corr 2 entrainement
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wordvec_model.ipynb
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wordvec_model.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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.decomposition import IncrementalPCA # inital reduction\n",
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"from sklearn.manifold import TSNE # final reduction\n",
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"import numpy as np "
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from gensim.models import Word2Vec"
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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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"p_1 = [\"le\", \"chat\", \"mange\", \"la\", \"souris\"]\n",
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"p_2 = [\"le\", \"chien\", \"regarde\", \"le\", \"canard\"]\n",
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"p_3 = [\"le\",\"canard\", \"regarde\", \"le\", \"chat\"]\n",
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"corpus = [p_1, p_2, p_3]"
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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": 12,
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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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"(7, 75)"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"model = Word2Vec(min_count=1, vector_size=5)\n",
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"model.build_vocab(corpus) # prepare the model vocabulary\n",
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"model.train(corpus, total_examples=model.corpus_count, epochs=model.epochs)"
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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": 15,
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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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"[-0.03632035 0.0575316 0.01983747 -0.1657043 -0.18897638]\n",
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"[-0.06811106 -0.01892909 0.1153778 -0.15044104 -0.07872642]\n",
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"[0.14623405 0.10140646 0.13515887 0.01525312 0.12701929]\n",
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"[('souris', 0.7668752074241638), ('canard', 0.6178626418113708)]\n",
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"[('le', 0.45935946702957153), ('mange', 0.17478135228157043)]\n"
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]
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}
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],
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"source": [
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"print(model.wv['chat'])\n",
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"print(model.wv['souris'])\n",
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"print(model.wv['chien'])\n",
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"\n",
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"print(model.wv.most_similar(positive=['chat'], topn=2))\n",
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"print(model.wv.most_similar(positive=['regarde'], topn=2))"
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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": 16,
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"metadata": {},
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"outputs": [
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{
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"ename": "ValueError",
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"evalue": "perplexity must be less than n_samples",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_19020\\1722442376.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[0mx_vals\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_vals\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreduce_dimensions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mplot_with_plotly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_vals\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_vals\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplot_in_notebook\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_19020\\1722442376.py\u001b[0m in \u001b[0;36mreduce_dimensions\u001b[1;34m(model)\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;31m# reduce using t-SNE\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0mtsne\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTSNE\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_components\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnum_dimensions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mvectors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtsne\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvectors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[0mx_vals\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mvectors\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32mc:\\Users\\Sasa\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\manifold\\_t_sne.py\u001b[0m in \u001b[0;36mfit_transform\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m 1120\u001b[0m \u001b[0mEmbedding\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mtraining\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlow\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mdimensional\u001b[0m \u001b[0mspace\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1121\u001b[0m \"\"\"\n\u001b[1;32m-> 1122\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_params_vs_input\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1123\u001b[0m \u001b[0membedding\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1124\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0membedding_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0membedding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32mc:\\Users\\Sasa\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\manifold\\_t_sne.py\u001b[0m in \u001b[0;36m_check_params_vs_input\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 791\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_check_params_vs_input\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 792\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mperplexity\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 793\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"perplexity must be less than n_samples\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 794\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 795\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mskip_num_points\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;31mValueError\u001b[0m: perplexity must be less than n_samples"
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]
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}
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],
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"source": [
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"\n",
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"def reduce_dimensions(model):\n",
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" num_dimensions = 2 # final num dimensions (2D, 3D, etc)\n",
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"\n",
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" # extract the words & their vectors, as numpy arrays\n",
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" vectors = np.asarray(model.wv.vectors)\n",
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" labels = np.asarray(model.wv.index_to_key) # fixed-width numpy strings\n",
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"\n",
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" # reduce using t-SNE\n",
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" tsne = TSNE(n_components=num_dimensions, random_state=0)\n",
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" vectors = tsne.fit_transform(vectors)\n",
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"\n",
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" x_vals = [v[0] for v in vectors]\n",
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" y_vals = [v[1] for v in vectors]\n",
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" return x_vals, y_vals, labels\n",
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"\n",
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"\n",
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"x_vals, y_vals, labels = reduce_dimensions(model)\n",
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"\n",
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"def plot_with_plotly(x_vals, y_vals, labels, plot_in_notebook=True):\n",
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" from plotly.offline import init_notebook_mode, iplot, plot\n",
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" import plotly.graph_objs as go\n",
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"\n",
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" trace = go.Scatter(x=x_vals, y=y_vals, mode='text', text=labels)\n",
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" data = [trace]\n",
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"\n",
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" if plot_in_notebook:\n",
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" init_notebook_mode(connected=True)\n",
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" iplot(data, filename='word-embedding-plot')\n",
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" else:\n",
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" plot(data, filename='word-embedding-plot.html')\n",
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"\n",
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"\n",
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"def plot_with_matplotlib(x_vals, y_vals, labels):\n",
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" import matplotlib.pyplot as plt\n",
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" import random\n",
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"\n",
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" random.seed(0)\n",
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"\n",
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" plt.figure(figsize=(12, 12))\n",
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" plt.scatter(x_vals, y_vals)\n",
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"\n",
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" #\n",
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" # Label randomly subsampled 25 data points\n",
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" #\n",
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" indices = list(range(len(labels)))\n",
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" selected_indices = random.sample(indices, 25)\n",
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" for i in selected_indices:\n",
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" plt.annotate(labels[i], (x_vals[i], y_vals[i]))\n",
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"\n",
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"try:\n",
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" get_ipython()\n",
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"except Exception:\n",
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" plot_function = plot_with_matplotlib\n",
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"else:\n",
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" plot_function = plot_with_plotly\n",
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"\n",
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"plot_function(x_vals, y_vals, labels)"
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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 3.9.4 64-bit",
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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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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "2ef431f6525756fa8a44688585fa332ef3b2e5fcfe8fe75df35bbf7028a8b511"
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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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}
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