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raghavrv/part_fit_test.ipynb

Last active December 28, 2015 16:12
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Test for non-reset upon partial fit/reset upon fit
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part_fit_test.ipynb
{
"metadata": {
"name": "",
"signature": "sha256:2190a1d956e09864439d4398f0e615fa400757b27467843876fd36eac70f0bc7"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin\n",
"from sklearn.svm import LinearSVC\n",
"from sklearn.utils import check_consistent_length\n",
"from sklearn.externals.joblib import Parallel, delayed\n",
"from sklearn.multiclass import _fit_binary, _fit_ovo_binary, OneVsOneClassifier\n",
"from sklearn.linear_model import SGDClassifier\n",
"from sklearn import datasets\n",
"from sklearn.preprocessing import scale\n",
"import numpy as np\n",
"import functools\n",
"\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.utils.testing import assert_raises, assert_equal, assert_array_almost_equal, assert_almost_equal"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"iris = datasets.load_iris()\n",
"X, y, bX, by, est_pfit = (None,)*5\n",
"\n",
"def init_data(init, shuffle):\n",
" global X, y, bX, by, est_pfit\n",
"\n",
" \n",
" if init:\n",
" X, y = iris.data, iris.target\n",
" X = scale(X)\n",
"\n",
" idx = np.arange(X.shape[0])\n",
" if shuffle:\n",
" rng = np.random.RandomState(0)\n",
" rng.shuffle(idx)\n",
"\n",
" # Shuffle X and y and split them into 10 smaller batches\n",
" bX = [ X[idx[i:i+10]] for i in range(0, 100, 10) ]\n",
" by = [ y[idx[i:i+10]] for i in range(0, 100, 10) ]\n",
"\n",
" state = (bX, by)\n",
" return state\n",
"\n",
"def test_error_upon_feature_size_change():\n",
" global X, y, bX, by, est_pfit\n",
" \n",
" # 1st batch of 10 with 4 features\n",
" est_pfit.partial_fit(bX[0], by[0], classes=np.unique(y))\n",
"\n",
" # Attempting batch of 2 with 4 features\n",
" assert_raises(ValueError, est_pfit.partial_fit, [[1, 0, 1.5], [1.5, 2, 1]], [1,2])\n",
"\n",
"def initialize():\n",
" global X, y, bX, by, est_pfit\n",
" \n",
" est_pfit = SGDClassifier(n_iter = 1, shuffle=False, loss=\"log\")\n",
" \n",
"def part_fit():\n",
" global X, y, bX, by, est_pfit\n",
"\n",
" # 1st batch of 10 with 4 features\n",
" est_pfit.partial_fit(bX[0], by[0], classes=np.unique(y))\n",
"\n",
" # Partially fit all other batches\n",
" for xi, yi in zip(bX[1:], by[1:]):\n",
" est_pfit.partial_fit(xi, yi)\n",
"\n",
" return round((np.where(y == est_pfit.predict(X))[0].shape[0])*100.0/150.0)\n",
"\n",
" \n",
"def full_fit():\n",
" global X, y, bX, by, est_pfit\n",
" \n",
" est_pfit.fit(X, y)\n",
" return round((np.where(y == est_pfit.predict(X))[0].shape[0])*100.0/150.0)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# for i in range(10):\n",
"# initialize()\n",
"# init_data(init=True, shuffle=False)\n",
"# print \"F: \", full_fit(), \"%\"\n",
"# print \"F: \", full_fit(), \"%\"\n",
"\n",
"initialize()\n",
"init_data(init=True, shuffle=False)\n",
"points = []\n",
"\n",
"for i in range(1000):\n",
" init_data(init=False, shuffle=True)\n",
" #print \"P: \", part_fit(), \"%\"\n",
" \n",
" points.append(part_fit())\n",
"\n",
"plt.plot(points)\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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fkoanjVLPBuAnVbUNeCtwWbP8MuCMxexg1y6/dlGShqWN4P894IvN9Jqq2gFQVduB1YvZ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\nAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f7da92f3050>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"initialize()\n",
"state = init_data(init=True, shuffle=True)\n",
"print \"F: \", full_fit(), \"%\" # n_iter = 1\n",
"print \"P: \", part_fit(), \"%\"\n",
"initialize()\n",
"bX, by = state\n",
"print \"P: \", part_fit(), \"%\"\n",
"print \"P: \", part_fit(), \"%\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"F: 35.0 %\n",
"P: 67.0 %\n",
"P: 66.0 %\n",
"P: 67.0 %\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# # Check if 2 Partial fits == 1 Fit\n",
"# est_pfit.partial_fit(X[:75], y[:75], classes = np.unique(y))\n",
"# est_pfit.partial_fit(X[75:], y[75:])\n",
"\n",
"c1 = est_pfit.coef_\n",
"\n",
"est_pfit.fit(X, y)\n",
"\n",
"c2 = est_pfit.coef_\n",
"\n",
"assert_array_almost_equal(c1, c2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "AssertionError",
"evalue": "\nArrays are not almost equal to 6 decimals\n\n(mismatch 100.0%)\n x: array([[ -0.28176201, 2.08103825, -3.71397985, -7.04148776],\n [ 5.8815943 , -4.56869085, 9.96897204, -14.77459225],\n [ -4.6004667 , -1.31077272, 32.12912944, 40.1287937 ]])\n y: array([[ 2.55751194, 10.86712161, -8.38886032, -8.82729875],\n [ 2.93208873, 2.35362305, -12.97457253, -19.76706177],\n [ -8.42037216, -0.39783461, 17.44550535, 25.90058516]])",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-6-b807e23876c9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0mc2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mest_pfit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0massert_array_almost_equal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m/usr/lib/python2.7/dist-packages/numpy/testing/utils.pyc\u001b[0m in \u001b[0;36massert_array_almost_equal\u001b[1;34m(x, y, decimal, err_msg, verbose)\u001b[0m\n\u001b[0;32m 809\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0maround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mz\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m10.0\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mdecimal\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 810\u001b[0m assert_array_compare(compare, x, y, err_msg=err_msg, verbose=verbose,\n\u001b[1;32m--> 811\u001b[1;33m header=('Arrays are not almost equal to %d decimals' % decimal))\n\u001b[0m\u001b[0;32m 812\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 813\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0massert_array_less\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merr_msg\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/python2.7/dist-packages/numpy/testing/utils.pyc\u001b[0m in \u001b[0;36massert_array_compare\u001b[1;34m(comparison, x, y, err_msg, verbose, header)\u001b[0m\n\u001b[0;32m 642\u001b[0m names=('x', 'y'))\n\u001b[0;32m 643\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcond\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 644\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 645\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 646\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mAssertionError\u001b[0m: \nArrays are not almost equal to 6 decimals\n\n(mismatch 100.0%)\n x: array([[ -0.28176201, 2.08103825, -3.71397985, -7.04148776],\n [ 5.8815943 , -4.56869085, 9.96897204, -14.77459225],\n [ -4.6004667 , -1.31077272, 32.12912944, 40.1287937 ]])\n y: array([[ 2.55751194, 10.86712161, -8.38886032, -8.82729875],\n [ 2.93208873, 2.35362305, -12.97457253, -19.76706177],\n [ -8.42037216, -0.39783461, 17.44550535, 25.90058516]])"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.where(y != est_pfit.predict(X))[0].shape[0]"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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