photodetectors/MUTC1750_Predict_Metrics.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "H6eOwX3UGpvG"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2025-01-07 11:12:58.334765: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
            "2025-01-07 11:12:58.412527: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2025-01-07 11:12:58.459900: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2025-01-07 11:12:58.470571: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2025-01-07 11:12:58.527198: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2025-01-07 11:12:59.359099: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
          ]
        }
      ],
      "source": [
        "import pathlib\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "# ML models\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import train_test_split\n",
        "# Multilayer Perceptron\n",
        "from keras import Model\n",
        "from keras.api.layers import Input\n",
        "from keras.api.layers import Dense\n",
        "from keras.api.layers import Dropout\n",
        "from keras.api.layers import concatenate\n",
        "from keras import optimizers\n",
        "from keras import backend\n",
        "from keras.api.layers import LeakyReLU, PReLU\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "xO6mZcSAGyp7"
      },
      "outputs": [],
      "source": [
        "#!git clone https://github.com/simsekergun/photodetectors.git\n",
        "df = pd.read_csv(\"./MUTC1750designs.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "1qLZ_FkrHJJB"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1755, 39)"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# let's take log10 of doping levels so that we deal with numbers in the similar ranges\n",
        "df[df.columns[22:40]] =np.log10(df[df.columns[22:40]])\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "x5h23R0y1MXk"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>phasenoise</th>\n",
              "      <th>phasenoise_15mA</th>\n",
              "      <th>current</th>\n",
              "      <th>IR_max</th>\n",
              "      <th>decay_time</th>\n",
              "      <th>t1</th>\n",
              "      <th>t2</th>\n",
              "      <th>t3</th>\n",
              "      <th>t4</th>\n",
              "      <th>t5</th>\n",
              "      <th>...</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-161.516</td>\n",
              "      <td>-170.938</td>\n",
              "      <td>0.001713</td>\n",
              "      <td>32002500000</td>\n",
              "      <td>184.775</td>\n",
              "      <td>20.7</td>\n",
              "      <td>121.5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>33.5</td>\n",
              "      <td>96.7</td>\n",
              "      <td>...</td>\n",
              "      <td>18.809560</td>\n",
              "      <td>15.110590</td>\n",
              "      <td>14.963316</td>\n",
              "      <td>15.107210</td>\n",
              "      <td>16.143015</td>\n",
              "      <td>15.408240</td>\n",
              "      <td>18.748188</td>\n",
              "      <td>18.170262</td>\n",
              "      <td>18.928396</td>\n",
              "      <td>18.424882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-162.136</td>\n",
              "      <td>-171.316</td>\n",
              "      <td>0.001812</td>\n",
              "      <td>28499800000</td>\n",
              "      <td>161.603</td>\n",
              "      <td>76.7</td>\n",
              "      <td>79.0</td>\n",
              "      <td>85.4</td>\n",
              "      <td>12.0</td>\n",
              "      <td>77.6</td>\n",
              "      <td>...</td>\n",
              "      <td>18.598791</td>\n",
              "      <td>15.753583</td>\n",
              "      <td>16.503791</td>\n",
              "      <td>15.918030</td>\n",
              "      <td>17.227887</td>\n",
              "      <td>14.868644</td>\n",
              "      <td>17.181844</td>\n",
              "      <td>18.193125</td>\n",
              "      <td>18.721811</td>\n",
              "      <td>18.832509</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-162.661</td>\n",
              "      <td>-172.271</td>\n",
              "      <td>0.001641</td>\n",
              "      <td>34642300000</td>\n",
              "      <td>174.001</td>\n",
              "      <td>86.5</td>\n",
              "      <td>94.5</td>\n",
              "      <td>7.0</td>\n",
              "      <td>16.6</td>\n",
              "      <td>111.4</td>\n",
              "      <td>...</td>\n",
              "      <td>18.836957</td>\n",
              "      <td>16.173186</td>\n",
              "      <td>15.372912</td>\n",
              "      <td>15.287802</td>\n",
              "      <td>16.491362</td>\n",
              "      <td>15.809560</td>\n",
              "      <td>18.100371</td>\n",
              "      <td>17.856124</td>\n",
              "      <td>19.404834</td>\n",
              "      <td>18.217484</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 39 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   phasenoise  phasenoise_15mA   current       IR_max  decay_time    t1  \\\n",
              "0    -161.516         -170.938  0.001713  32002500000     184.775  20.7   \n",
              "1    -162.136         -171.316  0.001812  28499800000     161.603  76.7   \n",
              "2    -162.661         -172.271  0.001641  34642300000     174.001  86.5   \n",
              "\n",
              "      t2    t3    t4     t5  ...         d8         d9        d10        d11  \\\n",
              "0  121.5   6.0  33.5   96.7  ...  18.809560  15.110590  14.963316  15.107210   \n",
              "1   79.0  85.4  12.0   77.6  ...  18.598791  15.753583  16.503791  15.918030   \n",
              "2   94.5   7.0  16.6  111.4  ...  18.836957  16.173186  15.372912  15.287802   \n",
              "\n",
              "         d12        d13        d14        d15        d16        d17  \n",
              "0  16.143015  15.408240  18.748188  18.170262  18.928396  18.424882  \n",
              "1  17.227887  14.868644  17.181844  18.193125  18.721811  18.832509  \n",
              "2  16.491362  15.809560  18.100371  17.856124  19.404834  18.217484  \n",
              "\n",
              "[3 rows x 39 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "iyFCz4P3xSgt"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>phasenoise</th>\n",
              "      <th>phasenoise_15mA</th>\n",
              "      <th>current</th>\n",
              "      <th>IR_max</th>\n",
              "      <th>decay_time</th>\n",
              "      <th>t1</th>\n",
              "      <th>t2</th>\n",
              "      <th>t3</th>\n",
              "      <th>t4</th>\n",
              "      <th>t5</th>\n",
              "      <th>...</th>\n",
              "      <th>d8</th>\n",
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              "      <th>d10</th>\n",
              "      <th>d11</th>\n",
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              "      <th>d13</th>\n",
              "      <th>d14</th>\n",
              "      <th>d15</th>\n",
              "      <th>d16</th>\n",
              "      <th>d17</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1755.000000</td>\n",
              "      <td>1755.000000</td>\n",
              "      <td>1755.000000</td>\n",
              "      <td>1.755000e+03</td>\n",
              "      <td>1755.000000</td>\n",
              "      <td>1755.000000</td>\n",
              "      <td>1755.000000</td>\n",
              "      <td>1755.000000</td>\n",
              "      <td>1755.000000</td>\n",
              "      <td>1755.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1755.000000</td>\n",
              "      <td>1755.000000</td>\n",
              "      <td>1755.000000</td>\n",
              "      <td>1755.000000</td>\n",
              "      <td>1755.000000</td>\n",
              "      <td>1755.000000</td>\n",
              "      <td>1755.000000</td>\n",
              "      <td>1755.000000</td>\n",
              "      <td>1755.000000</td>\n",
              "      <td>1755.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-170.904159</td>\n",
              "      <td>-177.668607</td>\n",
              "      <td>0.003658</td>\n",
              "      <td>5.396398e+10</td>\n",
              "      <td>62.732325</td>\n",
              "      <td>47.636524</td>\n",
              "      <td>106.206610</td>\n",
              "      <td>34.891556</td>\n",
              "      <td>43.307134</td>\n",
              "      <td>107.375670</td>\n",
              "      <td>...</td>\n",
              "      <td>17.712256</td>\n",
              "      <td>15.987027</td>\n",
              "      <td>16.378908</td>\n",
              "      <td>16.358700</td>\n",
              "      <td>17.047689</td>\n",
              "      <td>16.258266</td>\n",
              "      <td>17.985147</td>\n",
              "      <td>19.221367</td>\n",
              "      <td>19.013471</td>\n",
              "      <td>19.121494</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.845182</td>\n",
              "      <td>2.248727</td>\n",
              "      <td>0.002374</td>\n",
              "      <td>1.190861e+10</td>\n",
              "      <td>31.641076</td>\n",
              "      <td>24.608927</td>\n",
              "      <td>30.429793</td>\n",
              "      <td>23.385138</td>\n",
              "      <td>23.912232</td>\n",
              "      <td>29.172582</td>\n",
              "      <td>...</td>\n",
              "      <td>0.410801</td>\n",
              "      <td>0.442477</td>\n",
              "      <td>0.810166</td>\n",
              "      <td>0.812304</td>\n",
              "      <td>0.441059</td>\n",
              "      <td>0.833403</td>\n",
              "      <td>0.415166</td>\n",
              "      <td>0.545513</td>\n",
              "      <td>0.416695</td>\n",
              "      <td>0.555979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-178.621000</td>\n",
              "      <td>-183.613000</td>\n",
              "      <td>0.001459</td>\n",
              "      <td>2.849980e+10</td>\n",
              "      <td>22.224300</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>23.800000</td>\n",
              "      <td>...</td>\n",
              "      <td>16.505150</td>\n",
              "      <td>14.801404</td>\n",
              "      <td>14.801404</td>\n",
              "      <td>14.800029</td>\n",
              "      <td>15.947434</td>\n",
              "      <td>14.801404</td>\n",
              "      <td>16.808886</td>\n",
              "      <td>17.806858</td>\n",
              "      <td>17.800029</td>\n",
              "      <td>17.801404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-173.773000</td>\n",
              "      <td>-179.189500</td>\n",
              "      <td>0.002360</td>\n",
              "      <td>4.545390e+10</td>\n",
              "      <td>37.899700</td>\n",
              "      <td>31.850000</td>\n",
              "      <td>90.300000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>95.900000</td>\n",
              "      <td>...</td>\n",
              "      <td>17.482158</td>\n",
              "      <td>15.761552</td>\n",
              "      <td>15.868937</td>\n",
              "      <td>15.837904</td>\n",
              "      <td>16.781037</td>\n",
              "      <td>15.708421</td>\n",
              "      <td>17.797268</td>\n",
              "      <td>18.905256</td>\n",
              "      <td>18.872714</td>\n",
              "      <td>18.790637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-170.027000</td>\n",
              "      <td>-177.815000</td>\n",
              "      <td>0.002539</td>\n",
              "      <td>5.236430e+10</td>\n",
              "      <td>53.955800</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>26.800000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>17.698970</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.110590</td>\n",
              "      <td>16.064458</td>\n",
              "      <td>17.093422</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>19.155336</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>19.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-168.358000</td>\n",
              "      <td>-176.220500</td>\n",
              "      <td>0.003013</td>\n",
              "      <td>6.194950e+10</td>\n",
              "      <td>75.028400</td>\n",
              "      <td>52.600000</td>\n",
              "      <td>120.350000</td>\n",
              "      <td>47.200000</td>\n",
              "      <td>60.400000</td>\n",
              "      <td>120.300000</td>\n",
              "      <td>...</td>\n",
              "      <td>17.884795</td>\n",
              "      <td>16.254063</td>\n",
              "      <td>16.993636</td>\n",
              "      <td>16.980648</td>\n",
              "      <td>17.455600</td>\n",
              "      <td>16.897624</td>\n",
              "      <td>18.118923</td>\n",
              "      <td>19.850603</td>\n",
              "      <td>19.167317</td>\n",
              "      <td>19.589391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>-161.516000</td>\n",
              "      <td>-170.334000</td>\n",
              "      <td>0.009902</td>\n",
              "      <td>9.755770e+10</td>\n",
              "      <td>184.775000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>199.900000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>99.900000</td>\n",
              "      <td>199.800000</td>\n",
              "      <td>...</td>\n",
              "      <td>18.987666</td>\n",
              "      <td>17.193125</td>\n",
              "      <td>17.710963</td>\n",
              "      <td>17.687529</td>\n",
              "      <td>17.848805</td>\n",
              "      <td>17.923244</td>\n",
              "      <td>19.873902</td>\n",
              "      <td>20.193125</td>\n",
              "      <td>20.193125</td>\n",
              "      <td>20.195900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 39 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        phasenoise  phasenoise_15mA      current        IR_max   decay_time  \\\n",
              "count  1755.000000      1755.000000  1755.000000  1.755000e+03  1755.000000   \n",
              "mean   -170.904159      -177.668607     0.003658  5.396398e+10    62.732325   \n",
              "std       3.845182         2.248727     0.002374  1.190861e+10    31.641076   \n",
              "min    -178.621000      -183.613000     0.001459  2.849980e+10    22.224300   \n",
              "25%    -173.773000      -179.189500     0.002360  4.545390e+10    37.899700   \n",
              "50%    -170.027000      -177.815000     0.002539  5.236430e+10    53.955800   \n",
              "75%    -168.358000      -176.220500     0.003013  6.194950e+10    75.028400   \n",
              "max    -161.516000      -170.334000     0.009902  9.755770e+10   184.775000   \n",
              "\n",
              "                t1           t2           t3           t4           t5  ...  \\\n",
              "count  1755.000000  1755.000000  1755.000000  1755.000000  1755.000000  ...   \n",
              "mean     47.636524   106.206610    34.891556    43.307134   107.375670  ...   \n",
              "std      24.608927    30.429793    23.385138    23.912232    29.172582  ...   \n",
              "min      10.000000    36.000000     5.000000     6.000000    23.800000  ...   \n",
              "25%      31.850000    90.300000    15.000000    15.000000    95.900000  ...   \n",
              "50%      50.000000   100.000000    26.800000    47.000000   100.000000  ...   \n",
              "75%      52.600000   120.350000    47.200000    60.400000   120.300000  ...   \n",
              "max     178.000000   199.900000   100.000000    99.900000   199.800000  ...   \n",
              "\n",
              "                d8           d9          d10          d11          d12  \\\n",
              "count  1755.000000  1755.000000  1755.000000  1755.000000  1755.000000   \n",
              "mean     17.712256    15.987027    16.378908    16.358700    17.047689   \n",
              "std       0.410801     0.442477     0.810166     0.812304     0.441059   \n",
              "min      16.505150    14.801404    14.801404    14.800029    15.947434   \n",
              "25%      17.482158    15.761552    15.868937    15.837904    16.781037   \n",
              "50%      17.698970    16.000000    16.110590    16.064458    17.093422   \n",
              "75%      17.884795    16.254063    16.993636    16.980648    17.455600   \n",
              "max      18.987666    17.193125    17.710963    17.687529    17.848805   \n",
              "\n",
              "               d13          d14          d15          d16          d17  \n",
              "count  1755.000000  1755.000000  1755.000000  1755.000000  1755.000000  \n",
              "mean     16.258266    17.985147    19.221367    19.013471    19.121494  \n",
              "std       0.833403     0.415166     0.545513     0.416695     0.555979  \n",
              "min      14.801404    16.808886    17.806858    17.800029    17.801404  \n",
              "25%      15.708421    17.797268    18.905256    18.872714    18.790637  \n",
              "50%      16.000000    18.000000    19.155336    19.000000    19.000000  \n",
              "75%      16.897624    18.118923    19.850603    19.167317    19.589391  \n",
              "max      17.923244    19.873902    20.193125    20.193125    20.195900  \n",
              "\n",
              "[8 rows x 39 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "qBs0SZEek_Sh"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "phasenoise        -0.022499\n",
              "phasenoise_15mA   -0.012657\n",
              "current            0.649052\n",
              "IR_max             0.220677\n",
              "decay_time         0.504382\n",
              "dtype: float64"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "## Coefficient of Correlation\n",
        "df[df.columns[0:5]].std()/df[df.columns[0:5]].mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "lnIsvKeLIwZY"
      },
      "outputs": [],
      "source": [
        "# Let us some functions to normalize, de-normalize, and to calculate errors\n",
        "def normx(x, train_statsX):\n",
        "  return (x - train_statsX['mean']) / train_statsX['std']\n",
        "def norm(y, train_statsY):\n",
        "  return (y - train_statsY['mean']) / train_statsY['std']\n",
        "def denorm(y, train_statsY):\n",
        "  return (y* train_statsY['std'] + train_statsY['mean'])  \n",
        "def mean_aep(u1,u2):  \n",
        "  return (round(100*(100*sum(abs((u2-u1)/u1))/len(u1)))/100)\n",
        "def max_aep(u1,u2):  \n",
        "  return (round(100*(100*max(abs((u2-u1)/u1))))/100)  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "nAXW1rSrkJon"
      },
      "outputs": [],
      "source": [
        "# ANN parameters\n",
        "ac = 'relu'  # activation function\n",
        "nnno = 48    # number of neurons\n",
        "dr_rate = 0.2  # dropout rate\n",
        "EPOCHS = 400    # number of epocs\n",
        "LR = 0.001     # learning rate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "T9UuySubf9K9"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "************ 0 ************\n",
            "Mean Absolute Percentage Errors: LR, kNN, RF, ANN\n",
            "0.38 0.39 0.19 0.18\n",
            "Max Absolute Percentage Errors: LR, kNN, RF, ANN\n",
            "1.68 3.32 1.3 1.01\n"
          ]
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdMAAAGxCAYAAAA5wlz8AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAACccElEQVR4nOydeXxTVfr/P9nTpE26NwU6LEV2EJABC+KCrCI4IihLFYQBxfmpiKAwgyKjgLjg+J1RHLRSGFEULGDZwSIIFBFplaUslh2671vSLPf3R72hN7lZmiZNcnnerxcv7T0nJ+dzzsl57tmeI2IYhgFBEARBEB4j9ncGCIIgCCLYIWNKEARBEM2EjClBEARBNBMypgRBEATRTMiYEgRBEEQzIWNKEARBEM2EjClBEARBNBMypgRBEATRTKT+zkAwYrFYcPPmTYSFhUEkEvk7OwRBEEQTYBgGVVVVaNWqFcRi74wpyZh6wM2bN5GQkODvbBAEQRDN4Nq1a2jTpo1X0goaY7p06VJs374d2dnZkMvlKC8v542XmpqKlStX4vz589BoNJgwYQI++ugjAMAPP/yADz74AMeOHUNlZSXuuOMOzJ8/H1OmTGlSXsLCwgA0VIRGo2mWLm+Tn5+PNWvW4Omnn4ZOp/N3dryC0DSRnsBHaJpID5fKykokJCRY+3JvEDTGtL6+HhMmTEBSUhJSUlJ446xcuRLvv/8+3n33XQwYMAA1NTW4fPmyNfzIkSPo1asXXn31VcTFxWHbtm146qmnoNVq8fDDD7udF3ZqV6PRBJwxFYvFGDt2LFq1aoXQ0FB/Z8crCE0T6Ql8hKaJ9PDjzWU6UbA5uk9NTcWcOXPsRqZlZWVo3bo10tPT8eCDD7qd3ujRoxEXF4fPP//c7c9UVlZCq9WioqIi4IwpQRAE4Rxf9OFBMzJ1xd69e2GxWHDjxg107doVVVVVGDhwIN5//32n65sVFRXo2rWr07QNBgMMBoP178rKSgANUw01NTXW50qlEhERETCZTCgqKrJLJz4+HgBQXFwMo9HICQsPD0dISAhqamqs6bPI5XJERUXBYrGgoKDALt3Y2FhIJBKUlpaioqICN27cQOvWraFUKhEWFobQ0FDU1dXZvYBIpVLExMQAAPLy8uzSjY6OhkwmQ3l5Oerq6jhharUaGo0GBoMBpaWlnDCxWIy4uDgAQEFBASwWCyc8MjISCoUClZWVnPIDgJCQEISHh8NoNKK4uBgAoNfrcePGDbRp0wbt2rUDABQVFcFkMvGWYXV1NaqqqjhhCoUCkZGRMJvNKCwstNMaFxcHsViMkpIS1NfXc8I0Gg3UajVvGcpkMkRHRwPgL8OYmBhIpVKUlZVBr9dz9HTu3BkxMTG8ZSiRSBAbG+uwDKOioiCXy3nLUKVSQavVcsqQRSQSWafF+MowIiICSqWStwzZ9m1bhqyevn37QqVS8ZahVquFSqVCbW0tKioqOGFs+2YYBvn5+XZlyLbvxmXIwrZvvV6PsrIyTljj9p2fnw/bcQPbvisqKlBbW8sJk0gkKCoqQps2bezCGrfvwsJCmM1mTjjbvquqqlBdXc1bhi3dR4SFheHKlSuIiIiwG40FYx+hUChw4cIFhIWFQalUcsLZMnTVR3gbwRjTixcvwmKxYNmyZfjwww+h1WqxaNEiDBs2DL/99hvkcrndZ7755hv8/PPP+O9//+s07eXLl2PJkiV2z9esWcOpyJ49e2LcuHGorKzE6tWr7eIvXrwYALB161Zcv36dE/boo4+iV69eOH36NHbu3MkJS0xMRHJyMoxGI2+68+bNg1qtxu7du3H+/HlO2PDhw5GUlISLFy9i06ZNnDCdTodnnnkGAJCSkmLXKcyePRuxsbE4ePAgsrKyOGGDBg3C0KFDkZeXh7Vr13LCwsLCMHfuXADA+vXr7TrlqVOnol27djh27BgOHz7MCevTpw/Gjh2LsrIyO61isRivvfYaACAtLc2u4x0/fjy6d++OkydPYs+ePZywTp06YdKkSdDr9bxluGDBAigUCuzcuRO5ubmcsFGjRqF///64cOECNm/ezAlr06YNZsyYAQC86T7//POIjIzE/v37cfLkSU5YWVkZxowZg2vXrmH9+vWcsIiICLzwwgsAgHXr1tl16NOnT0dCQgIyMzNx9OhRTli/fv0wevRoFBcX2+VJLpdj4cKFAICNGzfadegTJ05E586dkZWVhYyMDE5Yt27dMGHCBNTU1PBqbdeuHVQqFdLT03HlyhVO2JgxY9C3b1+cPXsW6enpnLC2bdti2rRpMJvNvOm+9NJL0Gg02LdvH86cOcMJGzJkCAYPHowrV65gw4YNnLCYmBg899xzABp+q7YGftasWYiPj8ehQ4dw/PhxTljPnj1x8uRJPPLII9i6dSsnTKVSYf78+QCADRs22BnxKVOmoGPHjvjll19w4MABu3T90Uc8+eST2LRpE/70pz/h6tWrnLBg7CP+/Oc/2/0WgYaXoEWLFgFw3kfYtiNv4Ndp3gULFmDFihVO4+Tk5KBLly7Wvx1N8y5btgz/+Mc/sHv3bgwfPhxAw5uJTqfDjh07MGLECE78/fv34+GHH8aqVavw1FNPOc0D38g0ISEB586d4yxgB8LI9MaNG0hLS8O4ceMQHR0dlG+dtiPT4uJiq6aePXsCCO6RKasnOTkZiYmJQT8yZfXMnDkTrVq1EsTItLq6Gl9++SWefvppyGQyTlgwjkwtFgs+++wzTJw40W5aMxj7iLq6OqxevdrazzXGnZHpzZs30bp1a+FM87788suYNm2a0zgdOnRwKy22ALt162Z9FhMTg+joaLs3sQMHDmDMmDH44IMPXBpSoKEjVigUds91Oh1vRUilUmt++LCt/Mao1Wqo1WreMLFY7DTdyMhIq9GPjo7mxA0JCUFISIjDzzpLNzw8HOHh4bxhCoXC6WfZHwwfzjZwyWQyu3Qblxv7A+cjNDTU4aYEiUTiNL9RUVEOw5pThhEREXbPVCoVgJYtw8Z4uwzZ6UNnZahSqay6+T7f1DJkUSqVTj/rbMenVquFVqvlPGMNh6syZF94+AgLC3O4W7Sl+whWj0ajcfi9wdRHsIbbtp9rjKv27W38akxjYmKcCm4KgwYNAgCcO3fOem6otLQUxcXFaNu2rTXeDz/8gIcffhgrVqzArFmzvPLdBEEQxO1N0KyZXr16FaWlpbh69SrMZjOys7MBAB07dkRoaCg6deqERx55BC+++CJWr14NjUaDhQsXokuXLnjggQcA3JraffHFF/HYY49Zp5TkcjkiIyP9Jc2rSKVS6HQ6SKVBU7UuEZom0hP4CE0T6fE9QXM0Ztq0aXaL2ECDgbz//vsBNKxlvvTSS0hLS4NYLMZ9992HDz/80Lqb11Ea9913H3744Qe380JHYwiCIIIXX/ThQWNMAwkypgRBEMGLL/pwujVGYOTl5eGtt97i3XkXrAhNE+kJfISmKZD1mC0MMnNLsDX7BjJzS2C2uB7fBaKewJlwJryG7VZ9ISA0TaQn8BGapkDUs+tUHpakn0Fexa0jT/FaJRaP6YaRPRzvAAYCTw+NTAmCIIgWZ9epPMz+4gTHkAJAfoUes784gV2nAmfU6Q5kTAmCIIgWxWxhsCT9DPgmdNlnS9LP8E/5GgxQ7N/vy+x5BBlTgiAIokU5dqnUbkTaGAZAXoUexy5xPSfBYADGj0fklCnofeKEbzPZRGg3rwcE8m5eo9GIsrIyRERE2LlBC1aEpon0BD5C0xRoerZm38CLG7JdxvtwYm880rt1wx9/GFJs2wZGqUT5//6H0Ece8UgP3RpDuEQmkzl1cRaMCE0T6Ql8hKYp0PTEhildR2ocr5EhhVIJ0bZtiGjCVZstAU3zCozy8nJ89913dg6rgxmhaSI9gY/QNAWanv7tIxGvVcLR1dwiNOzq7d8+0s6QYts2lN91V0DpAciYCo66ujpkZWXZ3eAQzAhNE+kJfISmKdD0SMQiLB7TcCmJrUFl/148phskxno7Q4oHHww4PQAZU4IgCMIPjOwRj1XJfaHTcqd8dVolViX3xcg7Ijlrp
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "************ 1 ************\n",
            "Mean Absolute Percentage Errors: LR, kNN, RF, ANN\n",
            "0.26 0.32 0.19 0.15\n",
            "Max Absolute Percentage Errors: LR, kNN, RF, ANN\n",
            "1.37 2.25 1.47 1.0\n"
          ]
        },
        {
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "************ 2 ************\n",
            "Mean Absolute Percentage Errors: LR, kNN, RF, ANN\n",
            "16.81 5.16 3.5 2.71\n",
            "Max Absolute Percentage Errors: LR, kNN, RF, ANN\n",
            "110.66 57.08 36.18 21.57\n"
          ]
        },
        {
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "************ 3 ************\n",
            "Mean Absolute Percentage Errors: LR, kNN, RF, ANN\n",
            "4.81 5.48 3.14 2.88\n",
            "Max Absolute Percentage Errors: LR, kNN, RF, ANN\n",
            "30.07 47.61 30.65 25.43\n"
          ]
        },
        {
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "************ 4 ************\n",
            "Mean Absolute Percentage Errors: LR, kNN, RF, ANN\n",
            "9.91 11.34 7.03 5.3\n",
            "Max Absolute Percentage Errors: LR, kNN, RF, ANN\n",
            "99.57 61.48 52.24 58.61\n"
          ]
        },
        {
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "for var_index in np.arange(5):\n",
        "  X_Train, X_Test, Y_Train, Y_Test = train_test_split(df.iloc[0:-1,5:40],df.iloc[0:-1,var_index], test_size=0.2, random_state=55)\n",
        "\n",
        "  train_statsY = Y_Train.describe().transpose()\n",
        "  train_statsX = X_Train.describe().transpose()\n",
        "  XX = normx(X_Train, train_statsX)\n",
        "  YY = norm(Y_Train, train_statsY)\n",
        "  xx = normx(X_Test, train_statsX)\n",
        "  yy = norm(Y_Test, train_statsY)\n",
        "  #\n",
        "  visible = Input(shape=(len(X_Train.keys()),))\n",
        "  hidden1 = Dense(nnno, activation=ac)(visible)\n",
        "  hidden1 = Dropout(dr_rate)(hidden1)\n",
        "  hidden2 = Dense(nnno, activation=ac)(hidden1)\n",
        "  hidden2 = Dropout(dr_rate)(hidden2)\n",
        "  mergeA = concatenate([hidden2, visible])\n",
        "  hiddenB = Dense(nnno, activation=ac)(mergeA)\n",
        "  hiddenB = Dropout(dr_rate)(hiddenB)\n",
        "  hidden3 = Dense(nnno, activation=ac)(hiddenB)\n",
        "  hidden3 = Dropout(dr_rate)(hidden3)\n",
        "  merge = concatenate([hidden3, visible])\n",
        "  hidden4 = Dense(nnno, activation=ac)(merge)\n",
        "  hidden4 = Dropout(dr_rate)(hidden4)\n",
        "  predicted_value = Dense(1)(hidden4)\n",
        "  modelANN = Model(inputs=visible, outputs=predicted_value)\n",
        "  #\n",
        "  opt = optimizers.Adamax(learning_rate=LR)\n",
        "  modelANN.compile(optimizer=opt, loss=['mse'])\n",
        "  history = modelANN.fit(XX, YY,epochs=EPOCHS, validation_data = (xx,yy), verbose=0)\n",
        "  # plot losses\n",
        "  plt.figure(var_index+10)\n",
        "  plt.plot(history.history['loss'])\n",
        "  plt.plot(history.history['val_loss'])\n",
        "  plt.ylabel('loss')\n",
        "  plt.xlabel('epoch')\n",
        "  plt.legend(['train', 'test'], loc='upper right')\n",
        "  plt.show()\n",
        "  #\n",
        "  test_predictions = modelANN.predict(xx)\n",
        "  u1 = denorm(yy, train_statsY).to_numpy()\n",
        "  u2 = denorm(pd.Series(np.squeeze(test_predictions)), train_statsY)\n",
        "  # plot truth vs. prediction\n",
        "  x1 = min(min(u1),min(u2))\n",
        "  x2 = max(max(u1),max(u2))\n",
        "  plt.figure(var_index)\n",
        "  plt.plot([x1,x2],[x1,x2],color='red')\n",
        "  plt.scatter(u1, u2)\n",
        "  plt.xlabel('Ground Truth')\n",
        "  plt.ylabel('Prediction')\n",
        "  plt.gca().set_aspect('equal', adjustable='box')\n",
        "  plt.grid(color='grey', linestyle='--', linewidth=1)\n",
        "  # Errors\n",
        "  error_ANN, error_ANN_max = mean_aep(u1,u2), max_aep(u1,u2)  \n",
        "  # Save ANN Results\n",
        "  if var_index == 0:\n",
        "    np.savetxt(\"MUTC_training_loss.csv\", history.history['loss'], delimiter=\",\")\n",
        "    np.savetxt(\"MUTC_testing_loss.csv\", history.history['val_loss'], delimiter=\",\")\n",
        "    np.savetxt(\"MUTC_phasenoise_truth.csv\", u1, delimiter=\",\")\n",
        "    np.savetxt(\"MUTC_phasenoise_predictions.csv\", u2, delimiter=\",\")\n",
        "  ## LINEAR REGRESSION\n",
        "  modelLR = LinearRegression()\n",
        "  modelLR.fit(XX, YY)\n",
        "  yhat = modelLR.predict(xx)\n",
        "  u2 = denorm(pd.Series(np.squeeze(yhat)), train_statsY)\n",
        "  # calculate errors\n",
        "  error_LR, error_LR_max = mean_aep(u1,u2), max_aep(u1,u2)\n",
        "  ## k-Nearest Neighbors\n",
        "  modelkNN = KNeighborsRegressor()\n",
        "  modelkNN.fit(XX, YY)\n",
        "  yhat = modelkNN.predict(xx)\n",
        "  u2 = denorm(pd.Series(np.squeeze(yhat)), train_statsY)\n",
        "  # calculate errors\n",
        "  error_kNN, error_kNN_max = mean_aep(u1,u2), max_aep(u1,u2)\n",
        "  ## RANDOM FOREST  \n",
        "  modelRF = RandomForestRegressor()\n",
        "  modelRF.fit(XX, YY)\n",
        "  yhat = modelRF.predict(xx)\n",
        "  u2 = denorm(pd.Series(np.squeeze(yhat)), train_statsY)\n",
        "  # calculate errors\n",
        "  error_RF, error_RF_max = mean_aep(u1,u2), max_aep(u1,u2)\n",
        "  # PRINT ERRORS\n",
        "  print('************',var_index,'************')\n",
        "  print('Mean Absolute Percentage Errors: LR, kNN, RF, ANN')\n",
        "  print(error_LR, error_kNN, error_RF, error_ANN)\n",
        "  print('Max Absolute Percentage Errors: LR, kNN, RF, ANN')\n",
        "  print(error_LR_max, error_kNN_max, error_RF_max, error_ANN_max)\n",
        "  backend.clear_session()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FFtnWBnMmbAu"
      },
      "outputs": [],
      "source": []
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