s434804 added solution

README.md

@@ -1,2 +1,9 @@
-# challenging-america-word-gap-prediction
+Challenging America word-gap prediction
+===================================
 
+Guess a word in a gap.
+
+Evaluation metric
+-----------------
+
+LikelihoodHashed is the metric

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
+import pandas as pd
+import regex as re
+import nltk
+
+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,
+)
+
+
+def prepare_text(text):
+    text = text.lower().replace("-\\n", "").replace("\\n", " ")
+    text = re.sub(r"\p{P}", "", text)
+
+    return text
+
+
+def train_trigrams():
+    for _, row in train_data.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]):
+                model[(w2, w3)][w1] += 1
+                model[(w1, w2)][w3] += 1
+
+    for w_pair in model:
+        ngram_count = float(sum(model[w_pair].values()))
+        for w3 in model[w_pair]:
+            model[w_pair][w3] /= ngram_count
+
+
+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 "the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1"
+
+    remaining_prob = 1 - total_prob
+
+    if remaining_prob < 0.01:
+        remaining_prob = 0.01
+
+    str_prediction += f":{remaining_prob}"
+
+    return str_prediction
+
+
+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 = predict_probs(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 = predict_probs(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 = defaultdict(lambda: defaultdict(lambda: 0))
+
+    # train model
+    print("Model training...")
+    train_trigrams()
+
+    # write outputs
+    print("Writing outputs...")
+    write_output()