smoothing

run.py

@@ -3,84 +3,106 @@ import csv
 import regex as re
 from nltk import bigrams, word_tokenize
 from collections import Counter, defaultdict
-import string
-import unicodedata
-
-data = pd.read_csv(
-    "train/in.tsv.xz",
-    sep="\t",
-    error_bad_lines=False,
-    header=None,
-    quoting=csv.QUOTE_NONE,
-)
-train_labels = pd.read_csv(
-    "train/expected.tsv",
-    sep="\t",
-    error_bad_lines=False,
-    header=None,
-    quoting=csv.QUOTE_NONE,
-)
-
-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))
 
 
 def clean(text):
     text = str(text).lower().replace("-\\n", "").replace("\\n", " ")
     return re.sub(r"\p{P}", "", text)
 
-def train_model(data):
+
-    for _, row in data.iterrows():
+class Collection:
-        words = word_tokenize(clean(row["final"]))
+    def __init__(self, path: str) -> None:
+        self._path = path
+
+    def read(self, nrows=200_000):
+        self.data = pd.read_csv(
+            self._path,
+            sep="\t",
+            error_bad_lines=False,
+            header=None,
+            quoting=csv.QUOTE_NONE,
+            nrows=nrows,
+        )
+
+
+class Model:
+    def __init__(self, alpha: float = 0.01) -> None:
+        self.alpha = alpha
+        self.model = defaultdict(lambda: defaultdict(lambda: 0))
+        self.vocab = set()
+
+    def train(self, data):
+        for _, row in data.iterrows():
+            words = word_tokenize(clean(row["final"]))
         for w1, w2 in bigrams(words, pad_left=True, pad_right=True):
             if w1 and w2:
-                model[w1][w2] += 1
+                self.model[w1][w2] += 1
-    for w1 in model:
+                self.vocab.add(w1)
-        total_count = float(sum(model[w1].values()))
+                self.vocab.add(w2)
-        for w2 in model[w1]:
+
-            model[w1][w2] /= total_count
+        for w1 in self.model:
+            total_count = float(sum(self.model[w1].values()))
+            for w2 in self.model[w1]:
+                self.model[w1][w2] /= total_count
+                self.model[w1][w2] = (self.model[w1][w2] + self.alpha) / (
+                    total_count + self.alpha * len(self.vocab)
+                )
+
+    def _predict(self, word):
+        predictions = dict(self.model[word])
+        most_common = dict(Counter(predictions).most_common(5))
+
+        total_prob = 0.0
+        str_prediction = ""
+
+        for word, prob in most_common.items():
+            total_prob += prob
+            str_prediction += f"{word}:{prob} "
+
+        if not total_prob:
+            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
+
+    def _save(self, save_path: str, data):
+        with open(save_path, "w") as file:
+            for _, row in data.iterrows():
+                words = word_tokenize(clean(row[6]))
+                if len(words) < 3:
+                    prediction = "the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1"
+                else:
+                    prediction = self._predict(words[-1])
+                file.write(prediction + "\n")
+
+    def predict(self, read_path: str, save_path: str):
+        collection = Collection(read_path)
+        collection.read()
+        self._save(save_path, collection.data)
 
 
-def predict(word):
-    predictions = dict(model[word])
-    most_common = dict(Counter(predictions).most_common(5))
 
-    total_prob = 0.0
+if __name__ == '__main__':
-    str_prediction = ""
 
-    for word, prob in most_common.items():
+    data = Collection("train/in.tsv.xz")
-        total_prob += prob
+    data.read()
-        str_prediction += f"{word}:{prob} "
 
-    if not total_prob:
+    train_labels = Collection("train/expected.tsv")
-        return "the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1"
+    train_labels.read()
-
-    if 1 - total_prob >= 0.01:
-        str_prediction += f":{1-total_prob}"
-    else:
-        str_prediction += f":0.01"
-
-    return str_prediction
 
 
-def predict_data(read_path, save_path):
+    train_data = data.data[[6, 7]]
-    data = pd.read_csv(
+    train_data = pd.concat([train_data, train_labels.data], axis=1)
-        read_path, sep="\t", error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE
+
-    )
+    train_data["final"] = train_data[6] + train_data[0] + train_data[7]
-    with open(save_path, "w") as file:
-        for _, row in data.iterrows():
-            words = word_tokenize(clean(row[6]))
-            if len(words) < 3:
-                prediction = "the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1"
-            else:
-                prediction = predict(words[-1])
-            file.write(prediction + "\n")
 
 
-train_model(train_data)
+    model = Model()
-predict_data("dev-0/in.tsv.xz", "dev-0/out.tsv")
+    model.train(train_data)
-predict_data("test-A/in.tsv.xz", "test-A/out.tsv")
+
+    model.predict("dev-0/in.tsv.xz", "dev-0/out.tsv")
+    model.predict("test-A/in.tsv.xz", "test-A/out.tsv")