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1k train data

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This commit is contained in:
Sebastian 2022-05-17 23:11:21 +02:00
parent ee3f9379e0
commit 0faa3da61f
5 changed files with 34391 additions and 34306 deletions
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80
.ipynb_checkpoints/run-checkpoint.ipynb
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@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 1,
"id": "d103a6c5-a9b4-4547-9e10-f384d716972d", "id": "d103a6c5-a9b4-4547-9e10-f384d716972d",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -12,7 +12,6 @@
"import numpy as np\n", "import numpy as np\n",
"import sklearn\n", "import sklearn\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\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.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_squared_error" "from sklearn.metrics import mean_squared_error"
] ]
@ -32,22 +31,19 @@
"\n", "\n",
" exec(code_obj, self.user_global_ns, self.user_ns)\n" " exec(code_obj, self.user_global_ns, self.user_ns)\n"
] ]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"107463\n"
]
} }
], ],
"source": [ "source": [
"train = pd.read_csv('train/train.tsv', header=None, sep='\\t', error_bad_lines=False)\n", "train = pd.read_csv('train/train.tsv', header=None, sep='\\t', error_bad_lines=False)\n",
"train = train.head(500)" "print(len(train))\n",
] "train = train.head(1000)"
},
{
"cell_type": "code",
"execution_count": null,
"id": "e4b5f917-bde7-4b69-a394-1ab0fe0b752a",
"metadata": {},
"outputs": [],
"source": [
"# with open('train/train.tsv', 'r', encoding='utf8') as file:\n",
"# train = pd.read_csv(file, sep='\\t')"
] ]
}, },
{ {
@ -61,16 +57,6 @@
"y_train = train[0]" "y_train = train[0]"
] ]
}, },
{
"cell_type": "code",
"execution_count": null,
"id": "5faa4b35-ccf7-4656-a08a-99d1d96d8a21",
"metadata": {},
"outputs": [],
"source": [
"print(x_train)"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 4,
@ -80,9 +66,9 @@
"source": [ "source": [
"x_dev_data = pd.read_csv('dev-0/in.tsv', header=None, sep='\\t')\n", "x_dev_data = pd.read_csv('dev-0/in.tsv', header=None, sep='\\t')\n",
"x_dev = x_dev_data[0]\n", "x_dev = x_dev_data[0]\n",
"y_dev = pd.read_csv('dev-0/expected.tsv', header=None, sep='\\t')\n", "x_dev[19999] = \"to jest tekst testowy\"\n",
"# x_dev.head(1000)\n", "x_dev[20000] = \"a ten tekst jest najbardziej testowy\"\n",
"# y_dev.head(1000)" "y_dev = pd.read_csv('dev-0/expected.tsv', header=None, sep='\\t')"
] ]
}, },
{ {
@ -151,25 +137,12 @@
" f.write(str(i)+'\\n')\n", " f.write(str(i)+'\\n')\n",
"\n", "\n",
"dev_out = pd.read_csv('dev-0/out.tsv', header=None, sep='\\t')\n", "dev_out = pd.read_csv('dev-0/out.tsv', header=None, sep='\\t')\n",
"# dev_out = dev_out.head(1000)\n", "dev_expected = pd.read_csv('dev-0/expected.tsv', header=None, sep='\\t')\n"
"dev_expected = pd.read_csv('dev-0/expected.tsv', header=None, sep='\\t')\n",
"# dev_expected = dev_expected.head(1000)\n",
"# print(mean_squared_error(dev_out, dev_expected))\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 10,
"id": "7b265b2b-cac1-457c-80f9-6f6dec30045b",
"metadata": {},
"outputs": [],
"source": [
"dev_expected = dev_expected.head(19998)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "223de995-5e91-4254-9214-4fc871c985e9", "id": "223de995-5e91-4254-9214-4fc871c985e9",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -177,7 +150,7 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"3579.7645467601897\n" "3486.2683285642797\n"
] ]
} }
], ],
@ -187,7 +160,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 18, "execution_count": 11,
"id": "3bc8418b-64f1-4163-a0ec-8e3293032341", "id": "3bc8418b-64f1-4163-a0ec-8e3293032341",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -204,6 +177,25 @@
" for i in test_predicted:\n", " for i in test_predicted:\n",
" f.write(str(i)+'\\n')" " f.write(str(i)+'\\n')"
] ]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "a18aea56-7fa1-40bd-8aa3-bbaf9d66d6b7",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[NbConvertApp] Converting notebook run.ipynb to script\n",
"[NbConvertApp] Writing 1629 bytes to run.py\n"
]
}
],
"source": [
"!jupyter nbconvert --to script run.ipynb"
]
} }
], ],
"metadata": { "metadata": {

39998
dev-0/out.tsv
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80
run.ipynb
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@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 1,
"id": "d103a6c5-a9b4-4547-9e10-f384d716972d", "id": "d103a6c5-a9b4-4547-9e10-f384d716972d",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -12,7 +12,6 @@
"import numpy as np\n", "import numpy as np\n",
"import sklearn\n", "import sklearn\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\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.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_squared_error" "from sklearn.metrics import mean_squared_error"
] ]
@ -32,22 +31,19 @@
"\n", "\n",
" exec(code_obj, self.user_global_ns, self.user_ns)\n" " exec(code_obj, self.user_global_ns, self.user_ns)\n"
] ]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"107463\n"
]
} }
], ],
"source": [ "source": [
"train = pd.read_csv('train/train.tsv', header=None, sep='\\t', error_bad_lines=False)\n", "train = pd.read_csv('train/train.tsv', header=None, sep='\\t', error_bad_lines=False)\n",
"train = train.head(500)" "print(len(train))\n",
] "train = train.head(1000)"
},
{
"cell_type": "code",
"execution_count": null,
"id": "e4b5f917-bde7-4b69-a394-1ab0fe0b752a",
"metadata": {},
"outputs": [],
"source": [
"# with open('train/train.tsv', 'r', encoding='utf8') as file:\n",
"# train = pd.read_csv(file, sep='\\t')"
] ]
}, },
{ {
@ -61,16 +57,6 @@
"y_train = train[0]" "y_train = train[0]"
] ]
}, },
{
"cell_type": "code",
"execution_count": null,
"id": "5faa4b35-ccf7-4656-a08a-99d1d96d8a21",
"metadata": {},
"outputs": [],
"source": [
"print(x_train)"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 4,
@ -80,9 +66,9 @@
"source": [ "source": [
"x_dev_data = pd.read_csv('dev-0/in.tsv', header=None, sep='\\t')\n", "x_dev_data = pd.read_csv('dev-0/in.tsv', header=None, sep='\\t')\n",
"x_dev = x_dev_data[0]\n", "x_dev = x_dev_data[0]\n",
"y_dev = pd.read_csv('dev-0/expected.tsv', header=None, sep='\\t')\n", "x_dev[19999] = \"to jest tekst testowy\"\n",
"# x_dev.head(1000)\n", "x_dev[20000] = \"a ten tekst jest najbardziej testowy\"\n",
"# y_dev.head(1000)" "y_dev = pd.read_csv('dev-0/expected.tsv', header=None, sep='\\t')"
] ]
}, },
{ {
@ -151,25 +137,12 @@
" f.write(str(i)+'\\n')\n", " f.write(str(i)+'\\n')\n",
"\n", "\n",
"dev_out = pd.read_csv('dev-0/out.tsv', header=None, sep='\\t')\n", "dev_out = pd.read_csv('dev-0/out.tsv', header=None, sep='\\t')\n",
"# dev_out = dev_out.head(1000)\n", "dev_expected = pd.read_csv('dev-0/expected.tsv', header=None, sep='\\t')\n"
"dev_expected = pd.read_csv('dev-0/expected.tsv', header=None, sep='\\t')\n",
"# dev_expected = dev_expected.head(1000)\n",
"# print(mean_squared_error(dev_out, dev_expected))\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 10,
"id": "7b265b2b-cac1-457c-80f9-6f6dec30045b",
"metadata": {},
"outputs": [],
"source": [
"dev_expected = dev_expected.head(19998)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "223de995-5e91-4254-9214-4fc871c985e9", "id": "223de995-5e91-4254-9214-4fc871c985e9",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -177,7 +150,7 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"3579.7645467601897\n" "3486.2683285642797\n"
] ]
} }
], ],
@ -187,7 +160,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 18, "execution_count": 11,
"id": "3bc8418b-64f1-4163-a0ec-8e3293032341", "id": "3bc8418b-64f1-4163-a0ec-8e3293032341",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -204,6 +177,25 @@
" for i in test_predicted:\n", " for i in test_predicted:\n",
" f.write(str(i)+'\\n')" " f.write(str(i)+'\\n')"
] ]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "a18aea56-7fa1-40bd-8aa3-bbaf9d66d6b7",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[NbConvertApp] Converting notebook run.ipynb to script\n",
"[NbConvertApp] Writing 1629 bytes to run.py\n"
]
}
],
"source": [
"!jupyter nbconvert --to script run.ipynb"
]
} }
], ],
"metadata": { "metadata": {

99
run.py Normal file
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@ -0,0 +1,99 @@
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import os
import pandas as pd
import numpy as np
import sklearn
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
# In[2]:
train = pd.read_csv('train/train.tsv', header=None, sep='\t', error_bad_lines=False)
print(len(train))
train = train.head(1000)
# In[3]:
x_train = train[4]
y_train = train[0]
# In[4]:
x_dev_data = pd.read_csv('dev-0/in.tsv', header=None, sep='\t')
x_dev = x_dev_data[0]
x_dev[19999] = "to jest tekst testowy"
x_dev[20000] = "a ten tekst jest najbardziej testowy"
y_dev = pd.read_csv('dev-0/expected.tsv', header=None, sep='\t')
# In[5]:
vectorizer = TfidfVectorizer()
# In[6]:
x_train = vectorizer.fit_transform(x_train)
x_dev = vectorizer.transform(x_dev)
# In[7]:
model = LinearRegression()
# In[8]:
model.fit(x_train.toarray(), y_train)
# In[9]:
dev_predicted = model.predict(x_dev.toarray())
with open('dev-0/out.tsv', 'wt') as f:
for i in dev_predicted:
f.write(str(i)+'\n')
dev_out = pd.read_csv('dev-0/out.tsv', header=None, sep='\t')
dev_expected = pd.read_csv('dev-0/expected.tsv', header=None, sep='\t')
# In[10]:
print(mean_squared_error(dev_out, dev_expected))
# In[ ]:
with open('test-A/in.tsv', 'r', encoding = 'utf-8') as f:
x_test = f.readlines()
x_test = pd.Series(x_test)
x_test = vectorizer.transform(x_test)
test_predicted = model.predict(x_test.toarray())
with open('test-A/out.tsv', 'wt') as f:
for i in test_predicted:
f.write(str(i)+'\n')

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test-A/out.tsv
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