Test for non-reset upon partial fit/reset upon fit

part_fit_test.ipynb

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 "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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       "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": {}
  }
 ]
}