{
 "cells": [
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-07T12:38:15.344220Z",
     "start_time": "2018-09-07T12:38:15.340982Z"
    }
   },
   "outputs": [],
   "source": [
    "# we will study the hyperparamter tuning of fully connected scatternet\n",
    "\n",
    "# first find optimal number of layers and neuron numbers\n",
    "# second optimize the batch size and number of epochs for the best learned architecture\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-08T12:01:25.348660Z",
     "start_time": "2018-09-08T12:01:25.309257Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/ipyparallel/client/client.py:459: RuntimeWarning: \n",
      "            Controller appears to be listening on localhost, but not on this machine.\n",
      "            If this is true, you should specify Client(...,sshserver='you@Gamma')\n",
      "            or instruct your controller to listen on an external IP.\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "\n",
    "import ipyparallel as ipp\n",
    "rc = ipp.Client()\n",
    "rc.ids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the dataset here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-08T12:09:06.191974Z",
     "start_time": "2018-09-08T12:09:05.681850Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[stdout:0] \n",
      "Dataset has been loaded\n",
      "x-train (44999, 8)\n",
      "x-test  (55001, 8)\n",
      "y-train (44999, 256)\n",
      "y-test  (55001, 256)\n",
      "[stdout:1] \n",
      "Dataset has been loaded\n",
      "x-train (44999, 8)\n",
      "x-test  (55001, 8)\n",
      "y-train (44999, 256)\n",
      "y-test  (55001, 256)\n",
      "[stdout:2] \n",
      "Dataset has been loaded\n",
      "x-train (44999, 8)\n",
      "x-test  (55001, 8)\n",
      "y-train (44999, 256)\n",
      "y-test  (55001, 256)\n",
      "[stdout:3] \n",
      "Dataset has been loaded\n",
      "x-train (44999, 8)\n",
      "x-test  (55001, 8)\n",
      "y-train (44999, 256)\n",
      "y-test  (55001, 256)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[stderr:0] \n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "[stderr:1] \n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "[stderr:2] \n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "[stderr:3] \n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "%%px\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import h5py\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#now load this dataset \n",
    "h5f = h5py.File('./datasets/s8_sio2tio2_v2.h5','r')\n",
    "X = h5f['sizes'][:]\n",
    "Y = h5f['spectrum'][:]\n",
    "\n",
    "#get the ranges of the loaded data\n",
    "num_layers = X.shape[1]\n",
    "num_lpoints = Y.shape[1]\n",
    "size_max = np.amax(X)\n",
    "size_min = np.amin(X)\n",
    "size_av = 0.5*(size_max + size_min)\n",
    "\n",
    "#this information is not given in the dataset\n",
    "lam_min = 300\n",
    "lam_max = 1200\n",
    "lams = np.linspace(lam_min, lam_max, num_lpoints)\n",
    "\n",
    "#create a train - test split of the dataset\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.55, random_state=42)\n",
    "\n",
    "# normalize inputs \n",
    "x_train = (x_train - 50)/20 \n",
    "x_test = (x_test - 50)/20 \n",
    "\n",
    "print(\"Dataset has been loaded\")\n",
    "print(\"x-train\", x_train.shape)\n",
    "print(\"x-test \", x_test.shape)\n",
    "print(\"y-train\", y_train.shape)\n",
    "print(\"y-test \", y_test.shape)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### create models here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-08T12:09:43.998716Z",
     "start_time": "2018-09-08T12:09:43.987026Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AsyncResult: execute>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%px --targets 0 --noblock\n",
    "\n",
    "%autoreload 2\n",
    "\n",
    "\n",
    "\n",
    "import hyptune as htun\n",
    "import scnets as scn\n",
    "\n",
    "import os\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\" \n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
    "\n",
    "# #resnet\n",
    "func = scn.resnet\n",
    "param_grid = dict(num_units=[2,3,4,5,6], \n",
    "                  red_dim=[8,16],\n",
    "                  batch_size=[32],\n",
    "                  ker_size=[3, 5, 7])\n",
    "    \n",
    "cvresults = htun.get_cv_grid(modelfunc=func, \n",
    "                             param_grid=param_grid,\n",
    "                             num_epochs=250,\n",
    "                             x_train=x_train,\n",
    "                             y_train=y_train)\n",
    "\n",
    "htres = htun.print_tuning_results(cvresults, func)\n",
    "print(htres.to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-08T11:24:51.887905Z",
     "start_time": "2018-09-08T11:24:51.878241Z"
    }
   },
   "outputs": [],
   "source": [
    "#%%px --targets 1 --noblock\n",
    "\n",
    "%autoreload 2\n",
    "\n",
    "\n",
    "import hyptune as htun\n",
    "import scnets as scn\n",
    "\n",
    "import os\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\" \n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
    "\n",
    "# #resnet\n",
    "func = scn.resnet\n",
    "param_grid = dict(num_units=[2,3,4,5,6], \n",
    "                  red_dim=[8,16],\n",
    "                  batch_size=[64],\n",
    "                  ker_size=[3, 5, 7])\n",
    "    \n",
    "cvresults = htun.get_cv_grid(modelfunc=func, \n",
    "                             param_grid=param_grid,\n",
    "                             num_epochs=250,\n",
    "                             x_train=x_train,\n",
    "                             y_train=y_train)\n",
    "\n",
    "htres = htun.print_tuning_results(cvresults, func)\n",
    "print(htres.to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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