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@aboSamoor
Created September 13, 2013 15:38
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Polyglot Embeddings Characteristics
{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import cPickle as pickle\n",
"from io import open\n",
"\n",
"words, embeddings = pickle.load(open('/home/rmyeid/Downloads/polyglot-en.pkl', 'rb'))\n",
"\n",
"def moving_average(interval, window_size):\n",
" window = numpy.ones(int(window_size))/float(window_size)\n",
" front = [interval[0] for x in arange(1,window_size/2)]\n",
" back = [interval[-1] for x in arange(0,window_size/2)]\n",
" return convolve(front + interval + back, window, 'valid')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 75
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"vectors = embeddings[:100000]\n",
"x = arange(vectors.shape[0])\n",
"norms = (vectors ** 2).sum(axis=1) ** 0.5\n",
"y = moving_average(norms.tolist(), 500)\n",
"plt1 = plot(x, y)\n",
"\n",
"xl = xlabel('Word rank by frequency (Higher is less frequent)')\n",
"yl = ylabel('Vector L2 norm')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(500,)\n",
"<type 'numpy.ndarray'>\n",
"1\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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R2YzZBnMPDw+MGDECarUahYWFKCgoQEFBQV3EZhdat1YSBwBMnqxb/sknutfHjyvPf/+t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\n",
"text": [
"<matplotlib.figure.Figure at 0xd9ff990>"
]
}
],
"prompt_number": 66
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.neighbors import NearestNeighbors\n",
"knn = NearestNeighbors(n_neighbors=2, algorithm='ball_tree').fit(embeddings)\n",
"\n",
"x2 = []\n",
"y2 = []\n",
"\n",
"for i in arange(0, embeddings.shape[0], 50):\n",
" distances, indices = knn.kneighbors(embeddings[i])\n",
" x2.append(i)\n",
" y2.append(distances[0][1])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 113
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#print knn.kneighbors(embeddings[100000])[0][0][1]\n",
"#print y2\n",
"y2_ = y2[:2000]\n",
"x2_ = x2[:2000]\n",
"y2_ = moving_average(y2_, 50)\n",
"plot(x2_, y2_)\n",
"xl = xlabel('Word rank by frequency (Higher is less frequent)')\n",
"yl = ylabel('L2 distance to nearest neighbor')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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du8rud3ERk2UBwKxZyp0WGWNMWxWqWN+5cyf27dsHiUSCoKAg9O3bV+OJJ06c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\nyAhmZmZ4+PChvm6rUt566y0sXLgQdeo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du6NHjx5wcXGRpyksLAyHDh2Ch4cH4uLi5BWt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\nrXEQYYwxprX/B1Ghc6TSWntvAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0xd87f2d0>"
]
}
],
"prompt_number": 114
}
],
"metadata": {}
}
]
}
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