".py" version of LogReg

LogReg.py

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

@@ -2,43 +2,20 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
     "import torch\n",
-    "import csv\n",
     "from nltk.tokenize import word_tokenize\n",
-    "#from gensim.models import Word2Vec\n",
     "import gensim.downloader as api"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "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,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,34 +28,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Preprocessing danych\n",
-    "# lowercase\n",
+    "# lowercase-ing zbiorów\n",
     "# https://www.datacamp.com/community/tutorials/case-conversion-python\n",
     "X_train = X_train.content.str.lower()\n",
-    "y_train = y_train['label']\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",
-   "execution_count": 42,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# tokenize\n",
-    "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]"
+    "# 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": 44,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,9 +64,36 @@
     "# 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",
-    "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",
-    "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",
-    "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]"
+    "\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"
    ]
   },
   {
@@ -112,6 +117,7 @@
    "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",

dev-0/out.tsv

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test-A/out.tsv

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