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143
.ipynb_checkpoints/bayes-checkpoint.ipynb
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@ -1,143 +0,0 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 22,
"id": "ce420679-f5aa-4c83-a912-3c4afa982d7e",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\Users\\Adrian\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3444: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version.\n",
"\n",
"\n",
" exec(code_obj, self.user_global_ns, self.user_ns)\n",
"b'Skipping line 25706: expected 2 fields, saw 3\\nSkipping line 58881: expected 2 fields, saw 3\\nSkipping line 73761: expected 2 fields, saw 3\\n'\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.naive_bayes import MultinomialNB\n",
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.metrics import accuracy_score\n",
"\n",
"\n",
"\n",
"df = pd.read_csv(\"train/train.tsv\", sep=\"\\t\", header=None, error_bad_lines=False)\n",
"df = df.head(1000)\n",
"\n",
"\n",
"dev_x = pd.read_csv(\"dev-0/in.tsv\", sep=\"\\t\", header=None, error_bad_lines=False)\n",
"\n",
"\n",
"with open('test-A/in.tsv', 'r', encoding='utf8') as file:\n",
" test = file.readlines()\n",
"test = pd.Series(test)\n",
"\n",
"\n",
"x = df[1]\n",
"y = df[0]\n",
"\n",
"model = make_pipeline(TfidfVectorizer(), MultinomialNB())\n",
"model.fit(x,y)\n",
"\n",
"pred_dev = model.predict(dev_x[0])\n",
"pred_dev = pd.Series(pred_dev)\n",
"\n",
"with open('dev-0/out.tsv', 'wt') as file:\n",
" for pred in pred_dev:\n",
" file.write(str(pred)+'\\n')\n",
"\n",
"\n",
"pred_test = model.predict(test)\n",
"pred_test = pd.Series(pred_test)\n",
"pred_test = pred_test.astype('int')\n",
"\n",
"\n",
" \n",
"with open('test-A/out.tsv', 'wt') as file:\n",
" for pred in pred_test:\n",
" file.write(str(pred)+'\\n')\n",
"\n",
"\n",
"\n",
"\n",
"\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "3e2a9ef0-6da0-4934-8099-378d859ae04e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0\n",
"0 ATP Sztokholm: Juergen Zopp wykorzystał szansę...\n",
"1 Krowicki z reprezentacją kobiet aż do igrzysk ...\n",
"2 Wielki powrót Łukasza Kubota Odradza się zawsz...\n",
"3 Marcel Hirscher wygrał ostatni slalom gigant m...\n",
"4 Polki do Czarnogóry z pełnią zaangażowania. Sy...\n",
"... ...\n",
"5440 Biało-czerwona siła w Falun. Oni będą reprezen...\n",
"5441 Finał WTA Tokio na żywo: Woźniacka - Osaka LIV...\n",
"5442 Oni zapisali się w annałach. Hubert Hurkacz 15...\n",
"5443 Poprawia się stan Nikiego Laudy. Austriak może...\n",
"5444 Liga Mistrzów. Zabójcza końcówka Interu Mediol...\n",
"\n",
"[5445 rows x 1 columns]\n",
"0 ATP Sztokholm: Juergen Zopp wykorzystał szansę...\n",
"1 Krowicki z reprezentacją kobiet aż do igrzysk ...\n",
"2 Wielki powrót Łukasza Kubota Odradza się zawsz...\n",
"3 Marcel Hirscher wygrał ostatni slalom gigant m...\n",
"4 Polki do Czarnogóry z pełnią zaangażowania. Sy...\n",
" ... \n",
"5442 Biało-czerwona siła w Falun. Oni będą reprezen...\n",
"5443 Finał WTA Tokio na żywo: Woźniacka - Osaka LIV...\n",
"5444 Oni zapisali się w annałach. Hubert Hurkacz 15...\n",
"5445 Poprawia się stan Nikiego Laudy. Austriak może...\n",
"5446 Liga Mistrzów. Zabójcza końcówka Interu Mediol...\n",
"Length: 5447, dtype: object\n"
]
}
],
"source": [
"print(test)\n",
"\n",
"\n",
"\n",
"\n",
"print(Xtest)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

49
.ipynb_checkpoints/run-checkpoint.py
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import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import make_pipeline
from sklearn.metrics import accuracy_score
df = pd.read_csv("train/train.tsv", sep="\t", header=None, error_bad_lines=False)
df = df.head(1000)
dev_x = pd.read_csv("dev-0/in.tsv", sep="\t", header=None, error_bad_lines=False)
with open('test-A/in.tsv', 'r', encoding='utf8') as file:
test = file.readlines()
test = pd.Series(test)
x = df[1]
y = df[0]
model = make_pipeline(TfidfVectorizer(), MultinomialNB())
model.fit(x,y)
pred_dev = model.predict(dev_x[0])
pred_dev = pd.Series(pred_dev)
with open('dev-0/out.tsv', 'wt') as file:
for pred in pred_dev:
file.write(str(pred)+'\n')
pred_test = model.predict(test)
pred_test = pd.Series(pred_test)
pred_test = pred_test.astype('int')
with open('test-A/out.tsv', 'wt') as file:
for pred in pred_test:
file.write(str(pred)+'\n')

95
bayes.ipynb
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@ -1,95 +0,0 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 23,
"id": "ce420679-f5aa-4c83-a912-3c4afa982d7e",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\Users\\Adrian\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3444: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version.\n",
"\n",
"\n",
" exec(code_obj, self.user_global_ns, self.user_ns)\n",
"b'Skipping line 25706: expected 2 fields, saw 3\\nSkipping line 58881: expected 2 fields, saw 3\\nSkipping line 73761: expected 2 fields, saw 3\\n'\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.naive_bayes import MultinomialNB\n",
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.metrics import accuracy_score\n",
"\n",
"\n",
"\n",
"df = pd.read_csv(\"train/train.tsv\", sep=\"\\t\", header=None, error_bad_lines=False)\n",
"\n",
"\n",
"\n",
"dev_x = pd.read_csv(\"dev-0/in.tsv\", sep=\"\\t\", header=None, error_bad_lines=False)\n",
"\n",
"\n",
"with open('test-A/in.tsv', 'r', encoding='utf8') as file:\n",
" test = file.readlines()\n",
"test = pd.Series(test)\n",
"\n",
"\n",
"x = df[1]\n",
"y = df[0]\n",
"\n",
"model = make_pipeline(TfidfVectorizer(), MultinomialNB())\n",
"model.fit(x,y)\n",
"\n",
"pred_dev = model.predict(dev_x[0])\n",
"pred_dev = pd.Series(pred_dev)\n",
"\n",
"with open('dev-0/out.tsv', 'wt') as file:\n",
" for pred in pred_dev:\n",
" file.write(str(pred)+'\\n')\n",
"\n",
"\n",
"pred_test = model.predict(test)\n",
"pred_test = pd.Series(pred_test)\n",
"pred_test = pred_test.astype('int')\n",
"\n",
"\n",
" \n",
"with open('test-A/out.tsv', 'wt') as file:\n",
" for pred in pred_test:\n",
" file.write(str(pred)+'\\n')\n",
"\n",
"\n",
"\n",
"\n",
"\n",
" \n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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dev-0/.ipynb_checkpoints/expected-checkpoint.tsv
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dev-0/.ipynb_checkpoints/out-checkpoint.tsv
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dev-0/out.tsv
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49
run.py
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import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import make_pipeline
from sklearn.metrics import accuracy_score
df = pd.read_csv("train/train.tsv", sep="\t", header=None, error_bad_lines=False)
df = df.head(1000)
dev_x = pd.read_csv("dev-0/in.tsv", sep="\t", header=None, error_bad_lines=False)
with open('test-A/in.tsv', 'r', encoding='utf8') as file:
test = file.readlines()
test = pd.Series(test)
x = df[1]
y = df[0]
model = make_pipeline(TfidfVectorizer(), MultinomialNB())
model.fit(x,y)
pred_dev = model.predict(dev_x[0])
pred_dev = pd.Series(pred_dev)
with open('dev-0/out.tsv', 'wt') as file:
for pred in pred_dev:
file.write(str(pred)+'\n')
pred_test = model.predict(test)
pred_test = pd.Series(pred_test)
pred_test = pred_test.astype('int')
with open('test-A/out.tsv', 'wt') as file:
for pred in pred_test:
file.write(str(pred)+'\n')

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test-A/.ipynb_checkpoints/in-checkpoint.tsv
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test-A/.ipynb_checkpoints/out-checkpoint.tsv
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test-A/out.tsv
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train/train.tsv
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