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@ -1,41 +0,0 @@
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import lzma
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from naivebayes import NaiveBayesTextClassifier
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import nltk
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from nltk.corpus import stopwords
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nltk.download("stopwords")
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# Read train files
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with lzma.open("train/in.tsv.xz", "rt", encoding="utf-8") as train_file:
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x_train = [x.strip().lower() for x in train_file.readlines()]
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with open("train/expected.tsv", "r", encoding="utf-8") as train_file:
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y_train = [int(x.strip()) for x in train_file.readlines()]
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nbc = NaiveBayesTextClassifier(
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categories=[0, 1],
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stop_words=stopwords.words("english"),
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min_df=1
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)
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step = 15000
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for i in range(0, len(x_train), step):
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nbc.train(x_train[i:min(i+step, len(x_train))], y_train[i:min(i+step, len(x_train))])
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# Read dev files
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with lzma.open("dev-0/in.tsv.xz", "rt", encoding="utf-8") as dev_file:
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x_dev = [x.strip().lower() for x in dev_file.readlines()]
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# Read test file
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with lzma.open("test-A/in.tsv.xz", "rt", encoding="utf-8") as test_file:
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x_test = [x.strip().lower() for x in test_file.readlines()]
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# Predict dev
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pred_dev = [str(x) + "\n" for x in nbc.classify(x_dev)]
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with open("dev-0/out.tsv", "w", encoding="utf-8") as dev_out_file:
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dev_out_file.writelines(pred_dev)
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# Predict dev
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pred_test = [str(x) + "\n" for x in nbc.classify(x_test)]
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with open("test-A/out.tsv", "w", encoding="utf-8") as test_out_file:
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test_out_file.writelines(pred_test)
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dev-0/out.tsv
5272
dev-0/out.tsv
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@ -1,152 +0,0 @@
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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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@ -1,288 +0,0 @@
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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": 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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"metadata": {},
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"outputs": [],
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"source": [
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"import lzma\n",
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"import torch\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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"BATCH_SIZE = 10\n",
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"EPOCHS = 10\n",
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"FEATURES = 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": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"class NeuralNetworkModel(torch.nn.Module):\n",
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"\n",
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" def __init__(self):\n",
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" super(NeuralNetworkModel, self).__init__()\n",
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" self.fc1 = torch.nn.Linear(FEATURES, 1000)\n",
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" self.fc2 = torch.nn.Linear(1000, 500)\n",
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" self.fc3 = torch.nn.Linear(500, 1)\n",
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"\n",
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" def forward(self, x):\n",
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" x = self.fc1(x)\n",
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" x = torch.relu(x)\n",
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" x = self.fc2(x)\n",
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" x = torch.relu(x)\n",
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" x = self.fc3(x)\n",
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" x = torch.sigmoid(x)\n",
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" return x"
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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": 6,
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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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"\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": 7,
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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": 8,
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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(FEATURES)], 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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"model = NeuralNetworkModel()\n",
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"\n",
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"criterion = torch.nn.BCELoss()\n",
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"optimizer = torch.optim.ASGD(model.parameters(), lr=0.05)"
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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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"0\n",
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"0.5444966091123856 0.7128072132302411\n",
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"1\n",
|
||||
"0.5187017436751196 0.7303153888921503\n",
|
||||
"2\n",
|
||||
"0.5117590330604093 0.7348944502191112\n",
|
||||
"3\n",
|
||||
"0.5075270808198805 0.7376916143781145\n",
|
||||
"4\n",
|
||||
"0.5043017516287736 0.7403230206610286\n",
|
||||
"5\n",
|
||||
"0.5016950109024928 0.7418977204838748\n",
|
||||
"6\n",
|
||||
"0.49942716640870777 0.7432134236253319\n",
|
||||
"7\n",
|
||||
"0.49766424133924386 0.7448606425189672\n",
|
||||
"8\n",
|
||||
"0.49617289846816215 0.745534033890579\n",
|
||||
"9\n",
|
||||
"0.49471875689137873 0.7467116054686286\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for epoch in range(EPOCHS):\n",
|
||||
" print(epoch)\n",
|
||||
" loss_score = 0\n",
|
||||
" acc_score = 0\n",
|
||||
" items_total = 0\n",
|
||||
" for i in range(0, y_train.shape[0], BATCH_SIZE):\n",
|
||||
" x = x_train_w2v[i:i+BATCH_SIZE]\n",
|
||||
" x = torch.tensor(np.array(x).astype(np.float32))\n",
|
||||
" y = y_train[i:i+BATCH_SIZE]\n",
|
||||
" y = torch.tensor(y.astype(np.float32)).reshape(-1, 1)\n",
|
||||
" y_pred = model(x)\n",
|
||||
" acc_score += torch.sum((y_pred > 0.5) == y).item()\n",
|
||||
" items_total += y.shape[0]\n",
|
||||
"\n",
|
||||
" optimizer.zero_grad()\n",
|
||||
" loss = criterion(y_pred, y)\n",
|
||||
" loss.backward()\n",
|
||||
" optimizer.step()\n",
|
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"\n",
|
||||
" loss_score += loss.item() * y.shape[0]\n",
|
||||
" \n",
|
||||
" print((loss_score / items_total), (acc_score / items_total))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"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()]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"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_dev]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
|
||||
"y_dev = []\n",
|
||||
"with torch.no_grad():\n",
|
||||
" for i in range(0, len(x_dev_w2v), BATCH_SIZE):\n",
|
||||
" x = x_dev_w2v[i:i+BATCH_SIZE]\n",
|
||||
" x = torch.tensor(np.array(x).astype(np.float32))\n",
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" \n",
|
||||
" outputs = model(x)\n",
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" \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])"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
@ -1,34 +0,0 @@
|
||||
from simpletransformers.classification import ClassificationModel, ClassificationArgs
|
||||
import pandas as pd
|
||||
import logging
|
||||
import torch
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
transformer_logger = logging.getLogger("transformers")
|
||||
transformer_logger.setLevel(logging.WARNING)
|
||||
|
||||
|
||||
train_df = pd.read_csv("train/train.tsv", sep="\t")
|
||||
print(train_df)
|
||||
|
||||
dev_df = pd.read_csv("dev-0/dev.tsv", sep="\t")
|
||||
print(dev_df)
|
||||
|
||||
|
||||
args = {
|
||||
'train_batch_size': 32,
|
||||
'learning_rate': 2e-5,
|
||||
'evaluate_during_training': True,
|
||||
'save_steps': 1000,
|
||||
'evaluate_during_training_steps': 1000,
|
||||
'evaluate_during_training_verbose': True,
|
||||
'overwrite_output_dir': True,
|
||||
'save_eval_checkpoints': True,
|
||||
'use_early_stopping': True,
|
||||
'early_stopping_patience': 5,
|
||||
'num_train_epochs': 3
|
||||
}
|
||||
|
||||
model = ClassificationModel("deberta", "microsoft/deberta-base", use_cuda=True, args=args)
|
||||
|
||||
model.train_model(train_df, eval_df=dev_df)
|
||||
@ -1,19 +0,0 @@
|
||||
from simpletransformers.classification import ClassificationModel
|
||||
import pandas as pd
|
||||
|
||||
model = ClassificationModel("deberta", "outputs/best_model")
|
||||
|
||||
dev_df = pd.read_csv("dev-0/dev.tsv", sep="\t")
|
||||
|
||||
result, model_outputs, wrong_predictions = model.eval_model(dev_df)
|
||||
print(result)
|
||||
tp = result["tp"]
|
||||
fp = result["fp"]
|
||||
tn = result["tn"]
|
||||
fn = result["fn"]
|
||||
print(f"Accuracy: {(tp+tn)/(tp+fp+tn+fn)}")
|
||||
precision = tp/(tp+fp)
|
||||
print(f"Precision: {precision}")
|
||||
recall = tp/(tp+fn)
|
||||
print(f"Recall: {recall}")
|
||||
print(f"F1-score: {2*precision*recall/(precision+recall)}")
|
||||
5152
test-A/out.tsv
5152
test-A/out.tsv
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue
Block a user