CleanedConductanceAnalysisModels.ipynb

{
  "cells": [
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      "metadata": {
        "id": "q4X1XJRdPqEh"
      },
      "source": [
        "# Imports and Datasets\n"
      ]
    },
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      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "O92tV7rwPbMT"
      },
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      "source": [
        "# Imports\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from time import time\n",
        "\n",
        "# Scikit-learn stuff\n",
        "from sklearn.neural_network import MLPRegressor\n",
        "from sklearn.datasets import make_regression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import QuantileTransformer\n",
        "from sklearn.neural_network import MLPRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
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        "colab": {
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              "    addr            pw  vwl  vbsl        preread       postread\n",
              "0      0  2.000000e-08  0.5   0.5  151240.470700  115068.986900\n",
              "1      2  2.000000e-08  0.5   1.0  123855.302200  124977.565700\n",
              "2      4  2.000000e-08  0.5   1.5  127236.989700  106676.196700\n",
              "3      6  2.000000e-08  0.5   2.0   93031.982770  133852.645200\n",
              "4      8  2.000000e-08  0.5   2.5   75606.517090   57549.247700\n",
              "5     10  2.000000e-08  0.5   3.0   97572.302250   92171.401490\n",
              "6     12  2.000000e-08  0.6   0.5   46314.963040   50373.876070\n",
              "7     14  2.000000e-08  0.6   1.0  100303.315400  149505.440600\n",
              "8     16  2.000000e-08  0.6   1.5  212694.720700   75614.854910\n",
              "9     18  2.000000e-08  0.6   2.0    5747.716015    5608.268993\n",
              "10    20  2.000000e-08  0.6   2.5   99390.597620   68173.473680\n",
              "11    22  2.000000e-08  0.6   3.0  115261.443600   41095.737690\n",
              "12    24  2.000000e-08  0.7   0.5  138166.881800  131530.809600\n",
              "13    26  2.000000e-08  0.7   1.0   84032.373080   68859.159200\n",
              "14    28  2.000000e-08  0.7   1.5   77354.814580   35775.406770\n",
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              "17    34  2.000000e-08  0.7   3.0    5568.287628    5506.209298\n",
              "18    36  2.000000e-08  0.8   0.5  102392.493600  150220.804000\n",
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              "      <th>1</th>\n",
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              "      <td>2.000000e-08</td>\n",
              "      <td>0.6</td>\n",
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              "      <td>2.000000e-08</td>\n",
              "      <td>0.6</td>\n",
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              "      <td>1.0</td>\n",
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          "metadata": {},
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      ],
      "source": [
        "# Get conductange range data from file set_sweep.csv\n",
        "names = [\"addr\", \"pw\", \"vwl\", \"vbsl\", \"preread\", \"postread\"]\n",
        "data = pd.read_csv(\"/content/set_sweep.csv\", names=names)\n",
        "data.head(20)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"/content/reset_sweep.tsv\", sep='\\t', names=names)\n",
        "data.head(20)"
      ],
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        "id": "R-q84yfstuYU",
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      "execution_count": 8,
      "outputs": [
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              "    addr            pw  vwl  vbsl       preread      postread\n",
              "0      0  2.000000e-08  0.5   0.5   9726.189563  10018.123665\n",
              "1      2  2.000000e-08  0.5   1.0   9867.379098   9836.066607\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>9598.105929</td>\n",
              "      <td>9840.417267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>10</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10154.120560</td>\n",
              "      <td>10130.418229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>12</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.5</td>\n",
              "      <td>9576.502084</td>\n",
              "      <td>9832.650784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>14</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8517.708010</td>\n",
              "      <td>8714.310288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>16</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.5</td>\n",
              "      <td>8885.389926</td>\n",
              "      <td>9046.957580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>18</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8513.328536</td>\n",
              "      <td>8321.666349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>20</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2.5</td>\n",
              "      <td>9530.348939</td>\n",
              "      <td>9740.502149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>22</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>9421.936243</td>\n",
              "      <td>8959.038643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>24</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.5</td>\n",
              "      <td>8879.111880</td>\n",
              "      <td>8747.804518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>26</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8937.212556</td>\n",
              "      <td>8938.249624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>28</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>9533.422496</td>\n",
              "      <td>10336.489138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>30</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.7</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8357.976563</td>\n",
              "      <td>8239.564982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>32</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.7</td>\n",
              "      <td>2.5</td>\n",
              "      <td>9162.391859</td>\n",
              "      <td>9257.864014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>34</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>9354.664712</td>\n",
              "      <td>9286.621569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>36</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.5</td>\n",
              "      <td>9515.155062</td>\n",
              "      <td>9469.702176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>38</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9191.275222</td>\n",
              "      <td>9306.446592</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZOoja9TyQr0U"
      },
      "source": [
        "# Data Preprocessing\n",
        "\n",
        "Let us first convert resistance values (pre-read and post-read) to conductance values, and scale each column so that the maximum value is 1."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "j17xSRSON6vJ"
      },
      "outputs": [],
      "source": [
        "# Convert resistance to conductance\n",
        "data['preread'] = 1 / data['preread']\n",
        "data['postread'] = 1 / data['postread']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "eSVuw9x_Qp5j"
      },
      "outputs": [],
      "source": [
        "# Creating scaled versions of the columns which have very large or very small scales\n",
        "data['scaled_pw'] = data['pw'] / np.max(data['pw'])\n",
        "data['scaled_preread'] = data['preread'] / np.max(data['preread'])\n",
        "data['scaled_postread'] = data['postread'] / np.max(data['postread'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "xc9sL_lmTypp",
        "outputId": "471841aa-8baa-4031-857a-5ea9829ec18b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    addr            pw  vwl  vbsl   preread  postread  scaled_pw  \\\n",
              "0      0  2.000000e-08  0.5   0.5  0.000103  0.000100       0.01   \n",
              "1      2  2.000000e-08  0.5   1.0  0.000101  0.000102       0.01   \n",
              "2      4  2.000000e-08  0.5   1.5  0.000102  0.000101       0.01   \n",
              "3      6  2.000000e-08  0.5   2.0  0.000105  0.000107       0.01   \n",
              "4      8  2.000000e-08  0.5   2.5  0.000104  0.000102       0.01   \n",
              "5     10  2.000000e-08  0.5   3.0  0.000098  0.000099       0.01   \n",
              "6     12  2.000000e-08  0.6   0.5  0.000104  0.000102       0.01   \n",
              "7     14  2.000000e-08  0.6   1.0  0.000117  0.000115       0.01   \n",
              "8     16  2.000000e-08  0.6   1.5  0.000113  0.000111       0.01   \n",
              "9     18  2.000000e-08  0.6   2.0  0.000117  0.000120       0.01   \n",
              "10    20  2.000000e-08  0.6   2.5  0.000105  0.000103       0.01   \n",
              "11    22  2.000000e-08  0.6   3.0  0.000106  0.000112       0.01   \n",
              "12    24  2.000000e-08  0.7   0.5  0.000113  0.000114       0.01   \n",
              "13    26  2.000000e-08  0.7   1.0  0.000112  0.000112       0.01   \n",
              "14    28  2.000000e-08  0.7   1.5  0.000105  0.000097       0.01   \n",
              "15    30  2.000000e-08  0.7   2.0  0.000120  0.000121       0.01   \n",
              "16    32  2.000000e-08  0.7   2.5  0.000109  0.000108       0.01   \n",
              "17    34  2.000000e-08  0.7   3.0  0.000107  0.000108       0.01   \n",
              "18    36  2.000000e-08  0.8   0.5  0.000105  0.000106       0.01   \n",
              "19    38  2.000000e-08  0.8   1.0  0.000109  0.000107       0.01   \n",
              "\n",
              "    scaled_preread  scaled_postread  \n",
              "0         0.652873         0.602074  \n",
              "1         0.643531         0.613218  \n",
              "2         0.649740         0.609075  \n",
              "3         0.664560         0.643509  \n",
              "4         0.661585         0.612947  \n",
              "5         0.625358         0.595400  \n",
              "6         0.663077         0.613431  \n",
              "7         0.745501         0.692155  \n",
              "8         0.714652         0.666705  \n",
              "9         0.745885         0.724813  \n",
              "10        0.666289         0.619234  \n",
              "11        0.673955         0.673248  \n",
              "12        0.715157         0.689505  \n",
              "13        0.710508         0.674814  \n",
              "14        0.666074         0.583530  \n",
              "15        0.759749         0.732035  \n",
              "16        0.693046         0.651517  \n",
              "17        0.678802         0.649499  \n",
              "18        0.667353         0.636942  \n",
              "19        0.690869         0.648115  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2ecc1e60-212e-4d37-b833-c6909ca98250\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>addr</th>\n",
              "      <th>pw</th>\n",
              "      <th>vwl</th>\n",
              "      <th>vbsl</th>\n",
              "      <th>preread</th>\n",
              "      <th>postread</th>\n",
              "      <th>scaled_pw</th>\n",
              "      <th>scaled_preread</th>\n",
              "      <th>scaled_postread</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.000103</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.652873</td>\n",
              "      <td>0.602074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000101</td>\n",
              "      <td>0.000102</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.643531</td>\n",
              "      <td>0.613218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.000102</td>\n",
              "      <td>0.000101</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.649740</td>\n",
              "      <td>0.609075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.000105</td>\n",
              "      <td>0.000107</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.664560</td>\n",
              "      <td>0.643509</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0.000104</td>\n",
              "      <td>0.000102</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.661585</td>\n",
              "      <td>0.612947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>10</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.000098</td>\n",
              "      <td>0.000099</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.625358</td>\n",
              "      <td>0.595400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>12</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.000104</td>\n",
              "      <td>0.000102</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.663077</td>\n",
              "      <td>0.613431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>14</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000117</td>\n",
              "      <td>0.000115</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.745501</td>\n",
              "      <td>0.692155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>16</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.000113</td>\n",
              "      <td>0.000111</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.714652</td>\n",
              "      <td>0.666705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>18</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.000117</td>\n",
              "      <td>0.000120</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.745885</td>\n",
              "      <td>0.724813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>20</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0.000105</td>\n",
              "      <td>0.000103</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.666289</td>\n",
              "      <td>0.619234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>22</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.000106</td>\n",
              "      <td>0.000112</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.673955</td>\n",
              "      <td>0.673248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>24</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.000113</td>\n",
              "      <td>0.000114</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.715157</td>\n",
              "      <td>0.689505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>26</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000112</td>\n",
              "      <td>0.000112</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.710508</td>\n",
              "      <td>0.674814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>28</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.000105</td>\n",
              "      <td>0.000097</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.666074</td>\n",
              "      <td>0.583530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>30</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.7</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.000120</td>\n",
              "      <td>0.000121</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.759749</td>\n",
              "      <td>0.732035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>32</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.7</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0.000109</td>\n",
              "      <td>0.000108</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.693046</td>\n",
              "      <td>0.651517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>34</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.000107</td>\n",
              "      <td>0.000108</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.678802</td>\n",
              "      <td>0.649499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>36</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.000105</td>\n",
              "      <td>0.000106</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.667353</td>\n",
              "      <td>0.636942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>38</td>\n",
              "      <td>2.000000e-08</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000109</td>\n",
              "      <td>0.000107</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.690869</td>\n",
              "      <td>0.648115</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2ecc1e60-212e-4d37-b833-c6909ca98250')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2ecc1e60-212e-4d37-b833-c6909ca98250 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2ecc1e60-212e-4d37-b833-c6909ca98250');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# Take a peek at the preprocessed data\n",
        "data.head(20)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HlNBq4p9NUob"
      },
      "source": [
        "### Train Test Split\n",
        "Here, we make a 70%-30% split of the data into training and testing datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "b1pC9G4tgrjX"
      },
      "outputs": [],
      "source": [
        "# Train-test 70%-30% split\n",
        "x = data[[\"scaled_preread\",\"scaled_pw\",\"vwl\",\"vbsl\"]].to_numpy()\n",
        "y = data[\"scaled_postread\"].to_numpy()\n",
        "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iwFwLSaLrGDP"
      },
      "source": [
        "### Training/Testing Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M09Lil8DrFtz",
        "outputId": "977b79b4-d57c-4c71-a5a0-706fe8ba8da4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training MLPRegressor...\n",
            "Done in 3.510s\n",
            "Test R2 score: 0.60119\n"
          ]
        }
      ],
      "source": [
        "# Training the MLP regressor\n",
        "print(\"Training MLPRegressor...\")\n",
        "tic = time()\n",
        "est = make_pipeline(\n",
        "    QuantileTransformer(),\n",
        "    MLPRegressor(\n",
        "        hidden_layer_sizes=(45, 23),\n",
        "        learning_rate_init=0.03,\n",
        "        early_stopping=True,\n",
        "        random_state=0,\n",
        "    ),\n",
        ")\n",
        "est.fit(X_train, y_train)\n",
        "print(f\"Done in {time() - tic:.3f}s\")\n",
        "print(f\"Test R2 score: {est.score(X_test, y_test):.5f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T7xli8cKhfgm",
        "outputId": "bfdf37f3-93a0-4540-a4c7-18e67078220e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.041820615218246586"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# Root Mean Squared Error (RMSE) on training dataset\n",
        "predictions = est.predict(X_train)\n",
        "mean_squared_error(y_train, predictions, squared=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c6MY3CyyYK5V",
        "outputId": "2fe3603c-028c-42c2-89c3-cefabd28d8fd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0017489638572326378"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "# Mean Squared Error (MSE) on training dataset\n",
        "mean_squared_error(y_train, predictions, squared=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lMFmU1z7YEkZ",
        "outputId": "7175840a-3985-4273-9731-976b6da539b4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.04170019198952083"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# Root Mean Squared Error (RMSE) on testing dataset\n",
        "predictions = est.predict(X_test)\n",
        "predictions_orig = est.predict(X_test)\n",
        "mean_squared_error(y_test, predictions, squared=False)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test_orig = y_test"
      ],
      "metadata": {
        "id": "aIx3owj5JQTD"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F9kX7YF8SqlW",
        "outputId": "d7b8eff4-c9b8-4e1b-cf89-1789b83d6b0e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0017389060119628974"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# Mean Squared Error (MSE) on testing dataset\n",
        "mean_squared_error(y_test, predictions, squared=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aUzaDUixVAp-"
      },
      "source": [
        "### Testing on subset of testing data where there was a significant change in conductance (>= 50%)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "svaHoXF7VVbA",
        "outputId": "503c7d1a-54fb-4eb0-8ded-433835f9fa71"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[0.66111483, 0.5       , 3.3       , 3.        ],\n",
              "        [0.66379318, 1.        , 3.2       , 3.        ],\n",
              "        [0.70127194, 1.        , 2.9       , 3.        ],\n",
              "        [0.69663342, 0.5       , 3.2       , 3.        ],\n",
              "        [0.73510417, 0.5       , 2.9       , 3.        ],\n",
              "        [0.67853524, 1.        , 2.5       , 3.        ],\n",
              "        [0.76459209, 1.        , 3.2       , 3.        ],\n",
              "        [0.66774132, 1.        , 3.        , 3.        ],\n",
              "        [0.66258675, 1.        , 3.3       , 3.        ],\n",
              "        [0.69168001, 1.        , 2.6       , 3.        ],\n",
              "        [0.68577619, 1.        , 3.3       , 3.        ],\n",
              "        [0.64819991, 0.5       , 2.9       , 3.        ],\n",
              "        [0.82770967, 1.        , 2.7       , 3.        ],\n",
              "        [0.71040515, 1.        , 3.3       , 3.        ]]),\n",
              " array([0.14190148, 0.1626192 , 0.1045876 , 0.19321496, 0.15340063,\n",
              "        0.16902692, 0.20355361, 0.1650402 , 0.12579359, 0.18563967,\n",
              "        0.13951739, 0.14496318, 0.29503389, 0.1219447 ]))"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# Filter the testing data to a subset\n",
        "# Condition: |preread - postread| >= 50%\n",
        "condition = (np.abs(X_test[:,0] - y_test) >= 0.5)\n",
        "cond_X_test, cond_y_test = X_test[condition], y_test[condition]\n",
        "cond_X_test, cond_y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c_a1UwIlkWf_",
        "outputId": "623b09f7-8b99-4d23-8b23-43bac69156e9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.041820615218246586"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# Root Mean Squared Error (RMSE) on training dataset\n",
        "predictions = est.predict(X_train)\n",
        "mean_squared_error(y_train, predictions, squared=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8YEeGJ_vkWgA",
        "outputId": "6ec37a22-fc1b-4189-be33-cf003a8b8b01"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0017489638572326378"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "# Mean Squared Error (MSE) on training dataset\n",
        "mean_squared_error(y_train, predictions, squared=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h2oq2_imkWgB",
        "outputId": "f89b1858-3333-4276-b6ce-9a02ab9d6f65"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.40251856975808065"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# Root Mean Squared Error (RMSE) on testing dataset\n",
        "predictions = est.predict(cond_X_test)\n",
        "mean_squared_error(cond_y_test, predictions, squared=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gx1qj0FOkWgB",
        "outputId": "3975a6cb-ca17-4741-cf0a-132957dc5de8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.16202119900009082"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# Mean Squared Error (MSE) on testing dataset\n",
        "mean_squared_error(cond_y_test, predictions, squared=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P4lSMLUlkD4E"
      },
      "source": [
        "### Testing on subset of testing data where there was a miniscule change in conductance (<= 5%)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Evd-ntNUj7OC",
        "outputId": "e050d5a2-c190-4f1c-b0d0-fd1d8576b635"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[0.71827266, 0.2       , 3.3       , 3.        ],\n",
              "        [0.66499985, 0.5       , 1.8       , 2.        ],\n",
              "        [0.68991339, 0.02      , 2.8       , 2.5       ],\n",
              "        ...,\n",
              "        [0.64270547, 0.1       , 1.1       , 2.5       ],\n",
              "        [0.84469028, 1.        , 2.2       , 2.5       ],\n",
              "        [0.73692549, 0.1       , 2.8       , 3.        ]]),\n",
              " array([0.69400085, 0.64989859, 0.67550853, ..., 0.63111104, 0.85131999,\n",
              "        0.70778628]))"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "# Filter the testing data to a subset\n",
        "# NOTE: the first column of X_test is preread conductance\n",
        "# Condition: |preread - postread| <= 5%\n",
        "condition = (np.abs(X_test[:,0] - y_test) <= 0.05)\n",
        "cond_X_test, cond_y_test = X_test[condition], y_test[condition]\n",
        "cond_X_test, cond_y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-g7ceqtFkXf9",
        "outputId": "9f925994-1ed5-4004-c43e-5783c237a213"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.041820615218246586"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "# Root Mean Squared Error (RMSE) on training dataset\n",
        "predictions = est.predict(X_train)\n",
        "mean_squared_error(y_train, predictions, squared=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3HkK7OqckXf9",
        "outputId": "70ad24d2-34e3-478b-c4be-520ed038dc1b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0017489638572326378"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "# Mean Squared Error (MSE) on training dataset\n",
        "mean_squared_error(y_train, predictions, squared=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jXew1eRokXf-",
        "outputId": "7ff22ed2-6848-4e3d-d516-7c2d3f698b2e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.026237399351757996"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "# Root Mean Squared Error (RMSE) on testing dataset\n",
        "predictions = est.predict(cond_X_test)\n",
        "mean_squared_error(cond_y_test, predictions, squared=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5wHm5nb7kXf-",
        "outputId": "222c4868-ca64-457d-b1ee-61703b8028f8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.000688401124743631"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "# Mean Squared Error (MSE) on testing dataset\n",
        "mean_squared_error(cond_y_test, predictions, squared=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#NOTE\n",
        "#set_sweep mse was 0.0604 for overall test data, 0.0505 for >=50% data, 0.0466 for <=5% data\n",
        "#reset_sweep mse was 0.0417 for overall test data, 0.4025 for >=50% data, 0.0262 for <=5% data"
      ],
      "metadata": {
        "id": "uou414Q_uDK5"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Visualizations"
      ],
      "metadata": {
        "id": "frLivyhad2jf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#plot predicted conductance values vs real conductance values\n",
        "df2 = pd.DataFrame(list(zip(y_test_orig, predictions_orig)), columns = [\"real\",\"predicted\"])\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "jLktl5MGPZEg",
        "outputId": "2205e28b-a386-4098-b81c-306862071505"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          real  predicted\n",
              "0     0.609540   0.648763\n",
              "1     0.694001   0.629363\n",
              "2     0.649899   0.638597\n",
              "3     0.675509   0.657716\n",
              "4     0.616065   0.616976\n",
              "...        ...        ...\n",
              "9826  0.631111   0.621717\n",
              "9827  0.631509   0.660261\n",
              "9828  0.851320   0.794484\n",
              "9829  0.669521   0.682672\n",
              "9830  0.707786   0.701494\n",
              "\n",
              "[9831 rows x 2 columns]"
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              "      <td>0.657716</td>\n",
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              "      <th>4</th>\n",
              "      <td>0.616065</td>\n",
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              "      <td>0.794484</td>\n",
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              "      <td>0.669521</td>\n",
              "      <td>0.682672</td>\n",
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              "    <tr>\n",
              "      <th>9830</th>\n",
              "      <td>0.707786</td>\n",
              "      <td>0.701494</td>\n",
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              "<p>9831 rows × 2 columns</p>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          document.querySelector('#df-0536f0f4-e788-4c1c-9af5-ffb247012f45 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-0536f0f4-e788-4c1c-9af5-ffb247012f45');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(df2[\"real\"], df2[\"predicted\"], s=1)\n",
        "plt.plot([0, 1], [0, 1], \"r\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "TrQBR9sMO3xK",
        "outputId": "44e972fc-df2c-4903-f715-bcba1113b97d"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f6205d25d90>]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df2[\"real\"], df2[\"predicted\"], s=1)"
      ],
      "metadata": {
        "id": "xFX6jCr8uiIl",
        "outputId": "42dccc71-6e38-4ef2-900f-9ddb4b25a103",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f6205c66f50>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#attempt to graph a curve but didn't work (\"ValueError: Expect x to be a 1-D sorted array_like.\")\n",
        "from scipy.interpolate import make_interp_spline\n",
        "\n",
        "x = np.array(df2[\"real\"])\n",
        "y = np.array(df2[\"predicted\"])\n",
        "X_Y_Spline = make_interp_spline(x, y)\n",
        "X_ = np.linspace(x.min(), x.max(), 500)\n",
        "Y_ = X_Y_Spline(X_)\n",
        "plt.scatter(X_, Y_, s=1)"
      ],
      "metadata": {
        "id": "5tQJRtpOvQEL",
        "outputId": "e56d243f-b769-4267-fbd9-a64f7e170685",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-56-f093a2de7c59>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"real\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"predicted\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mX_Y_Spline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_interp_spline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mX_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m500\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mY_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_Y_Spline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/scipy/interpolate/_bsplines.py\u001b[0m in \u001b[0;36mmake_interp_spline\u001b[0;34m(x, y, k, t, bc_type, axis, check_finite)\u001b[0m\n\u001b[1;32m    797\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    798\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 799\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Expect x to be a 1-D sorted array_like.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    800\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mk\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    801\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Expect non-negative k.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Expect x to be a 1-D sorted array_like."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Pm4_JjyZwfGf"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "iAGoWTPjOqV_"
      ],
      "name": "CleanedConductanceAnalysisModels.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}