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logistic regression

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s434695 2021-05-25 20:59:48 +02:00
parent f02d3abb2f
commit 1dc429b554
5 changed files with 1832 additions and 3943 deletions
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269
.ipynb_checkpoints/LogisticRegression-checkpoint.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import torch\n",
"from nltk.tokenize import word_tokenize\n",
"import gensim.downloader"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#wczytywanie danych\n",
"x_train = pd.read_table('train/in.tsv', sep='\\t', error_bad_lines=False, quoting=3, header=None, names=['content', 'id'], usecols=['content'])\n",
"y_train = pd.read_table('train/expected.tsv', sep='\\t', error_bad_lines=False, quoting=3, header=None, names=['label'])\n",
"x_dev = pd.read_table('dev-0/in.tsv', sep='\\t', error_bad_lines=False, header=None, quoting=3, names=['content', 'id'], usecols=['content'])\n",
"x_test = pd.read_table('test-A/in.tsv', sep='\\t', error_bad_lines=False, header=None, quoting=3, names=['content', 'id'], usecols=['content'])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"x_train = x_train.content.str.lower()\n",
"x_dev = x_dev.content.str.lower()\n",
"x_test = x_test.content.str.lower()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package punkt to /home/tomasz/nltk_data...\n",
"[nltk_data] Unzipping tokenizers/punkt.zip.\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import nltk\n",
"nltk.download('punkt')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"x_train = [word_tokenize(content) for content in x_train]\n",
"x_dev = [word_tokenize(content) for content in x_dev]\n",
"x_test = [word_tokenize(content) for content in x_test]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"word2vec = gensim.downloader.load(\"word2vec-google-news-300\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def document_vector(doc):\n",
" \"\"\"Create document vectors by averaging word vectors. Remove out-of-vocabulary words.\"\"\"\n",
" return np.mean([word2vec[w] for w in doc if w in word2vec] or [np.zeros(300)], axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"x_train = [document_vector(doc) for doc in x_train]\n",
"x_dev = [document_vector(doc) for doc in x_dev]\n",
"x_test = [document_vector(doc) for doc in x_test]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"class NeuralNetwork(torch.nn.Module): \n",
" def __init__(self, hidden_size):\n",
" super(NeuralNetwork, self).__init__()\n",
" self.l1 = torch.nn.Linear(300, hidden_size)\n",
" self.l2 = torch.nn.Linear(hidden_size, 1)\n",
"\n",
" def forward(self, x):\n",
" x = self.l1(x)\n",
" x = torch.relu(x)\n",
" x = self.l2(x)\n",
" x = torch.sigmoid(x)\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"hidden_size = 600\n",
"epochs = 5\n",
"batch_size = 15\n",
"model = NeuralNetwork(hidden_size)\n",
"criterion = torch.nn.BCELoss()\n",
"optimizer = torch.optim.SGD(model.parameters(), lr=0.01)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/tomasz/.local/lib/python3.8/site-packages/torch/autograd/__init__.py:130: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:100.)\n",
" Variable._execution_engine.run_backward(\n"
]
}
],
"source": [
"for epoch in range(epochs):\n",
" model.train()\n",
" for i in range(0, y_train.shape[0], batch_size):\n",
" X = x_train[i:i+batch_size]\n",
" X = torch.tensor(X)\n",
" y = y_train[i:i+batch_size]\n",
" y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)\n",
" \n",
" outputs = model(X.float())\n",
" loss = criterion(outputs, y)\n",
" \n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"y_dev = []\n",
"y_test = []"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"NeuralNetwork(\n",
" (l1): Linear(in_features=300, out_features=600, bias=True)\n",
" (l2): Linear(in_features=600, out_features=1, bias=True)\n",
")"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.eval()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"with torch.no_grad():\n",
" for i in range(0, len(x_dev), batch_size):\n",
" X = x_dev[i:i+batch_size]\n",
" X = torch.tensor(X)\n",
" outputs = model(X.float()) \n",
" prediction = (outputs > 0.5)\n",
" y_dev += prediction.tolist()\n",
"\n",
" for i in range(0, len(x_test), batch_size):\n",
" X = x_test[i:i+batch_size]\n",
" X = torch.tensor(X)\n",
" outputs = model(X.float())\n",
" y = (outputs > 0.5)\n",
" y_test += prediction.tolist()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_dev = np.asarray(y_dev, dtype=np.int32)\n",
"y_test = np.asarray(y_test, dtype=np.int32)\n",
"\n",
"y_dev = pd.DataFrame({'label':y_dev})\n",
"y_test = pd.DataFrame({'label':y_test})\n",
"\n",
"y_dev.to_csv(r'dev-0/out.tsv', sep='\\t', index=False, header=False)\n",
"y_test.to_csv(r'test-A/out.tsv', sep='\\t', index=False, header=False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

95
Log_Reg.py Executable file
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import pandas as pd
import numpy as np
import torch
from nltk.tokenize import word_tokenize
import gensim.downloader
x_train = pd.read_table('train/in.tsv', sep='\t', error_bad_lines=False, quoting=3, header=None, names=['content', 'id'], usecols=['content'])
y_train = pd.read_table('train/expected.tsv', sep='\t', error_bad_lines=False, quoting=3, header=None, names=['label'])
x_dev = pd.read_table('dev-0/in.tsv', sep='\t', error_bad_lines=False, header=None, quoting=3, names=['content', 'id'], usecols=['content'])
x_test = pd.read_table('test-A/in.tsv', sep='\t', error_bad_lines=False, header=None, quoting=3, names=['content', 'id'], usecols=['content'])
x_train = x_train.content.str.lower()
x_dev = x_dev.content.str.lower()
x_test = x_test.content.str.lower()
x_train = [word_tokenize(content) for content in x_train]
x_dev = [word_tokenize(content) for content in x_dev]
x_test = [word_tokenize(content) for content in x_test]
word2vec = gensim.downloader.load("word2vec-google-news-300")
def document_vector(doc):
"""Create document vectors by averaging word vectors. Remove out-of-vocabulary words."""
return np.mean([word2vec[w] for w in doc if w in word2vec] or [np.zeros(300)], axis=0)
x_train = [document_vector(doc) for doc in x_train]
x_dev = [document_vector(doc) for doc in x_dev]
x_test = [document_vector(doc) for doc in x_test]
class NeuralNetwork(torch.nn.Module):
def __init__(self, hidden_size):
super(NeuralNetwork, self).__init__()
self.l1 = torch.nn.Linear(300, hidden_size)
self.l2 = torch.nn.Linear(hidden_size, 1)
def forward(self, x):
x = self.l1(x)
x = torch.relu(x)
x = self.l2(x)
x = torch.sigmoid(x)
return x
hidden_size = 600
epochs = 5
batch_size = 15
model = NeuralNetwork(hidden_size)
criterion = torch.nn.BCELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
for epoch in range(epochs):
model.train()
for i in range(0, y_train.shape[0], batch_size):
X = x_train[i:i+batch_size]
X = torch.tensor(X)
y = y_train[i:i+batch_size]
y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)
outputs = model(X.float())
loss = criterion(outputs, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
y_dev = []
y_test = []
model.eval()
with torch.no_grad():
for i in range(0, len(x_dev), batch_size):
X = x_dev[i:i+batch_size]
X = torch.tensor(X)
outputs = model(X.float())
prediction = (outputs > 0.5)
y_dev.extend(prediction)
for i in range(0, len(x_test), batch_size):
X = x_test[i:i+batch_size]
X = torch.tensor(X)
outputs = model(X.float())
y = (outputs > 0.5)
y_test.extend(prediction)
y_dev = np.asarray(y_dev, dtype=np.int32)
y_test = np.asarray(y_test, dtype=np.int32)
y_dev = pd.DataFrame({'label':y_dev})
y_test = pd.DataFrame({'label':y_test})
y_dev.to_csv(r'dev-0/out.tsv', sep='\t', index=False, header=False)
y_test.to_csv(r'test-A/out.tsv', sep='\t', index=False, header=False)

269
LogisticRegression.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import torch\n",
"from nltk.tokenize import word_tokenize\n",
"import gensim.downloader"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#wczytywanie danych\n",
"x_train = pd.read_table('train/in.tsv', sep='\\t', error_bad_lines=False, quoting=3, header=None, names=['content', 'id'], usecols=['content'])\n",
"y_train = pd.read_table('train/expected.tsv', sep='\\t', error_bad_lines=False, quoting=3, header=None, names=['label'])\n",
"x_dev = pd.read_table('dev-0/in.tsv', sep='\\t', error_bad_lines=False, header=None, quoting=3, names=['content', 'id'], usecols=['content'])\n",
"x_test = pd.read_table('test-A/in.tsv', sep='\\t', error_bad_lines=False, header=None, quoting=3, names=['content', 'id'], usecols=['content'])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"x_train = x_train.content.str.lower()\n",
"x_dev = x_dev.content.str.lower()\n",
"x_test = x_test.content.str.lower()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package punkt to /home/tomasz/nltk_data...\n",
"[nltk_data] Unzipping tokenizers/punkt.zip.\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import nltk\n",
"nltk.download('punkt')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"x_train = [word_tokenize(content) for content in x_train]\n",
"x_dev = [word_tokenize(content) for content in x_dev]\n",
"x_test = [word_tokenize(content) for content in x_test]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"word2vec = gensim.downloader.load(\"word2vec-google-news-300\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def document_vector(doc):\n",
" \"\"\"Create document vectors by averaging word vectors. Remove out-of-vocabulary words.\"\"\"\n",
" return np.mean([word2vec[w] for w in doc if w in word2vec] or [np.zeros(300)], axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"x_train = [document_vector(doc) for doc in x_train]\n",
"x_dev = [document_vector(doc) for doc in x_dev]\n",
"x_test = [document_vector(doc) for doc in x_test]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"class NeuralNetwork(torch.nn.Module): \n",
" def __init__(self, hidden_size):\n",
" super(NeuralNetwork, self).__init__()\n",
" self.l1 = torch.nn.Linear(300, hidden_size)\n",
" self.l2 = torch.nn.Linear(hidden_size, 1)\n",
"\n",
" def forward(self, x):\n",
" x = self.l1(x)\n",
" x = torch.relu(x)\n",
" x = self.l2(x)\n",
" x = torch.sigmoid(x)\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"hidden_size = 600\n",
"epochs = 5\n",
"batch_size = 15\n",
"model = NeuralNetwork(hidden_size)\n",
"criterion = torch.nn.BCELoss()\n",
"optimizer = torch.optim.SGD(model.parameters(), lr=0.01)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/tomasz/.local/lib/python3.8/site-packages/torch/autograd/__init__.py:130: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:100.)\n",
" Variable._execution_engine.run_backward(\n"
]
}
],
"source": [
"for epoch in range(epochs):\n",
" model.train()\n",
" for i in range(0, y_train.shape[0], batch_size):\n",
" X = x_train[i:i+batch_size]\n",
" X = torch.tensor(X)\n",
" y = y_train[i:i+batch_size]\n",
" y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)\n",
" \n",
" outputs = model(X.float())\n",
" loss = criterion(outputs, y)\n",
" \n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"y_dev = []\n",
"y_test = []"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"NeuralNetwork(\n",
" (l1): Linear(in_features=300, out_features=600, bias=True)\n",
" (l2): Linear(in_features=600, out_features=1, bias=True)\n",
")"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.eval()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"with torch.no_grad():\n",
" for i in range(0, len(x_dev), batch_size):\n",
" X = x_dev[i:i+batch_size]\n",
" X = torch.tensor(X)\n",
" outputs = model(X.float()) \n",
" prediction = (outputs > 0.5)\n",
" y_dev.extend(prediction)\n",
"\n",
" for i in range(0, len(x_test), batch_size):\n",
" X = x_test[i:i+batch_size]\n",
" X = torch.tensor(X)\n",
" outputs = model(X.float())\n",
" y = (outputs > 0.5)\n",
" y_test.extend(prediction)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"y_dev = np.asarray(y_dev, dtype=np.int32)\n",
"y_test = np.asarray(y_test, dtype=np.int32)\n",
"\n",
"y_dev = pd.DataFrame({'label':y_dev})\n",
"y_test = pd.DataFrame({'label':y_test})\n",
"\n",
"y_dev.to_csv(r'dev-0/out.tsv', sep='\\t', index=False, header=False)\n",
"y_test.to_csv(r'test-A/out.tsv', sep='\\t', index=False, header=False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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dev-0/out.tsv
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test-A/out.tsv
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