gram/faye.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pick me some facies, Robot.\n",
    "Hi. My name's <a href=\"https://gra.m-gan.sl\">gram</a>. My facies classification robot's name is Faye. Run this notebook to see how Faye was built in <a href=\"https://www.lua.org/\">Lua</a> using the <a href=\"http://torch.ch/\">Torch7</a> toolkit and how well she does at picking facies after looking at well logs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "-- deps\n",
    "require 'nn'\n",
    "Plot = require 'itorch.Plot'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Faye isn't wonderful at reading common data formats (what do you think Torch is? Python?). I swapped out the CSV files provided in this contest for Faye's favorite file type, T7. If you love your robot, you'll do this for him or her, too. My instructions are <a href=\"data_loading.ipynb\">here</a>; they're based on a script built by <a href=\"https://github.com/locklin/torch-things/blob/master/csv2t7.sh\">Scott Locklin</a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "-- load data\n",
    "file = torch.DiskFile('dat/facies_vectors.t7', 'r')\n",
    "facies = file:readObject()\n",
    "file:close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's build these data into Lua tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "facies size: \t4149\tx\t9\t\n"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-- build tables\n",
    "print(\"facies size: \", facies:size()[1], \"x\", facies:size()[2])\n",
    "\n",
    "\t-- initialize\n",
    "training_data = {}\n",
    "depth = {}\n",
    "\n",
    "\t-- build the training wells into the table\n",
    "training_data[\"shrimplin\"] = facies[{{1,471},{3,9}}]\n",
    "training_data[\"alexander\"] = facies[{{472,937},{3,9}}]\n",
    "training_data[\"shankle\"] = facies[{{938,1386},{3,9}}]\n",
    "training_data[\"luke\"] = facies[{{1387,1847},{3,9}}]\n",
    "training_data[\"kimzey\"] = facies[{{1848,2286},{3,9}}]\n",
    "training_data[\"cross\"] = facies[{{2287,2787},{3,9}}]\n",
    "training_data[\"nolan\"] = facies[{{2788,3202},{3,9}}]\n",
    "training_data[\"recruit\"] = facies[{{3203,3282},{3,9}}]\n",
    "training_data[\"newby\"] = facies[{{3283,3745},{3,9}}]\n",
    "training_data[\"churchman\"] = facies[{{3746,4149},{3,9}}]\n",
    "\n",
    "\t-- build a depth log for plotting\n",
    "depth[\"shrimplin\"] = facies[{{1,471},{2}}]\n",
    "depth[\"alexander\"] = facies[{{472,937},{2}}]\n",
    "depth[\"shankle\"] = facies[{{938,1386},{2}}]\n",
    "depth[\"luke\"] = facies[{{1387,1847},{2}}]\n",
    "depth[\"kimzey\"] = facies[{{1848,2286},{2}}]\n",
    "depth[\"cross\"] = facies[{{2287,2787},{2}}]\n",
    "depth[\"nolan\"] = facies[{{2788,3202},{2}}]\n",
    "depth[\"recruit\"] = facies[{{3203,3282},{2}}]\n",
    "depth[\"newby\"] = facies[{{3283,3745},{2}}]\n",
    "depth[\"churchman\"] = facies[{{3746,4149},{2}}]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## No artificial preservatives\n",
    "Faye likes nice ordered data with a zero mean and a standard deviation of one. Many other robots like their data in this format as well. Please be kind to your robot. Only feed him/her high grade, all natural, conditioned data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "-- normalize the data\n",
    "\t-- training data\n",
    "mean = {}\n",
    "stdv = {}\n",
    "\n",
    "for key,value in pairs(training_data) do --over each well\n",
    "    mean[key] = torch.Tensor(7)\n",
    "    stdv[key] = torch.Tensor(7)\n",
    "    for i = 1, 7 do --over each log\n",
    "        mean[key][i] = training_data[key][{{},{i}}]:mean()\n",
    "        training_data[key][{{},{i}}]:add(-mean[key][i])\n",
    "        \n",
    "        stdv[key][i] = training_data[key][{{},{i}}]:std()\n",
    "        training_data[key][{{},{i}}]:div(stdv[key][i])\n",
    "    end\n",
    "end\n",
    "\n",
    "-- facies labels for training\n",
    "facies_labels = {}\n",
    "\n",
    "facies_labels[\"shrimplin\"] = facies[{{1,471},{1}}]\n",
    "facies_labels[\"alexander\"] = facies[{{472,937},{1}}]\n",
    "facies_labels[\"shankle\"] = facies[{{938,1386},{1}}]\n",
    "facies_labels[\"luke\"] = facies[{{1387,1847},{1}}]\n",
    "facies_labels[\"kimzey\"] = facies[{{1848,2286},{1}}]\n",
    "facies_labels[\"cross\"] = facies[{{2287,2787},{1}}]\n",
    "facies_labels[\"nolan\"] = facies[{{2788,3202},{1}}]\n",
    "facies_labels[\"recruit\"] = facies[{{3203,3282},{1}}]\n",
    "facies_labels[\"newby\"] = facies[{{3283,3745},{1}}]\n",
    "facies_labels[\"churchman\"] = facies[{{3746,4149},{1}}]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just to make sure we're feeding Faye the best data possible, let's plot one well's log suite."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "  Bokeh.load_models(all_models);\n",
       "  var model = Bokeh.Collections(modeltype).get(modelid);\n",
       "  $(\"#f58a716c-0919-4ed1-ce81-4532f5591c71\").html(''); // clear any previous plot in window_id\n",
       "  var view = new model.default_view({model: model, el: \"#f58a716c-0919-4ed1-ce81-4532f5591c71\"});\n",
       "    } else {\n",
       "  load_lib(bokehjs_url, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", new Date())\n",
       "      var modelid = \"3ec781e8-420c-4d75-cdcb-dee6b0d0068c\";\n",
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       "  var modeltype = \"Plot\";\n",
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       "  Bokeh.load_models(all_models);\n",
       "  var model = Bokeh.Collections(modeltype).get(modelid);\n",
       "  $(\"#f58a716c-0919-4ed1-ce81-4532f5591c71\").html(''); // clear any previous plot in window_id\n",
       "  var view = new model.default_view({model: model, el: \"#f58a716c-0919-4ed1-ce81-4532f5591c71\"});\n",
       "    } else {\n",
       "  load_lib(bokehjs_url, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", new Date())\n",
       "      var modelid = \"3f6751a2-733b-4934-c931-1d1d86374e94\";\n",
       "      var modeltype = \"Plot\";\n",
       "      var all_models = 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       "      Bokeh.load_models(all_models);\n",
       "      var model = Bokeh.Collections(modeltype).get(modelid);\n",
       "      $(\"#f58a716c-0919-4ed1-ce81-4532f5591c71\").html(''); // clear any previous plot in window_id\n",
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       "  Bokeh.load_models(all_models);\n",
       "  var model = Bokeh.Collections(modeltype).get(modelid);\n",
       "  $(\"#f58a716c-0919-4ed1-ce81-4532f5591c71\").html(''); // clear any previous plot in window_id\n",
       "  var view = new model.default_view({model: model, el: \"#f58a716c-0919-4ed1-ce81-4532f5591c71\"});\n",
       "    } else {\n",
       "  load_lib(bokehjs_url, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", new Date())\n",
       "      var modelid = \"51b3bdeb-9054-46ac-c68b-ae4a6f851315\";\n",
       "      var modeltype = \"Plot\";\n",
       "      var all_models = 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       "  var modeltype = \"Plot\";\n",
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       "  Bokeh.load_models(all_models);\n",
       "  var model = Bokeh.Collections(modeltype).get(modelid);\n",
       "  $(\"#f58a716c-0919-4ed1-ce81-4532f5591c71\").html(''); // clear any previous plot in window_id\n",
       "  var view = new model.default_view({model: model, el: \"#f58a716c-0919-4ed1-ce81-4532f5591c71\"});\n",
       "    } else {\n",
       "  load_lib(bokehjs_url, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", new Date())\n",
       "      var modelid = \"1f4bd097-4ffe-4fdd-cf4e-0f2c2a6b99f4\";\n",
       "      var modeltype = \"Plot\";\n",
       "      var all_models = 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       "    bokehjs_url = \"https://cdn.pydata.org/bokeh-0.7.0.min.js\"\n",
       "\n",
       "    var elt = document.getElementById(\"f58a716c-0919-4ed1-ce81-4532f5591c71\");\n",
       "    if(elt==null) {\n",
       "  console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'f58a716c-0919-4ed1-ce81-4532f5591c71'\"\n",
       "        + \"but no matching script tag was found. \")\n",
       "  return false;\n",
       "    }\n",
       "\n",
       "    if(typeof(Bokeh) !== \"undefined\") {\n",
       "  console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "  var modelid = \"83cf6174-83d8-4560-c54d-eeb4c0590d89\";\n",
       "  var modeltype = \"Plot\";\n",
       "  var all_models = 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       "  Bokeh.load_models(all_models);\n",
       "  var model = Bokeh.Collections(modeltype).get(modelid);\n",
       "  $(\"#f58a716c-0919-4ed1-ce81-4532f5591c71\").html(''); // clear any previous plot in window_id\n",
       "  var view = new model.default_view({model: model, el: \"#f58a716c-0919-4ed1-ce81-4532f5591c71\"});\n",
       "    } else {\n",
       "  load_lib(bokehjs_url, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", new Date())\n",
       "      var modelid = \"83cf6174-83d8-4560-c54d-eeb4c0590d89\";\n",
       "      var modeltype = \"Plot\";\n",
       "      var all_models = 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       "  var modeltype = \"Plot\";\n",
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       "  Bokeh.load_models(all_models);\n",
       "  var model = Bokeh.Collections(modeltype).get(modelid);\n",
       "  $(\"#f58a716c-0919-4ed1-ce81-4532f5591c71\").html(''); // clear any previous plot in window_id\n",
       "  var view = new model.default_view({model: model, el: \"#f58a716c-0919-4ed1-ce81-4532f5591c71\"});\n",
       "    } else {\n",
       "  load_lib(bokehjs_url, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", new Date())\n",
       "      var modelid = \"9867469d-da41-4cfb-cc4e-312fe9f17017\";\n",
       "      var modeltype = \"Plot\";\n",
       "      var all_models = 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       "  var modeltype = \"Plot\";\n",
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       "  Bokeh.load_models(all_models);\n",
       "  var model = Bokeh.Collections(modeltype).get(modelid);\n",
       "  $(\"#f58a716c-0919-4ed1-ce81-4532f5591c71\").html(''); // clear any previous plot in window_id\n",
       "  var view = new model.default_view({model: model, el: \"#f58a716c-0919-4ed1-ce81-4532f5591c71\"});\n",
       "    } else {\n",
       "  load_lib(bokehjs_url, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", new Date())\n",
       "      var modelid = \"11e9f07e-a9b9-4695-c63e-d2e0ea283649\";\n",
       "      var modeltype = \"Plot\";\n",
       "      var all_models = 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       "  var modeltype = \"Plot\";\n",
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       "  Bokeh.load_models(all_models);\n",
       "  var model = Bokeh.Collections(modeltype).get(modelid);\n",
       "  $(\"#f58a716c-0919-4ed1-ce81-4532f5591c71\").html(''); // clear any previous plot in window_id\n",
       "  var view = new model.default_view({model: model, el: \"#f58a716c-0919-4ed1-ce81-4532f5591c71\"});\n",
       "    } else {\n",
       "  load_lib(bokehjs_url, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", new Date())\n",
       "      var modelid = \"39df18c0-5500-4893-c8f6-7f0d8b823929\";\n",
       "      var modeltype = \"Plot\";\n",
       "      var all_models = 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Logs\",\"extra_x_ranges\":{},\"left\":[{\"type\":\"LinearAxis\",\"id\":\"89325772-6ad0-46c7-c6e7-2e863ff20c14\"}],\"y_range\":{\"type\":\"DataRange1d\",\"id\":\"b028db9d-f21f-4c47-cc7f-31598ae7ef89\"},\"id\":\"39df18c0-5500-4893-c8f6-7f0d8b823929\",\"extra_y_ranges\":{},\"tags\":[]}}];\n",
       "      Bokeh.load_models(all_models);\n",
       "      var model = Bokeh.Collections(modeltype).get(modelid);\n",
       "      $(\"#f58a716c-0919-4ed1-ce81-4532f5591c71\").html(''); // clear any previous plot in window_id\n",
       "      var view = new model.default_view({model: model, el: \"#f58a716c-0919-4ed1-ce81-4532f5591c71\"});\n",
       "  });\n",
       "    }\n",
       "});\n",
       "</script>\n",
       "<div class=\"plotdiv\" id=\"863b08c1-f84e-4187-c124-7bccb92a0e7f\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "-- plot out a log suite for fun\n",
    "plot = Plot():line(training_data[\"luke\"][{{},{1}}]:reshape(461), depth[\"luke\"]:reshape(461),'red','gamma'):draw()\n",
    "plot:line(training_data[\"luke\"][{{},{2}}]:reshape(461)+4, depth[\"luke\"]:reshape(461),'blue','res'):redraw()\n",
    "plot:line(training_data[\"luke\"][{{},{3}}]:reshape(461)+8, depth[\"luke\"]:reshape(461),'green','por'):redraw()\n",
    "plot:line(training_data[\"luke\"][{{},{4}}]:reshape(461)+12, depth[\"luke\"]:reshape(461),'brown','neuDens'):redraw()\n",
    "plot:line(training_data[\"luke\"][{{},{5}}]:reshape(461)+16, depth[\"luke\"]:reshape(461),'grey','photo'):redraw()\n",
    "\n",
    "plot:title('Luke Logs'):redraw()\n",
    "plot:yaxis('TVD (m)'):redraw()\n",
    "plot:legend(true)\n",
    "plot:redraw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "-- chop out blind well\n",
    "blind_well = {}\n",
    "blind_labels = {}\n",
    "\n",
    "blind_well[\"newby\"] = training_data[\"newby\"][{{},{}}]\n",
    "training_data[\"newby\"] = nil\n",
    "\n",
    "blind_labels[\"newby\"] = facies_labels[\"newby\"][{{},{}}]\n",
    "facies_labels[\"newby\"] = nil"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The birth of a bot\n",
    "Get your cameras ready. We're about to witness the birth of Faye. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "-- build the neural net\n",
    "net = nil\n",
    "net = nn.Sequential()\n",
    "net:add(nn.Linear(7,200))\n",
    "net:add(nn.ReLU())\n",
    "net:add(nn.Linear(200,50))\n",
    "net:add(nn.ReLU())\n",
    "net:add(nn.Linear(50,9))\n",
    "net:add(nn.LogSoftMax())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Faye's first steps\n",
    "Now Faye is going to attempt to classify her first facies. Keep in mind she's just a youngster without any training in facies picking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "predicted facies = \t8\t\n",
       "actual facies = \t3\t\n"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-- test the net -> forward\n",
    "temp = torch.Tensor(7)\n",
    "for i = 1,7 do\n",
    "    temp[i] = training_data[\"shrimplin\"][1][i]\n",
    "end\n",
    "input = temp\n",
    "\n",
    "output = net:forward(input)\n",
    "\n",
    "-- zero gradients and initialize\n",
    "net:zeroGradParameters()\n",
    "\n",
    "gradInput = net:backward(input, torch.rand(9))\n",
    "\n",
    "-- untrained prediction\n",
    "temp1, temp2 = torch.sort(output, true)\n",
    "print(\"predicted facies = \", temp2[1])\n",
    "print(\"actual facies = \", facies_labels[\"shrimplin\"][1][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh no! She's predicted the wrong facies. Let's not judge to harshly; Faye still needs to go through facies prediction training school.\n",
    "\n",
    "You may have noticed Faye's output layer isn't a simple transfer function. That's because her training utilizes a negative log likelihood loss function. Keep in mind, if your (multi-class) classification robot has a logrithmic activation function in the output layer, you'll need an exponential function to recover class probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "-- define the loss function\n",
    "criterion = nn.ClassNLLCriterion()\n",
    "criterion:forward(output,facies_labels[\"shrimplin\"][1])\n",
    "\n",
    "gradients = criterion:backward(output, facies_labels[\"shrimplin\"][1])\n",
    "gradInput = net:backward(input, gradients)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data conditioning\n",
    "As mentioned above, Faye is quite picky about her data. Though Torch will put Faye through training school automatically, it does requires three things for the robots it trains: 1. the data is input as a `torch.DoubleTensor`, 2. the data has a size function capable of operating on the entire Lua table, 3. the table has an index function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "-- condition the data\n",
    "trainset = {}\n",
    "\n",
    "\t-- the data\n",
    "trainset[\"data\"] = torch.Tensor(facies:size()[1]-blind_well[\"newby\"]:size()[1],7) \n",
    "\n",
    "idx = 0\n",
    "for key,value in pairs(training_data) do\n",
    "    for i = 1,training_data[key]:size()[1] do\n",
    "        trainset[\"data\"][i + idx] = training_data[key][i]\n",
    "    end\n",
    "    idx = idx + training_data[key]:size()[1]\n",
    "end\n",
    "\n",
    "\t-- the answers\n",
    "trainset[\"facies\"] = torch.Tensor(facies:size()[1]-blind_labels[\"newby\"]:size()[1])\n",
    "\n",
    "idx = 0\n",
    "for key,value in pairs(facies_labels) do\n",
    "    for i = 1, facies_labels[key]:size()[1] do\n",
    "        trainset[\"facies\"][i + idx] = facies_labels[key][i]\n",
    "    end\n",
    "    idx = idx + facies_labels[key]:size()[1]\n",
    "end\n",
    "\n",
    "\n",
    "-- write index() and size() functions\n",
    "setmetatable(trainset, \n",
    "    {__index = function(t, i) \n",
    "                    return {t.data[i], t.facies[i]} \n",
    "                end}\n",
    ");\n",
    "\n",
    "function trainset:size() \n",
    "    return self.data:size(1) \n",
    "end\n",
    "\n",
    "-- condition the testing data\n",
    "testset = {}\n",
    "\n",
    "\t-- the data\n",
    "testset[\"data\"] = torch.Tensor(blind_well[\"newby\"]:size()[1],7) \n",
    "\n",
    "for i = 1,blind_well[\"newby\"]:size()[1] do\n",
    "    testset[\"data\"][i] = blind_well[\"newby\"][i]\n",
    "end\n",
    "\n",
    "\t-- the answers\n",
    "testset[\"facies\"] = torch.Tensor(blind_labels[\"newby\"]:size()[1])\n",
    "\n",
    "for i = 1, blind_labels[\"newby\"]:size()[1] do\n",
    "    testset[\"facies\"][i] = blind_labels[\"newby\"][i]\n",
    "end\n",
    "\n",
    "setmetatable(testset, \n",
    "    {__index = function(t, i) \n",
    "                    return {t.data[i], t.facies[i]} \n",
    "                end}\n",
    ");\n",
    "\n",
    "function testset:size() \n",
    "    return self.data:size(1) \n",
    "end\n",
    "\n",
    "-- eliminate NaNs\n",
    "nan_mask = trainset.data:ne(trainset.data)\n",
    "trainset.data[nan_mask] = 0\n",
    "nan_mask = testset.data:ne(testset.data)\n",
    "testset.data[nan_mask] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Faye goes to training school\n",
    "Here Torch automatically trains Faye to pick facies correctly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "starting training\t\n",
       "# StochasticGradient: training\t\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "# current error = 1.699766562147\t\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "# current error = 1.3167995469328\t\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "# current error = 1.1955562620453\t\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "# current error = 1.132782831007\t\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "# current error = 1.0948589200984\t\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "# current error = 1.068686200366\t\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "# current error = 1.0486755099724\t\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "# current error = 1.0325111903299\t\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "# current error = 1.0190135508263\t\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "# current error = 1.0072778907583\t\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "# current error = 0.99677809289167\t\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "# current error = 0.9871231951583\t\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "# current error = 0.97807527075243\t\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "# current error = 0.96953318003693\t\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "# current error = 0.96128729649097\t\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "# current error = 0.95338760763965\t\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "# current error = 0.94571454161906\t\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "# current error = 0.9381510632297\t\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "# current error = 0.93073322396093\t\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "# current error = 0.92352282746539\t\n",
       "# StochasticGradient: you have reached the maximum number of iterations\t\n",
       "# training error = 0.92352282746539\t\n",
       "training time =\t26.056068897247\t\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-- train the net\n",
    "trainer = nn.StochasticGradient(net, criterion)\n",
    "trainer.learningRate = .001\n",
    "trainer.maxIteration = 20\n",
    "\n",
    "print(\"starting training\")\n",
    "timer = torch.Timer()\n",
    "trainer:train(trainset)\n",
    "print(\"training time =\", timer:time().real)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Faye's accuracy\n",
    "Faye's going to pick all of the facies in a blind well. Well judge her performance against the known facies across the entire well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "correct: \t253\t54.643628509719 % \n",
       "\t\n"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-- overall performance\n",
    "correct = 0\n",
    "for i=1,testset:size() do\n",
    "    local groundtruth = testset.facies[i]\n",
    "    local prediction = net:forward(testset.data[i])\n",
    "    local confidences, indices = torch.sort(prediction, true)\n",
    "    if groundtruth == indices[1] then\n",
    "        correct = correct + 1\n",
    "    end\n",
    "end\n",
    "print(\"\\ncorrect: \", correct, 100*correct/testset:size() .. ' % \\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now we'll see which facies she classifies accurately, and which ones she struggles with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SS\tnan %\t\n",
       "CSiS\t84 %\t\n",
       "FSiS\t36 %\t\n",
       "SiSh\t83 %\t\n",
       "MS\t0 %\t\n",
       "WS\t44 %\t\n",
       "D\t63 %\t\n",
       "PS\t75 %\t\n",
       "BS\t0 %\t\n"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-- class performance\n",
    "counts = {0,0,0,0,0,0,0,0,0}\n",
    "for i = 1,testset.facies:size()[1] do\n",
    "    temp = testset.facies[i]\n",
    "    counts[temp] = counts[temp] + 1\n",
    "end\n",
    "--print(counts)\n",
    "\n",
    "classes = {'SS', 'CSiS', 'FSiS', 'SiSh', 'MS', 'WS', 'D', 'PS', 'BS'}\n",
    "\n",
    "class_performance = {0, 0, 0, 0, 0, 0, 0, 0, 0}\n",
    "for i=1,testset.facies:size()[1] do\n",
    "    local groundtruth = testset.facies[i]\n",
    "    local prediction = net:forward(testset.data[i])\n",
    "    local confidences, indices = torch.sort(prediction, true)\n",
    "    if groundtruth == indices[1] then\n",
    "        class_performance[groundtruth] = class_performance[groundtruth] + 1\n",
    "    end\n",
    "end\n",
    "\n",
    "for i = 1, #classes do\n",
    "    print(classes[i], torch.round(100 * class_performance[i] / counts[i]) .. ' %')\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "preds = torch.Tensor(testset.facies:size()[1])\n",
    "for i = 1, testset.facies:size()[1] do\n",
    "    local prediction = net:forward(testset.data[i])\n",
    "    confidences, indices = torch.sort(prediction, true)\n",
    "    preds[i] = indices[1]\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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Plot\",\"extra_x_ranges\":{},\"left\":[{\"type\":\"LinearAxis\",\"id\":\"3ce5acbc-7f69-44b4-c4a2-dd3f94b5a565\"}],\"y_range\":{\"type\":\"DataRange1d\",\"id\":\"6f714043-d69c-4664-c67d-dea4c07143a1\"},\"id\":\"ea7453d7-5c05-4e89-cec3-72ac5fd3116d\",\"extra_y_ranges\":{},\"tags\":[]}}];\n",
       "  Bokeh.load_models(all_models);\n",
       "  var model = Bokeh.Collections(modeltype).get(modelid);\n",
       "  $(\"#3a6034fd-6bb2-40ed-c00f-e75647865dfc\").html(''); // clear any previous plot in window_id\n",
       "  var view = new model.default_view({model: model, el: \"#3a6034fd-6bb2-40ed-c00f-e75647865dfc\"});\n",
       "    } else {\n",
       "  load_lib(bokehjs_url, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", new Date())\n",
       "      var modelid = \"ea7453d7-5c05-4e89-cec3-72ac5fd3116d\";\n",
       "      var modeltype = \"Plot\";\n",
       "      var all_models = 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       "      Bokeh.load_models(all_models);\n",
       "      var model = Bokeh.Collections(modeltype).get(modelid);\n",
       "      $(\"#3a6034fd-6bb2-40ed-c00f-e75647865dfc\").html(''); // clear any previous plot in window_id\n",
       "      var view = new model.default_view({model: model, el: \"#3a6034fd-6bb2-40ed-c00f-e75647865dfc\"});\n",
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       "  Bokeh.load_models(all_models);\n",
       "  var model = Bokeh.Collections(modeltype).get(modelid);\n",
       "  $(\"#3a6034fd-6bb2-40ed-c00f-e75647865dfc\").html(''); // clear any previous plot in window_id\n",
       "  var view = new model.default_view({model: model, el: \"#3a6034fd-6bb2-40ed-c00f-e75647865dfc\"});\n",
       "    } else {\n",
       "  load_lib(bokehjs_url, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", new Date())\n",
       "      var modelid = \"7401b029-d989-4536-c5ec-cf3a53e95083\";\n",
       "      var modeltype = \"Plot\";\n",
       "      var all_models = 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       "  Bokeh.load_models(all_models);\n",
       "  var model = Bokeh.Collections(modeltype).get(modelid);\n",
       "  $(\"#3a6034fd-6bb2-40ed-c00f-e75647865dfc\").html(''); // clear any previous plot in window_id\n",
       "  var view = new model.default_view({model: model, el: \"#3a6034fd-6bb2-40ed-c00f-e75647865dfc\"});\n",
       "    } else {\n",
       "  load_lib(bokehjs_url, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", new Date())\n",
       "      var modelid = \"14a5a30c-4dfe-4108-c11d-c4fe72f427fb\";\n",
       "      var modeltype = \"Plot\";\n",
       "      var all_models = 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       "  Bokeh.load_models(all_models);\n",
       "  var model = Bokeh.Collections(modeltype).get(modelid);\n",
       "  $(\"#3a6034fd-6bb2-40ed-c00f-e75647865dfc\").html(''); // clear any previous plot in window_id\n",
       "  var view = new model.default_view({model: model, el: \"#3a6034fd-6bb2-40ed-c00f-e75647865dfc\"});\n",
       "    } else {\n",
       "  load_lib(bokehjs_url, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", new Date())\n",
       "      var modelid = \"85130c37-77ef-4876-c8e1-47cc07402bfd\";\n",
       "      var modeltype = \"Plot\";\n",
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       "      Bokeh.load_models(all_models);\n",
       "      var model = Bokeh.Collections(modeltype).get(modelid);\n",
       "      $(\"#3a6034fd-6bb2-40ed-c00f-e75647865dfc\").html(''); // clear any previous plot in window_id\n",
       "      var view = new model.default_view({model: model, el: \"#3a6034fd-6bb2-40ed-c00f-e75647865dfc\"});\n",
       "  });\n",
       "    }\n",
       "});\n",
       "</script>\n",
       "<div class=\"plotdiv\" id=\"a5e4bdd8-c12b-4ea8-cee3-1bc48c728a13\"></div>\n"
      ]
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = Plot():circle(preds:reshape(463), depth[\"newby\"]:reshape(463),'red','predicted'):draw()\n",
    "plot:circle(testset.facies:reshape(463), depth[\"newby\"]:reshape(463), 'blue','true')\n",
    "\n",
    "plot:title('Predictions v. Truths'):redraw()\n",
    "plot:yaxis('TVD (m)'):redraw()\n",
    "plot:legend(true)\n",
    "plot:redraw()"
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   "source": [
    "## Cross validation\n",
    "Next we'll train Faye several times, holding a different well blind each iteration and record her accuracy as before. \n",
    "\n",
    "Since only one or two of you will want to run xval I'm not putting it in this notebook; you can access it in the <a href=\"crossValidation_cpu.lua\">crossValidation_cpu.lua</a> script in this directory.\n",
    "\n",
    "## What's Next?\n",
    "Don't tell her I said so, but Faye is pretty basic. She could definitely be optimized by way of complexity. The training data in this case is not perfectly spatially stochastic. Which means that the simple stochastic gradient descent training algorithm I've used isn't the best choice for this classifier. This problem could be somewhat mitigated by weighting the input feature vectors inverse proportionally to the frequency of the facies appearance.\n",
    "\n",
    "Additionally, using early stopping or a regularization algorithm during Faye's training would certainly improve her accuracy.\n",
    "\n",
    "Another question to be answered is whether or not the training feature vectors span the testing data field. This question does have an answer in this case, since we have access to the testing data facies labels.\n",
    "\n",
    "Though I've had fun playing with Faye it's time to move on to another, more sophisticated robot. Stay tuned in to my <a href=\"https://github.com/gganssle\">GitHub</a> page for more artificially intelligent critters.\n",
    "\n",
    "# Thanks a mill\n",
    "It's not easy to read other people's code. It's really not easy to read MY buggy code. So thanks to the TLE team for herding me like a cat. Thanks especially to the Hall boys, <a href=\"https://github.com/kwinkunks\">Matt</a> and <a href=\"https://github.com/brendonhall\">Brendon</a> (no relation, I guess) for encouraging me to participate in this wing-ding."
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