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".py" version of LogReg

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This commit is contained in:
Dominik Strzako 2021-05-22 18:52:56 +02:00
parent 265216824e
commit 6dbb5168eb
5 changed files with 343 additions and 225 deletions
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0
main.py → Bayes.py
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112
LogReg.py Normal file
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import pandas as pd
import numpy as np
import torch
from nltk.tokenize import word_tokenize
import gensim.downloader as api
# Wczytanie X i Y do Train oraz X do Dev i Test
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'])
# lowercase-ing zbiorów
# https://www.datacamp.com/community/tutorials/case-conversion-python
X_train = X_train.content.str.lower()
X_dev = X_dev.content.str.lower()
X_test = X_test.content.str.lower()
y_train = y_train['label'] #Df do Series?
# tokenizacja zbiorów
#https://www.nltk.org/_modules/nltk/tokenize.html
X_train = [word_tokenize(doc) for doc in X_train]
X_dev = [word_tokenize(doc) for doc in X_dev]
X_test = [word_tokenize(doc) for doc in X_test]
# word2vec zgodnie z poradą Pana Jakuba
# https://radimrehurek.com/gensim/auto_examples/howtos/run_downloader_api.html
# https://www.kaggle.com/kstathou/word-embeddings-logistic-regression
w2v = api.load('word2vec-google-news-300')
def document_vector(doc):
"""Create document vectors by averaging word vectors. Remove out-of-vocabulary words."""
return np.mean([w2v[w] for w in doc if w in w2v] 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]
#Sieć neuronowa z ćwiczeń 8
#https://git.wmi.amu.edu.pl/filipg/aitech-eks-pub/src/branch/master/cw/08_regresja_logistyczna.ipynb
class NeuralNetwork(torch.nn.Module):
def __init__(self, hidden_size):
super(NeuralNetwork, self).__init__()
self.l1 = torch.nn.Linear(300, hidden_size) #Korzystamy z word2vec-google-news-300 który ma zawsze na wejściu wymiar 300
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
model = NeuralNetwork(600)
criterion = torch.nn.BCELoss()
optimizer = torch.optim.SGD(model.parameters(), lr = 0.1)
batch_size = 15
# Trening modelu z ćwiczeń 8
#https://git.wmi.amu.edu.pl/filipg/aitech-eks-pub/src/branch/master/cw/08_regresja_logistyczna.ipynb
for epoch in range(5):
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 = []
#Predykcje
#model.eval() will notify all your layers that you are in eval mode
model.eval()
#torch.no_grad() impacts the autograd engine and deactivate it. It will reduce memory usage and speed up
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())
y = (outputs > 0.5)
y_dev.extend(y)
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(y)
#Wygenerowanie plików outputowych
y_dev = np.asarray(y_dev, dtype=np.int32)
y_test = np.asarray(y_test, dtype=np.int32)
y_dev_df = pd.DataFrame({'label':y_dev})
y_test_df = pd.DataFrame({'label':y_test})
y_dev_df.to_csv(r'dev-0/out.tsv', sep='\t', index=False, header=False)
y_test_df.to_csv(r'test-A/out.tsv', sep='\t', index=False, header=False)

84
LogReg_Test.ipynb
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@ -2,43 +2,20 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 38, "execution_count": 61,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"import pandas as pd\n", "import pandas as pd\n",
"import numpy as np\n", "import numpy as np\n",
"import torch\n", "import torch\n",
"import csv\n",
"from nltk.tokenize import word_tokenize\n", "from nltk.tokenize import word_tokenize\n",
"#from gensim.models import Word2Vec\n",
"import gensim.downloader as api" "import gensim.downloader as api"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 39, "execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"#Sieć neuronowa z ćwiczeń 8\n",
"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) #Korzystamy z Googlowego word2vec-google-news-300 który ma zawsze na wejściu wymiar 300\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": 40,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -51,34 +28,35 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 41, "execution_count": 63,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"# Preprocessing danych\n", "# lowercase-ing zbiorów\n",
"# lowercase\n",
"# https://www.datacamp.com/community/tutorials/case-conversion-python\n", "# https://www.datacamp.com/community/tutorials/case-conversion-python\n",
"X_train = X_train.content.str.lower()\n", "X_train = X_train.content.str.lower()\n",
"y_train = y_train['label']\n",
"X_dev = X_dev.content.str.lower()\n", "X_dev = X_dev.content.str.lower()\n",
"X_test = X_test.content.str.lower()" "X_test = X_test.content.str.lower()\n",
"\n",
"y_train = y_train['label'] #Df do Series?"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 42, "execution_count": 64,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"# tokenize\n", "# tokenizacja zbiorów\n",
"X_train = [word_tokenize(content) for content in X_train]\n", "#https://www.nltk.org/_modules/nltk/tokenize.html\n",
"X_dev = [word_tokenize(content) for content in X_dev]\n", "X_train = [word_tokenize(doc) for doc in X_train]\n",
"X_test = [word_tokenize(content) for content in X_test]" "X_dev = [word_tokenize(doc) for doc in X_dev]\n",
"X_test = [word_tokenize(doc) for doc in X_test]"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 44, "execution_count": 67,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -86,9 +64,36 @@
"# https://radimrehurek.com/gensim/auto_examples/howtos/run_downloader_api.html\n", "# https://radimrehurek.com/gensim/auto_examples/howtos/run_downloader_api.html\n",
"# https://www.kaggle.com/kstathou/word-embeddings-logistic-regression\n", "# https://www.kaggle.com/kstathou/word-embeddings-logistic-regression\n",
"w2v = api.load('word2vec-google-news-300')\n", "w2v = api.load('word2vec-google-news-300')\n",
"X_train = [np.mean([w2v[w] for w in content if w in w2v] or [np.zeros(300)], axis=0) for content in X_train]\n", "\n",
"X_dev = [np.mean([w2v[w] for w in content if w in w2v] or [np.zeros(300)], axis=0) for content in X_dev]\n", "def document_vector(doc):\n",
"X_test = [np.mean([w2v[w] for w in content if w in w2v] or [np.zeros(300)], axis=0) for content in X_test]" " \"\"\"Create document vectors by averaging word vectors. Remove out-of-vocabulary words.\"\"\"\n",
" return np.mean([w2v[w] for w in doc if w in w2v] or [np.zeros(300)], axis=0)\n",
"\n",
"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": null,
"metadata": {},
"outputs": [],
"source": [
"#Sieć neuronowa z ćwiczeń 8\n",
"#https://git.wmi.amu.edu.pl/filipg/aitech-eks-pub/src/branch/master/cw/08_regresja_logistyczna.ipynb\n",
"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) #Korzystamy z word2vec-google-news-300 który ma zawsze na wejściu wymiar 300\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"
] ]
}, },
{ {
@ -112,6 +117,7 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"# Trening modelu z ćwiczeń 8\n", "# Trening modelu z ćwiczeń 8\n",
"#https://git.wmi.amu.edu.pl/filipg/aitech-eks-pub/src/branch/master/cw/08_regresja_logistyczna.ipynb\n",
"for epoch in range(5):\n", "for epoch in range(5):\n",
" model.train()\n", " model.train()\n",
" for i in range(0, y_train.shape[0], batch_size):\n", " for i in range(0, y_train.shape[0], batch_size):\n",

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198
test-A/out.tsv
View File

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