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@byronyi
Last active August 29, 2015 14:13
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{
"metadata": {
"name": "",
"signature": "sha256:cdb5e98805ee701f996a8938971317dfe7555da1b2fb271ea6e49d2df17b102f"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Second Experiment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Yi Bairen, 8th January 2015"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this experiment, cosine similarity with consensus spectra are compared in the same setting as consine similarity with model coefficients. \n",
"\n",
"It shows that in spite of its simplicity, the idea majority voting achieves comparable result as state-of-the-art classification models, which requires complex tuning of hyper parameters."
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Data Preparation"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"from scipy import sparse as sp"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# numpy.ndarray\n",
"names = np.load('test.names.npy')\n",
"# Scipy.sparse.csr_matrix\n",
"spectra = np.load('test.spectra.npy')[()].astype(np.double)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"MASK_CONDITION = lambda counts: counts > 100\n",
"\n",
"_, encoded, counts = np.unique(names, return_inverse=True, return_counts=True)\n",
"mask = np.in1d(encoded, np.where(MASK_CONDITION(counts)))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.preprocessing import LabelEncoder\n",
"\n",
"le = LabelEncoder()\n",
"\n",
"X = spectra[mask, :]\n",
"y = le.fit_transform(names[mask])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 'Number of noise peptides: {}'.format(sum(~MASK_CONDITION(counts)))\n",
"print 'Number of noise spectra: {}'.format(sum(~mask))\n",
"print\n",
"print 'Number of masked peptide classes: {}'.format(np.max(y))\n",
"print 'Number of masked spectra: {}'.format(y.shape[0])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Number of noise peptides: 5467\n",
"Number of noise spectra: 70549\n",
"\n",
"Number of masked peptide classes: 702\n",
"Number of masked spectra: 439454\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.cross_validation import StratifiedShuffleSplit\n",
"\n",
"for train_index, test_index in StratifiedShuffleSplit(y, n_iter=1, test_size=0.1):\n",
" X_train, y_train = X[train_index], y[train_index]\n",
" X_test, y_test = X[test_index], y[test_index]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X_noise = spectra[~mask]\n",
"y_noise = names[~mask]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Consensus Building"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The consensus is built by simply taking the mean."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"index_sorted = np.argsort(y_train)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X_sorted = X_train[index_sorted]\n",
"y_sorted = y_train[index_sorted]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"_, starts = np.unique(y_sorted, return_index=True)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def groupby_mean(data, starts):\n",
" for i in range(len(starts)):\n",
" start = starts[i]\n",
" try:\n",
" end = starts[i+1]\n",
" s = slice(start, end)\n",
" except IndexError:\n",
" s = slice(start, data.shape[0])\n",
" # taking mean of the spectra\n",
" yield sp.csr_matrix(data[s].mean(axis=0))\n",
" \n",
"X_consensus = sp.vstack(groupby_mean(X_sorted, starts))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from matplotlib import pyplot as plt\n",
"\n",
"plt.plot(X_consensus[-3].T.todense())\n",
"plt.title('Example of consensus spectrum')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"<matplotlib.text.Text at 0x7fbd302bf1d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
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Lv/M7Z8yMNUZWeGU2hBisy1xHkePdFNiXRpJwbQr5iFAINSFjwd6e/uIV3XhG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\n",
"text": [
"<matplotlib.figure.Figure at 0x7fbd319ab150>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print repr(X_consensus)\n",
"print repr(X_test)\n",
"print repr(X_noise)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<703x2000 sparse matrix of type '<type 'numpy.float64'>'\n",
"\twith 870699 stored elements in Compressed Sparse Row format>\n",
"<43991x2000 sparse matrix of type '<type 'numpy.float64'>'\n",
"\twith 11588659 stored elements in Compressed Sparse Row format>\n",
"<70549x2000 sparse matrix of type '<type 'numpy.float64'>'\n",
"\twith 18672756 stored elements in Compressed Sparse Row format>\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Cosine-similarity with consensus spectra"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.metrics.pairwise import cosine_similarity\n",
"\n",
"test = cosine_similarity(X_test, X_consensus)\n",
"noise = cosine_similarity(X_noise, X_consensus)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"figure(figsize=(12, 4))\n",
"\n",
"fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True, figsize=(12, 4))\n",
"ax1.plot(test[300, :])\n",
"ax1.set_title('Score distribution of Testing data')\n",
"\n",
"ax2.plot(noise[300, :])\n",
"ax2.set_title('Score distribution of Noise data')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"<matplotlib.text.Text at 0x7fbd300f9e90>"
]
},
{
"metadata": {},
"output_type": "display_data",
"text": [
"<matplotlib.figure.Figure at 0x7fbd3019d750>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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ZABYAWAlOGgkhhGgEQ5f1QgAN6lYjRxrFwjCOB9CTSCQ+C+AwgF4AJwBYG3L8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\n",
"text": [
"<matplotlib.figure.Figure at 0x7fbd3019d650>"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print test.shape\n",
"print noise.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(43991, 703)\n",
"(70549, 703)\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"score_test = test.max(axis=-1)\n",
"score_noise = noise.max(axis=-1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.preprocessing import Normalizer\n",
"\n",
"normalized_score_test = Normalizer().fit_transform(test).max(axis=-1)\n",
"normalized_score_noise = Normalizer().fit_transform(noise).max(axis=-1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"true_mask = (test.argmax(axis=-1) == y_test)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.metrics import roc_curve, roc_auc_score\n",
"\n",
"true_discovery = np.hstack([true_mask, np.zeros(score_noise.shape)]).astype(np.bool)\n",
"\n",
"fig, axes = plt.subplots(nrows=1, ncols=2, sharex=True, sharey=True, figsize=(12, 4))\n",
"\n",
"titles = ['Raw', 'Normalized']\n",
"\n",
"fig.text(0.5, -0.04, 'ROC for mixture data', ha='center', va='center', fontsize=16)\n",
"for i, score in enumerate([np.hstack([score_test, score_noise]),\n",
" np.hstack([normalized_score_test, normalized_score_noise])]):\n",
"\n",
" fdr, tdr, _ = roc_curve(true_discovery, score)\n",
" axes[i].plot(fdr, tdr)\n",
" axes[i].set_xlabel('FDR')\n",
" axes[i].set_ylabel('TDR')\n",
" axes[i].set_title(titles[i])\n",
" axes[i].annotate('Area: {:.4f}'.format(roc_auc_score(true_discovery, score)), (0.25, 0.5), fontsize=16)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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073iHsQMX923l7AYXc45UIApug1sFt4iItFRaXcenG8ucugiaiCSPQBTcKYHI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\nJiMjg5NOOonjjjuOiy66qN3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KOfhcy9dl5dX19Eh3Z4bbxX1bObvBxZwjpYJbRERiantl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\nJKlDFtySJElSh/4F8toGY7KL+08AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7fbd2ffc3ad0>"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, axes = plt.subplots(nrows=1, ncols=2, sharex=False, figsize=(18, 3))\n",
"\n",
"titles = ['Raw', 'Normalized']\n",
"\n",
"fig.text(0.5, -0.04, 'Score distribution of cosine-similarity using consensus spectra',\n",
" ha='center', va='center', fontsize=16)\n",
"for i, (_score_test, _score_noise) in enumerate([[score_test, score_noise],\n",
" [normalized_score_test, normalized_score_noise]]):\n",
" \n",
" axes[i].hist([_score_test[true_mask], _score_test[~true_mask], _score_noise], \n",
" label=['True ID', 'False ID', 'Noise ID'], \n",
" color=['b', 'r', 'k'],\n",
" bins=50, \n",
" stacked=False)\n",
" axes[i].set_title(titles[i])\n",
" axes[i].legend(loc='best')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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Xk19ozod6br8e+Z20XOi9t1L5tNy9y08++SStrhvv+epZ11PPhzoaN24cJ5xw\nwqBJQt71rnfxrW99i82bN/Pggw9y22238c53vnPQ6wp1wYmiCO89GzduHDhAxo0bB8CkSZN485vf\nzEUXXcSmTZt4+eWXuffee1m5cmVN5c+WY+vWrdx7771cdtllnHHGGTVtU0REpFbe+y9671/vvX8D\ncA3wNeJhGAc653Zzzu0F7Om9XwEsqzJdpKTshJvpdOMm3RSR9qF7vuFr654PqVSK3t7ehm6/Ermt\nR6eddtqg2U0vuOAC5s2bx8EHH8yYMWO48MILmT49nt/q8ssv55vf/CavvPIKr7zyCqlUit133537\n7ruPKIpYtGgR8+fPJ4oiDj/8cL7xjW8MbPfaa6/lC1/4AkcccQSbN2/mwAMP5NJLLy1b1vxnvt5y\nyy0Dz3w1MxYvXsySJUuYMmUKZ555ph6zWUfptMb9haJYh6NYS7N47192zp0H3J0kzU3St1STLs3X\n6nM+5D7qr9rH8rUinbfDUaxr1yr3e6B7vnpo+8aHZl8AUqkUzzzzzMDyjBkzePbZZweWx40bx3e/\n+92Cr/3sZz/LZz/72YJ5I0aMYNGiIUNVB+y8884sXLiw6vJmn/layPz585k/f37V2xQREQnFe//v\nOf/3gC+wTlXpIiLSmlrhfi9bDt3z1U7DLkQCUct3OIp1OIq1iNSq0JwP0jg6b4ejWIsMpsYHERER\nEREREWkoNT6IBNJOjxBqd4p1OIq1iNSq1ed86DQ6b4ejWIsMpsYHEREREREREWmotplwMooioiiq\n6zNOZXiyn0Uny2QyA4/TqtdENxr3F45iHY5iLSK10pwPYem8HY5iXb1t27YxYoR+H2+2Rt3vtc0n\nO378eJ577rlmF0OA5557ruMrCtnHas2aNUvP9BYRERERabAJEybwxBNPsG3btmYXpes16n6vbXo+\njBkzhhdffJEnn3yy2UXpGP39/cM6qHp6ehgzZkwDStTZ9KzncBTrcBRrEamV5nwIS+ftcBTr6vT0\n9DBx4kTWrl1b9WuHe18jhTXqfq+ixgfn3FjgL8CV3vsrnXMOuBiIgHne+yXJelWlV2vChAnDeZkU\n8eijj/KGN7yh2cVoiuywilZ5drCIiIi0p0YM1RTpVj09PUyePLnq13XzfU07qbTnwwXAfUDknOsB\nFgBHAqOBu4Al1abX803I8HRzS2x2WEVvb++wKwnlGjDyKyPdHO/QFOtwFGsRqVW7/1qZrVMANdUr\nQtF5OxxN3bMHAAAgAElEQVTFOhzFuj2UnfPBObcfsBtwP2DATGCl936d9341sNo5N524caGadJG2\nlq1sFJsTQvNGiIiIiIiIxCqZcPKrwEU5y5OANc65Oc652cBaYA9gYpXp0mR69nBYinc4inU4irWI\n1EpzPoSl83Y4inU4inV7KNn44Jw7Gfhr0mNh0DMuvffXeu8X5b+mwvSSz+3IPXjS6bSWtdyw5f7+\n/pL52XVqyc/V7PerZS3Xe7mvr6+lyjOc5UzGSKdHcuutm1i2bF3R9QH6+9eXzM819HyzvmR+Jcsi\nIiIi7cpKPb/TOfcV4IPAK8AEYBuwEDjCe39yss5dwDnAWOC8StO99ysK7XPp0qXRoYceWp93J1JE\nOp0emPOh0BixbD5Qdp1atiEizZdOj2TWrHEA9Pau5+ijtxZdp1x+LduoxPLlyznuuOOs/JpSK9VH\ntqv1etft+SIinaSWukjJCSe9918EvgjgnLsQ2AB8E/iLc2434gkk9/Ter0gmljyw0vThFFak0+ip\nGyIiIiIi0g0qmfNhEO/9y8B5wN3AUmBukr6lmnRpvk7twpvJZJKu1K01yWOheJebtFKGp1OP7Vak\nWItIrTTnQ1g6b4ejWIejWLeHSh+1iff+33P+7wFfYJ2q0kUaoR6P0RQREREREZH6qbrng3QOjUkM\nS/EOR7EOR7EWkVqNHz++2UXoKjpvh6NYh6NYt4eKez6ISHjZOSEAzQshIiIiIiJtSz0fupjGRoU1\nnHhnh5BoXojq6NgOR7EWkVppzoewdN4OR7EOR7FuD+r5ICIiIi3NObcrcBuwA2DAJd5775xzwMVA\nBMzz3i9J1q8qXURERBpPPR+6mMZGhaV4h6NYh6NYSyD9wLHe+xnA24FrnHM7AAuAtwDHA1cDJI/4\nrjhdmk9zPoSVSr2VdHokmYw1uygdT9fIcBTr9qDGBxERkRaRyZhuCgrw3r/ivX8hWXwN8BJwJLDS\ne7/Oe78aWO2cmz6MdJGuksmMYNascWQyug0QkbB01uliGhsVluIdjmIdjmJdX7opKM45N8Y51wf0\nAZ8GJgFrnHNznHOzgbXAHsDEKtOlyTTnQ1j9/etL5pdrBFUjaeV0jQxHsW4Pqt2ItLlMJkM6ndaE\nlCLS0bz3G733BwOHApcDo5P0a733iwqsX0l6VGqfuZXZdDrd1cswuJGgUH4u5W/Pz2Qy3Hrrrdx6\n660D1+rW+DzXF8zPNoL29W0cVr6WtdyM5b6+vpYqTycv10ITTnYxjY0Kq1Hxzj4Ro7e3V4/iTOjY\nDkexltC89w875x4HHmdwz4VJwJPA2CrS15TaV+7xnX+sd9syDJ6bodx3v5r8QnM+1HP7zc7PZDJ8\n5CMfARi4Vjfz8xw/flzOv1uLvp9a8rWs5dDLOl+HW66FGh9ERESkpTnnJgMvee+fdc5NAvYD/gIc\n6JzbjbgXxJ7e+xXJxJIVpzfnHYmIiHQfDbvoYvXqPiOVUbzDUazDUawlkBRwl3NuBXAH8WMynwbO\nA+4GlgJzAbz3W6pJl+bTnA9hlZvzQepH18hwFOv2oJ4P0nYymQyZTIZUKqVhBiIiXcB7/wfgjQXS\nPeBrTRcREZHGU8+HLtauY7Wzcxy02wSL7RrvdqRYh6NYi7S/Zk9cXGjOBxm+ck+jyM75II2na2Q4\ninV7UOODSBdodsVSRERaV7s26kthzX5krx7FKSLFqPGhi2lsVFjNjHe3VSx1bIejWItIrTTnQ1iN\nnvOh2Y0frUTXyHAU6/ZQcs4H59yuwG3ADoABl3jvvXPOARcTPx97nvd+SbJ+VekiIiIiIiIi0vnK\nNUn2A8d672cAbweucc7tACwA3gIcD1wNkDzCquJ0aT6NjQpL8Q5HsQ6nHWKtLsAirU1zPoTV7Dkf\nuumc3A7XyE6hWLeHko0P3vtXvPcvJIuvAV4CjgRWeu/Xee9XA6udc9OHkS4iLUJzQkgnUxdgEZHW\noXOySPcq+613zo1xzvUBfcCngUnAGufcHOfcbGAtsAcwscp0aTKNjQqrlePdaXNCtHKsO41iLSK1\n0pwPYTV6zgfZTtfIcBTr9lC28cF7v9F7fzBwKHA5MDpJv9Z7v6jA+pWkR6X2mXvwpNNpLWt50HJu\nJWU4+bnL/f39JfOz69SSn6vQ/mvJr/f7LZevZS3nL/f19bVUeUof3+sr+L6vryk/V6H9l8qPv8/F\nt19qXyLSPbpp2IKIdJaSE07m8t4/7Jx7HHicwT0XJgFPAmOrSF9Tal+5Y3byx+9ouX7LhcZGtVL5\nii3nVsCHkw/bK/Hjx48vGJPcbeSORS0Us3L5lb620Drl8uv9fovla1nLxZbb4XjJfkXGjx9X5Pjf\n/pp4LPTWgturJL9UefLHWRf+Po8smb98+fKS+xRpR5rzoTrZYQu9vetJpbZW/fpmz/lQTiZjZDIj\nSKW2kUqV/L2y5WkegnAU6/ZQ7mkXk4GXvPfPOucmAfsBfwEOdM7tRtwLYk/v/YpkYsmK0xv5pkRE\nREREpP3U2rgiIq2r3LCLFHCXc24FcAfxYzKfBs4D7gaWAnMBvPdbqkmX5lMX3rAU73AU63AUaxGp\nVbfP+ZCd9DmdDjPxs+Z8CEfXyHAU6/ZQsueD9/4PwBsLpHvA15ouIiIiItLNspM+A/T29pJKpZpc\nIhGRxtAzbrqYxkaFpXiHo1iHo1iLSK0050NYrT7nQyfRNTIcxbo9qPFBRCqS7RbaKY/jFBERERGR\ncNT40MU0Niqsdo93tltoOzQ+tHus24liLSK16vY5H0LTnA/h6BoZjmLdHtT4ICIiIiIiIiINpcaH\nLtaKY6M6uWt/K8a7UynW4SjWIlIrzfkwWCZjpNMjyWSsIdvXnA/h6BoZjmLdHtT4IC2lnbr2y2Ch\nHxUmAtsr6Y2sqIuIhJTJjGDWrHFkMqqmi0hn0Vmti2lsVFidHu9sw1ErNB51eqxbSbNjna2kq6Iu\n0r4050Np9W7c15wP4TT7GtlNFOv28KpmF0BERESkFOfcFODHwGuAl4D53vtfOecccDEQAfO890uS\n9atKF2ll2cZ9gN7eXmBacwskIjJM+pmoi2lsVFiKdziKdTiKtQTyMvAv3vuDgPcC1zvndgAWAG8B\njgeuBnDO9VSTLs2nOR/Cavc5Hxo9J0Y96RoZjmLdHtT4ICIiIi3Ne/+0974v+X8G6AHeBKz03q/z\n3q8GVjvnpgNHVpkuIm1Ec2KItC99a7uYxkaFpXiHo1iHo1hLaM65dwD3A7sDa5xzc5xzs4G1wB7A\nxCrTpck050NYmvMhHF0jw1Gs24MaH0RERKQtOOcmAVcAn8ymee+v9d4vyl+3wvSo1P5yK7PZCf86\ndRkGNwIMJz+X8mvLh/KNBLn5hT+v7s3Xcvct9/X1tVR5Onm5FppwsotpbFRYinc4inU4irWE4pwb\nDSwinihylXNuMoN7LkwCngTGVpG+ptQ+c4/v/GO905Zh8NwLw8kvt/1i+YXmfKjn9tsxH7JzM2yt\nKL/w51U4Pzvnw3Bf3w75Wu6+5W46Xzd7uRZqfBCRYDKZDJlMhlQqRSqVanZxRKRNOOcMuA64yXv/\nyyR5GXCgc243YDSwp/d+RTKxZMXp4d+NdLtMxshkRpBKbSOVKtn5RkSko2jYRRerV/cZqYzivf1x\nYfV4TnkpinU4irUE8hbgFOBM59wfnXPLgV2B84C7gaXAXADv/ZZq0qX5um3Oh2ZPmKg5H8LRNTIc\nxbo9lO35oGdri4iISDN579PET7gYkpX85a9fVbqIiIg0XiVNrnq2dofSWO2wFO9wFOtwFGsRqVWh\nOR+kcbJzPkjj6RoZjmLdHso2PujZ2iIi0iyZjJFOjySTsWYXRUSkZWUyGdLpdMOHNYqI1KKqwWZ6\ntnZn0diosBTvcBTrcBod62aPjRaRxuu2OR8aoZo5lTp9zodWarRWfSQcxbo9VFybC/ls7VZ6jqmW\nwy7nV0AKrV/qOeOV5Ofvr9xzbBv53PNy77ce8ajm/dYjHrW+Hy2313Kjn6td7jnuufn9/evLHN+l\n87Pr1JKfq9D+S+VX8n5FRKQ0NVqLtK6KHrUZ+tnaek5rmOVCY6Maub/c1vjs4xbz188f91loe7kV\n8OHkw/ZK/Pjx4wvGJHcb9XruebnXFlqnHvGAyt9vsfxiZar2OeaVvB8tt9dyo8/X6fTIivPHjx9X\n5PiuLD+7Ti3PnS/1fvLHWQ/n/S5fvrzkPkXakeZ8CEtzPoSjeQjCUazbQyVPu9CztaUusl0CAXp7\ne0mlUk0ukbSaTCYz0EiVSqV0jIiIiNRB9voaX1enNbs4ItKlKumPpGdrdyh14Q1L8S4v20BV6bjV\nYhTrcBRrEamV5nxovNw5ITp9zodWomtkOIp1eyjb80HP1hYRERERERGRWmgmli6msVFhKd7hKNbh\nKNYiUivN+RBWt8/5EPJpGLpGhqNYtwc1PoiISFO00uPQRESkO+hpGCLNo29dF9PYqLAU73AU63Bq\nibUqgCICmvMhNM35EI7qI+Eo1u1BNT4RaSuZTIZ0Ol3ThJQiIiKNol5dIiKFqfGhi2lsVFiKd33k\nzthdjGIdjmItIrXqtDkfWr1XV6E5H9Sw3xi6RoajWLeH1jwrioiIiEhd6MZSyqmkYb9bqOeKSOOo\n8aGLaWxUWIp3OIp1OIq1SOtr9RtLzfkQluZ8KK2ePVd0jQxHsW4PanwQkY6SyWTo7+/Xr3wtQL8e\niYiIiEiWGh+6mMZGhaV4h5HJZPjIRz7S0r/ydZJSx3Wrj3sWkdbQaXM+tLpCcz5IY6juF45i3R5U\nIxQRERERERGRhlLjQxfT2KiwFG/pRDquRaRWmvMhLM35EI6ukeEo1u3hVc0ugIhIaJlMhkwmQyqV\nIpVKNbs4IlIB59wVwIeBdd77g5M0B1wMRMA87/2S4aSLSGm5102Y1uziiEibUs+HLlbvsVF6lFdp\nGovWOlp95vd2ouNaAvop8O7sgnOuB1gAvAU4Hrh6OOnSfJrzIazhzPmg6+bw6BoZjmLdHtT4IHWj\nC5OIiDSK9/73wLM5SUcCK73367z3q4HVzrnpw0gXERGRADTsooul02m1EgakeEsn0nEtTTQJWOOc\nmwM8B6wF9gDGVJn+QBPKLjnabc6HTMbIZEaQSm0jlYqaXZyqxXM+6IkXw5X9/IGyx4CukeEo1u2h\nbOODxliKiEi+bOVr1Kj9ml0U6XLe+2sBnHPvG0Z6yTvH3MpsdjKzdl2GwTf5jcjP1cn52ccI33jj\n30mldir4esje5IfJL9SAEzK/8PHSmfnZzx+gt3c9qdTWot+/rGZ//7thua+vr6XK08nLtaik58NP\ngR8B18OgMZNHAqOBu4Al1abXXHKpmVoHw1K824smpSwtW/nq7QXY2uziSHd6krjnQtakJG1sFelr\nSu0g97ydfw5vt2UYPLdCI/LL7b9YfqE5H+q5/UbkQ3buhOLnv5D5hWJYLD8758NwX18ov/Dx0tn5\nuZr9/dby0R11vm715VqUnfNBYyxFpBtpDhORlrcMONA5t5tzbi9gT+/9imGki0iNNOm4iFRiOBNO\nDoyxdM7NZvuYyYlVpkuT6Xm4YSnenaWTK1qZjJFOjySdHkkmYyXX1fPiJRTn3ELgHmA/59xq4B3A\necDdwFJgLoD3fks16dJ87TbnQ7trxHlbDfaFqe4XjmLdHoY94aTGWGq50DLUNiY0vwJSaH+ltl9J\nfq7+/v6Cx1v+OqXeTyPfbz3iUc37LZbfTu83N7+R7zdb0brxxhsHhmU0+/tXr2U4dmA8a+6Y5kLx\n2bRpE8XGzOZPalZtfvx5HjKwTrn8/v71pNN/LPh+KsnfXqbhjxHO1Yj3u+OOOxbcVzfw3p8NnF0o\nq8C6vpp0EZF6yZ2QUvMiiQw2nMYHjbHskOVCFeZ6jAGqZUxo/pjCQvvLvYEcTj5sr9SPHz++YExy\nt1GvMbDlXltonXrEAyp/v8Xyi5Wp3p9vvT//EO+3VH67Lue20ZQb8zp58h5F8/OfJV9tfvx5jqw4\nf/z4cUU+78ryt5epMWOA6/F+ly9fXnKfIu2o0HwC0jj55yKpr8ETUoLmRQqj3DVZWsNwGh8GxkwS\nTyC5p/d+RTKxZMXp9XoDIiIiIiL10u6P0mxFuZM4w7RmF6epqnlUp0inKTvng8ZYdi6NjQpL8ZZO\npDkfRKRWrTbnQ/aX6+wNYqdpxnm7W+eEKBTr7PHVycdYM6ie3R7K9nzQGEsRkc6iX/VEREREJLRh\nTzgp7a+asVHZ7nIAqVRqYII9qZzGonWf3G6mhb4zzfpeZX916e1dTypV21jUcs+hFxEpR3M+hNWK\ncz6UG5bRrsM2dI0MR/Xs9qC+PlKRbHe5buwyJzJc5bqZNuJ7Vc1jMkVERFpBpdfLbqiDlruO6zov\n7UyND11MY6PCUrwlhNBjSTXng4jUqtXmfOh0Om+HM5xYl7uOa86IwlTPbg8adiEi0kY0X4OISG10\nHu087TosQ6TbqLmsi2lsVFiKtwxHJpMhnU4PdDXNn4U9m5+7TkitOHZYRNpL6DkfOv1pFuV04nm7\nVYdldGKsW5Xq2e2hO8+6IiJtohnzRoiIiEh70pwQ0srU+NDFcsdG5f+6KvWnsWhSTjtWGDR2WERq\npTkfwtJ5O5xmxLpb54RQPbs9dM8RKSW1anc1kW7SrRUGEREREel8mnCyi2lsVFiKtzRrkrPcibji\nybjqR88wF5Fa1XvOB00oWVo3zkPQrAkpdY0MR/Xs9qCf1kRE6iA7ZKLUcIlmTXKmnk0i0k26fUJJ\nGUrXQZHWoLNyF9PYqLAU787WzpXdWp6YobHDIlIrzfkQls7b4bRirNtxfqlKqJ7dHjTsQkSkAp3c\njTf7ixBAb28veka6iIhIZ8r+WALQ27ueVErDQiSc9vuJTqpW7EkWGhsVluLdulp5yESrKHYe6cax\nwyJSX/We80FK03l7qEY99U2xDkf17PbQnbXoLqNxbtLNKule2O0NC5Wo5DxSj8qbHvsrUr2nnnqK\nRx99lGeffbbZRQmikgZjkWqUu8aVuzbp2iVSGdW0u5jGRoWleFevsoaD0pVQPb6ysXLHs9Zaeatm\nG6oEimx3zz33cPjhh/O3v/2t2UUZlmrnfFCDcW1acR6CVlfJtalQfjvGul3nhFA9uz0Em/PBOeeA\ni4EImOe9XxJq352ukY/RE2mkcvMoVDIuMbuOxi22vmzlrLe3d9jnqkrmp6jHfqRzqT4iIqE161Gf\nw1Gu7pWtuwEdOQ+WNFaQxgfnXA+wADgSGA3cBehiX4FKGhaGW9HW2KiwOi3e5RsOyl+c1HDQ/trx\nGebZ8yrQFhVBqR/VR1pT/pwPnTzBbyvQPAThZK+RuXX1dr/mNLpxYrivb6V6ts5hxYXqr3YksNJ7\nv857vxpY7ZybHmjfLa2SMWSar0FCq8cEjBruIK0qe15thXOrhogEp/pIG9CwCuk2tc4p0UrXknL1\nv3LDOiqpP5YfctvYeWEqHfLbqPK387w3oYZdTATWOOfmAM8Ba4E9gAcC7b8qW7duZeTIkUP+X61G\n9lqoh3Q63VKthO2uXCvnsmXreOmlSUCpngDxNg7dZRVjn8vAiy8WzK9qG1B0O8X3E7ZHwn6jHmNk\n+vEh5QhtoBxQtCzlylqPbVRV1ibHLB7PulNN26gkZqGVO39X0nui1a8BXaqt6iOVOvjgg7nuuuuY\nOHFis4sCVN4zLptfbM6HWs9zjX59y+RDwXWK5cfn7XHDfn2l+eU0+/UhVHqNLNczoh75pYZ9hMzP\nZKYN6TlRzevjbQyup2YyGfr6+jj44IOL5g9+fXXnqHL7L6fa19ee37o9LyyKGl8g59z7gRO992cm\nyz8Crvfe356/7tKlS1srQiIiIi3kuOOOa7+fOlqE6iMiIiK1G25dJFTPhzXEvyxkTUrShlClSkRE\nRBpE9REREZEmCdX4sAw40Dm3G/EET3t671cE2reIiIgIqD4iIiLSNEFm8vHebwHOA+4GlgJzQ+xX\nREREJEv1ERERkeYJMueDiIiIiIiIiHQvPcNIRERERERERBpKjQ8iIiIiIiIi0lChJpwcxDnngIuB\nCJjnvV9Sj3VlqErj55ybAvwYeA3wEjDfe/+rYAXtANUeq865scBfgCu991cGKGJHqfI8ciTwXeJz\nXp/3/gNhStkZqoz1hYBLFn/svf9ygCJ2DOfcFcCHgXXe+4PLrKvrY41UHwlH9ZFwVB8JS/WRcFQf\nCaORdZHgPR+ccz3AAuAtwPHA1fVYV4aqMn4vA//ivT8IeC9wfcML2EGGeaxeANxH/GWVKlR5HhkB\n3ACc5b0/APhkkEJ2iCpjPQ34CHAwMAP4qHPutSHK2UF+Cry73Eq6PtZO9ZFwVB8JR/WRsFQfCUf1\nkaAaVhdpxrCLI4GV3vt13vvVwGrn3PQ6rCtDVRw/7/3T3vu+5P8ZoMc5t0PAsra7qo5V59x+wG7A\n/YCeJV+9auJ9GHHL7T0A3vtnQxWyQ1QT6/XENw6vTv62AP1hitkZvPe/Byo5RnV9rJ3qI+GoPhKO\n6iNhqT4SjuojgTSyLtKMYRcTgTXOuTnAc8BaYA/ggRrXlaGGFT/n3DuA+733Lze+iB2j2lh/FTgH\n+FiY4nWcauKdAvqdc79IXvdd7/23gpW0/VUca+/9s865rwOriRu353nv/y9kYbuIro+1U30kHNVH\nwlF9JCzVR8JRfaT1VH1ub9qEk977a733i5LFkt28qllXhqomfs65ScAVqCvYsFQSa+fcycBfkxZC\n/cpQgwqP7dHE3cHOAI4F5ibd8aQKFR7bU4GzgNcC+wCfTc4p0iC6PtZO9ZFwVB8JR/WRsFQfCUf1\nkdZTzbm9GY0Pa4hbRLImJWm1ritDVRU/59xoYBFx6+CqBpet01QT65nAKc65h4Czgc855/65weXr\nNNXEey3woPf+7977DcRdS/dvcPk6STWxPhJY5r3fkHQn/SNwSIPL1610fayd6iPhqD4SjuojYak+\nEo7qI62n6mtjM4ZdLAMOdM7tRtwCuKf3fgWAc+6rQOS9P7/culKRimPtnDPgOuAm7/0vm1XgNlZx\nrL33XwS+mORdCGzw3v+oOcVuW9WcR+4DUs65nYFNxJMPPdKEMreramL9CPD5ZAKikcChwEXhi9x5\ndH1sCNVHwlF9JBzVR8JSfSQc1UearB7XxuA9H7z3W4DzgLuBpcDcnOxJyV8l60oZ1cSauBvYKcCZ\nzrk/Jn/qnlShKmMtNaryPNKf5N8JLCeu0P41XGnbW5Wxvg9YTPwLw33E41n/Eq607c85txC4B9jP\nObfaOfePSZauj3Wm+kg4qo+Eo/pIWKqPhKP6SDiNrItYFGnIooiIiIiIiIg0TtMmnBQRERERERGR\n7qDGBxERERERERFpKDU+iIiIiIiIiEhDqfFBRERERERERBpKjQ8iIiIiIiIi0lBqfBARERERERGR\nhlLjg4iIiIiIiIg0lBofRERERERERKSh1PggIiIiIiIiIg2lxgcRERERERERaSg1PoiIiIiIiIhI\nQ6nxQUREREREREQaSo0PIiIiIiIiItJQanwQaQIzO9bM7jSzZ8zseTNbZmYXN7tcjWZmF5nZtiJ5\np5nZNjNL1biP8cl+pteynbxtPmZm3y+St83MLqzXvpJtXmRmx1awXl1i1ixmtpuZ/XfyHdhmZnc2\nu0zVMLOpSblPbXZZhisp/5fqtK1fm9ldBdIPSvaT/buuHvurolxvS/Z7TIl1/snMzglZrnordX4V\naTXJ+fMiM3tts8siIuG8qtkFEOk2ZnYSsAT4MfARYDNwDPBh4AtNLFooUZH0JcBRwNoat78z8CXg\nUeCBGreV9R5gfYn8Yu9puL4EbAN+U2a9esWsWb4AvA04HXiC0jFuRU8Sx/+RZhekBkcBf6/Tts4q\nkv63ZD8GLKb+35dy7k/2/1CJdf4JOBb4epASNcZ3gZ83uxAiFZpKfK27E3i8uUURkVDU+CAS3r8B\nf4qi6J9z0n5tZpc2q0CBWaHEKIqeAZ5p9H6GI4qiejViVKNs+RsQs9AOIP4uLG52QYYjiqItwP80\nuxy1iKKobuWPoujhIukvksTJzF6q1/4qFUXRBtr8c6pEFEVPEDfiibSTul2rRaT1adiFSHh7AE/n\nJ0ZR9Ep+mplNMbPvm9mTZrbZzP5sZnPz1tnLzBYlXddfMLPfmtmbCmwr2/X4RDP7hpmtM7ONZvbL\nnHXMzM41s4fN7MVkuMG/DedNmtlIM1tgZk+b2QYzuwHYqcB6P8zrkl1wCIGZvdPM/mBm683s/8zs\nXjN7d07+aUmX40eTpOtytjlkyETy3q4zs/eZ2YNJfP9mZjOT/NdV0VV8TPI+Niaf1QV5+xoyPCK/\ny37O55PtNn1hzr7vzNtepTF7q5mlk+PiOTP7sZlNLhKHz5nZE8lx9EMzG/JZVSLpvv7HJJ5Pmdl3\nzGxc3jrZ93kccEyx91nFPseb2dfNLJMct/9rZl/NW2dnM/tecjxuNrP7zewfC2yr5HGWrPOJvPh/\ntMB2sp/nbDNbnBwbq8zs4wXWPcrM7kj22Z98ThOHGYsjLR7S9VzyvXvAzE7PW+f4vPIPGTZk8RCK\nH5nZo2a2Jvlc/5AcH5/PWe9VedsaMuyiirKPMrP/MLO+pOz9ZnabmR1WYN1KzmcX55VtyFCm5Pjf\nBpwKvLbQ98rMDk6WP5D32hHJMXd9le9zyPAIKzA0JIntJclxszn5HG42s13zXpvOLXeRfV6fbOdd\nZrYyidXvzOz1eetNMDOf5K+1+LxQcDhNhe+1Lt/NbMzM7HCLhylusvg8MzNvvUpjNt7MrjGzvyfl\n+qOZvWuYMRtrZguT97g5+feHZjYiZ51S14CP5m3vMxZfg19IYvILM3tdtbFPtlXJ+ewxM7sh+Zz+\nz+LhoFeb2avy1isbs2S9UWb278ln/aKZPZ68bsckP3v8Z8/3d+Ucv0OGgCXpF5rZWRafjzYnn8dr\nk4yeKaoAABBSSURBVPwTzexWi6+9L1p8Hb8ov/wi0hrU+CAS3h+B481snpntVmwlM5sA/B44EbgI\neDfwLeAdOeuMBpYSdyk+G3DEvyIsNbP9imz6UuIGkI8B/ww8lpP3deBrwE+Bk4BrgUvM7Oxq3yTw\neeJeHt8E3kfc0+pshna5/lJS/qJzXpjZNOBnxMMLTgFmA73E3TazskMQ3pcsfyVZPir5f74ImA5c\nDlxGHN/vs72BJJO89k3Jfkt1FT8beBl4L3Aj8BUz+1iJ9QvJdg3PNhz9Z075P5m3biUxOwC4A9hK\nHK9/BY4mPjZ6claNgHcBhwOnAV8lPi7mVVl+zOx44GZgFXE39i8RH5P/nbdq9n3+EVhO8fdZyT5H\nAXcR3zz+B/FxuwB4e96qPyM+dr6QlC0D/LeZvS1nW5UcZyTvJ/dYK3VsXA78Ntnnw8C1ZjawPYsb\nCn8DjAQ+BHwceCNwS8k3XoCZjQF+AexAPKTrPcD1QP6Y6nsZfKwVK/+hwBzgeeJzwveBHwD/nr2R\nSBpNcz/PWoZUvBpIAVcBJwMfIB6W9mszm1TkNaXOZ99i+7mRImV7T1L2nxN/7kfl/K0FiKKojzhm\n+d/p44A9iYc7VKuSOH0u+buG+DrwKWADMD5vvTOS8n6vzHZ3If5Ofj55zf7E5/hcNyb7mgt8gvh7\ncGiF5R2knt/NHN8CriD+rMcB/2Vmub+cl41Zcv77FfHxdTHwj8THbq8NnSuokphdSfx9+3Kyz/nE\n15FKb34HYmtmH0q2dzPwzmSffwOKHf9FVXE+I8l/HXFMLiM+Fw80SlYZs5uJY/BD4uvqBcm2JyT5\n32Xw9/KTbP/O/WeRt3My8bno/OT/twCjk7yDgPuAfwFOID7nziWuy4hIq4miSH/601/AP+LKdR/x\nmP6txDdfnwd2ylvvEuAVYP+89JE5//9gsp0Tc9JeA2wCFua97m3JuncUKdfrkvJ8NS/9KmANYFW8\nxx2AZ4Ef5KU/Amwt8prTkvKlCuS9P8k7tIJ9T03WPbXMeo8BLwFTK9jmKuD7RfK2AQ/lpf0CeLjU\neytVziT9SxWUq1TMvg1sBMbnpJ2YrD87Lw4ZYERO2u+A3w7j2L6twLbOTPZ5RIH1fw3cWe1+8rZx\nRrL9E/LSc8twVLLOmTlpI5Oy3jqc46yCzzD7fftqTto+SdrpebF+FOjJSXtjst5JVcbi8OR176vi\nNQWPNeKbxquS//8H8Fzy/+nJa944nM+z1HepwLojgV2T/X2qSHwLns+KrHtMiXWuB1aVyD+d+Hy8\nV07afwErh3HMXgRsK1dG4gbVFbVsN+/9bQP2y0n7CvE5f2TecXdGzjqp5H1X/T2t83fzomS9d+Sk\nfTxJm1ZNzHJe94689OWAryZmSdqfgd4y+zyNCq4BxI0mG6qNdZF9VnQ+I74GPA+8OiftW0laT5Ux\nOyH/GCr0uRc75ouUbxvx8MKxFbxnI270+TrwTD3iqD/96a++f+r5IBJYFEUZ4BDiXw6uBcYQNzQs\nT365zDoeWBbljaOOomhrzuIRJBXwnPz/A/4ADOqOmuOHRdKPI75wL0q6rr4q6bb4e2Ai8S98ldqL\neOLHX+Wl/4rhje/sI67wXW5m7ynxK2i17omi6LE6bCd/uMCvgX2TninNcgTx8dOfk/Yr4huJI/LW\n/Z8oinK7az/GMH5pS7a7NG9bt+XkNcLxwFNRFN2Rm5hXhuy+b8/J30ocj9zvSSOOs7tz/v9Y8u8k\ngKT3wJuIf53clvOde5C44ejw3A1ZPJQp97uZ75HkdReY2Qet9lnks8fOBrZPBroh+XdsjdsuyMw+\nYGb/Y2b/R9ybaF2StXuRlxQ7n9Xbj4ljexqAxUOJ/om4t0Gj/Ak40OLhI28ys1fXuL3noij6S87y\nY8Tn42xss8Nbcr8nGeB/h7m/en43s4p+nxKVxOx44hvrpXnfp3vI+85RPmbZfR5n8RCVw/J6llXr\nT8BOZvYti4fi1PI9q+Z89vsoijbnLP+auLfItGS50pgdn/x7Y/4O8j73at0SxXO3DGFme1g8vC8D\nbEn+/hXY2XKGvohIa9CXUqQJoih6JYqin0dR9MkoivYlniX+9cS/LmTtQtzjoJTxQH8URfldYp9l\naNfcrNVF0rNdIu9j+wV8C3GlOyJuUKhUtmL2XIFyVS2p/L2HuKHlJuDJZLzpIcPZXnazFI9FtfLf\n5/PJv0WH1QQwnrx4J5W/54l7xwwkM/QpE1uJe69Ua1z+Ptk+IeZraIxKvydQuGwD5WrQcTYQ25yG\nw2xsdya+Dp/D4O/cFuJu2/kNfktz17G8R3xGUfQ88S+PTxA3bK5Kxl2fMMyyZ88r2/L+D/Gv03Vl\nZicDPyJuRPkA8U3NUUl2sS7s9foOlxRF0QvEx8RpSdIHiGPwgwbu9ivJ33uBNPCcxfMQDLdRs9D3\nHLYfj9l5Roqdz6pVt+9mVhRFG3MW88sPlcVsAvF3L/8790mGfufKxQzi4QPfIR4WsAx4xsyuyC97\nhb4HnEvc8HJHUv6fWd6cFZWo4nwWMTT+2c88e0xUGrNdiOskL1Zb3jIKfs+TITc/Ix6KcQnxk8MO\nJx4ilu0FISItRF9KkRYQRdF3zOxrwL45yc8Ak4u8JKsfGG9mI/J+VZgA/F+R1wyZ2DKRrXycyNDK\nJ8Bfy5Ql11PJv7vkpVddgcqKouj/b+9cY+0qqjj+WxABSUosNoRqeURqTKgGQjREBbQfCh+ILwKJ\nCBG9JoQQgbQfqEahUoix/QA0lULqA21ipFgwNbGQcEslPNLyaCsJqTz0QktRpC0tbW/Loyw//Gf3\nzt13n3P2vuecWyzrl+yc15zZs2fPzJ5Zsx6rgFVpt2UWmuz9HqkKj5dWddGU8nVOTq9FPbybXnOt\nj2N7dO5W7GREoKSTaxfoeFq3jW7Zxdh7XJShX+fcDnyuQ5piB//jaPe6YAqlhVWf2lkr3kQLg18C\nyyp+L0cyuRJpShW8XP6Du68Dvp4m5ecAS4DlZjaly53HXtHOd8C3gX96FgmohvZGr/pwHZYCV5nZ\nTCSEWOnu4xGovlvx3ZjxwN3fRuYGP0s+gK5On59F5nBN6aR1VoxX5X5yPAor25Se9s061Kyzbajv\nXFwjyzpRh3YBs4HZZjYN+SWYY2ZPuPv9KVmtZ0DaSLgduD1pPVyOTAjmAdfWKG85vzrjmTF23C4/\nw7ZTr862oznJMT0WQLTq59ORsOEKdz+obWFmPReOBkHQG0LzIQgmGKvwYp++m8SIijFol/PzyXFg\nnjZ/qD6J+vGs7PfJaLewaWi51WhhcKK7r6849nTKIGMLmuDl5TJk2tGNQ7pCa+QB5PSvShuj2Knq\ni1p4C2ZWfN7kCsUIIxO4XJhUdtKVs5vuy/8U8AUzy3cPZ6Hd2jptYzz36SnkTDVvo4U39H6FOhwE\nTjCzC/IvK/oJyIFb8XsxGa8sV4121jVpN30tMKNFn9tcSv9C6fcqIWGR1t39UWS3/jEmtj+0Ywcl\noVjGR9Fuas6lVQn7wFuMFuyMwd03Is2wW5C5TCvneJ14HaQunn3XbjzA3be5+3y0QB9ve+zUp59J\nr7lT41OQVt546EvfrEubOlsNfBJ4s6rflbNpeM5XgRvSx/ycTZ8BuPtud78TeJ4ux6Aa49mXSiYq\nM5FgaCh9HqRenRWmlqMieMBB4XdOL57VRZkPCjqS+erX6HKuEQRBfwjNhyCYeO42s33Ie/yraCJw\nPTDM6N3P25AH7YfMbD6yu/002nkoFvUrkYryb8xsLnqYz02/LW5SKHd/0czuApaY2aeQr4ejgTOR\nQ6jzG+T1npndCtxiZi+iSeR3kW3uwZ2ftLMzI308Lb2eZQoH+ba7b0jpvoPUR/+C6uwzaEeo8CeQ\nn3uHmW0BBsxsA1rs7HT3/5SStt3RSkKfIkTkMWgSXaiAv+Tu+a70KaZwnvcgAcv5yEFdwVp0b643\ns6uQmupcWrMRuMTMHkQq9MPFQrRunaHd9O8jb+QL0QJ0AdJgWVmjHsbjm+NWdE9WmNlSFGVhAfCI\nuz/dw/PkLENezpenfvJ3VL/XkGyR3X2dmT0OLJAMjM1IRXoqapcqSM12lrWDYvE4PftufSZ0qsNc\nZEd9P7KT3oXu64XAIndfUzcjMzsXOa/9E1o0TENe39flvj+S2vXR2V9Pysr/XLKtrnVfzOw0RsyL\njgPczM5O/9/i7ltLf3kY+KGZXY68+P/X3YvwuKuAb5rZL5DK+RdRhInGGhvJ7v6s9LEQ4M4ws3cA\n3H1t6S8bUrlmp3O/h/p5ecd1aTq2lH0ZNGAQXdNPzOwmpB1wZcU1/AE5I30SORG+FJnjPJilmYJ2\nfyGpv2f1v8vdN+VZtiuUuz+bxpyFZubIZOIGxq+11LO+WZc6dZaVazCNjc+jNvxl5LBzTp5ljXM+\nhNr1RmSWcTUSoq3OktV6BqTnpiFHtDvQs2QGGs8b0eS5icxIVpjZYqQVMYCc5RZjWa06c/dBM1sF\nLErCtceRsHEARU95JTvnS+geXWtmryMh0RsNtYk2IY2Mm1PfPgLNp/YyViMxCIIPAv4B8HoZRxwf\npgNNBlaiSda+9LocOL0i7SeQ7eK/kWT/H8CcUpqTgRVogjiMJi3nVOT1VTQx6uRZ+hqkoroPqVA+\nRsnTfM3rPAKFwSsmFctQCK8DpTK9nx0Hsvf/ytKdAfwZaVTsRxOYRbTwfg2ci3bh96S8xnjXp4PX\nfeTtv6pcBxjtnfz9dF1/RBOercCPKvK7MN2/YRR6sbDFrYqU8Fnk8GtnSvNw0zpLab+CJn/DKa97\ngWmd6gG4u5xXg/t+EVrI7UeaPL8mi7hRUcddRbtI+RyHVJVfSecdAhaW0kxGfemNlGY98I1Smlrt\nrE39HyB5s6dFf6MiugSy7/4rUjMfRhP7xcDUhvVwMrLtHkL9d2u6l1NL6YbalP+87N7cmN7PK9oD\n8tCfp/td6f95flVRNCahKBFF2y63vXmp/veixdvpSGX956V0lfWb/X5qu/tUkf5I4A5kXlBcR1UU\nmRNSHjd32WZ/gBZNe1GEhivK14MWe0+gRehupJlwWSmf77W5znzcGNOn039HXSdSv78XjZ2vIXOC\nx4D7DnHfnFe+b1VtoE6dZeW6jZGoR6+h53IeTaNund2YyvwWatePUooKkdJ1fAagzYU1qS72okga\n142z7uuOZ0Po+bwECT+3pbo5smmdpXRHATehDZP9SPBxF3BsRRm/la5xH63HjLbRn5Dw7m+pzW5G\nZi8/TffpqDp1FUcccUzcYe6hlRQEQRAEQdAJMxsAfgVMd/ehTun/3zGzj6DF42/d/ceHujxB7zGz\nIWCNuw8c6rIEQXD4E2YXQRAEQRAEbUhmWNOB+cjR5GEpeEjmMCch7SVH6vKTgDsPZbmCvtKt6VsQ\nBEFtQvgQBEEQBEHQnjuQffsjKDTy4coe4BKkuv4OEkKc5yXnp8FhRahAB0EwYYTZRRAEQRAEQRAE\nQRAEfSVCbQZBEARBEARBEARB0FdC+BAEQRAEQRAEQRAEQV8J4UMQBEEQBEEQBEEQBH0lhA9BEARB\nEARBEARBEPSVED4EQRAEQRAEQRAEQdBX/gdgycmukulEMwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7fbd301ff690>"
]
}
],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Comparison: cosine similarity with model coefficients"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that scaling is changed to normalizing, as norm of the vector is used in cosine similarity."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.preprocessing import Normalizer\n",
"from sklearn.linear_model import SGDClassifier\n",
"from sklearn.pipeline import Pipeline\n",
"\n",
"normalizer = Normalizer()\n",
"sgd = SGDClassifier(loss='log', shuffle=True, warm_start=False,\n",
" alpha=1e-6, class_weight='auto',\n",
" penalty='l2', l1_ratio=0,\n",
" n_jobs=30)\n",
"clf = Pipeline([\n",
" ('normalize', normalizer),\n",
" ('sgd', sgd)\n",
"])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf.fit(X_train, y_train)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"Pipeline(steps=[('normalize', Normalizer(copy=True, norm='l2')), ('sgd', SGDClassifier(alpha=1e-06, class_weight='auto', epsilon=0.1, eta0=0.0,\n",
" fit_intercept=True, l1_ratio=0, learning_rate='optimal', loss='log',\n",
" n_iter=5, n_jobs=30, penalty='l2', power_t=0.5, random_state=None,\n",
" shuffle=True, verbose=0, warm_start=False))])"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"coef = sgd.coef_"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"test = cosine_similarity(X_test, coef)\n",
"noise = cosine_similarity(X_noise, coef)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"figure(figsize=(12, 4))\n",
"\n",
"fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True, figsize=(12, 4))\n",
"ax1.plot(test[300, :])\n",
"ax1.set_title('Score distribution of Testing data')\n",
"\n",
"ax2.plot(noise[300, :])\n",
"ax2.set_title('Score distribution of Noise data')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 29,
"text": [
"<matplotlib.text.Text at 0x7fbd2d644650>"
]
},
{
"metadata": {},
"output_type": "display_data",
"text": [
"<matplotlib.figure.Figure at 0x7fbd2ff3ff90>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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U1xEluVQmklyj0TyiWl4jhUY0IYSQCYlm/k9/dLSo8QQjZAJBIzogUY3noVzh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\ngiAIPvn/HnTBJNL4GNoAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7fbd2ff3ff50>"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"score_test = test.max(axis=-1)\n",
"score_noise = noise.max(axis=-1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"normalized_score_test = Normalizer().fit_transform(test).max(axis=-1)\n",
"normalized_score_noise = Normalizer().fit_transform(noise).max(axis=-1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"true_mask = (test.argmax(axis=-1) == y_test)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 34
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.metrics import roc_curve, roc_auc_score\n",
"\n",
"true_discovery = np.hstack([true_mask, np.zeros(score_noise.shape)]).astype(np.bool)\n",
"\n",
"fig, axes = plt.subplots(nrows=1, ncols=2, sharex=True, sharey=True, figsize=(12, 4))\n",
"\n",
"titles = ['raw', 'normalized']\n",
"\n",
"fig.text(0.5, -0.04, \n",
" 'ROC curve for mixture data', ha='center', va='center', fontsize=16)\n",
"for i, score in enumerate([np.hstack([score_test, score_noise]),\n",
" np.hstack([normalized_score_test, normalized_score_noise])]):\n",
"\n",
" fdr, tdr, _ = roc_curve(true_discovery, score)\n",
" axes[i].plot(fdr, tdr)\n",
" axes[i].set_xlabel('FDR')\n",
" axes[i].set_ylabel('TDR')\n",
" axes[i].set_title('ROC curve of {} cosine similarity'.format(titles[i]))\n",
" axes[i].annotate('Area: {:.4f}'.format(roc_auc_score(true_discovery, score)), (0.25, 0.5), fontsize=16)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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MpGNQny2p1GrR7jhOkeM4NzmO84zjOFcG9/V1HOe3wErgG5kIsi3G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\nH5zINzjAWkOwAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7fbd2d588250>"
]
}
],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, axes = plt.subplots(nrows=1, ncols=2, sharex=False, figsize=(18, 3))\n",
"\n",
"titles = ['Raw', 'Normalized']\n",
"\n",
"fig.text(0.5, -0.04, \n",
" 'Score distribution of cosine-similarity using coefficients',\n",
" ha='center', va='center', fontsize=16)\n",
"for i, (_score_test, _score_noise) in enumerate([[score_test, score_noise],\n",
" [normalized_score_test, normalized_score_noise]]):\n",
" \n",
" axes[i].hist([_score_test[true_mask], _score_test[~true_mask], _score_noise], \n",
" label=['True ID', 'False ID', 'Noise ID'], \n",
" color=['b', 'r', 'k'],\n",
" bins=50, \n",
" stacked=False)\n",
" axes[i].set_title(titles[i])\n",
" axes[i].legend(loc='best')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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oMJ6BPRjGAKuBERHS1xTbafY5lXt+xWG5UHyZXpDZE7Nl8jOV0K6urryvL5af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\nGGOMMcYYY8zI+R9rj4zpt4enHwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7fbd2ff3fed0>"
]
}
],
"prompt_number": 36
}
],
"metadata": {}
}
]
}
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