Added neural network classifiers

keras_classifier.ipynb

@@ -0,0 +1,152 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.9.5 64-bit",
+   "metadata": {
+    "interpreter": {
+     "hash": "ac59ebe37160ed0dfa835113d9b8498d9f09ceb179beaac4002f036b9467c963"
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# https://gonito.net/challenge/paranormal-or-skeptic\n",
+    "# dane + wyniki -> https://git.wmi.amu.edu.pl/s444380/paranormal-or-skeptic-ISI-public"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import lzma\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "from gensim import downloader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read train files\n",
+    "with lzma.open(\"train/in.tsv.xz\", \"rt\", encoding=\"utf-8\") as train_file:\n",
+    "    x_train = [x.strip().lower() for x in train_file.readlines()]\n",
+    "\n",
+    "with open(\"train/expected.tsv\", \"r\", encoding=\"utf-8\") as train_file:\n",
+    "    y_train = np.array([int(x.strip()) for x in train_file.readlines()])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "word2vec = downloader.load(\"glove-twitter-200\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train_w2v = [np.mean([word2vec[word.lower()] for word in doc.split() if word.lower() in word2vec]\n",
+    "                   or [np.zeros(200)], axis=0) for doc in x_train]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read dev files\n",
+    "with lzma.open(\"dev-0/in.tsv.xz\", \"rt\", encoding=\"utf-8\") as dev_file:\n",
+    "    x_dev = [x.strip().lower() for x in dev_file.readlines()]\n",
+    "\n",
+    "with open(\"dev-0/expected.tsv\", \"r\", encoding=\"utf-8\") as train_file:\n",
+    "    y_dev = np.array([int(x.strip()) for x in train_file.readlines()])\n",
+    "\n",
+    "x_dev_w2v = [np.mean([word2vec[word.lower()] for word in doc.split() if word.lower() in word2vec]\n",
+    "                   or [np.zeros(200)], axis=0) for doc in x_dev]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# y_train = y_train.reshape(-1, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Dense(1000, activation='relu', input_dim=200))\n",
+    "model.add(Dense(500, activation='relu'))\n",
+    "model.add(Dense(1, activation='sigmoid'))\n",
+    "model.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Epoch 1/5\n",
+      "9050/9050 [==============================] - 48s 5ms/step - loss: 0.5244 - accuracy: 0.7303 - val_loss: 0.5536 - val_accuracy: 0.6910\n",
+      "Epoch 2/5\n",
+      "9050/9050 [==============================] - 47s 5ms/step - loss: 0.5132 - accuracy: 0.7367 - val_loss: 0.5052 - val_accuracy: 0.7475\n",
+      "Epoch 3/5\n",
+      "9050/9050 [==============================] - 47s 5ms/step - loss: 0.5067 - accuracy: 0.7396 - val_loss: 0.5091 - val_accuracy: 0.7320\n",
+      "Epoch 4/5\n",
+      "9050/9050 [==============================] - 47s 5ms/step - loss: 0.5025 - accuracy: 0.7429 - val_loss: 0.5343 - val_accuracy: 0.7071\n",
+      "Epoch 5/5\n",
+      "9050/9050 [==============================] - 47s 5ms/step - loss: 0.4992 - accuracy: 0.7447 - val_loss: 0.5143 - val_accuracy: 0.7381\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = model.fit(tf.stack(x_train_w2v), tf.stack(y_train), epochs=5, validation_data=(tf.stack(x_dev_w2v), tf.stack(y_dev)))"
+   ]
+  }
+ ]
+}

pytorch_classifier.ipynb

@@ -26,6 +26,16 @@
  "nbformat": 4,
  "nbformat_minor": 2,
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# https://gonito.net/challenge/paranormal-or-skeptic\n",
+    "# dane + wyniki -> https://git.wmi.amu.edu.pl/s444380/paranormal-or-skeptic-ISI-public"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -189,17 +199,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
     "x_dev_w2v = [np.mean([word2vec[word.lower()] for word in doc.split() if word.lower() in word2vec]\n",
-    "                   or [np.zeros(FEATURES)], axis=0) for doc in x_train]"
+    "                   or [np.zeros(FEATURES)], axis=0) for doc in x_dev]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -209,11 +219,70 @@
     "        x = x_dev_w2v[i:i+BATCH_SIZE]\n",
     "        x = torch.tensor(np.array(x).astype(np.float32))\n",
     "        \n",
-    "        outputs = model(x\n",
+    "        outputs = model(x)\n",
     "        \n",
     "        y = (outputs > 0.5)\n",
     "        y_dev.extend(y)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"dev-0/out.tsv\", \"w\", encoding=\"utf-8\") as f:\n",
+    "    f.writelines([str(y.int()[0].item()) + \"\\n\" for y in y_dev])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read test files\n",
+    "with lzma.open(\"test-A/in.tsv.xz\", \"rt\", encoding=\"utf-8\") as test_file:\n",
+    "    x_test = [x.strip().lower() for x in test_file.readlines()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_test_w2v = [np.mean([word2vec[word.lower()] for word in doc.split() if word.lower() in word2vec]\n",
+    "                   or [np.zeros(FEATURES)], axis=0) for doc in x_test]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test = []\n",
+    "with torch.no_grad():\n",
+    "    for i in range(0, len(x_test_w2v), BATCH_SIZE):\n",
+    "        x = x_test_w2v[i:i+BATCH_SIZE]\n",
+    "        x = torch.tensor(np.array(x).astype(np.float32))\n",
+    "        \n",
+    "        outputs = model(x)\n",
+    "        \n",
+    "        y = (outputs > 0.5)\n",
+    "        y_test.extend(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"test-A/out.tsv\", \"w\", encoding=\"utf-8\") as f:\n",
+    "    f.writelines([str(y.int()[0].item()) + \"\\n\" for y in y_test])"
+   ]
   }
  ]
 }