add removed solutions

bigram/bigram.py

@@ -0,0 +1,105 @@
+import csv
+import pandas as pd
+import regex as re
+import nltk
+import tqdm
+from nltk import bigrams, word_tokenize
+from collections import Counter, defaultdict
+import string 
+
+nltk.download("punkt")
+
+most_common_en_word = "the:0.4 be:0.2 to:0.1 of:0.05 and:0.025 a:0.0125 :0.2125"
+train_count = 125000
+# train set
+train_data = pd.read_csv("train/in.tsv.xz", sep="\t", on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE, nrows=train_count)
+
+# training labels
+train_labels = pd.read_csv("train/expected.tsv", sep="\t", on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE,nrows=train_count)
+
+dev_data = pd.read_csv("dev-0/in.tsv.xz", sep="\t", on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE)
+
+test_data = pd.read_csv("test-A/in.tsv.xz", sep="\t", on_bad_lines='skip', 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_bigrams():
+    for _, row in tqdm.tqdm(train_data.iterrows()):
+        text = prepare_text(str(row["final"]))
+        words = word_tokenize(text)
+        for w1, w2 in bigrams(words, pad_right=True, pad_left=True):
+            if all([w1, w2]):
+                model[w2][w1] += 1
+
+    for w_pair in model:
+        ngram_count = float(sum(model[w_pair].values()))
+        for w2 in model[w_pair]:
+            model[w_pair][w2] /= ngram_count
+
+
+def predict_probs(word):
+    raw_prediction = dict(model[word])
+    prediction = dict(Counter(raw_prediction).most_common(6))
+
+    total_prob = 0.0
+    str_prediction = ""
+
+    for w, prob in prediction.items():
+        total_prob += prob
+        str_prediction += f"{w}:{prob} "
+
+    if total_prob == 0.0:
+        return most_common_en_word
+
+    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) < 2:
+                prediction = most_common_en_word
+            else:
+                prediction = predict_probs(words[0])
+            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) < 2:
+                prediction = most_common_en_word
+            else:
+                prediction = predict_probs(words[0])
+            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_bigrams()
+
+    # write outputs
+    print("Writing outputs...")
+    write_output()

trigram/trigram.py

@@ -0,0 +1,107 @@
+import csv
+import pandas as pd
+import regex as re
+import nltk
+import tqdm
+from nltk import trigrams, word_tokenize
+from collections import Counter, defaultdict
+import string 
+
+nltk.download("punkt")
+
+most_common_en_word = "the:0.4 be:0.2 to:0.1 of:0.05 and:0.025 a:0.0125 :0.2125"
+train_count = 125000
+# train set
+train_data = pd.read_csv("train/in.tsv.xz", sep="\t", on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE, nrows=train_count)
+
+# training labels
+train_labels = pd.read_csv("train/expected.tsv", sep="\t", on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE,nrows=train_count)
+
+dev_data = pd.read_csv("dev-0/in.tsv.xz", sep="\t", on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE)
+
+test_data = pd.read_csv("test-A/in.tsv.xz", sep="\t", on_bad_lines='skip', 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 tqdm.tqdm(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 most_common_en_word
+
+    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 = most_common_en_word
+            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 = most_common_en_word
+            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()