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patrycjalazna
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dev-0/out.tsv
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dev-0/out.tsv
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dev-0/out2.tsv
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dev-0/out2.tsv
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regresja-logistyczna.ipynb
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regresja-logistyczna.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "d2b899fb",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import os\n",
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"import gensim\n",
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"from gensim.models import Word2Vec\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.gridspec as gridspec\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"import torch\n",
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"import csv"
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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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"id": "39a1f19a",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"b'Skipping line 25706: expected 2 fields, saw 3\\nSkipping line 58881: expected 2 fields, saw 3\\nSkipping line 73761: expected 2 fields, saw 3\\n'\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"98129\n",
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"98129\n",
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"5452\n",
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"5452\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"b'Skipping line 1983: expected 1 fields, saw 2\\nSkipping line 5199: expected 1 fields, saw 2\\n'\n"
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]
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}
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],
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"source": [
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"# wczytanie danych\n",
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"train = pd.read_table('train/train.tsv', error_bad_lines=False, sep='\\t', quoting=csv.QUOTE_NONE, header=None)\n",
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"x_dev = pd.read_table('dev-0/in.tsv', error_bad_lines=False, sep='\\t', header=None, quoting=csv.QUOTE_NONE)\n",
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"y_dev = pd.read_table('dev-0/expected.tsv', error_bad_lines=False, sep='\\t', header=None, quoting=csv.QUOTE_NONE)\n",
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"x_test = pd.read_table('test-A/in.tsv', error_bad_lines=False, sep='\\t', header=None, quoting=csv.QUOTE_NONE)\n",
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"\n",
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"# podzial na x i y\n",
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"x_train = train[1].values\n",
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"y_train = train[0].values\n",
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"x_dev = x_dev[0].values\n",
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"x_test = x_test[0].values\n",
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"\n",
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"print(len(x_train))\n",
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"print(len(y_train))\n",
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"print(len(x_dev))\n",
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"print(len(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": 26,
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"id": "c637937e",
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"metadata": {},
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"outputs": [],
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"source": [
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"import nltk\n",
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"#nltk.download('punkt')\n",
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"\n",
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"\n",
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"# tokenizacja \n",
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"def tokenize_data(data):\n",
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" data_tokenize = [nltk.word_tokenize(x) for x in data]\n",
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" \n",
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" for doc in data_tokenize:\n",
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" i = 0\n",
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" while i < len(doc):\n",
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" if doc[i].isalpha():\n",
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" doc[i] = doc[i].lower()\n",
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" else:\n",
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" del doc[i]\n",
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" i += 1\n",
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" return data_tokenize\n",
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"\n",
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"x_train_tokenized = tokenize_data(x_train)\n",
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"x_dev_tokenized = tokenize_data(x_dev)\n",
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"x_test_tokenized = tokenize_data(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": 34,
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"id": "890b3cca",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[('róż', 0.8955456018447876), ('kwiatek', 0.8504886031150818), ('fiołek', 0.831953763961792), ('chryzantema', 0.8315931558609009), ('bukiet', 0.8306410908699036), ('wiśnia', 0.8005671501159668), ('żonkil', 0.8005172610282898), ('liść', 0.7998315095901489), ('lilia', 0.7931062579154968), ('peonia', 0.7918344140052795)]\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/usr/local/Cellar/jupyterlab/3.0.14/libexec/lib/python3.9/site-packages/gensim/models/keyedvectors.py:772: RuntimeWarning: invalid value encountered in true_divide\n",
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" dists = dot(self.vectors[clip_start:clip_end], mean) / self.norms[clip_start:clip_end]\n"
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]
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}
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],
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"source": [
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"from gensim.models import KeyedVectors\n",
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"\n",
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"word2vec_model = KeyedVectors.load(\"word2vec.bin\")\n",
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"\n",
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"# sprawdzenie czy dziala\n",
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"print(word2vec_model.similar_by_word(\"kwiat\"))"
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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": 62,
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"id": "6bd92640",
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train = [np.mean([word2vec_model[word] for word in content if word in word2vec_model] or [np.zeros(100)], axis=0) for content in x_train]\n",
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"x_train_tensor = torch.tensor(np.array(x_train, dtype=np.float32).astype(np.float32))\n",
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"x_train_vec = np.array(x_train, dtype=np.float32)\n",
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"\n",
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"x_dev = [np.mean([word2vec_model[word] for word in content if word in word2vec_model] or [np.zeros(100)], axis=0) for content in x_dev]\n",
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"x_dev_vec = np.array(x_dev, dtype=np.float32)\n",
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"\n",
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"\n",
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"x_test = [np.mean([word2vec_model[word] for word in content if word in word2vec_model] or [np.zeros(100)], axis=0) for content in x_test]\n",
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"x_test_vec = np.array(x_test, dtype=np.float32)\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": 56,
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"id": "df544bfb",
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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(100,200)\n",
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" self.fc2 = torch.nn.Linear(200,1)\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.sigmoid(x)\n",
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" return x\n",
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" \n",
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"nn_model = NeuralNetworkModel()"
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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": 67,
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"id": "884d80ec",
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"metadata": {},
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"outputs": [],
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"source": [
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"criterion = torch.nn.BCELoss()\n",
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"optimizer = torch.optim.SGD(model.parameters(), lr=0.01)"
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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": 68,
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"id": "eacc269d",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"1"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"2"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"3"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"4"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"5"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"batch_size = 12\n",
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"for epoch in range(6):\n",
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" loss_score = 0\n",
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" acc_score = 0\n",
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" items_total = 0\n",
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" nn_model.train()\n",
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" \n",
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" for i in range(0, y_train.shape[0], batch_size):\n",
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" X = x_train_vec[i:i + batch_size]\n",
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" X = torch.tensor(X.astype(np.float32))\n",
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" Y = y_train[i:i + batch_size]\n",
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" Y = torch.tensor(Y.astype(np.float32)).reshape(-1, 1)\n",
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" Y_predictions = nn_model(X)\n",
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" acc_score += torch.sum((Y_predictions > 0.5) == Y).item()\n",
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" items_total += Y.shape[0]\n",
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"\n",
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" optimizer.zero_grad()\n",
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" loss = criterion(Y_predictions, Y)\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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"\n",
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" loss_score += loss.item() * Y.shape[0]\n",
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" display(epoch)"
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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": 70,
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"id": "daa85677",
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"metadata": {},
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"outputs": [],
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"source": [
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"# predykcje \n",
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"y_pred_dev = nn_model(torch.tensor(x_dev_vec.astype(np.float32)))\n",
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"y_pred_dev = y_pred_dev.cpu().detach().numpy()\n",
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"y_pred_dev = (y_pred_dev > 0.5)\n",
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"y_pred_dev = np.asarray(y_pred_dev, dtype=np.int32)\n",
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"y_pred_dev.tofile('dev-0/out2.tsv', sep='\\n')\n",
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"\n",
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"\n",
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"y_pred_test = nn_model(torch.tensor(x_dev_vec.astype(np.float32)))\n",
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"y_pred_test = y_pred_test.cpu().detach().numpy()\n",
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"y_pred_test = (y_pred_test > 0.5)\n",
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"y_pred_test = np.asarray(y_pred_test, dtype=np.int32)\n",
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"y_pred_test.tofile('test-A/out2.tsv', sep='\\n')\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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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": "3.9.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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5452
test-A/out.tsv
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5452
test-A/out.tsv
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5452
test-A/out2.tsv
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test-A/out2.tsv
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98132
train/train.tsv
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98132
train/train.tsv
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