{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
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       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import warnings\n",
    "import time\n",
    "import copy\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.metrics import mean_squared_error, explained_variance_score, r2_score\n",
    "from sklearn.model_selection import train_test_split, KFold\n",
    "from sklearn.linear_model import LinearRegression, Lasso, lasso_path, lars_path, LassoLarsIC\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' #Hide messy TensorFlow warnings\n",
    "warnings.filterwarnings(\"ignore\") #Hide messy Numpy warnings\n",
    "\n",
    "import keras\n",
    "from keras.layers.core import Dense, Activation, Dropout\n",
    "from keras.layers import Input\n",
    "from keras.models import Model\n",
    "\n",
    "from keras.layers.recurrent import LSTM, GRU\n",
    "from keras.regularizers import l1\n",
    "from keras.models import Sequential\n",
    "from keras.models import load_model\n",
    "\n",
    "#plotly charts\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "from plotly.graph_objs import *\n",
    "import plotly.figure_factory as ff\n",
    "init_notebook_mode(connected=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splines chart\n",
    "# R code\n",
    "\"\"\"\n",
    "testdata <- data.frame(seq(from=1, to = 1000))\n",
    "testdata['x'] <- seq(from=0, to = 3*pi, length.out=1000)\n",
    "testdata['y'] <- -cos(testdata$x) + testdata$x/(3*pi) + rnorm(1000)*0.25\n",
    "nrows <- 300\n",
    "indexes <- sample(nrow(testdata), nrows)\n",
    "indexes <- indexes[order(indexes)]\n",
    "testdata <- testdata[indexes, ]\n",
    "plot(testdata$x, testdata$y, main=\"Scatterplot\", xlab=\"x\", ylab=\"y\", pch=1)\n",
    "\n",
    "fit <- smooth.spline(testdata$x,testdata$y,df=2)\n",
    "testdata$fit2 <- predict(fit, newdata=testdata$x)[2]$y\n",
    "plot(testdata$x, testdata$fit2, main=\"Scatterplot\", xlab=\"x\", ylab=\"y\", pch=1)\n",
    "\n",
    "fit <- smooth.spline(testdata$x,testdata$y,df=3)\n",
    "testdata$fit3 <- predict(fit, newdata=testdata$x)[2]$y\n",
    "plot(testdata$x, testdata$fit3, main=\"Scatterplot\", xlab=\"x\", ylab=\"y\", pch=1)\n",
    "\n",
    "fit <- smooth.spline(testdata$x,testdata$y,df=4)\n",
    "testdata$fit4 <- predict(fit, newdata=testdata$x)[2]$y\n",
    "plot(testdata$x, testdata$fit4, main=\"Scatterplot\", xlab=\"x\", ylab=\"y\", pch=1)\n",
    "\n",
    "fit <- smooth.spline(testdata$x,testdata$y,df=5)\n",
    "testdata$fit5 <- predict(fit, newdata=testdata$x)[2]$y\n",
    "plot(testdata$x, testdata$fit5, main=\"Scatterplot\", xlab=\"x\", ylab=\"y\", pch=1)\n",
    "\n",
    "fit <- smooth.spline(testdata$x,testdata$y,df=6)\n",
    "testdata$fit6 <- predict(fit, newdata=testdata$x)[2]$y\n",
    "plot(testdata$x, testdata$fit6, main=\"Scatterplot\", xlab=\"x\", ylab=\"y\", pch=1)\n",
    "\n",
    "fit <- smooth.spline(testdata$x,testdata$y,df=20)\n",
    "testdata$fit20 <- predict(fit, newdata=testdata$x)[2]$y\n",
    "plot(testdata$x, testdata$fit20, main=\"Scatterplot\", xlab=\"x\", ylab=\"y\", pch=1)\n",
    "\n",
    "fit <- smooth.spline(testdata$x,testdata$y,df=40)\n",
    "testdata$fit40 <- predict(fit, newdata=testdata$x)[2]$y\n",
    "plot(testdata$x, testdata$fit40, main=\"Scatterplot\", xlab=\"x\", ylab=\"y\", pch=1)\n",
    "\n",
    "fit <- smooth.spline(testdata$x,testdata$y,df=80)\n",
    "testdata$fit80 <- predict(fit, newdata=testdata$x)[2]$y\n",
    "plot(testdata$x, testdata$fit80, main=\"Scatterplot\", xlab=\"x\", ylab=\"y\", pch=1)\n",
    "\n",
    "fit <- smooth.spline(testdata$x,testdata$y,df=160)\n",
    "testdata$fit160 <- predict(fit, newdata=testdata$x)[2]$y\n",
    "plot(testdata$x, testdata$fit160, main=\"Scatterplot\", xlab=\"x\", ylab=\"y\", pch=1)\n",
    "\n",
    "fit <- smooth.spline(testdata$x,testdata$y,df=240)\n",
    "testdata$fit240 <- predict(fit, newdata=testdata$x)[2]$y\n",
    "plot(testdata$x, testdata$fit240, main=\"Scatterplot\", xlab=\"x\", ylab=\"y\", pch=1)\n",
    "\n",
    "fit <- smooth.spline(testdata$x,testdata$y,cv=TRUE)\n",
    "testdata$fitcv <- predict(fit, newdata=testdata$x)[2]$y\n",
    "plot(testdata$x, testdata$fitcv, main=\"Scatterplot\", xlab=\"x\", ylab=\"y\", pch=1)\n",
    "\n",
    "write.csv(testdata, \"splines.csv\")\n",
    "print(fit)\n",
    "\"\"\"\n",
    "splines = pd.read_csv('splines.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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true})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "data = splines.values\n",
    "\n",
    "x = data[:,2]\n",
    "y = data[:,3]  \n",
    "fit2 = data[:,4]\n",
    "fit3 = data[:,5]\n",
    "fit4 = data[:,6]\n",
    "fit5 = data[:,7]\n",
    "fit6 = data[:,8]\n",
    "fit20 = data[:,9]\n",
    "fit40 = data[:,10]\n",
    "fit80 = data[:,11]\n",
    "fit160 = data[:,12]\n",
    "fit240 = data[:,13]\n",
    "fitcv = data[:,14]\n",
    "\n",
    "plotdata = []\n",
    "plotdata.append(Scatter(x=x,\n",
    "                        y=y,\n",
    "                        name='Raw data',\n",
    "                        mode = 'markers',\n",
    "                        marker = dict(\n",
    "                            size = 3,\n",
    "                            line = dict(\n",
    "                                width = 2,\n",
    "                                color = 'rgba(128, 128, 192, .75)',\n",
    "                            )\n",
    "                        )\n",
    "                       )\n",
    "               )\n",
    "plotdata.append(Scatter(x=x,\n",
    "                    y=fit2,\n",
    "                    name='Linear (high bias / low variance)',\n",
    "                    mode = 'line',\n",
    "                        line = dict(\n",
    "                            color = ('orange'),\n",
    "                            width = 2)                        \n",
    "                       ))\n",
    "plotdata.append(Scatter(x=x,\n",
    "                    y=fit4,\n",
    "                    name='Underfit (high bias / low variance)',\n",
    "                    mode = 'line',\n",
    "                        line = dict(\n",
    "                            width = 2,\n",
    "                        color= ('blue'),                                                \n",
    "                        )                                                \n",
    "                       ))\n",
    "plotdata.append(Scatter(x=x,\n",
    "                        y=fitcv,\n",
    "                        name='Lowest CV error',\n",
    "                        mode = 'line',\n",
    "                        line = dict(\n",
    "                            color = ('green'),\n",
    "                            width = 5)\n",
    ")               )\n",
    "plotdata.append(Scatter(x=x,\n",
    "                    y=fit240,\n",
    "                    name='Overfit (high variance / low bias)',\n",
    "                    mode = 'line',\n",
    "                        line = dict(\n",
    "                            width = 2,\n",
    "                        color= ('rgb(192,0,0)'))                                                \n",
    "                        \n",
    "                       ))\n",
    "\n",
    "layout = Layout(\n",
    "    yaxis=dict(\n",
    "        autorange=True))\n",
    "\n",
    "\n",
    "fig = Figure(data=plotdata, layout=layout)\n",
    "\n",
    "iplot(fig) # png to save notebook w/static image   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a data set, sin wave plus trend plus random noise\n",
    "nobs = 2000\n",
    "x = np.linspace(0, 3*np.pi, num=nobs)\n",
    "y = -np.cos(x) + x/(3*np.pi) + np.random.normal(0, 0.25, nobs)\n",
    "\n",
    "x2 = np.linspace(0, 15*np.pi, num=nobs)\n",
    "y1 = -np.sin(x2) + x2/(3*np.pi) + np.random.normal(0, 0.25, nobs)\n",
    "y2 = -np.cos(x2) + x2/(3*np.pi) + np.random.normal(0, 0.25, nobs)\n",
    "\n",
    "# sample 20% to make it more sparse\n",
    "#arr = np.arange(nobs)\n",
    "#np.random.shuffle(arr)\n",
    "#x = arr[:200]\n",
    "#y = y[x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# chart it\n",
    "\n",
    "def mychart(*args):\n",
    "\n",
    "    # pass some 2d n x 1 arrays, x, y, z\n",
    "    \n",
    "    # 1st array is independent vars\n",
    "    # reshape to 1 dimensional array\n",
    "    x = args[0].reshape(-1)\n",
    "    \n",
    "    # following are dependent vars plotted on y axis\n",
    "    data = []\n",
    "    for i in range(1, len(args)):\n",
    "        data.append(Scatter(x=x,\n",
    "                            y=args[i].reshape(-1),\n",
    "                            mode = 'markers'))\n",
    "\n",
    "    layout = Layout(\n",
    "        yaxis=dict(\n",
    "            autorange=True))\n",
    "    \n",
    "    fig = Figure(data=data, layout=layout)\n",
    "    \n",
    "    return iplot(fig) # png to save notebook w/static image   \n",
    "    \n",
    "mychart(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "mychart(x2,y1, y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>0.292461</td>\n",
       "      <td>-0.971105</td>\n",
       "      <td>-0.000803</td>\n",
       "      <td>-0.162264</td>\n",
       "      <td>-0.564433</td>\n",
       "      <td>-0.825382</td>\n",
       "      <td>-0.910901</td>\n",
       "      <td>-0.875433</td>\n",
       "      <td>-0.863846</td>\n",
       "      <td>-0.815897</td>\n",
       "      <td>-0.824983</td>\n",
       "      <td>-0.824983</td>\n",
       "      <td>-0.894611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>0.311329</td>\n",
       "      <td>-0.441006</td>\n",
       "      <td>0.001310</td>\n",
       "      <td>-0.156713</td>\n",
       "      <td>-0.552002</td>\n",
       "      <td>-0.809184</td>\n",
       "      <td>-0.894369</td>\n",
       "      <td>-0.873216</td>\n",
       "      <td>-0.878066</td>\n",
       "      <td>-0.846558</td>\n",
       "      <td>-0.836715</td>\n",
       "      <td>-0.836715</td>\n",
       "      <td>-0.882226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>0.320763</td>\n",
       "      <td>-0.812196</td>\n",
       "      <td>0.002366</td>\n",
       "      <td>-0.153938</td>\n",
       "      <td>-0.545787</td>\n",
       "      <td>-0.801084</td>\n",
       "      <td>-0.886100</td>\n",
       "      <td>-0.871895</td>\n",
       "      <td>-0.885199</td>\n",
       "      <td>-0.863802</td>\n",
       "      <td>-0.847200</td>\n",
       "      <td>-0.847200</td>\n",
       "      <td>-0.876009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>0.339632</td>\n",
       "      <td>-1.107151</td>\n",
       "      <td>0.004479</td>\n",
       "      <td>-0.148389</td>\n",
       "      <td>-0.533356</td>\n",
       "      <td>-0.784882</td>\n",
       "      <td>-0.869555</td>\n",
       "      <td>-0.868770</td>\n",
       "      <td>-0.899093</td>\n",
       "      <td>-0.902248</td>\n",
       "      <td>-0.881082</td>\n",
       "      <td>-0.881082</td>\n",
       "      <td>-0.863520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>0.386803</td>\n",
       "      <td>-1.002422</td>\n",
       "      <td>0.009760</td>\n",
       "      <td>-0.134517</td>\n",
       "      <td>-0.502283</td>\n",
       "      <td>-0.744365</td>\n",
       "      <td>-0.828147</td>\n",
       "      <td>-0.857574</td>\n",
       "      <td>-0.925840</td>\n",
       "      <td>-0.994434</td>\n",
       "      <td>-0.994687</td>\n",
       "      <td>-0.994687</td>\n",
       "      <td>-0.831932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>0.415105</td>\n",
       "      <td>-1.195662</td>\n",
       "      <td>0.012929</td>\n",
       "      <td>-0.126197</td>\n",
       "      <td>-0.483642</td>\n",
       "      <td>-0.720046</td>\n",
       "      <td>-0.803265</td>\n",
       "      <td>-0.848041</td>\n",
       "      <td>-0.931322</td>\n",
       "      <td>-1.022434</td>\n",
       "      <td>-1.038721</td>\n",
       "      <td>-1.038721</td>\n",
       "      <td>-0.812685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>0.443408</td>\n",
       "      <td>-0.967867</td>\n",
       "      <td>0.016097</td>\n",
       "      <td>-0.117879</td>\n",
       "      <td>-0.465006</td>\n",
       "      <td>-0.695720</td>\n",
       "      <td>-0.778353</td>\n",
       "      <td>-0.836071</td>\n",
       "      <td>-0.925555</td>\n",
       "      <td>-1.014256</td>\n",
       "      <td>-1.035864</td>\n",
       "      <td>-1.035864</td>\n",
       "      <td>-0.793187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>0.490579</td>\n",
       "      <td>-0.757764</td>\n",
       "      <td>0.021378</td>\n",
       "      <td>-0.104024</td>\n",
       "      <td>-0.433956</td>\n",
       "      <td>-0.655163</td>\n",
       "      <td>-0.736759</td>\n",
       "      <td>-0.810934</td>\n",
       "      <td>-0.893917</td>\n",
       "      <td>-0.940020</td>\n",
       "      <td>-0.951372</td>\n",
       "      <td>-0.951372</td>\n",
       "      <td>-0.760116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>0.556619</td>\n",
       "      <td>-0.882360</td>\n",
       "      <td>0.028772</td>\n",
       "      <td>-0.084649</td>\n",
       "      <td>-0.390525</td>\n",
       "      <td>-0.598362</td>\n",
       "      <td>-0.678368</td>\n",
       "      <td>-0.766014</td>\n",
       "      <td>-0.817432</td>\n",
       "      <td>-0.794839</td>\n",
       "      <td>-0.783219</td>\n",
       "      <td>-0.783219</td>\n",
       "      <td>-0.712597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>0.566053</td>\n",
       "      <td>-0.747627</td>\n",
       "      <td>0.029828</td>\n",
       "      <td>-0.081884</td>\n",
       "      <td>-0.384326</td>\n",
       "      <td>-0.590246</td>\n",
       "      <td>-0.670011</td>\n",
       "      <td>-0.758767</td>\n",
       "      <td>-0.804596</td>\n",
       "      <td>-0.777175</td>\n",
       "      <td>-0.764024</td>\n",
       "      <td>-0.764024</td>\n",
       "      <td>-0.705691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>66</td>\n",
       "      <td>66</td>\n",
       "      <td>0.613224</td>\n",
       "      <td>-0.823935</td>\n",
       "      <td>0.035109</td>\n",
       "      <td>-0.068069</td>\n",
       "      <td>-0.353349</td>\n",
       "      <td>-0.549667</td>\n",
       "      <td>-0.628167</td>\n",
       "      <td>-0.719926</td>\n",
       "      <td>-0.736604</td>\n",
       "      <td>-0.707282</td>\n",
       "      <td>-0.696022</td>\n",
       "      <td>-0.696022</td>\n",
       "      <td>-0.670730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>0.669829</td>\n",
       "      <td>-0.523625</td>\n",
       "      <td>0.041445</td>\n",
       "      <td>-0.051521</td>\n",
       "      <td>-0.316238</td>\n",
       "      <td>-0.500979</td>\n",
       "      <td>-0.577829</td>\n",
       "      <td>-0.668861</td>\n",
       "      <td>-0.651602</td>\n",
       "      <td>-0.642244</td>\n",
       "      <td>-0.648028</td>\n",
       "      <td>-0.648028</td>\n",
       "      <td>-0.627878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>0.858513</td>\n",
       "      <td>-0.622196</td>\n",
       "      <td>0.062566</td>\n",
       "      <td>0.003297</td>\n",
       "      <td>-0.193244</td>\n",
       "      <td>-0.338960</td>\n",
       "      <td>-0.409136</td>\n",
       "      <td>-0.486504</td>\n",
       "      <td>-0.415746</td>\n",
       "      <td>-0.399757</td>\n",
       "      <td>-0.434665</td>\n",
       "      <td>-0.434665</td>\n",
       "      <td>-0.478897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>0.877382</td>\n",
       "      <td>-0.275332</td>\n",
       "      <td>0.064678</td>\n",
       "      <td>0.008742</td>\n",
       "      <td>-0.181025</td>\n",
       "      <td>-0.322799</td>\n",
       "      <td>-0.392199</td>\n",
       "      <td>-0.468776</td>\n",
       "      <td>-0.399977</td>\n",
       "      <td>-0.378763</td>\n",
       "      <td>-0.402004</td>\n",
       "      <td>-0.402004</td>\n",
       "      <td>-0.463549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>0.915119</td>\n",
       "      <td>-0.238423</td>\n",
       "      <td>0.068901</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>-0.156638</td>\n",
       "      <td>-0.290509</td>\n",
       "      <td>-0.358291</td>\n",
       "      <td>-0.434364</td>\n",
       "      <td>-0.374523</td>\n",
       "      <td>-0.342332</td>\n",
       "      <td>-0.328025</td>\n",
       "      <td>-0.328025</td>\n",
       "      <td>-0.432644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>0.933987</td>\n",
       "      <td>-0.123531</td>\n",
       "      <td>0.071013</td>\n",
       "      <td>0.025028</td>\n",
       "      <td>-0.144473</td>\n",
       "      <td>-0.274381</td>\n",
       "      <td>-0.341322</td>\n",
       "      <td>-0.417781</td>\n",
       "      <td>-0.365058</td>\n",
       "      <td>-0.328908</td>\n",
       "      <td>-0.291046</td>\n",
       "      <td>-0.291046</td>\n",
       "      <td>-0.417092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>101</td>\n",
       "      <td>101</td>\n",
       "      <td>0.943421</td>\n",
       "      <td>-0.386109</td>\n",
       "      <td>0.072069</td>\n",
       "      <td>0.027735</td>\n",
       "      <td>-0.138398</td>\n",
       "      <td>-0.266322</td>\n",
       "      <td>-0.332833</td>\n",
       "      <td>-0.409645</td>\n",
       "      <td>-0.361105</td>\n",
       "      <td>-0.325847</td>\n",
       "      <td>-0.280151</td>\n",
       "      <td>-0.280151</td>\n",
       "      <td>-0.409291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>0.962290</td>\n",
       "      <td>-0.727077</td>\n",
       "      <td>0.074181</td>\n",
       "      <td>0.033142</td>\n",
       "      <td>-0.126262</td>\n",
       "      <td>-0.250213</td>\n",
       "      <td>-0.315848</td>\n",
       "      <td>-0.393661</td>\n",
       "      <td>-0.354619</td>\n",
       "      <td>-0.332577</td>\n",
       "      <td>-0.291137</td>\n",
       "      <td>-0.291137</td>\n",
       "      <td>-0.393637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>0.981158</td>\n",
       "      <td>-0.092702</td>\n",
       "      <td>0.076292</td>\n",
       "      <td>0.038539</td>\n",
       "      <td>-0.114149</td>\n",
       "      <td>-0.234118</td>\n",
       "      <td>-0.298853</td>\n",
       "      <td>-0.378022</td>\n",
       "      <td>-0.349691</td>\n",
       "      <td>-0.358963</td>\n",
       "      <td>-0.356338</td>\n",
       "      <td>-0.356338</td>\n",
       "      <td>-0.377915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>0.990592</td>\n",
       "      <td>-0.178196</td>\n",
       "      <td>0.077348</td>\n",
       "      <td>0.041233</td>\n",
       "      <td>-0.108100</td>\n",
       "      <td>-0.226076</td>\n",
       "      <td>-0.290352</td>\n",
       "      <td>-0.370308</td>\n",
       "      <td>-0.347513</td>\n",
       "      <td>-0.374982</td>\n",
       "      <td>-0.398651</td>\n",
       "      <td>-0.398651</td>\n",
       "      <td>-0.370026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>1.018895</td>\n",
       "      <td>-0.648567</td>\n",
       "      <td>0.080515</td>\n",
       "      <td>0.049303</td>\n",
       "      <td>-0.089988</td>\n",
       "      <td>-0.201973</td>\n",
       "      <td>-0.264834</td>\n",
       "      <td>-0.347455</td>\n",
       "      <td>-0.340412</td>\n",
       "      <td>-0.403746</td>\n",
       "      <td>-0.490174</td>\n",
       "      <td>-0.490174</td>\n",
       "      <td>-0.346246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>1.056632</td>\n",
       "      <td>-0.366328</td>\n",
       "      <td>0.084738</td>\n",
       "      <td>0.060024</td>\n",
       "      <td>-0.065924</td>\n",
       "      <td>-0.169893</td>\n",
       "      <td>-0.230780</td>\n",
       "      <td>-0.317344</td>\n",
       "      <td>-0.326463</td>\n",
       "      <td>-0.352344</td>\n",
       "      <td>-0.408403</td>\n",
       "      <td>-0.408403</td>\n",
       "      <td>-0.314260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>115</td>\n",
       "      <td>115</td>\n",
       "      <td>1.075500</td>\n",
       "      <td>-0.486962</td>\n",
       "      <td>0.086849</td>\n",
       "      <td>0.065368</td>\n",
       "      <td>-0.053930</td>\n",
       "      <td>-0.153881</td>\n",
       "      <td>-0.213741</td>\n",
       "      <td>-0.302373</td>\n",
       "      <td>-0.318127</td>\n",
       "      <td>-0.315264</td>\n",
       "      <td>-0.327709</td>\n",
       "      <td>-0.327709</td>\n",
       "      <td>-0.298144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>1.103803</td>\n",
       "      <td>-0.263969</td>\n",
       "      <td>0.090016</td>\n",
       "      <td>0.073360</td>\n",
       "      <td>-0.035991</td>\n",
       "      <td>-0.129900</td>\n",
       "      <td>-0.188169</td>\n",
       "      <td>-0.279950</td>\n",
       "      <td>-0.304810</td>\n",
       "      <td>-0.282838</td>\n",
       "      <td>-0.231644</td>\n",
       "      <td>-0.231644</td>\n",
       "      <td>-0.273814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>1.122671</td>\n",
       "      <td>0.053620</td>\n",
       "      <td>0.092127</td>\n",
       "      <td>0.078673</td>\n",
       "      <td>-0.024067</td>\n",
       "      <td>-0.113939</td>\n",
       "      <td>-0.171114</td>\n",
       "      <td>-0.264966</td>\n",
       "      <td>-0.295037</td>\n",
       "      <td>-0.275382</td>\n",
       "      <td>-0.191377</td>\n",
       "      <td>-0.191377</td>\n",
       "      <td>-0.257487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>1.198145</td>\n",
       "      <td>-0.612992</td>\n",
       "      <td>0.100570</td>\n",
       "      <td>0.099790</td>\n",
       "      <td>0.023316</td>\n",
       "      <td>-0.050340</td>\n",
       "      <td>-0.102857</td>\n",
       "      <td>-0.203814</td>\n",
       "      <td>-0.241514</td>\n",
       "      <td>-0.288249</td>\n",
       "      <td>-0.248066</td>\n",
       "      <td>-0.248066</td>\n",
       "      <td>-0.191293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>130</td>\n",
       "      <td>130</td>\n",
       "      <td>1.217013</td>\n",
       "      <td>-0.087615</td>\n",
       "      <td>0.102681</td>\n",
       "      <td>0.105033</td>\n",
       "      <td>0.035078</td>\n",
       "      <td>-0.034507</td>\n",
       "      <td>-0.085788</td>\n",
       "      <td>-0.188020</td>\n",
       "      <td>-0.222981</td>\n",
       "      <td>-0.286090</td>\n",
       "      <td>-0.319283</td>\n",
       "      <td>-0.319283</td>\n",
       "      <td>-0.174515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>883</td>\n",
       "      <td>883</td>\n",
       "      <td>8.320975</td>\n",
       "      <td>1.090773</td>\n",
       "      <td>0.893429</td>\n",
       "      <td>0.977505</td>\n",
       "      <td>1.064809</td>\n",
       "      <td>1.134710</td>\n",
       "      <td>1.186539</td>\n",
       "      <td>1.277704</td>\n",
       "      <td>1.207429</td>\n",
       "      <td>1.028304</td>\n",
       "      <td>0.953941</td>\n",
       "      <td>0.953941</td>\n",
       "      <td>1.270916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>885</td>\n",
       "      <td>885</td>\n",
       "      <td>8.339844</td>\n",
       "      <td>1.000214</td>\n",
       "      <td>0.895564</td>\n",
       "      <td>0.984115</td>\n",
       "      <td>1.077449</td>\n",
       "      <td>1.150509</td>\n",
       "      <td>1.203084</td>\n",
       "      <td>1.298065</td>\n",
       "      <td>1.242501</td>\n",
       "      <td>1.059962</td>\n",
       "      <td>0.978819</td>\n",
       "      <td>0.978819</td>\n",
       "      <td>1.287540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>889</td>\n",
       "      <td>889</td>\n",
       "      <td>8.377580</td>\n",
       "      <td>1.070650</td>\n",
       "      <td>0.899835</td>\n",
       "      <td>0.997383</td>\n",
       "      <td>1.102828</td>\n",
       "      <td>1.182170</td>\n",
       "      <td>1.236144</td>\n",
       "      <td>1.339964</td>\n",
       "      <td>1.329076</td>\n",
       "      <td>1.247071</td>\n",
       "      <td>1.206912</td>\n",
       "      <td>1.206912</td>\n",
       "      <td>1.320577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>897</td>\n",
       "      <td>897</td>\n",
       "      <td>8.453054</td>\n",
       "      <td>1.666208</td>\n",
       "      <td>0.908379</td>\n",
       "      <td>1.024105</td>\n",
       "      <td>1.153949</td>\n",
       "      <td>1.245697</td>\n",
       "      <td>1.302093</td>\n",
       "      <td>1.423501</td>\n",
       "      <td>1.510355</td>\n",
       "      <td>1.728304</td>\n",
       "      <td>1.809082</td>\n",
       "      <td>1.809082</td>\n",
       "      <td>1.385514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>902</td>\n",
       "      <td>902</td>\n",
       "      <td>8.500225</td>\n",
       "      <td>2.164331</td>\n",
       "      <td>0.913720</td>\n",
       "      <td>1.040920</td>\n",
       "      <td>1.186119</td>\n",
       "      <td>1.285510</td>\n",
       "      <td>1.343161</td>\n",
       "      <td>1.471858</td>\n",
       "      <td>1.587764</td>\n",
       "      <td>1.832285</td>\n",
       "      <td>1.896199</td>\n",
       "      <td>1.896199</td>\n",
       "      <td>1.425103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>910</td>\n",
       "      <td>910</td>\n",
       "      <td>8.575699</td>\n",
       "      <td>1.598121</td>\n",
       "      <td>0.922266</td>\n",
       "      <td>1.067986</td>\n",
       "      <td>1.237899</td>\n",
       "      <td>1.349337</td>\n",
       "      <td>1.408590</td>\n",
       "      <td>1.540403</td>\n",
       "      <td>1.640360</td>\n",
       "      <td>1.720218</td>\n",
       "      <td>1.699297</td>\n",
       "      <td>1.699297</td>\n",
       "      <td>1.486653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>912</td>\n",
       "      <td>912</td>\n",
       "      <td>8.594567</td>\n",
       "      <td>1.674038</td>\n",
       "      <td>0.924403</td>\n",
       "      <td>1.074781</td>\n",
       "      <td>1.250897</td>\n",
       "      <td>1.365312</td>\n",
       "      <td>1.424890</td>\n",
       "      <td>1.555704</td>\n",
       "      <td>1.641260</td>\n",
       "      <td>1.671724</td>\n",
       "      <td>1.646877</td>\n",
       "      <td>1.646877</td>\n",
       "      <td>1.501679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>914</td>\n",
       "      <td>914</td>\n",
       "      <td>8.613436</td>\n",
       "      <td>1.414879</td>\n",
       "      <td>0.926539</td>\n",
       "      <td>1.081586</td>\n",
       "      <td>1.263914</td>\n",
       "      <td>1.381293</td>\n",
       "      <td>1.441165</td>\n",
       "      <td>1.570257</td>\n",
       "      <td>1.638037</td>\n",
       "      <td>1.625459</td>\n",
       "      <td>1.610072</td>\n",
       "      <td>1.610072</td>\n",
       "      <td>1.516558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>916</td>\n",
       "      <td>916</td>\n",
       "      <td>8.632304</td>\n",
       "      <td>1.742757</td>\n",
       "      <td>0.928676</td>\n",
       "      <td>1.088402</td>\n",
       "      <td>1.276950</td>\n",
       "      <td>1.397280</td>\n",
       "      <td>1.457418</td>\n",
       "      <td>1.584188</td>\n",
       "      <td>1.632486</td>\n",
       "      <td>1.585905</td>\n",
       "      <td>1.584810</td>\n",
       "      <td>1.584810</td>\n",
       "      <td>1.531294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>926</td>\n",
       "      <td>926</td>\n",
       "      <td>8.726646</td>\n",
       "      <td>1.570245</td>\n",
       "      <td>0.939362</td>\n",
       "      <td>1.122621</td>\n",
       "      <td>1.342384</td>\n",
       "      <td>1.477277</td>\n",
       "      <td>1.538342</td>\n",
       "      <td>1.648121</td>\n",
       "      <td>1.609439</td>\n",
       "      <td>1.527509</td>\n",
       "      <td>1.535119</td>\n",
       "      <td>1.535119</td>\n",
       "      <td>1.603040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>928</td>\n",
       "      <td>928</td>\n",
       "      <td>8.745515</td>\n",
       "      <td>1.643629</td>\n",
       "      <td>0.941499</td>\n",
       "      <td>1.129490</td>\n",
       "      <td>1.355517</td>\n",
       "      <td>1.493288</td>\n",
       "      <td>1.554462</td>\n",
       "      <td>1.660238</td>\n",
       "      <td>1.609349</td>\n",
       "      <td>1.537043</td>\n",
       "      <td>1.539531</td>\n",
       "      <td>1.539531</td>\n",
       "      <td>1.617032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>932</td>\n",
       "      <td>932</td>\n",
       "      <td>8.783252</td>\n",
       "      <td>1.318655</td>\n",
       "      <td>0.945774</td>\n",
       "      <td>1.143251</td>\n",
       "      <td>1.381823</td>\n",
       "      <td>1.525316</td>\n",
       "      <td>1.586643</td>\n",
       "      <td>1.684328</td>\n",
       "      <td>1.617467</td>\n",
       "      <td>1.568683</td>\n",
       "      <td>1.562898</td>\n",
       "      <td>1.562898</td>\n",
       "      <td>1.644693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>933</td>\n",
       "      <td>933</td>\n",
       "      <td>8.792686</td>\n",
       "      <td>1.618969</td>\n",
       "      <td>0.946843</td>\n",
       "      <td>1.146695</td>\n",
       "      <td>1.388408</td>\n",
       "      <td>1.533324</td>\n",
       "      <td>1.594676</td>\n",
       "      <td>1.690366</td>\n",
       "      <td>1.621550</td>\n",
       "      <td>1.578440</td>\n",
       "      <td>1.571771</td>\n",
       "      <td>1.571771</td>\n",
       "      <td>1.651545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>950</td>\n",
       "      <td>950</td>\n",
       "      <td>8.953067</td>\n",
       "      <td>2.012024</td>\n",
       "      <td>0.965012</td>\n",
       "      <td>1.205484</td>\n",
       "      <td>1.500747</td>\n",
       "      <td>1.669515</td>\n",
       "      <td>1.730547</td>\n",
       "      <td>1.791749</td>\n",
       "      <td>1.773085</td>\n",
       "      <td>1.792390</td>\n",
       "      <td>1.810661</td>\n",
       "      <td>1.810661</td>\n",
       "      <td>1.764372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>960</td>\n",
       "      <td>960</td>\n",
       "      <td>9.047409</td>\n",
       "      <td>1.624429</td>\n",
       "      <td>0.975702</td>\n",
       "      <td>1.240220</td>\n",
       "      <td>1.567092</td>\n",
       "      <td>1.749627</td>\n",
       "      <td>1.809931</td>\n",
       "      <td>1.845555</td>\n",
       "      <td>1.864767</td>\n",
       "      <td>1.904327</td>\n",
       "      <td>1.900205</td>\n",
       "      <td>1.900205</td>\n",
       "      <td>1.827779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>961</td>\n",
       "      <td>961</td>\n",
       "      <td>9.056844</td>\n",
       "      <td>1.996643</td>\n",
       "      <td>0.976771</td>\n",
       "      <td>1.243698</td>\n",
       "      <td>1.573734</td>\n",
       "      <td>1.757636</td>\n",
       "      <td>1.817850</td>\n",
       "      <td>1.850507</td>\n",
       "      <td>1.871202</td>\n",
       "      <td>1.912575</td>\n",
       "      <td>1.902017</td>\n",
       "      <td>1.902017</td>\n",
       "      <td>1.834010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>962</td>\n",
       "      <td>962</td>\n",
       "      <td>9.066278</td>\n",
       "      <td>1.777070</td>\n",
       "      <td>0.977840</td>\n",
       "      <td>1.247176</td>\n",
       "      <td>1.580376</td>\n",
       "      <td>1.765645</td>\n",
       "      <td>1.825766</td>\n",
       "      <td>1.855370</td>\n",
       "      <td>1.876986</td>\n",
       "      <td>1.919627</td>\n",
       "      <td>1.903155</td>\n",
       "      <td>1.903155</td>\n",
       "      <td>1.840221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>965</td>\n",
       "      <td>965</td>\n",
       "      <td>9.094581</td>\n",
       "      <td>1.932855</td>\n",
       "      <td>0.981047</td>\n",
       "      <td>1.257615</td>\n",
       "      <td>1.600309</td>\n",
       "      <td>1.789670</td>\n",
       "      <td>1.849495</td>\n",
       "      <td>1.869428</td>\n",
       "      <td>1.890573</td>\n",
       "      <td>1.928173</td>\n",
       "      <td>1.912138</td>\n",
       "      <td>1.912138</td>\n",
       "      <td>1.858747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>967</td>\n",
       "      <td>967</td>\n",
       "      <td>9.113449</td>\n",
       "      <td>2.122920</td>\n",
       "      <td>0.983185</td>\n",
       "      <td>1.264578</td>\n",
       "      <td>1.613602</td>\n",
       "      <td>1.805684</td>\n",
       "      <td>1.865300</td>\n",
       "      <td>1.878369</td>\n",
       "      <td>1.896750</td>\n",
       "      <td>1.919566</td>\n",
       "      <td>1.929082</td>\n",
       "      <td>1.929082</td>\n",
       "      <td>1.871011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>971</td>\n",
       "      <td>971</td>\n",
       "      <td>9.151186</td>\n",
       "      <td>1.849789</td>\n",
       "      <td>0.987461</td>\n",
       "      <td>1.278507</td>\n",
       "      <td>1.640196</td>\n",
       "      <td>1.837708</td>\n",
       "      <td>1.896880</td>\n",
       "      <td>1.895414</td>\n",
       "      <td>1.905165</td>\n",
       "      <td>1.883622</td>\n",
       "      <td>1.952431</td>\n",
       "      <td>1.952431</td>\n",
       "      <td>1.895362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>973</td>\n",
       "      <td>973</td>\n",
       "      <td>9.170054</td>\n",
       "      <td>2.085739</td>\n",
       "      <td>0.989599</td>\n",
       "      <td>1.285474</td>\n",
       "      <td>1.653497</td>\n",
       "      <td>1.853718</td>\n",
       "      <td>1.912656</td>\n",
       "      <td>1.903625</td>\n",
       "      <td>1.908997</td>\n",
       "      <td>1.867122</td>\n",
       "      <td>1.934527</td>\n",
       "      <td>1.934527</td>\n",
       "      <td>1.907461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>978</td>\n",
       "      <td>978</td>\n",
       "      <td>9.217225</td>\n",
       "      <td>1.845186</td>\n",
       "      <td>0.994944</td>\n",
       "      <td>1.302898</td>\n",
       "      <td>1.686757</td>\n",
       "      <td>1.893738</td>\n",
       "      <td>1.952067</td>\n",
       "      <td>1.923569</td>\n",
       "      <td>1.921577</td>\n",
       "      <td>1.858799</td>\n",
       "      <td>1.789746</td>\n",
       "      <td>1.789746</td>\n",
       "      <td>1.937531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>980</td>\n",
       "      <td>980</td>\n",
       "      <td>9.236094</td>\n",
       "      <td>1.432716</td>\n",
       "      <td>0.997082</td>\n",
       "      <td>1.309869</td>\n",
       "      <td>1.700063</td>\n",
       "      <td>1.909744</td>\n",
       "      <td>1.967820</td>\n",
       "      <td>1.931353</td>\n",
       "      <td>1.927733</td>\n",
       "      <td>1.868867</td>\n",
       "      <td>1.738972</td>\n",
       "      <td>1.738972</td>\n",
       "      <td>1.949500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>986</td>\n",
       "      <td>986</td>\n",
       "      <td>9.292699</td>\n",
       "      <td>2.110915</td>\n",
       "      <td>1.003496</td>\n",
       "      <td>1.330786</td>\n",
       "      <td>1.739989</td>\n",
       "      <td>1.957758</td>\n",
       "      <td>2.015055</td>\n",
       "      <td>1.954210</td>\n",
       "      <td>1.948939</td>\n",
       "      <td>1.941426</td>\n",
       "      <td>1.871168</td>\n",
       "      <td>1.871168</td>\n",
       "      <td>1.985259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>988</td>\n",
       "      <td>988</td>\n",
       "      <td>9.311567</td>\n",
       "      <td>2.150585</td>\n",
       "      <td>1.005634</td>\n",
       "      <td>1.337760</td>\n",
       "      <td>1.753298</td>\n",
       "      <td>1.973761</td>\n",
       "      <td>2.030794</td>\n",
       "      <td>1.961701</td>\n",
       "      <td>1.956536</td>\n",
       "      <td>1.976895</td>\n",
       "      <td>2.053107</td>\n",
       "      <td>2.053107</td>\n",
       "      <td>1.997144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>990</td>\n",
       "      <td>990</td>\n",
       "      <td>9.330436</td>\n",
       "      <td>1.970253</td>\n",
       "      <td>1.007772</td>\n",
       "      <td>1.344733</td>\n",
       "      <td>1.766609</td>\n",
       "      <td>1.989764</td>\n",
       "      <td>2.046531</td>\n",
       "      <td>1.969119</td>\n",
       "      <td>1.963709</td>\n",
       "      <td>2.006993</td>\n",
       "      <td>2.207295</td>\n",
       "      <td>2.207295</td>\n",
       "      <td>2.009016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>993</td>\n",
       "      <td>993</td>\n",
       "      <td>9.358738</td>\n",
       "      <td>2.289554</td>\n",
       "      <td>1.010979</td>\n",
       "      <td>1.355194</td>\n",
       "      <td>1.786574</td>\n",
       "      <td>2.013768</td>\n",
       "      <td>2.070134</td>\n",
       "      <td>1.980067</td>\n",
       "      <td>1.972180</td>\n",
       "      <td>2.016287</td>\n",
       "      <td>2.120013</td>\n",
       "      <td>2.120013</td>\n",
       "      <td>2.026805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>994</td>\n",
       "      <td>994</td>\n",
       "      <td>9.368173</td>\n",
       "      <td>1.766312</td>\n",
       "      <td>1.012048</td>\n",
       "      <td>1.358681</td>\n",
       "      <td>1.793230</td>\n",
       "      <td>2.021769</td>\n",
       "      <td>2.078001</td>\n",
       "      <td>1.983659</td>\n",
       "      <td>1.974087</td>\n",
       "      <td>2.004609</td>\n",
       "      <td>1.957363</td>\n",
       "      <td>1.957363</td>\n",
       "      <td>2.032730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>995</td>\n",
       "      <td>995</td>\n",
       "      <td>9.377607</td>\n",
       "      <td>1.850753</td>\n",
       "      <td>1.013117</td>\n",
       "      <td>1.362168</td>\n",
       "      <td>1.799885</td>\n",
       "      <td>2.029770</td>\n",
       "      <td>2.085867</td>\n",
       "      <td>1.987228</td>\n",
       "      <td>1.975583</td>\n",
       "      <td>1.986822</td>\n",
       "      <td>1.770286</td>\n",
       "      <td>1.770286</td>\n",
       "      <td>2.038654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>999</td>\n",
       "      <td>999</td>\n",
       "      <td>9.415344</td>\n",
       "      <td>1.948860</td>\n",
       "      <td>1.017394</td>\n",
       "      <td>1.376116</td>\n",
       "      <td>1.826506</td>\n",
       "      <td>2.061773</td>\n",
       "      <td>2.117333</td>\n",
       "      <td>2.001399</td>\n",
       "      <td>1.979900</td>\n",
       "      <td>1.906551</td>\n",
       "      <td>1.950956</td>\n",
       "      <td>1.950956</td>\n",
       "      <td>2.062340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  seq.from...1..to...1000.         x         y      fit2  \\\n",
       "0             7                         7  0.056605 -0.894776 -0.027209   \n",
       "1            14                        14  0.122645 -0.639564 -0.019815   \n",
       "2            29                        29  0.264158 -0.858491 -0.003971   \n",
       "3            32                        32  0.292461 -0.971105 -0.000803   \n",
       "4            34                        34  0.311329 -0.441006  0.001310   \n",
       "5            35                        35  0.320763 -0.812196  0.002366   \n",
       "6            37                        37  0.339632 -1.107151  0.004479   \n",
       "7            42                        42  0.386803 -1.002422  0.009760   \n",
       "8            45                        45  0.415105 -1.195662  0.012929   \n",
       "9            48                        48  0.443408 -0.967867  0.016097   \n",
       "10           53                        53  0.490579 -0.757764  0.021378   \n",
       "11           60                        60  0.556619 -0.882360  0.028772   \n",
       "12           61                        61  0.566053 -0.747627  0.029828   \n",
       "13           66                        66  0.613224 -0.823935  0.035109   \n",
       "14           72                        72  0.669829 -0.523625  0.041445   \n",
       "15           92                        92  0.858513 -0.622196  0.062566   \n",
       "16           94                        94  0.877382 -0.275332  0.064678   \n",
       "17           98                        98  0.915119 -0.238423  0.068901   \n",
       "18          100                       100  0.933987 -0.123531  0.071013   \n",
       "19          101                       101  0.943421 -0.386109  0.072069   \n",
       "20          103                       103  0.962290 -0.727077  0.074181   \n",
       "21          105                       105  0.981158 -0.092702  0.076292   \n",
       "22          106                       106  0.990592 -0.178196  0.077348   \n",
       "23          109                       109  1.018895 -0.648567  0.080515   \n",
       "24          113                       113  1.056632 -0.366328  0.084738   \n",
       "25          115                       115  1.075500 -0.486962  0.086849   \n",
       "26          118                       118  1.103803 -0.263969  0.090016   \n",
       "27          120                       120  1.122671  0.053620  0.092127   \n",
       "28          128                       128  1.198145 -0.612992  0.100570   \n",
       "29          130                       130  1.217013 -0.087615  0.102681   \n",
       "..          ...                       ...       ...       ...       ...   \n",
       "270         883                       883  8.320975  1.090773  0.893429   \n",
       "271         885                       885  8.339844  1.000214  0.895564   \n",
       "272         889                       889  8.377580  1.070650  0.899835   \n",
       "273         897                       897  8.453054  1.666208  0.908379   \n",
       "274         902                       902  8.500225  2.164331  0.913720   \n",
       "275         910                       910  8.575699  1.598121  0.922266   \n",
       "276         912                       912  8.594567  1.674038  0.924403   \n",
       "277         914                       914  8.613436  1.414879  0.926539   \n",
       "278         916                       916  8.632304  1.742757  0.928676   \n",
       "279         926                       926  8.726646  1.570245  0.939362   \n",
       "280         928                       928  8.745515  1.643629  0.941499   \n",
       "281         932                       932  8.783252  1.318655  0.945774   \n",
       "282         933                       933  8.792686  1.618969  0.946843   \n",
       "283         950                       950  8.953067  2.012024  0.965012   \n",
       "284         960                       960  9.047409  1.624429  0.975702   \n",
       "285         961                       961  9.056844  1.996643  0.976771   \n",
       "286         962                       962  9.066278  1.777070  0.977840   \n",
       "287         965                       965  9.094581  1.932855  0.981047   \n",
       "288         967                       967  9.113449  2.122920  0.983185   \n",
       "289         971                       971  9.151186  1.849789  0.987461   \n",
       "290         973                       973  9.170054  2.085739  0.989599   \n",
       "291         978                       978  9.217225  1.845186  0.994944   \n",
       "292         980                       980  9.236094  1.432716  0.997082   \n",
       "293         986                       986  9.292699  2.110915  1.003496   \n",
       "294         988                       988  9.311567  2.150585  1.005634   \n",
       "295         990                       990  9.330436  1.970253  1.007772   \n",
       "296         993                       993  9.358738  2.289554  1.010979   \n",
       "297         994                       994  9.368173  1.766312  1.012048   \n",
       "298         995                       995  9.377607  1.850753  1.013117   \n",
       "299         999                       999  9.415344  1.948860  1.017394   \n",
       "\n",
       "         fit3      fit4      fit5      fit6     fit20     fit40     fit80  \\\n",
       "0   -0.231666 -0.719842 -1.027753 -1.117166 -0.879794 -0.781292 -0.858112   \n",
       "1   -0.212231 -0.676326 -0.971100 -1.059450 -0.880533 -0.784838 -0.700174   \n",
       "2   -0.170590 -0.583081 -0.849676 -0.935686 -0.877862 -0.843645 -0.775587   \n",
       "3   -0.162264 -0.564433 -0.825382 -0.910901 -0.875433 -0.863846 -0.815897   \n",
       "4   -0.156713 -0.552002 -0.809184 -0.894369 -0.873216 -0.878066 -0.846558   \n",
       "5   -0.153938 -0.545787 -0.801084 -0.886100 -0.871895 -0.885199 -0.863802   \n",
       "6   -0.148389 -0.533356 -0.784882 -0.869555 -0.868770 -0.899093 -0.902248   \n",
       "7   -0.134517 -0.502283 -0.744365 -0.828147 -0.857574 -0.925840 -0.994434   \n",
       "8   -0.126197 -0.483642 -0.720046 -0.803265 -0.848041 -0.931322 -1.022434   \n",
       "9   -0.117879 -0.465006 -0.695720 -0.778353 -0.836071 -0.925555 -1.014256   \n",
       "10  -0.104024 -0.433956 -0.655163 -0.736759 -0.810934 -0.893917 -0.940020   \n",
       "11  -0.084649 -0.390525 -0.598362 -0.678368 -0.766014 -0.817432 -0.794839   \n",
       "12  -0.081884 -0.384326 -0.590246 -0.670011 -0.758767 -0.804596 -0.777175   \n",
       "13  -0.068069 -0.353349 -0.549667 -0.628167 -0.719926 -0.736604 -0.707282   \n",
       "14  -0.051521 -0.316238 -0.500979 -0.577829 -0.668861 -0.651602 -0.642244   \n",
       "15   0.003297 -0.193244 -0.338960 -0.409136 -0.486504 -0.415746 -0.399757   \n",
       "16   0.008742 -0.181025 -0.322799 -0.392199 -0.468776 -0.399977 -0.378763   \n",
       "17   0.019608 -0.156638 -0.290509 -0.358291 -0.434364 -0.374523 -0.342332   \n",
       "18   0.025028 -0.144473 -0.274381 -0.341322 -0.417781 -0.365058 -0.328908   \n",
       "19   0.027735 -0.138398 -0.266322 -0.332833 -0.409645 -0.361105 -0.325847   \n",
       "20   0.033142 -0.126262 -0.250213 -0.315848 -0.393661 -0.354619 -0.332577   \n",
       "21   0.038539 -0.114149 -0.234118 -0.298853 -0.378022 -0.349691 -0.358963   \n",
       "22   0.041233 -0.108100 -0.226076 -0.290352 -0.370308 -0.347513 -0.374982   \n",
       "23   0.049303 -0.089988 -0.201973 -0.264834 -0.347455 -0.340412 -0.403746   \n",
       "24   0.060024 -0.065924 -0.169893 -0.230780 -0.317344 -0.326463 -0.352344   \n",
       "25   0.065368 -0.053930 -0.153881 -0.213741 -0.302373 -0.318127 -0.315264   \n",
       "26   0.073360 -0.035991 -0.129900 -0.188169 -0.279950 -0.304810 -0.282838   \n",
       "27   0.078673 -0.024067 -0.113939 -0.171114 -0.264966 -0.295037 -0.275382   \n",
       "28   0.099790  0.023316 -0.050340 -0.102857 -0.203814 -0.241514 -0.288249   \n",
       "29   0.105033  0.035078 -0.034507 -0.085788 -0.188020 -0.222981 -0.286090   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "270  0.977505  1.064809  1.134710  1.186539  1.277704  1.207429  1.028304   \n",
       "271  0.984115  1.077449  1.150509  1.203084  1.298065  1.242501  1.059962   \n",
       "272  0.997383  1.102828  1.182170  1.236144  1.339964  1.329076  1.247071   \n",
       "273  1.024105  1.153949  1.245697  1.302093  1.423501  1.510355  1.728304   \n",
       "274  1.040920  1.186119  1.285510  1.343161  1.471858  1.587764  1.832285   \n",
       "275  1.067986  1.237899  1.349337  1.408590  1.540403  1.640360  1.720218   \n",
       "276  1.074781  1.250897  1.365312  1.424890  1.555704  1.641260  1.671724   \n",
       "277  1.081586  1.263914  1.381293  1.441165  1.570257  1.638037  1.625459   \n",
       "278  1.088402  1.276950  1.397280  1.457418  1.584188  1.632486  1.585905   \n",
       "279  1.122621  1.342384  1.477277  1.538342  1.648121  1.609439  1.527509   \n",
       "280  1.129490  1.355517  1.493288  1.554462  1.660238  1.609349  1.537043   \n",
       "281  1.143251  1.381823  1.525316  1.586643  1.684328  1.617467  1.568683   \n",
       "282  1.146695  1.388408  1.533324  1.594676  1.690366  1.621550  1.578440   \n",
       "283  1.205484  1.500747  1.669515  1.730547  1.791749  1.773085  1.792390   \n",
       "284  1.240220  1.567092  1.749627  1.809931  1.845555  1.864767  1.904327   \n",
       "285  1.243698  1.573734  1.757636  1.817850  1.850507  1.871202  1.912575   \n",
       "286  1.247176  1.580376  1.765645  1.825766  1.855370  1.876986  1.919627   \n",
       "287  1.257615  1.600309  1.789670  1.849495  1.869428  1.890573  1.928173   \n",
       "288  1.264578  1.613602  1.805684  1.865300  1.878369  1.896750  1.919566   \n",
       "289  1.278507  1.640196  1.837708  1.896880  1.895414  1.905165  1.883622   \n",
       "290  1.285474  1.653497  1.853718  1.912656  1.903625  1.908997  1.867122   \n",
       "291  1.302898  1.686757  1.893738  1.952067  1.923569  1.921577  1.858799   \n",
       "292  1.309869  1.700063  1.909744  1.967820  1.931353  1.927733  1.868867   \n",
       "293  1.330786  1.739989  1.957758  2.015055  1.954210  1.948939  1.941426   \n",
       "294  1.337760  1.753298  1.973761  2.030794  1.961701  1.956536  1.976895   \n",
       "295  1.344733  1.766609  1.989764  2.046531  1.969119  1.963709  2.006993   \n",
       "296  1.355194  1.786574  2.013768  2.070134  1.980067  1.972180  2.016287   \n",
       "297  1.358681  1.793230  2.021769  2.078001  1.983659  1.974087  2.004609   \n",
       "298  1.362168  1.799885  2.029770  2.085867  1.987228  1.975583  1.986822   \n",
       "299  1.376116  1.826506  2.061773  2.117333  2.001399  1.979900  1.906551   \n",
       "\n",
       "       fit160    fit240     fitcv  \n",
       "0   -0.892830 -0.892830 -1.046363  \n",
       "1   -0.645704 -0.645704 -1.004173  \n",
       "2   -0.809460 -0.809460 -0.913081  \n",
       "3   -0.824983 -0.824983 -0.894611  \n",
       "4   -0.836715 -0.836715 -0.882226  \n",
       "5   -0.847200 -0.847200 -0.876009  \n",
       "6   -0.881082 -0.881082 -0.863520  \n",
       "7   -0.994687 -0.994687 -0.831932  \n",
       "8   -1.038721 -1.038721 -0.812685  \n",
       "9   -1.035864 -1.035864 -0.793187  \n",
       "10  -0.951372 -0.951372 -0.760116  \n",
       "11  -0.783219 -0.783219 -0.712597  \n",
       "12  -0.764024 -0.764024 -0.705691  \n",
       "13  -0.696022 -0.696022 -0.670730  \n",
       "14  -0.648028 -0.648028 -0.627878  \n",
       "15  -0.434665 -0.434665 -0.478897  \n",
       "16  -0.402004 -0.402004 -0.463549  \n",
       "17  -0.328025 -0.328025 -0.432644  \n",
       "18  -0.291046 -0.291046 -0.417092  \n",
       "19  -0.280151 -0.280151 -0.409291  \n",
       "20  -0.291137 -0.291137 -0.393637  \n",
       "21  -0.356338 -0.356338 -0.377915  \n",
       "22  -0.398651 -0.398651 -0.370026  \n",
       "23  -0.490174 -0.490174 -0.346246  \n",
       "24  -0.408403 -0.408403 -0.314260  \n",
       "25  -0.327709 -0.327709 -0.298144  \n",
       "26  -0.231644 -0.231644 -0.273814  \n",
       "27  -0.191377 -0.191377 -0.257487  \n",
       "28  -0.248066 -0.248066 -0.191293  \n",
       "29  -0.319283 -0.319283 -0.174515  \n",
       "..        ...       ...       ...  \n",
       "270  0.953941  0.953941  1.270916  \n",
       "271  0.978819  0.978819  1.287540  \n",
       "272  1.206912  1.206912  1.320577  \n",
       "273  1.809082  1.809082  1.385514  \n",
       "274  1.896199  1.896199  1.425103  \n",
       "275  1.699297  1.699297  1.486653  \n",
       "276  1.646877  1.646877  1.501679  \n",
       "277  1.610072  1.610072  1.516558  \n",
       "278  1.584810  1.584810  1.531294  \n",
       "279  1.535119  1.535119  1.603040  \n",
       "280  1.539531  1.539531  1.617032  \n",
       "281  1.562898  1.562898  1.644693  \n",
       "282  1.571771  1.571771  1.651545  \n",
       "283  1.810661  1.810661  1.764372  \n",
       "284  1.900205  1.900205  1.827779  \n",
       "285  1.902017  1.902017  1.834010  \n",
       "286  1.903155  1.903155  1.840221  \n",
       "287  1.912138  1.912138  1.858747  \n",
       "288  1.929082  1.929082  1.871011  \n",
       "289  1.952431  1.952431  1.895362  \n",
       "290  1.934527  1.934527  1.907461  \n",
       "291  1.789746  1.789746  1.937531  \n",
       "292  1.738972  1.738972  1.949500  \n",
       "293  1.871168  1.871168  1.985259  \n",
       "294  2.053107  2.053107  1.997144  \n",
       "295  2.207295  2.207295  2.009016  \n",
       "296  2.120013  2.120013  2.026805  \n",
       "297  1.957363  1.957363  2.032730  \n",
       "298  1.770286  1.770286  2.038654  \n",
       "299  1.950956  1.950956  2.062340  \n",
       "\n",
       "[300 rows x 15 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.read_csv('splines.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the format of your plot grid:\n",
      "[ (1,1) x1,y1 ]    [ (1,2) x2,y2 ]    [ (1,3) x3,y3 ]    [ (1,4) x4,y4 ]  \n",
      "[ (2,1) x5,y5 ]    [ (2,2) x6,y6 ]    [ (2,3) x7,y7 ]    [ (2,4) x8,y8 ]  \n",
      "[ (3,1) x9,y9 ]    [ (3,2) x10,y10 ]  [ (3,3) x11,y11 ]  [ (3,4) x12,y12 ]\n",
      "[ (4,1) x13,y13 ]  [ (4,2) x14,y14 ]  [ (4,3) x15,y15 ]  [ (4,4) x16,y16 ]\n",
      "\n",
      "Layer 1 units: 1, Layer 2 units 1:\n",
      "Running experiment 1 of 16 : 1 1\n",
      "0.45123687843071003\n",
      "10:59:18 Train MSE: 0.45123687843071003\n",
      "10:59:18 Train R-squared: 0.360194\n",
      "Layer 1 units: 1, Layer 2 units 2:\n",
      "Running experiment 2 of 16 : 1 2\n",
      "0.4501787438606833\n",
      "10:59:18 Train MSE: 0.4501787438606833\n",
      "10:59:18 Train R-squared: 0.361694\n",
      "Layer 1 units: 1, Layer 2 units 4:\n",
      "Running experiment 3 of 16 : 1 4\n",
      "0.449447662622746\n",
      "10:59:19 Train MSE: 0.449447662622746\n",
      "10:59:19 Train R-squared: 0.362731\n",
      "Layer 1 units: 1, Layer 2 units 8:\n",
      "Running experiment 4 of 16 : 1 8\n",
      "0.44960316888835167\n",
      "10:59:19 Train MSE: 0.44960316888835167\n",
      "10:59:19 Train R-squared: 0.362511\n",
      "Layer 1 units: 2, Layer 2 units 1:\n",
      "Running experiment 5 of 16 : 2 1\n",
      "0.4493035300677745\n",
      "10:59:19 Train MSE: 0.4493035300677745\n",
      "10:59:19 Train R-squared: 0.362935\n",
      "Layer 1 units: 2, Layer 2 units 2:\n",
      "Running experiment 6 of 16 : 2 2\n",
      "0.3769890459200242\n",
      "10:59:19 Train MSE: 0.3769890459200242\n",
      "10:59:19 Train R-squared: 0.465470\n",
      "Layer 1 units: 2, Layer 2 units 4:\n",
      "Running experiment 7 of 16 : 2 4\n",
      "0.06425241086551657\n",
      "10:59:20 Train MSE: 0.06425241086551657\n",
      "10:59:20 Train R-squared: 0.908897\n",
      "Layer 1 units: 2, Layer 2 units 8:\n",
      "Running experiment 8 of 16 : 2 8\n",
      "0.06460457742750453\n",
      "10:59:20 Train MSE: 0.06460457742750453\n",
      "10:59:20 Train R-squared: 0.908398\n",
      "Layer 1 units: 4, Layer 2 units 1:\n",
      "Running experiment 9 of 16 : 4 1\n",
      "0.0646415331604307\n",
      "10:59:20 Train MSE: 0.0646415331604307\n",
      "10:59:20 Train R-squared: 0.908345\n",
      "Layer 1 units: 4, Layer 2 units 2:\n",
      "Running experiment 10 of 16 : 4 2\n",
      "0.44931457571832617\n",
      "10:59:20 Train MSE: 0.44931457571832617\n",
      "10:59:20 Train R-squared: 0.362920\n",
      "Layer 1 units: 4, Layer 2 units 4:\n",
      "Running experiment 11 of 16 : 4 4\n",
      "0.06389679796982471\n",
      "10:59:20 Train MSE: 0.06389679796982471\n",
      "10:59:20 Train R-squared: 0.909401\n",
      "Layer 1 units: 4, Layer 2 units 8:\n",
      "Running experiment 12 of 16 : 4 8\n",
      "0.0636990840102081\n",
      "10:59:21 Train MSE: 0.0636990840102081\n",
      "10:59:21 Train R-squared: 0.909681\n",
      "Layer 1 units: 8, Layer 2 units 1:\n",
      "Running experiment 13 of 16 : 8 1\n",
      "0.4493031378114045\n",
      "10:59:21 Train MSE: 0.4493031378114045\n",
      "10:59:21 Train R-squared: 0.362936\n",
      "Layer 1 units: 8, Layer 2 units 2:\n",
      "Running experiment 14 of 16 : 8 2\n",
      "0.06367152233486137\n",
      "10:59:21 Train MSE: 0.06367152233486137\n",
      "10:59:21 Train R-squared: 0.909721\n",
      "Layer 1 units: 8, Layer 2 units 4:\n",
      "Running experiment 15 of 16 : 8 4\n",
      "0.06352021208219046\n",
      "10:59:22 Train MSE: 0.06352021208219046\n",
      "10:59:22 Train R-squared: 0.909935\n",
      "Layer 1 units: 8, Layer 2 units 8:\n",
      "Running experiment 16 of 16 : 8 8\n",
      "0.06369116044117601\n",
      "10:59:22 Train MSE: 0.06369116044117601\n",
      "10:59:22 Train R-squared: 0.909693\n"
     ]
    },
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fit with sklearn MLPRegressor\n",
    "\n",
    "layer1_sizes=[1,2,4,8]\n",
    "layer2_sizes=[1,2,4,8]\n",
    "import itertools\n",
    "from plotly import tools\n",
    "                         \n",
    "def run_grid(build_model_fn, layer1_sizes, layer2_sizes, x, y, epochs=None):\n",
    "    nrows = len(layer1_sizes)\n",
    "    ncols = len(layer2_sizes)\n",
    "    \n",
    "    hyperparameter_list = list(itertools.product(layer1_sizes, layer2_sizes))\n",
    "    subplot_titles = [\"%d units, %d units\" % \n",
    "                      (layer1_size, layer2_size) for (layer1_size, layer2_size) in hyperparameter_list]\n",
    "    \n",
    "    fig = tools.make_subplots(rows=nrows, \n",
    "                              cols=ncols, \n",
    "                              subplot_titles=subplot_titles)\n",
    "    \n",
    "    for count, (layer1_size, layer2_size) in enumerate(hyperparameter_list):\n",
    "        print(\"Layer 1 units: %d, Layer 2 units %d:\" % (layer1_size, layer2_size))\n",
    "        \n",
    "        print(\"Running experiment %d of %d : %d %d\" % (count+1, len(hyperparameter_list), layer1_size, layer2_size))\n",
    "        model = build_model_fn(hidden_layer_sizes=(layer1_size, layer2_size),\n",
    "                               activation='tanh',\n",
    "                               max_iter=10000, tol=1e-10,\n",
    "                               solver='lbfgs'\n",
    "                              )\n",
    "        x = x.reshape(-1,1)\n",
    "        \n",
    "        if epochs:\n",
    "            model.fit(x,y, epochs=EPOCHS)\n",
    "        else:\n",
    "            model.fit(x,y)\n",
    "            \n",
    "        y_pred = model.predict(x)\n",
    "        train_score = mean_squared_error(y, y_pred)\n",
    "        print(train_score)\n",
    "        print(\"%s Train MSE: %s\" % (time.strftime(\"%H:%M:%S\"), str(train_score)))\n",
    "        print(\"%s Train R-squared: %.6f\" % (time.strftime(\"%H:%M:%S\"), 1-train_score/y.var()))\n",
    "\n",
    "        z = model.predict(x)\n",
    "        \n",
    "        trace = Scatter(\n",
    "            x = x.reshape(-1),\n",
    "            y = z.reshape(-1),\n",
    "            name = 'fit',    \n",
    "            mode = 'markers',\n",
    "            marker = dict(size = 2)\n",
    "        )\n",
    "        fig.append_trace(trace, count // nrows + 1, count % ncols +1)\n",
    "    return(iplot(fig))\n",
    "    \n",
    "run_grid(MLPRegressor, layer1_sizes, layer2_sizes, x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sklearn MLP regression didn't work as well as expected\n",
    "# let's build our own keras model by wrappping it in sklearn interface\n",
    "\n",
    "def build_ols_model(input_size = 1, hidden_layer_sizes=[4]):\n",
    "    \n",
    "    main_input = Input(shape=(input_size,), name='main_input')\n",
    "\n",
    "    lastlayer=main_input\n",
    "    for layer_size in hidden_layer_sizes:\n",
    "        lastlayer = Dense(layer_size, \n",
    "                          kernel_initializer=keras.initializers.glorot_normal(seed=None),\n",
    "                          bias_initializer=keras.initializers.glorot_normal(seed=None),\n",
    "                          activation='tanh')(lastlayer)\n",
    "    \n",
    "    output = Dense(1, activation='linear')(lastlayer)\n",
    "    \n",
    "    model = Model(inputs=[main_input], outputs=[output])\n",
    "\n",
    "    model.compile(loss=\"mse\",\n",
    "                  optimizer='rmsprop'\n",
    "                 )\n",
    "    print(model.summary())\n",
    "    \n",
    "    return model\n",
    "\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS=5000\n",
    "BATCH_SIZE=1000\n",
    "def run_experiment (model, x, y):\n",
    "    \n",
    "    models = []\n",
    "    losses = []\n",
    "\n",
    "    for epoch in range(EPOCHS):\n",
    "        fit = model.fit(\n",
    "            x,\n",
    "            y,\n",
    "            batch_size=BATCH_SIZE,\n",
    "            shuffle=True,\n",
    "            epochs=1,\n",
    "            verbose=0\n",
    "        )\n",
    "        \n",
    "        train_loss = fit.history['loss'][-1]\n",
    "            \n",
    "        losses.append(train_loss)\n",
    "        models.append(copy.copy(model))\n",
    "        \n",
    "        bestloss_index = np.argmin(losses)\n",
    "        bestloss_value = losses[bestloss_index]\n",
    "        if epoch % 100 == 99:\n",
    "            print(\"%s Epoch %d of %d Loss %.6f Best Loss %.6f\" % (time.strftime(\"%H:%M:%S\"), \n",
    "                                                                  epoch+1, \n",
    "                                                                  EPOCHS, train_loss, \n",
    "                                                                  bestloss_value))\n",
    "        # stop if loss rises by 20% from best\n",
    "        #if train_loss / bestloss_value > 1.2:\n",
    "        #    print(\"%s Stopping...\" % (time.strftime(\"%H:%M:%S\")))\n",
    "        #    break\n",
    "            \n",
    "    print (\"%s Best training loss epoch %d, value %f\" % (time.strftime(\"%H:%M:%S\"), bestloss_index, bestloss_value))\n",
    "    model = models[bestloss_index]\n",
    "    \n",
    "    train_score = model.evaluate(x, y)\n",
    "    print(train_score)\n",
    "    print(\"%s Train MSE: %s\" % (time.strftime(\"%H:%M:%S\"), str(train_score)))\n",
    "    print(\"%s Train R-squared: %.6f\" % (time.strftime(\"%H:%M:%S\"), 1-train_score/y.var()))\n",
    "\n",
    "    return mychart(x, y, model.predict(x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "main_input (InputLayer)      (None, 1)                 0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 4)                 8         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 4)                 20        \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 5         \n",
      "=================================================================\n",
      "Total params: 33\n",
      "Trainable params: 33\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "10:59:28 Epoch 100 of 5000 Loss 0.454866 Best Loss 0.454866\n",
      "10:59:30 Epoch 200 of 5000 Loss 0.440300 Best Loss 0.440300\n",
      "10:59:31 Epoch 300 of 5000 Loss 0.430287 Best Loss 0.430266\n",
      "10:59:32 Epoch 400 of 5000 Loss 0.423292 Best Loss 0.423292\n",
      "10:59:34 Epoch 500 of 5000 Loss 0.419050 Best Loss 0.419013\n",
      "10:59:35 Epoch 600 of 5000 Loss 0.415203 Best Loss 0.415203\n",
      "10:59:37 Epoch 700 of 5000 Loss 0.411413 Best Loss 0.411413\n",
      "10:59:38 Epoch 800 of 5000 Loss 0.407389 Best Loss 0.407328\n",
      "10:59:39 Epoch 900 of 5000 Loss 0.402804 Best Loss 0.402804\n",
      "10:59:41 Epoch 1000 of 5000 Loss 0.398626 Best Loss 0.398626\n",
      "10:59:42 Epoch 1100 of 5000 Loss 0.395037 Best Loss 0.394761\n",
      "10:59:43 Epoch 1200 of 5000 Loss 0.391516 Best Loss 0.391516\n",
      "10:59:45 Epoch 1300 of 5000 Loss 0.389361 Best Loss 0.389119\n",
      "10:59:46 Epoch 1400 of 5000 Loss 0.387390 Best Loss 0.387390\n",
      "10:59:47 Epoch 1500 of 5000 Loss 0.386087 Best Loss 0.386087\n",
      "10:59:48 Epoch 1600 of 5000 Loss 0.385108 Best Loss 0.385108\n",
      "10:59:49 Epoch 1700 of 5000 Loss 0.384455 Best Loss 0.384379\n",
      "10:59:50 Epoch 1800 of 5000 Loss 0.383898 Best Loss 0.383654\n",
      "10:59:51 Epoch 1900 of 5000 Loss 0.382989 Best Loss 0.382975\n",
      "10:59:52 Epoch 2000 of 5000 Loss 0.382186 Best Loss 0.382186\n",
      "10:59:53 Epoch 2100 of 5000 Loss 0.382344 Best Loss 0.381253\n",
      "10:59:55 Epoch 2200 of 5000 Loss 0.379681 Best Loss 0.379681\n",
      "10:59:56 Epoch 2300 of 5000 Loss 0.376322 Best Loss 0.375842\n",
      "10:59:57 Epoch 2400 of 5000 Loss 0.357396 Best Loss 0.357396\n",
      "10:59:58 Epoch 2500 of 5000 Loss 0.242111 Best Loss 0.242111\n",
      "10:59:59 Epoch 2600 of 5000 Loss 0.097278 Best Loss 0.097278\n",
      "11:00:00 Epoch 2700 of 5000 Loss 0.078167 Best Loss 0.076910\n",
      "11:00:01 Epoch 2800 of 5000 Loss 0.073316 Best Loss 0.070604\n",
      "11:00:02 Epoch 2900 of 5000 Loss 0.069464 Best Loss 0.067773\n",
      "11:00:03 Epoch 3000 of 5000 Loss 0.068371 Best Loss 0.066624\n",
      "11:00:04 Epoch 3100 of 5000 Loss 0.066509 Best Loss 0.066264\n",
      "11:00:05 Epoch 3200 of 5000 Loss 0.067152 Best Loss 0.066055\n",
      "11:00:06 Epoch 3300 of 5000 Loss 0.067296 Best Loss 0.065978\n",
      "11:00:07 Epoch 3400 of 5000 Loss 0.066435 Best Loss 0.065866\n",
      "11:00:09 Epoch 3500 of 5000 Loss 0.065987 Best Loss 0.065753\n",
      "11:00:10 Epoch 3600 of 5000 Loss 0.068419 Best Loss 0.065646\n",
      "11:00:11 Epoch 3700 of 5000 Loss 0.067809 Best Loss 0.065501\n",
      "11:00:12 Epoch 3800 of 5000 Loss 0.065614 Best Loss 0.065393\n",
      "11:00:13 Epoch 3900 of 5000 Loss 0.065301 Best Loss 0.065238\n",
      "11:00:14 Epoch 4000 of 5000 Loss 0.065297 Best Loss 0.065232\n",
      "11:00:16 Epoch 4100 of 5000 Loss 0.065132 Best Loss 0.065132\n",
      "11:00:17 Epoch 4200 of 5000 Loss 0.067115 Best Loss 0.065132\n",
      "11:00:20 Epoch 4300 of 5000 Loss 0.065320 Best Loss 0.065060\n",
      "11:00:21 Epoch 4400 of 5000 Loss 0.065299 Best Loss 0.065060\n",
      "11:00:22 Epoch 4500 of 5000 Loss 0.068100 Best Loss 0.065059\n",
      "11:00:23 Epoch 4600 of 5000 Loss 0.066901 Best Loss 0.065020\n",
      "11:00:24 Epoch 4700 of 5000 Loss 0.068272 Best Loss 0.065020\n",
      "11:00:25 Epoch 4800 of 5000 Loss 0.066469 Best Loss 0.065017\n",
      "11:00:26 Epoch 4900 of 5000 Loss 0.068633 Best Loss 0.064932\n",
      "11:00:27 Epoch 5000 of 5000 Loss 0.065355 Best Loss 0.064932\n",
      "11:00:27 Best training loss epoch 4861, value 0.064932\n",
      "2000/2000 [==============================] - 0s 71us/step\n",
      "0.06788910901546479\n",
      "11:00:27 Train MSE: 0.06788910901546479\n",
      "11:00:27 Train R-squared: 0.903740\n"
     ]
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{\"linkText\": \"Export to plot.ly\", \"showLink\": true})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = build_ols_model(hidden_layer_sizes=[4,4])\n",
    "run_experiment(model, x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# wrap our keras model in sklearn wrapper so we can run grid like above MLPRegressor\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "\n",
    "# closure for sklearn wrapper\n",
    "def make_build(layer_sizes):\n",
    "    def myclosure():\n",
    "        return build_ols_model(input_size=1, hidden_layer_sizes=layer_sizes)\n",
    "    return myclosure\n",
    "\n",
    "def make_keras_model(**kwargs):\n",
    "    # make a function that takes hidden layer sizes kwarg and returns estimator of correct layer sizes\n",
    "    build_fn = make_build(kwargs['hidden_layer_sizes'])\n",
    "\n",
    "    keras_estimator = KerasRegressor(build_fn=build_fn, \n",
    "                                     epochs=500, \n",
    "                                     batch_size=32, \n",
    "                                     verbose=0)\n",
    "    return keras_estimator\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the format of your plot grid:\n",
      "[ (1,1) x1,y1 ]    [ (1,2) x2,y2 ]    [ (1,3) x3,y3 ]    [ (1,4) x4,y4 ]  \n",
      "[ (2,1) x5,y5 ]    [ (2,2) x6,y6 ]    [ (2,3) x7,y7 ]    [ (2,4) x8,y8 ]  \n",
      "[ (3,1) x9,y9 ]    [ (3,2) x10,y10 ]  [ (3,3) x11,y11 ]  [ (3,4) x12,y12 ]\n",
      "[ (4,1) x13,y13 ]  [ (4,2) x14,y14 ]  [ (4,3) x15,y15 ]  [ (4,4) x16,y16 ]\n",
      "\n",
      "Layer 1 units: 1, Layer 2 units 1:\n",
      "Running experiment 1 of 16 : 1 1\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "main_input (InputLayer)      (None, 1)                 0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 2         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 1)                 2         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 1)                 2         \n",
      "=================================================================\n",
      "Total params: 6\n",
      "Trainable params: 6\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "0.4519322147661302\n",
      "11:02:40 Train MSE: 0.4519322147661302\n",
      "11:02:40 Train R-squared: 0.359208\n",
      "Layer 1 units: 1, Layer 2 units 2:\n",
      "Running experiment 2 of 16 : 1 2\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "main_input (InputLayer)      (None, 1)                 0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 1)                 2         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 2)                 4         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 1)                 3         \n",
      "=================================================================\n",
      "Total params: 9\n",
      "Trainable params: 9\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "0.45160741952506867\n",
      "11:04:51 Train MSE: 0.45160741952506867\n",
      "11:04:51 Train R-squared: 0.359669\n",
      "Layer 1 units: 1, Layer 2 units 4:\n",
      "Running experiment 3 of 16 : 1 4\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "main_input (InputLayer)      (None, 1)                 0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 1)                 2         \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 4)                 8         \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 1)                 5         \n",
      "=================================================================\n",
      "Total params: 15\n",
      "Trainable params: 15\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "0.7053004490393621\n",
      "11:07:17 Train MSE: 0.7053004490393621\n",
      "11:07:17 Train R-squared: -0.000041\n",
      "Layer 1 units: 1, Layer 2 units 8:\n",
      "Running experiment 4 of 16 : 1 8\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "main_input (InputLayer)      (None, 1)                 0         \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 1)                 2         \n",
      "_________________________________________________________________\n",
      "dense_14 (Dense)             (None, 8)                 16        \n",
      "_________________________________________________________________\n",
      "dense_15 (Dense)             (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 27\n",
      "Trainable params: 27\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "0.4510370600120921\n",
      "11:09:26 Train MSE: 0.4510370600120921\n",
      "11:09:26 Train R-squared: 0.360477\n",
      "Layer 1 units: 2, Layer 2 units 1:\n",
      "Running experiment 5 of 16 : 2 1\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "main_input (InputLayer)      (None, 1)                 0         \n",
      "_________________________________________________________________\n",
      "dense_16 (Dense)             (None, 2)                 4         \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 1)                 3         \n",
      "_________________________________________________________________\n",
      "dense_18 (Dense)             (None, 1)                 2         \n",
      "=================================================================\n",
      "Total params: 9\n",
      "Trainable params: 9\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "0.45114841289054414\n",
      "11:11:41 Train MSE: 0.45114841289054414\n",
      "11:11:41 Train R-squared: 0.360320\n",
      "Layer 1 units: 2, Layer 2 units 2:\n",
      "Running experiment 6 of 16 : 2 2\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "main_input (InputLayer)      (None, 1)                 0         \n",
      "_________________________________________________________________\n",
      "dense_19 (Dense)             (None, 2)                 4         \n",
      "_________________________________________________________________\n",
      "dense_20 (Dense)             (None, 2)                 6         \n",
      "_________________________________________________________________\n",
      "dense_21 (Dense)             (None, 1)                 3         \n",
      "=================================================================\n",
      "Total params: 13\n",
      "Trainable params: 13\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "0.06812825100574675\n",
      "11:14:05 Train MSE: 0.06812825100574675\n",
      "11:14:05 Train R-squared: 0.903401\n",
      "Layer 1 units: 2, Layer 2 units 4:\n",
      "Running experiment 7 of 16 : 2 4\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "main_input (InputLayer)      (None, 1)                 0         \n",
      "_________________________________________________________________\n",
      "dense_22 (Dense)             (None, 2)                 4         \n",
      "_________________________________________________________________\n",
      "dense_23 (Dense)             (None, 4)                 12        \n",
      "_________________________________________________________________\n",
      "dense_24 (Dense)             (None, 1)                 5         \n",
      "=================================================================\n",
      "Total params: 21\n",
      "Trainable params: 21\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "0.3792664590602151\n",
      "11:16:15 Train MSE: 0.3792664590602151\n",
      "11:16:15 Train R-squared: 0.462241\n",
      "Layer 1 units: 2, Layer 2 units 8:\n",
      "Running experiment 8 of 16 : 2 8\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "main_input (InputLayer)      (None, 1)                 0         \n",
      "_________________________________________________________________\n",
      "dense_25 (Dense)             (None, 2)                 4         \n",
      "_________________________________________________________________\n",
      "dense_26 (Dense)             (None, 8)                 24        \n",
      "_________________________________________________________________\n",
      "dense_27 (Dense)             (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 37\n",
      "Trainable params: 37\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4000896874315654\n",
      "11:18:35 Train MSE: 0.4000896874315654\n",
      "11:18:35 Train R-squared: 0.432715\n",
      "Layer 1 units: 4, Layer 2 units 1:\n",
      "Running experiment 9 of 16 : 4 1\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "main_input (InputLayer)      (None, 1)                 0         \n",
      "_________________________________________________________________\n",
      "dense_28 (Dense)             (None, 4)                 8         \n",
      "_________________________________________________________________\n",
      "dense_29 (Dense)             (None, 1)                 5         \n",
      "_________________________________________________________________\n",
      "dense_30 (Dense)             (None, 1)                 2         \n",
      "=================================================================\n",
      "Total params: 15\n",
      "Trainable params: 15\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "0.4504766956211491\n",
      "11:20:56 Train MSE: 0.4504766956211491\n",
      "11:20:56 Train R-squared: 0.361272\n",
      "Layer 1 units: 4, Layer 2 units 2:\n",
      "Running experiment 10 of 16 : 4 2\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "main_input (InputLayer)      (None, 1)                 0         \n",
      "_________________________________________________________________\n",
      "dense_31 (Dense)             (None, 4)                 8         \n",
      "_________________________________________________________________\n",
      "dense_32 (Dense)             (None, 2)                 10        \n",
      "_________________________________________________________________\n",
      "dense_33 (Dense)             (None, 1)                 3         \n",
      "=================================================================\n",
      "Total params: 21\n",
      "Trainable params: 21\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "0.06517304845517428\n",
      "11:23:05 Train MSE: 0.06517304845517428\n",
      "11:23:05 Train R-squared: 0.907592\n",
      "Layer 1 units: 4, Layer 2 units 4:\n",
      "Running experiment 11 of 16 : 4 4\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "main_input (InputLayer)      (None, 1)                 0         \n",
      "_________________________________________________________________\n",
      "dense_34 (Dense)             (None, 4)                 8         \n",
      "_________________________________________________________________\n",
      "dense_35 (Dense)             (None, 4)                 20        \n",
      "_________________________________________________________________\n",
      "dense_36 (Dense)             (None, 1)                 5         \n",
      "=================================================================\n",
      "Total params: 33\n",
      "Trainable params: 33\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "0.08572741041763401\n",
      "11:25:31 Train MSE: 0.08572741041763401\n",
      "11:25:31 Train R-squared: 0.878448\n",
      "Layer 1 units: 4, Layer 2 units 8:\n",
      "Running experiment 12 of 16 : 4 8\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "main_input (InputLayer)      (None, 1)                 0         \n",
      "_________________________________________________________________\n",
      "dense_37 (Dense)             (None, 4)                 8         \n",
      "_________________________________________________________________\n",
      "dense_38 (Dense)             (None, 8)                 40        \n",
      "_________________________________________________________________\n",
      "dense_39 (Dense)             (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 57\n",
      "Trainable params: 57\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "0.07093969956793557\n",
      "11:27:46 Train MSE: 0.07093969956793557\n",
      "11:27:46 Train R-squared: 0.899415\n",
      "Layer 1 units: 8, Layer 2 units 1:\n",
      "Running experiment 13 of 16 : 8 1\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "main_input (InputLayer)      (None, 1)                 0         \n",
      "_________________________________________________________________\n",
      "dense_40 (Dense)             (None, 8)                 16        \n",
      "_________________________________________________________________\n",
      "dense_41 (Dense)             (None, 1)                 9         \n",
      "_________________________________________________________________\n",
      "dense_42 (Dense)             (None, 1)                 2         \n",
      "=================================================================\n",
      "Total params: 27\n",
      "Trainable params: 27\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "0.06676349956404776\n",
      "11:29:59 Train MSE: 0.06676349956404776\n",
      "11:29:59 Train R-squared: 0.905336\n",
      "Layer 1 units: 8, Layer 2 units 2:\n",
      "Running experiment 14 of 16 : 8 2\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "main_input (InputLayer)      (None, 1)                 0         \n",
      "_________________________________________________________________\n",
      "dense_43 (Dense)             (None, 8)                 16        \n",
      "_________________________________________________________________\n",
      "dense_44 (Dense)             (None, 2)                 18        \n",
      "_________________________________________________________________\n",
      "dense_45 (Dense)             (None, 1)                 3         \n",
      "=================================================================\n",
      "Total params: 37\n",
      "Trainable params: 37\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "0.06960820754959089\n",
      "11:32:26 Train MSE: 0.06960820754959089\n",
      "11:32:26 Train R-squared: 0.901303\n",
      "Layer 1 units: 8, Layer 2 units 4:\n",
      "Running experiment 15 of 16 : 8 4\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "main_input (InputLayer)      (None, 1)                 0         \n",
      "_________________________________________________________________\n",
      "dense_46 (Dense)             (None, 8)                 16        \n",
      "_________________________________________________________________\n",
      "dense_47 (Dense)             (None, 4)                 36        \n",
      "_________________________________________________________________\n",
      "dense_48 (Dense)             (None, 1)                 5         \n",
      "=================================================================\n",
      "Total params: 57\n",
      "Trainable params: 57\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "0.06459001586471468\n",
      "11:34:40 Train MSE: 0.06459001586471468\n",
      "11:34:40 Train R-squared: 0.908418\n",
      "Layer 1 units: 8, Layer 2 units 8:\n",
      "Running experiment 16 of 16 : 8 8\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "main_input (InputLayer)      (None, 1)                 0         \n",
      "_________________________________________________________________\n",
      "dense_49 (Dense)             (None, 8)                 16        \n",
      "_________________________________________________________________\n",
      "dense_50 (Dense)             (None, 8)                 72        \n",
      "_________________________________________________________________\n",
      "dense_51 (Dense)             (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 97\n",
      "Trainable params: 97\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "0.06490714871662286\n",
      "11:36:52 Train MSE: 0.06490714871662286\n",
      "11:36:52 Train R-squared: 0.907969\n"
     ]
    },
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "run_grid(make_keras_model, layer1_sizes, layer2_sizes, x, y)\n",
    "# works but not as conistently as expected\n",
    "# expected all to work well but show increasing degrees of freedom\n",
    "# takeaway: feedforward NN a bit hinky for regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters currently in use:\n",
      "\n",
      "{'bootstrap': True,\n",
      " 'criterion': 'mse',\n",
      " 'max_depth': None,\n",
      " 'max_features': 'auto',\n",
      " 'max_leaf_nodes': None,\n",
      " 'min_impurity_decrease': 0.0,\n",
      " 'min_impurity_split': None,\n",
      " 'min_samples_leaf': 1,\n",
      " 'min_samples_split': 2,\n",
      " 'min_weight_fraction_leaf': 0.0,\n",
      " 'n_estimators': 10,\n",
      " 'n_jobs': 1,\n",
      " 'oob_score': False,\n",
      " 'random_state': 42,\n",
      " 'verbose': 0,\n",
      " 'warm_start': False}\n",
      "Search grid:\n",
      "{'bootstrap': [True, False],\n",
      " 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None],\n",
      " 'max_features': ['auto', 'sqrt'],\n",
      " 'min_samples_leaf': [1, 2, 4],\n",
      " 'min_samples_split': [2, 5, 10],\n",
      " 'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000]}\n"
     ]
    }
   ],
   "source": [
    "# try RandomForestRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "rf = RandomForestRegressor(random_state = 42)\n",
    "from pprint import pprint\n",
    "# Look at parameters used by our current forest\n",
    "print('Parameters currently in use:\\n')\n",
    "pprint(rf.get_params())\n",
    "\n",
    "# First 2 are most important\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(a) for a in np.linspace(start = 200, stop = 2000, num = 10)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(a) for a in np.linspace(10, 110, num = 11)]\n",
    "max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]\n",
    "\n",
    "# Create the random grid\n",
    "print(\"Search grid:\")\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}\n",
    "pprint(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 200.,  400.,  600.,  800., 1000., 1200., 1400., 1600., 1800.,\n",
       "       2000.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(start = 200, stop = 2000, num = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=x.reshape(x.shape[0],1)\n",
    "y=y.reshape(y.shape[0],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = RandomForestRegressor()\n",
    "rf.fit(x, y)\n",
    "z = rf.predict(x)\n",
    "mychart(x, y, z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=5, max_depth=30 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=5, max_depth=30, total=   0.7s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=5, max_depth=30 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.7s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=5, max_depth=30, total=   0.6s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=5, max_depth=30 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=5, max_depth=30, total=   0.7s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=10 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=10, total=   3.1s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=10 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=10, total=   2.9s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=10 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=10, total=   3.1s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=5, max_depth=10 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=5, max_depth=10, total=   2.0s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=5, max_depth=10 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=5, max_depth=10, total=   1.9s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=5, max_depth=10 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=5, max_depth=10, total=   2.0s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=30 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=30, total=   4.1s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=30 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=30, total=   3.2s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=30 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=30, total=   3.3s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=10 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=10, total=   2.3s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=10 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=10, total=   2.2s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=10 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=10, total=   2.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=30 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=30, total=   1.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=30 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=30, total=   1.4s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=30 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=30, total=   1.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=100 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=100, total=   1.8s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=100 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=100, total=   1.7s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=100 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=100, total=   1.7s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=60 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=60, total=   1.1s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=60 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=60, total=   1.2s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=60 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=60, total=   1.1s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=auto, min_samples_split=2, max_depth=50 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=auto, min_samples_split=2, max_depth=50, total=   2.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=auto, min_samples_split=2, max_depth=50 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=auto, min_samples_split=2, max_depth=50, total=   2.4s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=auto, min_samples_split=2, max_depth=50 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=auto, min_samples_split=2, max_depth=50, total=   2.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=10 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=10, total=   2.8s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=10 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=10, total=   2.7s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=10 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=10, total=   2.8s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=70 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=70, total=   0.6s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=70 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=70, total=   0.6s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=70 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=70, total=   0.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=90 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=90, total=   1.5s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=90 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=90, total=   1.5s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=90 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=90, total=   1.4s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=10, max_depth=10 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=10, max_depth=10, total=   3.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=10, max_depth=10 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=10, max_depth=10, total=   3.2s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=10, max_depth=10 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=10, max_depth=10, total=   3.1s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=1600, max_features=sqrt, min_samples_split=5, max_depth=10 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=1600, max_features=sqrt, min_samples_split=5, max_depth=10, total=   2.5s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=1600, max_features=sqrt, min_samples_split=5, max_depth=10 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=1600, max_features=sqrt, min_samples_split=5, max_depth=10, total=   2.5s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=1600, max_features=sqrt, min_samples_split=5, max_depth=10 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=1600, max_features=sqrt, min_samples_split=5, max_depth=10, total=   2.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=30 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=30, total=   1.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=30 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=30, total=   1.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=30 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=30, total=   1.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=50 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=50, total=   3.2s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=50 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=50, total=   3.0s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=50 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=50, total=   3.0s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=70 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=70, total=   1.1s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=70 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=70, total=   1.0s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=70 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=70, total=   1.0s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=20 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=20, total=   1.8s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=20 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=20, total=   1.6s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=20 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=20, total=   1.8s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=10, max_depth=110 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=10, max_depth=110, total=   3.2s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=10, max_depth=110 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=10, max_depth=110, total=   2.9s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=10, max_depth=110 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=10, max_depth=110, total=   3.0s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=80 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=80, total=   0.9s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=80 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=80, total=   1.0s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=80 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=80, total=   0.9s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1800, max_features=sqrt, min_samples_split=10, max_depth=30 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1800, max_features=sqrt, min_samples_split=10, max_depth=30, total=   3.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1800, max_features=sqrt, min_samples_split=10, max_depth=30 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1800, max_features=sqrt, min_samples_split=10, max_depth=30, total=   2.9s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1800, max_features=sqrt, min_samples_split=10, max_depth=30 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1800, max_features=sqrt, min_samples_split=10, max_depth=30, total=   3.0s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=5, max_depth=70 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=5, max_depth=70, total=   2.8s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=5, max_depth=70 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=5, max_depth=70, total=   2.7s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=5, max_depth=70 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=5, max_depth=70, total=   2.8s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=80 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=80, total=   1.9s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=80 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=80, total=   1.9s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=80 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=80, total=   1.9s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=None \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=None, total=   2.6s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=None \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=None, total=   2.6s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=None \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=None, total=   2.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=80 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=80, total=   2.4s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=80 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=80, total=   2.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=80 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=80, total=   2.4s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=None \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=None, total=   0.8s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=None \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=None, total=   0.7s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=None \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=None, total=   0.8s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=40 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=40, total=   2.7s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=40 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=40, total=   2.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=40 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=40, total=   2.7s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=20 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=20, total=   1.4s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=20 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=20, total=   1.4s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=20 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=20, total=   1.4s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=100 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=100, total=   3.1s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=100 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=100, total=   3.1s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=100 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=100, total=   3.1s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=20 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=20, total=   1.7s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=20 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=20, total=   1.7s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=20 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=20, total=   1.7s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=20 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=20, total=   1.9s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=20 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=20, total=   1.8s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=20 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=20, total=   1.9s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=2, max_depth=50 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=2, max_depth=50, total=   1.4s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=2, max_depth=50 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=2, max_depth=50, total=   1.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=2, max_depth=50 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=2, max_depth=50, total=   1.4s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=100 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=100, total=   1.4s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=100 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=100, total=   1.4s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=100 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=100, total=   1.4s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=50 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=50, total=   1.1s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=50 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=50, total=   1.1s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=50 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=50, total=   1.1s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=1800, max_features=sqrt, min_samples_split=2, max_depth=90 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=1800, max_features=sqrt, min_samples_split=2, max_depth=90, total=   2.4s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=1800, max_features=sqrt, min_samples_split=2, max_depth=90 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=1800, max_features=sqrt, min_samples_split=2, max_depth=90, total=   2.4s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=1800, max_features=sqrt, min_samples_split=2, max_depth=90 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=1800, max_features=sqrt, min_samples_split=2, max_depth=90, total=   2.4s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=20 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=20, total=   1.5s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=20 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=20, total=   1.7s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=20 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=20, total=   1.8s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=5, max_depth=20 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=5, max_depth=20, total=   2.0s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=5, max_depth=20 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=5, max_depth=20, total=   2.8s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=5, max_depth=20 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=5, max_depth=20, total=   2.0s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=100 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=100, total=   1.3s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=100 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=100, total=   1.3s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=100 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=100, total=   1.2s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=None \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=None, total=   1.1s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=None \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=None, total=   1.1s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=None \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=None, total=   1.1s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=60 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=60, total=   1.4s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=60 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=60, total=   1.4s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=60 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=60, total=   1.4s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=10 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=10, total=   0.3s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=10 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=10, total=   0.3s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=10 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=10, total=   0.3s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=10, max_depth=60 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=10, max_depth=60, total=   0.8s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=10, max_depth=60 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=10, max_depth=60, total=   0.8s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=10, max_depth=60 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=10, max_depth=60, total=   0.8s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=2, max_depth=90 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=2, max_depth=90, total=   1.2s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=2, max_depth=90 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=2, max_depth=90, total=   1.1s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=2, max_depth=90 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=2, max_depth=90, total=   1.2s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=90 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=90, total=   0.6s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=90 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=90, total=   0.6s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=90 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=90, total=   0.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=90 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=90, total=   0.4s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=90 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=90, total=   0.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=90 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=90, total=   0.3s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=110 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=110, total=   1.5s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=110 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=110, total=   1.5s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=110 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=110, total=   1.5s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=90 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=90, total=   3.0s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=90 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=90, total=   2.8s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=90 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=90, total=   2.8s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=80 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=80, total=   0.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=80 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=80, total=   0.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=80 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=80, total=   0.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=70 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=70, total=   1.9s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=70 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=70, total=   1.8s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=70 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=70, total=   1.9s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=110 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=110, total=   1.0s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=110 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=110, total=   1.0s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=110 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=110, total=   1.0s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=20 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=20, total=   3.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=20 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=20, total=   3.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=20 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=20, total=   3.4s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=50 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=50, total=   3.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=50 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=50, total=   3.1s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=50 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=50, total=   3.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=10, max_depth=50 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=10, max_depth=50, total=   1.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=10, max_depth=50 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=10, max_depth=50, total=   1.5s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=10, max_depth=50 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=10, max_depth=50, total=   1.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=30 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=30, total=   1.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=30 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=30, total=   1.5s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=30 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=30, total=   1.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=None \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=None, total=   2.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=None \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=None, total=   2.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=None \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=None, total=   2.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=60 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=60, total=   1.0s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=60 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=60, total=   0.9s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=60 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=60, total=   1.0s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=5, max_depth=10 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=5, max_depth=10, total=   2.2s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=5, max_depth=10 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=5, max_depth=10, total=   2.2s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=5, max_depth=10 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=5, max_depth=10, total=   2.2s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=80 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=80, total=   2.6s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=80 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=80, total=   2.5s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=80 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=80, total=   2.6s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=100 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=100, total=   2.2s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=100 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=100, total=   2.2s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=100 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=100, total=   2.2s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1400, max_features=sqrt, min_samples_split=10, max_depth=80 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1400, max_features=sqrt, min_samples_split=10, max_depth=80, total=   1.9s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1400, max_features=sqrt, min_samples_split=10, max_depth=80 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1400, max_features=sqrt, min_samples_split=10, max_depth=80, total=   2.1s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1400, max_features=sqrt, min_samples_split=10, max_depth=80 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1400, max_features=sqrt, min_samples_split=10, max_depth=80, total=   2.0s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=2, max_depth=50 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=2, max_depth=50, total=   0.3s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=2, max_depth=50 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=2, max_depth=50, total=   0.3s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=2, max_depth=50 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=2, max_depth=50, total=   0.4s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=10 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=10, total=   0.7s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=10 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=10, total=   0.6s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=10 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=10, total=   0.7s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=10, max_depth=80 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=10, max_depth=80, total=   1.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=10, max_depth=80 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=10, max_depth=80, total=   1.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=10, max_depth=80 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=10, max_depth=80, total=   1.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=auto, min_samples_split=10, max_depth=None \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=auto, min_samples_split=10, max_depth=None, total=   2.1s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=auto, min_samples_split=10, max_depth=None \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=auto, min_samples_split=10, max_depth=None, total=   1.9s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=auto, min_samples_split=10, max_depth=None \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=auto, min_samples_split=10, max_depth=None, total=   2.1s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=20 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=20, total=   2.6s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=20 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=20, total=   2.6s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=20 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=20, total=   2.5s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=None \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=None, total=   2.5s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=None \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=None, total=   2.5s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=None \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=None, total=   2.2s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=10 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=10, total=   3.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=10 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=10, total=   3.4s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=10 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=2, max_depth=10, total=   2.9s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=70 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=70, total=   2.2s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=70 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=70, total=   2.1s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=70 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=70, total=   2.1s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=80 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=80, total=   1.7s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=80 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=80, total=   1.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=80 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=80, total=   1.7s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=60 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=60, total=   3.5s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=60 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=60, total=   3.2s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=60 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=60, total=   3.9s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1400, max_features=sqrt, min_samples_split=2, max_depth=80 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1400, max_features=sqrt, min_samples_split=2, max_depth=80, total=   2.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1400, max_features=sqrt, min_samples_split=2, max_depth=80 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1400, max_features=sqrt, min_samples_split=2, max_depth=80, total=   2.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=1400, max_features=sqrt, min_samples_split=2, max_depth=80 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=1400, max_features=sqrt, min_samples_split=2, max_depth=80, total=   2.5s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=2, max_depth=20 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=2, max_depth=20, total=   1.5s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=2, max_depth=20 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=2, max_depth=20, total=   1.4s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=2, max_depth=20 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=2, max_depth=20, total=   1.3s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1800, max_features=sqrt, min_samples_split=5, max_depth=60 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1800, max_features=sqrt, min_samples_split=5, max_depth=60, total=   2.7s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1800, max_features=sqrt, min_samples_split=5, max_depth=60 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1800, max_features=sqrt, min_samples_split=5, max_depth=60, total=   2.7s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1800, max_features=sqrt, min_samples_split=5, max_depth=60 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1800, max_features=sqrt, min_samples_split=5, max_depth=60, total=   2.7s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=auto, min_samples_split=5, max_depth=90 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=auto, min_samples_split=5, max_depth=90, total=   0.7s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=auto, min_samples_split=5, max_depth=90 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=auto, min_samples_split=5, max_depth=90, total=   0.7s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=auto, min_samples_split=5, max_depth=90 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=auto, min_samples_split=5, max_depth=90, total=   0.8s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=5, max_depth=90 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=5, max_depth=90, total=   2.5s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=5, max_depth=90 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=5, max_depth=90, total=   2.3s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=5, max_depth=90 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=5, max_depth=90, total=   2.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=90 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=90, total=   0.7s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=90 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=90, total=   0.7s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=90 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=90, total=   0.8s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=None \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=None, total=   2.6s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=None \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=None, total=   2.5s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=None \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=None, total=   2.3s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=100 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=100, total=   3.0s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=100 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=100, total=   3.0s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=100 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=100, total=   3.1s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=10 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=10, total=   1.4s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=10 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=10, total=   1.5s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=10 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=10, total=   1.4s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=80 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=80, total=   0.3s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=80 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=80, total=   0.3s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=80 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=80, total=   0.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=60 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=60, total=   3.4s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=60 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=60, total=   3.1s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=60 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=60, total=   3.5s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=600, max_features=auto, min_samples_split=10, max_depth=100 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=600, max_features=auto, min_samples_split=10, max_depth=100, total=   0.8s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=600, max_features=auto, min_samples_split=10, max_depth=100 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=600, max_features=auto, min_samples_split=10, max_depth=100, total=   0.8s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=600, max_features=auto, min_samples_split=10, max_depth=100 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=600, max_features=auto, min_samples_split=10, max_depth=100, total=   0.8s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=100 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=100, total=   1.4s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=100 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=100, total=   1.4s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=100 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=100, total=   1.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=auto, min_samples_split=10, max_depth=None \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=auto, min_samples_split=10, max_depth=None, total=   1.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=auto, min_samples_split=10, max_depth=None \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=auto, min_samples_split=10, max_depth=None, total=   1.2s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=auto, min_samples_split=10, max_depth=None \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=800, max_features=auto, min_samples_split=10, max_depth=None, total=   1.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=70 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=70, total=   1.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=70 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=70, total=   1.2s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=70 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=70, total=   1.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=10, max_depth=40 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=10, max_depth=40, total=   1.0s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=10, max_depth=40 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=10, max_depth=40, total=   0.9s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=10, max_depth=40 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=10, max_depth=40, total=   1.0s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=5, max_depth=100 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=5, max_depth=100, total=   0.6s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=5, max_depth=100 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=5, max_depth=100, total=   0.6s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=5, max_depth=100 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=5, max_depth=100, total=   0.6s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=80 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=80, total=   2.3s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=80 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=80, total=   2.1s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=80 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=80, total=   2.1s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=100 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=100, total=   1.1s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=100 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=100, total=   1.1s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=100 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=100, total=   1.1s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=10 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=10, total=   1.3s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=10 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=10, total=   1.3s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=10 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=10, total=   1.3s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=10, max_depth=110 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=10, max_depth=110, total=   0.8s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=10, max_depth=110 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=10, max_depth=110, total=   0.8s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=10, max_depth=110 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=10, max_depth=110, total=   0.8s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=None \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=None, total=   3.2s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=None \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=None, total=   3.0s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=None \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=None, total=   3.3s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=40 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=40, total=   1.2s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=40 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=40, total=   1.2s\n",
      "[CV] bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=40 \n",
      "[CV]  bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=40, total=   1.2s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=600, max_features=auto, min_samples_split=2, max_depth=40 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=600, max_features=auto, min_samples_split=2, max_depth=40, total=   0.8s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=600, max_features=auto, min_samples_split=2, max_depth=40 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=600, max_features=auto, min_samples_split=2, max_depth=40, total=   0.8s\n",
      "[CV] bootstrap=True, min_samples_leaf=4, n_estimators=600, max_features=auto, min_samples_split=2, max_depth=40 \n",
      "[CV]  bootstrap=True, min_samples_leaf=4, n_estimators=600, max_features=auto, min_samples_split=2, max_depth=40, total=   0.8s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=30 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=30, total=   1.0s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=30 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=30, total=   0.9s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=30 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=30, total=   1.0s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=40 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=40, total=   0.7s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=40 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=40, total=   0.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=40 \n",
      "[CV]  bootstrap=False, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=40, total=   0.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=auto, min_samples_split=2, max_depth=10 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=auto, min_samples_split=2, max_depth=10, total=   1.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=auto, min_samples_split=2, max_depth=10 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=auto, min_samples_split=2, max_depth=10, total=   1.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=auto, min_samples_split=2, max_depth=10 \n",
      "[CV]  bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=auto, min_samples_split=2, max_depth=10, total=   1.6s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=100 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=100, total=   0.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=100 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=100, total=   0.3s\n",
      "[CV] bootstrap=False, min_samples_leaf=4, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=100 \n",
      "[CV]  bootstrap=False, min_samples_leaf=4, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=100, total=   0.3s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=20 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=20, total=   2.8s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=20 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=20, total=   2.8s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=20 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=20, total=   3.0s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=40 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=40, total=   2.7s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=40 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=40, total=   2.7s\n",
      "[CV] bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=40 \n",
      "[CV]  bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=40, total=   2.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed:  9.4min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score='raise',\n",
       "          estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False),\n",
       "          fit_params=None, iid=True, n_iter=100, n_jobs=1,\n",
       "          param_distributions={'bootstrap': [True, False], 'min_samples_leaf': [1, 2, 4], 'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000], 'min_samples_split': [2, 5, 10], 'max_features': ['auto', 'sqrt'], 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None]},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score='warn', scoring=None, verbose=2)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the random grid to search for best hyperparameters\n",
    "# First create the base model to tune\n",
    "rf = RandomForestRegressor()\n",
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations, and use all available cores\n",
    "rf_random = RandomizedSearchCV(estimator = rf, \n",
    "                               param_distributions = random_grid, \n",
    "                               n_iter = 100, \n",
    "                               cv = 3, verbose=2, \n",
    "                               random_state=42, \n",
    "                               n_jobs = 1)\n",
    "# Fit the random search model\n",
    "rf_random.fit(x, y)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': False,\n",
       " 'max_depth': 10,\n",
       " 'max_features': 'sqrt',\n",
       " 'min_samples_leaf': 2,\n",
       " 'min_samples_split': 5,\n",
       " 'n_estimators': 1200}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = RandomForestRegressor(bootstrap=False,\n",
    "                           n_estimators=1200,\n",
    "                           max_features='sqrt',\n",
    "                           max_depth=10,\n",
    "                           min_samples_leaf=2,\n",
    "                           min_samples_split=5,\n",
    "                          )\n",
    "rf.fit(x, y)\n",
    "z = rf.predict(x)\n",
    "mychart(x, y, z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
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true})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators=600,\n",
    "                           max_features='auto',\n",
    "                           max_depth=None,\n",
    "                           min_samples_leaf=4,\n",
    "                           min_samples_split=2,\n",
    "                          )\n",
    "rf.fit(x, y)\n",
    "z = rf.predict(x)\n",
    "mychart(x, y, z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.interpolate import UnivariateSpline\n",
    "spl = UnivariateSpline(x, y)\n",
    "z = spl(x)\n",
    "mychart(x, y, z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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plot.ly\", \"showLink\": true})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spl.set_smoothing_factor(101.0)\n",
    "#print(spl.get_knots())\n",
    "z = spl(x)\n",
    "mychart(x, y, z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4146407058.9861517\n",
      "(10.0, 33247092630076.83)\n",
      "(11.0, 29475614624086.59)\n",
      "(12.0, 29558362233432.53)\n",
      "(13.0, 27736768535424.715)\n",
      "(14.0, 26414569025152.24)\n",
      "(15.0, 25709370408353.465)\n",
      "(16.0, 24634112258771.12)\n",
      "(17.0, 22070573209369.37)\n",
      "(18.0, 34762440049306.203)\n",
      "(19.0, 28995785902109.6)\n",
      "(20.0, 25770131493033.906)\n",
      "(21.0, 26265698632396.156)\n",
      "(22.0, 23697753834507.715)\n",
      "(23.0, 28310324440186.1)\n",
      "(24.0, 4059767967770.148)\n",
      "(25.0, 3916644608906.1465)\n",
      "(26.0, 4824453016586.01)\n",
      "(27.0, 56376321055.55424)\n",
      "(28.0, 55562653311.56177)\n",
      "(29.0, 52594047044.55418)\n",
      "(30.0, 61455155857.17878)\n",
      "(31.0, 56449841551.18958)\n",
      "(32.0, 57019560639.7848)\n",
      "(33.0, 53770603740.183395)\n",
      "(34.0, 57195783200.04348)\n",
      "(35.0, 58654224000.13448)\n",
      "(36.0, 68212678411.61669)\n",
      "(37.0, 78709333835.38673)\n",
      "(38.0, 84730456041.5764)\n",
      "(39.0, 6011720459.68354)\n",
      "(40.0, 6231192279.7697)\n",
      "(41.0, 5760476682.374887)\n",
      "(42.0, 5992944422.7285185)\n",
      "(43.0, 5368627878.031981)\n",
      "(44.0, 5106394139.494643)\n",
      "(45.0, 4798101260.433854)\n",
      "(46.0, 4692589740.948821)\n",
      "(47.0, 4383231967.811253)\n",
      "(48.0, 4169875675.4103723)\n",
      "(49.0, 4087905363.489609)\n",
      "(50.0, 4119941566.538889)\n",
      "(51.0, 304534373059.7632)\n",
      "(52.0, 319659183461.79486)\n",
      "(53.0, 332016260700.8769)\n",
      "(54.0, 342055577738.312)\n",
      "(55.0, 348457597114.5676)\n",
      "(56.0, 352151813819.3777)\n",
      "(57.0, 353037412881.7689)\n",
      "(58.0, 351325948727.9931)\n",
      "(59.0, 347297681624.6608)\n",
      "(60.0, 341278589697.5876)\n",
      "(61.0, 333526211145.6957)\n",
      "(62.0, 441726740163.7495)\n",
      "(63.0, 430653018385.7642)\n",
      "(64.0, 413905328309.2967)\n",
      "(65.0, 395040356994.88696)\n",
      "(66.0, 371131700725.1827)\n",
      "(67.0, 347834956206.02094)\n",
      "(68.0, 341390951622.9299)\n",
      "(69.0, 323821608636.58655)\n",
      "(70.0, 256146963309.88522)\n",
      "(71.0, 254264662682.2758)\n",
      "(72.0, 253581454777.15063)\n",
      "(73.0, 255192642640.8915)\n",
      "(74.0, 249116269545.6131)\n",
      "(75.0, 255390352518.3961)\n",
      "(76.0, 247069897775.83905)\n",
      "(77.0, 238388147339.28958)\n",
      "(78.0, 199245200724.46246)\n",
      "(79.0, 1390347540.060241)\n",
      "(80.0, 1460575869.1399848)\n",
      "(81.0, 507637087.6948515)\n",
      "(82.0, 24313904.205273505)\n",
      "(83.0, 35902735.79875658)\n",
      "(84.0, 108113116.80639756)\n",
      "(85.0, 601348749.000735)\n",
      "(86.0, 660088695.3629776)\n",
      "(87.0, 450959.1560572498)\n",
      "(88.0, 818997.3135701473)\n",
      "(89.0, 445227.5400082507)\n",
      "(90.0, 435738.8472340024)\n",
      "(91.0, 414369.39111643395)\n",
      "(92.0, 814941.6070000506)\n",
      "(93.0, 383793.93787504005)\n",
      "(94.0, 7175365.42675651)\n",
      "(95.0, 840549.2243547873)\n",
      "(96.0, 1539370.3420854488)\n",
      "(97.0, 115513.70913762086)\n",
      "(98.0, 56918.81033437237)\n",
      "(99.0, 15364.46737816732)\n",
      "(100.0, 2552.420702278856)\n",
      "(101.0, 0.1968513560366662)\n",
      "(102.0, 2.8564629246408826)\n",
      "(103.0, 7.01178867660263)\n",
      "(104.0, 11.827185995071453)\n",
      "(105.0, 11.940081221867297)\n",
      "(106.0, 11.380104011537451)\n",
      "(107.0, 10.819879403613854)\n",
      "(108.0, 10.225661266430416)\n",
      "(109.0, 9.689135004107914)\n",
      "(110.0, 12.782728637570663)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def spline_cv_test(smoothing_factor):\n",
    "    kf = KFold(n_splits=5)\n",
    "    errors = []\n",
    "    for train_index, test_index in kf.split(x):\n",
    "        x_train, x_test = x[train_index], x[test_index]\n",
    "        y_train, y_test = y[train_index], y[test_index]\n",
    "        spline = UnivariateSpline(x_train, y_train)\n",
    "        spline.set_smoothing_factor(smoothing_factor)\n",
    "        y_pred_test = spline(x_test)\n",
    "        errors.append(mean_squared_error(y_test, y_pred_test))\n",
    "        return np.mean(np.array(errors))\n",
    "\n",
    "print(spline_cv_test(49.856))\n",
    "for sf in np.linspace(10, 110, num=101):\n",
    "    print(sf, spline_cv_test(sf))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'colsample_bytree': 0.6,\n",
       " 'gamma': 3.0,\n",
       " 'learning_rate': 0.05,\n",
       " 'max_depth': 64,\n",
       " 'min_child_weight': 90,\n",
       " 'n_estimators': 10,\n",
       " 'reg_alpha': 100,\n",
       " 'subsample': 0.99}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBRegressor\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "import scipy.stats as st\n",
    "\n",
    "xgbreg = XGBRegressor(nthreads=1)  \n",
    "\n",
    "one_to_left = st.beta(10, 1)  \n",
    "from_zero_positive = st.expon(0, 50)\n",
    "\n",
    "param_grid = {\n",
    "        'silent': [False],\n",
    "        'max_depth': [6, 10, 15, 20],\n",
    "        'learning_rate': [0.001, 0.01, 0.1, 0.2, 0,3],\n",
    "        'subsample': [0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
    "        'colsample_bytree': [0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
    "        'colsample_bylevel': [0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
    "        'min_child_weight': [0.5, 1.0, 3.0, 5.0, 7.0, 10.0],\n",
    "        'gamma': [0, 0.25, 0.5, 1.0],\n",
    "        'reg_lambda': [0.1, 1.0, 5.0, 10.0, 50.0, 100.0],\n",
    "        'n_estimators': [100]}\n",
    "\n",
    "params = {  \n",
    "    \"n_estimators\": [10, 20, 40, 80, 160, 320, 640],\n",
    "    \"max_depth\": [4, 8, 16, 32, 64, 128],\n",
    "    \"learning_rate\": [0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50],\n",
    "    \"colsample_bytree\": [1.0, 0.99, 0.95, 0.90, 0.85, 0.80, 0.60],\n",
    "    \"subsample\": [1.0, 0.99, 0.95, 0.90, 0.85, 0.80, 0.60],\n",
    "    \"gamma\": [0, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0],\n",
    "    'reg_alpha': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100],\n",
    "    \"min_child_weight\": [10, 20, 30, 40, 50, 60, 70, 80, 90, 100],\n",
    "}\n",
    "\n",
    "gs = RandomizedSearchCV(xgbreg, params, n_jobs=1)  \n",
    "gs.fit(x, y)  \n",
    "gs.best_params_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgbreg = XGBRegressor(nthreads=1,\n",
    "                      colsample_bytree=0.6,\n",
    "                      gamma=3.0,\n",
    "                      learning_rate=0.05,\n",
    "                      max_depth=64,\n",
    "                      min_child_weight=90,\n",
    "                      n_estimators=10,\n",
    "                      reg_alpha=100,\n",
    "                      subsample=0.99)\n",
    "\n",
    "xgbreg.fit(x, y)\n",
    "z = xgbreg.predict(x)\n",
    "mychart(x, y, z)                      \n",
    "                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#LSTM/GRU\n",
    "def generator_factory(data, # Xs \n",
    "                      lookback, # sequence length\n",
    "                      min_index=0, # start index\n",
    "                      max_index=None, # max index for e.g. train/xval/text, if None use all of X\n",
    "                      shuffle=False, # if True, just repeatedly sample randomly\n",
    "                      batch_size=128): # how many (predictors, responses) tuples to generate\n",
    "    \n",
    "    if max_index == None:\n",
    "        max_index = data.shape[0] - 2 # last row of predictors that has a y to predict\n",
    "\n",
    "    first_row = min_index + lookback # first row of responses to return\n",
    "    i = first_row\n",
    "    while True: # keep generating forever\n",
    "        if shuffle: # pick rows at random\n",
    "            rows = np.random.choice(range(first_row,max_index), size=batch_size, replace=False)\n",
    "        else:\n",
    "            if i + batch_size >= max_index: # start from beginning\n",
    "                i = first_row\n",
    "            rows = np.arange(i, i + batch_size)\n",
    "        \n",
    "        predictors = np.zeros((len(rows), lookback, data.shape[-1]))\n",
    "        responses = np.zeros((len(rows), data.shape[-1]))\n",
    "        \n",
    "        for j, row in enumerate(rows):\n",
    "            indices = range(rows[j] - lookback, rows[j])\n",
    "            predictors[j] = data[indices]\n",
    "            responses[j] = data[rows[j]]\n",
    "\n",
    "        yield predictors, [responses[:,0], responses[:,1]]\n",
    "        \n",
    "            \n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[[ 0.06066273, -0.5962953 ],\n",
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       " \n",
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       " \n",
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       " \n",
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       " \n",
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       "         [-0.10820698, -0.84829179],\n",
       "         [ 0.09578219, -1.10095065],\n",
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       " \n",
       "        [[-0.33741347, -1.28745857],\n",
       "         [-0.10820698, -0.84829179],\n",
       "         [ 0.09578219, -1.10095065],\n",
       "         [-0.40179584, -1.13244153],\n",
       "         [-0.26829727, -0.63113494],\n",
       "         [-0.14780634, -1.15089659],\n",
       "         [-0.52769753, -0.65231528],\n",
       "         [-0.67783355, -0.93284959],\n",
       "         [-0.25965463, -0.66408584],\n",
       "         [-0.21045462, -1.04332307]],\n",
       " \n",
       "        [[-0.10820698, -0.84829179],\n",
       "         [ 0.09578219, -1.10095065],\n",
       "         [-0.40179584, -1.13244153],\n",
       "         [-0.26829727, -0.63113494],\n",
       "         [-0.14780634, -1.15089659],\n",
       "         [-0.52769753, -0.65231528],\n",
       "         [-0.67783355, -0.93284959],\n",
       "         [-0.25965463, -0.66408584],\n",
       "         [-0.21045462, -1.04332307],\n",
       "         [-0.34603607, -0.8710234 ]],\n",
       " \n",
       "        [[ 0.09578219, -1.10095065],\n",
       "         [-0.40179584, -1.13244153],\n",
       "         [-0.26829727, -0.63113494],\n",
       "         [-0.14780634, -1.15089659],\n",
       "         [-0.52769753, -0.65231528],\n",
       "         [-0.67783355, -0.93284959],\n",
       "         [-0.25965463, -0.66408584],\n",
       "         [-0.21045462, -1.04332307],\n",
       "         [-0.34603607, -0.8710234 ],\n",
       "         [-0.11911523, -0.90433275]],\n",
       " \n",
       "        [[-0.40179584, -1.13244153],\n",
       "         [-0.26829727, -0.63113494],\n",
       "         [-0.14780634, -1.15089659],\n",
       "         [-0.52769753, -0.65231528],\n",
       "         [-0.67783355, -0.93284959],\n",
       "         [-0.25965463, -0.66408584],\n",
       "         [-0.21045462, -1.04332307],\n",
       "         [-0.34603607, -0.8710234 ],\n",
       "         [-0.11911523, -0.90433275],\n",
       "         [-0.24633775, -0.50097024]],\n",
       " \n",
       "        [[-0.26829727, -0.63113494],\n",
       "         [-0.14780634, -1.15089659],\n",
       "         [-0.52769753, -0.65231528],\n",
       "         [-0.67783355, -0.93284959],\n",
       "         [-0.25965463, -0.66408584],\n",
       "         [-0.21045462, -1.04332307],\n",
       "         [-0.34603607, -0.8710234 ],\n",
       "         [-0.11911523, -0.90433275],\n",
       "         [-0.24633775, -0.50097024],\n",
       "         [-0.10660787, -1.11483104]],\n",
       " \n",
       "        [[-0.14780634, -1.15089659],\n",
       "         [-0.52769753, -0.65231528],\n",
       "         [-0.67783355, -0.93284959],\n",
       "         [-0.25965463, -0.66408584],\n",
       "         [-0.21045462, -1.04332307],\n",
       "         [-0.34603607, -0.8710234 ],\n",
       "         [-0.11911523, -0.90433275],\n",
       "         [-0.24633775, -0.50097024],\n",
       "         [-0.10660787, -1.11483104],\n",
       "         [-0.79584369, -0.71976902]]]),\n",
       " [array([-0.33741347, -0.10820698,  0.09578219, -0.40179584, -0.26829727,\n",
       "         -0.14780634, -0.52769753, -0.67783355, -0.25965463, -0.21045462,\n",
       "         -0.34603607, -0.11911523, -0.24633775, -0.10660787, -0.79584369,\n",
       "         -0.35641191]),\n",
       "  array([-1.28745857, -0.84829179, -1.10095065, -1.13244153, -0.63113494,\n",
       "         -1.15089659, -0.65231528, -0.93284959, -0.66408584, -1.04332307,\n",
       "         -0.8710234 , -0.90433275, -0.50097024, -1.11483104, -0.71976902,\n",
       "         -1.03635705])])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2 = y2.reshape(y2.shape[0],1)\n",
    "y1 = y1.reshape(y1.shape[0],1)\n",
    "X = np.hstack((y1,y2))\n",
    "X.shape\n",
    "\n",
    "mygen = generator_factory(X, 10, batch_size=16)\n",
    "next(mygen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "OUTPUT_DIM = 2\n",
    "\n",
    "def build_model(hidden_layers = [[16, 0.0001, 0.2], \n",
    "                                 [16, 0.0001, 0.2], \n",
    "                                 [1, 0.0, 0.0]]):\n",
    "    \"\"\"Take list of [layer_size, reg_penalty, dropout], last layer is linear, rest LSTM\"\"\"\n",
    "\n",
    "    main_input = Input(shape=(None, \n",
    "                              OUTPUT_DIM), \n",
    "                       dtype='float32', \n",
    "                       name='main_input')\n",
    "    lastlayer=main_input\n",
    "\n",
    "    n_lstms = len(hidden_layers)-1\n",
    "    for i in range(n_lstms):\n",
    "        layer_size, reg_penalty, dropout = hidden_layers[i]\n",
    "        # first n-1 layers are LSTM, return_sequences=True\n",
    "        return_sequences = True\n",
    "        if i == n_lstms-1:\n",
    "            return_sequences= False\n",
    "        LSTM_layer = GRU(layer_size, \n",
    "                         return_sequences=return_sequences,\n",
    "                         kernel_regularizer=keras.regularizers.l1(reg_penalty),\n",
    "                         dropout=dropout, \n",
    "                         recurrent_dropout=dropout,\n",
    "                         name = \"GRU%02d\" % i\n",
    "                        )(lastlayer)\n",
    "        lastlayer = LSTM_layer\n",
    "        \n",
    "    layer_size, reg_penalty, dropout = hidden_layers[-1]\n",
    "    if dropout:\n",
    "        lastlayer = Dropout(dropout)(lastlayer)\n",
    "    \n",
    "    outputs = []\n",
    "    for i in range(OUTPUT_DIM):\n",
    "        # OUTPUT_DIM outputs\n",
    "        output01 = Dense(1, \n",
    "                         activation='linear', \n",
    "                         kernel_regularizer=keras.regularizers.l1(reg_penalty),\n",
    "                         name='output%02d' % i)(lastlayer)\n",
    "        outputs.append(output01)\n",
    "    \n",
    "    model = Model(inputs=[main_input], outputs=outputs)\n",
    "    print(model.summary())\n",
    "    model.compile(loss=\"mae\", metrics=['mae'], optimizer=\"rmsprop\", loss_weights=[1.]*OUTPUT_DIM)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "main_input (InputLayer)         (None, None, 2)      0                                            \n",
      "__________________________________________________________________________________________________\n",
      "GRU00 (GRU)                     (None, None, 16)     912         main_input[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "GRU01 (GRU)                     (None, 16)           1584        GRU00[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "output00 (Dense)                (None, 1)            17          GRU01[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "output01 (Dense)                (None, 1)            17          GRU01[0][0]                      \n",
      "==================================================================================================\n",
      "Total params: 2,530\n",
      "Trainable params: 2,530\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model = build_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "32/32 [==============================] - 11s 346ms/step - loss: 3.7321 - output00_loss: 1.8919 - output01_loss: 1.8273 - output00_mean_absolute_error: 1.8919 - output01_mean_absolute_error: 1.8273\n",
      "Epoch 2/40\n",
      "32/32 [==============================] - 10s 326ms/step - loss: 2.0985 - output00_loss: 1.0409 - output01_loss: 1.0450 - output00_mean_absolute_error: 1.0409 - output01_mean_absolute_error: 1.0450\n",
      "Epoch 3/40\n",
      "32/32 [==============================] - 10s 324ms/step - loss: 1.8702 - output00_loss: 0.9141 - output01_loss: 0.9439 - output00_mean_absolute_error: 0.9141 - output01_mean_absolute_error: 0.9439\n",
      "Epoch 4/40\n",
      "32/32 [==============================] - 10s 324ms/step - loss: 1.7142 - output00_loss: 0.8325 - output01_loss: 0.8698 - output00_mean_absolute_error: 0.8325 - output01_mean_absolute_error: 0.8698\n",
      "Epoch 5/40\n",
      "32/32 [==============================] - 10s 320ms/step - loss: 1.6417 - output00_loss: 0.7942 - output01_loss: 0.8359 - output00_mean_absolute_error: 0.7942 - output01_mean_absolute_error: 0.8359\n",
      "Epoch 6/40\n",
      "32/32 [==============================] - 10s 323ms/step - loss: 1.5260 - output00_loss: 0.7415 - output01_loss: 0.7731 - output00_mean_absolute_error: 0.7415 - output01_mean_absolute_error: 0.7731\n",
      "Epoch 7/40\n",
      "32/32 [==============================] - 10s 313ms/step - loss: 1.4786 - output00_loss: 0.7208 - output01_loss: 0.7465 - output00_mean_absolute_error: 0.7208 - output01_mean_absolute_error: 0.7465\n",
      "Epoch 8/40\n",
      "32/32 [==============================] - 10s 313ms/step - loss: 1.3636 - output00_loss: 0.6567 - output01_loss: 0.6957 - output00_mean_absolute_error: 0.6567 - output01_mean_absolute_error: 0.6957\n",
      "Epoch 9/40\n",
      "32/32 [==============================] - 10s 314ms/step - loss: 1.3350 - output00_loss: 0.6556 - output01_loss: 0.6684 - output00_mean_absolute_error: 0.6556 - output01_mean_absolute_error: 0.6684\n",
      "Epoch 10/40\n",
      "32/32 [==============================] - 10s 313ms/step - loss: 1.3357 - output00_loss: 0.6492 - output01_loss: 0.6755 - output00_mean_absolute_error: 0.6492 - output01_mean_absolute_error: 0.6755\n",
      "Epoch 11/40\n",
      "32/32 [==============================] - 10s 324ms/step - loss: 1.2915 - output00_loss: 0.6241 - output01_loss: 0.6566 - output00_mean_absolute_error: 0.6241 - output01_mean_absolute_error: 0.6566\n",
      "Epoch 12/40\n",
      "32/32 [==============================] - 12s 379ms/step - loss: 1.3242 - output00_loss: 0.6344 - output01_loss: 0.6790 - output00_mean_absolute_error: 0.6344 - output01_mean_absolute_error: 0.6790\n",
      "Epoch 13/40\n",
      "32/32 [==============================] - 12s 385ms/step - loss: 1.2485 - output00_loss: 0.5992 - output01_loss: 0.6385 - output00_mean_absolute_error: 0.5992 - output01_mean_absolute_error: 0.6385\n",
      "Epoch 14/40\n",
      "32/32 [==============================] - 12s 388ms/step - loss: 1.2267 - output00_loss: 0.5955 - output01_loss: 0.6204 - output00_mean_absolute_error: 0.5955 - output01_mean_absolute_error: 0.6204\n",
      "Epoch 15/40\n",
      "32/32 [==============================] - 12s 378ms/step - loss: 1.2452 - output00_loss: 0.6054 - output01_loss: 0.6290 - output00_mean_absolute_error: 0.6054 - output01_mean_absolute_error: 0.6290\n",
      "Epoch 16/40\n",
      "32/32 [==============================] - 12s 379ms/step - loss: 1.2232 - output00_loss: 0.5869 - output01_loss: 0.6254 - output00_mean_absolute_error: 0.5869 - output01_mean_absolute_error: 0.6254\n",
      "Epoch 17/40\n",
      "32/32 [==============================] - 12s 382ms/step - loss: 1.1922 - output00_loss: 0.5702 - output01_loss: 0.6111 - output00_mean_absolute_error: 0.5702 - output01_mean_absolute_error: 0.6111\n",
      "Epoch 18/40\n",
      "32/32 [==============================] - 12s 383ms/step - loss: 1.1964 - output00_loss: 0.5760 - output01_loss: 0.6094 - output00_mean_absolute_error: 0.5760 - output01_mean_absolute_error: 0.6094\n",
      "Epoch 19/40\n",
      "32/32 [==============================] - 12s 384ms/step - loss: 1.1761 - output00_loss: 0.5770 - output01_loss: 0.5881 - output00_mean_absolute_error: 0.5770 - output01_mean_absolute_error: 0.5881\n",
      "Epoch 20/40\n",
      "32/32 [==============================] - 12s 379ms/step - loss: 1.1678 - output00_loss: 0.5629 - output01_loss: 0.5940 - output00_mean_absolute_error: 0.5629 - output01_mean_absolute_error: 0.5940\n",
      "Epoch 21/40\n",
      "32/32 [==============================] - 12s 381ms/step - loss: 1.1443 - output00_loss: 0.5468 - output01_loss: 0.5865 - output00_mean_absolute_error: 0.5468 - output01_mean_absolute_error: 0.5865\n",
      "Epoch 22/40\n",
      "32/32 [==============================] - 12s 389ms/step - loss: 1.1685 - output00_loss: 0.5754 - output01_loss: 0.5820 - output00_mean_absolute_error: 0.5754 - output01_mean_absolute_error: 0.5820\n",
      "Epoch 23/40\n",
      "32/32 [==============================] - 12s 379ms/step - loss: 1.1917 - output00_loss: 0.5690 - output01_loss: 0.6117 - output00_mean_absolute_error: 0.5690 - output01_mean_absolute_error: 0.6117\n",
      "Epoch 24/40\n",
      "32/32 [==============================] - 12s 386ms/step - loss: 1.1651 - output00_loss: 0.5730 - output01_loss: 0.5809 - output00_mean_absolute_error: 0.5730 - output01_mean_absolute_error: 0.5809\n",
      "Epoch 25/40\n",
      "32/32 [==============================] - 10s 323ms/step - loss: 1.1103 - output00_loss: 0.5364 - output01_loss: 0.5627 - output00_mean_absolute_error: 0.5364 - output01_mean_absolute_error: 0.5627\n",
      "Epoch 26/40\n",
      "32/32 [==============================] - 10s 310ms/step - loss: 1.1416 - output00_loss: 0.5571 - output01_loss: 0.5733 - output00_mean_absolute_error: 0.5571 - output01_mean_absolute_error: 0.5733\n",
      "Epoch 27/40\n",
      "32/32 [==============================] - 10s 312ms/step - loss: 1.0820 - output00_loss: 0.5221 - output01_loss: 0.5487 - output00_mean_absolute_error: 0.5221 - output01_mean_absolute_error: 0.5487\n",
      "Epoch 28/40\n",
      "32/32 [==============================] - 10s 313ms/step - loss: 1.1204 - output00_loss: 0.5620 - output01_loss: 0.5472 - output00_mean_absolute_error: 0.5620 - output01_mean_absolute_error: 0.5472\n",
      "Epoch 29/40\n",
      "32/32 [==============================] - 10s 312ms/step - loss: 1.1241 - output00_loss: 0.5423 - output01_loss: 0.5705 - output00_mean_absolute_error: 0.5423 - output01_mean_absolute_error: 0.5705\n",
      "Epoch 30/40\n",
      "32/32 [==============================] - 10s 314ms/step - loss: 1.0786 - output00_loss: 0.5265 - output01_loss: 0.5408 - output00_mean_absolute_error: 0.5265 - output01_mean_absolute_error: 0.5408\n",
      "Epoch 31/40\n",
      "32/32 [==============================] - 10s 315ms/step - loss: 1.0626 - output00_loss: 0.5176 - output01_loss: 0.5337 - output00_mean_absolute_error: 0.5176 - output01_mean_absolute_error: 0.5337\n",
      "Epoch 32/40\n",
      "32/32 [==============================] - 10s 314ms/step - loss: 1.1126 - output00_loss: 0.5407 - output01_loss: 0.5605 - output00_mean_absolute_error: 0.5407 - output01_mean_absolute_error: 0.5605\n",
      "Epoch 33/40\n",
      "32/32 [==============================] - 10s 311ms/step - loss: 1.0792 - output00_loss: 0.5205 - output01_loss: 0.5472 - output00_mean_absolute_error: 0.5205 - output01_mean_absolute_error: 0.5472\n",
      "Epoch 34/40\n",
      "32/32 [==============================] - 10s 317ms/step - loss: 1.0806 - output00_loss: 0.5274 - output01_loss: 0.5417 - output00_mean_absolute_error: 0.5274 - output01_mean_absolute_error: 0.5417\n",
      "Epoch 35/40\n",
      "32/32 [==============================] - 10s 312ms/step - loss: 1.0872 - output00_loss: 0.5187 - output01_loss: 0.5571 - output00_mean_absolute_error: 0.5187 - output01_mean_absolute_error: 0.5571\n",
      "Epoch 36/40\n",
      "32/32 [==============================] - 11s 349ms/step - loss: 1.0806 - output00_loss: 0.5205 - output01_loss: 0.5485 - output00_mean_absolute_error: 0.5205 - output01_mean_absolute_error: 0.5485\n",
      "Epoch 37/40\n",
      "32/32 [==============================] - 10s 316ms/step - loss: 1.0773 - output00_loss: 0.5148 - output01_loss: 0.5509 - output00_mean_absolute_error: 0.5148 - output01_mean_absolute_error: 0.5509\n",
      "Epoch 38/40\n",
      "32/32 [==============================] - 10s 313ms/step - loss: 1.0587 - output00_loss: 0.5243 - output01_loss: 0.5228 - output00_mean_absolute_error: 0.5243 - output01_mean_absolute_error: 0.5228\n",
      "Epoch 39/40\n",
      "32/32 [==============================] - 10s 307ms/step - loss: 1.0545 - output00_loss: 0.5165 - output01_loss: 0.5263 - output00_mean_absolute_error: 0.5165 - output01_mean_absolute_error: 0.5263\n",
      "Epoch 40/40\n",
      "32/32 [==============================] - 10s 314ms/step - loss: 1.0867 - output00_loss: 0.5333 - output01_loss: 0.5416 - output00_mean_absolute_error: 0.5333 - output01_mean_absolute_error: 0.5416\n"
     ]
    }
   ],
   "source": [
    "LOOKBACK = 128\n",
    "BATCH_SIZE = 64\n",
    "mygen = generator_factory(X, LOOKBACK, batch_size=BATCH_SIZE, shuffle=True)\n",
    "history = model.fit_generator(mygen,\n",
    "                              steps_per_epoch=32,\n",
    "                              epochs=40,\n",
    "                             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[0.54338735]], dtype=float32), array([[1.1517216]], dtype=float32)]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X[:128].reshape(1,LOOKBACK,OUTPUT_DIM))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.25599367,  1.20011222])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[129]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict using X\n",
    "X_pred = np.zeros_like(X)\n",
    "# copy first 128 rows to X_pred\n",
    "for i in range(LOOKBACK):\n",
    "    X_pred[i]=X[i]\n",
    "# forecast remainder of rows using X\n",
    "for i in range(LOOKBACK, X.shape[0]):\n",
    "    predlist = model.predict(X[i-LOOKBACK:i].reshape(1,LOOKBACK,OUTPUT_DIM))\n",
    "    X_pred[i] = np.array([predlist[0][0][0],predlist[1][0][0]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.487040\n",
      "0.512351\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "print(\"%.6f\" % mean_absolute_error(y1, X_pred[:,0]))\n",
    "print(\"%.6f\" % mean_absolute_error(y2, X_pred[:,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "mode": "markers",
         "type": "scatter",
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\"type\": \"scatter\", \"mode\": \"markers\"}], {\"yaxis\": {\"autorange\": true}}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mychart(x2,X_pred[:,0],X_pred[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict recursively after bootstrapping first 128 rows of X\n",
    "X_pred = np.zeros_like(X)\n",
    "# copy first 128 rows to X_pred\n",
    "for i in range(LOOKBACK):\n",
    "    X_pred[i]=X[i]\n",
    "# recursively forecast remainder of rows using X_pred\n",
    "for i in range(LOOKBACK, X.shape[0]):\n",
    "    predlist = model.predict(X_pred[i-LOOKBACK:i].reshape(1,LOOKBACK,OUTPUT_DIM))\n",
    "    X_pred[i] = np.array([predlist[0][0][0],predlist[1][0][0]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
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