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6
.ipynb_checkpoints/LogReg_Test-checkpoint.ipynb
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6
.ipynb_checkpoints/LogReg_Test-checkpoint.ipynb
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{
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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69
Bayes.py
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69
Bayes.py
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'''
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Autor: Dominik Strzałko
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Data: 05.08.2021
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Zadanie: naiwny bayes2 gotowa biblioteka (Skeptic vs paranormal subreddits)
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Wyniki z geval:
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Likelihood 0.0000
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Accuracy 0.7367
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F1.0 0.4367
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Precision 0.8997
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Recall 0.2883
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'''
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import numpy as np
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from sklearn.preprocessing import LabelEncoder
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.pipeline import make_pipeline
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from sklearn.feature_extraction.text import TfidfVectorizer
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def open_tsv(tsv):
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'''
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Funkcja do zamiany plików tsv jako listy linii tekstu.
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Na wejście potrzebuje ścieżkę do pliku .tsv
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np. X = open_tsv("train/expected.tsv")
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'''
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with open(tsv) as f:
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return f.readlines()
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def Create_model(X_tsv, Y_tsv):
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'''
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Funkcja przeznaczona do tworzenia modelu uczenia maszynowego.
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Na wejście trzeba podać zbiór X_train oraz Y_train w formie plików tsv.
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np. model = Create_model("train/in.tsv", "train/expected.tsv")
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'''
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X = open_tsv(X_tsv)
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Y = open_tsv(Y_tsv)
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Y = LabelEncoder().fit_transform(Y)
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pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())
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return pipeline.fit(X, Y)
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def predict(model, X_tsv, file_name):
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'''
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Funkcja przeznaczona do predykcji wyników na podstawie modelu oraz zbiory X. trzecim argumentem w funkcji jest nazwa pliku z predykcjami, do zapisania na dysku.
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np. predict(model, "dev-0/in.tsv", "dev-0/out.tsv")
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'''
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X = open_tsv(X_tsv)
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prediction = model.predict(X)
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np.savetxt(file_name, prediction, fmt='%d')
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def main():
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model = Create_model("train/in.tsv", "train/expected.tsv")
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predict(model, "dev-0/in.tsv", "dev-0/out.tsv")
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predict(model, "test-A/in.tsv", "test-A/out.tsv")
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if __name__ == '__main__':
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main()
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112
LogReg.py
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112
LogReg.py
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import pandas as pd
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import numpy as np
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import torch
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from nltk.tokenize import word_tokenize
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import gensim.downloader as api
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# Wczytanie X i Y do Train oraz X do Dev i Test
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X_train = pd.read_table('train/in.tsv', sep='\t', error_bad_lines=False, quoting=3, header=None, names=['content', 'id'], usecols=['content'])
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y_train = pd.read_table('train/expected.tsv', sep='\t', error_bad_lines=False, quoting=3, header=None, names=['label'])
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X_dev = pd.read_table('dev-0/in.tsv', sep='\t', error_bad_lines=False, header=None, quoting=3, names=['content', 'id'], usecols=['content'])
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X_test = pd.read_table('test-A/in.tsv', sep='\t', error_bad_lines=False, header=None, quoting=3, names=['content', 'id'], usecols=['content'])
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# lowercase-ing zbiorów
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# https://www.datacamp.com/community/tutorials/case-conversion-python
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X_train = X_train.content.str.lower()
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X_dev = X_dev.content.str.lower()
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X_test = X_test.content.str.lower()
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y_train = y_train['label'] #Df do Series?
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# tokenizacja zbiorów
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#https://www.nltk.org/_modules/nltk/tokenize.html
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X_train = [word_tokenize(doc) for doc in X_train]
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X_dev = [word_tokenize(doc) for doc in X_dev]
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X_test = [word_tokenize(doc) for doc in X_test]
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# word2vec zgodnie z poradą Pana Jakuba
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# https://radimrehurek.com/gensim/auto_examples/howtos/run_downloader_api.html
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# https://www.kaggle.com/kstathou/word-embeddings-logistic-regression
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w2v = api.load('word2vec-google-news-300')
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def document_vector(doc):
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"""Create document vectors by averaging word vectors. Remove out-of-vocabulary words."""
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return np.mean([w2v[w] for w in doc if w in w2v] or [np.zeros(300)], axis=0)
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X_train = [document_vector(doc) for doc in X_train]
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X_dev = [document_vector(doc) for doc in X_dev]
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X_test = [document_vector(doc) for doc in X_test]
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#Sieć neuronowa z ćwiczeń 8
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#https://git.wmi.amu.edu.pl/filipg/aitech-eks-pub/src/branch/master/cw/08_regresja_logistyczna.ipynb
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class NeuralNetwork(torch.nn.Module):
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def __init__(self, hidden_size):
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super(NeuralNetwork, self).__init__()
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self.l1 = torch.nn.Linear(300, hidden_size) #Korzystamy z word2vec-google-news-300 który ma zawsze na wejściu wymiar 300
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self.l2 = torch.nn.Linear(hidden_size, 1)
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def forward(self, x):
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x = self.l1(x)
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x = torch.relu(x)
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x = self.l2(x)
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x = torch.sigmoid(x)
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return x
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model = NeuralNetwork(600)
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criterion = torch.nn.BCELoss()
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optimizer = torch.optim.SGD(model.parameters(), lr = 0.1)
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batch_size = 15
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# Trening modelu z ćwiczeń 8
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#https://git.wmi.amu.edu.pl/filipg/aitech-eks-pub/src/branch/master/cw/08_regresja_logistyczna.ipynb
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for epoch in range(5):
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model.train()
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for i in range(0, y_train.shape[0], batch_size):
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X = X_train[i:i+batch_size]
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X = torch.tensor(X)
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y = y_train[i:i+batch_size]
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y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1,1)
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outputs = model(X.float())
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loss = criterion(outputs, y)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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y_dev = []
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y_test = []
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#Predykcje
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#model.eval() will notify all your layers that you are in eval mode
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model.eval()
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#torch.no_grad() impacts the autograd engine and deactivate it. It will reduce memory usage and speed up
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with torch.no_grad():
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for i in range(0, len(X_dev), batch_size):
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X = X_dev[i:i+batch_size]
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X = torch.tensor(X)
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outputs = model(X.float())
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y = (outputs > 0.5)
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y_dev.extend(y)
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for i in range(0, len(X_test), batch_size):
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X = X_test[i:i+batch_size]
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X = torch.tensor(X)
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outputs = model(X.float())
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y = (outputs > 0.5)
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y_test.extend(y)
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#Wygenerowanie plików outputowych
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y_dev = np.asarray(y_dev, dtype=np.int32)
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y_test = np.asarray(y_test, dtype=np.int32)
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y_dev_df = pd.DataFrame({'label':y_dev})
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y_test_df = pd.DataFrame({'label':y_test})
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y_dev_df.to_csv(r'dev-0/out.tsv', sep='\t', index=False, header=False)
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y_test_df.to_csv(r'test-A/out.tsv', sep='\t', index=False, header=False)
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215
LogReg_Test.ipynb
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215
LogReg_Test.ipynb
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@ -0,0 +1,215 @@
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||||
{
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||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 61,
|
||||
"metadata": {},
|
||||
"outputs": [],
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||||
"source": [
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||||
"import pandas as pd\n",
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||||
"import numpy as np\n",
|
||||
"import torch\n",
|
||||
"from nltk.tokenize import word_tokenize\n",
|
||||
"import gensim.downloader as api"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 62,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Wczytanie X i Y do Train oraz X do Dev i Test\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": 63,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# lowercase-ing zbiorów\n",
|
||||
"# https://www.datacamp.com/community/tutorials/case-conversion-python\n",
|
||||
"X_train = X_train.content.str.lower()\n",
|
||||
"X_dev = X_dev.content.str.lower()\n",
|
||||
"X_test = X_test.content.str.lower()\n",
|
||||
"\n",
|
||||
"y_train = y_train['label'] #Df do Series?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 64,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# tokenizacja zbiorów\n",
|
||||
"#https://www.nltk.org/_modules/nltk/tokenize.html\n",
|
||||
"X_train = [word_tokenize(doc) for doc in X_train]\n",
|
||||
"X_dev = [word_tokenize(doc) for doc in X_dev]\n",
|
||||
"X_test = [word_tokenize(doc) for doc in X_test]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 67,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# word2vec zgodnie z poradą Pana Jakuba\n",
|
||||
"# https://radimrehurek.com/gensim/auto_examples/howtos/run_downloader_api.html\n",
|
||||
"# https://www.kaggle.com/kstathou/word-embeddings-logistic-regression\n",
|
||||
"w2v = api.load('word2vec-google-news-300')\n",
|
||||
"\n",
|
||||
"def document_vector(doc):\n",
|
||||
" \"\"\"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"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 45,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = NeuralNetwork(600)\n",
|
||||
"\n",
|
||||
"criterion = torch.nn.BCELoss()\n",
|
||||
"optimizer = torch.optim.SGD(model.parameters(), lr = 0.1)\n",
|
||||
"\n",
|
||||
"batch_size = 15"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 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",
|
||||
" 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": 59,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_dev = []\n",
|
||||
"y_test = []\n",
|
||||
"\n",
|
||||
"#model.eval() will notify all your layers that you are in eval mode\n",
|
||||
"model.eval()\n",
|
||||
"\n",
|
||||
"#torch.no_grad() impacts the autograd engine and deactivate it. It will reduce memory usage and speed up\n",
|
||||
"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",
|
||||
" \n",
|
||||
" outputs = model(X.float())\n",
|
||||
" \n",
|
||||
" y = (outputs > 0.5)\n",
|
||||
" y_dev.extend(y)\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",
|
||||
"\n",
|
||||
" outputs = model(X.float())\n",
|
||||
"\n",
|
||||
" y = (outputs > 0.5)\n",
|
||||
" y_test.extend(y)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 60,
|
||||
"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_df = pd.DataFrame({'label':y_dev})\n",
|
||||
"y_test_df = pd.DataFrame({'label':y_test})\n",
|
||||
"\n",
|
||||
"y_dev_df.to_csv(r'dev-0/out.tsv', sep='\\t', index=False, header=False)\n",
|
||||
"y_test_df.to_csv(r'test-A/out.tsv', sep='\\t', index=False, header=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
26
README.md
26
README.md
@ -11,3 +11,29 @@ Sources
|
||||
-------
|
||||
|
||||
Data taken from <https://archive.org/details/2015_reddit_comments_corpus>.
|
||||
|
||||
Results from geval (Using Naive Bayes)
|
||||
-------
|
||||
|
||||
Likelihood 0.0000
|
||||
|
||||
Accuracy 0.7367
|
||||
|
||||
F1.0 0.4367
|
||||
|
||||
Precision 0.8997
|
||||
|
||||
Recall 0.2883
|
||||
|
||||
Results from geval (Using Log Reg (NN from Pytorch))
|
||||
-------
|
||||
|
||||
Likelihood 0.0000
|
||||
|
||||
Accuracy 0.7561
|
||||
|
||||
F1.0 0.6152
|
||||
|
||||
Precision 0.6965
|
||||
|
||||
Recall 0.5509
|
6
Wyniki_z_geval.txt
Normal file
6
Wyniki_z_geval.txt
Normal file
@ -0,0 +1,6 @@
|
||||
Wyniki z geval:
|
||||
Likelihood 0.0000
|
||||
Accuracy 0.7367
|
||||
F1.0 0.4367
|
||||
Precision 0.8997
|
||||
Recall 0.2883
|
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Loading…
Reference in New Issue
Block a user