Added neural network classifiers
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{
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"metadata": {
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.5-final"
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},
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"orig_nbformat": 2,
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3.9.5 64-bit",
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"metadata": {
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"interpreter": {
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"hash": "ac59ebe37160ed0dfa835113d9b8498d9f09ceb179beaac4002f036b9467c963"
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}
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2,
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# https://gonito.net/challenge/paranormal-or-skeptic\n",
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"# dane + wyniki -> https://git.wmi.amu.edu.pl/s444380/paranormal-or-skeptic-ISI-public"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"import lzma\n",
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"from keras.models import Sequential\n",
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"from keras.layers import Dense\n",
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"import tensorflow as tf\n",
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"import numpy as np\n",
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"from gensim import downloader"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Read train files\n",
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"with lzma.open(\"train/in.tsv.xz\", \"rt\", encoding=\"utf-8\") as train_file:\n",
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" x_train = [x.strip().lower() for x in train_file.readlines()]\n",
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"\n",
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"with open(\"train/expected.tsv\", \"r\", encoding=\"utf-8\") as train_file:\n",
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" y_train = np.array([int(x.strip()) for x in train_file.readlines()])\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"word2vec = downloader.load(\"glove-twitter-200\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train_w2v = [np.mean([word2vec[word.lower()] for word in doc.split() if word.lower() in word2vec]\n",
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" or [np.zeros(200)], axis=0) for doc in x_train]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Read dev files\n",
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"with lzma.open(\"dev-0/in.tsv.xz\", \"rt\", encoding=\"utf-8\") as dev_file:\n",
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" x_dev = [x.strip().lower() for x in dev_file.readlines()]\n",
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"\n",
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"with open(\"dev-0/expected.tsv\", \"r\", encoding=\"utf-8\") as train_file:\n",
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" y_dev = np.array([int(x.strip()) for x in train_file.readlines()])\n",
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"\n",
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"x_dev_w2v = [np.mean([word2vec[word.lower()] for word in doc.split() if word.lower() in word2vec]\n",
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" or [np.zeros(200)], axis=0) for doc in x_dev]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"# y_train = y_train.reshape(-1, 1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = Sequential()\n",
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"model.add(Dense(1000, activation='relu', input_dim=200))\n",
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"model.add(Dense(500, activation='relu'))\n",
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"model.add(Dense(1, activation='sigmoid'))\n",
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"model.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Epoch 1/5\n",
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"9050/9050 [==============================] - 48s 5ms/step - loss: 0.5244 - accuracy: 0.7303 - val_loss: 0.5536 - val_accuracy: 0.6910\n",
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"Epoch 2/5\n",
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"9050/9050 [==============================] - 47s 5ms/step - loss: 0.5132 - accuracy: 0.7367 - val_loss: 0.5052 - val_accuracy: 0.7475\n",
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"Epoch 3/5\n",
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"9050/9050 [==============================] - 47s 5ms/step - loss: 0.5067 - accuracy: 0.7396 - val_loss: 0.5091 - val_accuracy: 0.7320\n",
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"Epoch 4/5\n",
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"9050/9050 [==============================] - 47s 5ms/step - loss: 0.5025 - accuracy: 0.7429 - val_loss: 0.5343 - val_accuracy: 0.7071\n",
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"Epoch 5/5\n",
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"9050/9050 [==============================] - 47s 5ms/step - loss: 0.4992 - accuracy: 0.7447 - val_loss: 0.5143 - val_accuracy: 0.7381\n"
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]
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}
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],
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"source": [
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"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)))"
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]
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}
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]
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}
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@ -26,6 +26,16 @@
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"nbformat": 4,
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"nbformat_minor": 2,
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# https://gonito.net/challenge/paranormal-or-skeptic\n",
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"# dane + wyniki -> https://git.wmi.amu.edu.pl/s444380/paranormal-or-skeptic-ISI-public"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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@ -189,17 +199,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"execution_count": 39,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_dev_w2v = [np.mean([word2vec[word.lower()] for word in doc.split() if word.lower() in word2vec]\n",
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" or [np.zeros(FEATURES)], axis=0) for doc in x_train]"
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" or [np.zeros(FEATURES)], axis=0) for doc in x_dev]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 40,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -209,11 +219,70 @@
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" x = x_dev_w2v[i:i+BATCH_SIZE]\n",
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" x = torch.tensor(np.array(x).astype(np.float32))\n",
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" \n",
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" outputs = model(x\n",
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" outputs = model(x)\n",
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" \n",
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" y = (outputs > 0.5)\n",
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" y_dev.extend(y)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(\"dev-0/out.tsv\", \"w\", encoding=\"utf-8\") as f:\n",
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" f.writelines([str(y.int()[0].item()) + \"\\n\" for y in y_dev])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Read test files\n",
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"with lzma.open(\"test-A/in.tsv.xz\", \"rt\", encoding=\"utf-8\") as test_file:\n",
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" x_test = [x.strip().lower() for x in test_file.readlines()]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_test_w2v = [np.mean([word2vec[word.lower()] for word in doc.split() if word.lower() in word2vec]\n",
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" or [np.zeros(FEATURES)], axis=0) for doc in x_test]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_test = []\n",
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"with torch.no_grad():\n",
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" for i in range(0, len(x_test_w2v), BATCH_SIZE):\n",
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" x = x_test_w2v[i:i+BATCH_SIZE]\n",
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" x = torch.tensor(np.array(x).astype(np.float32))\n",
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" \n",
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" outputs = model(x)\n",
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" \n",
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" y = (outputs > 0.5)\n",
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" y_test.extend(y)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(\"test-A/out.tsv\", \"w\", encoding=\"utf-8\") as f:\n",
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" f.writelines([str(y.int()[0].item()) + \"\\n\" for y in y_test])"
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]
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}
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]
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}
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