s430705 plusalpha

run.py

@@ -1,62 +1,24 @@
-import string
-import unicodedata
-
 from nltk.tokenize import word_tokenize
 from nltk import trigrams
 from collections import defaultdict, Counter
 import pandas as pd
 import csv
-import regex as re
 
 
-DEFAULT_PREDICTION = 'the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1'
-
+class GapPredictor:
+    def __init__(self, alpha):
+        self.model = defaultdict(lambda: defaultdict(lambda: 0))
+        self.alpha = alpha
+        self.vocab = set()
+        self.DEFAULT_PREDICTION = "the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1"
 
+    @staticmethod
     def preprocess_text(text):
         text = text.lower().replace("-\\n", "").replace("\\n", " ")
-
         return text
 
-
-def predict_probs(word1, word2):
-    raw_prediction = dict(model[word1, word2])
-    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} '
-
-    if total_prob == 0.0:
-        return DEFAULT_PREDICTION
-
-    remaining_prob = 1 - total_prob
-
-    if remaining_prob < 0.01:
-        remaining_prob = 0.01
-
-    str_prediction += f':{remaining_prob}'
-
-    return str_prediction
-
-
-def train_model(training_data):
-    for index, row in training_data.iterrows():
-        text = preprocess_text(str(row["final"]))
-        words = word_tokenize(text)
-        for w1, w2, w3 in trigrams(words, pad_right=True, pad_left=True):
-            if w1 and w2 and w3:
-                model[(w2, w3)][w1] += 1
-                model[(w1, w2)][w3] += 1
-
-    for word_pair in model:
-        num_n_grams = float(sum(model[word_pair].values()))
-        for word in model[word_pair]:
-            model[word_pair][word] /= num_n_grams
-
-
+    @staticmethod
+    def _prepare_train_data():
         data = pd.read_csv(
             "train/in.tsv.xz",
             sep="\t",
@@ -64,7 +26,7 @@ data = pd.read_csv(
             warn_bad_lines=False,
             header=None,
             quoting=csv.QUOTE_NONE,
-    nrows=100000,
+            nrows=90000,
         )
 
         train_labels = pd.read_csv(
@@ -73,39 +35,81 @@ train_labels = pd.read_csv(
             error_bad_lines=False,
             header=None,
             quoting=csv.QUOTE_NONE,
-    nrows=100000,
+            nrows=90000,
         )
 
         train_data = 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]
 
-model = defaultdict(lambda: defaultdict(lambda: 0))
+        return train_data
 
-
-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)
-
-
-train_model(train_data)
-
-with open("dev-0/out.tsv", "w") as file:
-    for _, row in dev_data.iterrows():
-        text = preprocess_text(str(row[7]))
+    def train_model(self):
+        training_data = self._prepare_train_data()
+        for index, row in training_data.iterrows():
+            text = self.preprocess_text(str(row["final"]))
             words = word_tokenize(text)
+            for w1, w2, w3 in trigrams(words, pad_right=True, pad_left=True):
+                if w1 and w2 and w3:
+                    self.model[(w2, w3)][w1] += 1
+                    self.model[(w1, w2)][w3] += 1
+                    self.vocab.add(w1)
+                    self.vocab.add(w2)
+                    self.vocab.add(w3)
+
+        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] = (
+                    self.model[word_pair][word] + self.alpha
+                ) / (num_n_grams + self.alpha * len(self.vocab))
+
+    def predict_probs(self, words):
         if len(words) < 3:
-            prediction = DEFAULT_PREDICTION
-        else:
-            prediction = predict_probs(words[0], words[1])
+            return self.DEFAULT_PREDICTION
+
+        word1, word2 = words[0], words[1]
+        raw_prediction = dict(self.model[word1, word2])
+        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} "
+
+        if total_prob == 0.0:
+            return self.DEFAULT_PREDICTION
+
+        remaining_prob = 1 - total_prob
+
+        if remaining_prob < 0.01:
+            remaining_prob = 0.01
+
+        str_prediction += f":{remaining_prob}"
+
+        return str_prediction
+
+    def prepare_output(self, input_file, output_file):
+        with open(output_file, "w") as file:
+            data = pd.read_csv(
+                input_file,
+                sep="\t",
+                error_bad_lines=False,
+                warn_bad_lines=False,
+                header=None,
+                quoting=csv.QUOTE_NONE,
+            )
+            for _, row in data.iterrows():
+                text = self.preprocess_text(str(row[7]))
+                words = word_tokenize(text)
+                prediction = self.predict_probs(words)
                 file.write(prediction + "\n")
 
-with open("test-A/out.tsv", "w") as file:
-    for _, row in test_data.iterrows():
-        text = preprocess_text(str(row[7]))
-        words = word_tokenize(text)
-        if len(words) < 3:
-            prediction = DEFAULT_PREDICTION
-        else:
-            prediction = predict_probs(words[0], words[1])
-        file.write(prediction + "\n")
 
+predictor = GapPredictor(alpha=0.00002)
+predictor.train_model()
+
+predictor.prepare_output("dev-0/in.tsv.xz", "dev-0/out.tsv")
+predictor.prepare_output("test-A/in.tsv.xz", "test-A/out.tsv")