2017-11-27_JC_model.ipynb

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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Cython extension is already loaded. To reload it, use:\n",
      "  %reload_ext Cython\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from matplotlib import pyplot as plt\n",
    "from numpy import clip\n",
    "from torch.autograd import Variable\n",
    "import torch\n",
    "from torch import Tensor\n",
    "from torch.optim import SGD, Adam\n",
    "from IPython.display import clear_output\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact\n",
    "from torch.nn.functional import softplus\n",
    "w = widgets.IntText()\n",
    "%matplotlib inline\n",
    "%run utils.py\n",
    "%load_ext Cython\n",
    "from numpy.random import permutation\n",
    "M, N = 20, 70\n",
    "\n",
    "def log_sum_exp(value):\n",
    "    m = torch.max(value)\n",
    "    sum_exp = torch.sum(torch.exp(value - m))\n",
    "    return m + torch.log(sum_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "%%cython \n",
    "import numpy as np\n",
    "cimport numpy as np\n",
    "\n",
    "cdef int M = 20\n",
    "cdef int N = 70\n",
    "DTYPE = int\n",
    "ctypedef np.int_t DTYPE_t\n",
    "\n",
    "\n",
    "def random_path(int sx, int sy, int tx, int ty, int L):\n",
    "    \"\"\"sample L random paths from sx, sy to tx, ty\"\"\"\n",
    "    cdef int hor_dir, ver_dir, X, Y, XY\n",
    "    \n",
    "    hor_dir = (tx - sx) > 0\n",
    "    ver_dir = (ty - sy) > 0\n",
    "    \n",
    "    cdef np.ndarray[DTYPE_t, ndim=3] paths = np.zeros([M, N, L], dtype=DTYPE)\n",
    "    paths[sx,sy,:] = 1\n",
    "    \n",
    "    X = abs(tx - sx) \n",
    "    Y = abs(ty - sy) \n",
    "    XY = X + Y\n",
    "    \n",
    "    cdef np.ndarray[DTYPE_t, ndim=1] path = np.zeros(X+Y, dtype=DTYPE)\n",
    "    path[:X] = 1\n",
    "    \n",
    "    cdef int l, i, x, y\n",
    "\n",
    "    for l in range(L):\n",
    "        x, y = sx, sy\n",
    "        np.random.shuffle(path)\n",
    "        for i in range(XY):\n",
    "            if path[i]:\n",
    "                x += 2 * hor_dir - 1\n",
    "            else:\n",
    "                y += 2 * ver_dir - 1\n",
    "            paths[x,y,l] = 1\n",
    "    return paths"
   ]
  },
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      "8 6\n"
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      "\n",
      "12 8\n"
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      "\n",
      "18 5\n"
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      "\n"
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   "source": [
    "# hyperparameters of the model\n",
    "L = 20\n",
    "s2 = 10 ** 2 # sigma2 = (10 s) ** 2\n",
    "\n",
    "for hour in [4, 8, 12, 18]:\n",
    "    # file to run model on\n",
    "    filename = 'data/split/JC_week1_hour{}_40x140.csv'.format(hour)\n",
    "    df = pd.read_csv(filename)\n",
    "    df[['sx', 'sy','tx','ty']] = df[['sx', 'sy','tx','ty']] // 2\n",
    "    weights = Variable((15 * torch.ones((M,N))).float(), requires_grad=True)\n",
    "    optimizer = Adam([weights], lr = 0.005)\n",
    "    num_epochs = int(500000//len(df))\n",
    "    print(hour, num_epochs)\n",
    "    def loss_fn(paths, t):\n",
    "        \"\"\"Implement the loss function\"\"\"\n",
    "        weighted_paths = (Variable(Tensor(paths.astype(float)))\n",
    "                                  * weights.unsqueeze(-1))\n",
    "        weight_sums = weighted_paths.sum(dim=0).sum(dim=0)\n",
    "        first = (-1/(2 * L * s2) * (t - weight_sums).pow(2).sum())\n",
    "        second = - weighted_paths.sum() / L\n",
    "        third = - log_sum_exp(-weight_sums)\n",
    "        loss = -(first + second + third)\n",
    "        return loss \n",
    "    pbar = tqdm(total=num_epochs * len(df))\n",
    "    losses = []\n",
    "    for epoch in range(num_epochs):\n",
    "        for _, duration, sx, sy, tx, ty in (df.sample(frac=1).itertuples()):\n",
    "            pbar.update(1)\n",
    "            paths = random_path(int(sx), int(sy), \n",
    "                                int(tx), int(ty), L)\n",
    "            t = float(duration)\n",
    "            loss = loss_fn(paths, t)\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            losses.append(loss.data[0])\n",
    "    np.save('results/weights_week1hour{}_f.npy'.format(hour), weights.data.numpy())\n",
    "    np.save('results/losses_week1hour{}_f.npy'.format(hour), np.array(losses))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The loss function is $$\n",
    "\\sum_{k=1}^N\\left\\{-\\frac{1}{2L\\sigma^2}\\sum_{j=1}^L \\left(T^{(k)} - \\sum_{\\text{path}_j} w_i\\right) - \\frac{1}{L} \\sum_{j=1}^L \\sum_{\\text{path}} w_i - \\text{logsumexp}\\left(-\\sum_{\\text{path}_j}w_i\\right)\\right\\} + \\log p(W).\n",
    "$$\n",
    "\n",
    "We tentatively ignore the prior on $W$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Adam' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-27de1cbc398d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# df = pd.read_csv(filename)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0moptimizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAdam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.01\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# using Adam since we are doing some stochastic optimization things\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mnum_epochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mloss_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpaths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;34m\"\"\"Implement the loss function\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'Adam' is not defined"
     ]
    }
   ],
   "source": [
    "# df = pd.read_csv(filename)\n",
    "optimizer = Adam([weights], lr = 0.01) # using Adam since we are doing some stochastic optimization things\n",
    "num_epochs = \n",
    "def loss_fn(paths, t):\n",
    "    \"\"\"Implement the loss function\"\"\"\n",
    "    weighted_paths = (Variable(Tensor(paths.astype(float)))\n",
    "                              * weights.unsqueeze(-1))\n",
    "    weight_sums = weighted_paths.sum(dim=0).sum(dim=0)\n",
    "    first = (-1/(2 * L * s2) * (t - weight_sums).pow(2).sum())\n",
    "    second = - weighted_paths.sum() / L\n",
    "    third = - log_sum_exp(-weight_sums)\n",
    "    loss = -(first + second + third)\n",
    "    return loss \n",
    "\n",
    "pbar = tqdm(total=num_epochs * len(df))\n",
    "losses = []\n",
    "for epoch in range(num_epochs):\n",
    "    for _, duration, sx, sy, tx, ty in (df.sample(frac=1).itertuples()):\n",
    "        pbar.update(1)\n",
    "        paths = random_path(int(sx), int(sy), \n",
    "                            int(tx), int(ty), L)\n",
    "        t = float(duration)\n",
    "        loss = loss_fn(paths, t)\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        losses.append(loss.data[0])\n",
    "        \n",
    "np.save('results/weights_week1hour{}_f.npy', weights.data.numpy())\n",
    "np.save('results/losses_week1hour{}_f.npy', np.array(losses))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for epoch in range(num_epochs):\n",
    "#     for _, duration, sx, sy, tx, ty in (df.sample(frac=1).itertuples()):\n",
    "#         pbar.update(1)\n",
    "#         paths = random_path(int(sx), int(sy), \n",
    "#                             int(tx), int(ty), L)\n",
    "#         t = float(duration)\n",
    "#         loss = loss_fn(paths, t)\n",
    "#         optimizer.zero_grad()\n",
    "#         loss.backward()\n",
    "#         optimizer.step()\n",
    "#         losses.append(loss.data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('results/weights_week1hour4_1.npy', weights.data.numpy())\n",
    "np.save('results/losses_week1hour4_1.npy', np.array(losses))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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s8z1vemo5fe5utd+jpJkpAETQdSN7UHi8hLxdNcfo+wNABPoAAMb2SSc1OaHe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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1246ede48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.array(losses).cumsum()/np.arange(1,len(losses)+1))\n",
    "plt.xscale('log')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x131eed588>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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srn3FGSl0x5CjeOD8itpjXm4OJx3ezDexhfvoW6eJ6bFhXTmzS/w3k44cekzI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122c29ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot((pd.Series(losses).rolling(len(df)//10).mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 311.29456444])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.convolve(np.array(losses), np.ones((N,))/N, mode='valid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VCjRNAD6qbmrXlUBVvyJswsGfXcSCbOfsYp0trMYTTQA+6Ojsoq6lQ/cCVgMS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\nhSlNAEopFaY0ASilVJj6/41DCTXda6sDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1206fa978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cumsum / arange\n",
    "plt.plot(np.array(losses).cumsum()/np.arange(1,len(losses)+1))\n",
    "plt.xscale('log')\n",
    "#plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1206eaba8>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1C4KiXyw0XZgZQ+RC6DhEuLJve+TnZkdYW1w2I9wv2LR+ZO1qrrhkJVhrahle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\nOf8P12mYVZ0DNKkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12388cfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# running average via convolution\n",
    "N = 100000\n",
    "plt.plot(\n",
    "    np.convolve(np.array(losses), np.ones((N,))/N, mode='valid'))\n",
    "# # plt.xscale('log')\n",
    "# #plt.yscale('log')\n",
    "# plt.xlim((10000,len(losses)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1375e48d0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Am939g7+tHO+Xi+P+Nu3u89y9xd1beBdKkeorZzPchQA+Z2afBdAHwCAcvSM0mFmP0l2g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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122e494a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(softplus(weights).data.numpy().T, origin='lower',\n",
    "           clim=(20,70),cmap='bwr')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('results/losses_week1hour8_4.npy', arr=np.array(losses))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       "  80.0000   80.0000   80.0000  ...    80.0000   80.0000   80.0000\n",
       "  80.0000   80.0000   80.0000  ...    80.0000   80.0000   80.0000\n",
       "  80.3176   80.3176   80.9529  ...    79.6824   80.0000   80.0000\n",
       "             ...                ⋱                ...             \n",
       "  80.0000   80.0000   79.6824  ...    68.5620   73.0259   73.5459\n",
       "  80.0000   80.0000   80.0000  ...    64.7392   67.5195   67.4749\n",
       "  80.0000   80.0000   80.0000  ...    85.2549   90.1408   76.4488\n",
       "[torch.FloatTensor of size 20x70]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}