{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "progressive-festival",
   "metadata": {},
   "source": [
    "# Artificial Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "supported-proof",
   "metadata": {},
   "source": [
    "Keras library is used here. Keras is included in the lower-level library TensorFlow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "sorted-scout",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "solar-ecuador",
   "metadata": {},
   "source": [
    "Data in from SDSS asteroid taxonomy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "floppy-powder",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ASTNUM</th>\n",
       "      <th>ASTNAME</th>\n",
       "      <th>PROVDESIG</th>\n",
       "      <th>T</th>\n",
       "      <th>SC</th>\n",
       "      <th>MOID</th>\n",
       "      <th>B</th>\n",
       "      <th>LRU</th>\n",
       "      <th>EU</th>\n",
       "      <th>LRG</th>\n",
       "      <th>GE</th>\n",
       "      <th>LRR</th>\n",
       "      <th>ER</th>\n",
       "      <th>LRI</th>\n",
       "      <th>EI</th>\n",
       "      <th>LRZ</th>\n",
       "      <th>EZ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>166</td>\n",
       "      <td>Rhodope</td>\n",
       "      <td>-</td>\n",
       "      <td>C</td>\n",
       "      <td>78</td>\n",
       "      <td>s394c9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1.036</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1.056</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1.048</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>183</td>\n",
       "      <td>Istria</td>\n",
       "      <td>-</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>seef1c</td>\n",
       "      <td>0</td>\n",
       "      <td>0.856</td>\n",
       "      <td>0.012</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1.084</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1.124</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1.080</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>208</td>\n",
       "      <td>Lacrimosa</td>\n",
       "      <td>-</td>\n",
       "      <td>S</td>\n",
       "      <td>85</td>\n",
       "      <td>s458db</td>\n",
       "      <td>0</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.012</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1.072</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1.112</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1.084</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>220</td>\n",
       "      <td>Stephania</td>\n",
       "      <td>-</td>\n",
       "      <td>X</td>\n",
       "      <td>65</td>\n",
       "      <td>s03bbc</td>\n",
       "      <td>0</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.012</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1.020</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1.060</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1.040</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>227</td>\n",
       "      <td>Philosophia</td>\n",
       "      <td>-</td>\n",
       "      <td>C</td>\n",
       "      <td>82</td>\n",
       "      <td>sd038c</td>\n",
       "      <td>0</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1.024</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1.036</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1.016</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107461</th>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>6865 P-L</td>\n",
       "      <td>Q</td>\n",
       "      <td>45</td>\n",
       "      <td>s0983d</td>\n",
       "      <td>1</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.052</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1.100</td>\n",
       "      <td>0.012</td>\n",
       "      <td>1.108</td>\n",
       "      <td>0.016</td>\n",
       "      <td>1.036</td>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107462</th>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>7590 P-L</td>\n",
       "      <td>C</td>\n",
       "      <td>9</td>\n",
       "      <td>sc03d7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.048</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.012</td>\n",
       "      <td>1.056</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1.048</td>\n",
       "      <td>0.012</td>\n",
       "      <td>1.076</td>\n",
       "      <td>0.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107463</th>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>7602 P-L</td>\n",
       "      <td>C</td>\n",
       "      <td>42</td>\n",
       "      <td>s40598</td>\n",
       "      <td>0</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.024</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107464</th>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>7602 P-L</td>\n",
       "      <td>C</td>\n",
       "      <td>23</td>\n",
       "      <td>s3c767</td>\n",
       "      <td>0</td>\n",
       "      <td>0.964</td>\n",
       "      <td>0.016</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1.004</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107465</th>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>7602 P-L</td>\n",
       "      <td>C</td>\n",
       "      <td>32</td>\n",
       "      <td>s3e41f</td>\n",
       "      <td>1</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.040</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.964</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107466 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ASTNUM      ASTNAME PROVDESIG  T  SC    MOID  B    LRU     EU  LRG  \\\n",
       "0          166      Rhodope         -  C  78  s394c9  0  0.860  0.008  1.0   \n",
       "1          183       Istria         -  S   0  seef1c  0  0.856  0.012  1.0   \n",
       "2          208    Lacrimosa         -  S  85  s458db  0  0.780  0.012  1.0   \n",
       "3          220    Stephania         -  X  65  s03bbc  0  0.924  0.012  1.0   \n",
       "4          227  Philosophia         -  C  82  sd038c  0  0.908  0.008  1.0   \n",
       "...        ...          ...       ... ..  ..     ... ..    ...    ...  ...   \n",
       "107461       0            -  6865 P-L  Q  45  s0983d  1  0.832  0.052  1.0   \n",
       "107462       0            -  7590 P-L  C   9  sc03d7  1  0.980  0.048  1.0   \n",
       "107463       0            -  7602 P-L  C  42  s40598  0  0.936  0.024  1.0   \n",
       "107464       0            -  7602 P-L  C  23  s3c767  0  0.964  0.016  1.0   \n",
       "107465       0            -  7602 P-L  C  32  s3e41f  1  0.924  0.040  1.0   \n",
       "\n",
       "           GE    LRR     ER    LRI     EI    LRZ     EZ  \n",
       "0       0.008  1.036  0.004  1.056  0.008  1.048  0.012  \n",
       "1       0.004  1.084  0.004  1.124  0.004  1.080  0.004  \n",
       "2       0.008  1.072  0.008  1.112  0.004  1.084  0.008  \n",
       "3       0.008  1.020  0.008  1.060  0.008  1.040  0.008  \n",
       "4       0.004  1.024  0.004  1.036  0.008  1.016  0.008  \n",
       "...       ...    ...    ...    ...    ...    ...    ...  \n",
       "107461  0.008  1.100  0.012  1.108  0.016  1.036  0.020  \n",
       "107462  0.012  1.056  0.008  1.048  0.012  1.076  0.024  \n",
       "107463  0.008  0.992  0.004  0.988  0.004  0.960  0.012  \n",
       "107464  0.008  1.004  0.008  0.988  0.008  0.996  0.008  \n",
       "107465  0.012  0.992  0.008  0.996  0.008  0.964  0.016  \n",
       "\n",
       "[107466 rows x 17 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.read_fwf('sdsstax.dat')\n",
    "temp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "systematic-malaysia",
   "metadata": {},
   "source": [
    "Perhaps too much data for somple example, let's take only 10,000 first asteroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "involved-brand",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = temp.head(10000).copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "comfortable-heating",
   "metadata": {},
   "source": [
    "Classification to be learned: variable 'T', the taxonomic classification of an asteroid. Variables from which to classify: logarithmic reflectancies in filters u, r, i, and z, so variables 'LRU', 'LRR', 'LRI', LRZ'."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "going-regard",
   "metadata": {},
   "source": [
    "We wil simplify the task by reducing the 27 classes in the taxonomy into 9 by taking just the first letter of the class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "north-excitement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A' 'AQ' 'C' 'CL' 'CO' 'CQ' 'CS' 'CX' 'D' 'DL' 'DS' 'L' 'LS' 'O' 'Q' 'QO'\n",
      " 'QV' 'S' 'SA' 'SQ' 'SV' 'V' 'X' 'XD' 'XL' 'XQ' 'XS']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "types = np.unique(data['T'].tolist())\n",
    "print(types)\n",
    "len(types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "pursuant-thickness",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A' 'C' 'D' 'L' 'O' 'Q' 'S' 'V' 'X']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['ST'] = data.apply(lambda x: x['T'][0], axis=1)\n",
    "stypes = np.unique(data['ST'].tolist())\n",
    "print(stypes)\n",
    "len(stypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "killing-violence",
   "metadata": {},
   "source": [
    "Let's try to see classification pattern with filter pairs..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "corrected-hierarchy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive']\n",
    "plt.axes().set_aspect(1)\n",
    "for i in range(len(stypes)):\n",
    "    plt.scatter(x=data[data['ST']==stypes[i]]['LRU'], y=data[data['ST']==stypes[i]]['LRR'], color=cols[i])\n",
    "plt.show()\n",
    "for i in range(len(stypes)):\n",
    "    plt.scatter(x=data[data['ST']==stypes[i]]['LRI'], y=data[data['ST']==stypes[i]]['LRZ'], color=cols[i])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "traditional-dollar",
   "metadata": {},
   "source": [
    "Let's build a neural network classifier. We will use 1 layer only, and select 15 nodes. The second layer is the output layer with softmax activation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "given-waters",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(15, input_dim=4, activation='tanh'))\n",
    "model.add(Dense(len(stypes), activation='softmax'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "plastic-process",
   "metadata": {},
   "source": [
    "Model needs to be compiled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "loaded-honey",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "foreign-montreal",
   "metadata": {},
   "source": [
    "Divide data into testing and training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "political-speaker",
   "metadata": {},
   "outputs": [],
   "source": [
    "datar = data.sample(10000)\n",
    "dataTrain = datar.head(8000)\n",
    "dataTest = datar.tail(2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "available-driver",
   "metadata": {},
   "outputs": [],
   "source": [
    "XTrain = dataTrain[['LRU','LRR','LRI','LRZ']].to_numpy()\n",
    "XTest = dataTest[['LRU','LRR','LRI','LRZ']].to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exotic-advisory",
   "metadata": {},
   "source": [
    "For character lables, encode into integers, and then into one-hot-encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "natural-transformation",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = preprocessing.LabelEncoder()\n",
    "encoder.fit(stypes)\n",
    "yTrain = keras.utils.to_categorical(encoder.transform(dataTrain['ST']), num_classes=len(stypes))\n",
    "yTest = keras.utils.to_categorical(encoder.transform(dataTest['ST']), num_classes=len(stypes))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "useful-vertex",
   "metadata": {},
   "source": [
    "Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ready-receptor",
   "metadata": {},
   "outputs": [],
   "source": [
    "history = model.fit(XTrain, yTrain, epochs=3000, validation_split=0.2, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sunrise-cologne",
   "metadata": {},
   "source": [
    "Show the training history with validation loss and accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "portable-remedy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21db10f2f10>]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,2,figsize=[14,4])\n",
    "axs[0].plot(history.history['val_loss'])\n",
    "axs[0].plot(history.history['loss'])\n",
    "axs[0].set_yscale('log')\n",
    "axs[1].plot(history.history['val_accuracy'])\n",
    "axs[1].plot(history.history['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "radical-terrace",
   "metadata": {},
   "source": [
    "Evaluate final accuracy with test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "clear-bathroom",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 0s 1ms/step - loss: 0.2709 - accuracy: 0.9205\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.27086976170539856, 0.9204999804496765]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(XTest, yTest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "disabled-crazy",
   "metadata": {},
   "source": [
    "Confusion matrix. First, get predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "stock-metadata",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = np.argmax(model.predict(XTest),axis=1)\n",
    "corr = np.argmax(yTest,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "technological-category",
   "metadata": {},
   "source": [
    "Then, confusion matrix and nice print."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "broadband-poland",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x21db2453af0>"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(corr,pred)\n",
    "ConfusionMatrixDisplay(cm, display_labels=stypes).plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}