2017-11-27_JC_model-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "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 = 40, 140\n",
    "\n",
    "# file to run model on\n",
    "filename = 'data/split/JC_week1_hour8_40x140.csv'\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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cython \n",
    "import numpy as np\n",
    "cimport numpy as np\n",
    "\n",
    "cdef int M = 40\n",
    "cdef int N = 140\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = Variable((15 * torch.rand((M,N))).float(), requires_grad=True)\n",
    "\n",
    "# hyperparameters of the model\n",
    "L = 20\n",
    "s2 = 10 ** 2 # sigma2 = (10 s) ** 2"
   ]
  },
  {
   "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(filename)\n",
    "num_epochs = 5\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",
    "losses = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c2ef0f25e4d54b779bc0bd1818ac6d02",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimizer = Adam([weights], lr = 0.005) # using Adam since we are doing some stochastic optimization things\n",
    "pbar = tqdm(total=num_epochs * len(df))\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), 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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16139e390>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lNmPMJL/9BgGd7Z+TgYn276Sqm5/L3gM6jkAppaLlqkQgIm2BIcALLnYfCrxmLHOAJp5F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\nCJRSKsv9P+kdDiN3oMHdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1454467b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(pd.Series(losses).rolling(window=len(losses)//50).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  9.8254   4.6442   4.4170  ...   13.2968   4.9182   2.9221\n",
       " 14.7198   1.8721  13.8976  ...    5.8079   9.6465   4.7782\n",
       " 13.3657   0.4518   5.9805  ...   10.7860  12.4293   7.7268\n",
       "           ...               ⋱              ...            \n",
       "  0.3766  13.7394   2.3137  ...    8.8649  13.4068  15.2418\n",
       " 10.7051   4.3956   2.6510  ...    5.1553  11.9776   5.5710\n",
       "  7.4097  14.1579   6.9340  ...   19.2705   8.9629  13.4105\n",
       "[torch.FloatTensor of size 40x140]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('results/weights_week1hour8_2.npy', arr=weights.data.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.save('results/weights_week1hour10_1.npy', weights.data.numpy())\n",
    "# np.save('results/losses_week1hour8_3.npy', np.array(losses))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nx4Qqmx2HU7Nw3f5mrztYWdOoZ0etkQBkIJho6KtZZwFQabNzqKrW5GiEWdr9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\nzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10599c278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1275c6438>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xeu6pp9Q+/ni1J2N/h+OxG4rLRxwngy8Ox8ngi8NxMpQi5jDTFjhsIc8xAtzH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\ni8NxMvjicJwM/w+hZ/NUyFPq1gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12c85cda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(softplus(weights).data.numpy().T, origin='lower',\n",
    "           clim=(0,35), cmap='bwr')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94128668"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.abs((softplus(weights).data.numpy() \n",
    "- softplus(Variable(Tensor(np.load('results/weights_week1hour8_2.npy')))).data.numpy())).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('results/weights_week1hour8_3.npy', weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('results/weights_week1hour8_3.npy', weights.data.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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