predict generation using a neural network.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import networkx as nx\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "from neupy import environment\n",
    "from neupy import algorithms, layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "g = nx.read_gexf(\"datasets/influences.gexf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame.from_csv(\"datasets/dates.birthdeath.csv\",encoding=\"utf-8\")\n",
    "df_train = pd.DataFrame.from_csv(\"datasets/generations.training_sample.csv\",encoding=\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "samples = map(lambda idx: (list(df_train.fillna(0).loc[idx,:].as_matrix()), [df.fillna(0).loc[idx,\"birthyear\"]]) , df_train.index)\n",
    "(input1, target) = zip(*samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [3.0, 1033.66666667, 1236.44503854, -384.0, 1889.0, 1101.33333333, 1243.37336843, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 1946.0, 0.0, 1946.0, 1946.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [2.0, 1916.0, 19.7989898732, 1902.0, 1930.0, 1980.0, 16.970562748500001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [4.0, 1828.5, 30.555959593299999, 1809.0, 1874.0, 1887.75, 41.8041066563, 4.0, 1923.75, 4.7871355387800003, 1918.0, 1929.0, 1993.0, 7.8740078740100001], [4.0, 1539.5, 272.83511504199998, 1137.0, 1743.0, 1610.75, 276.03547477299998, 3.0, 1798.66666667, 87.065109736000011, 1743.0, 1899.0, 1864.0, 94.873600121399988], [1.0, 1889.0, 0.0, 1889.0, 1889.0, 1976.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 1493.0, 0.0, 1493.0, 1493.0, 1541.0, 0.0, 3.0, 1812.33333333, 53.799008665000002, 1775.0, 1874.0, 1887.33333333, 52.6244556583], [1.0, 1953.0, 0.0, 1953.0, 1953.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 1775.0, 0.0, 1775.0, 1775.0, 1854.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])\n",
      "([1895.0], [1926.0], [1963.0], [1952.0], [1912.0], [1729.0], [1930.0], [1575.0], [1964.0], [1916.0])\n"
     ]
    }
   ],
   "source": [
    "g_train = g.subgraph([ n for n in df_train.index if n in g.nodes()])\n",
    "input2 = nx.to_numpy_matrix(g_train)\n",
    "\n",
    "print input1[0:10]\n",
    "print target[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_scaler = preprocessing.MinMaxScaler()\n",
    "target_scaler = preprocessing.MinMaxScaler()\n",
    "\n",
    "data = data_scaler.fit_transform(input1)\n",
    "target = target_scaler.fit_transform(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "environment.reproducible()\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(\n",
    "    data, target, train_size=0.85\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Main information\n",
      "\n",
      "[ALGORITHM] ConjugateGradient\n",
      "[ARCHITECTURE] Sigmoid(14) > Sigmoid(50) > Output(1)\n",
      "\n",
      "Network options\n",
      "\n",
      "Verbose:\n",
      "[OPTION] verbose = True\n",
      "\n",
      "BaseNetwork:\n",
      "[OPTION] epoch_end_signal = None\n",
      "[OPTION] show_epoch = 25\n",
      "[OPTION] shuffle_data = False\n",
      "[OPTION] step = 0.1\n",
      "[OPTION] train_end_signal = None\n",
      "\n",
      "ConstructableNetwork:\n",
      "[OPTION] error = mse\n",
      "\n",
      "GradientDescent:\n",
      "[OPTION] addons = ['LinearSearch']\n",
      "\n",
      "ConjugateGradient:\n",
      "[OPTION] update_function = fletcher_reeves\n",
      "\n",
      "LinearSearch:\n",
      "[OPTION] maxiter = 10\n",
      "[OPTION] search_method = golden\n",
      "[OPTION] tol = 0.1\n",
      "\n",
      "[THEANO] Initializing Theano variables and functions.\n",
      "[THEANO] Initialization finished sucessfully. It took 25.21 seconds\n"
     ]
    }
   ],
   "source": [
    "cgnet = algorithms.ConjugateGradient(\n",
    "    connection=[\n",
    "        layers.Sigmoid(len(input1[0])),\n",
    "        layers.Sigmoid(50),\n",
    "        layers.Output(1),\n",
    "    ],\n",
    "    search_method='golden',\n",
    "    show_epoch=25,\n",
    "    verbose=True,\n",
    "    addons=[algorithms.LinearSearch],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Start train\n",
      "\n",
      "[TRAIN DATA] 765 samples, feature shape: (14,)\n",
      "[TEST DATA] 135 samples, feature shape: (14,)\n",
      "[TRAINING] Total epochs: 100\n",
      "\n",
      "\r",
      "------------------------------------------------\n",
      "\r",
      "| Epoch # | Train err | Valid err | Time       |\n",
      "\r",
      "------------------------------------------------\n"
     ]
    },
    {
     "ename": "error",
     "evalue": "(25, 'Inappropriate ioctl for device')",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31merror\u001b[0m                                     Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-e5d2486e2503>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcgnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/neupy/network/constructor.pyc\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, input_train, target_train, input_test, target_test, *args, **kwargs)\u001b[0m\n\u001b[1;32m    449\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_input_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    450\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_target_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 451\u001b[0;31m             \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    452\u001b[0m         )\n\u001b[1;32m    453\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/neupy/network/learning.pyc\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, input_train, target_train, input_test, target_test, epochs, epsilon)\u001b[0m\n\u001b[1;32m     41\u001b[0m             \u001b[0minput_train\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_train\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtarget_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m             \u001b[0minput_test\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_test\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtarget_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m             \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepsilon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepsilon\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m         )\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/neupy/network/base.pyc\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, input_train, target_train, input_test, target_test, epochs, epsilon)\u001b[0m\n\u001b[1;32m    437\u001b[0m         \u001b[0mlast_epoch_shown\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable_user_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    440\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterepochs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m                 \u001b[0mepoch_start_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/contextlib.pyc\u001b[0m in \u001b[0;36m__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__enter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"generator didn't yield\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/neupy/helpers/logs.pyc\u001b[0m in \u001b[0;36mdisable_user_input\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    171\u001b[0m                 \u001b[0;32myield\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m                 \u001b[0mterminal_echo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menabled\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    174\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m             \u001b[0;32myield\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/neupy/helpers/logs.pyc\u001b[0m in \u001b[0;36mterminal_echo\u001b[0;34m(enabled, file_descriptor)\u001b[0m\n\u001b[1;32m     31\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_windows\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m         \u001b[0mtermios\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimportlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimport_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'termios'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m         \u001b[0mattributes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtermios\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtcgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_descriptor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m         \u001b[0mlflag\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mattributes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31merror\u001b[0m: (25, 'Inappropriate ioctl for device')"
     ]
    }
   ],
   "source": [
    "cgnet.train(x_train, y_train, x_test, y_test, epochs=100)"
   ]
  }
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