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Author SHA1 Message Date
Kornelia Girejko
1b803cf24e Zaktualizuj 'run.py' 2022-05-17 23:35:15 +02:00
Kornelia Girejko
b16fab206e Prześlij pliki do '' 2022-05-17 23:33:18 +02:00
Kornelia Girejko
b0f937e53e Prześlij pliki do 'test-A' 2022-05-17 23:26:18 +02:00
Kornelia Girejko
e2950edac7 Usuń 'test-A/out.tsv' 2022-05-17 23:26:00 +02:00
Kornelia Girejko
e3d0d2ac06 Prześlij pliki do 'dev-1' 2022-05-17 23:25:40 +02:00
Kornelia Girejko
a566846eda Usuń 'dev-1/out.tsv' 2022-05-17 23:24:53 +02:00
Kornelia Girejko
825092b0a8 Prześlij pliki do 'dev-0' 2022-05-17 23:24:21 +02:00
Kornelia Girejko
179b150f10 Usuń 'dev-0/out.tsv' 2022-05-17 23:23:51 +02:00
Kornelia Girejko
e0630161e7 Prześlij pliki do 'dev-1' 2022-05-17 23:06:34 +02:00
Kornelia Girejko
142af2ddec Usuń 'dev-1/out.tsv' 2022-05-17 23:06:17 +02:00
Kornelia Girejko
c85a694955 Prześlij pliki do 'dev-0' 2022-05-17 22:50:34 +02:00
Kornelia Girejko
510135f5ae Usuń 'dev-0/out.tsv' 2022-05-17 22:50:21 +02:00
Kornelia Girejko
4e71bc526a Prześlij pliki do 'test-A' 2022-05-17 22:49:59 +02:00
Kornelia Girejko
a8273ecd11 Usuń 'test-A/out.tsv' 2022-05-17 22:49:45 +02:00
korne
14fbbecf94 second 2022-05-17 22:26:42 +02:00
korne
5389bd1d5b ten thousand 2022-05-17 22:07:26 +02:00
11 changed files with 177081 additions and 0 deletions
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167
.ipynb_checkpoints/retroc2-checkpoint.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 10,
"id": "greenhouse-technician",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sklearn\n",
"import pandas as pd\n",
"from gzip import open as open_gz\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_squared_error"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "acoustic-dividend",
"metadata": {},
"outputs": [],
"source": [
"def predict_year(x, path_out, model):\n",
" results = model.predict(x)\n",
" with open(path_out, 'wt') as file:\n",
" for r in results:\n",
" file.write(str(r) + '\\n') "
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "senior-harassment",
"metadata": {},
"outputs": [],
"source": [
"with open('train/train.tsv', 'r', encoding='utf8') as file:\n",
" train = pd.read_csv(file, sep='\\t', names=['Date1', 'Date2', 'Title', 'Author', 'Text'])\n",
" \n",
"#train = train[0:10000]\n",
"train_x = train['Text']\n",
"train['Date'] = (train['Date1'].astype(float) + train['Date2'].astype(float))/2\n",
"train_y=train['Date1']"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "polyphonic-coach",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()),\n",
" ('linearregression', LinearRegression())])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = make_pipeline(TfidfVectorizer(), LinearRegression())\n",
"model.fit(train_x, train_y)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "varying-wright",
"metadata": {},
"outputs": [],
"source": [
"with open('dev-0/in.tsv', 'r', encoding='utf8') as file:\n",
" x_dev0 = pd.read_csv(file, header=None, sep='\\t')\n",
"x_dev0 = x_dev0[0] \n",
"x_dev0[19999] = 'nie jest'\n",
"x_dev0[20000] = 'nie wiem'"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "frozen-ticket",
"metadata": {},
"outputs": [],
"source": [
"with open('dev-1/in.tsv', 'r', encoding='utf8') as file:\n",
" x_dev1 = pd.read_csv(file, header=None, sep='\\t')\n",
"x_dev1 = x_dev1[0] "
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "8e3a18db-f966-45e4-b881-4b336f188055",
"metadata": {},
"outputs": [],
"source": [
"with open('test-A/in.tsv', 'r', encoding='utf8') as file:\n",
" x_test = pd.read_csv(file, header=None, sep='\\t')\n",
"x_test = x_test[0] "
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "traditional-amount",
"metadata": {},
"outputs": [],
"source": [
"#y_dev = pd.read_csv('dev-0/out.tsv',header = None, sep = '/t',engine = 'python')\n",
"#y_dev = y_dev[0]\n",
"#y_dev_exp = pd.read_csv('dev-0/expected.tsv',header = None, sep = '/t',engine = 'python')\n",
"#y_dev_exp = y_dev_exp[0]\n",
"#RMSE_dev = mean_squared_error(y_dev_exp, y_dev) "
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "close-clinton",
"metadata": {},
"outputs": [],
"source": [
"predict_year(x_dev0, 'dev-0/out.tsv', model)\n",
"predict_year(x_dev1,'dev-1/out.tsv', model)\n",
"predict_year(x_test,'test-A/out.tsv', model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "official-sweet",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

167
.ipynb_checkpoints/run-checkpoint.py Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 10,
"id": "greenhouse-technician",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sklearn\n",
"import pandas as pd\n",
"from gzip import open as open_gz\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_squared_error"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "acoustic-dividend",
"metadata": {},
"outputs": [],
"source": [
"def predict_year(x, path_out, model):\n",
" results = model.predict(x)\n",
" with open(path_out, 'wt') as file:\n",
" for r in results:\n",
" file.write(str(r) + '\\n') "
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "senior-harassment",
"metadata": {},
"outputs": [],
"source": [
"with open('train/train.tsv', 'r', encoding='utf8') as file:\n",
" train = pd.read_csv(file, sep='\\t', names=['Date1', 'Date2', 'Title', 'Author', 'Text'])\n",
" \n",
"#train = train[0:10000]\n",
"train_x = train['Text']\n",
"train['Date'] = (train['Date1'].astype(float) + train['Date2'].astype(float))/2\n",
"train_y=train['Date1']"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "polyphonic-coach",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()),\n",
" ('linearregression', LinearRegression())])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = make_pipeline(TfidfVectorizer(), LinearRegression())\n",
"model.fit(train_x, train_y)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "varying-wright",
"metadata": {},
"outputs": [],
"source": [
"with open('dev-0/in.tsv', 'r', encoding='utf8') as file:\n",
" x_dev0 = pd.read_csv(file, header=None, sep='\\t')\n",
"x_dev0 = x_dev0[0] \n",
"x_dev0[19999] = 'nie jest'\n",
"x_dev0[20000] = 'nie wiem'"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "frozen-ticket",
"metadata": {},
"outputs": [],
"source": [
"with open('dev-1/in.tsv', 'r', encoding='utf8') as file:\n",
" x_dev1 = pd.read_csv(file, header=None, sep='\\t')\n",
"x_dev1 = x_dev1[0] "
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "8e3a18db-f966-45e4-b881-4b336f188055",
"metadata": {},
"outputs": [],
"source": [
"with open('test-A/in.tsv', 'r', encoding='utf8') as file:\n",
" x_test = pd.read_csv(file, header=None, sep='\\t')\n",
"x_test = x_test[0] "
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "traditional-amount",
"metadata": {},
"outputs": [],
"source": [
"#y_dev = pd.read_csv('dev-0/out.tsv',header = None, sep = '/t',engine = 'python')\n",
"#y_dev = y_dev[0]\n",
"#y_dev_exp = pd.read_csv('dev-0/expected.tsv',header = None, sep = '/t',engine = 'python')\n",
"#y_dev_exp = y_dev_exp[0]\n",
"#RMSE_dev = mean_squared_error(y_dev_exp, y_dev) "
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "close-clinton",
"metadata": {},
"outputs": [],
"source": [
"predict_year(x_dev0, 'dev-0/out.tsv', model)\n",
"predict_year(x_dev1,'dev-1/out.tsv', model)\n",
"predict_year(x_test,'test-A/out.tsv', model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "official-sweet",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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167
retroc2.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 10,
"id": "greenhouse-technician",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sklearn\n",
"import pandas as pd\n",
"from gzip import open as open_gz\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_squared_error"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "acoustic-dividend",
"metadata": {},
"outputs": [],
"source": [
"def predict_year(x, path_out, model):\n",
" results = model.predict(x)\n",
" with open(path_out, 'wt') as file:\n",
" for r in results:\n",
" file.write(str(r) + '\\n') "
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "senior-harassment",
"metadata": {},
"outputs": [],
"source": [
"with open('train/train.tsv', 'r', encoding='utf8') as file:\n",
" train = pd.read_csv(file, sep='\\t', names=['Date1', 'Date2', 'Title', 'Author', 'Text'])\n",
" \n",
"#train = train[0:10000]\n",
"train_x = train['Text']\n",
"train['Date'] = (train['Date1'].astype(float) + train['Date2'].astype(float))/2\n",
"train_y=train['Date1']"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "polyphonic-coach",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()),\n",
" ('linearregression', LinearRegression())])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = make_pipeline(TfidfVectorizer(), LinearRegression())\n",
"model.fit(train_x, train_y)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "varying-wright",
"metadata": {},
"outputs": [],
"source": [
"with open('dev-0/in.tsv', 'r', encoding='utf8') as file:\n",
" x_dev0 = pd.read_csv(file, header=None, sep='\\t')\n",
"x_dev0 = x_dev0[0] \n",
"x_dev0[19999] = 'nie jest'\n",
"x_dev0[20000] = 'nie wiem'"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "frozen-ticket",
"metadata": {},
"outputs": [],
"source": [
"with open('dev-1/in.tsv', 'r', encoding='utf8') as file:\n",
" x_dev1 = pd.read_csv(file, header=None, sep='\\t')\n",
"x_dev1 = x_dev1[0] "
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "8e3a18db-f966-45e4-b881-4b336f188055",
"metadata": {},
"outputs": [],
"source": [
"with open('test-A/in.tsv', 'r', encoding='utf8') as file:\n",
" x_test = pd.read_csv(file, header=None, sep='\\t')\n",
"x_test = x_test[0] "
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "traditional-amount",
"metadata": {},
"outputs": [],
"source": [
"#y_dev = pd.read_csv('dev-0/out.tsv',header = None, sep = '/t',engine = 'python')\n",
"#y_dev = y_dev[0]\n",
"#y_dev_exp = pd.read_csv('dev-0/expected.tsv',header = None, sep = '/t',engine = 'python')\n",
"#y_dev_exp = y_dev_exp[0]\n",
"#RMSE_dev = mean_squared_error(y_dev_exp, y_dev) "
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "close-clinton",
"metadata": {},
"outputs": [],
"source": [
"predict_year(x_dev0, 'dev-0/out.tsv', model)\n",
"predict_year(x_dev1,'dev-1/out.tsv', model)\n",
"predict_year(x_test,'test-A/out.tsv', model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "official-sweet",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

159
run.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "greenhouse-technician",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sklearn\n",
"import pandas as pd\n",
"from gzip import open as open_gz\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_squared_error"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "acoustic-dividend",
"metadata": {},
"outputs": [],
"source": [
"def predict_year(x, path_out, model):\n",
" results = model.predict(x)\n",
" with open(path_out, 'wt') as file:\n",
" for r in results:\n",
" file.write(str(r) + '\\n') "
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "78c79a98-8309-4c1c-b27d-faad2ee7a2af",
"metadata": {},
"outputs": [],
"source": [
"def read_file(filename):\n",
" result = []\n",
" with open(filename, 'r', encoding=\"utf-8\") as file:\n",
" for line in file:\n",
" text = line.split(\"\\t\")[0].strip()\n",
" result.append(text)\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "senior-harassment",
"metadata": {},
"outputs": [],
"source": [
"with open('train/train.tsv', 'r', encoding='utf8') as file:\n",
" train = pd.read_csv(file, sep='\\t', names=['Begin', 'End', 'Title', 'Author', 'Text'])\n",
" \n",
"train = train[0:12000]\n",
"train_x = train['Text']\n",
"#train['Date'] = (train['Date1'].astype(float) + train['Date2'].astype(float))/2\n",
"train_y = train['Begin']"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "polyphonic-coach",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()),\n",
" ('linearregression', LinearRegression())])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = make_pipeline(TfidfVectorizer(), LinearRegression())\n",
"model.fit(train_x, train_y)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "varying-wright",
"metadata": {},
"outputs": [],
"source": [
"x_dev_0 = read_file('dev-0/in.tsv')\n",
"predict_year(x_dev_0, 'dev-0/out.tsv', model)\n",
"x_dev_1 = read_file('dev-1/in.tsv')\n",
"predict_year(x_dev_1,'dev-1/out.tsv', model)\n",
"x_test = read_file('test-A/in.tsv')\n",
"predict_year(x_test,'test-A/out.tsv', model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "traditional-amount",
"metadata": {},
"outputs": [],
"source": [
"#y_dev = pd.read_csv('dev-0/out.tsv',header = None, sep = '/t',engine = 'python')\n",
"#y_dev = y_dev[0]\n",
"#y_dev_exp = pd.read_csv('dev-0/expected.tsv',header = None, sep = '/t',engine = 'python')\n",
"#y_dev_exp = y_dev_exp[0]\n",
"#RMSE_dev = mean_squared_error(y_dev_exp, y_dev) "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "close-clinton",
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "official-sweet",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

41
run.py Normal file
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import os
import sklearn
import pandas as pd
from gzip import open as open_gz
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
def predict_year(x, path_out, model):
results = model.predict(x)
with open(path_out, 'wt') as file:
for r in results:
file.write(str(r) + '\n')
def read_file(filename):
result = []
with open(filename, 'r', encoding="utf-8") as file:
for line in file:
text = line.split("\t")[0].strip()
result.append(text)
return result
with open('train/train.tsv', 'r', encoding='utf8') as file:
train = pd.read_csv(file, sep='\t', names=['Start', 'End', 'Title', 'Author', 'Text'])
train = train[0:12000]
train_x = train['Text']
#train['Date'] = (train['Start'].astype(float) + train['End'].astype(float))/2
train_y = train['Start']
model = make_pipeline(TfidfVectorizer(), LinearRegression())
model.fit(train_x, train_y)
x_dev_0 = read_file('dev-0/in.tsv')
predict_year(x_dev_0, 'dev-0/out.tsv', model)
x_dev_1 = read_file('dev-1/in.tsv')
predict_year(x_dev_1,'dev-1/out.tsv', model)
x_test = read_file('test-A/in.tsv')
predict_year(x_test,'test-A/out.tsv', model)

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test-A/out.tsv Normal file
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107471
train/train.tsv Normal file
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