.ipynb_checkpoints/LogReg_Test-checkpoint.ipynb

@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 4
-}

Bayes.py

@@ -1,69 +0,0 @@
-'''
-Autor: Dominik Strzałko
-Data: 05.08.2021
-Zadanie: naiwny bayes2 gotowa biblioteka (Skeptic vs paranormal subreddits)
-
-Wyniki z geval:
-Likelihood      0.0000
-Accuracy        0.7367
-F1.0            0.4367
-Precision       0.8997
-Recall          0.2883
-'''
-import numpy as np
-from sklearn.preprocessing import LabelEncoder
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.pipeline import make_pipeline
-from sklearn.feature_extraction.text import TfidfVectorizer
-
-def open_tsv(tsv):
-    '''
-    Funkcja do zamiany plików tsv jako listy linii tekstu.
-
-    Na wejście potrzebuje ścieżkę do pliku .tsv
-
-    np. X = open_tsv("train/expected.tsv")
-    '''
-    with open(tsv) as f:
-        return f.readlines()
-
-def Create_model(X_tsv, Y_tsv):
-    '''
-    Funkcja przeznaczona do tworzenia modelu uczenia maszynowego. 
-    
-    Na wejście trzeba podać zbiór X_train oraz Y_train w formie plików tsv.
-
-    np. model = Create_model("train/in.tsv", "train/expected.tsv")
-    '''
-
-    X = open_tsv(X_tsv)
-    Y = open_tsv(Y_tsv)
-
-    Y = LabelEncoder().fit_transform(Y)
-    pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())
-
-    return pipeline.fit(X, Y)
-
-
-def predict(model, X_tsv, file_name):
-    '''
-    Funkcja przeznaczona do predykcji wyników na podstawie modelu oraz zbiory X. trzecim argumentem w funkcji jest nazwa pliku z predykcjami, do zapisania na dysku.
-
-    np. predict(model, "dev-0/in.tsv", "dev-0/out.tsv")
-    '''
-    X = open_tsv(X_tsv)
-
-    prediction = model.predict(X)
-    np.savetxt(file_name, prediction, fmt='%d')
-
-
-def main():
-
-    model = Create_model("train/in.tsv", "train/expected.tsv")
-
-    predict(model, "dev-0/in.tsv", "dev-0/out.tsv")
-    predict(model, "test-A/in.tsv", "test-A/out.tsv")
-
-
-if __name__ == '__main__':
-    main()

LogReg.py

@@ -1,112 +0,0 @@
-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)

LogReg_Test.ipynb

@@ -1,215 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "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 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
-}

README.md

@@ -11,29 +11,3 @@ 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

Wyniki_z_geval.txt

@@ -1,6 +0,0 @@
-Wyniki z geval:
-Likelihood      0.0000
-Accuracy        0.7367
-F1.0            0.4367
-Precision       0.8997
-Recall          0.2883