Benchmarking a revision of the MSMBuilder3 transitions_counts method

transition_counts_bench.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "from scipy.sparse import coo_matrix\n",
      "import time\n",
      "\n",
      "import matplotlib.pyplot as plt\n",
      "import matplotlib.gridspec as gridspec\n",
      "\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def _transition_counts1(sequences, lag_time=1, sliding_window=True):\n",
      "    \"\"\"Count the number of directed transitions in a collection of sequences\n",
      "    in a discrete space.\n",
      "    Parameters\n",
      "    ----------\n",
      "    sequences : list of array-like\n",
      "        List of sequences, or a single sequence. Each sequence should be a\n",
      "        1D iterable of state labels. Labels can be integers, strings, or\n",
      "        other orderable objects.\n",
      "    lag_time : int\n",
      "        The time (index) delay for the counts.\n",
      "    Returns\n",
      "    -------\n",
      "    counts : array, shape=(n_states, n_states)\n",
      "        ``counts[i][j]`` counts the number of times a sequences was in state\n",
      "        `i` at time t, and state `j` at time `t+self.lag_time`, over the\n",
      "        full set of trajectories.\n",
      "    mapping : dict\n",
      "        Mapping from the items in the sequences to the indices in\n",
      "        ``(0, n_states-1)`` used for the count matrix.\n",
      "    Examples\n",
      "    --------\n",
      "    >>> sequence = [0, 0, 0, 1, 1]\n",
      "    >>> counts, mapping = _transition_counts([sequence])\n",
      "    >>> print counts\n",
      "    [[2, 1],\n",
      "     [0, 1]]\n",
      "    >>> print mapping\n",
      "    {0: 0, 1: 1}\n",
      "    >>> sequence = [100, 200, 300]\n",
      "    >>> counts, mapping = _transition_counts([sequence])\n",
      "    >>> print counts\n",
      "    [[ 0.  1.  0.]\n",
      "     [ 0.  0.  1.]\n",
      "     [ 0.  0.  0.]]\n",
      "    >>> print mapping\n",
      "    {100: 0, 200: 1, 300: 2}\n",
      "    Notes\n",
      "    -----\n",
      "    `None` and `NaN` are recognized immediately as invalid labels. Therefore,\n",
      "    transition counts from or to a sequence item which is NaN or None will not\n",
      "    be counted. The mapping return value will not include the NaN or None.\n",
      "    \"\"\"\n",
      "    if (not sliding_window) and lag_time > 1:\n",
      "        return  _transition_counts([X[::lag_time] for X in sequences], lag_time=1)\n",
      "\n",
      "    classes = np.unique(np.concatenate(sequences))\n",
      "    contains_nan = (classes.dtype.kind == 'f') and np.any(np.isnan(classes))\n",
      "    contains_none = any(c is None for c in classes)\n",
      "\n",
      "    if contains_nan:\n",
      "        classes = classes[~np.isnan(classes)]\n",
      "    if contains_none:\n",
      "        classes = [c for c in classes if c is not None]\n",
      "\n",
      "    n_states = len(classes)\n",
      "\n",
      "    mapping = dict(zip(classes, range(n_states)))\n",
      "    mapping_is_identity = np.all(classes == np.arange(n_states))\n",
      "    mapping_fn = np.vectorize(mapping.get, otypes=[np.int])\n",
      "    none_to_nan = np.vectorize(lambda x: np.nan if x is None else x,\n",
      "                               otypes=[np.float])\n",
      "\n",
      "    counts = np.zeros((n_states, n_states), dtype=float)\n",
      "    for y in sequences:\n",
      "        y = np.asarray(y)\n",
      "        from_states = y[: -lag_time: 1]\n",
      "        to_states = y[lag_time::1]\n",
      "\n",
      "        if contains_none:\n",
      "            from_states = none_to_nan(from_states)\n",
      "            to_states = none_to_nan(to_states)\n",
      "\n",
      "        if contains_nan or contains_none:\n",
      "            # mask out nan in either from_states or to_states\n",
      "            mask = ~(np.isnan(from_states) + np.isnan(to_states))\n",
      "            from_states = from_states[mask]\n",
      "            to_states = to_states[mask]\n",
      "\n",
      "        if (not mapping_is_identity) and len(from_states) > 0 and len(to_states) > 0:\n",
      "            from_states = mapping_fn(from_states)\n",
      "            to_states = mapping_fn(to_states)\n",
      "\n",
      "        transitions = np.row_stack((from_states, to_states))\n",
      "        C = coo_matrix((np.ones(transitions.shape[1], dtype=int), transitions),\n",
      "            shape=(n_states, n_states))\n",
      "        counts = counts + np.asarray(C.todense())\n",
      "\n",
      "    return counts / float(lag_time), mapping\n",
      "\n",
      "\n",
      "def _transition_counts2(sequences, lag_time=1, sliding_window=True):\n",
      "    \"\"\"Count the number of directed transitions in a collection of sequences\n",
      "    in a discrete space.\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    sequences : list of array-like\n",
      "        List of sequences, or a single sequence. Each sequence should be a\n",
      "        1D iterable of state labels. Labels can be integers, strings, or\n",
      "        other orderable objects.\n",
      "    lag_time : int\n",
      "        The time (index) delay for the counts.\n",
      "\n",
      "    Returns\n",
      "    -------\n",
      "    counts : array, shape=(n_states, n_states)\n",
      "        ``counts[i][j]`` counts the number of times a sequences was in state\n",
      "        `i` at time t, and state `j` at time `t+self.lag_time`, over the\n",
      "        full set of trajectories.\n",
      "    mapping : dict\n",
      "        Mapping from the items in the sequences to the indices in\n",
      "        ``(0, n_states-1)`` used for the count matrix.\n",
      "\n",
      "    Examples\n",
      "    --------\n",
      "    >>> sequence = [0, 0, 0, 1, 1]\n",
      "    >>> counts, mapping = _transition_counts([sequence])\n",
      "    >>> print counts\n",
      "    [[2, 1],\n",
      "     [0, 1]]\n",
      "    >>> print mapping\n",
      "    {0: 0, 1: 1}\n",
      "\n",
      "    >>> sequence = [100, 200, 300]\n",
      "    >>> counts, mapping = _transition_counts([sequence])\n",
      "    >>> print counts\n",
      "    [[ 0.  1.  0.]\n",
      "     [ 0.  0.  1.]\n",
      "     [ 0.  0.  0.]]\n",
      "    >>> print mapping\n",
      "    {100: 0, 200: 1, 300: 2}\n",
      "\n",
      "    Notes\n",
      "    -----\n",
      "    `None` and `NaN` are recognized immediately as invalid labels. Therefore,\n",
      "    transition counts from or to a sequence item which is NaN or None will not\n",
      "    be counted. The mapping return value will not include the NaN or None.\n",
      "    \"\"\"\n",
      "    if (not sliding_window) and lag_time > 1:\n",
      "        return  _transition_counts([X[::lag_time] for X in sequences], lag_time=1)\n",
      "\n",
      "    classes = np.unique(np.concatenate(sequences))\n",
      "    contains_nan = (classes.dtype.kind == 'f') and np.any(np.isnan(classes))\n",
      "    contains_none = any(c is None for c in classes)\n",
      "\n",
      "    if contains_nan:\n",
      "        classes = classes[~np.isnan(classes)]\n",
      "    if contains_none:\n",
      "        classes = [c for c in classes if c is not None]\n",
      "\n",
      "    n_states = len(classes)\n",
      "\n",
      "    mapping = dict(zip(classes, range(n_states)))\n",
      "    mapping_is_identity = np.all(classes == np.arange(n_states))\n",
      "    mapping_fn = np.vectorize(mapping.get, otypes=[np.int])\n",
      "    none_to_nan = np.vectorize(lambda x: np.nan if x is None else x,\n",
      "                               otypes=[np.float])\n",
      "\n",
      "    counts = np.zeros((n_states, n_states), dtype=float)\n",
      "    _transitions = []\n",
      "    \n",
      "    for y in sequences:\n",
      "        y = np.asarray(y)\n",
      "        from_states = y[: -lag_time: 1]\n",
      "        to_states = y[lag_time::1]\n",
      "\n",
      "        if contains_none:\n",
      "            from_states = none_to_nan(from_states)\n",
      "            to_states = none_to_nan(to_states)\n",
      "\n",
      "        if contains_nan or contains_none:\n",
      "            # mask out nan in either from_states or to_states\n",
      "            mask = ~(np.isnan(from_states) + np.isnan(to_states))\n",
      "            from_states = from_states[mask]\n",
      "            to_states = to_states[mask]\n",
      "\n",
      "        if (not mapping_is_identity) and len(from_states) > 0 and len(to_states) > 0:\n",
      "            from_states = mapping_fn(from_states)\n",
      "            to_states = mapping_fn(to_states)\n",
      "\n",
      "        _transitions.append(np.row_stack((from_states, to_states)))\n",
      "\n",
      "    transitions = np.hstack(_transitions)\n",
      "    C = coo_matrix((np.ones(transitions.shape[1], dtype=int), transitions),\n",
      "        shape=(n_states, n_states))\n",
      "    counts = counts + np.asarray(C.todense())\n",
      "\n",
      "    return counts / float(lag_time), mapping\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trajlengths = np.logspace(3,5,10).astype(np.int)\n",
      "ntrajs = [5, 10, 50, 100]\n",
      "nstates = [100, 1000, 5000]\n",
      "\n",
      "transcnt_methods = [_transition_counts1, _transition_counts2]\n",
      "timings = np.zeros((len(nstates), 2, len(ntrajs), len(N)))\n",
      "\n",
      "for nsi, _nstates in enumerate(nstates):\n",
      "    print 'nstates: ', _nstates\n",
      "    for ti, _ntrajs in enumerate(ntrajs):\n",
      "        print 'ntrajs: ', _ntrajs\n",
      "        for ni, n in enumerate(trajlengths):\n",
      "            assignments = [np.random.random_integers(0,_nstates, size=(n,)) for k in xrange(_ntrajs)]\n",
      "\n",
      "            t1 = time.time()\n",
      "            T1, mapping1 = _transition_counts1(assignments, lag_time=5)\n",
      "            timings[nsi, 0, ti, ni] = (time.time() - t1)\n",
      "\n",
      "            t1 = time.time()\n",
      "            T2, mapping2 = _transition_counts2(assignments, lag_time=5)\n",
      "            timings[nsi, 1, ti, ni] = (time.time() - t1)\n",
      "\n",
      "            if not np.allclose(T1, T2):\n",
      "                print n_states,_ntrajs, n\n",
      "\n",
      "            del assignments\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "nstates:  100\n",
        "ntrajs:  5\n",
        "ntrajs: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10\n",
        "ntrajs: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 50\n",
        "ntrajs: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 100\n",
        "nstates: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1000\n",
        "ntrajs:  5\n",
        "ntrajs: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10\n",
        "ntrajs: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 50\n",
        "ntrajs: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 100\n",
        "nstates: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5000\n",
        "ntrajs:  5\n",
        "ntrajs: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10\n",
        "ntrajs: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 50\n",
        "ntrajs: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 100\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(10,6))\n",
      "gs = gridspec.GridSpec(2, len(nstates))\n",
      "\n",
      "for nsi, _nstates in enumerate(nstates):\n",
      "    ax = plt.subplot(gs[0, nsi])\n",
      "    \n",
      "    eff = timings[nsi, 0, ...] / timings[nsi, 1, ...]\n",
      "    for k in xrange(len(ntrajs)):\n",
      "        ax.loglog(trajlengths, eff[k,:], '.-', label='{}'.format(ntrajs[k]))\n",
      "    \n",
      "    if nsi == 0:\n",
      "        ax.legend(loc=\"upper right\", ncol=2, title=\"# of trajs\")\n",
      "    ax.set_xlabel('Traj Length')\n",
      "    ax.set_ylabel('Speed-up')\n",
      "    ax.set_ylim(.1, 100)\n",
      "    ax.text(0.05, 0.05, \"{} states\".format(_nstates),\n",
      "        horizontalalignment='left',\n",
      "        verticalalignment='bottom',\n",
      "        transform=ax.transAxes)\n",
      "    \n",
      "    ax = plt.subplot(gs[1, nsi])\n",
      "    for k in xrange(len(ntrajs)):\n",
      "        ax.loglog(trajlengths, timings[nsi, 1, k, :], '.-', label='{}'.format(ntrajs[k]))\n",
      "        \n",
      "    ax.set_xlabel('Traj Length')\n",
      "    ax.set_ylabel('Time (s) revised method')\n",
      "    ax.set_ylim(.0001, 10)\n",
      "    ax.text(0.05, 0.05, \"{} states\".format(_nstates),\n",
      "        horizontalalignment='left',\n",
      "        verticalalignment='bottom',\n",
      "        transform=ax.transAxes)\n",
      "    \n",
      "    \n",
      "plt.tight_layout()\n",
      "fig.savefig('timings.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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sLBaLyMvLE4GBgWLZsmXCZDKJp556Slx++eWtKl9rMVRUiGn79wtDRUW759Ha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\n4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gNJSrKJXHmPhc9zcmzJZufOEBoKCTvTmHd0Fk/3WcySd4Po2bO639ZZZ/LPEJ8aX5QE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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ae7a5d0>"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!open ."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}