Digit_Recognizer_NN2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST Digit Recognizer Neural Network\n",
    "\n",
    "My first attempt at running the MNIST digit image data through scikit's RandomForestClassifier hit an accuracy around 96%, which I'd like to improve upon.  As noted before, the image data are pretty diverse, coming as they do from a pretty diverse group of examples of human handwriting, and it's entirely possible that a more sophisticated machine learning approach could generate a better model to predict the digit labels from the pixels.\n",
    "\n",
    "The basic properties of neural networks have been summarized [elsewhere](https://www.youtube.com/playlist?list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU); suffice it to say that they constitute a series of nodes and edges representing greatly simplified virtual analogs of actual neurons in an animal brain.  The nodes are arranged in layers, with an input layer (here with one node per pixel of the train/test data), an output layer (with 10 nodes, each representing the strength of prediction that the entire digit represents the number 0, the number 1, etc.), and one or more hidden layers, where the inputs are \"weighed\" and integrated, to determine if the \"neuron\" will \"fire\" (i.e., how strong of a signal it sends along to the next layer, and ultimately to the output layer).\n",
    "\n",
    "The machine learning comes in varying the weights that represent the edges of the network; deciding how strong the association between each input pixel, hidden layer neuron, and output digit should be.  The algorithm decides this on the basis of \"backpropagating\" the error between the digit's label and the model's output call; obtaining the derivative of a particular weight with respect to the error function, to tell in a computationally-tractable manner whether increasing or decreasing that weight will render an output call that's closer to the label.  The output model is just a list of list of weights, telling which associations between neurons represents the best way to transition from input to output.\n",
    "\n",
    "That's all done by the algorithm; in this notebook I'm just trying to get a handle on the \"hyperparameters\" of the method: how will tweaking the various arguments of sklearn's multilayer perceptron classifier affect the accuracy of the predictions?  What results is an empirical (read: theory-poor) dive into cranking every little knob on this machine, to see how pretty the outcome can be.  [Spoiler alert: nothing I do makes a big difference, but it was still educational to try.]\n",
    "\n",
    "I'll start by copying over the relevant code from my RandomForest notebook to import and split the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from time import clock\n",
    "import random\n",
    "from itertools import combinations, repeat\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn import model_selection\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 10.3 s, sys: 1.77 s, total: 12.1 s\n",
      "Wall time: 19.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# on the PC:\n",
    "# train = pd.read_csv('C:\\\\Users\\\\DMacKellar\\\\Documents\\\\Python\\\\Kaggle\\\\Digit\\\\train.csv')\n",
    "\n",
    "# test = pd.read_csv('C:\\\\Users\\\\DMacKellar\\\\Documents\\\\Python\\\\Kaggle\\\\Digit\\\\test.csv')\n",
    "\n",
    "# on the Mac:\n",
    "train = pd.read_csv('/Users/drew/Documents/Data/R/Kaggle_Digit_Recognizer/train.csv')\n",
    "\n",
    "test = pd.read_csv('/Users/drew/Documents/Data/R/Kaggle_Digit_Recognizer/test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Rescale the data\n",
    "\n",
    "I learned pretty quickly that Neural Network approaches don't work well with the raw data (integers between 0 and 255); they need to be rescaled, and I choose to rescale all pixel values to floats between 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>pixel0</th>\n",
       "      <th>pixel1</th>\n",
       "      <th>pixel2</th>\n",
       "      <th>pixel3</th>\n",
       "      <th>pixel4</th>\n",
       "      <th>pixel5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5\n",
       "0      1     0.0     0.0     0.0     0.0     0.0     0.0\n",
       "1      0     0.0     0.0     0.0     0.0     0.0     0.0\n",
       "2      1     0.0     0.0     0.0     0.0     0.0     0.0\n",
       "3      4     0.0     0.0     0.0     0.0     0.0     0.0\n",
       "4      0     0.0     0.0     0.0     0.0     0.0     0.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train2 = train.iloc[:,1:].apply(lambda x: x / 255)\n",
    "train3 = pd.concat([train.iloc[:,0], train2], axis=1, join='inner')\n",
    "train3.iloc[0:5,0:7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll take a quick look at the columns, plotting their maximal pixel intensities:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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WOjHv54T8Gh11rPstYCnwI2AkcESOY+uuCkp3ZT0BuB44Lr/GBaQbtD3WzXaLTiXdDO3X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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2031dceb2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(train.iloc[:,1:].max(), bins=25)\n",
    "plt.title('Max pixel intensity per column \\nfor raw training data')\n",
    "plt.xlabel('Pixel intensity')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlim(0, 260)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mZmaVcaJhZmZmlXGiYWZmZpVxomFmZmaVcaJhZmZmlXGiYWZmZpVxomFmZmaV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2031dde9588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(train3.iloc[:,1:].max(), bins=25)\n",
    "plt.title('Max pixel intensity per column \\nfor rescaled training data')\n",
    "plt.xlabel('Pixel intensity')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlim(0, 1.05)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rZn2v+2sWL/6hVPPt2btQK/rnnysSwk4+R7WhuzVsWKrPzAdvDteDHyf6QwgL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\nLRl+UPwaMCRUVtH1rd2B/ZxzTfBClcg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KXP7o+7GOSf51nGrc69bdHoCnB+h4r/2uah3b\nUMdz4b3Dvqzo/qwLO2l/e8+zumfp1ivOB+CFp3sD0ObENgCc1f6Q3BQ2DcwSYRiGYRiGYRhGRuTU\nEvHNF4na3W4ndwQKLRAb6sw0XYZOVo13KutDJmy22RYA7Lf/4QA0jmiaKzNBOzRo4kQAHrpevSEM\nG/YCUOg54uRTdb3+8ZceD8DeTRoX5LFrgwYJeV16ra597fPQjQCMnNENgOP22adM3qEsCe/0/Ief\nAHDVyepB4ozzri6Ic/+duj6zapUqWeU9YLx6Zfvll3klKmtFIX7+zchJusZ68Y+LARj0omoow/r0\nNWv+jmexHi88+18A9j/6AKBwnW1lZeyU6QAsWPBt0vA6dbYF4OEBbwBwzN7qEecfVasCcFoX9QA2\n+P1nAPh8pmqXD9pppzIqcW4Y863K47vvtD0782xdM33XzZcBhRaIeN+6sVcfXnHGCQCMG3INAG/0\nu68gzsAJEwA45YADyqLo5ZKhI98t+L3k998BuOYC9QD24YcvZpRXs2ZaB7/9dnzC/dAeXHKGtqnP\n93+kIKw8e+FJRXifafPVStr9RLUCBgvEqx+9D5gFIk4yOYQ+9T8XqUXs7ut0/8jvv+sqnkuP01U7\nHefPBaBujZplXcxiMUuEYRiGYRiGYRgZUaaWiLjHmxEf6zkPhxyi2s16tWoBNjMtDbbaStdVNm6i\nHjX2OehgAPY6VE9HvOV89Ry0aJHuR2naVO+/+vIdQOX/G4S6OGqWrjM/+UD1qPTXX1o3d9hBrQaP\nvaUn3hblmSQuq0a7NgoPAWDZit9LqdS5I26B6HHK6UChn/2vxxVq09YWsx49Vd5PDtAzSR69Qdd3\n/vHHioR4F17x3wxLnV/Cew32Vq3Ph41LGm/U4E8AmDxZT01esmRhWvkn+yYL1ln7/++8QjXIbfbV\nvU17eE92GwrBM9qbH7wGQLO6RWtyV67UM4o+GPQ5UPEtEeMnzEi47t7zHKB4T4bhflXvGeiKXuoV\nZuig/xXEeeE2bQtPGVj5LRFBHvUjZ1DdcafKIlMLRODCntcD4NZqe9mn991A4WnqYY/EW0M7F6S5\n+syTsnpWPght0Q+/ah9x+lFnAoV9aq8n1epnFoiiie6d+WOV7mG97X799oIsA7//ru3XmrWZ9cFl\niVkiDMMwDMMwDMPIiJzsiXjvC9XQhXMhGjRQ7U/wJ1xZ/CSXNdvU1PVvNWpsVXBv+XJdV/3g23pe\nxGmtWyekWeW9d/TfXTV2H330EgAnnHMWUPm1A3Fr2FlHqtYnzPBPPV1PFX28j+5nqL2F7hXJRC4L\n56hmOayB7dr24JIWO2cE+SxeoVaBB6++FYDffvslId629RsX/M7WW8tzvR8FYPr00UnD7+h9RVb5\n5pqCU3+n6zr9zge3B2DlyuVZ5Vejeh0AGjdRa8IpF6tWuFmLJgVxRryhnqye8vtu4mVpUck8vMRp\n2GAboLDtC+1e1arqAS1YIOLfbZDPxT11n1L/1++jMnHyEdqu9ztAPcLt3bgxkH77FeIFi8zZl91Q\nEPbSk7rXa+FvdwGwbc38r78ubUL9ePPLLwHo3KpVJLRoGW6/vZ5zsN/+urdz2tfargUPas2aNwbg\neL83rv8T2vd+992ExDKsq1he6eLnQVzfXb+p0K7fcLd6ULuqi+63qexjjNLkjkfVy9pd/7k0afh+\n+2ldq+PHKeUBs0QYhmEYhmEYhpEROTEBHNJC/eDuuutBAIwfr6dltvIz+CNP0z0SJ55yGADNtlGt\nU0X2s1+ahJl8m531ZO8dd9y3IOyrr9SX92t365rg4/qp1qPapuqV5PZHdD1nsEAEFnxXOfylp8ut\n/hyI2bPVw1XbtmqRuOpW9cOcygIRtC59Bn5QcK/J/7N35vFWTe8ffy80K4UyZiZDKikJkSHKGJll\n+GYoZJ5lJmNCkWQKIfmVRJnnIRmKNJkrkUpkLmT//njWunef3bnn3nPudO69n/frdV/nnL3WXnvt\n5z5r7b3Ws9bzbLQuULhvonEz29vTvPmWacuoCiz580/79JF9k2zaZtOC77neX1FxIDbayORYe5Wq\nFevgr3/Mi1LHjjbj9vLLD6Wkr+zXpe+553Epx1vvYm20zZ5muQqe0TputllKvri0Hr4utexA14Ns\nHXJV1LniiN9Tt9atAdh996MBePrpQUXmTUfHzS1exL77nlKWVaw0wv2uv7pZsdZZx3Tn7c/MA2KI\nBpwtOx+wU8H3wTdaXzDPr3mvTpaI0BeNeMti+1xweGijhXoUZFxgrdn5EABOv+kcADZc06xiO2xq\nfWOIdr3oV7Pqhr0AheWlesoq+Fyparbd8X4v2MjHzFK19962H+eqs+2zOvZJ5c3mba0dN2myVsrx\noK8ffmj7CnfueAAAd48aAhTqYGUgS4QQQgghhBAiK8rVEhFGomHEfssI84c8+FIbPQXvBDdc/Jr/\ntPybbNIKgM02s5m6biccCMBph+0PVM/orCUhjOs7H1DoEz74oH72WVuHOG6yedUJa+beHv9a2rLa\ndql6MQxKwyfvpnrO2dxbEcIIfv4S88P88RzzXvXMQxbhdvjQ6wFYtuzPgnPr1VsVgImzpgFwcR+b\nHe3StXAWL99J7oU48XDbj/Dtt7PS5u9xeJecr/HcJ+aNZNq0t9Lm69zFZviC9SzfCf1a11bWT+39\nwv0AfLXg+rT5NlsrdVapuHKD3F769NOCtEIPMcE+YXmv659+7Wx15fKBpqfvv/8sULgOe599bB/J\nsEdtVjQ8cwq8EHn/65feZjFOpn/xTQXVuGK4brDFHAjr1HNl2w0KvXvVrlU12mNp6NezNwA//jhv\nhbTVVjML4fGnmZelSy6wGfamjRql5As61nL95mmPlzTCdb4T7iN4ELrs+LOBwthKo56y97uirPkf\nfWNtbshNtiriy5kWL2f+/K8L8q6xxjoAdD3avFQd3H0PAFpvsEFZ3kpeEZdXiER93OLvU/Is/dvi\n3NwyzFac9D/fIs7vvKVZHI/saTo6dKjFiQmx1yoCWSKEEEIIIYQQWVEheyIKZu5a28xd1/FDAVjw\ny40A3DbERlfPP2FxJEJE5hCF88UXhwPw+O3mgSKMfAF6X2cjsoPbtSu3+ucLQY439jut4Ni3n9kM\nyqjHzZPGKXvvl3JO8Isezu03YBgAx3XdvXwrm2fMn/9Vyu/vvjZPYTvsYPL6/vsv/KflW8lHeq1f\n32adli4tnE3q0uUEYEWPODv5ddf5TJgVCnsgunc7ASiMlpxkr71srXDLUnj/ufNSs5LFrTlxzrjE\nPOdUtRm6UN8wE7P52mtnzJct5x55asH3f/75O5QGwI47mnV2jVUb5lR2PhJ085tFtha/T8+LC9J+\n+sn2cIW4JfHZS4CXXhoOwF4dzeq18cb2rFnLR5efN9va9cnXWSTnXVpvU+b1rwyCbm3t7zNbkt7r\nfl+6tCBt5VXMevPZ9yb7dptsknM984Vwv1cONOvhggXfpByP06bNngAMvPaslOPFxd6o7rw83SwI\nM2a8C0DXrravsFG9ekChpWK8j3g+/n5bxz9u9D0A/PWXxVEKeztbtd6toOwxo28DYNIki3L9wADb\nZ/jah68DxceBqeoUpUN1vUfEy/va8/jQg003u3Sw97gRw80K3rqz9XvnH39YudYzjiwRQgghhBBC\niKyo0AANyVHWWquZV5sbL+mT8hlmBW5/bCwAzz30DADvvmt7KOKRbl980SL7jX5/ElBTLBKF3x98\n4GoA3n1rHADz5n2W9pywZvjS082bS1gjXFPoc4WtGTznGFtr+fzz96Wkr722+eS/sP9dAGzko1AP\nvdIsPFOnvlGQt1uvbkDVmnkKbeovv7ay255HAvD++xMynvfaaxZ/ZM3VxxYc23Zbmznauavtk9hp\nX4toW1TbC9awJIccYmtqW1Xx9a5lpQfhf/TajBkAzJ4zbYU8a65pM86XDbkSgJVXqvrzQOG+rx9q\nPtIfus08L4X9XtkQfPAnffEHFp5ie57GvPx/WZedzxSlg0G2Icr8tHlmuR52h1n/P59qs8qhf1u0\n6NsVyjjzIJvV7OfjmbRoYe19izbmfWj9FmalPPlws+quvuqqpbmVciHIYd5PPwEw5kHzdrZ8+b9F\nntPnerNaldYbXYhBMXv21JzKqWySOjTwXIu8vYq3VPW5zvaVPPHeewBce9IFQKGlok4ds1AccJBZ\nVg88/SAAenbaBYAvFxR6BPz1F/v/BE93YY/e/ruZ5fXZN+w9p7pbJJIkdXArb3lcYw37DCsohl57\nAyBLhBBCCCGEECKP0SBCCCGEEEIIkRUVupwpSXEblM455uCUz4W/2Kabh556oSDv9eecAcAFR54M\nwJ6fmgktbPKp7tz3jAVBW9E1p5kggymy7R7tgcJlTFVpKU5Z0PcIM4cevd/PANz9sC3P2Ww7C+6y\nf9vtUvJfeqUtawpm/i5dji9I+9++e5VvZcuRs86z5Vlh41ohqa5DA//+awHVfv+9cEnSxInjUj5r\nXWebvi68zlz8XXVeLwD284G93nxzVEqZwQy+8yFmzl6phulikuRm93OOtmUU8WWbob22aWMuD/dt\n0ybleFUk3PcBB5ijiJdfNje2f/+9tMhzSkqdOhaotEUL6/e6HGLLGHud2B2ALYrYBF/VKXAPPM2W\nwk0Y+TIAr4yz/q4oN8uZ+OmnH1I+58yx5XYvvpia7/4bbaPs559/kPU1KorD9jM3rWGpTZL+94wo\nzNuhQ5lc88nbRwOwcOHclONnXGx98bnHHFIm1ylv/vRLYd9880kAVlvN3CjP/fo7AM483NpWndrm\nXvSMiwcAcGQvC4yWDKZZ6AK7cGnSU+PMCcdNd5m79OsusKVSs2a9D8ADw80BSP8LTy6bm6pmFBXQ\ntTyRJUIIIYQQQgiRFZVqiSiO5Cxbs9VWA+D8Ew4vOPbNVHPRNvT2S4HCwFZH7LhjRVSxQkkGewH4\n4LmiZn1MdkGG999qLsCuO/+k8qtgHhLuP2xADYGCrvCu0pIceKAF73r22aEpxx8ceWvB96oc7PC+\nu64AYGV/D2Fj1t77HQvAJq3NjeO3s2yD5bOjzRVicLEJK25GDO5HXx5jDhDqNrSZqOB2M1Dg6rmr\nbfI/+6juKcdrOrfc+SiQuok/sNtuthE+uLuuyjIL/dgN9z4OFOpJoRvbFWm4ahMA1l+/BQCHn2pW\nrnU2MYvC4wPMivHGG7ZheL31zN3ylCmvpC2vKssPVnTN+uxHtgl94FlXAYVu0v/zm2GTNGtmzgxa\ntzbLVovtzOXtN9MLXeeOH2+zwlfcbn1Asw1sxvi9Z2wD7efTbfN6cM7w5Zf2+53PPwdg5y22yPHu\nypPU52L4bNLEgpxdfNKRhTlLuaH64ussqO6TIwekvebJpx5WqutUNr/9ZpugL+hpMgsWiBFvvQ5A\nj/btU/KXxDVug7oW4PBi/3yeMXEmAGPG3A7ApJe8Ja2GWiKSbrB/+CE1aGbr1hXvul+WCCGEEEII\nIURW5LUlIknBiDW27uuA47sChZaIWR94F6fV0BIReHXG9ILvjdcyN7nJUX737mcCsGTJIgDee8/W\nrwd3c8lZgupOcS4QL7zW9kC88MIDANSrZ24KB4609fxNGjQotqyqwE0P2ExtvVVt1qjvoQdkzB/d\ndRkAQ8c9X3Bs4riJALz9mq1PnT3b1l+///74lM9AUl6dj9gz7fGaRtC9SV9+CcDAK88pMu+mW28F\nVC+Z3XCe7f1IWiCC1WGXTocWHDvparOgdt9++7RlrbeuzZK/sf3IlOPVSV5QqDOv+oBfZx9lltOi\n9jqsuaa5X+124AkA7PO/fQA4YPu2ADSsW9eXa/nPveKOwpN9M95//12BwmBzp3XfF4Dl/9lJ9zz9\nHAAvPWybJNrkkcvmpLxCUNGkXlw0cGDa47lca9o8s+KOfyKzLlZ13QxWrmV+D1OwboV3i1zuL5xT\nv47ts9uvt7kNDpYIKn7Jf14QdOuf5csBuPA0c7ObdMm8edvUfScVgSwRQgghhBBCiKyoUpaIdPz8\n+x+VXYVyJ4xCJ/oZy//tfWBB2s8/L0jJ23+YeZc45zjzRvKKn4E5YHtbf/zNjNmWsYZZIpIEmQ5+\n0iw0g/pbgJyVVzbPQb3Pu8Y+D9inEmpX9oQZngtOOCzt8eLOO/WgbgXHwvcoskCHd4w0i8R1Z9is\naFInA9tsY96Yeh6wZ1Z1r24E3ftsvu0zefQ+08EosX69XbuuBd9vvunsCqpdxZH0JNKgge15u220\nWf/+t9eK63uT+hrKeOah51fIW515+dl3gBUtEHXrmsV0t92OAODRJ82jYXEB4P79z2Y4J73yesGx\npk2bA7DFOrZfICn7lf0U5GkH75vymY9M/tA8SgVLRKBRI/MwFPZ75ELSAnFYF+tjk8ESg8ew86+3\nWfXN1lor52tWBg3q2H6FU8+zgGZ333pJSvq225rFqiysOfOXLAHgiVsfTUlv2WG7Fc6pzgR5LPvH\nvCQedYQFzR079o60+XfZt2w8imWDLBFCCCGEEEKIrKhQS0QYVX27eDEAg++yNYO3XNk3q/P/+rtw\nDe3Np18WUgHYqXPbsqhqXvL+h2ZVSDfTu8sutn74jGPM403tVexfu2jJLyn5xg8fA1RsWPR8IujQ\nvRNs/e5Fx/cE4O+/zePVwQefCsDAa84Cqv661SS53k+688Kx4GVpwRzzI39zv/TtuVYtm8mq7eNE\n1DSS8SBOOtxi3LzzzlMp+Tp2PAiA0RMeLDgW35NTXQiWhz///BWAPfawtnhM505AZl1Neqr7eNJ7\n5VbPqkD79mYdvGeU7e1qs+GGKenF7Qm7+T57Foe9cwD77Wc++ouKuVQd+sba3qNQwwb1sz63wAtT\nf/NiNX6kWfuDBSJpaTv9YvOQeNWZJ+RU18oi/J/DjPNl/cwzWtISETwFBSvCOo0bl6j8uJwefdss\nbAPOMC+Cn3zyOgBbbmkz7Mee1D3L2lctgix++v13AN72ns7uvHgQAC+//EhK/pW818mz+pn3yH1a\nta6QeqbUocKvKIQQQgghhKjSVMqeiClz5gDw2DBb13Vm36MAaL7GGmnzh9HZH8vMC8DRh19YkBb8\nYZ963o0A7L711uVQ4/zg6WGjVjgWZi1vfdBmOer7dYvnXGZrYQfdYGv9w2zC3Lkzy72e+UjQoSFj\nzOXIyNsfAmDpUttT07WreX95bKTpUXWYZatoNthqw4zp7XbpXDEVyTOC7n0y1yLW3nSpxSBJWiBa\ntrQZ+MuGXAnA2j4uDlRPfTzjqmsBuOL0EwB45hmbRT/ycIthsmrjQutLn0vMb/yPv1kU7ycHm+wW\n+X0l7096tvwrnEesvnaTlN877d0FgG3WN29MxelL0Ml3v7A9AndedfkK5104oPpZY5NxM0IU6ScH\nmYX+4IfbrXBOkNXIieaV7phdOoUU/5lZPiEKdohBUdXluZbvl+574SUALjzKYgxNn25WhI3XXheA\nTTZpA0CLFunX6X/+mUWh/urrjwuOhWj1QeadvIe2Wx6w95u2G21UNjeRR8QtMW/MtPezy3vbM+Dt\nt0enPadWLfNedf39plvn9exRnlXMiCwRQgghhBBCiKyoUEtEGIEf2Nb2LYzoZOvbRj33OrDiaCq5\nh+LMk2zm6rnnhhXkOerYiwG49srTgMLIxNWRxBJLoDDSZi2/B6JDh/0BmDLl5bRlbLpp9d0zko6g\nQ9///DMAD99ss8Ahymrz5lsC8H9P2ZrDOrVq5nr9XAiy/fWvvwAYdPHVafMFr0ynnXcMAI3rZ7/+\nuDrw1NhX7XP0oLTp3U+w2fZurW1da1WfsSyKcF+X9jZ9uPs669fnz/8KgKefXlE+jz50Q4nKDt6J\n9j7wiFLXM5858VDzhPTFZJPdHf3PBeD/hluU5C77mmw3b2eRuzvsbDo17weLGzRpvO0hGTbILBDL\nvf/5086/seAanVq0KL8bqGDqr2Z6ETwk/f33Xynpox67BYCvP59WcMy51HeJ335b7I+b/obncTIS\ndbjG6RfZ7Hl1sUAk90b06mLRznvMNWvWtbdYjKXJb5huvf667RGZOTP9fqXVVjPPWGEvFMCW7S1y\netu97D3lyJ06AoXvdVVZhkkvXp98Yytyxg4aW5Dns88+tDwJr2ur+H2EB/cw6+CDw817ZFh5Uply\nqb5v3EIIIYQQQohyoVLjROzf22bNzz7EZo0mjn0XgJa7bgvAwtnmhej/Rtjsys8/m/eXcy4fWFDG\nTZefllJmVR6pFsfRFxwPwFtvFe6NeOMNG+13afcMAIsXf5f23DBD16f/qeVZxbwhjPpnL7KZt313\ns70js2ZNAmD77fcG4KbhA4D8GNFXVW6842EAPv/8w7TpJ19ms6StfSTbmiLj5F6IQVeZN5Nly8w7\n06o+MvNFN9vesPNPtH6wpsgneBb59ttZABx1jMlnrLfU/PPv3+lPTENYIzxojK1tP3Gf6hmLJOhG\n8NY15DazxO/QbQcARg96EoAnRpi3lr/u/b1E5W60UUsAbu1fGI+kOuhhYZwbs9w80tpmzz/8JlFm\ntAAAIABJREFUcEJKvtBW339/fMGxlUq4qiFco0OHAwA4uM/RQOHKiuogxzjJ+1nNW5YHFHjZDJ8j\nyu2aVZnbrjevew8OTW+5j7PJJq0AuOA2sxAm41blg1xkiRBCCCGEEEJkRaVYIsLo6bjOFuFw3Tdt\nl/8dl5jF4ZqzTwQK1xz2PMFmqA7wlovDOnRYoayaQPttbI1q06YbFBxbtMhmOf/445e059Sv3xCA\nGbMt2nVRHrCqK72OOAcotEBss83OAJx6vXmt2mMbW4NZk/SorNlg61SvTA0brg5AjyMtgnX7tuYx\nrabIOLlX5KBdbRZ0yZKFPofJ4dxrzArWr09PajJhxnfkYzbbFkW2/6H/3YXRaj966SMAxo0bDMCR\nx5iHvnU2NU8wPU+wWeAQH6G661q4v7BW/MSue6V8fjK3HwCjRr4AwMujLar8F1+YHBs3tgjNHTpa\nfImzrzbvdNV1T1hQh6232x4o1LmwdzC5RyJzWVbYmmuaJ6wddrD2ff8I09umjRql5Kvu1JT7LCtu\nuMH2NUx4yjxELlgwuyCtTh2LydL7vOsAOL2v7afZfO21gfyUtSwRQgghhBBCiKyo1D0RYVS1V0tb\nj7nXM0N8ypAizkg9r6YQ7jfMsh181CkFacMGXZb2nCFjzW/64XuYT+vgEae6yy7MAo/+4AMA3nzz\nCQAaNTILzBX3mReOYM2q7vKoCE7x6zSP/M0iD6/kZVpUpNvqSjIi9SH7WztNxma5qL/FQ7j8TNvj\nJB00kl5uLjstZqEp+H5bicqoKRR1v2H/UesLTwagv//MtbyqTriv+4eYN6qwymHo07Y3YvgN9s7x\n4YfPF1nGxTdaZOrGa1kk5k229M/jdqmxJaqrDEXpCHrRzMfZCN7osjk3H5ElQgghhBBCCJEVlWqJ\nCOTzKCufCHIaMvCigmPx7yU5t6Yw5i7zvRwilN73ollmDt3BvJjUNHmUB0m/4cn4DzVFxsEC8dPv\n5gnnqB5nAoV+0ldf3daz9ht0JwC9e9ga6uoc06YsqCn6Ux5IdukptHbZ71O775vyWZoyhSgJ1U1f\n9BQTQgghhBBCZEVeWCJEdlS3kWxZEmTz6PD+KZ/JdFF2SKbG9O8sRstLL5nXjW23Ne9zfW8w73JH\n7mG/FZNEiPxAbVCI0iFLhBBCCCGEECIrZIkQ1RLNMImKIuhapxYWx+W//5aXKL8QQghRlZElQggh\nhBBCCJEVJbVE1AWYOXNmcfmqNLH7q1uGxUp2uSG55Y5klxulklvw0lQclW2JkM7lTr7pXFVBOpc7\n0rnckM7lTlayi6Ko2D/gaCCqQX9Hl0Qukl35yU5yk+ykc1VDbpKd5Cadqzqyk9wku7KUnfOCyYhz\nbg1gH2A2sLTYE6oudYGNgBeiKFpcFgVKdrkhueWOZJcbklvuSHa5IbnljmSXG5Jb7kh2K1KiQYQQ\nQgghhBBCBLSxWgghhBBCCJEVGkQIIYQQQgghskKDCCGEEEIIIURWaBAhhBBCCCGEyAoNIoQQQggh\nhBBZoUGEEEIIIYQQIis0iBBCCCGEEEJkhQYRQgghhBBCiKzQIEIIIYQQQgiRFRpECCGEEEIIIbJC\ngwghhBBCCCFEVmgQIYQQQgghhMgKDSKEEEIIIYQQWaFBhBBCCCGEECIrNIgQQgghhBBCZIUGEUII\nIYQQQois0CBCCCGEEEIIkRUaRAghhBBCCCGyQoMIIYQQQgghRFZoECGEEEIIIYTICg0ihBBCCCGE\nEFmhQYQQQgghhBAiKzSIEEIIIYQQQmSFBhFCCCGEEEKIrNAgQgghhBBCCJEVGkQIIYQQQgghskKD\nCCGEEEIIIURWaBAhhBBCCCGEyAoNIoQQQgghhBBZoUGEEEIIIYQQIis0iBBCCCGEEEJkhQYRQggh\nhBBCiKzQIEIIIYQQQgiRFRpECCGEEEIIIbJCgwghhBBCCCFEVmQ9iHDOjXLOdYj97uGcm+qc+9R/\nblCCMro75z5xzk12zk1zzl0bS3POuVudc9N9nlecc5v4tI2ccx/68z51zj3hnFstdu6TzrnvnHP/\nOecaxY7Xcc495Zyb5Zyb4px7wTm3aeKedsxWFtmQlJs/1sw594NzbkwJyzjC13+q/zs3luaccwO8\nXGY45+51zq2SpozhaeTzn5f1FC/bnWNpmznn3nHOfeacm+Sc2zpxT+Uqt9h1Ovjv+3odWOqcG5hF\nGcXJriida+mce8PLdKpz7j7nXB2fVt85954v92Pn3IS4/ueD7BIyKAs5Hu+cW+L1ZIpz7pVY2m7O\nuT9jaZNjssrYdmNlXO31sVXs2JvOuQ1Ld/eloyxk58/dzTn3vrN+b1qszB1jMvvUOXe3c66WTyuu\n3zve6+YU59xHzrlusbQKl11CVs45N9g596Vz7nPn3OklLKOdbzt/uET/6Jzb0Dn3WtDDRFqR7dWn\n521fl5DbabH/6VTn3BlZllXXy2By7Fgmue0dk8kUZ8/RD2PpjZ1zI7xsPnXOXe+Prx47b7JP/9s5\n1zh2TxX9jMhJdsXo3AkJ+Sxyzv1fLP0iZ8+PKc65d51z7WNpaduny4P3kiSubN7v6jrnHoqdM9Y5\nt4ZPc66I95TidDBW/tUuD54RCZ07w9/TJ87eBY7JsqwV2qs/fqKzfvML59w9zrmVY2nNnXPjvP5M\nc7G+tSid82ll289FUVTiP6A98FLs93bADGAt/7sBULcE5TSIfV8FmAQc5H8fBEwEVvK/+wEj/ffa\nQJ3YubcDt8V+7wGsCSwHGsWO1wG6xn6fDrwW+90aeCMbWZRGbrHjY4B7gTElLKcj0Mx/bwR8Aezq\nf58EvAys7H8PA85PnH+wP56Uz3KgYRHXfAU41n/vAbxfUXIrQuc2A7YFrgEGZlFOJtll0rnNgJb+\nuwNGAlfEfsd1+WxgbL7IrpzkeHxR+grsBkwuIq1WprYbq+N44GugVez4gcBDFSWrcpTdOsA3wBYx\nmTTy3+uGtut/jwHOKk52QBPgF6Cp/70zsKCyZJdGVseF376us4GtSlDOukA74OSkvvlydgK6JfUt\nU3v1x/Kyr0sjt4ax76sCc4DWWZR3B3BPXD6Z5Jbm/GeAsxP6eE7sd7MizjsPeLqi5FaWssukc2ny\nfgp0j93jbKCe/30MMCkm87Ttk0p+LymBHHN9vzsLGBX7PQy40X8/mWLeU4rSwVgdK/0ZkUZWuwe9\nA9YHFgEbZ1Feuva6EfBdTHeeBk6NpX8IHBL7HfIV90wo034uW0tEb+Cx2O9zsYfoAoAoiv6Iomhp\ncYVEUfRH7Gd9bHAQhWSscdV3zjnshW+eP+/vKIqWAfgRWYPYeURR9GoURT9iD4/49ZZFUfR87NB7\nwIax9E+ANZ1zLYqre44k5YZzrhfWEN4uaSFRFE2Momih//4rMAtTNLB//stRFC33v58DesautxZw\nCXAOCfn438ljOOeaAtsDj/prjgaaOz9LXwFyg4Tsoij6MoqiT7GXgRJTjOwy6dyXURRN898j4INw\nXmT8ATbD4s/7z//OB9nFKRM5elbQleLSoij6J1Pbdc7VA+4ETklTxgSgm3OuYQ51LQvKSnanAY9G\nUfS5L+cfr4tEUbQ0tF3nXF2gHl4+xcgu9OHBMtEE+DZ2zYqWXbKvOxybKCGKop+BJ4CjiiskiqLv\noyj6EPg7TdrPURS9C/yZJq3I9urJ174uqWO/xdIaYpNtJcI5txf2QpzyzMkkt8T56wJ7AiP8702B\n7aMoui1W1sIiTj8RuC+WrzKeETnJLpPOxfGzz02xl1ywtriKvxZAYwrbYJHtMw/eS5KUyfsdJo/6\nzrla3sqwKoXyaEWG95RAUgf9sXx6RiR17rWgd1EUzQN+AJqXpKCi2itwKDYgX+R/D8X3nc65PYGl\nURQVWMxi+YrUufLo57IdRHTGrAaBrYENnXOve5PJNf5lqliccx2dc1MxYb8aRdE4n/QM8IY//h1m\nXbgidl4t59wUYCE263RllvcANlIemzj2Hqa05UFnYnJzzm2MKWG/XAv0JqgdsVE9wEfAgc65hs6W\nQRxO6sNzGHBBYgAXiIDXvOlrgHOuvj/eHJgfRdF/sbxzgbhJszzlBivqXKlJI7uMOhc7rwFm8Rmb\nOP4SMB9r9MGkmA+yi9OZspPjLl5X3nbOHZpI29Sboyc5506NJxTTdm8G7oqi6LvkxaIo+heb+etU\nRvXPls6Ujey2xh6uL3kZ3eEfjEDBcpOPMfksAYbE0tLKLoqixcCpwGTn3DfYC9wJ4bxKkF1nUmW1\nATYTHJhNahsoN2Lt9enY4Xzt6zqT0DFnS0mmYZNNA/wDPiPOlrndCPQpRV2OB8b7CTkwvf3OOTfU\n2bK6551zbdJceyfsBXp8IqnCnxG5yC4LegGPhBfhKIqmYtbBb5xzczGL9Bk+LWP7TFDR7yVJOlM2\n73f3AL9jfdV8zNp6l08r7j0lkNRByK9nRGeKeCb4QUFjbAIjI8W010x959bAj865x/2zZLR/ryxO\n58q8n8t2ELE+sCD2exWgDbA39s/byVe+WPzMcCvspto553bxSe2BbYB1oihaF3gVU8pw3j9RFG0H\nrIXNJmfVWTrnLgU2BS5NJP3g7688SMrtfqCvn10s0aArjnNufayz6R1F0fcAURQNB57HXoZfAz4D\n/vX5TwLmRFH0RhFFbhhF0fbY/68Z1lhLSnnKDVaUXalIJzvMhF2kzvnzamFLI56PDXgBiKKoSxRF\na2OzrJdlUZ3yll2cspLjM0Bz3wZPAgY653bwaR8B60VR1BY4BOgTH2QU1Xadc10wHXw4w3UrUlZJ\nykp2q2D9ZA+sn1sduDokRlE0J4qiNsDamGXskFhaUbJrhL18tIuiaGP8INel7oeqinpWKhLtNT6I\nyNe+bgW5RVE0OoqilkAL4Fjn3OYlKOdO4Hr/IpH1s8XTi5g1AdPbHYDHoihqh70wP+ti67Nj5z2c\neEGBSnhG5Ci7YvGDziOxZ3g4thHWVjeJomgDTD6jfFpJ2mdlvZckKav3u30w3WuGLeH8xfl9r5ne\nUxKk6KB/Mc+nZ0Tafs45ty3wAHB4FEV/laCcXNvrKtgSqqv98/ZFstS5DGQlx2wHEX9ga3cDc4HR\nkS0z+hNbN5nVpgwvvAnAYf7QsZhlIpgkH8JGfcnz/gWGk8YURmyZRBzn3PlAd2wdYtIsVxcoyT89\nFwrk5v/B2wJPOOe+Bm4B9vaz2cXizXwvAdfETVkAURRdE0VR2yiKdgFmAtN9UmfgIOfc135kCjDV\nOdfanxeW7vyFzX6G0fy3wDrOubiebID93wPlKTdYUedyJoPsjiODzvnG9wTwXRRF52S4xH2Y/kJ+\nyC5OmcgxiqKfQtuJomgW1nZ39r9/j5l0vwMeJ83MUJq2uzuwXUw/1wcmOOf2i51WkbJKUlY6OBeb\nXfvVz2I+Tpr+0velT2Brq5NpSdl1AX6OCpdIPYstq9swdlpl6tncRF02IrUNlDmZ2mse93VF6lgU\nRXOxWc/9S1DOLsAA/2x5HNjGOTezpJVwznXGBrAvxg7PBeZFUfSmr8/z2BLkDWPnNcBmlR9IU2yl\nPSOylF1JOByY5vu+QA9galj2AzwI7OT1sNj2WYnvJUnK6v3uZOApP/HxL7Z0pnNIzPCeAhSpg3uQ\nX8+IFXTOr3AYB5wQRdHEEpaTqb1m6jvnAlNievgIJp+VyaxzZd7PZTuImIqN7AOPYS/AzjeYvYFP\nAJxz7Z1zL6cpA+dci2AWc7aGbb9wHmZ+3MPPJAEcAEzzeTcI5n9//mG+TitcIs01z8VmELpEqWsm\nA1vF6lDWFMjNv0A0jaJokyiKNgHOB16MoqiLr2cmua2DLcG5MYqiEYm0Oq7QI8aawMXATf6aPaMo\n2tBfc2N/yrZRFH3izOtGkOlKwBHAFH/eImAy/sXYzyp/G0XR17FLl6fcYEWdi5Pyf85VdmTWuZWx\nF5LFURSlWL2cc2sFmXuOxMsiT2QXp6zkuG7s+1pY5z7F/1470a73j6UV2XajKLo0iqLmMf2cB3SL\noii+LKIiZZWkTGSH9Ze7O+dq+9/dKOwvN3WFXkpqY04Qpvrfmfq9r4E2/n+Bc64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9lpl6yvVVbIEiGEEEIIIYTIikq1RIRRVLtNNwFg1Ms2K9Jj9wMA+P57CxDUpcsJAAy85qyU80Tu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w5swPgGqNh4e5Yzuuu25Z0p5ElgghhBBCCCFEQZTUEhFbQeuuYKtknnmZeWm59ExbF6JfL/NR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fb7b5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# just to check the rescaled data still make sense visually\n",
    "subplot1 = 1\n",
    "plt.figure(figsize=(8, 8))\n",
    "for digit in random.sample(range(len(train3)), 70):\n",
    "    plt.subplot(7, 10, subplot1)\n",
    "    grid_data = train3.iloc[digit, 1:].as_matrix().reshape(28,28)  # reshape from 1d to 2d pixel array\n",
    "    plt.imshow(grid_data, interpolation = \"none\", cmap = \"bone_r\")\n",
    "    title = int(train3.iloc[digit, 0])\n",
    "    plt.title((title, digit), fontsize=8)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    subplot1 += 1\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course the test data set doesn't have labels, so it can be rescaled directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test2 = test.apply(lambda x: x / 255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LYqvGcODe/PdMYJCkoRWtGsPzvB6NHTuWYcOGrVDW0dFBR0dH3ftgZmbWLjo7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\neqoPMb5Q8TpY+Q+htUldOz+tEsuMWtY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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1147f8f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "   pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6\n",
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      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.hist(test2.max(), bins=25)\n",
    "plt.xlim(0, 1.05)\n",
    "plt.title('Max pixel intensity per column \\nfor rescaled test data')\n",
    "plt.xlabel('Pixel intensity')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n",
    "test2.iloc[0:5,0:7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: First pass with MLPClassifier\n",
    "\n",
    "There are several different flavors of neural networks, in addition to the different hyperparameters that can be set within any given approach.  From scikit-learn's [page](http://scikit-learn.org/stable/modules/neural_networks_supervised.html), it sounds as though the first listed kind, a multi-layer perceptron classifier, would be a good place to start."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try a first pass using the a sample with 4,000 rows for training, and 1,000 rows for testing.  I'll just stick with default settings for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.934934934935\n",
      "CPU times: user 36.6 s, sys: 5.31 s, total: 41.9 s\n",
      "Wall time: 22.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "clf = MLPClassifier()\n",
    "clf.fit(train3.iloc[0:4000, 1:], train3.iloc[0:4000, 0])\n",
    "print(clf.score(train3.iloc[4001:5000, 1:], train3.iloc[4001:5000, 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
      "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
      "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
      "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
      "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
      "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
      "       verbose=False, warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "print(clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, 93% accuracy out of the box: not bad.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3: Digging deeper\n",
    "\n",
    "The major choices in a neural network are the number of hidden layers desired, the number of neurons per hidden layer, the choice of backpropagation approach, the activation function applied at each neuron, the size of the steps by which to vary the weights in each iteration, the increase in the score/decrease in the penalty of the difference between the output and the objective function that will be tolerated before you stop iterating, the size of the regularization parameter (to avoid overfitting), and perhaps others that I haven't thought of yet.\n",
    "\n",
    "According to print(clf), the default settings are:\n",
    "\n",
    "activation = ['relu'](https://en.wikipedia.org/wiki/Rectifier_(neural_networks))\n",
    "The other alternatives are 'identity', 'logistic', and 'tanh'.\n",
    "\n",
    "alpha = 1e-4 \n",
    "This is the regularization term: the penalty applied to weights varying too much.  It is essentially attempting to reduce the likelihood of overfitting.  This may be something I could try tweaking, but it's not a high priority\n",
    "\n",
    "batch_size = 'auto'\n",
    "\"Size of minibatches for stochastic optimizers.\"\n",
    "\n",
    "beta_1 = 0.9\n",
    "\"Exponential decay rate for estimates of first moment vector in adam\"\n",
    "\n",
    "beta_2 = 0.999\n",
    "\"Exponential decay rate for estimates of second moment vector in adam\"\n",
    "\n",
    "early_stopping = False  \n",
    "Whether to stop iterating (prior to max_iter or convergence to tol) if score isn't improving.  \"Only effective when solver='sgd' or 'adam'\"\n",
    "\n",
    "epsilon = 1e-8\n",
    "\"Value for numerical stability in adam\"\n",
    "\n",
    "<b>hidden_layer_sizes = (100,) \n",
    "Sounds like 100 neurons in a single hidden layer.  Definitely something to try tweaking.</b>\n",
    "\n",
    "<b>learning_rate = 'constant'  \n",
    "\"Only used when solver='sgd'\"</b>\n",
    "\n",
    "learning_rate_init = 0.001  \n",
    "\"Only used when solver='sgd' or 'adam'\"\n",
    "\n",
    "max_iter = 200  \n",
    "Might be worth playing with, but lower priority than hidden layer size or solver\n",
    "\n",
    "momentum = 0.9  \n",
    "Momentum for gradient descent update; \"only used when solver='sgd'\".\n",
    "\n",
    "nesterovs_momentum = True  \n",
    "\"Only used when solver='sgd' and momentum>0\"\n",
    "\n",
    "power_t = 0.5  \n",
    "When sgd solver is used: \"effective_learning_rate = learning_rate_init / pow(t, power_t)\"\n",
    "\n",
    "random_state = None  \n",
    "Just the seed for a pseudorandom number generator.\n",
    "\n",
    "shuffle = True  \n",
    "Used with solver='sgd' or 'adam' to change the sample order when drawing inputs to train the network.\n",
    "\n",
    "<b>solver = 'adam'  \n",
    "\"The default solver [‘adam’](https://arxiv.org/abs/1412.6980) works pretty well on relatively large datasets (with thousands of training samples or more) in terms of both training time and validation score. For small datasets, however, ‘lbfgs’ can converge faster and perform better.\"  It sounds like adam is a special case of 'sgd', which is just stochastic gradient descent.</b>\n",
    "\n",
    "tol = 0.0001  \n",
    "\"Tolerance for the optimization. When the loss or score is not improving by at least tol for two consecutive iterations, unless learning_rate is set to ‘adaptive’, convergence is considered to be reached and training stops.\"  This could be a biggie; lots of reasons networks may get stuck in local energy minima.  Maybe need an adaptive learning rate, and then playing with the rate_init.\n",
    "\n",
    "validation fraction = 0.1  \n",
    "The proportion of training data to set aside for early stopping.  Only used if early_stopping is true.\n",
    "\n",
    "verbose = False  \n",
    "Whether or not to spit info out to the terminal during training.\n",
    "\n",
    "warm_start = False  \n",
    "If set to True, clf.fit() will start with the output weights matrix of the previous clf.fit() call.  Probably leave off.\n",
    "\n",
    "---\n",
    "\n",
    "I don't want to try playing with all of these parameters, but some of them are definitely targets for seeing if I can improve on the performance above.  My approach will be to leave all options on their default settings until I try permuting a particular option; at that point, I will leave that option on the best setting that I find for all future train/test cycles.  I will stick with 4,000 samples for training and 1,000 for testing.  My priorities for options to try changing are, in order:\n",
    "\n",
    "* Hidden layer size(s)\n",
    "* Solver\n",
    "* Learning rate (if sgd chosen for solver)\n",
    "* Activation function\n",
    "* Alpha\n",
    "* Tol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 4: Hidden layer size\n",
    "\n",
    "\n",
    "It definitely seems that the hidden layer size and overall shape of the network is the first thing to change.  I'm having difficulty interpreting the description of that argument's format from the docs, which say:\n",
    "\n",
    "hidden_layer_sizes : tuple, length = n_layers - 2, default (100,)\n",
    "The ith element represents the number of neurons in the ith hidden layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "[0 1 2 3 4 5 6 7 8 9] 10\n",
      "2 784 100\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "print(clf.n_layers_)\n",
    "print(clf.classes_, clf.n_outputs_)\n",
    "print(len(clf.coefs_), len(clf.coefs_[0]), len(clf.coefs_[1]))\n",
    "print(len(clf.coefs_[0][1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the argument \"hidden_layer_sizes=(100,)\" in the MLPClassifier call that defined clf did indeed end up with just 3 layers: inputs, one hidden layer, and one output.\n",
    "\n",
    "-The inputs are 784 pixels from each digit, and have no weights, activation function, or inputs that went in to generating them.  \n",
    "\n",
    "-There is one hidden layer with 100 neurons, each of which has inputs from all 784 pixels, meaning there are 784 coefficients defining the weights leading from each input node to each neuron.\n",
    "\n",
    "-Put another way, the first layer of the coefficients matrix has 784 lists, representing each pixel of the input image as a node in the input layer.  Each of these 784 lists has 100 floats within it, representing 100 different weights leading from that node to the 100 different neurons of the hidden layer.\n",
    "\n",
    "-The final layer is the output layer, with 10 neurons/nodes, to weigh in on which of the 10 possible digits the image most likely represents.  Each of these neurons receives inputs from each of the 100 neurons of the hidden layer.  So the second layer of the coefficients matrix has 100 lists, and each of those lists has 10 elements, representing the 10 different weights leading from each node of the hidden layer to each node of the output layer.\n",
    "\n",
    "So the total number of nodes in the network should be:\n",
    "1 * 784 + 1 * 100 + 1 * 10 = 894\n",
    "and the number of edges (the coefficients present in the matrix defining the weights of the final model) should be:\n",
    "0 X 784 + 784 X 100 + 10 X 100 = 79,400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79400\n"
     ]
    }
   ],
   "source": [
    "print(len(clf.coefs_[0]) * len(clf.coefs_[0][0]) + len(clf.coefs_[1]) * len(clf.coefs_[1][0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly, when it comes to selecting the network topology, this is an in-depth topic that involves a lot of opinion and empiricism, but [one discussion on stackexchange](http://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw) was readable and offered some concrete tips.\n",
    "\n",
    "One was to take 2/3rds the number of input neurons plust the number of output neurons.  That would be 784/3 + 10: 271.\n",
    "Another was to apply the formula taking a ratio of the number of samples in the training set to the product of an arbitrary scaling factor between 2 and 10 (with 2 being suggested) and the sum of the input and output layer neurons.  This would be 42000/(2 * (784 + 10)) = 26.\n",
    "\n",
    "Both suggested a single hidden layer would probably be enough in most cases.  The discussion did hinge somewhat on tradeoffs between accuracy and computational performance, and the latter is not a consideration in my case because the nature of the Kaggle competition is to run the test data set locally and upload predictions, not to build a model that will either be modified in the future with new data, nor to judge a stream of new test data.  The more important consideration for network topology for me is overfitting or underfitting, rather than processor time, but this can be addressed through the extra factor of alpha, and its effect judged by judicious splitting of the training set.\n",
    "\n",
    "I will simply try a range of hidden layer sizes and evaluating scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.\n",
      "  % (), ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of hidden layer neurons:  10 \tTime taken:  8.49  seconds\n",
      "Number of hidden layer neurons:  20 \tTime taken:  9.44  seconds\n",
      "Number of hidden layer neurons:  30 \tTime taken:  9.284  seconds\n",
      "Number of hidden layer neurons:  40 \tTime taken:  8.681  seconds\n",
      "Number of hidden layer neurons:  50 \tTime taken:  8.822  seconds\n",
      "Number of hidden layer neurons:  60 \tTime taken:  8.952  seconds\n",
      "Number of hidden layer neurons:  70 \tTime taken:  10.621  seconds\n",
      "Number of hidden layer neurons:  80 \tTime taken:  10.215  seconds\n",
      "Number of hidden layer neurons:  90 \tTime taken:  11.309  seconds\n",
      "Number of hidden layer neurons:  100 \tTime taken:  10.377  seconds\n",
      "Number of hidden layer neurons:  110 \tTime taken:  14.359  seconds\n",
      "Number of hidden layer neurons:  120 \tTime taken:  11.048  seconds\n",
      "Number of hidden layer neurons:  130 \tTime taken:  12.061  seconds\n",
      "Number of hidden layer neurons:  140 \tTime taken:  14.159  seconds\n",
      "Number of hidden layer neurons:  150 \tTime taken:  13.183  seconds\n",
      "Number of hidden layer neurons:  160 \tTime taken:  14.103  seconds\n",
      "Number of hidden layer neurons:  170 \tTime taken:  21.39  seconds\n",
      "Number of hidden layer neurons:  180 \tTime taken:  21.411  seconds\n",
      "Number of hidden layer neurons:  190 \tTime taken:  22.293  seconds\n",
      "Number of hidden layer neurons:  200 \tTime taken:  22.292  seconds\n",
      "Number of hidden layer neurons:  210 \tTime taken:  24.358  seconds\n",
      "Number of hidden layer neurons:  220 \tTime taken:  24.65  seconds\n",
      "Number of hidden layer neurons:  230 \tTime taken:  25.314  seconds\n",
      "Number of hidden layer neurons:  240 \tTime taken:  25.884  seconds\n",
      "Number of hidden layer neurons:  250 \tTime taken:  28.367  seconds\n",
      "Number of hidden layer neurons:  260 \tTime taken:  27.832  seconds\n",
      "Number of hidden layer neurons:  270 \tTime taken:  31.848  seconds\n",
      "Number of hidden layer neurons:  280 \tTime taken:  29.862  seconds\n",
      "Number of hidden layer neurons:  290 \tTime taken:  34.612  seconds\n",
      "Number of hidden layer neurons:  300 \tTime taken:  36.41  seconds\n",
      "Number of hidden layer neurons:  310 \tTime taken:  33.05  seconds\n",
      "Number of hidden layer neurons:  320 \tTime taken:  36.139  seconds\n",
      "Number of hidden layer neurons:  330 \tTime taken:  32.924  seconds\n",
      "Number of hidden layer neurons:  340 \tTime taken:  31.078  seconds\n",
      "Number of hidden layer neurons:  350 \tTime taken:  33.74  seconds\n",
      "Number of hidden layer neurons:  360 \tTime taken:  33.08  seconds\n",
      "Number of hidden layer neurons:  370 \tTime taken:  34.583  seconds\n",
      "Number of hidden layer neurons:  380 \tTime taken:  34.091  seconds\n",
      "Number of hidden layer neurons:  390 \tTime taken:  40.5  seconds\n",
      "Number of hidden layer neurons:  400 \tTime taken:  39.742  seconds\n",
      "Number of hidden layer neurons:  410 \tTime taken:  37.646  seconds\n",
      "Number of hidden layer neurons:  420 \tTime taken:  40.998  seconds\n",
      "Number of hidden layer neurons:  430 \tTime taken:  40.949  seconds\n",
      "Number of hidden layer neurons:  440 \tTime taken:  40.403  seconds\n",
      "Number of hidden layer neurons:  450 \tTime taken:  36.439  seconds\n",
      "Number of hidden layer neurons:  460 \tTime taken:  48.3  seconds\n",
      "Number of hidden layer neurons:  470 \tTime taken:  47.986  seconds\n",
      "Number of hidden layer neurons:  480 \tTime taken:  46.576  seconds\n",
      "Number of hidden layer neurons:  490 \tTime taken:  40.85  seconds\n",
      "Number of hidden layer neurons:  500 \tTime taken:  40.717  seconds\n"
     ]
    }
   ],
   "source": [
    "neurons_scores = []\n",
    "sizes = list(range(10, 501, 10))\n",
    "times = []\n",
    "timer = clock()\n",
    "\n",
    "for i in sizes:\n",
    "    j = train3.sample(n=5000)\n",
    "    clf = MLPClassifier(hidden_layer_sizes=(i,))\n",
    "    clf.fit(j.iloc[:4000, 1:], j.iloc[:4000, 0])\n",
    "    neurons_scores.append(clf.score(j.iloc[4001:, 1:], j.iloc[4001:, 0]))\n",
    "    times.append(round(clock() - timer, 3))\n",
    "    print('Number of hidden layer neurons: ', i, '\\tTime taken: ', round(clock() - timer, 3), ' seconds')\n",
    "    timer = clock()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50 50 50\n"
     ]
    }
   ],
   "source": [
    "print(len(sizes), len(neurons_scores), len(times))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vgxUC7AvcAwwAO2VpryK1X04IByvWIHlpWYmo7uy73Do33HBDbvar3vL0Ho6F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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x255e7254ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sPGZmZjYCFCJYAYiIBcCCKq8dVSGt5kymiNi5SVUzMzOzYVSEqctmZmZmVTlY\nMTMzs0JzsGJmZmaF5mDFzMzMCs3BipmZmRWagxUzMzMrNAcrZmZmVmgOVszMzKzQHKyYmZlZoTlY\nMTMzs0JzsGJmZmaF5mDFzMzMCs3BipmZmRWagxUzMzMrNAcrZmZmVmgOVszMzKzQHKyYmZlZoTlY\nMTMzs0JzsGJmZmaF5mDFzMzMCs3BipmZmRWagxUzMzMrNAcrZmZmVmgOVszMzKzQXjDcFbCxraen\nhxUrVjBp0iQmT5483NUxM7MCcsuKDYtVq1YxY8ahTJ06lVmzZjFlyhRmzDiU1atXAymIufzyy+nt\n7R3mmpqZ2XBzy4oNi9mz59LVtRRYCOwPLKGrax7vfOe72HzzzensXLQub1vbLDo6FjJhwoThqq6Z\nmQ0jByu2yfX09GTByELgiCz1CPr6giuvPJKWlq3IBzHt7XNYvPiydce768jMbOxwN5BtcitWrMh+\n2j/3yquBtfT1nU0KYl5NCmLOorNzEdddd13NriMzMxudHKzYgDRjLMmuu+6a/bQk98ql2b/5IOYA\nAI455viyrqO7gYV0dS2lvX1Ow3UxM7Pic7BidelvQOxATJkyhba2WbS0zCMFHvcACxk37jtZjnwQ\ncxUAy5ZdV7XVxQNxzcxGLwcrVpcNB8QOvlWjo2Mhra17A3OBHYC5HHzwfhx44MEbBTEtLScwbdqe\n2ZGVW12WL1/eUD3MzKz4PMDW+lVrQGxn51x6e3sHPNB1woQJLF58Gb29vSxfvnzdYNnVq1fT3j6H\nzs656/K2ts7ilFM+zxvf+EZSq8sRZSWlVpdJkyYN4grNzKzICtOyIul4SXdIekrSUkl71cg7UdIP\nJd0mqU/SGVXy/YekW7Iyb5Q0c+iuYPSqPiB28K0akydPZubMmeuCnVIQ09PTw6JFi+jp6WHx4svY\na6+9KnYdtbScQFvbLM8KMjMbxQoRrEg6HPgqcBKwB3Aj0Clp2yqHbAH8DTgFuKFKmfsCPwK+A7wB\n+AXwc0mvbW7tR7/qA2KHrlUjH8RA5a6j1ta96ehY2PTzm5lZcRSlG2g+cG5EXAAg6RjgUOBo4Cv5\nzBFxV3YMkt5bpcx5wOURUWp1+Zykg4EPAcc1t/qjW2lAbFfXPPr6gtSichUtLSfQ2tp/q0az1kWp\n1nVkZmaj27C3rEjaDJgOXFFKi4gAuoB9BlH0PlkZ5ToHWeaY1UirRjNnEJWr1OpiZmaj17AHK8C2\nQAvwUC4V7TgBAAAgAElEQVT9IWDiIMqdOARljlnVxpLUWgK/2TOIzMxsbCpKN5CNEJMnT66rRWMo\nZhCZmdnYVIRgZSXQB2yfS98eeHAQ5T7YaJnz589n/PjxG6S1t7fT3t4+iOqMLfXMIHKwYmY2MnV0\ndNDR0bFB2po1a4bsfMMerETEc5K6gYOASwAkKXt+9iCKvqZCGQdn6TWdeeaZTJs2bRCntg1nEHld\nFDOz0aTSH/DLli1j+vTpQ3K+YQ9WMmcA52dBy7WkmT5bAucDSDoVeEVEHFk6QNLugIAXA9tlz5+N\niFuyLGcBv5X0EeAyoJ00kPf9m+SKxrjBziAyMzMrKUSwEhEXZWuqnEzqqrkBaIuIh7MsE0mbwZS7\nHojs52nAbOAuYJeszGskzQa+lD16gbdFxM1DeS22XkfHwoqr0XpdFDMzG4hCBCsAEbEAWFDltaMq\npPU7kyki/g/4v8HXzhrhdVHMzKwZChOs2MhWa+G3emcQmZmZVVKEdVZsBBuqhd/MzMxK6mpZkTSv\n3gIjYjAzeGyE2XDht/2BJXR1zaO9fQ6LF182zLUzM7PRoN5uoPm559uRZus8mj3fCniStLmgg5Ux\nwgu/mZnZplBXN1BE7Fx6AJ8mzdbZLSK2joitgd2AZcBnh66qVjT1LPxmZmY2WI2MWTkF+HBE3FZK\nyH6eD3yxWRWz4ttw4bdyXvjNzMyap5Fg5eVU7j5qYePl7W0UKy381tIyj9QVdA+wkJaWE2hr88Jv\nZmbWHI0EK1cA50patx69pOnAt4CuZlXMRoaOjoW0tu4NzAV2AObS2rp34RZ+6+np4fLLL6e3t3e4\nq2JmZgPUyDorRwM/AP4k6bmycjqB9zWrYjYyFH3ht1WrVjF79txsIHDS1pZW0Z0wYcIw1szMzOo1\n4GAlWwJ/lqQpwGuy5FsjoqepNbMRpagLv3lqtZnZyNfwCrZZcOIAxQrLU6vNzEaHAQcrklqA/wQO\nAl5GbtxLRBzYlJqZDVI9U6sdrJiZFV8jLStnkYKVy4CbWL/zsVmhbDi1+oiyVzy12sxsJGkkWHk3\n8K6IWNRvTrNhVJpa3dU1j76+ILWoXEVLywm0tvY/tbrW5oxmZrbpNDJ1+VnAS5PaiNDI1Gpvzmhm\nViyNBCtfBU6QpGZXxqzZSlOre3p6WLRoET09PSxefFnNacsbziC6G1hIV9dS2tvnbKpqm5lZmUa6\ngd4M/AswU9JfgefKX4yIdzSjYmbNVO/Uas8gMjMrnkaClUeBnzW7ImZF4BlEZmbF08iicEcNRUXM\nisAziMzMiqfhReEkbQdMzZ7elq1sazaiDXYGkZmZNd+AB9hKepGk7wMPkP78XALcL+l7krZsdgXN\nNrWRsjmjmdlY0UjLyhmkPzffCvw+S3szcDZpptCxzama2fAo+uaMZmZjTSPByjuBwyLit2VpiyQ9\nBVyEgxUbJYq6OaOZ2VjTSLCyJfBQhfS/Za+ZDRuvOmtmNvo0sijcNcAXJP1DKUHSC4GTstfMNjmv\nOmtmNno1EqycAOwH3CvpCklXAPcA+2avmW1yRVp1tqenh8svv5ze3t5Nfm4zs9GokXVWbpI0mbQI\nxWuy5A7ghxHxVDMrZ1aPoqw6u2rVKmbPnpvVJWlrm0VHx8Kay/ubmVltjbSsEBFPRsR3IuKj2eO7\nDlRsuNSz6uymUKTWHTOz0aSRdVY+KWmjVWwlHS3p482plln9Nlx1ttymW3W21LrT13c2qXXn1aTW\nnbPo7FzkLiEzs0FopGXlg8DNFdL/ChwzuOqYDVxp1dmWlnmkVo17gIW0tJxAW9umWXW2KK07Zmaj\nUSPBykTSNOW8h4GXD646Zo0Z7lVni9C6Y2Y2WjWyzso9pNlAd+TS9wPuH3SNzBowmFVnm7E2i/cU\nMjMbOo0EK98BviZpM+A3WdpBwFdIy+2bDZuBrDrb7Nk7HR0LaW+fQ2fn3HVpra2zvKeQmdkgNRKs\nnA5sAywANs/SngZOi4hTm1Uxs6G24eyd/YEldHXNo719DosXXzbg8rynkJnZ0BjwmJVIPg5sB+wN\n7A5sHREnD6Yiko6XdIekpyQtlbRXP/nfIqlb0tOSeiQdWSHPf0m6VdKTku6WdIakLQZTTxsdhnL2\nzuTJk5k5c6YDFTOzJmlonZXMRGBrYEVEPCNJjRYk6XBSF9JJwB7AjUCnpG2r5N8JuBS4ghQsnQV8\nV9LBZXlmA6dmZb4GOBp4F/ClRutpo4dn75iZjRyNrLOyTbbEfg+wiPUzgL4nqdExK/OBcyPigoi4\nlTQF+klSgFHJscDtEXFiRNwWEd8ELs7KKdkH+F1EXBgRd0dEF/Bj4I0N1tFGEc/eMTMbORppWTkT\neI40P/TJsvQLgRkDLSwbqDud1EoCpK4moIsUcFSyd/Z6uc5c/j8A00vdSZJ2AWYBAx+MYKNOEdZm\nMTOz+jQywPYQoC0i7s31/PQCOzZQ3rZAC/BQLv0hYGqVYyZWyf9SSVtExDMR0ZF1I/0u66JqAc6J\niNMaqKONQp69Y2Y2MjQSrLyIDVtUSrYGnhlcdZpH0luAT5G6lK4FJgFnS3ogIr5Y69j58+czfvz4\nDdLa29tpb28fotracPDsHTOzxnR0dNDR0bFB2po1a4bsfEo9LgM4QFoEdEfEZyX9HXg9cBdpPMi4\niDhsgOVtRgp+3hkRl5Slnw+Mj4h/r3DMVVkdPlKW9p/AmRExIXu+BFgaESeW5TmCNDbmxVXqMg3o\n7u7uZtq0aQO5DLN+NWPxOTOzolq2bBnTp08HmB4Ry5pZdiNjVk4EPiDpctI6K18BbiJNqxjwRoYR\n8RzQTVpYDoCs2+Yg0riTSq4pz585JEsv2RJ4PpdnbVn5ZpvEqlWrmDHjUKZOncqsWbOYMmUKM2Yc\nyurVq4e7amZmI0Ij66zcBEwBfgf8gtQt9FNgj4hYUevYGs4A3i/pPZJeA5xDCjbOB5B0qqQflOU/\nB9hF0mmSpko6DjgsK6fkl8Bxkg6XtFM2rflk4JIYaHOS2SBsuPjc3cBCurqW0t4+Z5hrZmY2MjQy\nZoWIWEMT1yuJiIuywbAnA9sDN5AG8T6cZZlIWrWrlP9OSYeSZibNA+4F3ptNTy45hdSScgrwStJG\ni5cAn2lWvc36U1p8LgUqR2SpR9DXF3R2zqW3t9ddQmZm/RhwsCJpBvB4RPwue3488H7gZuD4iGio\nbTsiFpCW8K/02lEV0paQpjxXK68UqJzSSH3MmqGexeccrJiZ1dbImJXTgZcCSHodqetlEbAzG3bD\nmI15XnzOzGzwGukG2pnUigLwTuCXEfGpbCbNouqHmY09pcXnurrm0dcXpBaVq2hpOYHWVi8+Z2ZW\nj0ZaVp4lDX4FaAV+lf28iqzFxczW6+hYSGvr3sBc0sLPc2lt3duLz5mZ1amRlpXfAWdI+j1pn53D\ns/QppIGuZlbGi8+ZmQ1OI8HKh0gDYQ8Djo2I+7L0mcDiZlXMbLSZPHmygxQzswYMOFiJiLuBf62Q\nPr9CdjMzM7NBaWTMipmZmdkm42DFzMzMCs3BipmZmRWagxUzMzMrtLqDFUl3S/qGpEMkNbSnkJmZ\nmdlADaRlZS7wDPBNYKWkCyUdIWmroamamZmZ2QCClYi4KiI+GhGTgf1IOyN/GHhQ0m8k/ZekXYaq\nomZjVU9PD5dffjm9vb3DXRUzs2HR0JiViPhrRJwaEXsDOwEdwEHATZJuknRoE+toNiatWrWKGTMO\nZerUqcyaNYspU6YwY8ahrF7d0MbmZmYj1qAH2EbEgxHxnYh4K7At8FlSd5GZDcLs2XPp6loKLATu\nBhbS1bWU9vY5w1wzM7NNq6kDZSPiSeBnzSzTbCzq6emhs3MRKVA5Iks9gr6+oLNzLr29vV6638zG\nDE9dNiugFStWZD/tn3vlAACWL1++SetjZjacHKyYFdCuu+6a/bQk98pVAEyaNGmT1sfMbDg5WDEr\noClTptDWNouWlnmkrqB7gIW0tJxAW9ssdwGZ2ZjScLAiaZKkNkkvzJ6redUys46OhbS27k1a4mgH\nYC6trXvT0bFwmGtmZrZpDXiAraRtgAuBA4EAJgO3A9+TtDoiPtrcKpqNTRMmTGDx4svo7e1l+fLl\nTJo0yS0qZjYmNTIb6EzgedKfereUpV8InAE4WDFrosmTJztIMbMxrZFg5RCgLSLuzfX89AI7NqVW\nZtavnp4eVqxY4RYXMxv1Ghmz8iLgyQrpW+PF4MyGnFe2NbOxppFg5WrgPWXPQ9I44ETgyqbUysyq\n8sq2ZjbWNNINdCJwhaQ9gc2BrwD/SGpZ2a+JdTMrrGZ3wdRbnle2NbOxaMAtKxFxEzAF+B3wC1K3\n0E+BPSJiRa1jzUa6ZnfBDLQ8r2xrZmPRgIIVSS+Q9DngJRHxpYh4V0TMiojPRMQDQ1RHs8JodhfM\nQMvzyrZmNhYNKFiJiOdJ3UBN3QDRbCQodcH09Z1N6oJ5NakL5iw6OxfR29s75OV5ZVszG4saGWB7\nBaU2Z7MxpNldMI2W55VtzWysaaSF5HLgy5JeB3QDT5S/GBGXNKNiZkWzYRfMEWWvNNYF02h5XtnW\nzMaaRoKVBdm/H6nwWgAtjVfHrLhKXTBdXfPo6wtSC8hVtLScQGvrwLtgBlueV7Y1s7GikdlA42o8\nHKjYqNbsLhh36ZiZ9a/hXZebTdLxku6Q9JSkpZL26if/WyR1S3paUo+kIyvkGS/pm5Luz/LdKmnG\n0F2FjXalLpienh4WLVpET08PixdfxoQJEwpRnpnZaNTQrB5JBwAfA3bLkm4GTo+Iqxss73Dgq8AH\ngGuB+UCnpCkRsbJC/p2AS0ldUrOBVuC7ku6PiF9neTYDuoAHgXcA95P2Lnq0kTqalWt2F4y7dMzM\nqhtwy4qkOaQg4Eng7OzxFGlV29kN1mM+cG5EXBARtwLHZOUfXSX/scDtEXFiRNwWEd8ELs7KKXkv\nsBXw9ohYGhF3R8TVEfGXButoZmZmw6CRbqBPAydGxOERcXb2OBz4BPDZgRaWtYBMJ02JBiAighQQ\n7VPlsL2z18t15vK/FbgGWCDpQUl/kfTJbB8jMzMzGyEa+eLeBfhlhfRLgJ0bKG9b0gyih3LpDwET\nqxwzsUr+l0raoqye/0G6xpnAycBHScGWmZmZjRCNjFm5BzgIyK9Y1Zq9VhTjSAHMB7KWmuslvYo0\n1uaUYa2ZmZmZ1a2RYOWrwNmS3gD8IUvbD/hP4IQGylsJ9AHb59K3Jw2OreTBKvkfi4hnsucPAM9m\ngUrJLcBESS/Itg6oaP78+YwfP36DtPb2dtrb22teiJmZ2VjQ0dFBR0fHBmlr1qwZsvNpw+/yOg+S\n/p3UpVKaDXQLaTbQLxqqhLQU+GNEnJA9F2lXt7Mj4vQK+b8MzIyI3cvSfgRsFRGzsudfAtojYpey\nPCcA/y8iXlWlHtOA7u7ubqZNm9bIpZiZmY1Jy5YtY/r06QDTI2JZM8tuaOpyRPwM+FkT63EGcL6k\nbtZPXd4SOB9A0qnAKyKitJbKOcDxkk4Dvk/qljoMmFVW5reyPGcDXwemAJ8EvtbEepuZmdkQG3Cw\nki3WNi4i/phLfxPQFxF/GmiZEXGRpG1Jg2C3B24A2iLi4SzLRNKWtKX8d0o6FDgTmAfcC7w3IrrK\n8twrqS3LcyNwX/bzVwZaPzMzMxs+jbSsfBM4tUL6K4GPA29qpCIRsYD1+w7lXzuqQtoS0pTnWmX+\nEdi3kfqYmZlZMTQydfm1pJaPvOuz18zMzMyappFg5Rkqr3/ycqDqDBszMzOzRjQSrPwKOFXSurm9\nkrYC/hv4dbMqZmZmZgaNjVn5GLAEuEvS9VnaG0gLsM1tVsXMzMzMoIFgJSLuk/R64Ahgd9ImhucB\nHRHxXJPrZ2ZmZmNco+usPAF8u8l1MTMzM9vIgMesSDoyW+Ok9Pwrkh6V9AdJOza3emZmZjbWNTLA\n9lOkrh8k7QN8CDiRtMfPmc2rmpmZmVlj3UCvZv2Oy28HLo6Ib0v6PfDbZlXMzMzMDBprWXkc2Cb7\n+RDWT1d+GnhhMyplZmZmVtJIy8qvge9m05anAIuy9H8E7mxSvczMzMyAxlpWjgeuAbYD3hkRj2Tp\n04GOZlXMzMzMDBpbZ+VR0qDafPpJ5c8lLQA+FxErG6+emZmZjXWNtKzUaw7w0iEs38zMzMaAoQxW\nNIRlm5mZ2RgxlMGKmZmZ2aA5WDEzM7NCc7BiZmZmheZgxczMzAqtrmBF0k8lvTT7+T2StqjjsIXA\nY4OpnJmZmVm9LSv/Crwo+/k8YHx/B0TEsV5jxczMzAar3kXhbgVOlXQlaUryuyRVbDWJiAuaVTkz\nMzOzeoOVY4AzgEOBAL6Y/ZsXgIMVMzMza5q6gpWI+AOwN4CktcCUiPjbUFbMzMzMDBqbDbQz8HCz\nK2JmZmZWSV0tK5Jen0t6nVR5Nf2I+PNgK2VmZmZWUu+YlRtI41Gq7fdTei2AlibUy8yGSE9PDytW\nrGDSpElMnjx5uKtjZtaveoOVnYe0FmY25FatWsXs2XPp7Fy0Lq2tbRYdHQuZMGHCMNbMzKy2egfY\n3jXUFTGz5qnUejJ79ly6upaS1mvcH1hCV9c82tvnsHjxZcNZXTOzmgY8wFbSJyUdVSH9aEkfb061\nzKwRq1atYsaMQ5k6dSqzZs1iypQpzJhxKNdddx2dnYvo6zsbOAJ4NXAEfX1n0dm5iN7e3mGuuZlZ\ndfV2A5X7IHB4hfS/Aj8GThtUjcysYdVaTx5++Lgsx/65Iw4AYPny5URE1bEsHudiZsOpkWBlIlBp\njZWHgZcPrjpm1qienp5sPMpCUusJpNaTYNmyudnzJWWvAVwFwKmnnsbVV1+1LrU0liUiPM7FzIZd\nI+us3APsVyF9P+D+wVXHzBq1YsWK7KfKrSfTpu1FS8s8UjBzD7CQlpYT2Gab7fnDH/6Spd8NLKSr\naynt7XNyLTUbvmZmtqk0Eqx8B/iapKMk7Zg9jgbOzF5riKTjJd0h6SlJSyXt1U/+t0jqlvS0pB5J\nR9bI+25JayX9tNH6mRXdrrvumv20JPdKajE599wFtLbuDcwFdgDmss8+/8QjjzxUdSyLx7mYWRE0\n0g10OrANsADYPEt7GjgtIk5tpBKSDge+CnwAuBaYD3RKmlJp52ZJOwGXZnWYDbQC35V0f0T8ukLe\n09n4N7jZqDJlyhTa2mbR1TWPvr4gtahcRUvLCbS2zmLPPfdk8eLL6O3tZfny5UyaNInly5cza9YS\nqrXGJNXHuXj8ipltCgNuWYnk48B2pP2Cdge2joiTB1GP+cC5EXFBRNxK2jjxSeDoKvmPBW6PiBMj\n4raI+CZwcVbOOpLGkdqvPwfcMYj6mY0IHR0LN2o9aW3dm46OhevyTJ48mZkzZzJ58uR+W2NqvTZp\n0qSm1t3MrJpGWlYAiIjHgesGWwFJmwHTgf8uKzskdQH7VDlsb6Arl9ZJ6ooqdxLwUEScJyn/56HZ\nqDNhwoSNWk9qtX701xoDVH3NrSpmtqk0HKw00bakJfofyqU/BEytcszEKvlfKmmLiHhG0puBo0gt\nP2ZjyuTJk+sOJjo6FtLePofOzrnr0lpbZ61rjan1mpnZplCEYKXpJL0YuAB4f0SsHu76mBVZf60x\n/bXUeA0WMxtqRQhWVgJ9wPa59O2BB6sc82CV/I9lrSqvAXYEfqn120OPA5D0LDA1IqqOYZk/fz7j\nx4/fIK29vZ329vY6LsdsZKrVGlPpNe81ZDZ2dXR00NHRsUHamjVrhux8ioghK7zuSkhLgT9GxAnZ\nc5EWdTg7Ik6vkP/LwMyI2L0s7UfAVhExS9IWwK65w74EvBiYB/RGxPMVyp0GdHd3dzNt2rQmXZ3Z\n6DRjxqF0dS3Npjan1XJbWubR2ro3ixdf5hYXszFm2bJlTJ8+HWB6RCxrZtlFaFkBOAM4X1I366cu\nbwmcDyDpVOAVEVFaS+Uc4HhJpwHfBw4CDgNmAUTEM8DN5SeQ9Gh6KW4Z8qsxG+VqrZbb2TmX/fd/\nS8UVcd3iYmaNaGRRuKaLiIuAjwEnA9cDrwfaIuLhLMtE0opUpfx3AoeS1le5gRTcvDci8jOEzGwI\n1F4tdxy///2fqbXqbU9PD5dffrkXljOzuhSlZYWIWEBa5K3Saxvt8hwRS0hTnustf6MyzKwxG67P\nUr7X0I+Btaxd+3Uqtbhcd911fPazn/c4FzMbkEK0rJjZyFJanyW/19C4caW1ISuvenvMMcfX3GvI\nLS5mVomDFTNrSKXVcvfdtzQwvfKqt8uWXVd1r6H9938LU6dOZdasWUyZMoUZMw5l9WqvPGBmDlbM\nrEGl9Vl6enpYtGgRPT09XH31VRVbXFpaTmDatD2zIxsb52JmY5eDFTMblPK9hqD6/kTnnFMakpZv\ndcmPc/Huzma2ocIMsDWz0aHWiriV9iEaN+5k1q4F7+5sZtW4ZcXMhkS+xQUaG+dSz+7OHphrNrq5\nZcXMNplqrS5pNdyB7+7sJf/Nxga3rJjZJlfvOJfy3Z0rtZ7Mnj235lRoMxsd3LJiZsOu1jiXaq0n\np5zy+ZpL/vf29nqsi9ko4WDFzAqj0u7OG7aepA0Tu7rm8fDDx2U5PDDXbLRzsGJmhVVrw8Rly+Zm\nz/NL/tc/MNfMRgYHK2ZWWLU3TIRp0/bixhsHPjAXUiC0YsWKDbqczKyYPMDWzAprww0Ty6XWk3PP\nXdDvwNy8VatWMWPGoV7a32wEcbBiZoVVbcPElpYTaGubxZ577rnRkv+LF19Wc9rySJlB5LVjzNZz\nN5CZFVpHx0La2+fQ2Tl3XVpr66wNWk8qDcytpNYYmKLMIPLaMWYbc8uKmRVapQ0T+2s9Kcm3TvQ3\nBmb58uVNrHljRkrLj9mm5JYVMxsR6m09gdprsyTFmEGUH+Q72JYfDxq20crBipmNOtXWZoHPV9xM\nsd4ZRLUMJFCoFkwdffSR2bPaa8fkz+WuIxvtHKyY2ajSX+vEddddB5xUcwxMMwKPWoFCtWDqiSce\nz3JUbvnZdtttmTHj0I3O9dxzz3HVVd0bldfePofFiy+rWX+zESEi/MgewDQguru7w8xGpkWLFgUQ\ncHdAlD3uDiAWLVoUERE9PT2xaNGi6OnpWXfsI488Em1ts7Lj06OtbVasWrWq6vna2mZFS8vWAQuz\ncyyMlpato61tVsX8t912W1b2wlz9/jeA+Od/PiAr73+z8v53XXmVzjVu3Pia5ZWu77bbbtvoemul\nmw1Ud3d36f/NtGj293OzCxzJDwcrZiNff8FArS/lZgcelc7VXzB14YUXVgyYrr322irn+lhD5a1Y\nsWLAgZlZLUMZrHg2kJmNKv2tzVKtW6fUfdTXdzapC+bVpO6js+jsXFRxvZNGZhf1t9DdHnvsUXH2\n08qVK6uc619rlveNbyyoOLvojW/c17OObMTwmBUzG3XqWZslr57AIyI2GMuyYeBRfXZR+RiYUjDV\n3yDf/Oyn6ue6BxhHS8vG5e2zz/5cffVVbDx+534eeeTECunFWW+mHp79NIY0u6lmJD9wN5DZqFJp\nXEo19YwloUKXyfquo43HmFQbA3P77bc31AVT7VwHHnhwxfIuvPDCKl1EP6hrXE9RNTK2yIaex6w4\nWDGzTaBaMLDNNttXHctSClhqBzKVx8AMJJiKiJrnqlRe9QDsKw2P6ymCgY4tsk3DwYqDFTPbBCoF\nA29+8/51fbHXHygMPiAYSJDTfwC2cYvQYAz17KKhvK82OB5ga2a2CVRa2v9Tn/pE9mrtQbSTJ09m\n5syZ68ZODOXS/vlz1dLRsbDiztTXXXfNgHesrmVT7WY9ErZMsObzAFszs5zywa2RWl0Z6BL99Q6+\nHWqlAKy3t5fly5dvMBi1Wnojqi101+yF6YpyX23TcrBiZlZDvbN3mnXcUKm2t1KtPZdqzbYpfy0i\nBr2bdbVz5dOLdl89I2kTaXa/0kh+4DErZlZBfwNbm33ccKs126bSa9Om7VnX7KJK41mqnavWonXD\ncV/zdfeMpI15gK2DFTMrgIHO3hnsccOl1mybRpb8v/baawc8Y6rWDKySTXFfqwUlBx54sGck5ThY\ncbBiZrZJ9Dfbpvpr4wa0p1FLy9Y1ZloVZ2r1YPZjGms8G8jMzDaJ/mbbVH9tLbvvviv52UWnnPL5\nqtsY/O53S6qUt33NOmyqGT/VtmBYu/b9hajfWFKYYEXS8ZLukPSUpKWS9uon/1skdUt6WlKPpCNz\nr79P0hJJq7LHr/sr08xsrOtv76Jar/34xz8cwJ5G5cFPvryHap5nU834qR641d6PyTOSmq8Qs4Ek\nHQ58FfgAcC0wH+iUNCUiVlbIvxNwKbAAmA20At+VdH9E/DrLdgDwI+APwNPAJ4BfSXptRDwwtFdk\nZjYy9TfbBqhrX6OS/qYa//M/H8Af/pAv78tstdX2PPropp3xk5/Z08h+TMMxI2lMaHa/UiMPYClw\nVtlzAfcCJ1bJfxrw51xaB7CoxjnGAWuAOTXyeMyKmY15tWbbNDITp9b+SdXKa3T/pEbUmtkz0P2Y\nPBtolA6wBTYDngP+LZd+PvCzKsdcBZyRS/tPYHWN87wEeBKYVSOPgxUzs0yt2TYDmYlTT4BTrbxG\nZ/xUW/a/Unqt2U8D3Y9pLBvtwcrLgbXAm3LppwHXVDnmNuDjubSZQB+wRZVjFgC9wOY16uJgxcxs\niAznVONq67Zce+21dc3scVDSv6EMVgoxZmWoSfoE8C7ggIh4drjrY2Y2FtVaLbdZqi37/8Y37suj\njz63UfrDDx+XHVl9Zk+p3h6LMnyKEKysJLWIbJ9L3x54sMoxD1bJ/1hEPFOeKOljwInAQRHx13oq\nNH/+fMaPH79BWnt7O+3t7fUcbmZmw6A01XjjZf/v55FHTqyQHixbNjd77r2GBqKjo4OOjo4N0tas\nWTNk5xv2YCUinpPUDRwEXAIgSdnzs6scdg2p26fcIVn6OpJOBD4JHBIR19dbpzPPPJNp06bVm93M\nzETvVr0AABBhSURBVAqg+lTj2uu2TJu2Fzfe6Jk9A1HpD/hly5Yxffr0ITlfUdZZOQN4v6T3SHoN\ncA6wJWmQLZJOlfSDsvznALtIOk3SVEnHAYdl5ZAd83HgZOBo4G5J22ePF22aSzIzs02p+hoxtddt\nOffcBbS27k1+QbuOjoVDVVUboGFvWQGIiIskbUsKLrYHbgDaIuLhLMtE0tKBpfx3SjoUOBOYR5rm\n/N6I6Cor9hjSTKOLc6f7QnYeMzMbRaqvEVN73ZY999yTxYsvo7e3l+XLl3sH5QJSpFkwBkiaBnR3\nd3e7G8jMbARavXo17e1zsrErSVvbLL71rW9w7LEf2ii9o2MhEyZMGI6qjjpl3UDTI2JZM8suRMuK\nmZlZM0yYMKFqK4lbT0YuBytmZjbqVJtq7CnII1NRBtiamZmZVeRgxczMzArNwYqZmZkVmoMVMzMz\nKzQHK2ZmZlZoDlbMzMys0BysmJmZWaE5WDEzM7NCc7BiZmZmheZgxczMzArNwYqZmZkVmoMVMzMz\nKzQHK2ZmZlZoDlbMzMys0BysmJmZWaE5WDEzM7NCc7BiZmZmheZgxczMzArNwYqZmZkVmoMVMzMz\nKzQHK2ZmZlZoDlbMzMys0BysmJmZWaE5WDEzM7NCc7BiZmZmheZgxczMzArNwYqZmZkVmoMVMzMz\nKzQHK2ZmZlZoDlbMzMys0BysmJmZWaE5WDEzM7NCK0ywIul4SXdIekrSUkl79ZP/LZK6JT0tqUfS\nkRXy/IekW7Iyb5Q0c+iuYHTp6OgY7ioUgu/Der4Xie/Der4Xie/D0CtEsCLpcOCrwEnAHsCNQKek\nbavk3wm4FLgC2J3/3969R1lVnncc//7wXhVNvRFaIgoJoigGWEZQ0ViFFJearLioSY2miStVk3hJ\nGi5pA0bTipelUSuJqy6viUmNjZfEpBIi0QSxVMcbgmjFWyogNxGFKJenf7zvdDbbOTMDzMw5Z87v\ns9ZecPZ+9nve95k5c56z97vPhmuBmySdWIgZBdwJ/BtwOHAfcK+kg7tsID2IX3yJ89DCuUichxbO\nReI8dL2aKFaAi4AbI+L2iHgeOAdYC3ypQvy5wKKImBARCyPiBuDu3E6z84FfR8TVOWYK0AR8reuG\nYWZmZp2t6sWKpB2A4aSjJABERAAzgZEVdjsyby96sBQ/sgMxZmZmVuOqXqwAewPbAUtL65cCfSrs\n06dCfG9JO7UTU6lNMzMzq0HbV7sDNWZngAULFlS7H1W3evVqmpqaqt2NqnMeWjgXifPQwrlInIek\n8N65c2e3XQvFynJgI7Bfaf1+wJIK+yypEP92RLzXTkylNgH6A5xxxhlt97hBDB8+vNpdqAnOQwvn\nInEeWjgXifOwmf7Ao53ZYNWLlYhYL+kJ4K+A+wEkKT++rsJuc4DyZchj8vpiTLmNE0sxZQ8Cfwu8\nAvypYyMwMzMz0hGV/qT30k6lNJe1uiSNB24lXQU0l3RVz2nAQRGxTNJlQN+IOCvH9weeBaYDN5OK\nku8D4yJiZo4ZCfwOmAw8AHwOmAQMi4j53TQ0MzMz20ZVP7ICEBF35e9UuYR0quYpYGxELMshfYB+\nhfhXJJ0EXEO6RPmPwJebC5UcM0fS54F/zsuLwKkuVMzMzOpLTRxZMTMzM6ukFi5dNjMzM6uoIYsV\nSd+WNFvSu5JWVojpJ+mBHLNE0hWSepViDpP0SL730KuSvtU9I+haW3qfpnoj6RhJ90v6X0mbJJ3S\nSswlkt6QtFbSbyQNLG3fSdINkpZLWiPpbkn7dt8otp2kyZLmSnpb0lJJ90j6WCtxPToXks7J9w5b\nnZdHJX2qFNOjc9AaSZPy6+Pq0voenwtJU/PYi8v8UkyPzwOApL6S7sjjWJtfK8NKMV2ei4YsVoAd\ngLuAH7S2MRclvyLN6TkSOAv4ImlOTXPM7qQZzy8Dw4BvARdLOrsrO97VtIX3aapTu5LmRZ0HfOA8\nqKSJpNsyfAU4AniXlIMdC2HfB04CPguMBvoC/9G13e50xwDXA58ATiC9LmZI2qU5oEFy8TowkfQ6\nHg48BNwnaTA0TA42kz+gfIX0+i+ub6RczCPNoeyTl6ObNzRKHiTtCcwG3gPGAoOBbwKrCjHdk4uI\naNiFVISsbGX9XwPrgb0L6/4+/4C2z4/PJX1HzPaFmMuA+dUe1zbm5DHg2sJjkSYwT6h237povJuA\nU0rr3gAuKjzuDawDxhcevwd8phAzKLd1RLXHtA252DuP4WjnghXA3zViDoDdgIXA8cAs4OpG+30g\nfVhramN7o+RhGvBwOzHdkotGPbLSniOBZyNieWHdg8AewCGFmEciYkMpZpCkPbqnm51LW3efph5F\n0gGkT1HFHLwN/BctORhBOupWjFkIvEZ952lP0pGmldCYuZDUS9LpwJ8BjzZiDoAbgF9ExEPFlQ2Y\ni4/mU8UvSfqRpH7QcHk4GXhc0l35VHFT8exBd+bCxUrrKt1XqHlbR2Pqzdbcp6mn6UN6w24rB/sB\n7+cXZaWYuiJJpEO1f4iWy/sbJheShkhaQ/oEOJ30KXAhDZQDgFyoHU76fqqyRsrFY6RT/2NJ3/91\nAPCIpF1prDwcSDqLsJD0xas/AK6T9IW8vdtyURPfs9IZlL44bmIbIQEMjogXuqlLZvVkOnAwcFS1\nO1IlzwNDSUdPTwNulzS6ul3qXpL+klSwnhAR66vdn2qKiOI3sM6TNBd4FRhP+l1pFL2AuRHxnfz4\naUlDSAXcHd3dkZ7iKuCgNpbBwKIOtlXpvkLN2zoaU2+25j5NPc0S0jydtnKwBNhRUu82YuqGpH8F\nxgHHRcTiwqaGyUVEbIiIRRHxZET8I2li6QU0UA5Ip4D3AZokrZe0HjgWuEDS+6RPwo2Si81ExGrg\nBWAgjfU7sRgo39l3AfCR/P9uy0WPKVYiYkVEvNDOsqH9loB0/6BDS1fAjAFWA/MLMaMlbVeKWZh/\nsetO/jTVfJ8mYLP7NHXqTalqVUS8THoBFXPQm3TFTHMOngA2lGIGkV7Abd17qubkQuVU4JMR8Vpx\nW6PloqQXsFOD5WAmcCjpNNDQvDwO/AgYGhGLaJxcbEbSbqRC5Y0G+52YTZoMWzSIdJSpe/9GVHu2\ncTUW0lf3DwWmkAqQ5hfmrnl7L9Inq18Dh5HOWy4FLi200Zs0C/o20uHzvwHeIX3tf9XHuA25GQ+s\nBc4kHZG6kXRlxD7V7lsnjnHX/PM+nDQj/cL8uF/ePiGP+WTSH+97Sbdr2LHQxnTSZevHkT6RzgZ+\nX+2xbWEeppOucDuG9Cmnedm5ENPjcwH8S87B/sAQ0lV9G4DjGyUHbeSmfDVQQ+QCuJJ0ie3+wCjg\nN/k9YK8Gy8MI0jyuycAA4PPAGuD07v6dqHoyqvQDuIV0uqO8jC7E9AN+SSpAlgKXA71K7QwBHia9\nub8G/EO1x9ZJ+TmPdOfpdaTKd0S1+9TJ4zuWVKSUf/43F2IuJhWja0lXeQ0stbET6TtKlucX78+A\nfas9ti3MQ2s52AicWYrr0bkAbiKdIl5H+pQ4g1yoNEoO2sjNQxSKlUbJBfAT0lc2rMt/2+8EDmi0\nPORxjAOeyeN8DvhSKzFdngvfG8jMzMxqWo+Zs2JmZmY9k4sVMzMzq2kuVszMzKymuVgxMzOzmuZi\nxczMzGqaixUzMzOraS5WzMzMrKa5WDEzM7Oa5mLFzMzMapqLFTNrl6T9JW2SdFi1+9JM0iBJcySt\nk9RU7f6YWddxsWJWByTdmouFCaX1p0ra1E3dqLV7c3yXdO+uj1K4o6uZ9TwuVszqQ5BuqjZR0h6t\nbOsO6vQGpR22YfcBwB8i4o8Rsaqz+tQZtnFcZlbiYsWsfswk3RX425UCJE2V9GRp3QWSXi48vkXS\nPZImS1oiaZWkf5K0naQrJK2Q9LqkL7byFIMlzc6nXp6VNLr0XEMk/UrSmtz27ZL2KmyfJel6SddI\nWgb8Z4VxSNKU3I8/SXpS0tjC9k3AMGCqpI2SplRoZ5akayVdnse1WNLUUswekm6S9Kak1ZJmFk93\n5Xz9vLTPNZJmtTcuSf0k3ZfzsVrSv0vat7Df1Dy2MyS9LOktST+RtGsh5jRJz0haK2m5pBmSdmlt\nvGY9lYsVs/qxkVSofF1S3zbiWjvSUl53PPBh4BjgIuAS4JfASuAI4IfAja08zxXAlcDhwBzgF5I+\nBOlNH/gt8ASpkBgL7AvcVWrjTOA9YBRwToUxXJj79Q3gUNJt5++XNCBv7wPMB67K47iqQjvNz/dO\nHtcEYIqk4mmju4G9cn+HAU3AbyXt2Uab8MGcbjYuSQLuB/Yk5fkE4EDgp6X9BgCnAuOAk4BjgUkA\nkvoAdwI3AQflbT+nC45ymdUyFytmdSQi7gOeIs3X2BYrIuL8iHgxIm4FFgK7RMS0iHgJuAx4Hzi6\ntN/1EXFvRCwEzgVWA1/O274GNEXEd3K7TwNnA5+UNLDQxosRMSnHvFihf98EpkXEz3LcpDzuC3Me\n3gQ2AO9ExJsRsbaNsT4TEZdGxEsRcQfwOHmOi6SjgRHA+Ih4MsdMAN4CTms7hR9QHtcJwCHA5yLi\nqYj4b1JBc5yk4YX9BJwVEQsiYjZwBy1zcD4MbAfcExGvRcRzEfHDdsZr1uO4WDGrPxOBsyQN2oY2\nnis9Xgo82/wgIjYBK0hHRooeK8RsJL3xD86rhgLH51MeayStARaQjkAMKLTxRFsdk7Q70Bd4tLRp\nduG5tsQzpceLaRnXYcDuwMpSv/uX+twR5XEdBLweEW80r4iIBaRCqDiOV0rFR7F/T5OOVs2TdJek\nsztwxMesx9m+2h0wsy0TEb+X9CAwDbi1tHkTHzxF0Npkz/XlZius25IPNLuRTntMaKUPiwv/f3cL\n2uwMbY1rN+AN0umVcp/fyv92NKdbO66K/ctF4xhJI4ExwNeB70n6RES8upXPZ1Z3fGTFrD5NBk4G\nRpbWLyPN5yj6eCc+75HN/5G0HTCcNHcE0lyPQ4BXI2JRaVnX0SeIiDWkAuKo0qajCs/VWZpI+drY\nSp9X5phlpNMxRYd3oO0FQD9Jf9G8QtLBpDks5SNbbYqIORHxXdLPcj3wmS3Z36zeuVgxq0MRMQ/4\nMXB+adPvgH0kTZB0oKSvAp/qxKf+qqRP51NQ00lvvLfkbTcAfw78VNKI/PxjJd2cJ5tuiStJl2mP\nl/QxSdNIp5mu7ayBAETETNJE4Xslnaj05XejJH1P0rAc9hAwQtIXJA2UdDEwpINtzwN+LOnjko4A\nbgNmRcSTbe+dSDpC6aqt4ZL6AZ8F9qbzizazmuZixax+TSG9hv//qpSIeB44Ly9PkSaPXtmBtjpy\nBVGQrlJpnuw6Cji5+QhERCwmHf3oRbp65xngamBVRESFNiu5Lu97VW5nTH6ul9rpc3tjaM044BHg\nZtJE4zuBj5Dm8RARM4BLgcuBuaRTR7d18HlOAVYBDwMzgP8BTu9An5q9DYwGHsh9uwT4Ru6TWcNQ\ny98QMzMzs9rjIytmZmZW01ysmJmZWU1zsWJmZmY1zcWKmZmZ1TQXK2ZmZlbTXKyYmZlZTXOxYmZm\nZjXNxYqZmZnVNBcrZmZmVtNcrJiZmVlNc7FiZmZmNc3FipmZmdW0/wOsAgnpcDuhKQAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x255e367c748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(sizes, neurons_scores)\n",
    "plt.title('Effect of hidden layer size on prediction accuracy')\n",
    "plt.xlabel('Number of neurons')\n",
    "plt.ylabel('clf_score')\n",
    "plt.show()\n",
    "\n",
    "scores_times = [i/j for i, j in zip(neurons_scores, times)]\n",
    "plt.scatter(sizes, scores_times)\n",
    "plt.title('Score per second training model \\nbased on hidden layer size')\n",
    "plt.xlabel('Number of neurons')\n",
    "plt.ylabel('clf_score / second')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The effect of adding more neurons continues to trend upward indefinitely overall, with smaller fluctuations around an average value, but 80 neurons seems like plenty; the effect quickly saturates after that.  I highly doubt that the minor fluctuations are reproducible with other data sets (i.e., I have no reason to suspect that 80 neurons really does outperform 100 neurons consistently), but I'll check real quick."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of hidden layer neurons:  80 \tTime taken:  50.096  seconds\n",
      "Number of hidden layer neurons:  105 \tTime taken:  57.625  seconds\n",
      "Number of hidden layer neurons:  130 \tTime taken:  68.767  seconds\n",
      "Number of hidden layer neurons:  155 \tTime taken:  74.621  seconds\n",
      "Number of hidden layer neurons:  180 \tTime taken:  112.241  seconds\n",
      "Number of hidden layer neurons:  205 \tTime taken:  125.788  seconds\n"
     ]
    }
   ],
   "source": [
    "neurons_scores2 = []\n",
    "timer = clock()\n",
    "sizes2 = [i for i in range(80, 206, 25)]\n",
    "\n",
    "for h, size in enumerate(sizes2):\n",
    "    neurons_scores2.append(list())\n",
    "    clf = MLPClassifier(hidden_layer_sizes=(size,))\n",
    "    for i in range(5):\n",
    "        j = train3.sample(n=5000)\n",
    "        clf.fit(j.iloc[0:4000, 1:], j.iloc[0:4000, 0])\n",
    "        neurons_scores2[h].append(clf.score(j.iloc[4001:, 1:], j.iloc[4001:, 0]))    \n",
    "    print('Number of hidden layer neurons: ', size, '\\tTime taken: ', round(clock() - timer, 3), ' seconds')\n",
    "    timer = clock()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kbSZpqqRx2XbVi3eZmZmZQYOBh6TXSroGuB+4Clg/23WOpJMHq3JmZmY2sjTa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\nrDT/H0tZnp7nqrXgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x255e7254080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "neurons_scores2_avg = list(np.mean(i) for i in neurons_scores2)\n",
    "neurons_scores2_std = list(np.std(i) for i in neurons_scores2)\n",
    "\n",
    "plt.errorbar(sizes2, neurons_scores2_avg, yerr=neurons_scores2_std)\n",
    "plt.title('clf_scores by hidden layer size')\n",
    "plt.xlabel('Neurons in hidden layer')\n",
    "plt.ylabel('clf_score')\n",
    "plt.xlim(75, 210)\n",
    "plt.ylim(0.6, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, so the answer is no: changing the hidden layer size doesn't matter much beyond 80-100 neurons.\n",
    "\n",
    "One more quick question is whether more than one hidden layer makes any difference.  Most of the references I've read online suggest that, for many tasks, multiple layers are unnecessary, but again it will be pretty quick to check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of hidden layer neurons:  (100,) \tTime taken:  72.828  seconds\n",
      "Number of hidden layer neurons:  (100, 100) \tTime taken:  51.265  seconds\n",
      "Number of hidden layer neurons:  (100, 100, 100) \tTime taken:  45.214  seconds\n",
      "Number of hidden layer neurons:  (100, 100, 100, 100) \tTime taken:  44.383  seconds\n"
     ]
    }
   ],
   "source": [
    "neurons_scores3 = []\n",
    "timer = clock()\n",
    "sizes3 = [(100,), (100, 100), (100, 100, 100), (100, 100, 100, 100)]\n",
    "layers3 = []\n",
    "neurons3 = []\n",
    "\n",
    "\n",
    "for h, size in enumerate(sizes3):\n",
    "    neurons_scores3.append(list())\n",
    "    layers3.append(list())\n",
    "    neurons3.append(list())\n",
    "    clf = MLPClassifier(hidden_layer_sizes=size)\n",
    "    for i in range(5):\n",
    "        j = train3.sample(n=5000)\n",
    "        clf.fit(j.iloc[0:4000, 1:], j.iloc[0:4000, 0])\n",
    "        neurons_scores3[h].append(clf.score(j.iloc[4001:, 1:], j.iloc[4001:, 0]))    \n",
    "    print('Number of hidden layer neurons: ', size, '\\tTime taken: ', round(clock() - timer, 3), ' seconds')\n",
    "    layers3[h].append(clf.n_layers_)\n",
    "    neurons3[h].append(clf.coefs_[k] for k in list(range(len(clf.coefs_))))\n",
    "    timer = clock()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "[0 1 2 3 4 5 6 7 8 9] 10\n",
      "5 784 100 100 100 100\n",
      "[0, 1, 2, 3, 4]\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "print(clf.n_layers_)\n",
    "print(clf.classes_, clf.n_outputs_)\n",
    "print(len(clf.coefs_), len(clf.coefs_[0]), len(clf.coefs_[1]), len(clf.coefs_[2]), len(clf.coefs_[3]), len(clf.coefs_[4]))\n",
    "print(list(range(len(clf.coefs_))))\n",
    "print(len(clf.coefs_[0][1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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E3LKvA3tExGuy+4cDZ0ZEXT3brTySTqH/a4oEsE2svIaK5UhaQWr3/xmpL4sTNBsVsg7i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\nGhEX9rXSiUeOpE2AA4EFQG9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ndTTNf/HNIfWb+JWkvSVtI+ntkvYcSrBKE13tmsU3IRsV\nskuuyPeAbSV9RdLLJX0QqHzJVnwTeJOk47MypwJTgW8PJTbgFOAkSR+WNDkbGfQ+SZURNXcDDwKn\nStpe0ltInUVr8TXgREnvkrSDpC+zcrhwRb/DbiWdmY3KmSSpA3gducRF0takJp1ix1uzEcOJh1mL\ny/oSdAHvyi1bSPo1PYaUmNxG+uJ+MjLAu0lf1reTko6PF/b7HKkW4RHg19k+TmRls0O9LieNtvkv\n4LWkX+ddueMuIDUlvIE0QmMWcGREXJUrcyPwHuCorMzbgbflOl3WJRvmOxM4gnS+1wKHA/dm658H\nDiGNeLkV+F/S5G21OIv0HHw92/cBwFsj4p58CNXCyv0/lpRczSc9jj3Acbn17wGujIgHa4zJrOX4\n6rRmI4CkNwNfjYidmh2LNYektYG7gEMi4qZmx2NWL3cuNRsBIuLyrOr/pRHxz2bHY02xFfAFJx02\n0rnGw8zMzErjPh5mZmZWGiceZmZmVhonHmZmZlYaJx5mZmZWGiceZmZmVhonHmZmZlYaJx5mZmZW\nGiceZmZmVhonHmZmZlaa/w+LgpLzMI7/zAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f70b545a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(neurons_scores3)\n",
    "plt.title('clf_scores by number of hidden layers')\n",
    "# plt.xlabel('solver')\n",
    "plt.ylabel('clf_score')\n",
    "plt.xticks(range(1,5), list(range(1, 6)))\n",
    "plt.xlabel('Number of hidden layers \\n(each 100 neurons)')\n",
    "# plt.xlim(75, 115)\n",
    "# plt.ylim(0.6, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So there's a very marginal improvement made by adding successive hidden layers of neurons.  Surprisingly, the models train faster with more layers, at least on my PC.  I'll stick with 1 layer for now, and revisit whether additional layers provide benefit at the end, after I've finalized the other options that I want to use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 5: Solver\n",
    "\n",
    "Next: which solver to use?  \n",
    "\n",
    "[SGD](https://en.wikipedia.org/wiki/Stochastic_gradient_descent) is the standard stochastic gradient descent: you use backpropagation to define the objective function in terms of the derivatives of all the weights in each layer.  Then you start at a random point in the vector space of that derivative, choose a nearby point at random (i.e., altering the weights by small steps at random), see whether it reduces the penalty/gets closer to the desired value from the objective function, and repeat until you can't improve any more (at least, under the rules you've set).\n",
    "\n",
    "[LBFGS](https://en.wikipedia.org/wiki/Limited-memory_BFGS) stands for limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm(s?), which is apparently part of a family of such approaches for finding minima that are called quasi-Newtonian.  It appears to differ from gradient descent primarily by the incorporation of second-derivative information about a matrix.  Using an analogy from physics, I might interpret this as attempting to predict the future position of a body by using knowledge about both its acceleration and velocity at some timepoint, rather than just the latter.\n",
    "\n",
    "[Adam](https://arxiv.org/abs/1412.6980) is an update to/modification of SGD, intended to improve its performance in ways dependent upon particulars I haven't read about yet.\n",
    "\n",
    "The sklearn docs say:\n",
    "\n",
    "\"The default solver ‘adam’ works pretty well on relatively large datasets (with thousands of training samples or more) in terms of both training time and validation score. For small datasets, however, ‘lbfgs’ can converge faster and perform better.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solver algorithm used:  lbfgs \tTime taken:  17.765  seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.\n",
      "  % (), ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solver algorithm used:  sgd \tTime taken:  121.547  seconds\n",
      "Solver algorithm used:  adam \tTime taken:  71.2  seconds\n"
     ]
    }
   ],
   "source": [
    "solvers = ['lbfgs', 'sgd', 'adam']\n",
    "solver_scores = []\n",
    "timer = clock()\n",
    "\n",
    "for h, solver1 in enumerate(solvers):\n",
    "    solver_scores.append(list())\n",
    "    clf = MLPClassifier(hidden_layer_sizes=(100,), solver=solver1)\n",
    "    for i in range(5):\n",
    "        j = train3.sample(n=5000)\n",
    "        clf.fit(j.iloc[0:4000, 1:], j.iloc[0:4000, 0])\n",
    "        solver_scores[h].append(clf.score(j.iloc[4001:, 1:], j.iloc[4001:, 0]))\n",
    "    print('Solver algorithm used: ', solver1, '\\tTime taken: ', round(clock() - timer, 3), ' seconds')\n",
    "    timer = clock()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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VKaB5nLTl9iWkLZebMTMzs5rIRXASETdnNwu7lDRM8wBwbMES3zHAXgWXDCXdeGsCKZi4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\ncXBiZmZmueLgxMzMzHLl/wP5CWlvAq9oiwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f7000c7320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(solver_scores)\n",
    "plt.title('clf_scores by solver type')\n",
    "# plt.xlabel('solver')\n",
    "plt.ylabel('clf_score')\n",
    "plt.xticks(range(1,4), (solver for solver in solvers))\n",
    "# plt.xlim(75, 115)\n",
    "# plt.ylim(0.6, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmm... mixed results.  No clear winner, but a ConvergenceWarning was raised, saying it had hit max iter.  I wonder if I try increasing the max_iter value.  This one is kind of important to get right, because there are multiple other options that only become relevant with one or another solver: sgd involves picking a learning schedule, power_t value, and momentum bool, whereas adam has epsilon and beta values to set.  So I'll retry with a larger training set to ensure that I'm getting the best representation for the choice of solver here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solver algorithm used:  lbfgs \tTime taken:  111.956  seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.\n",
      "  % (), ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solver algorithm used:  sgd \tTime taken:  1038.98  seconds\n",
      "Solver algorithm used:  adam \tTime taken:  153.253  seconds\n"
     ]
    }
   ],
   "source": [
    "solvers = ['lbfgs', 'sgd', 'adam']\n",
    "solver_scores = []\n",
    "timer = clock()\n",
    "\n",
    "for h, solver1 in enumerate(solvers):\n",
    "    solver_scores.append(list())\n",
    "    clf = MLPClassifier(hidden_layer_sizes=(100,), solver=solver1, max_iter=500)\n",
    "    for i in range(5):\n",
    "        j = train3.sample(n=20000)\n",
    "        clf.fit(j.iloc[0:16000, 1:], j.iloc[0:16000, 0])\n",
    "        solver_scores[h].append(clf.score(j.iloc[16001:, 1:], j.iloc[16001:, 0]))\n",
    "    print('Solver algorithm used: ', solver1, '\\tTime taken: ', round(clock() - timer, 3), ' seconds')\n",
    "    timer = clock()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EHAEsK4wfWQT8JwcmFR3AZpLWiog1Br5GxFJJdwJb91bpWbNmMdXthH0yZQrM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\nzKxU/j9PU6VZEMIh9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dacf037160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(solver_scores)\n",
    "plt.title('clf_scores by solver type \\n(16,000 sample training set)')\n",
    "# plt.xlabel('solver')\n",
    "plt.ylabel('clf_score')\n",
    "plt.xticks(range(1,4), (solver for solver in solvers))\n",
    "# plt.xlim(75, 115)\n",
    "# plt.ylim(0.6, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, the scores overall improved, but the trend remains roughly the same: sgd is underperforming the other two options (it also took preposterously long to compute: 1038 seconds?).  It might get better, of course, if I optimize the parameters for it, but for simplicity's sake I'll work with adam going forward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.\n",
      "  % (), ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate scheduler used:  constant \tTime taken:  122.83  seconds\n"
     ]
    }
   ],
   "source": [
    "learning = ['constant', 'invscaling', 'adaptive']\n",
    "learning_scores = []\n",
    "timer = clock()\n",
    "\n",
    "for h, learn in enumerate(learning):\n",
    "    learning_scores.append(list())\n",
    "    clf = MLPClassifier(hidden_layer_sizes=(100,), solver='sgd', learning_rate = learn)\n",
    "    for i in range(5):\n",
    "        j = train3.sample(n=5000)\n",
    "        clf.fit(j.iloc[0:4000, 1:], j.iloc[0:4000, 0])\n",
    "        learning_scores[h].append(clf.score(j.iloc[4001:, 1:], j.iloc[4001:, 0]))\n",
    "    print('Learning rate scheduler used: ', learn, '\\tTime taken: ', round(clock() - timer, 3), ' seconds')\n",
    "    timer = clock()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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L7AZcSwlBR1FuUPfqoQVGxDuBDwB9wPKIGGpRuTcz7xunekuSpDHo9O60TwDu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\nSZJUm/8PDwr4tM7yIB8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14f02631e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(learning_scores)\n",
    "plt.title('clf_scores by learning schedule')\n",
    "plt.ylabel('clf_score')\n",
    "plt.xticks(range(1,4), learning)\n",
    "# plt.xlim(75, 115)\n",
    "# plt.ylim(0.6, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 6: Max_iter\n",
    "\n",
    "In order to process more samples in the latter example above, I bumped the maximum iterations allowed while training up from 200 (default) to 500.  Even still, a ConvergenceWarning was raised, but either for the sgd or lbfgs solver run; I'm pretty sure adam didn't trip it.  I don't want to waste time with the remaining tests: how many maximum iterations should be used?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.\n",
      "  % (), ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_iter value used:  1 \tTime taken:  1.031  seconds\n",
      "max_iter value used:  2 \tTime taken:  1.439  seconds\n",
      "max_iter value used:  5 \tTime taken:  2.843  seconds\n",
      "max_iter value used:  9 \tTime taken:  6.159  seconds\n",
      "max_iter value used:  16 \tTime taken:  8.783  seconds\n",
      "max_iter value used:  31 \tTime taken:  16.885  seconds\n",
      "max_iter value used:  59 \tTime taken:  30.043  seconds\n",
      "max_iter value used:  112 \tTime taken:  51.627  seconds\n",
      "max_iter value used:  213 \tTime taken:  57.26  seconds\n",
      "max_iter value used:  405 \tTime taken:  57.106  seconds\n",
      "max_iter value used:  769 \tTime taken:  57.55  seconds\n",
      "max_iter value used:  1460 \tTime taken:  56.179  seconds\n",
      "max_iter value used:  2772 \tTime taken:  55.666  seconds\n",
      "max_iter value used:  5265 \tTime taken:  54.385  seconds\n",
      "max_iter value used:  10000 \tTime taken:  53.564  seconds\n",
      "Wall time: 8min 30s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "iterations = list(int(i) for i in (list(np.round(np.logspace(1e-1, 4, num=15)))))\n",
    "scores_iterations = []\n",
    "timer = clock()\n",
    "\n",
    "for h, max1 in enumerate(iterations):\n",
    "    scores_iterations.append(list())\n",
    "    clf = MLPClassifier(hidden_layer_sizes=(100,), solver='adam', max_iter=max1)\n",
    "    for i in range(5):\n",
    "        j = train3.sample(n=5000)\n",
    "        clf.fit(j.iloc[0:4000, 1:], j.iloc[0:4000, 0])\n",
    "        scores_iterations[h].append(clf.score(j.iloc[4001:, 1:], j.iloc[4001:, 0]))\n",
    "    print('max_iter value used: ', max1, '\\tTime taken: ', round(clock() - timer, 3), ' seconds')\n",
    "    timer = clock()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7HDgN+DaEMDta7IumbDPdgAED6NixY62y/v3701919s2WnlhUV0NFhSciDdVc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\n/Ouqp74c1ikAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dac0e8e978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iterations_scores_avg = list(np.mean(i) for i in scores_iterations)\n",
    "iterations_scores_std = list(np.std(i) for i in scores_iterations)\n",
    "\n",
    "plt.errorbar(iterations, iterations_scores_avg, yerr=iterations_scores_std)\n",
    "plt.title('clf_scores by number of iterations')\n",
    "plt.xlabel('max_iter value')\n",
    "plt.ylabel('clf_score')\n",
    "plt.xlim(0.7, 14000)\n",
    "plt.xscale('log')\n",
    "# plt.ylim(0.6, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 5, 9, 16, 31, 59, 112, 213, 405, 769, 1460, 2772, 5265, 10000]\n",
      "1 \t 0.784984984985\n",
      "2 \t 0.846246246246\n",
      "5 \t 0.892692692693\n",
      "9 \t 0.909309309309\n",
      "16 \t 0.921321321321\n",
      "31 \t 0.932932932933\n",
      "59 \t 0.925125125125\n",
      "112 \t 0.928728728729\n",
      "213 \t 0.926526526527\n",
      "405 \t 0.926126126126\n",
      "769 \t 0.933133133133\n",
      "1460 \t 0.933133133133\n",
      "2772 \t 0.929329329329\n",
      "5265 \t 0.932132132132\n",
      "10000 \t 0.926526526527\n"
     ]
    }
   ],
   "source": [
    "iterations = list(int(i) for i in (list(np.round(np.logspace(1e-1, 4, num=15)))))\n",
    "print(iterations)\n",
    "for i, j in zip(iterations, iterations_scores_avg):\n",
    "    print(i, '\\t', j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, anything more than 30 iterations is overkill, at least for a training set with 4,000 samples.  Yet if I permute other options I might expect slower performance, so I'll build in some wiggle room.  I'll use the default max_iter=200 going forward, and only modify this if I see ConvergenceWarnings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 7: Activation function\n",
    "\n",
    "I've been using the default 'relu' activation function so far, and didn't expect that this would make a big difference on the results, but I'll try comparing them briefly here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activation function used:  identity \tTime taken:  73.179  seconds\n",
      "Activation function used:  logistic \tTime taken:  115.182  seconds\n",
      "Activation function used:  tanh \tTime taken:  64.919  seconds\n",
      "Activation function used:  relu \tTime taken:  57.626  seconds\n"
     ]
    }
   ],
   "source": [
    "activation = ['identity', 'logistic', 'tanh', 'relu']\n",
    "scores_activation = []\n",
    "timer = clock()\n",
    "\n",
    "for h, activation1 in enumerate(activation):\n",
    "    scores_activation.append(list())\n",
    "    clf = MLPClassifier(hidden_layer_sizes=(100,), solver='adam', max_iter=200, activation=activation1)\n",
    "    for i in range(5):\n",
    "        j = train3.sample(n=5000)\n",
    "        clf.fit(j.iloc[0:4000, 1:], j.iloc[0:4000, 0])\n",
    "        scores_activation[h].append(clf.score(j.iloc[4001:5000, 1:], j.iloc[4001:5000, 0]))\n",
    "    print('Activation function used: ', activation1, '\\tTime taken: ', round(clock() - timer, 3), ' seconds')\n",
    "    timer = clock()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YmZnZiDmBMDMzs8JUnHhIOkDS9ZL+lP0slPRP1QzOzMzMmktFiYekWaR7qfSR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\neJiZmVlhnHiYmZlZYZx4mJm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dacfe56978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(scores_activation)\n",
    "plt.title('clf_scores by activation function')\n",
    "# plt.xlabel('solver')\n",
    "plt.ylabel('clf_score')\n",
    "plt.xticks(range(1,5), activation)\n",
    "# plt.xlim(75, 115)\n",
    "# plt.ylim(0.6, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, it's pretty clear that you don't want to use \"identity\".  It's kind of a tossup between the rest; other than the fact that logistic took significantly longer.  The default, 'relu' setting, works fine and should be kept."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 8: Learning_rate_init\n",
    "\n",
    "The choice of step sizes for altering the weights while training could be important.  I'll try a few different values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.\n",
      "  % (), ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning_rate_init value used:  1e-06 \tTime taken:  120.323  seconds\n",
      "Learning_rate_init value used:  2.68269579528e-06 \tTime taken:  118.777  seconds\n",
      "Learning_rate_init value used:  7.19685673001e-06 \tTime taken:  119.821  seconds\n",
      "Learning_rate_init value used:  1.93069772888e-05 \tTime taken:  119.403  seconds\n",
      "Learning_rate_init value used:  5.17947467923e-05 \tTime taken:  114.857  seconds\n",
      "Learning_rate_init value used:  0.000138949549437 \tTime taken:  117.908  seconds\n",
      "Learning_rate_init value used:  0.000372759372031 \tTime taken:  110.07  seconds\n",
      "Learning_rate_init value used:  0.001 \tTime taken:  56.807  seconds\n",
      "Learning_rate_init value used:  0.00268269579528 \tTime taken:  34.543  seconds\n",
      "Learning_rate_init value used:  0.00719685673001 \tTime taken:  20.555  seconds\n",
      "Learning_rate_init value used:  0.0193069772888 \tTime taken:  13.477  seconds\n",
      "Learning_rate_init value used:  0.0517947467923 \tTime taken:  9.538  seconds\n",
      "Learning_rate_init value used:  0.138949549437 \tTime taken:  10.403  seconds\n",
      "Learning_rate_init value used:  0.372759372031 \tTime taken:  8.737  seconds\n",
      "Learning_rate_init value used:  1.0 \tTime taken:  6.648  seconds\n"
     ]
    }
   ],
   "source": [
    "inits = list(np.logspace(-6, 0, num=15))\n",
    "scores_inits = []\n",
    "timer = clock()\n",
    "\n",
    "for h, init1 in enumerate(inits):\n",
    "    scores_inits.append(list())\n",
    "    clf = MLPClassifier(hidden_layer_sizes=(100,), solver='adam', max_iter=200, activation='relu',\n",
    "                        learning_rate_init=init1)\n",
    "    for i in range(5):\n",
    "        j = train3.sample(n=5000)\n",
    "        clf.fit(j.iloc[0:4000, 1:], j.iloc[0:4000, 0])\n",
    "        scores_inits[h].append(clf.score(j.iloc[4001:5000, 1:], j.iloc[4001:5000, 0]))\n",
    "    print('Learning_rate_init value used: ', init1, \n",
    "          '\\tTime taken: ', round(clock() - timer, 3), ' seconds')\n",
    "    timer = clock()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SUefuvDOctvj663V2UREpj8GDw/+ZP/4Rxo2LOxopJyUVdWzGjFCZfeaZYd5+\nEZFyOfHEcLnwwnDIutSmxCQVZnaRmc02s2VmNtXM9u1i+SYzm25mH5rZG2Z2i5ltWql4q92774YJ\nrnbeOfyC0ORWIlJuN9wAffrABRdoGKRWJSKpMLOTgGuBMcA+wAxggpkNyrP8AcDtwK+BXYETgP2A\nX1Uk4Cq3Zg2cfjq8807ojlx//bgjEpF6MGgQ3HRTmBDrrrvijkbKIRFJBTAauNnd73D3WcAFwFLg\n7DzLfwKY7e43uPtcd58C3ExILKQLP/kJ/OUv4UO9/fZxRyMi9eSLXwznFfr61+GNN+KORkot9qTC\nzPoCKWBSus3dHZgI7J9ntb8BW5vZ0dE2BgNfBnRevC60tMAPfgBjxsBnPxt3NCJSj66/PhzCfv75\nGgapNbEnFcAgoDewIKt9ATAk1wpRz8SpwD1mtgJ4E3gX0LxtnZgzJxwz/pnPhMRCRCQOm20WZtt8\n8EG4/fa4o5FSSkJS0W1mtivwC+B/gJHAUcB2hCEQyWH58jDB1UYbwe9+B72q8i8vIrXiuONCbdfF\nF4cZfaU2JGFGzUXAamBwVvtgIN+BR5cCT7n7ddH958zsQuAJM/t/7p7d6/Efo0ePpqGhYa22xsZG\nGmt87umvfx2eew6mTIFNdYyMiCTAL34BEyfCuefCj38cdzSS1tzcTHNz81ptixcvLmjd2JMKd19p\nZm3A4cB4ADOz6P71eVbbAFiR1bYGcKDTgyPHjh3LyJEjexRztbnlFvjNb+C3v4U6e+kikmAbbxz+\nN332s7Bvp5MISCXl+qHd3t5OKpXqct2kdIJfB5xnZqeb2S7ATYTE4TYAM7vSzDJH3v4MHG9mF5jZ\ndtEhpr8AnnZ3TauSoa0NLrooFESddVbc0YiIrO3oo+Gcc2Ds2LgjkVKIvacCwN3HRXNSXE4Y9pgO\nHOXuC6NFhgBbZyx/u5kNBC4Cfga8Rzh65NKKBp5wb78dzgy4556h2lpEJImuvTYc5v7hhzoapNol\npacCd7/R3bd19/XdfX93fzbjsbPc/bCs5W9w9z3cfaC7b+XuZ7j7m5WPPJlWrw6nG16yJJwobL31\n4o5IRCS3hgb41rfC7TlzYg1FeigRPRVSej/8YZiTYsIE2GabuKMREelcuqZi5sx445CeUVJRg/7y\nF7jiilBNfcQRcUcjItK1jTYK10oqqltihj+kNF55BU47LRwDfqkqTESkyiipqG5KKmrI0qWhMHOz\nzcIsdZoOlbeuAAAgAElEQVTgSkSqzYsvhpowqU762qkR7uF0wi+9BPfdF47/FhGpNkuXhv9jUp2U\nVNSIm26CO++EX/0qHEIqIlKt2trijkCKpaSiBjz9dJg//2tfg1NPjTsaEZHibbWVkopqpqSiyr31\nVjhR2KhRYQIZEZFqNmKEkopqpqSiiq1aBSefDCtWwL33Qr9+cUckItIzI0ZAe7uKNauVkooq9v3v\nw+OPwz33wJZbxh2NiEjPjRgRputWsWZ1UlJRpe6/H376U7jqKjjkkLijEREpjV12CdcaAqlOmlEz\nwZqbwwVg+XKYOxeGDQvDHk88Afvt1zFfvohILdhoI9hhh5BUqPC8+iipSLDGxnCBMMaYSsEtt4T5\nKHbYASZOBLN4YxQRKbVUCp59tuvlJHk0/FFlrrginMXvvvtgww3jjkZEpPRSKZg2TcWa1UhJRZVp\naYHf/hZ23TXuSEREymPUKBVrVislFVXiuefCdVMTnHhivLGIiJTTyJHhWsWa1UdJRZW4775w/fWv\nxxuHiEi5bbxxqBtTXUX1UVJRBdzDVNwAffvGG4uISCWkUuqpqEZKKqrAyy/D/PlxRyEiUjmjRqlY\nsxrpkNIq0NICffqE+SlERGpF9lw8w4fDpZdC//6waFFHseaIEfHGKYVTUlEFWlthr73UFSgitSVz\nLp5s770Hm2wS6iqUVFQPDX8kWHMzfO5z8NBD4QOWzuKPOy5c0hm+iEitSRdr6sdUdVFPRYI1NsLW\nW8ODD4YEYt99445IRKRyRo1SUlFtEtNTYWYXmdlsM1tmZlPNLO9XqJndamZrzGx1dJ2+/LOSMVdC\na2voAkwfty0iUi80s2b1SURSYWYnAdcCY4B9gBnABDMblGeVbwBDgKHR9VbAO8C48kdbWa2tcPjh\n0Lt33JGIiFRWKhWKNV98Me5IpFCJSCqA0cDN7n6Hu88CLgCWAmfnWtjdP3D3t9IXYD9gY+C2SgVc\nCe+9F+anOPLIuCMREak8zaxZfWJPKsysL5ACJqXb3N2BicD+BW7mbGCiu88rfYTxmTwZ1qyBI46I\nOxIRkcrbeGPYcUclFdUk9qQCGAT0BhZktS8gDG10ysyGAkcDvy59aPFqaQkfqG23jTsSEZF4aGbN\n6pKEpKKnzgTeBR6IOY6Sa23V0IeI1LdUCtrbVaxZLZJwSOkiYDUwOKt9MFDI5NRnAXe4e0HzTY4e\nPZqGhoa12hobG2nMNwNLTP79b3jlFQ19iEh9S6Vg6dJQrLnrrnFHUx+am5tpzpoIafHixQWta6F8\nIV5mNhV42t0vju4b8Cpwvbtf08l6hxBqMXZ395ldPMdIoK2trY2RVXB85s03w0UXwdtvQ1YOJCJS\nN9Iza95xB5x2WtzR1K/29nZSqRRAyt3b8y2XlOGP64DzzOx0M9sFuAnYgOhoDjO70sxuz7HeOYRk\npNOEohq1tsLHP66EQkTqm4o1q0sShj9w93HRnBSXE4Y9pgNHufvCaJEhwNaZ65jZRsAXCXNW1JTV\nq2HSJLj44rgjERGJXyoVzgEiyZeIpALA3W8Ebszz2Fk52t4HBpY7rjg8+2zo8lORpohISCr+/Ofw\ng0sTASZbUoY/JENrK2y0Eey3X9yRiIjEb9SojmJNSTYlFQnU2gqHHgp9EtOPJCISH82sWT2UVCTM\nBx/AlCka+hARSWtoCMWaqqtIPiUVCfPYY7BqleanEBHJpJk1q4OSioRpbYVhw0JWLiIiwahROg16\nNVBSkTAtLWHowyzuSEREkiNzZk1JLiUVCfLaazBrloY+RESypYs1VVeRbEoqEqS1NfRQHHZY3JGI\niCRLulhTdRXJpqQiQVpawrjhZpvFHYmISPKMGqWkIumUVCTEmjUwcaKGPkRE8kmlVKyZdEoqEmL6\ndFi0SPNTiIjkky7WnDUr7kgkHyUVCdHaCgMGwP77xx2JiEgyaWbN5FNSkRCtrXDwwdCvX9yRiIgk\nU0MD7LSTkookU1KRAEuXwhNPaOhDRKQrmlkz2ZRUJMATT8CKFSrSFBHpioo1k01JRQK0tsIWW8CI\nEXFHIiKSbCrWTDYlFQmgqblFRAqjYs1kU1IRs/nz4Z//1NCHiEghVKyZbD1KKsxsRzM7yszWj+7r\nt3Y3TZwYrj/96XjjEBGpFqmUzgGSVEUlFWa2mZlNBF4CHgKGRg/dYmbXliq4etDSAnvvDZtvHnck\nIiLVIZUKEwaqWDN5iu2pGAusArYBlma03wN8pqdB1Qt3Tc0tItJdo0apWDOpik0qjgS+6+6vZbW/\nDAzrWUj14/nn4c03lVSIiHTHPvuEa9VVJE+xScUA1u6hSNsU+Kj4cOpLSwv07w+f+lTckYiIVI90\nsabqKpKn2KTiCeD0jPtuZr2A7wCTexxVnWhthQMPhPXXjzsSEZHqotOgJ1OxScV3gPPN7GGgH3A1\n8BxwEPDdYjZoZheZ2WwzW2ZmU81s3y6W72dmPzazOWa23Mz+bWZnFvPccfjoI3jsMU3NLSJSDBVr\nJlNRSYW7PwcMB54EHiAMh9wH7OPur3R3e2Z2EnAtMAbYB5gBTDCzQZ2sdi9wKHBWFEsj8GJ3nzsu\nTz0Fy5apnkJEpBiaWTOZ+nR3BTPrA1wG/Nbdf1yiOEYDN7v7HdFzXAAcA5xN6AXJjuEzwIHA9u7+\nXtT8aoliqYjW1nAY6R57xB2JiEj1SRdrPvss7LZbvLFIh273VLj7KsLwR7cTklzMrC+QAiZlPIcD\nE4H986x2LPAs8F0ze83MXjSza8ysfyliqoSWltBL0UtzmoqIdFtDAwwfrrqKpCn2K20ScHCJYhgE\n9AYWZLUvAIbkWWd7Qk/FbsAXgIuBE4AbShRTWS1aFM6yp6EPEZHi6TToyVNsb8PDwFVmtgfQBnyY\n+aC7j+9pYF3oBawBTnH3JQBmdglwr5ld6O6JPqx10qQw8ZWm5hYRKV4qBQ88AKtWQZ+S9J1LTxX7\nZ7gxur4kx2NO6Hko1CJgNTA4q30wMD/POm8Cr6cTishMwICtgLzFoqNHj6ahoWGttsbGRhobG7sR\ncs+0tIQxwC23rNhTiojUnMxizd13jzua2tHc3Exzc/NabYsXLy5o3aKSCncvWSWAu680szbgcGA8\n/OfEZIcD1+dZ7SngBDPbwN3Tk3DtTOi9yJ7lcy1jx45lZPrcuTFwD0Waxx8fWwgiIjUh8zToSipK\nJ9cP7fb2dlKpVJfrJqVM8DrgPDM73cx2AW4CNgBuAzCzK83s9ozl7wbeBm41sxFmdhDhKJFbkj70\n8dJLMG+e6ilERHpqo41UrJk0RScVZnawmf3ZzP4VXcab2YHFbMvdxwHfBi4HpgF7Ake5+8JokSHA\n1hnLfwgcAWwM/B24kzBfxsXFvp5KaWmBvn3h4FKVuYqI1DEVayZLUcMfZnYqcCthwqv0EMUBwCQz\nO9Pd7+7uNt39RjpqNbIfOytH20vAUd19nri1tsIBB8CAAXFHIiJS/VIp+NOfVKyZFMX2VPw/4Dvu\nfpK7Xx9dTgIuBf67dOHVlpUrYfJkDX2IiJTKqFFhdmLNrJkMxSYV2wN/ztE+Htiu+HBq29SpsGSJ\nzvchIlIqOg16shSbVMwjHJ2R7dPRY5JDaytsumnHh0BERHpGxZrJUuwI1LXA9Wa2NzAlajsAOJMq\nKJaMS2trmPCqd3dm8RARkU6lUuEcIBK/Yuep+D8zmw98Czgxap4JnOTuD5QquFry7rvwzDNwzjlx\nRyIiUltGjVKxZlIUvfvd/X7g/hLGUtMmT4Y1a1SkKSJSaqlUR7GmJsGKV1E1FWa2r5l9PEf7x81s\nVM/Dqj0tLWHcb9iwuCMREaktKtZMjmILNW8AtsjRviVVcqbQSmttVS+FiEg5pIs1VVcRv2KTil2B\n6Tnap0WPSYZXXoF//1tJhYhIuYwapZ6KJCg2qfiIMHV2tqHAquLDqU2treGIj0MPjTsSEZHalErB\n9OmhWFPiU2xS0QJcaWb/OYe4mW0M/ARoLUVgtaS1FT7xidBFJyIipZdZrCnxKTap+DbhBF9zzWyy\nmU0GZhN6L75VquBqwapV8Ne/auhDRKSc0sWaqquIV1FJhbu/TjiT6HeAF4A2wqRXe7i7ZtTM8Oyz\n8N57mppbRKScNtoIdt5ZdRVx68k8FR8CvyphLDWptRUaGmDffeOORESktuk06PErdp6KM8zsmIz7\nV5vZe2Y2xcw0E0OG1tZQoKlZ3kREykvFmvErtqbiMmAZgJntD3yNMBSyCBhbmtCq3wcfwN/+pqEP\nEZFKSBdrzpwZdyT1q9ikYmvgX9HtLwB/cPdfAd8DDixFYLXg0UdDxqwiTRGR8ttnHzDTEEicik0q\nlgCbRbePpOMw0uXA+j0Nqla0tsK228IOO8QdiYhI7dNp0ONX7Eh/K/AbM5sGDAceitp3A+aUIK6a\n0NIShj7M4o5ERKQ+qFgzXsX2VFwE/A34GHC8u78dtaeA5lIEVu3mzYMXX9TQh4hIJalYM15F9VS4\n+3uE4szs9jGZ983sRuAH7r6ouPCqV2sr9OoFhx0WdyQiIvVj1KiOYs099og7mvpTbE9FoU4F6nJy\n6paW8ObedNO4IxERqR8q1oxXuZOKuqwmWLMGJk7U0IeISKVtuKGKNeNU7qSiYGZ2kZnNNrNlZjbV\nzPLOQWlmB5vZmqzLajPbvJIx5zNtGrz9tuanEBGJQyqlc4DEJRFJhZmdBFwLjAH2AWYAE8xsUCer\nObAT4SRmQ4Ch7v5WuWMtRGsrDBgQzkwqIiKVNWoUzJihYs04JCKpAEYDN7v7He4+C7gAWAqc3cV6\nC939rfSl7FEWqLUVDjkE+vWLOxIRkfqjmTXjE3tSYWZ9CYeiTkq3ubsDE4H9O1sVmG5mb5hZi5l9\nsryRFmbpUnjySQ19iIjERcWa8Sk4qTCz+8xso+j26Wa2XgGr/Q54v4tlBgG9gQVZ7QsIwxq5vAl8\nBTge+BIwD3jUzPYuIKayevxxWLFCRZoiInFJF2uqrqLyujNPxeeAAYQk4VbgEaDTIQd3/2rxoXW6\n3ZeAlzKapprZDoRhlDPK8ZwAzc3hArB8OcydC8OGQf/+oa2xMbyJt9wSdtmlXFGIiEhXRo1ST0Uc\nupNUzAKuNLPJhKGHE80sZy+Eu9/Rje0uAlYDg7PaBwPzu7GdZ4ADulpo9OjRNDQ0rNXW2NhIY2Nj\nl0/Q2BguAO3tYdyuuRlGjuxY5ic/0dTcIiJxS6Xgj38MxZp9ij0hRZ1qbm6muXntybEXL15c0Lrd\n2dUXANcBxxCOvPhRdJ3NgYKTCndfaWZtwOHAeAAzs+j+9d2Ib2/CsEinxo4dy8jMLKCE3nwTnnsO\nLrusLJsXEZECpVKhR/mFF2DPPeOOprrk+qHd3t5OKpXqct2Ckwp3nwJ8AsDM1gDDS3jExXXAbVFy\n8QxhGGMD4Lbo+a4EtnD3M6L7FwOzgeeB/sB5wKFArJUMEyeG68MPjzMKERHJLNZUUlE5xR79sR2w\nsFRBuPs44NvA5cA0YE/gKHdPP8cQYOuMVfoR5rX4B/AosAdwuLs/WqqYitHSEt7ImydiCi4Rkfq1\n4Yaw886qq6i0gnsqzCw719vD8hQOuPs/uhuIu98I3JjnsbOy7l8DXNPd5ygn99BTcfrpcUciIiKg\n06DHoTs1FdMJ9RL5ShDTjznhENG68txzMH++5qcQEUkKFWtWXnd283Zli6IGtLSEQ0sP6PL4ExER\nqQQVa1ZewTUV7j43fQFOAQ7LbIvaDwdOLlewSdbaCgcd1DFnhYiIxEsza1ZesYWaXwFeyNH+POHQ\n07qyfHmYSVNDHyIiyaFizcorNqkYQu7ZNBcCQ4sPpzo99VQ4eY2m5hYRSRadBr2yik0q5pF79soD\ngDeKD6c6tbbC4MGwxx5xRyIiIpl0GvTKKrYe9tfAz6MzjP41ajscuJowf0RdaWkJvRSamltEJFlU\nrFlZxSYV1wCbEeaV6Be1LQd+6u5XliKwavHuuzBtGnzzm3FHIiIi2TSzZmUVNfzhwXeBjxGm7t4L\n2NTdLy9lcNXgmWfC9ac/HW8cIiKyroEDQ7Gm6ioqo0fTgbj7EuDvJYqlKk2dCrvvDltsEXckIiKS\ni06DXjnFFmpK5OmnddSHiEiSpVIq1qwUJRU9tGCBkgoRkSTLLNaU8lJS0UN9+4aZNEVEJJnSxZqq\nqyg/JRU9tNdeMGBA3FGIiEg+AwfCLruorqISlFQUaeXKcP2JT8Qbh4iIdE2nQa8MJRVF+uc/w7WS\nChGR5EsXa6Z/EEp5KKko0tSp4XrnneONQ0REuqZizcpQUlGkp58O1720B0VEEk+nQa8MfSUWYeVK\nWLMm7ihERKRQKtasDCUVRejbF+68M+4oRESkO1SsWX5KKkREpC6kUjB9uoo1y0lJhYiI1IVRo+Cj\nj1SsWU49OqGYiIhItdh771CsecYZsM024WiQuXNh2DDo3z8s09gYLlIcJRUiIlIX0sWaBxwAN9wA\n7e1hSKS5GUaOjDu62pCY4Q8zu8jMZpvZMjObamb7FrjeAWa20szayx2jiIhUt1RK5wApp0QkFWZ2\nEnAtMAbYB5gBTDCzQV2s1wDcDkwse5AiIlL1Ro3SzJrllIikAhgN3Ozud7j7LOACYClwdhfr3QTc\nBUwtc3wiIlIDUikVa5ZT7EmFmfUFUsCkdJu7O6H3Yf9O1jsL2A74YbljFBGR2pAu1tR8FeURe1IB\nDAJ6Awuy2hcAQ3KtYGY7AT8Bmtxdc1uKiEhB0sWaqqsoj6o7+sPMehGGPMa4+yvp5kLXHz16NA0N\nDWu1NTY20qhjiERE6sKoUaGn4txz444kmZqbm2lubl6rbfHixQWtm4SkYhGwGhic1T4YmJ9j+Q2B\nUcDeZnZD1NYLMDNbARzp7o/me7KxY8cyUscOiYjUrVQKxo1TsWY+uX5ot7e3k0qlulw39uEPd18J\ntAGHp9vMzKL7U3Ks8j6wO7A3sFd0uQmYFd1+uswhi4hIFUsXa86eHXcktScJPRUA1wG3mVkb8Azh\naJANgNsAzOxKYAt3PyMq4lyrbtfM3gKWu/vMikYtIiJVJ12sqSNASi8RSYW7j4vmpLicMOwxHTjK\n3RdGiwwBto4rPhERqR0DB8KIETBTP0NLLhFJBYC73wjcmOexs7pY94fo0FIRESlQKhWm6ZbSir2m\nQkREpNJSKXj55bijqD1KKkREpO6kUrBiRdxR1B4lFSIiUnf23ht66Ruw5LRLRUSk7gwcCNtvH3cU\ntUdJhYiI1KXDo9mRli+PN45aoqRCRETq0tFHh+snnog3jlqipEJEROrS1tHsRw8/HG8ctSQx81RU\ng+bmcIHQXTZ8OFx6KfTvH9oaG8NFRESqx1NPwTvvwKabxh1J9VNS0Q1KGkREas+aNfDHP8J558Ud\nSfXT8IeIiNS1ffeFu+6KO4raoKRCRETq2tFHw2OPwbx5cUdS/ZRUiIhIXTv00FAb9/vfxx1J9VNS\nISIidW3gQDj2WA2BlIKSChERqXtNTTBjBjz/fNyRVDclFSIiUveOPho22QTuvjvuSKqbkgoREal7\n/frBCSeEpMI97miql5IKERERwhDInDkwZUrckVQvJRUiIiLAgQfCVltpCKQnlFSIiIgAvXqFWZPH\njYOVK+OOpjopqRAREYk0NcGiRdDaGnck1Unn/hARkbrR1YkhTz4ZdtstzFnx2c/GF2e1UlIhIiJ1\no5ATQ86ZAz/+MSxZEibGksJp+ENERCRDYyMsXQrjx8cdSfVJTFJhZheZ2WwzW2ZmU81s306WPcDM\nnjSzRWa21Mxmmtk3KxmviIjUpu22g09+UtN2FyMRSYWZnQRcC4wB9gFmABPMbFCeVT4E/hc4ENgF\nuAL4kZmdW4FwRUSkxjU1wYQJsHBh3JFUl0QkFcBo4GZ3v8PdZwEXAEuBs3Mt7O7T3f0ed5/p7q+6\n+93ABEKSISIi0iMnnghmcO+9cUdSXWJPKsysL5ACJqXb3N2BicD+BW5jn2jZR8sQooiI1JlBg+DI\nIzUE0l2xJxXAIKA3sCCrfQEwpLMVzWyemS0HngFucPdbyxOiiIjUm6amMGX37NlxR1I9kpBU9MSn\nCL0cFwCjo9oMERGRHvv852GDDTrmtZCuJWGeikXAamBwVvtgYH5nK7r73Ojm82Y2BPgf4J7O1hk9\nejQNDQ1rtTU2NtLY1YHLIiJSVwYMgC98IQyBfO97ocaiHjQ3N9OclUktXry4oHXNE3COVzObCjzt\n7hdH9w14Fbje3a8pcBs/AM509+3zPD4SaGtra2PkyJElilxERGrZQw/BMcfAtGmw995xRxOf9vZ2\nUqkUQMrd2/Mtl4SeCoDrgNvMrI1QHzEa2AC4DcDMrgS2cPczovsXEpKOWdH6BwPfAn5e2bBFRKSW\nHXFEKNq8++76TioKlYikwt3HRXNSXE4Y9pgOHOXu6SOEhwBbZ6zSC7gS2BZYBbwC/Je7/6piQYuI\nSM3r2zccXtrcDFddFc5kKvklZve4+43uvq27r+/u+7v7sxmPneXuh2Xc/6W77+HuG7r7Ju4+SgmF\niIiUQ1MTvPYaPPFE3JEkX2KSChERkSTaf3/YdlvNWVEIJRUiIiKdMINTTgmza370UdzRJJuSChER\nkS40NcF778Ejj8QdSbIpqRAREenCrrvCXntpCKQrSipEREQK0NQEf/4zvP9+3JEkl5IKERGRAjQ2\nhpqK+++PO5LkUlIhIiJSgK22goMO0hBIZ5RUiIiIFKipCSZNgvmdnpmqfimpEBERKdAJJ0Dv3nBP\np6eurF9KKkRERAq0ySbw2c9qCCQfJRUiIiLd0NQEf/87vPxy3JEkj5IKERGRbvjc52DDDcOZS2Vt\nSipERES6Yf314UtfCkMg7nFHkyxKKkRERLqpqSkMf7S1xR1JsiipEBER6aZDD4XBg1WwmU1JhYiI\nSDf16QMnnwy//z2sXh13NMmhpEJERKQITU1hEqzJk+OOJDmUVIiIiBRh1CjYcUcNgWRSUiEiIlIE\ns9Bb8cc/wrJlcUeTDEoqREREinTKKfDBB/Dgg3FHkgxKKkRERIo0fHgYBtEQSKCkQkREpAeamuCh\nh+Ddd+OOJH5KKkRERHrgpJNg1apQW1HvlFSIiIj0wNChcNhhGgKBBCUVZnaRmc02s2VmNtXM9u1k\n2S+aWYuZvWVmi81sipkdWcl4RURE0pqa4LHH4LXX4o4kXolIKszsJOBaYAywDzADmGBmg/KschDQ\nAhwNjAQmA382s70qEK6IiMhavvhF6NcvzLBZzxKRVACjgZvd/Q53nwVcACwFzs61sLuPdvefuXub\nu7/i7v8PeBk4tnIhi4iIBA0NcOyxGgKJPakws75ACpiUbnN3ByYC+xe4DQM2BN4pR4wiIiJdaWqC\n6dPhhRfijiQ+sScVwCCgN7Agq30BMKTAbfwXMAAYV8K4RERECnb00bDxxnD33XFHEp8+cQfQU2Z2\nCvDfwHHuvqir5UePHk1DQ8NabY2NjTQ2NpYpQhERqQfrrQcnnBCSiiuuCNN4V6Pm5maam5vXalu8\neHFB61oYaYhPNPyxFDje3cdntN8GNLj7FztZ92TgN8AJ7v5IF88zEmhra2tj5MiRJYldREQk06OP\nwqGHwpQpsH9BA/jVob29nVQqBZBy9/Z8y8U+/OHuK4E24PB0W1QjcTgwJd96ZtYI3AKc3FVCISIi\nUgkHHQRbblm/BZuxJxWR64DzzOx0M9sFuAnYALgNwMyuNLPb0wtHQx63A98C/m5mg6PLRpUPXURE\nJOjVCxob4Z57YOXKuKOpvEQkFe4+Dvg2cDkwDdgTOMrdF0aLDAG2zljlPEJx5w3AGxmXn1cqZhER\nkVyammDRIpg4Me5IKi8xhZrufiNwY57Hzsq6f2hFghIREemmvfaCESPCEMjRR8cdTWUloqdCRESk\nVpiF3oo//Qk+/DDuaCpLSYWIiEiJNTaGhGL8+K6XrSVKKkREREps++3DIaX1dhSIkgoREZEyaGqC\nCRNC0Wa9SEyhpoiISC358pfhG9+Aww6DbbeF5cth7lwYNgz69w/LNDaGS61QUiEiIlIGm28ORx0F\n778faiva2yGVguZmqNWJnTX8ISIiUiZNTfDUUzBnTtyRVIaSChERkTL5/Odhgw1C70Q9UFIhIiJS\nJgMHhsTirrsg5vN3VoSSChERkTJqaoLnn4d//SvuSMpPSYWIiEgZHXkkbLYZPPxw3JGUn5IKERGR\nMurbF048ER55JO5Iyk9JhYiISJmdcgosWBB3FOWnpEJERKTMPvlJGDo07ijKT0mFiIhImfXqBZ/5\nTLi9cmW8sZSTkgoREZEKOOaYcP3RR/HGUU5KKkRERCpgu+3C9cCB8cZRTkoqREREpCSUVIiIiEhJ\nKKkQERGRklBSISIiIiWhpEJERERKQkmFiIiIlERikgozu8jMZpvZMjObamb7drLsEDO7y8xeNLPV\nZnZdJWNNa25ujuNpE0f7oYP2RaD9EGg/dNC+SKvt/ZCIpMLMTgKuBcYA+wAzgAlmNijPKusBbwFX\nANMrEmQO+pAE2g8dtC8C7YdA+6GD9kVabe+HPnEHEBkN3OzudwCY2QXAMcDZwNXZC7v73GgdzOyc\nCsYpIiJSsObmcAFYvhwGDIBLL4X+/UNbY2O41IrYeyrMrC+QAial29zdgYnA/uV+/uzsubv3yxlL\nT5fNt0yu9kLaMu8nZT8Usnxnj3f3dRdyv5T0nihu28W+Jwptr/X9kO+xrtqS+tkoZPlyvSdC0tDM\n+PHQ0gKHHRaux48Pl54mFJV8TxQi9qQCGAT0BrJPCrsAGFLuJ9cXSOFt9fqPU++J/G219p5QUtH5\nY0oq8rfXw3uiEEkZ/qiE/gAzZ85cq3Hx4sW0t7cXdT/7sZ7qzvYKWTbfMrnaC2nL99rj3A+FLN/Z\n49193V3d13si/v1QyPLd2Q+52mt9P+R7rKu2pH42Clle74nOH8/47uzf2fYtjDTEJxr+WAoc7+7j\nM1HHkHMAAArySURBVNpvAxrc/YtdrD8ZmObul3Sx3CnAXT2PWEREpG41ufvd+R6MvafC3VeaWRtw\nODAewMwsun99CZ9qAtAEzAGWl3C7IiIita4/sC3huzSv2JOKyHXAbVFy8QzhyI4NgNsAzOxKYAt3\nPyO9gpntBRgwEPhYdH+Fu88kB3d/G8ibXYmIiEinpnS1QCKSCncfF81JcTkwmDD3xFHuvjBaZAiw\nddZq04D02M1I4BRgLrB9+SMWERGRbLHXVIiIiEhtSMIhpSIiIlIDlFSIiIhISSipKCMz29bM/mpm\nz5vZDDNbP+6Y4mJmc8xsuplNM7NJXa9Ru8xs/Wh/rDMFfT0wswYz+7uZtZvZP8zs3LhjiouZbWVm\nk6P/EdPN7IS4Y4qLmd1nZu+Y2bi4Y4mLmX3OzGZFJ8usylNQqKaijMzsUeAyd59iZhsD77v7mpjD\nioWZ/RvYzd2XxR1L3MzsR8AOwDx3/07c8VRadMj4eu6+PEq0nwdS7v5uzKFVnJkNATZ393+Y2WCg\nDdipHj8nZnYQsCFwhrufGHc8lWZmvYEXgIOBJUA78PFq+1yop6JMzGxXwiGuUwDc/b16TSgiht5v\nmNmOwM7Aw3HHEhcP0nPFpHvvLK544uTu8939H9HtBcAiYNN4o4qHuz9O+DKtV/sBz0XviSXAg8CR\nMcfUbXX/T76MdgI+NLPxZvasmX0v7oBi5sDjZvZ0NLtpvfoZ8D3q9Es0LRoCmQ68Clzj7u/EHVPc\nzCwF9HL31+OORWKxBZD5t38d2DKmWIqmpCJiZgdGCcDrZrbGzI7LscxFZjbbzJaZ2VQz27eTTfYB\nPgVcAHwSOMLMDi9T+CVVhn0BcIC7p4DPA5eZ2e5lCb6ESr0fovVfdPd/pZvKFXspleP94O6L3X1v\nYDugycw+Vq74S6lMnw3MbFPgduC8csRdauXaD9VK+6ODkooOAwiTbl1Ix6Ra/2FmJwHXAmOAfYAZ\nwIRo0q70MhdGhYjtwGvAs+7+hruvAB4C9i7/yyiJku4LM1vP3d+E0N1L2Bcjy/8yeqzU74mDgZOj\n+pKfAeea2ffL/zJ6rOTvh3R7NMHdDODA8r6Ekin5vjCzfsD9wE/c/elKvIgSKNt7okr1eH8AbwBb\nZdzfMmqrLu6uS9YFWAMcl9U2FfhFxn0jJA7fybON3oSiqwb+f3v3GmNHXcZx/PtrQ4WlKSCGpSq0\nimlU0phIlYCX9UaTQlTQYDRelsQXXqLVipqmxtYX+IKmKVHSRBNDCgmtDUmNQIqKsZJSmkDrJlRE\niqabSsRWUshSqlh2H1/853Rnh3N2zznMnNmz/X2Sye5c95lnZ3ee8//PJRVv9wLX1r1vNeViAFiY\nfb8Q2E+6MK/2/etlHgrrDgMb696vmo6Hi3LHw3nAQdJFvLXvXx3HBLAdWF/3/tSdh2y5DwH31L1P\ndeQjO2c8BSzO/k8+CVxQ9/50Orilog1Kb1K9Ajh9K2Sko+D3wFXN1omIcWAdsIdUwR6KiF3VR1ut\nbnJBevT6w5JGSM+O3xoRB6qOtUpd5mHO6TIPS4A92fHwEOmf7RNVx1q1bnIh6X3AjcD1uU/tl/ci\n3qp0+7ch6UFgB7BK0hFJV1Yday+0m4/snHEz8EfSnR+bos/u/IBZ8u6PPvAGUhV5tDD9KOlK/qYi\n4rfM8Ea3PtRxLiLiMP3T9dOuro6Jhoi4s4qgatDN8fAYqQl4rukmF3uZe/+Hu/1/eU2VQdWo7XxE\nxP3A/T2KqxJuqTAzM7NSuKhoz3PAOKkZP28Q+Ffvw6mVc5E4D4nzMMm5SJyHqc6ofLioaENEnCJd\ndHn6llBJysZnfL/8XOJcJM5D4jxMci4S52GqMy0fc60vr2uSzgXexuSzA94q6V3A8Yj4B7AZ2Crp\nAPAosIZ0V8PWGsKtlHOROA+J8zDJuUich6mcj5y6bz+ZLQPpGQITpGaq/HBHbpmvA6PAf4B9wIq6\n43YunAfnwblwHpyP2TL4hWJmZmZWCl9TYWZmZqVwUWFmZmalcFFhZmZmpXBRYWZmZqVwUWFmZmal\ncFFhZmZmpXBRYWZmZqVwUWFmZmalcFFhZmZmpXBRYWZmZqVwUWE2i0naLWnzLIhjSNK4pEV1x1IH\nScOSjne4zqz43Zn1kosKM2vHXmBxRIzVHchMsgLg+ZI3+0tgWYfr3AD8sDEi6bCk1aVGZTbL+NXn\nZmcwSWdFxKmZlouIV4BjPQippXZjJb1+utQ3JUbEy8DLHa7zQpkxmPUDt1SY9QlJCyRtkvSMpBOS\n9kkays1/vaRt2fyXJD0u6bOFbeyWdLuk2yT9G/hNNn1C0pcl7czWPSTp47n1hrJlFmXjw5Kel7RS\n0l8kvSjpAUmDuXXmS/ppttwxST+WtFXSr9rc31axrsn27YSkI5K2SBpoxAncAZyXxTsuaX07+Zsh\nlimtH5I2SBqR9IWsBeIFSdslnVuIf3Pje2AJcFsjrnZ+rlm/cVFh1j+2AFcCnwGWA/cAD0i6LJt/\nNrAfWAVcDvwcuEvSisJ2vkT61H018NXc9PWkZv7lwC7gbknn5+YXP/0PADcDnwc+AFwKbMrNXwt8\nDhgG3g9cAFzfZDvTaRbrOPBN4J3Z/A8DG7N5jwDfBsaAQWBxLqaZ8jeTYtyXAZ8ErgWuA4ZI+9zM\np4BnSN0hF2dxmc09EeHBg4dZOgC7gc3AJcAp4OLC/AeBW6ZZ/z5gY2F7+5ssNwH8KDc+kE1bmY0P\nkU7mi7Lx4Wx8aW6drwH/zI0/C6zJjc8DRoGdHez7q2JtstyngWO58WHgeGGZrvLXapvABuBFYCA3\n7VbgkeLvLjd+GFhd9zHlwUOVg6+pMOsPy4H5wCFJyk1fADwHIGke8APgRuBN2bwFwEuFbR1o8TMO\nNr6JiJOSxoCLponpZESM5safbSyfdZMMAo/ltjkh6QDpmod2vSpWSR8jtQi8HVhEujbsdZLOjoj/\nttjOjPnrwmhEnMyNn95/szOViwqz/rAQeAV4N6kFIe9E9vX7pG6BbwF/JhUTPyGdOPOKRUZD8SLI\nYPou0mbLd1IwtGNKrJKWkFpftgDrgOOkrpdfkPazVVHRTv461Wm+zOY8FxVm/WGE9Pc6GBF7Wyxz\nNfDriNgOkH0iXwY80ZsQJ0XEmKSjwHuAh7N45pFO6iOvYdNXAIqI7zYmFC9GBf5HapXIG8mmTZe/\nqjWLy2xOcVVt1gci4mngbtKFlzdIWirpvZLWSlqVLfY0cI2kqyS9g3Sh5mCrbXah01aI24F1kj4h\naRmp1eR8Xtvtnn8DzpK0WtJbJH0R+EphmVFgoaSPSLpQ0jlZ/rYxff6qNgp8UNIbJV3Yo59p1lMu\nKsxmt/wJ+CbgLtLdDH8FdgIrgCPZ/FuAP5FuvfwDqY+/ePtmqxN6s+nFaZ0WA7eSTuR3ku7KOAH8\njtZdFDPGFBGPA98hdfUcJN1dsrawzD7gZ8AO0rM1vpfNuonp81e2YvzrgaXA36n5mR9mVVFEqc+I\nMTNrKuuOeRLYEREb6o7HzMrnayrMrBKSLgVWAg+RnqHxDdIn9W01hmVmFXL3h5lVZYLU5fAosIf0\nQK6PRsRTki7JnsI5ln3ND2OS3tzLQCXtahJHI5ZWD7QyswJ3f5hZz0maT3psdSujEVG89bPKeBYD\n57SYfTz8Hg+ztrioMDMzs1K4+8PMzMxK4aLCzMzMSuGiwszMzErhosLMzMxK4aLCzMzMSuGiwszM\nzErhosLMzMxK4aLCzMzMSvF/4nks5DUdP5UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dac0e48b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1e-06 \t 0.432832832833\n",
      "2.68269579528e-06 \t 0.737937937938\n",
      "7.19685673001e-06 \t 0.843843843844\n",
      "1.93069772888e-05 \t 0.892892892893\n",
      "5.17947467923e-05 \t 0.917117117117\n",
      "0.000138949549437 \t 0.92012012012\n",
      "0.000372759372031 \t 0.933133133133\n",
      "0.001 \t 0.924924924925\n",
      "0.00268269579528 \t 0.932332332332\n",
      "0.00719685673001 \t 0.941941941942\n",
      "0.0193069772888 \t 0.939139139139\n",
      "0.0517947467923 \t 0.90970970971\n",
      "0.138949549437 \t 0.786786786787\n",
      "0.372759372031 \t 0.373973973974\n",
      "1.0 \t 0.140540540541\n"
     ]
    }
   ],
   "source": [
    "inits_avg = list(np.mean(i) for i in scores_inits)\n",
    "inits_std = list(np.std(i) for i in scores_inits)\n",
    "\n",
    "plt.errorbar(inits, inits_avg, yerr=inits_std)\n",
    "plt.title('clf_scores by learning_rate_init value')\n",
    "plt.ylabel('clf_score')\n",
    "plt.xlabel('learning_rate_init')\n",
    "plt.xscale('log')\n",
    "plt.xlim(5e-7, 5)\n",
    "plt.show()\n",
    "\n",
    "for i, j in zip(inits, inits_avg):\n",
    "    print(i, '\\t', j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, values close to 0.1 and 0.2 performed best; there's a big drop-off in scores for any step of larger size.  The default value of 0.001 is definitely acceptable, but 0.01 is slightly better; that's what I'll use going forward."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 9: Solver-specific values\n",
    "\n",
    "If I were using sgd as the solver, the next variables to maybe try would be changing the learning rate from constant to adaptive.  But with adam as the flagship solver for this run, I'm more concerned about whether choices of values for beta and epsilon may make some difference, and finally whether or not to enable early stopping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning_rate_init value used:  0.1 \tTime taken:  17.894  seconds\n",
      "Learning_rate_init value used:  0.129121927361 \tTime taken:  19.085  seconds\n",
      "Learning_rate_init value used:  0.166724721255 \tTime taken:  19.263  seconds\n",
      "Learning_rate_init value used:  0.215278173472 \tTime taken:  17.606  seconds\n",
      "Learning_rate_init value used:  0.277971326776 \tTime taken:  20.154  seconds\n",
      "Learning_rate_init value used:  0.358921934645 \tTime taken:  18.25  seconds\n",
      "Learning_rate_init value used:  0.463446919736 \tTime taken:  17.3  seconds\n",
      "Learning_rate_init value used:  0.59841159506 \tTime taken:  17.982  seconds\n",
      "Learning_rate_init value used:  0.772680585096 \tTime taken:  14.987  seconds\n",
      "Learning_rate_init value used:  0.997700063823 \tTime taken:  14.474  seconds\n"
     ]
    }
   ],
   "source": [
    "beta1s = list(np.logspace(-1, -1e-3, num=10))\n",
    "scores_beta1s = []\n",
    "timer = clock()\n",
    "\n",
    "for h, beta1 in enumerate(beta1s):\n",
    "    scores_beta1s.append(list())\n",
    "    clf = MLPClassifier(hidden_layer_sizes=(100,), solver='adam', max_iter=200, activation='relu',\n",
    "                        learning_rate_init=0.01, beta_1=beta1)\n",
    "    for i in range(5):\n",
    "        j = train3.sample(n=5000)\n",
    "        clf.fit(j.iloc[0:4000, 1:], j.iloc[0:4000, 0])\n",
    "        scores_beta1s[h].append(clf.score(j.iloc[4001:, 1:], j.iloc[4001:, 0]))\n",
    "    print('Beta1 value used: ', beta1, \n",
    "          '\\tTime taken: ', round(clock() - timer, 3), ' seconds')\n",
    "    timer = clock()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TpkDnzvDQQ7G/MrLy2acRR01fs6bOX93nrebzldktIhPJibuPSxYSG0Y007wF7JPTRNMG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b20ab9f908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1 \t 0.941741741742\n",
      "0.129121927361 \t 0.936536536537\n",
      "0.166724721255 \t 0.934534534535\n",
      "0.215278173472 \t 0.940540540541\n",
      "0.277971326776 \t 0.942942942943\n",
      "0.358921934645 \t 0.944144144144\n",
      "0.463446919736 \t 0.947347347347\n",
      "0.59841159506 \t 0.936536536537\n",
      "0.772680585096 \t 0.935935935936\n",
      "0.997700063823 \t 0.929329329329\n"
     ]
    }
   ],
   "source": [
    "beta1_avg = list(np.mean(i) for i in scores_beta1s)\n",
    "beta1_std = list(np.std(i) for i in scores_beta1s)\n",
    "\n",
    "plt.errorbar(beta1s, beta1_avg, yerr=beta1_std)\n",
    "plt.title('clf_scores by beta_1 value')\n",
    "plt.ylabel('clf_score')\n",
    "plt.xlabel('beta_1 value')\n",
    "plt.xscale('log')\n",
    "plt.xlim(9e-2, 1.1)\n",
    "plt.show()\n",
    "\n",
    "for i, j in zip(beta1s, beta1_avg):\n",
    "    print(i, '\\t', j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, so no huge difference overall; 0.5 or so is fine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beta1 value used:  0.3 \tTime taken:  15.603  seconds\n",
      "Beta1 value used:  0.333333333333 \tTime taken:  17.594  seconds\n",
      "Beta1 value used:  0.366666666667 \tTime taken:  18.084  seconds\n",
      "Beta1 value used:  0.4 \tTime taken:  18.371  seconds\n",
      "Beta1 value used:  0.433333333333 \tTime taken:  18.465  seconds\n",
      "Beta1 value used:  0.466666666667 \tTime taken:  18.314  seconds\n",
      "Beta1 value used:  0.5 \tTime taken:  15.809  seconds\n",
      "Beta1 value used:  0.533333333333 \tTime taken:  15.296  seconds\n",
      "Beta1 value used:  0.566666666667 \tTime taken:  16.477  seconds\n",
      "Beta1 value used:  0.6 \tTime taken:  17.225  seconds\n"
     ]
    }
   ],
   "source": [
    "beta1s = list(np.linspace(0.3, 0.6, num=10))\n",
    "scores_beta1s = []\n",
    "timer = clock()\n",
    "\n",
    "for h, beta1 in enumerate(beta1s):\n",
    "    scores_beta1s.append(list())\n",
    "    clf = MLPClassifier(hidden_layer_sizes=(100,), solver='adam', max_iter=200, activation='relu',\n",
    "                        learning_rate_init=0.01, beta_1=beta1)\n",
    "    for i in range(5):\n",
    "        j = train3.sample(n=5000)\n",
    "        clf.fit(j.iloc[0:4000, 1:], j.iloc[0:4000, 0])\n",
    "        scores_beta1s[h].append(clf.score(j.iloc[4001:, 1:], j.iloc[4001:, 0]))\n",
    "    print('Beta1 value used: ', beta1, \n",
    "          '\\tTime taken: ', round(clock() - timer, 3), ' seconds')\n",
    "    timer = clock()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZwzljgH5m9oS7V5jZTsDJQJvkejPMbE/gRGD7WvKeAJwAvE/0m7kYGGdm27j7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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1de0088c3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3 \t 0.93973973974\n",
      "0.333333333333 \t 0.939339339339\n",
      "0.366666666667 \t 0.942542542543\n",
      "0.4 \t 0.941741741742\n",
      "0.433333333333 \t 0.944944944945\n",
      "0.466666666667 \t 0.949149149149\n",
      "0.5 \t 0.941341341341\n",
      "0.533333333333 \t 0.941341341341\n",
      "0.566666666667 \t 0.935335335335\n",
      "0.6 \t 0.938538538539\n"
     ]
    }
   ],
   "source": [
    "beta1_avg = list(np.mean(i) for i in scores_beta1s)\n",
    "beta1_std = list(np.std(i) for i in scores_beta1s)\n",
    "\n",
    "plt.errorbar(beta1s, beta1_avg, yerr=beta1_std)\n",
    "plt.title('clf_scores by beta_1 value')\n",
    "plt.ylabel('clf_score')\n",
    "plt.xlabel('beta_1 value')\n",
    "# plt.xscale('log')\n",
    "plt.xlim(0.27, 0.63)\n",
    "plt.show()\n",
    "\n",
    "for i, j in zip(beta1s, beta1_avg):\n",
    "    print(i, '\\t', j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eh, no clear winner in that range.  The scores are all between 0.93 and 0.96.  0.45 should be fine for beta1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beta_2 value used:  0.1 \tTime taken:  7.154  seconds\n",
      "Beta_2 value used:  0.129154636068 \tTime taken:  7.871  seconds\n",
      "Beta_2 value used:  0.166809200179 \tTime taken:  11.195  seconds\n",
      "Beta_2 value used:  0.21544181542 \tTime taken:  8.099  seconds\n",
      "Beta_2 value used:  0.278253092644 \tTime taken:  10.392  seconds\n",
      "Beta_2 value used:  0.359376769153 \tTime taken:  10.889  seconds\n",
      "Beta_2 value used:  0.464151758314 \tTime taken:  11.458  seconds\n",
      "Beta_2 value used:  0.599473514255 \tTime taken:  11.322  seconds\n",
      "Beta_2 value used:  0.774247835661 \tTime taken:  10.548  seconds\n",
      "Beta_2 value used:  0.999976974414 \tTime taken:  15.533  seconds\n"
     ]
    }
   ],
   "source": [
    "beta2s = list(np.logspace(-1, -1e-5, num=10))\n",
    "scores_beta2s = []\n",
    "timer = clock()\n",
    "\n",
    "for h, beta2 in enumerate(beta2s):\n",
    "    scores_beta2s.append(list())\n",
    "    clf = MLPClassifier(hidden_layer_sizes=(100,), solver='adam', max_iter=200, activation='relu',\n",
    "                        learning_rate_init=0.01, beta_1=0.45, beta_2=beta2)\n",
    "    for i in range(5):\n",
    "        j = train3.sample(n=5000)\n",
    "        clf.fit(j.iloc[0:4000, 1:], j.iloc[0:4000, 0])\n",
    "        scores_beta2s[h].append(clf.score(j.iloc[4001:, 1:], j.iloc[4001:, 0]))\n",
    "    print('Beta_2 value used: ', beta2, \n",
    "          '\\tTime taken: ', round(clock() - timer, 3), ' seconds')\n",
    "    timer = clock()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pv+OO+P91773Z+6JSF0pORFq4H3+M5OSEE5puNkNjatcOevaMHWKvuSaWxt9w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\nv9sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1de5d8e7ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1 \t 0.922122122122\n",
      "0.129154636068 \t 0.926526526527\n",
      "0.166809200179 \t 0.928928928929\n",
      "0.21544181542 \t 0.939339339339\n",
      "0.278253092644 \t 0.936736736737\n",
      "0.359376769153 \t 0.935735735736\n",
      "0.464151758314 \t 0.928528528529\n",
      "0.599473514255 \t 0.93953953954\n",
      "0.774247835661 \t 0.935535535536\n",
      "0.999976974414 \t 0.939139139139\n"
     ]
    }
   ],
   "source": [
    "beta2_avg = list(np.mean(i) for i in scores_beta2s)\n",
    "beta2_std = list(np.std(i) for i in scores_beta2s)\n",
    "\n",
    "plt.errorbar(beta2s, beta2_avg, yerr=beta2_std)\n",
    "plt.title('clf_scores by beta_2 value')\n",
    "plt.ylabel('clf_score')\n",
    "plt.xlabel('beta_2 value')\n",
    "plt.xscale('log')\n",
    "plt.xlim(0.08, 1.1)\n",
    "plt.show()\n",
    "\n",
    "for i, j in zip(beta2s, beta2_avg):\n",
    "    print(i, '\\t', j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not a big effect, but higher values seem better, overall.  The default of 0.999 is probably fine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epsilon value used:  1e-10 \tTime taken:  19.212  seconds\n",
      "Epsilon value used:  1e-09 \tTime taken:  17.7  seconds\n",
      "Epsilon value used:  1e-08 \tTime taken:  18.326  seconds\n",
      "Epsilon value used:  1e-07 \tTime taken:  17.77  seconds\n",
      "Epsilon value used:  1e-06 \tTime taken:  18.24  seconds\n",
      "Epsilon value used:  1e-05 \tTime taken:  18.639  seconds\n",
      "Epsilon value used:  0.0001 \tTime taken:  21.418  seconds\n",
      "Epsilon value used:  0.001 \tTime taken:  25.25  seconds\n",
      "Epsilon value used:  0.01 \tTime taken:  52.173  seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.\n",
      "  % (), ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epsilon value used:  0.1 \tTime taken:  117.198  seconds\n"
     ]
    }
   ],
   "source": [
    "epsilons = list(np.logspace(-10, -1, num=10))\n",
    "scores_epsilons = []\n",
    "timer = clock()\n",
    "\n",
    "for h, epsilon1 in enumerate(epsilons):\n",
    "    scores_epsilons.append(list())\n",
    "    clf = MLPClassifier(hidden_layer_sizes=(100,), solver='adam', max_iter=200, activation='relu',\n",
    "                        learning_rate_init=0.01, beta_1=0.45, beta_2=0.999, epsilon=epsilon1)\n",
    "    for i in range(5):\n",
    "        j = train3.sample(n=5000)\n",
    "        clf.fit(j.iloc[0:4000, 1:], j.iloc[0:4000, 0])\n",
    "        scores_epsilons[h].append(clf.score(j.iloc[4001:, 1:], j.iloc[4001:, 0]))\n",
    "    print('Epsilon value used: ', epsilon1, \n",
    "          '\\tTime taken: ', round(clock() - timer, 3), ' seconds')\n",
    "    timer = clock()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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65BGfW+LKK310TWO2007+obzrrt7Z9ZJLfPunn/rw14aUnE6Z4sOfr7rKl1B4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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1de352116a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1e-10 \t 0.937337337337\n",
      "1e-09 \t 0.945545545546\n",
      "1e-08 \t 0.934534534535\n",
      "1e-07 \t 0.946346346346\n",
      "1e-06 \t 0.941141141141\n",
      "1e-05 \t 0.940540540541\n",
      "0.0001 \t 0.945345345345\n",
      "0.001 \t 0.93993993994\n",
      "0.01 \t 0.929329329329\n",
      "0.1 \t 0.924124124124\n"
     ]
    }
   ],
   "source": [
    "epsilon_avg = list(np.mean(i) for i in scores_epsilons)\n",
    "epsilon_std = list(np.std(i) for i in scores_epsilons)\n",
    "\n",
    "plt.errorbar(epsilons, epsilon_avg, yerr=epsilon_std)\n",
    "plt.title('clf_scores by epsilon value')\n",
    "plt.ylabel('clf_score')\n",
    "plt.xlabel('epsilon value')\n",
    "plt.xscale('log')\n",
    "plt.xlim(5e-11, 0.15)\n",
    "plt.show()\n",
    "\n",
    "for i, j in zip(epsilons, epsilon_avg):\n",
    "    print(i, '\\t', j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again: no huge diff; we're still seeing scores lingering around 0.94.  But again, I'll take the max score seen here:1e-7 will do."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 10: Everything else\n",
    "\n",
    "I'll conclude with permuting the alpha, tol, and learning_rate_init values, too (unless any of the runs ends up taking too long).  I foresee very little chance that any will perform much better when tweaked from their current settings, but it will be quick to check that.  Each will need a log range:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha value used:  1e-06 \tTime taken:  19.88  seconds\n",
      "Alpha value used:  2.27584592607e-06 \tTime taken:  22.437  seconds\n",
      "Alpha value used:  5.17947467923e-06 \tTime taken:  21.96  seconds\n",
      "Alpha value used:  1.17876863479e-05 \tTime taken:  20.018  seconds\n",
      "Alpha value used:  2.68269579528e-05 \tTime taken:  18.857  seconds\n",
      "Alpha value used:  6.10540229659e-05 \tTime taken:  18.767  seconds\n",
      "Alpha value used:  0.000138949549437 \tTime taken:  20.11  seconds\n",
      "Alpha value used:  0.000316227766017 \tTime taken:  19.724  seconds\n",
      "Alpha value used:  0.000719685673001 \tTime taken:  20.443  seconds\n",
      "Alpha value used:  0.00163789370695 \tTime taken:  20.939  seconds\n",
      "Alpha value used:  0.00372759372031 \tTime taken:  28.804  seconds\n",
      "Alpha value used:  0.00848342898244 \tTime taken:  35.124  seconds\n",
      "Alpha value used:  0.0193069772888 \tTime taken:  23.819  seconds\n",
      "Alpha value used:  0.0439397056076 \tTime taken:  13.927  seconds\n",
      "Alpha value used:  0.1 \tTime taken:  9.455  seconds\n"
     ]
    }
   ],
   "source": [
    "alphas = list(np.logspace(-6, -1, num=15))\n",
    "scores_alphas = []\n",
    "timer = clock()\n",
    "\n",
    "for h, alpha1 in enumerate(alphas):\n",
    "    scores_alphas.append(list())\n",
    "    clf = MLPClassifier(hidden_layer_sizes=(100,), solver='adam', max_iter=200, activation='relu',\n",
    "                        learning_rate_init=0.01, beta_1=0.45, beta_2=0.999, epsilon=1e-7, alpha=alpha1)\n",
    "    for i in range(5):\n",
    "        j = train3.sample(n=5000)\n",
    "        clf.fit(j.iloc[0:4000, 1:], j.iloc[0:4000, 0])\n",
    "        scores_alphas[h].append(clf.score(j.iloc[4001:5000, 1:], j.iloc[4001:5000, 0]))\n",
    "    print('Alpha value used: ', alpha1, '\\tTime taken: ', round(clock() - timer, 3), ' seconds')\n",
    "    timer = clock()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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3jlVWif4fN97YtuMtWBDDY6+6Kpq/Jk5sfg0OkazlZ0F98cXoiP6HP8SQe6ms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\nJElSZwwekiSpMwYPSZLUmZ8AcrUqeKOB9QYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1de009d66a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1e-06 \t 0.942742742743\n",
      "2.27584592607e-06 \t 0.938738738739\n",
      "5.17947467923e-06 \t 0.935335335335\n",
      "1.17876863479e-05 \t 0.941541541542\n",
      "2.68269579528e-05 \t 0.942342342342\n",
      "6.10540229659e-05 \t 0.931531531532\n",
      "0.000138949549437 \t 0.942742742743\n",
      "0.000316227766017 \t 0.945945945946\n",
      "0.000719685673001 \t 0.936936936937\n",
      "0.00163789370695 \t 0.937337337337\n",
      "0.00372759372031 \t 0.942142142142\n",
      "0.00848342898244 \t 0.934734734735\n",
      "0.0193069772888 \t 0.900900900901\n",
      "0.0439397056076 \t 0.883083083083\n",
      "0.1 \t 0.943143143143\n"
     ]
    }
   ],
   "source": [
    "alphas_avg = list(np.mean(i) for i in scores_alphas)\n",
    "alphas_std = list(np.std(i) for i in scores_alphas)\n",
    "\n",
    "plt.errorbar(alphas, alphas_avg, yerr=alphas_std)\n",
    "plt.title('clf_scores by alpha')\n",
    "plt.ylabel('clf_score')\n",
    "plt.xlabel('alpha value')\n",
    "plt.xscale('log')\n",
    "plt.xlim(7e-7, 0.15)\n",
    "plt.show()\n",
    "\n",
    "for i, j in zip(alphas, alphas_avg):\n",
    "    print(i, '\\t', j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, the higher alpha values are clearly poorer (although 0.1 appears to be ok, for some reason); it's safest to stick with something in the middle of that range, I think: alpha=0.0003, which is very close to the default value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.\n",
      "  % (), ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tolerance value used:  1e-06 \tTime taken:  148.315  seconds\n",
      "Tolerance value used:  2.68269579528e-06 \tTime taken:  81.404  seconds\n",
      "Tolerance value used:  7.19685673001e-06 \tTime taken:  56.064  seconds\n",
      "Tolerance value used:  1.93069772888e-05 \tTime taken:  35.514  seconds\n",
      "Tolerance value used:  5.17947467923e-05 \tTime taken:  28.044  seconds\n",
      "Tolerance value used:  0.000138949549437 \tTime taken:  19.961  seconds\n",
      "Tolerance value used:  0.000372759372031 \tTime taken:  14.097  seconds\n",
      "Tolerance value used:  0.001 \tTime taken:  11.142  seconds\n"
     ]
    }
   ],
   "source": [
    "tols = list(np.logspace(-6, -3, num=8))\n",
    "scores_tols = []\n",
    "timer = clock()\n",
    "\n",
    "for h, tol1 in enumerate(tols):\n",
    "    scores_tols.append(list())\n",
    "    clf = MLPClassifier(hidden_layer_sizes=(100,), solver='adam', max_iter=200, activation='relu',\n",
    "                        learning_rate_init=0.01, beta_1=0.45, beta_2=0.999, epsilon=1e-7, alpha=3e-4, tol=tol1)\n",
    "    for i in range(5):\n",
    "        j = train3.sample(n=5000)\n",
    "        clf.fit(j.iloc[0:4000, 1:], j.iloc[0:4000, 0])\n",
    "        scores_tols[h].append(clf.score(j.iloc[4001:, 1:], j.iloc[4001:, 0]))\n",
    "    print('Tolerance value used: ', tol1, '\\tTime taken: ', round(clock() - timer, 3), ' seconds')\n",
    "    timer = clock()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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3iEwkJ+5+W7I41dlEs8oUYLecJppewOo5u1QBxxNzmMwDHgG2zxuyuRyx1kMvYprvicB2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1de5cceb9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tols_avg = list(np.mean(i) for i in scores_tols)\n",
    "tols_std = list(np.std(i) for i in scores_tols)\n",
    "\n",
    "plt.errorbar(tols, tols_avg, yerr=tols_std)\n",
    "plt.title('clf_scores by tolerance score')\n",
    "plt.ylabel('clf_score')\n",
    "plt.xlabel('alpha value')\n",
    "plt.xscale('log')\n",
    "plt.xlim(7e-7, 0.0015)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Huh.  The choice of tol values over a pretty broad range made surprisingly little difference in output model scores.  In any case, 1e-4 appears to be as good a choice as any other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 11: Hidden layers, revisited\n",
    "\n",
    "Ok, I've permuted all of the variables I want to, and converged on values startlingly similar to the defaults; the output model score hasn't budged much.  This outcome is kind of depressing, but it is at least somewhat reassuring in that it suggests that a simple neural network approach as implemented in sklearn is a very robust way to score around 94% accuracy in classifying MNIST digit data with 4,000 training samples.  The \".score()\" method (outputting accuracy) is built-in to the MLPClassifier module, but perhaps I should've been looking for alternate metrics, such as recall and precision along the way?  For now, I'll go with the settings I have determined to work best from the above, and re-run the comparison of one to multiple hidden layers (performed in part 4), with a larger subset of training data, in anticipation of finalizing the project and submitting a model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of hidden layer neurons:  (100,) \tTime taken:  97.02  seconds\n",
      "Number of hidden layer neurons:  (100, 100) \tTime taken:  99.331  seconds\n",
      "Number of hidden layer neurons:  (100, 100, 100) \tTime taken:  125.985  seconds\n",
      "Number of hidden layer neurons:  (100, 100, 100, 100) \tTime taken:  138.879  seconds\n"
     ]
    }
   ],
   "source": [
    "neurons_scores3 = []\n",
    "timer = clock()\n",
    "sizes3 = [(100,), (100, 100), (100, 100, 100), (100, 100, 100, 100)]\n",
    "layers3 = []\n",
    "neurons3 = []\n",
    "\n",
    "\n",
    "for h, size in enumerate(sizes3):\n",
    "    neurons_scores3.append(list())\n",
    "    layers3.append(list())\n",
    "    neurons3.append(list())\n",
    "    clf = MLPClassifier(hidden_layer_sizes=size, solver='adam', max_iter=200, activation='relu',\n",
    "                        learning_rate_init=0.01, beta_1=0.45, beta_2=0.999, epsilon=1e-7, alpha=3e-4, tol=1e-4)\n",
    "\n",
    "    for i in range(5):\n",
    "        j = train3.sample(frac=1)\n",
    "        clf.fit(j.iloc[0:30000, 1:], j.iloc[0:30000, 0])\n",
    "        neurons_scores3[h].append(clf.score(j.iloc[30001:, 1:], j.iloc[30001:, 0]))    \n",
    "    print('Number of hidden layer neurons: ', size, '\\tTime taken: ', round(clock() - timer, 3), ' seconds')\n",
    "    layers3[h].append(clf.n_layers_)\n",
    "    neurons3[h].append(clf.coefs_[k] for k in list(range(len(clf.coefs_))))\n",
    "    timer = clock()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wG6D1jYBnbNnIcXBiZlYjk1Z00UELtNa7JrXRBHQAnSvaYW/P2LKRM6TgRNLO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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1de3553bcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(neurons_scores3)\n",
    "plt.title('clf_scores by number of hidden layers')\n",
    "# plt.xlabel('solver')\n",
    "plt.ylabel('clf_score')\n",
    "plt.xticks(range(1,5), list(range(1, 6)))\n",
    "plt.xlabel('Number of hidden layers \\n(each 100 neurons)')\n",
    "# plt.xlim(75, 115)\n",
    "# plt.ylim(0.6, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, that's a little surprising.  While the number of hidden layers made little difference with these settings and this large a training set, it actually did best with only one layer.  Another variable that might make a difference is fan out: should there be fewer neurons in the later hidden layers?  I tried the equal number in each layer because it was easiest.  Anyways, I'm not particularly inclined to test it here.  A mean accuracy of ~97% apparently isn't bad for a simple neural network (although it's very little improved from the starting point of this notebook, with the default settings, and very little improved over my random forest trial).\n",
    "\n",
    "Actually; that's a good sanity check to perform: compare my \"optimized\" arguments (above) with the same number of samples, training with the default hyperparameters in everything (below).  Have I improved anything at all?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of hidden layer neurons:  (100,) \tTime taken:  327.562  seconds\n",
      "Number of hidden layer neurons:  (100, 100) \tTime taken:  309.575  seconds\n",
      "Number of hidden layer neurons:  (100, 100, 100) \tTime taken:  298.614  seconds\n",
      "Number of hidden layer neurons:  (100, 100, 100, 100) \tTime taken:  285.098  seconds\n"
     ]
    }
   ],
   "source": [
    "neurons_scores3 = []\n",
    "timer = clock()\n",
    "sizes3 = [(100,), (100, 100), (100, 100, 100), (100, 100, 100, 100)]\n",
    "layers3 = []\n",
    "neurons3 = []\n",
    "\n",
    "\n",
    "for h, size in enumerate(sizes3):\n",
    "    neurons_scores3.append(list())\n",
    "    layers3.append(list())\n",
    "    neurons3.append(list())\n",
    "    clf = MLPClassifier()\n",
    "\n",
    "    for i in range(5):\n",
    "        j = train3.sample(frac=1)\n",
    "        clf.fit(j.iloc[0:30000, 1:], j.iloc[0:30000, 0])\n",
    "        neurons_scores3[h].append(clf.score(j.iloc[30001:, 1:], j.iloc[30001:, 0]))    \n",
    "    print('Number of hidden layer neurons: ', size, '\\tTime taken: ', round(clock() - timer, 3), ' seconds')\n",
    "    layers3[h].append(clf.n_layers_)\n",
    "    neurons3[h].append(clf.coefs_[k] for k in list(range(len(clf.coefs_))))\n",
    "    timer = clock()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9xseR5vz/a1a3joj4/fZfvdVLNrPmD0bI9lhElJoqu8OT9D7gNaTFzv5+hOxmLUPSXNKi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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1de355efc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(neurons_scores3)\n",
    "plt.title('clf_scores by number of hidden layers')\n",
    "# plt.xlabel('solver')\n",
    "plt.ylabel('clf_score')\n",
    "plt.xticks(range(1,5), list(range(1, 6)))\n",
    "plt.xlabel('Number of hidden layers \\n(each 100 neurons)')\n",
    "# plt.xlim(75, 115)\n",
    "# plt.ylim(0.6, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, that's a big negatory.  The highest mean score with my \"optimized\" hyperparameters is about 96.9%; the highest mean with default parameters is 97.08%.  So there's really no great difference, and my results are slightly worse.  This surprises me personally, but it was still kind of a worthwhile exercise.  Like I said: the biggest takeaway is that, at least within the range of conditions I tried, MLPClassifier is pretty robust to hyperparameter permutation: the defaults work as well as anything I picked."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Part 12: Wrapping up\n",
    "\n",
    "While there's no great reason to expect that the individual options I've chosen based on their performance in isolation will present a best solution in conjunction, it's the best option I have right now to just throw them together, train on a large dataset, and prepare for submission.\n",
    "\n",
    "So there you have it.  After lots of permuting options within MLPClassifier, building models, and visualizing their scores, the only values that I ended up changing from the default were learning_rate_init, beta_1, epsilon, and alpha, and all of these were by relatively small amounts.  The difference from the default output isn't that great, either."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Part 13: Oh, wait.\n",
    "\n",
    "After all that work looking at how these hyperparameters compare one-by-one, a friend told me about a tool called [grid search](http://scikit-learn.org/stable/modules/grid_search.html) available within sklearn, which automates the process of searching through a subset of hyperparameter space and comparing results, usually with [cross-validation](http://scikit-learn.org/stable/modules/cross_validation.html#cross-validation) of the results using another sklearn tool (instead of the manual partition of the randomly sampled data which I have used above, which is presumably less robust).  A slightly similar [example](http://scikit-learn.org/stable/auto_examples/model_selection/grid_search_digits.html#sphx-glr-auto-examples-model-selection-grid-search-digits-py) is available in the docs.\n",
    "\n",
    "I'm sure that the trials I ran above will take a prohibitively long time on my laptop to compare exhaustively in an all-vs-all comparison, so I'll try the related function RandomizedSearchCV, which drastically reduces the search space among hyperparameters by sampling.  Also, since I don't really expect a performance boost relative to my own arduous approach above, I'll just run a greatly pared-down hyperparameter space to demonstrate this tool's use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "\n",
    "sizes3 = [(100,), (100, 100), (100, 100, 100), (100, 100, 100, 100)]\n",
    "tols = list(np.logspace(-6, -3, num=8))\n",
    "solvers = ['lbfgs', 'adam']\n",
    "activation = ['logistic', 'tanh', 'relu']\n",
    "learning = ['constant', 'invscaling', 'adaptive']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, re-run the default network, to demonstrate time and accuracy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.938938938939\n",
      "CPU times: user 37.9 s, sys: 5.55 s, total: 43.4 s\n",
      "Wall time: 23.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "clf = MLPClassifier()\n",
    "clf.fit(train3.iloc[0:4000, 1:], train3.iloc[0:4000, 0])\n",
    "print(clf.score(train3.iloc[4001:5000, 1:], train3.iloc[4001:5000, 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
      "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
      "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
      "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
      "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
      "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
      "       verbose=False, warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "print(clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, run RandomizedSearchCV, permuting just neural network sizes, solvers, and activation functions.  I'll just try 5 iterations (with 5 cross-validations each, so at least 25x times the default runtime above; maybe 10 min).  5 iterations isn't nearly enough to give a fair comparison between 4 * 2 * 3 hyperparameter space represented by these three arguments, but it'll at least show how this tool works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "param_distributions = dict(activation=activation, hidden_layer_sizes=sizes3, solver=solvers)\n",
    "clf = RandomizedSearchCV(MLPClassifier(), param_distributions=param_distributions, \n",
    "                         cv=5, scoring='accuracy', n_iter=5, n_jobs=-1)\n",
    "X = train3.iloc[:, 1:]\n",
    "y = train3.iloc[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3min 18s, sys: 34.6 s, total: 3min 53s\n",
      "Wall time: 1h 22min 58s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise',\n",
       "          estimator=MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
       "       verbose=False, warm_start=False),\n",
       "          fit_params={}, iid=True, n_iter=5, n_jobs=-1,\n",
       "          param_distributions={'hidden_layer_sizes': [(100,), (100, 100), (100, 100, 100), (100, 100, 100, 100)], 'solver': ['lbfgs', 'adam'], 'activation': ['logistic', 'tanh', 'relu']},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score=True, scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "clf.fit(X, y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'hidden_layer_sizes': (100,), 'solver': 'adam', 'activation': 'relu'} \n",
      " 0.971452380952 \n",
      " MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
      "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
      "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
      "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
      "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
      "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
      "       verbose=False, warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "print(clf.best_params_, '\\n', clf.best_score_, '\\n', clf.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, that took over an hour on my ancient mac, and found that the best outcome was with the default single hidden layer, the default 'relu' activation function, and the default 'adam' solver.  This supports my previous conclusion that the default settings still seem best for this data set, and the accuracy is still hovering around 97%.  Just to be thorough, I'll read in the results to pandas, see what combinations of these arguments it tried, and whether the accuracy scores varied much across them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5, 23)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>param_activation</th>\n",
       "      <th>param_hidden_layer_sizes</th>\n",
       "      <th>param_solver</th>\n",
       "      <th>params</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>819.061999</td>\n",
       "      <td>0.549951</td>\n",
       "      <td>0.970024</td>\n",
       "      <td>0.999958</td>\n",
       "      <td>tanh</td>\n",
       "      <td>(100, 100, 100, 100)</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>{'hidden_layer_sizes': (100, 100, 100, 100), '...</td>\n",
       "      <td>3</td>\n",
       "      <td>0.970256</td>\n",
       "      <td>...</td>\n",
       "      <td>0.969282</td>\n",
       "      <td>0.999970</td>\n",
       "      <td>0.968917</td>\n",
       "      <td>0.999940</td>\n",
       "      <td>0.970581</td>\n",
       "      <td>0.999940</td>\n",
       "      <td>514.444409</td>\n",
       "      <td>0.077452</td>\n",
       "      <td>0.000807</td>\n",
       "      <td>0.000024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>255.224919</td>\n",
       "      <td>0.183490</td>\n",
       "      <td>0.971452</td>\n",
       "      <td>0.999244</td>\n",
       "      <td>relu</td>\n",
       "      <td>(100,)</td>\n",
       "      <td>adam</td>\n",
       "      <td>{'hidden_layer_sizes': (100,), 'solver': 'adam...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.972397</td>\n",
       "      <td>...</td>\n",
       "      <td>0.968687</td>\n",
       "      <td>0.999851</td>\n",
       "      <td>0.970942</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.973678</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>45.532469</td>\n",
       "      <td>0.023168</td>\n",
       "      <td>0.001659</td>\n",
       "      <td>0.001439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>154.178520</td>\n",
       "      <td>0.286214</td>\n",
       "      <td>0.971405</td>\n",
       "      <td>0.997815</td>\n",
       "      <td>relu</td>\n",
       "      <td>(100, 100, 100)</td>\n",
       "      <td>adam</td>\n",
       "      <td>{'hidden_layer_sizes': (100, 100, 100), 'solve...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.969899</td>\n",
       "      <td>...</td>\n",
       "      <td>0.971187</td>\n",
       "      <td>0.999077</td>\n",
       "      <td>0.968798</td>\n",
       "      <td>0.997976</td>\n",
       "      <td>0.974035</td>\n",
       "      <td>0.998482</td>\n",
       "      <td>23.128809</td>\n",
       "      <td>0.047749</td>\n",
       "      <td>0.001945</td>\n",
       "      <td>0.001338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>224.134807</td>\n",
       "      <td>0.319989</td>\n",
       "      <td>0.965429</td>\n",
       "      <td>0.999905</td>\n",
       "      <td>logistic</td>\n",
       "      <td>(100,)</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>{'hidden_layer_sizes': (100,), 'solver': 'lbfg...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.968709</td>\n",
       "      <td>...</td>\n",
       "      <td>0.964401</td>\n",
       "      <td>0.999911</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.999940</td>\n",
       "      <td>0.966293</td>\n",
       "      <td>0.999940</td>\n",
       "      <td>44.772799</td>\n",
       "      <td>0.079272</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.000044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>421.749656</td>\n",
       "      <td>0.356897</td>\n",
       "      <td>0.964976</td>\n",
       "      <td>0.999786</td>\n",
       "      <td>logistic</td>\n",
       "      <td>(100, 100)</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>{'hidden_layer_sizes': (100, 100), 'solver': '...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.968590</td>\n",
       "      <td>...</td>\n",
       "      <td>0.965472</td>\n",
       "      <td>0.999911</td>\n",
       "      <td>0.963558</td>\n",
       "      <td>0.999762</td>\n",
       "      <td>0.964745</td>\n",
       "      <td>0.999792</td>\n",
       "      <td>104.662181</td>\n",
       "      <td>0.108437</td>\n",
       "      <td>0.002071</td>\n",
       "      <td>0.000115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  mean_score_time  mean_test_score  mean_train_score  \\\n",
       "0     819.061999         0.549951         0.970024          0.999958   \n",
       "1     255.224919         0.183490         0.971452          0.999244   \n",
       "2     154.178520         0.286214         0.971405          0.997815   \n",
       "3     224.134807         0.319989         0.965429          0.999905   \n",
       "4     421.749656         0.356897         0.964976          0.999786   \n",
       "\n",
       "  param_activation param_hidden_layer_sizes param_solver  \\\n",
       "0             tanh     (100, 100, 100, 100)        lbfgs   \n",
       "1             relu                   (100,)         adam   \n",
       "2             relu          (100, 100, 100)         adam   \n",
       "3         logistic                   (100,)        lbfgs   \n",
       "4         logistic               (100, 100)        lbfgs   \n",
       "\n",
       "                                              params  rank_test_score  \\\n",
       "0  {'hidden_layer_sizes': (100, 100, 100, 100), '...                3   \n",
       "1  {'hidden_layer_sizes': (100,), 'solver': 'adam...                1   \n",
       "2  {'hidden_layer_sizes': (100, 100, 100), 'solve...                2   \n",
       "3  {'hidden_layer_sizes': (100,), 'solver': 'lbfg...                4   \n",
       "4  {'hidden_layer_sizes': (100, 100), 'solver': '...                5   \n",
       "\n",
       "   split0_test_score       ...         split2_test_score  split2_train_score  \\\n",
       "0           0.970256       ...                  0.969282            0.999970   \n",
       "1           0.972397       ...                  0.968687            0.999851   \n",
       "2           0.969899       ...                  0.971187            0.999077   \n",
       "3           0.968709       ...                  0.964401            0.999911   \n",
       "4           0.968590       ...                  0.965472            0.999911   \n",
       "\n",
       "   split3_test_score  split3_train_score  split4_test_score  \\\n",
       "0           0.968917            0.999940           0.970581   \n",
       "1           0.970942            1.000000           0.973678   \n",
       "2           0.968798            0.997976           0.974035   \n",
       "3           0.962963            0.999940           0.966293   \n",
       "4           0.963558            0.999762           0.964745   \n",
       "\n",
       "   split4_train_score  std_fit_time  std_score_time  std_test_score  \\\n",
       "0            0.999940    514.444409        0.077452        0.000807   \n",
       "1            1.000000     45.532469        0.023168        0.001659   \n",
       "2            0.998482     23.128809        0.047749        0.001945   \n",
       "3            0.999940     44.772799        0.079272        0.001953   \n",
       "4            0.999792    104.662181        0.108437        0.002071   \n",
       "\n",
       "   std_train_score  \n",
       "0         0.000024  \n",
       "1         0.001439  \n",
       "2         0.001338  \n",
       "3         0.000044  \n",
       "4         0.000115  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_results = pd.DataFrame.from_dict(data=clf.cv_results_)\n",
    "print(clf_results.shape)\n",
    "clf_results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, so none of them made a big difference in performance.  I think I've beaten this dead horse for long enough.  Time to train the default MLPClassifier on the full training set, predict test values, and submit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 46s, sys: 29.6 s, total: 3min 16s\n",
      "Wall time: 1min 59s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "clf = MLPClassifier()\n",
    "clf_final = clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "results=clf_final.predict(test2)\n",
    "\n",
    "test2['Label'] = pd.Series(results).astype(int)\n",
    "test2['ImageId'] = test2.index +1\n",
    "sub = test2[['ImageId','Label']]\n",
    "\n",
    "sub.to_csv('digit_nn_submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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E5rSNh425hm1zbtt6nTtK0sby5xH+R9as+cmZ07gOFHpOb7O/uGxzivdpY8yW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\npVzq/x5Zlb6d6jEJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114002a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot some of the numbers\n",
    "plt.figure(figsize=(5, 5))\n",
    "for digit in range(0,30):\n",
    "    plt.subplot(3,10,digit+1)\n",
    "    grid_data = test2.iloc[digit,:-2].as_matrix().reshape(28,28)  # reshape from 1d to 2d pixel array\n",
    "    plt.imshow(grid_data, interpolation = \"none\", cmap = \"bone_r\", label=train.iloc[digit,0])\n",
    "    title = int(test2.iloc[digit,-2])\n",
    "    plt.title(title)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Huh.  Just like the random forest, they both seem to think the fourth digit is a nine, when I'm betting by eyeballing it that it should be a zero.  Also, the 16th digit is given as a 5 here, when it probably should be a 3.  Oh well.  Submit!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Update: Kaggle says it's slightly better than the random forest model:\n",
    "\"Your submission scored 0.97114, which is an improvement of your previous score of 0.96657. Great job!\"\n",
    "\n",
    "I'm so glad those hours of work finally paid off.\n",
    "\n",
    "Next up: dimensional reduction and support vector machines!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}