EQL-DIV-ICML-Python3/Evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-13T10:52:02.201337Z",
     "start_time": "2018-03-13T10:52:02.184485Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:98% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "HTML(\"<style>.container { width:98% !important; }</style>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-13T10:52:02.865959Z",
     "start_time": "2018-03-13T10:52:02.298627Z"
    }
   },
   "outputs": [],
   "source": [
    "colors=[(0.368417, 0.506779, 0.709798), (0.880722, 0.611041, 0.142051), (0.560181, 0.691569, 0.194885), (0.922526, 0.385626, 0.209179),(0.2,0.2,0.2),(155.0/255,69.0/255,213.0/255)]\n",
    "def darker(color):\n",
    "    return (color[0]*0.5,color[1]*0.5,color[2]*0.5)\n",
    "#!/usr/bin/env python\n",
    "import pandas as pd\n",
    "from theano import *\n",
    "import theano.tensor as T\n",
    "from theano.ifelse import ifelse\n",
    "from theano import pp\n",
    "import numpy as np\n",
    "import numpy\n",
    "import copy\n",
    "import cPickle\n",
    "import gzip\n",
    "import collections\n",
    "\n",
    "import src.mlfg_final as mlfg_div\n",
    "import src.mlp as mlp\n",
    "from src.utils import *\n",
    "import src.model_selection_val_sparsity as model_sel\n",
    "import src.noise as noise\n",
    "from src.graph_div import *\n",
    "\n",
    "import pylab\n",
    "from pylab import subplot,subplot2grid,figure,plot,matshow\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-13T10:52:03.218279Z",
     "start_time": "2018-03-13T10:52:03.213989Z"
    }
   },
   "outputs": [],
   "source": [
    "def getRuns(header,data,key,typeconv, value):\n",
    "    return [d for d in data if typeconv(d[getIdx(header,key)])==value]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FG 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-13T10:52:04.414870Z",
     "start_time": "2018-03-13T10:52:04.410286Z"
    }
   },
   "outputs": [],
   "source": [
    "def FG1a(x):\n",
    "    x1,x2,x3,x4 = x\n",
    "    return [(np.sin(x1*np.pi)+np.sin(x2*np.pi*2+np.pi/8)+x2-x3*x4)/3.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-13T10:52:16.096909Z",
     "start_time": "2018-03-13T10:52:16.058741Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['k', 'iter', 'layers', 'epochs', 'nodes', 'lr', 'L1', 'L2', 'shortcut', 'batchsize', 'regstart', 'regend', 'id', 'dataset', 'gradient', 'numactive', 'bestnumactive', 'bestepoch', 'runtime', 'extrapol1', 'extrapol2', 'extrapol3', 'extrapolbest1', 'extrapolbest2', 'extrapolbest3', 'valerror', 'valerrorbest', 'testerror']\n",
      "25\n"
     ]
    }
   ],
   "source": [
    "#f = gzip.open(\"data/fg3-12k.dat.gz\", 'rb')\n",
    "f = gzip.open(\"data/fg1a-n-10k-1.dat.gz\", 'rb')\n",
    "\n",
    "data = cPickle.load(f)\n",
    "base=\"result_fg1a-fg/\"\n",
    "\n",
    "(runs,comments)=readCSVTable(base + \"all.dat\")\n",
    "header=comments[0][0].split(' ')\n",
    "assert(header[0]==\"#C\")\n",
    "header = header[1:]\n",
    "print header\n",
    "print len(runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-13T10:52:24.363377Z",
     "start_time": "2018-03-13T10:52:24.356670Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'src.model_selection_val_sparsity' from 'src/model_selection_val_sparsity.pyc'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reload(model_sel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-13T10:52:24.805604Z",
     "start_time": "2018-03-13T10:52:24.797150Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lines:  25\n",
      "(' best extrapolating model: (only for information):', (1, 9.94299902638e-05))\n",
      "selected instance model: score: 0.00318949593359 id: 1 #active: 4.0\t val-error: 0.000102293056778\t extra-pol2-error: 9.94299902638e-05\n"
     ]
    }
   ],
   "source": [
    "best_instance = model_sel.select_instance(base + \"all.dat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-09T14:55:19.000344Z",
     "start_time": "2018-03-09T14:55:18.996464Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extrapol2': 9.94299902638e-05,\n",
       " 'id': 1,\n",
       " 'num_active': 4.0,\n",
       " 'score': 0.0031894959335853797,\n",
       " 'valerror': 0.000102293056778}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-09T14:57:35.364862Z",
     "start_time": "2018-03-09T14:57:35.355569Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'99999999999 0 3 4000 10 0.01 0.000316227766017 0.0 False 20 500 3500 1 data/fg1a-n-10k-1.dat.gz sgd 4 4 3800 18.5196628531 9.99167471211e-05 9.94299902638e-05 9.80261459605e-05 9.93677612041e-05 0.000235331374806 0.000968852830731 0.000102293056778 0.000100712484516 inf'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runid = best_instance['id']\n",
    "state = cPickle.load(open(base + str(runid) + \".last_state\",\"rb\"))\n",
    "runparams=getRuns(header,runs,\"id\",int, runid)[0]\n",
    "\" \".join(runparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-09T14:58:02.949202Z",
     "start_time": "2018-03-09T14:57:59.548061Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max input value is:  0.999951215607\n",
      "... building the model\n",
      "Gradient: sgd\n",
      "... training\n",
      "Optimization complete. Best validation score of inf obtained at epoch 1, with test performance 0.000000 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The code for file mlfg_final.py ran for 0.00m\n"
     ]
    }
   ],
   "source": [
    "reload(mlfg_div)\n",
    "#reload(mlfg_redexp)\n",
    "trained = mlfg_div.test_mlfg(datasets=data,n_epochs=0,verbose=False,learning_rate=0.005, basefuncs1=[0,1,2],\n",
    "                        L1_reg=float(runparams[getIdx(header,\"L1\")]),L2_reg=float(runparams[getIdx(header,\"L2\")]),reg_start=0, batch_size=40,\n",
    "                        n_layer=int(runparams[getIdx(header,\"layers\")]), n_per_base=int(runparams[getIdx(header,\"nodes\")]),\n",
    "                        classifier=None, gradient=runparams[getIdx(header,\"gradient\")],\n",
    "                        init_state=state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-09T14:58:59.732359Z",
     "start_time": "2018-03-09T14:58:59.503085Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumActive: 4\n",
      "1.98206528607e-06\n"
     ]
    },
    {
     "data": {
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Fwpb/wi6/FjTqE+dQP35hwST3GEf/w7PZs9i59ww48Y+XsGGn6Vf/qdJ2babfRxG+7L3T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\nZeyG+N8/Ym39QUR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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3c2ba13d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classifier_c = trained[\"classifier\"]\n",
    "classifier_c.set_state(state)\n",
    "print \"NumActive:\",classifier_c.get_num_active_units()\n",
    "xs = np.arange(-3,3,0.01)\n",
    "inputs=np.asarray([[x,x,x,0.1*x] for x in xs])\n",
    "outputs=np.asarray([FG1a(x) for x in inputs])\n",
    "cout=classifier_c.evaluate(inputs)\n",
    "\n",
    "print np.mean((cout[:,0]-outputs[:,0])**2)\n",
    "plot(xs,cout[:,0],c='b')\n",
    "plot(xs,outputs[:,0],c='r')\n",
    "pylab.axvspan(-1,1, facecolor='0.8', alpha=0.5)\n",
    "for extra in [1.5,2.0]:\n",
    "    pylab.axvline(-extra*1,c='0.8')\n",
    "    pylab.axvline(extra*1,c='0.8')\n",
    "#inputs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-09T14:59:56.509415Z",
     "start_time": "2018-03-09T14:59:56.419679Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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