file)
+        for index, (_, row) in enumerate(rows):
+            if index % 1000 == 0:
+                print(f'uczenie modelu: {index / rows_len}')
+            words = word_tokenize(preprocess(str(row['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:
+                    self.model[(word_1, word_3)][word_2] += 1
+        model_len = len(self.model)
+        for index, words_1_3 in enumerate(self.model):
+            if index % 100000 == 0:
+                print(f'normalizacja i wygładzanie: {index / model_len}')
+            occurrences = 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(occurrences + self.alpha + len(word_2))
+    
+    def predict_row(self, word_before, word_after):
+        prediction = dict(Counter(dict(self.model[word_before, word_after])).most_common(6))
+        result = []
+        prob = 0.0
+        for key, value in prediction.items():
+            prob += value
+            result.append(f'{key}:{value}')
+        if prob == 0.0:
+            return DEFAULT_PREDICTION
+        result.append(f':{max(1 - prob, 0.01)}')
+        return ' '.join(result)
+    
+    def predict(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():
+                words_before, words_after = word_tokenize(preprocess(str(row[6]))), word_tokenize(preprocess(str(row[7])))
+                if len(words_before) < 3 or len(words_after) < 3:
+                    prediction = DEFAULT_PREDICTION
+                else:
+                    prediction = self.predict_row(words_before[-1], words_after[0])
+                file_out.write(prediction + '\n')
+        
 
 
-def make_prediction(file):
-    data = pd.read_csv(f'{file}/in.tsv.xz', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE)
-    with open(f'{file}/out.tsv', 'w', encoding='utf-8') as file_out:
-        for _, row in data.iterrows():
-            words_before, words_after = word_tokenize(preprocess(str(row[6]))), word_tokenize(preprocess(str(row[7])))
-            if len(words_before) < 3 or len(words_after) < 3:
-                prediction = DEFAULT_PREDICTION
-            else:
-                prediction = predict(words_before[-1], words_after[0])
-            file_out.write(prediction + '\n')
-
-
-file_in = pd.read_csv('train/in.tsv.xz', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE, nrows=200000)
-file_expected = pd.read_csv('train/expected.tsv', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE, nrows=200000)
-file_in = file_in[[6, 7]]
-file_concat = pd.concat([file_in, file_expected], axis=1)
-file_concat['text'] = file_concat[6] + file_concat[0] + file_concat[7]
-file_concat = file_concat[['text']]
-trigrams_list = []
-model = defaultdict(lambda: defaultdict(lambda: 0))
-
-rows = file_concat.iterrows()
-rows_len = len(file_concat)
-for index, (_, row) in enumerate(rows):
-    if index % 1000 == 0:
-        print(f'uczenie modelu: {index / rows_len}')
-    words = word_tokenize(preprocess(str(row['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:
-            model[(word_1, word_3)][word_2] += 1
-
-alpha = 0.25
-model_len = len(model)
-for index, words_1_3 in enumerate(model):
-    if index % 100000 == 0:
-        print(f'normalizacja: {index / model_len}')
-    occurrences = sum(model[words_1_3].values())
-    for word_2 in model[words_1_3]:
-        model[words_1_3][word_2] += alpha
-        model[words_1_3][word_2] /= float(occurrences + alpha + len(word_2))
-
-
-make_prediction('test-A')
-make_prediction('dev-0')
-
-print('koniec')
+alpha = float(sys.argv[1])
+print(f'alfa: {alpha}')
+model = Model(alpha, 'dev-0', 'test-A')
+model.train()
+model.predict()