Update 'README.md'

.ipynb_checkpoints/LogReg_Test-checkpoint.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Bayes.py

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+'''
+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

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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)

LogReg_Test.ipynb

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+{
+ "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

@@ -11,3 +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

Wyniki_z_geval.txt

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+Wyniki z geval:
+Likelihood      0.0000
+Accuracy        0.7367
+F1.0            0.4367
+Precision       0.8997
+Recall          0.2883