added plusalpha parameter

LICENSE

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+MIT License
+
+Copyright (c) <year> <copyright holders>
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

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+Challenging America word-gap prediction
+===================================
+
+Guess a word in a gap.
+
+Evaluation metric
+-----------------
+
+LikelihoodHashed is the metric

alpha_test.md

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+## Tested on 10k rows
+
+|alpha|result|
+|---|---|
+|__0.0001__|__603.35__|
+|0.001|656.93|
+|0.1|933.45|
+|0.2|962.28|
+|0.3|975.78|
+|0.4|983.92|
+|0.5|989.45|
+|0.6|993.51|
+|0.7|996.63|
+|0.8|999.11|
+|0.9|1001.13|
+|1.0|1002.83|

config.txt

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+--metric PerplexityHashed --precision 2  --in-header in-header.tsv  --out-header out-header.tsv

in-header.tsv

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+FileId	Year	LeftContext	RightContext

out-header.tsv

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+Word

run.py

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+import csv
+from lib2to3.pytree import Base
+from logging import raiseExceptions
+import pandas as pd
+import regex as re
+import nltk
+import sys
+
+from nltk import trigrams, word_tokenize
+from collections import Counter, defaultdict
+
+# nltk.download("punkt")
+
+# train set
+train_data = pd.read_csv(
+    "train/in.tsv.xz",
+    sep="\t",
+    error_bad_lines=False,
+    warn_bad_lines=False,
+    header=None,
+    quoting=csv.QUOTE_NONE,
+    nrows=100_000
+)
+
+# training labels
+train_labels = pd.read_csv(
+    "train/expected.tsv",
+    sep="\t",
+    error_bad_lines=False,
+    warn_bad_lines=False,
+    header=None,
+    quoting=csv.QUOTE_NONE,
+    nrows=100_000
+)
+
+# dev set
+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 set
+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,
+)
+
+class Model():
+    def __init__(self, vocab_size, alpha):
+        self.alpha = alpha
+        self.model = defaultdict(lambda: defaultdict(lambda: 0))
+        self.vocab = set()
+        self.vocab_size = vocab_size
+
+    def train(self, corpus: list):
+        for _, row in corpus[:self.vocab_size].iterrows():
+            text = prepare_text(str(row["final"]))
+            words = word_tokenize(text)
+            for w1, w2, w3 in trigrams(words, pad_right=True, pad_left=True):
+                if all([w1, w2, w3]):
+                    self.vocab.add(w1)
+                    self.vocab.add(w2)
+                    self.vocab.add(w3)
+                    self.model[(w2, w3)][w1] += 1
+                    self.model[(w1, w2)][w3] += 1
+
+        for w_pair in self.model:
+            ngram_count = float(sum(self.model[w_pair].values()))
+            denominator = ngram_count + self.alpha * len(self.vocab)
+            for w3 in self.model[w_pair]:
+                nominator = self.model[w_pair][w3] + self.alpha
+                self.model[w_pair][w3] = nominator / denominator
+
+    def predict(self, word1, word2):
+        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} "
+
+        remaining_prob = 1 - total_prob
+
+        str_prediction += f":{remaining_prob}"
+
+        return str_prediction
+
+
+def prepare_text(text):
+    text = text.lower().replace("-\\n", "").replace("\\n", " ")
+    text = re.sub(r"\p{P}", "", text)
+
+    return text
+
+# def write_output():
+#     with open('dev-0/out.tsv', 'w') as file:
+#         for _, row in dev_data.iterrows():
+#             left_text, right_text = prepare_text(str(row[6])), prepare_text(str(row[7]))
+#             left_words, right_words = word_tokenize(left_text), word_tokenize(right_text)
+#             if len(left_words) < 2 or len(right_words) < 2:
+#                 prediction = ':1.0'
+#             else:
+#                 prediction = model.predict(left_words[len(left_words) - 1], right_words[0])
+#             file.write(prediction + '\n')
+
+#     with open('test-A/out.tsv', 'w') as file:
+#         for _, row in test_data.iterrows():
+#             left_text, right_text = prepare_text(str(row[6])), prepare_text(str(row[7]))
+#             left_words, right_words = word_tokenize(left_text), word_tokenize(right_text)
+#             if len(left_words) < 2 or len(right_words) < 2:
+#                 prediction = ':1.0'
+#             else:
+#                 prediction = model.predict(left_words[len(left_words) - 1], right_words[0])
+#             file.write(prediction + '\n')
+
+def write_output():
+    with open("dev-0/out.tsv", "w") as file:
+        for _, row in dev_data.iterrows():
+            text = prepare_text(str(row[7]))
+            words = word_tokenize(text)
+            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 = model.predict(words[0], words[1])
+            file.write(prediction + "\n")
+
+    with open("test-A/out.tsv", "w") as file:
+        for _, row in test_data.iterrows():
+            text = prepare_text(str(row[7]))
+            words = word_tokenize(text)
+            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 = model.predict(words[0], words[1])
+            file.write(prediction + "\n")
+                
+if __name__ == "__main__":
+    # Preapare train data
+    print("Preparing data...")
+    train_data = train_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]
+    
+    # declare model
+    print("Preparing model...")
+    model = Model(100_000, 0.0001)
+
+    # train model
+    print("Model training...")
+    model.train(train_data)
+
+    # write outputs
+    print("Writing outputs...")
+    write_output()
+