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Created August 15, 2018 11:05
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Visualization of word similarity with t-SNE and word2vec
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{
"cells": [
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"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
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"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
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"source": [
"import gensim\n",
"import numpy as np\n",
"from sklearn.manifold import TSNE\n",
"from nltk.corpus import stopwords\n",
"import plotly.offline as plt\n",
"import plotly.graph_objs as go\n",
"plt.init_notebook_mode(connected=True) "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"model = gensim.models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin.gz', binary=True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def get_coordinates(model, words):\n",
" arr = np.empty((0,300), dtype='f')\n",
" labels = []\n",
" for wrd_score in words:\n",
" try:\n",
" wrd_vector = model[wrd_score]\n",
" arr = np.append(arr, np.array([wrd_vector]), axis=0)\n",
" labels.append(wrd_score)\n",
" except:\n",
" pass\n",
" tsne = TSNE(n_components=3, random_state=0)\n",
" np.set_printoptions(suppress=True)\n",
" Y = tsne.fit_transform(arr)\n",
" x_coords = Y[:, 0]\n",
" y_coords = Y[:, 1]\n",
" z_coords = Y[:, 2]\n",
" return x_coords, y_coords, z_coords"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
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" {\"title\": \"Ok Computer lyrics\"},\n",
" {\"showLink\": true, \"linkText\": \"Export to plot.ly\"}\n",
" ).then(function () {return Plotly.addFrames('ee092938-8c2f-48c9-a368-1dc57c8792dd',{});}).then(function(){Plotly.animate('ee092938-8c2f-48c9-a368-1dc57c8792dd');})\n",
" });</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"with open('ok_computer.txt', 'r') as f:\n",
" data = f.read()\n",
" \n",
"words = np.unique(data.replace('\\n', '').replace(',', ' ').replace('(','').replace(')','').split(' '))\n",
"ok_computer = [word for word in words if word not in stopwords.words('english')]\n",
"\n",
"x, y, z = get_coordinates(model, ok_computer)\n",
"\n",
"plot = [go.Scatter3d(x = x,\n",
" y = y,\n",
" z = z,\n",
" mode = 'markers+text',\n",
" text = words,\n",
" textposition='bottom center',\n",
" hoverinfo = 'text',\n",
" marker=dict(size=5,opacity=0.8))]\n",
"\n",
"layout = go.Layout(title='Ok Computer lyrics')\n",
"fig = go.Figure(data=plot, layout=layout)\n",
"plt.iplot(fig) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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