440054
This commit is contained in:
Norbert Litkowski
parent
61e88a9c8c
commit
86f2757aee
2
.gitignore
vendored
2
.gitignore
vendored
@ -6,3 +6,5 @@
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*.o
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.DS_Store
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.token
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.vscode/*
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.ipynb_c*
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106
run.py
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106
run.py
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import pandas as pd
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import csv
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import regex as re
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import nltk
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from collections import Counter, defaultdict
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import string
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import unicodedata
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def main():
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try:
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nltk.data.find('tokenizers/punkt')
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except LookupError:
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nltk.download('punkt')
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with open("in-header.tsv") as f:
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in_cols = f.read().strip().split("\t")
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with open("out-header.tsv") as f:
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out_cols = f.read().strip().split("\t")
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data = pd.read_csv(
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"train/in.tsv.xz",
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sep="\t",
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on_bad_lines='skip',
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header=None,
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# names=in_cols,
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quoting=csv.QUOTE_NONE,
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)
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train_labels = pd.read_csv(
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"train/expected.tsv",
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sep="\t",
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on_bad_lines='skip',
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header=None,
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# names=out_cols,
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quoting=csv.QUOTE_NONE,
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)
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train_data = data[[7, 6]]
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train_data = pd.concat([train_data, train_labels], axis=1)
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train_data["final"] = train_data[7] + train_data[0] + train_data[6]
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model = defaultdict(lambda: defaultdict(lambda: 0))
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train_model(train_data, model)
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predict_data("dev-0/in.tsv.xz", "dev-0/out.tsv", model)
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predict_data("test-A/in.tsv.xz", "test-A/out.tsv", model)
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def clean_text(text):
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return re.sub(r"\p{P}", "", str(text).lower().replace("-\\n", "").replace("\\n", " "))
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def train_model(data, model):
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for _, row in data.iterrows():
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words = nltk.word_tokenize(clean_text(row["final"]))
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for w1, w2 in nltk.bigrams(words, pad_left=True, pad_right=True):
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if w1 and w2:
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model[w2][w1] += 1
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for w1 in model:
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total_count = float(sum(model[w1].values()))
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for w2 in model[w1]:
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model[w2][w1] /= total_count
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def predict(word, model):
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predictions = dict(model[word])
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most_common = dict(Counter(predictions).most_common(5))
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total_prob = 0.0
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str_prediction = ""
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for word, prob in most_common.items():
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total_prob += prob
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str_prediction += f"{word}:{prob} "
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if not total_prob:
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return "the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1"
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if 1 - total_prob >= 0.01:
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str_prediction += f":{1-total_prob}"
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else:
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str_prediction += f":0.01"
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return str_prediction
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def predict_data(read_path, save_path, model):
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data = pd.read_csv(
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read_path,
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sep="\t",
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error_bad_lines=False,
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header=None,
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quoting=csv.QUOTE_NONE
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)
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with open(save_path, "w") as file:
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for _, row in data.iterrows():
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words = nltk.word_tokenize(clean_text(row[6]))
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if len(words) < 3:
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prediction = "the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1"
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else:
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prediction = predict(words[-1], model)
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file.write(prediction + "\n")
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if __name__ == "__main__":
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main()
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211
testing.ipynb
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211
testing.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "21c9b695",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import csv\n",
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"import regex as re\n",
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"import nltk\n",
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"from collections import Counter, defaultdict\n",
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"import string\n",
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"import unicodedata\n",
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"\n",
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"def clean_text(text): \n",
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" return re.sub(r\"\\p{P}\", \"\", str(text).lower().replace(\"-\\\\n\", \"\").replace(\"\\\\n\", \" \"))\n",
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"\n",
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"def train_model(data, model):\n",
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" for _, row in data.iterrows():\n",
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" words = nltk.word_tokenize(clean_text(row[\"final\"]))\n",
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" for w1, w2 in nltk.bigrams(words, pad_left=True, pad_right=True):\n",
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" if w1 and w2:\n",
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" model[w2][w1] += 1\n",
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" for w1 in model:\n",
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" total_count = float(sum(model[w1].values()))\n",
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" for w2 in model[w1]:\n",
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" model[w2][w1] /= total_count\n",
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"\n",
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"\n",
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"def predict(word, model):\n",
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" predictions = dict(model[word])\n",
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" most_common = dict(Counter(predictions).most_common(5))\n",
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"\n",
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" total_prob = 0.0\n",
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" str_prediction = \"\"\n",
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"\n",
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" for word, prob in most_common.items():\n",
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" total_prob += prob\n",
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" str_prediction += f\"{word}:{prob} \"\n",
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"\n",
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" if not total_prob:\n",
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" return \"the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1\"\n",
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"\n",
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" if 1 - total_prob >= 0.01:\n",
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" str_prediction += f\":{1-total_prob}\"\n",
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" else:\n",
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" str_prediction += f\":0.01\"\n",
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"\n",
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" return str_prediction\n",
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"\n",
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"\n",
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"def predict_data(read_path, save_path, model):\n",
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" data = pd.read_csv(\n",
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" read_path, sep=\"\\t\", error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE\n",
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" )\n",
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" with open(save_path, \"w\") as file:\n",
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" for _, row in data.iterrows():\n",
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" words = nltk.word_tokenize(clean_text(row[7]))\n",
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" if len(words) < 3:\n",
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" prediction = \"the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1\"\n",
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" else:\n",
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" prediction = predict(words[-1], model)\n",
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" file.write(prediction + \"\\n\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "e39473e2",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(\"in-header.tsv\") as f:\n",
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" in_cols = f.read().strip().split(\"\\t\")\n",
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"\n",
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"with open(\"out-header.tsv\") as f:\n",
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" out_cols = f.read().strip().split(\"\\t\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "bde510c9",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['FileId', 'Year', 'LeftContext', 'RightContext']"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"in_cols"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "0e8b31dd",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['Word']"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"out_cols"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7662d802",
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"metadata": {},
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"outputs": [],
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"source": [
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"data = pd.read_csv(\n",
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" \"train/in.tsv.xz\",\n",
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" sep=\"\\t\",\n",
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" on_bad_lines='skip',\n",
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" header=None,\n",
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" # names=in_cols,\n",
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" quoting=csv.QUOTE_NONE,\n",
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")\n",
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"\n",
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"train_labels = pd.read_csv(\n",
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" \"train/expected.tsv\",\n",
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" sep=\"\\t\",\n",
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" on_bad_lines='skip',\n",
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" header=None,\n",
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" # names=out_cols,\n",
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" quoting=csv.QUOTE_NONE,\n",
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")\n",
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"\n",
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"train_data = data[[7, 6]]\n",
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"train_data = pd.concat([train_data, train_labels], axis=1)\n",
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"\n",
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"train_data[\"final\"] = train_data[7] + train_data[0] + train_data[6]\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c3d2cfec",
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"metadata": {},
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"outputs": [],
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"source": [
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"train_data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bd92ba07",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"model = defaultdict(lambda: defaultdict(lambda: 0))\n",
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"\n",
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"train_model(train_data, model)\n",
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"predict_data(\"dev-0/in.tsv.xz\", \"dev-0/out.tsv\", model)\n",
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"predict_data(\"test-A/in.tsv.xz\", \"test-A/out.tsv\", model)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ad23240e",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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