{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 2450,
     "status": "ok",
     "timestamp": 1615991459232,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "TGIxH9Tmt5zp"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras.optimizers as tf_optim\n",
    "tf.get_logger().setLevel('ERROR')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "import random\n",
    "import pickle\n",
    "import json\n",
    "import math\n",
    "import datetime\n",
    "import os\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "fig_dpi = 70"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fksHv5rXACEX"
   },
   "source": [
    "# Neural Network Training\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l4zqVWyRAM0Z"
   },
   "source": [
    "## Load Dataset\n",
    "\n",
    "Read CSVs dumped from MatLab and parse into Pandas DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 331
    },
    "executionInfo": {
     "elapsed": 2441,
     "status": "ok",
     "timestamp": 1615991459234,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "Hj5l_tdZuYh7",
    "outputId": "fbfa9406-f662-4ebc-8ba2-67950714627c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>Clump thickness</th>\n",
       "      <th>Uniformity of cell size</th>\n",
       "      <th>Uniformity of cell shape</th>\n",
       "      <th>Marginal adhesion</th>\n",
       "      <th>Single epithelial cell size</th>\n",
       "      <th>Bare nuclei</th>\n",
       "      <th>Bland chomatin</th>\n",
       "      <th>Normal nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.441774</td>\n",
       "      <td>0.313448</td>\n",
       "      <td>0.320744</td>\n",
       "      <td>0.280687</td>\n",
       "      <td>0.321602</td>\n",
       "      <td>0.354363</td>\n",
       "      <td>0.343777</td>\n",
       "      <td>0.286695</td>\n",
       "      <td>0.158941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.281574</td>\n",
       "      <td>0.305146</td>\n",
       "      <td>0.297191</td>\n",
       "      <td>0.285538</td>\n",
       "      <td>0.221430</td>\n",
       "      <td>0.360186</td>\n",
       "      <td>0.243836</td>\n",
       "      <td>0.305363</td>\n",
       "      <td>0.171508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Clump thickness  Uniformity of cell size  Uniformity of cell shape  \\\n",
       "count       699.000000               699.000000                699.000000   \n",
       "mean          0.441774                 0.313448                  0.320744   \n",
       "std           0.281574                 0.305146                  0.297191   \n",
       "min           0.100000                 0.100000                  0.100000   \n",
       "25%           0.200000                 0.100000                  0.100000   \n",
       "50%           0.400000                 0.100000                  0.100000   \n",
       "75%           0.600000                 0.500000                  0.500000   \n",
       "max           1.000000                 1.000000                  1.000000   \n",
       "\n",
       "       Marginal adhesion  Single epithelial cell size  Bare nuclei  \\\n",
       "count         699.000000                   699.000000   699.000000   \n",
       "mean            0.280687                     0.321602     0.354363   \n",
       "std             0.285538                     0.221430     0.360186   \n",
       "min             0.100000                     0.100000     0.100000   \n",
       "25%             0.100000                     0.200000     0.100000   \n",
       "50%             0.100000                     0.200000     0.100000   \n",
       "75%             0.400000                     0.400000     0.500000   \n",
       "max             1.000000                     1.000000     1.000000   \n",
       "\n",
       "       Bland chomatin  Normal nucleoli     Mitoses  \n",
       "count      699.000000       699.000000  699.000000  \n",
       "mean         0.343777         0.286695    0.158941  \n",
       "std          0.243836         0.305363    0.171508  \n",
       "min          0.100000         0.100000    0.100000  \n",
       "25%          0.200000         0.100000    0.100000  \n",
       "50%          0.300000         0.100000    0.100000  \n",
       "75%          0.500000         0.400000    0.100000  \n",
       "max          1.000000         1.000000    1.000000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('features.csv', header=None).T\n",
    "data.columns = ['Clump thickness', 'Uniformity of cell size', 'Uniformity of cell shape', 'Marginal adhesion', 'Single epithelial cell size', 'Bare nuclei', 'Bland chomatin', 'Normal nucleoli', 'Mitoses']\n",
    "labels = pd.read_csv('targets.csv', header=None).T\n",
    "labels.columns = ['Benign', 'Malignant']\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "executionInfo": {
     "elapsed": 2436,
     "status": "ok",
     "timestamp": 1615991459236,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "qc1Mku6h041u",
    "outputId": "94e38c34-0419-4a02-ac8c-17bbc83f777b"
   },
   "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>Benign</th>\n",
       "      <th>Malignant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Benign  Malignant\n",
       "0       1          0\n",
       "1       1          0\n",
       "2       1          0\n",
       "3       0          1\n",
       "4       1          0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h9QsJjWEMbLu"
   },
   "source": [
    "### Explore Dataset\n",
    "\n",
    "The classes are uneven in their occurences, stratify when splitting later on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2430,
     "status": "ok",
     "timestamp": 1615991459237,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "rjjiSYAZMa4k",
    "outputId": "ae0c3772-00be-4f2b-80d2-9cd62a6b6e08"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Benign       458\n",
       "Malignant    241\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.astype(bool).sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E9lVYI14AUMf"
   },
   "source": [
    "## Split Dataset\n",
    "\n",
    "Using a 50/50 split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 2604,
     "status": "ok",
     "timestamp": 1615991459418,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "L83Ae5l9wM35"
   },
   "outputs": [],
   "source": [
    "data_train, data_test, labels_train, labels_test = train_test_split(data, labels, test_size=0.5, stratify=labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qf2U199fNjmJ"
   },
   "source": [
    "## Generate & Retrieve Model\n",
    "\n",
    "Get a shallow model with a single hidden layer of varying nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 2598,
     "status": "ok",
     "timestamp": 1615991459419,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "SgoQ-NjWB0T5"
   },
   "outputs": [],
   "source": [
    "def get_model(hidden_nodes=9, activation=lambda: 'sigmoid', weight_init=lambda: 'glorot_uniform'):\n",
    "    layers = [tf.keras.layers.InputLayer(input_shape=(9,), name='Input'), \n",
    "              tf.keras.layers.Dense(hidden_nodes, activation=activation(), kernel_initializer=weight_init(), name='Hidden'), \n",
    "              tf.keras.layers.Dense(2, activation='softmax', kernel_initializer=weight_init(), name='Output')]\n",
    "\n",
    "    model = tf.keras.models.Sequential(layers)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get a Keras Tensorboard callback for dumping data for later analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tensorboard_callback(path='tensorboard-logs', prefix=''):\n",
    "    return tf.keras.callbacks.TensorBoard(\n",
    "        log_dir=os.path.normpath(os.path.join(path, prefix + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))), histogram_freq=1\n",
    "    ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QT5B9PTUN3pj"
   },
   "source": [
    "# Example Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mQGAUtIPAd6e"
   },
   "source": [
    "## Define Model\n",
    "\n",
    "Variable number of hidden nodes. All using 9D outputs except the last layer which is 2D for binary classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7889,
     "status": "ok",
     "timestamp": 1615991464716,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "fYA34P0Vu_pX",
    "outputId": "aded18b8-aa7f-4362-a614-837c8a0f526f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_2 (Dense)              (None, 9)                 90        \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 2)                 20        \n",
      "=================================================================\n",
      "Total params: 110\n",
      "Trainable params: 110\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = get_model(9)\n",
    "model.compile('sgd', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KZSwFe-AAs1y"
   },
   "source": [
    "## Train Model\n",
    "\n",
    "Example 10 epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 11304,
     "status": "ok",
     "timestamp": 1615991468137,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "s8U9Atu3zelS",
    "outputId": "8439e8d2-7a5d-495f-a192-a34f01e95bfa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "11/11 [==============================] - 1s 2ms/step - loss: 0.6257 - accuracy: 0.6607\n",
      "Epoch 2/5\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.6226 - accuracy: 0.6651\n",
      "Epoch 3/5\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.6326 - accuracy: 0.6424\n",
      "Epoch 4/5\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.6158 - accuracy: 0.6696\n",
      "Epoch 5/5\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.6228 - accuracy: 0.6534\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2cd249f3400>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(data_train.to_numpy(), labels_train.to_numpy(), epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 11294,
     "status": "ok",
     "timestamp": 1615991468137,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "VnUEJdXovzi-",
    "outputId": "02075086-352c-4a23-fac5-ad54d11e0e35"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['loss', 'accuracy']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metrics_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 11285,
     "status": "ok",
     "timestamp": 1615991468138,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "r0vxP3Ah42ib",
    "outputId": "061113ba-52db-4fbe-c7f9-b5d3d85438ed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=float32, numpy=0.6561605>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metrics[1].result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z7bn8pKTAynt",
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "# Experiment 1\n",
    "\n",
    "The below function runs an iteration of layer/epoch investigations.\n",
    "Returns the amount of layers/epochs used as well as the results and the model.\n",
    "\n",
    "Using cancer dataset (as in E2) and 'trainscg' or an optimiser of your choice, vary nodes and epochs (that is using early stopping for epochs) over suitable range, to find optimal choice in terms of classification test error rate of node/epochs for 50/50% random train/test split (no validation set). It is suggested that you initially try epochs = [ 1 2 4 8 16 32 64], nodes = [2 8 32], so there would be 21 node/epoch combinations. \n",
    "\n",
    "(Hint1: from the 'advanced script' in E2, nodes can be changed to xx, with hiddenLayerSize = xx; and epochs changed to xx by addingnet. trainParam.epochs = xx; placed afternet = patternnet(hiddenLayerSize, trainFcn); --see 'trainscg' help documentation for changing epochs). \n",
    "\n",
    "Repeat each of the 21 node/epoch combinations at least thirty times, with different 50/50 split and take average and report classification error rate and standard deviation (std). Graph classification train and test error rate and std as node-epoch changes, that is plot error rate vs epochs for different number of nodes. Report the optimal value for test error rate and associated node/epoch values. \n",
    "\n",
    "(Hint2: as epochs increases you can expect the test error rate to reach a minimum and then start increasing, you may need to set the stopping criteria to achieve the desired number of epochs - Hint 3: to find classification error rates for train and test set, you need to check the code from E2, to determine how you may obtain the train and test set patterns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "executionInfo": {
     "elapsed": 11274,
     "status": "ok",
     "timestamp": 1615991468138,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "mYWhCSW4A57V",
    "tags": [
     "exp1",
     "exp-func"
    ]
   },
   "outputs": [],
   "source": [
    "# hidden_nodes = [2, 8, 16, 24, 32]\n",
    "# epochs = [1, 2, 4, 8, 16, 32, 64, 100, 150, 200]\n",
    "hidden_nodes = [2, 8, 16]\n",
    "epochs = [1, 2, 4, 8]\n",
    "\n",
    "def evaluate_parameters(hidden_nodes=hidden_nodes, \n",
    "                        epochs=epochs, \n",
    "                        batch_size=128,\n",
    "                        optimizer=lambda: 'sgd',\n",
    "                        loss=lambda: 'categorical_crossentropy',\n",
    "                        metrics=['accuracy'],\n",
    "                        callbacks=None,\n",
    "                        validation_split=None,\n",
    "\n",
    "                        verbose=0,\n",
    "                        print_params=True,\n",
    "                        return_model=True,\n",
    "                        run_eagerly=False,\n",
    "                        tboard=True,\n",
    "                        \n",
    "                        dtrain=data_train,\n",
    "                        dtest=data_test,\n",
    "                        ltrain=labels_train,\n",
    "                        ltest=labels_test):\n",
    "    for idx1, hn in enumerate(hidden_nodes):\n",
    "        for idx2, e in enumerate(epochs):\n",
    "            if print_params:\n",
    "                print(f\"Nodes: {hn}, Epochs: {e}\")\n",
    "\n",
    "            model = get_model(hn)\n",
    "            model.compile(\n",
    "                optimizer=optimizer(),\n",
    "                loss=loss(),\n",
    "                metrics=metrics,\n",
    "                run_eagerly=run_eagerly\n",
    "                )\n",
    "            \n",
    "            if tboard:\n",
    "                if callbacks is not None:\n",
    "                    cb = [i() for i in callbacks] + [tensorboard_callback(prefix=f'exp1-{hn}-{e}-')]\n",
    "                else:\n",
    "                    cb = [tensorboard_callback(prefix=f'exp1-{hn}-{e}-')]\n",
    "            \n",
    "            response = {\"nodes\": hn, \n",
    "                        \"epochs\": e,\n",
    "                        ##############\n",
    "                        ##   TRAIN\n",
    "                        ##############\n",
    "                        \"history\": model.fit(dtrain.to_numpy(), \n",
    "                                             ltrain.to_numpy(), \n",
    "                                             epochs=e, \n",
    "                                             verbose=verbose,\n",
    "                                             \n",
    "                                             callbacks=cb,\n",
    "                                             validation_split=validation_split).history,\n",
    "                        ##############\n",
    "                        ##   TEST\n",
    "                        ##############\n",
    "                        \"results\": model.evaluate(dtest.to_numpy(), \n",
    "                                                 ltest.to_numpy(),\n",
    "                                                 callbacks=cb,\n",
    "                                                 batch_size=batch_size, \n",
    "                                                 verbose=verbose),\n",
    "                        \"optimizer\": model.optimizer.get_config(),\n",
    "                        \"loss\": model.loss,\n",
    "                        \"model_config\": json.loads(model.to_json())\n",
    "                       }\n",
    "\n",
    "            if return_model:\n",
    "                response[\"model\"] = model\n",
    "\n",
    "            yield response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r-63V9qb-i4w",
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "## Single Iteration\n",
    "Run a single iteration of epoch/layer investigations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 313592,
     "status": "ok",
     "timestamp": 1615991770468,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "ZmGFkE9y8E4H",
    "outputId": "243fb136-bc07-4438-afb7-f2d21758168d",
    "tags": [
     "exp1"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nodes: 2, Epochs: 1\n",
      "Nodes: 2, Epochs: 2\n",
      "Nodes: 2, Epochs: 4\n",
      "Nodes: 2, Epochs: 8\n",
      "Nodes: 8, Epochs: 1\n",
      "Nodes: 8, Epochs: 2\n",
      "Nodes: 8, Epochs: 4\n",
      "Nodes: 8, Epochs: 8\n",
      "Nodes: 16, Epochs: 1\n",
      "Nodes: 16, Epochs: 2\n",
      "Nodes: 16, Epochs: 4\n",
      "Nodes: 16, Epochs: 8\n"
     ]
    }
   ],
   "source": [
    "# es = tf.keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', patience = 5)\n",
    "single_results = list(evaluate_parameters(return_model=False, validation_split=0.2\n",
    "                                          , optimizer = lambda: tf.keras.optimizers.SGD(learning_rate=0.5, momentum=0.5)\n",
    "#                                           , callbacks=[es]\n",
    "                                         ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mdWK3-M6SK8_",
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "### Train/Test Curves\n",
    "\n",
    "For a single test from the set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 517
    },
    "executionInfo": {
     "elapsed": 314527,
     "status": "ok",
     "timestamp": 1615991771417,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "F9Xre1o6SesD",
    "outputId": "d6b817aa-58cd-4510-807f-e5e6bcf62f18",
    "tags": [
     "exp1"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nodes: 2, Epochs: 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1050x490 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "single_result = random.choice([i for i in single_results if i[\"epochs\"] > 1])\n",
    "single_history = single_result[\"history\"]\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15,7))\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "################\n",
    "##    LOSS\n",
    "################\n",
    "ax = axes[0]\n",
    "ax.set_title(\"Training vs Validation Loss\")\n",
    "ax.plot(single_history['loss'], label=\"train\", lw=2)\n",
    "ax.plot(single_history['val_loss'], label=\"validation\", lw=2, c=(1,0,0))\n",
    "ax.set_xlabel(\"Epochs\")\n",
    "ax.grid()\n",
    "ax.legend()\n",
    "\n",
    "################\n",
    "##  ACCURACY\n",
    "################\n",
    "ax = axes[1]\n",
    "ax.set_title(\"Training vs Validation Accuracy\")\n",
    "ax.plot(single_history['accuracy'], label=\"train\", lw=2)\n",
    "ax.plot(single_history['val_accuracy'], label=\"validation\", lw=2, c=(1,0,0))\n",
    "ax.set_xlabel(\"Epochs\")\n",
    "# ax.set_ylim(0, 1)\n",
    "ax.grid()\n",
    "ax.legend()\n",
    "\n",
    "print(f\"Nodes: {single_result['nodes']}, Epochs: {single_result['epochs']}\")\n",
    "# plt.tight_layout()\n",
    "# plt.savefig('fig.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0IQ7HfJCSDud",
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "### Accuracy Surface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 705
    },
    "executionInfo": {
     "elapsed": 315450,
     "status": "ok",
     "timestamp": 1615991772345,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "X3MWHLxJElbc",
    "outputId": "134671d0-bfd3-4ee6-aa02-1a2a5b23f3ca",
    "tags": [
     "exp1"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x350 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, Y = np.meshgrid(epochs, hidden_nodes)\n",
    "\n",
    "shaped_result = np.reshape([r[\"results\"][1] for r in single_results], \n",
    "                           (len(hidden_nodes), len(epochs)))\n",
    "\n",
    "fig = plt.figure(figsize=(8, 5))\n",
    "fig.set_dpi(fig_dpi)\n",
    "ax = plt.axes(projection='3d')\n",
    "\n",
    "surf = ax.plot_surface(X, Y, shaped_result, cmap='viridis')\n",
    "ax.set_title('Model test accuracy over different training periods with different numbers of nodes')\n",
    "ax.set_xlabel('Epochs')\n",
    "ax.set_ylabel('Hidden Nodes')\n",
    "ax.set_zlabel('Accuracy')\n",
    "ax.view_init(30, -110)\n",
    "# ax.set_zlim([0, 1])\n",
    "fig.colorbar(surf, shrink=0.3, aspect=6)\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('fig.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C793_RHvSGai",
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "### Error Rate Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 668
    },
    "executionInfo": {
     "elapsed": 316211,
     "status": "ok",
     "timestamp": 1615991773109,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "tpClZMptrq-q",
    "outputId": "f9fe93f9-7b67-4772-83e4-9e3567fd9318",
    "tags": [
     "exp1"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x350 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 5))\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "for layer in hidden_nodes:\n",
    "    plt.plot(epochs, \n",
    "             1 - np.array([i[\"results\"][1] \n",
    "                  for i in single_results \n",
    "                  if i[\"nodes\"] == layer]), \n",
    "             label=f'{layer} Nodes')\n",
    "\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.title(\"Test error rates for a single iteration of different epochs and hidden node training\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Error Rate\")\n",
    "plt.ylim(0)\n",
    "\n",
    "# plt.savefig('fig.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7mJaKjlCxEkt",
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "## Multiple Iterations\n",
    "\n",
    "Run multiple iterations of the epoch/layer investigations and average\n",
    "\n",
    "### CSV Results\n",
    "\n",
    "| test | learning rate | momentum | batch size | hidden nodes | epochs |\n",
    "| --- | --- | --- | --- | --- | --- |\n",
    "|1|0.01|0|128|2, 8, 12, 16, 24, 32, 64, 128, 256|1, 2, 4, 8, 16, 32, 64, 100, 150, 200|\n",
    "|2|0.5|0.1|128|2, 8, 12, 16, 24, 32, 64, 128|1, 2, 4, 8, 16, 32, 64, 100|\n",
    "|3|0.2|0.05|128|2, 8, 12, 16, 24, 32, 64, 128|1, 2, 4, 8, 16, 32, 64, 100|\n",
    "|4|0.08|0.04|128|2, 8, 12, 16, 24, 32, 64, 128|1, 2, 4, 8, 16, 32, 64, 100|\n",
    "|5|0.08|0|128|2, 8, 12, 16, 24, 32, 64, 128|1, 2, 4, 8, 16, 32, 64, 100|\n",
    "|6|0.06|0|128|1, 2, 3, 4, 5, 6, 7, 8|1, 2, 4, 8, 16, 32, 64, 100|\n",
    "|7|0.06|0|35|2, 8, 12, 16, 24, 32, 64, 128|1, 2, 4, 8, 16, 32, 64, 100|\n",
    "\n",
    "### Pickle Results\n",
    "\n",
    "| test | learning rate | momentum | batch size | hidden nodes | epochs |\n",
    "| --- | --- | --- | --- | --- | --- |\n",
    "|1|0.01|0|128|2, 8, 12, 16, 24, 32, 64, 128, 256|1, 2, 4, 8, 16, 32, 64, 100, 150, 200|\n",
    "|2|0.5|0.1|128|2, 8, 12, 16, 24, 32, 64, 128|1, 2, 4, 8, 16, 32, 64, 100|\n",
    "|3|1|0.3|20|2, 8, 12, 16, 24, 32, 64, 128|1, 2, 4, 8, 16, 32, 64, 100|\n",
    "|4|0.6|0.1|20|2, 8, 16, 24, 32|1, 2, 4, 8, 16, 32, 64, 100, 150, 200|\n",
    "|5|0.05|0.01|20|2, 8, 16, 24, 32|1, 2, 4, 8, 16, 32, 64, 100, 150, 200|\n",
    "|6|1.5|0.5|20|2, 8, 16, 24, 32|1, 2, 4, 8, 16, 32, 64, 100, 150, 200|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "-lsKo4BCP3yw",
    "tags": [
     "exp1"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1/30\n",
      "Iteration 2/30\n",
      "Iteration 3/30\n",
      "Iteration 4/30\n",
      "Iteration 5/30\n",
      "Iteration 6/30\n",
      "Iteration 7/30\n",
      "Iteration 8/30\n",
      "Iteration 9/30\n",
      "Iteration 10/30\n",
      "Iteration 11/30\n",
      "Iteration 12/30\n",
      "Iteration 13/30\n",
      "Iteration 14/30\n",
      "Iteration 15/30\n",
      "Iteration 16/30\n",
      "Iteration 17/30\n",
      "Iteration 18/30\n",
      "Iteration 19/30\n",
      "Iteration 20/30\n",
      "Iteration 21/30\n",
      "Iteration 22/30\n",
      "Iteration 23/30\n",
      "Iteration 24/30\n",
      "Iteration 25/30\n",
      "Iteration 26/30\n",
      "Iteration 27/30\n",
      "Iteration 28/30\n",
      "Iteration 29/30\n",
      "Iteration 30/30\n"
     ]
    }
   ],
   "source": [
    "multi_param_results = list()\n",
    "multi_iterations = 30\n",
    "for i in range(multi_iterations):\n",
    "    print(f\"Iteration {i+1}/{multi_iterations}\")\n",
    "    data_train, data_test, labels_train, labels_test = train_test_split(data, labels, test_size=0.5, stratify=labels)\n",
    "    multi_param_results.append(list(evaluate_parameters(dtrain=data_train, \n",
    "                                                  dtest=data_test, \n",
    "                                                  ltrain=labels_train, \n",
    "                                                  ltest=labels_test,\n",
    "                                                  optimizer=lambda: tf.keras.optimizers.SGD(learning_rate=1.5, momentum=0.5),\n",
    "                                                  return_model=False,\n",
    "                                                  print_params=False,\n",
    "                                                  batch_size=20)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "### Accuracy Tensor\n",
    "\n",
    "Create a tensor for holding the accuracy results\n",
    "\n",
    "(Iterations x [Test/Train] x Number of nodes x Number of epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30 Tests\n",
      "Nodes: [2, 8, 12, 16, 24, 32, 64, 128, 256]\n",
      "Epochs: [1, 2, 4, 8, 16, 32, 64, 100, 150, 200]\n",
      "\n",
      "Loss: categorical_crossentropy\n",
      "LR: 0.01\n",
      "Momentum: 0.0\n"
     ]
    }
   ],
   "source": [
    "multi_param_epochs = sorted(list({i[\"epochs\"] for i in multi_param_results[0]}))\n",
    "multi_param_nodes = sorted(list({i[\"nodes\"] for i in multi_param_results[0]}))\n",
    "multi_param_iter = len(multi_param_results)\n",
    "\n",
    "accuracy_tensor = np.zeros((multi_param_iter, 2, len(multi_param_nodes), len(multi_param_epochs)))\n",
    "for iter_idx, iteration in enumerate(multi_param_results):\n",
    "    for single_test in iteration:\n",
    "        accuracy_tensor[iter_idx, :,\n",
    "                        multi_param_nodes.index(single_test['nodes']), \n",
    "                        multi_param_epochs.index(single_test['epochs'])] = [single_test[\"results\"][1], \n",
    "                                                                            single_test[\"history\"][\"accuracy\"][-1]]\n",
    "        \n",
    "mean_param_accuracy = np.mean(accuracy_tensor, axis=0)\n",
    "std_param_accuracy = np.std(accuracy_tensor, axis=0)\n",
    "\n",
    "print(f'{multi_param_iter} Tests')\n",
    "print(f'Nodes: {multi_param_nodes}')\n",
    "print(f'Epochs: {multi_param_epochs}')\n",
    "print()\n",
    "print(f'Loss: {multi_param_results[0][0][\"loss\"]}')\n",
    "print(f'LR: {multi_param_results[0][0][\"optimizer\"][\"learning_rate\"]:.3}')\n",
    "print(f'Momentum: {multi_param_results[0][0][\"optimizer\"][\"momentum\"]:.3}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "#### Export/Import Test Sets\n",
    "\n",
    "Export mean and standard deviations for retrieval and visualisation "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "pickle.dump(multi_param_results, open(\"result.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "outputs": [],
   "source": [
    "exp1_testname = 'exp1-test1'\n",
    "multi_param_results = pickle.load(open(f\"results/{exp1_testname}.p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "np.savetxt(\"exp1-mean.csv\", mean_param_accuracy, delimiter=',')\n",
    "np.savetxt(\"exp1-std.csv\", std_param_accuracy, delimiter=',')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "mean_param_accuracy = np.loadtxt(\"results/test1-exp1-mean.csv\", delimiter=',')\n",
    "std_param_accuracy = np.loadtxt(\"results/test1-exp1-std.csv\", delimiter=',')\n",
    "# multi_iterations = 30"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "### Best Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nodes: 256, Epochs: 200, 1e+02% Accurate\n"
     ]
    }
   ],
   "source": [
    "best_param_accuracy_idx = np.unravel_index(np.argmax(mean_param_accuracy[0, :, :]), mean_param_accuracy.shape)\n",
    "best_param_accuracy = mean_param_accuracy[best_param_accuracy_idx]\n",
    "best_param_accuracy_nodes = multi_param_nodes[best_param_accuracy_idx[1]]\n",
    "best_param_accuracy_epochs = multi_param_epochs[best_param_accuracy_idx[2]]\n",
    "\n",
    "print(f'Nodes: {best_param_accuracy_nodes}, Epochs: {best_param_accuracy_epochs}, {best_param_accuracy * 100:.1}% Accurate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "### Test Accuracy Surface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "executionInfo": {
     "elapsed": 2653358,
     "status": "aborted",
     "timestamp": 1615994110345,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "ZGJVhz6iJU-7",
    "tags": [
     "exp1"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 420x280 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, Y = np.meshgrid(multi_param_epochs, multi_param_nodes)\n",
    "\n",
    "# fig = plt.figure(figsize=(10, 5))\n",
    "fig = plt.figure()\n",
    "fig.set_dpi(fig_dpi)\n",
    "ax = plt.axes(projection='3d')\n",
    "\n",
    "surf = ax.plot_surface(X, Y, mean_param_accuracy[0, :, :], cmap='coolwarm')\n",
    "ax.set_title(f'Average Accuracy')\n",
    "ax.set_xlabel('Epochs')\n",
    "ax.set_ylabel('Hidden Nodes')\n",
    "ax.set_zlabel('Accuracy')\n",
    "ax.view_init(30, -110)\n",
    "# ax.set_zlim([0, 1])\n",
    "fig.colorbar(surf, shrink=0.3, aspect=6)\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig(f'graphs/{exp1_testname}-acc-surf.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "### Test Error Rate Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "executionInfo": {
     "elapsed": 2653349,
     "status": "aborted",
     "timestamp": 1615994110347,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "Jrn3hKQAlGcc",
    "tags": [
     "exp1"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 420x280 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig = plt.figure(figsize=(7, 5))\n",
    "fig = plt.figure()\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "for idx, layer in enumerate(mean_param_accuracy[0, :, :]):\n",
    "#     plt.errorbar(epochs, 1- layer, yerr=std_param_accuracy[idx], label=f'{hidden_nodes[idx]} Nodes')\n",
    "    plt.plot(multi_param_epochs, 1 - layer, '-', label=f'{multi_param_nodes[idx]} Nodes', lw=2)\n",
    "\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.title(f\"Test error rates for different epochs and hidden nodes\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Error Rate\")\n",
    "plt.ylim(0)\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig(f'graphs/{exp1_testname}-error-rate-curves.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "### Test/Train Error Over Nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x933.333 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(math.ceil(len(multi_param_nodes) / 2), 2, figsize=(8, 8*math.ceil(len(multi_param_nodes) / 2)/3))\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "for idx, (nodes, ax) in enumerate(zip(multi_param_nodes, axes.flatten())):\n",
    "    ax.set_title(f'Error Rates For {nodes} Nodes')\n",
    "#     ax.errorbar(multi_param_epochs, 1 - mean_param_accuracy[0, idx, :], fmt='x', ls='-', yerr=std_param_accuracy[0, idx, :], markersize=4, lw=1, label='Test', capsize=4, c=(0, 0, 1), ecolor=(0, 0, 1, 0.5))\n",
    "#     ax.errorbar(multi_param_epochs, 1 - mean_param_accuracy[1, idx, :], fmt='x', ls='-', yerr=std_param_accuracy[1, idx, :], markersize=4, lw=1, label='Train', capsize=4, c=(1, 0, 0), ecolor=(1, 0, 0, 0.5))\n",
    "    ax.plot(multi_param_epochs, 1 - mean_param_accuracy[0, idx, :], 'x', ls='-', lw=1, label='Test', c=(0, 0, 1))\n",
    "    ax.plot(multi_param_epochs, 1 - mean_param_accuracy[1, idx, :], 'x', ls='-', lw=1, label='Train', c=(1, 0, 0))\n",
    "    ax.set_ylim(0, np.round(np.max(1 - mean_param_accuracy + std_param_accuracy) + 0.05, 1))\n",
    "    ax.legend()\n",
    "    ax.grid()\n",
    "\n",
    "fig.tight_layout()\n",
    "# fig.savefig(f'graphs/{exp1_testname}-test-train-error-rate.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x560 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(math.ceil(len(multi_param_nodes) / 2), 2, figsize=(8, 8*math.ceil(len(multi_param_nodes) / 2)/3))\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "for idx, (nodes, ax) in enumerate(zip(multi_param_nodes, axes.flatten())):\n",
    "    ax.set_title(f'Error Rate Std Dev. For {nodes} Nodes')\n",
    "    ax.plot(multi_param_epochs, std_param_accuracy[0, idx, :], 'x', ls='-', lw=1, label='Test', c=(0, 0, 1))\n",
    "    ax.plot(multi_param_epochs, std_param_accuracy[1, idx, :], 'x', ls='-', lw=1, label='Train', c=(1, 0, 0))\n",
    "    ax.set_ylim(0, np.round(np.max(std_param_accuracy) + 0.05, 1))\n",
    "    ax.legend()\n",
    "    ax.grid()\n",
    "\n",
    "fig.tight_layout()\n",
    "# fig.savefig(f'graphs/{exp1_testname}-test-train-error-rate-std.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eUPJuxUtVUc3",
    "tags": [
     "exp2"
    ]
   },
   "source": [
    "# Experiment 2\n",
    "\n",
    "For cancer dataset, choose an appropriate value of node and epochs, based on Exp 1) and use ensemble of individual (base) classifiers with random starting weights and Majority Vote to see if performance improves - repeat the majority vote ensemble at least thirty times with different 50/50 split and average and graph (Each classifier in the ensemble sees the same training patterns). Repeat for a different odd number (prevents tied vote) of individual classifiers between 3 and 25, and comment on the result of individualclassifier accuracy vs ensemble accuracy as number of base classifiers varies. Consider changing the number of nodes/epochs (both less complex and more complex) to see if you obtain better performance, and comment on the result with respect to why the optimal node/epoch combination may be different for an ensemble compared with the base classifier, as in Exp 1). \n",
    "\n",
    "(Hint4: to implement majority vote you need to determine the predicted class labels -probably easier to implement yourself rather than use the ensemble matlab functions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "exp2",
     "exp-func"
    ]
   },
   "outputs": [],
   "source": [
    "num_models=[1, 3, 9, 15, 25]\n",
    "\n",
    "def evaluate_ensemble_vote(hidden_nodes=16, \n",
    "                           epochs=50, \n",
    "                           batch_size=128,\n",
    "                           optimizer=lambda: 'sgd',\n",
    "                           weight_init=lambda: 'glorot_uniform',\n",
    "                           loss=lambda: 'categorical_crossentropy',\n",
    "                           metrics=['accuracy'],\n",
    "                           callbacks=None,\n",
    "                           validation_split=None,\n",
    "                           round_predictions=True,\n",
    "\n",
    "                           nmodels=num_models,\n",
    "                           tboard=True,\n",
    "                           exp='2',\n",
    "\n",
    "                           verbose=0,\n",
    "                           print_params=True,\n",
    "                           return_model=True,\n",
    "\n",
    "                           dtrain=data_train,\n",
    "                           dtest=data_test,\n",
    "                           ltrain=labels_train,\n",
    "                           ltest=labels_test):\n",
    "    for m in nmodels:\n",
    "        if print_params:\n",
    "            print(f\"Models: {m}\")\n",
    "            \n",
    "        response = {\"epochs\": list(),\n",
    "                    \"num_models\": m}\n",
    "            \n",
    "        ###################\n",
    "        ##   GET MODELS\n",
    "        ###################\n",
    "        if isinstance(hidden_nodes, tuple): # for range of hidden nodes, calculate value per model\n",
    "            if m == 1:\n",
    "                models = [get_model(int(np.mean(hidden_nodes)), weight_init=weight_init)]\n",
    "                response[\"nodes\"] = [int(np.mean(hidden_nodes))]\n",
    "                \n",
    "            else:\n",
    "                models = [get_model(int(i), weight_init=weight_init) \n",
    "                          for i in np.linspace(*hidden_nodes, num=m)]\n",
    "                response[\"nodes\"] = [int(i) for i in np.linspace(*hidden_nodes, num=m)]\n",
    "                \n",
    "        else: # not a range of epochs, just set to given value\n",
    "            models = [get_model(hidden_nodes, weight_init=weight_init) for _ in range(m)]\n",
    "            response[\"nodes\"] = hidden_nodes\n",
    "\n",
    "        for model in models:        \n",
    "            model.compile(\n",
    "                optimizer=optimizer(),\n",
    "                loss=loss(),\n",
    "                metrics=metrics\n",
    "                )        \n",
    "        \n",
    "        if tboard:\n",
    "            if callbacks is not None:\n",
    "                cb = [i() for i in callbacks] + [tensorboard_callback(prefix=f'exp{exp}-{m}-')]\n",
    "            else:\n",
    "                cb = [tensorboard_callback(prefix=f'exp{exp}-{m}-')]\n",
    "        \n",
    "        ###################\n",
    "        ##  TRAIN MODELS\n",
    "        ###################\n",
    "        histories = list()\n",
    "        for idx, model in enumerate(models):\n",
    "            if isinstance(epochs, tuple): # for range of epochs, calculate value per model\n",
    "                if m == 1:\n",
    "                    e = np.mean(epochs) # average, not lower bound if single model\n",
    "                else:\n",
    "                    e = np.linspace(*epochs, num=m)[idx]\n",
    "                e = int(e)\n",
    "            else: # not a range of epochs, just set to given value\n",
    "                e = epochs\n",
    "                \n",
    "#             print(m, e) # debug\n",
    "            \n",
    "            history = model.fit(dtrain.to_numpy(), \n",
    "                                ltrain.to_numpy(), \n",
    "                                epochs=e, \n",
    "                                verbose=verbose,\n",
    "\n",
    "                                callbacks=cb,\n",
    "                                validation_split=validation_split)\n",
    "            histories.append(history.history)\n",
    "            response[\"epochs\"].append(e)\n",
    "\n",
    "        ########################\n",
    "        ##  FEEDFORWARD TEST\n",
    "        ########################\n",
    "        # TEST DATA PREDICTIONS\n",
    "        response[\"predictions\"] = [model(dtest.to_numpy()) for model in models]\n",
    "        # TEST LABEL TENSOR\n",
    "        ltest_tensor = tf.constant(ltest.to_numpy())\n",
    "\n",
    "        ########################\n",
    "        ##  ENSEMBLE ACCURACY\n",
    "        ########################\n",
    "        ensem_sum_rounded = sum(tf.math.round(pred) for pred in response[\"predictions\"])\n",
    "        ensem_sum = sum(response[\"predictions\"])\n",
    "        # round predictions to onehot vectors and sum over all ensemble models\n",
    "        # take argmax for ensemble predicted class\n",
    "        \n",
    "        correct = 0 # number of correct ensemble predictions\n",
    "        correct_num_models = 0 # when correctly predicted ensembley, proportion of models correctly classifying\n",
    "        individual_accuracy = 0 # proportion of models correctly classifying\n",
    "        \n",
    "         # pc = predicted class, pcr = rounded predicted class, gt = ground truth\n",
    "        for pc, pcr, gt in zip(ensem_sum, ensem_sum_rounded, ltest_tensor):\n",
    "            gt_argmax = tf.math.argmax(gt)\n",
    "            \n",
    "            if round_predictions:\n",
    "                pred_val = pcr\n",
    "            else:\n",
    "                pred_val = pc\n",
    "            \n",
    "            correct_models = pcr[gt_argmax] / m # use rounded value so will divide nicely\n",
    "            individual_accuracy += correct_models\n",
    "            \n",
    "            if tf.math.argmax(pred_val) == gt_argmax: # ENSEMBLE EVALUATE HERE\n",
    "                correct += 1\n",
    "                correct_num_models += correct_models\n",
    "                \n",
    "#                 print(pc.numpy(), pcr.numpy(), gt.numpy(), (pcr[gt_argmax] / m).numpy(), True) # debug\n",
    "#             else:\n",
    "#                 print(pc.numpy(), pcr.numpy(), gt.numpy(), (pcr[gt_argmax] / m).numpy(), False)\n",
    "                \n",
    "        ########################\n",
    "        ##      RESULTS\n",
    "        ########################\n",
    "        response.update({\n",
    "            \"history\": histories,\n",
    "            \"optimizer\": model.optimizer.get_config(),\n",
    "            \"model_config\": json.loads(model.to_json()),\n",
    "            \"loss\": model.loss,\n",
    "            \"round_predictions\": round_predictions,\n",
    "            \n",
    "            \"accuracy\": correct / len(ltest), # average number of correct ensemble predictions\n",
    "            \"agreement\": correct_num_models / correct, # when correctly predicted ensembley, average proportion of models correctly classifying\n",
    "            \"individual_accuracy\": individual_accuracy / len(ltest) # average proportion of individual models correctly classifying\n",
    "        })\n",
    "\n",
    "        if return_model:\n",
    "            response[\"models\"] = models\n",
    "\n",
    "        yield response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "source": [
    "## Single Iteration\n",
    "Run a single iteration of ensemble model investigations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models: 1\n",
      "13 [50]\n",
      "Models: 3\n",
      "[1, 13, 25] [50, 50, 50]\n",
      "Models: 9\n",
      "[1, 4, 7, 10, 13, 16, 19, 22, 25] [50, 50, 50, 50, 50, 50, 50, 50, 50]\n",
      "Models: 15\n",
      "[1, 2, 4, 6, 7, 9, 11, 13, 14, 16, 18, 19, 21, 23, 25] [50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50]\n",
      "Models: 25\n",
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25] [50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50]\n"
     ]
    }
   ],
   "source": [
    "single_ensem_results = list()\n",
    "# for test in evaluate_ensemble_vote(epochs=(5, 300), optimizer=lambda: tf.keras.optimizers.SGD(learning_rate=0.02)):\n",
    "for test in evaluate_ensemble_vote(hidden_nodes=(1, 25), optimizer=lambda: tf.keras.optimizers.SGD(learning_rate=0.02)):\n",
    "    single_ensem_results.append(test)\n",
    "    print(test[\"nodes\"], test[\"epochs\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 560x350 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 5))\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "ensem_x = [i[\"num_models\"] for i in single_ensem_results]\n",
    "\n",
    "plt.plot(ensem_x, 1 - np.array([i[\"accuracy\"] for i in single_ensem_results]), 'x-', label='Ensemble Test')\n",
    "plt.plot(ensem_x, 1 - np.array([i[\"individual_accuracy\"] for i in single_ensem_results]), 'x-', label='Individual Test')\n",
    "plt.plot(ensem_x, 1 - np.array([i[\"agreement\"] for i in single_ensem_results]), 'x-', label='Disagreement')\n",
    "\n",
    "plt.title(\"Test Error Rates for Horizontal Model Ensembles\")\n",
    "plt.ylim(0)\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.ylabel(\"Error Rate\")\n",
    "plt.xlabel(\"Number of Models\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "source": [
    "## Multiple Iterations\n",
    "Run multiple iterations of the ensemble model investigations and average\n",
    "\n",
    "### CSV Results\n",
    "\n",
    "| test | learning rate | momentum | batch size | hidden nodes | epochs | models |\n",
    "| --- | --- | --- | --- | --- | --- | --- |\n",
    "|1|0.06|0|128|16|50|1, 3, 9, 15, 25|\n",
    "|2|0.06|0|35|16|1 - 100|1, 3, 9, 15, 25|\n",
    "\n",
    "### Pickle Results\n",
    "\n",
    "| test | learning rate | momentum | batch size | hidden nodes | epochs | models |\n",
    "| --- | --- | --- | --- | --- | --- | --- |\n",
    "|3|0.06|0.05|35|16|1 - 300|1, 3, 9, 15, 25|\n",
    "|4|0.06|0.05|35|1 - 50|50|1, 3, 9, 15, 25|\n",
    "|5|0.06|0.05|35|1 - 300|50|1, 3, 9, 15, 25|\n",
    "|6|0.001|0.01|35|1 - 400|50|1, 3, 9, 15, 25|\n",
    "|7|0.01|0.01|35|1 - 400|30 - 150|1, 3, 9, 15, 25|\n",
    "|8|0.03|0.01|35|1 - 400|5 - 100|1, 3, 9, 15, 25|\n",
    "|9|0.1|0.01|35|1 - 400|20|1, 3, 9, 15, 25|\n",
    "|10|0.15|0.01|35|1 - 400|20|1, 3, 9, 15, 25, 35, 45|\n",
    "|11|0.15|0.01|35|1 - 400|10|1, 3, 9, 15, 25, 35, 45|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size=35\n",
    "test_size=0.5\n",
    "epochs=10\n",
    "lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(0.1,\n",
    "                                                             decay_steps=100000,\n",
    "                                                             decay_rate=0.96)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1/30\n",
      "Iteration 2/30\n",
      "Iteration 3/30\n",
      "Iteration 4/30\n",
      "Iteration 5/30\n",
      "Iteration 6/30\n",
      "Iteration 7/30\n",
      "Iteration 8/30\n",
      "Iteration 9/30\n",
      "Iteration 10/30\n",
      "Iteration 11/30\n",
      "Iteration 12/30\n",
      "Iteration 13/30\n",
      "Iteration 14/30\n",
      "Iteration 15/30\n",
      "Iteration 16/30\n",
      "Iteration 17/30\n",
      "Iteration 18/30\n",
      "Iteration 19/30\n",
      "Iteration 20/30\n",
      "Iteration 21/30\n",
      "Iteration 22/30\n",
      "Iteration 23/30\n",
      "Iteration 24/30\n",
      "Iteration 25/30\n",
      "Iteration 26/30\n",
      "Iteration 27/30\n",
      "Iteration 28/30\n",
      "Iteration 29/30\n",
      "Iteration 30/30\n"
     ]
    }
   ],
   "source": [
    "multi_ensem_results = list()\n",
    "multi_ensem_iterations = 30\n",
    "for i in range(multi_ensem_iterations):\n",
    "    print(f\"Iteration {i+1}/{multi_ensem_iterations}\")\n",
    "    data_train, data_test, labels_train, labels_test = train_test_split(data, labels, test_size=test_size, stratify=labels)\n",
    "    multi_ensem_results.append(list(evaluate_ensemble_vote(epochs=10,\n",
    "                                                           hidden_nodes=(1, 400),\n",
    "                                                           nmodels=[1, 3, 9, 15, 25, 35, 45],\n",
    "                                                           optimizer=lambda: tf.keras.optimizers.SGD(learning_rate=0.15, momentum=0.01),\n",
    "                                                           weight_init=lambda: 'random_uniform',\n",
    "                                                           batch_size=batch_size,\n",
    "                                                           dtrain=data_train, \n",
    "                                                           dtest=data_test, \n",
    "                                                           ltrain=labels_train, \n",
    "                                                           ltest=labels_test,\n",
    "                                                           return_model=False,\n",
    "                                                           print_params=False)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "699"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "source": [
    "### Accuracy Tensor\n",
    "\n",
    "Create a tensor for holding the accuracy results\n",
    "\n",
    "(Iterations x Param x Number of models)\n",
    "\n",
    "#### Params\n",
    "0. Test Accuracy\n",
    "1. Train Accuracy\n",
    "2. Individual Accuracy\n",
    "3. Agreement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_tensor_data(test):\n",
    "    return [test[\"accuracy\"], \n",
    "            np.mean([i[\"accuracy\"][-1] for i in test[\"history\"]]), # avg train acc\n",
    "            test[\"individual_accuracy\"], \n",
    "            test[\"agreement\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30 Tests\n",
      "Models: [1, 3, 9, 15, 25, 35, 45]\n",
      "\n",
      "Loss: categorical_crossentropy\n",
      "LR: 0.15\n",
      "Momentum: 0.01\n"
     ]
    }
   ],
   "source": [
    "multi_ensem_models = sorted(list({i[\"num_models\"] for i in multi_ensem_results[0]}))\n",
    "multi_ensem_iter = len(multi_ensem_results)\n",
    "\n",
    "accuracy_ensem_tensor = np.zeros((multi_ensem_iter, 4, len(multi_ensem_models)))\n",
    "for iter_idx, iteration in enumerate(multi_ensem_results):\n",
    "    for single_test in iteration:\n",
    "        \n",
    "        ensem_models_idx = multi_ensem_models.index(single_test['num_models'])\n",
    "        accuracy_ensem_tensor[iter_idx, :, ensem_models_idx] = test_tensor_data(single_test)\n",
    "        \n",
    "mean_ensem_accuracy = np.mean(accuracy_ensem_tensor, axis=0)\n",
    "std_ensem_accuracy = np.std(accuracy_ensem_tensor, axis=0)\n",
    "\n",
    "print(f'{multi_ensem_iter} Tests')\n",
    "print(f'Models: {multi_ensem_models}')\n",
    "print()\n",
    "print(f'Loss: {multi_ensem_results[0][0][\"loss\"]}')\n",
    "print(f'LR: {multi_ensem_results[0][0][\"optimizer\"][\"learning_rate\"]:.3}')\n",
    "print(f'Momentum: {multi_ensem_results[0][0][\"optimizer\"][\"momentum\"]:.3}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "source": [
    "#### Export/Import Test Sets\n",
    "\n",
    "Export mean and standard deviations for retrieval and visualisation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "outputs": [],
   "source": [
    "pickle.dump(multi_ensem_results, open(\"results/exp2-test11.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp2_testname = 'exp2-test11'\n",
    "multi_ensem_results = pickle.load(open(f\"results/{exp2_testname}.p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "np.savetxt(\"exp2-mean.csv\", mean_ensem_accuracy, delimiter=',')\n",
    "np.savetxt(\"exp2-std.csv\", std_ensem_accuracy, delimiter=',')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "mean_ensem_accuracy = np.loadtxt(\"results/test1-exp2-mean.csv\", delimiter=',')\n",
    "std_ensem_accuracy = np.loadtxt(\"results/test1-exp2-std.csv\", delimiter=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "source": [
    "### Best Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models: 1, 71.8% Accurate\n"
     ]
    }
   ],
   "source": [
    "best_ensem_accuracy_idx = np.unravel_index(np.argmax(mean_ensem_accuracy[0, :]), mean_ensem_accuracy.shape)\n",
    "best_ensem_accuracy = mean_ensem_accuracy[best_ensem_accuracy_idx]\n",
    "best_ensem_accuracy_models = multi_ensem_models[best_ensem_accuracy_idx[1]]\n",
    "\n",
    "print(f'Models: {best_ensem_accuracy_models}, {best_ensem_accuracy * 100:.3}% Accurate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "source": [
    "### Test/Train Error Over Model Numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x350 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 5))\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "# plt.plot(multi_ensem_models, 1 - mean_ensem_accuracy[0, :], 'x-', label='Ensemble Test')\n",
    "# plt.plot(multi_ensem_models, 1 - mean_ensem_accuracy[2, :], 'x-', label='Individual Test')\n",
    "# plt.plot(multi_ensem_models, 1 - mean_ensem_accuracy[1, :], 'x-', label='Individual Train')\n",
    "# plt.plot(multi_ensem_models, 1 - mean_ensem_accuracy[3, :], 'x-', label='Agreement')\n",
    "\n",
    "plt.errorbar(multi_ensem_models, 1 - mean_ensem_accuracy[0, :], yerr=std_ensem_accuracy[0, :], capsize=4, label='Ensemble Test')\n",
    "plt.errorbar(multi_ensem_models, 1 - mean_ensem_accuracy[2, :], yerr=std_ensem_accuracy[2, :], capsize=4, label='Individual Test')\n",
    "plt.errorbar(multi_ensem_models, 1 - mean_ensem_accuracy[1, :], yerr=std_ensem_accuracy[1, :], capsize=4, label='Individual Train')\n",
    "plt.errorbar(multi_ensem_models, 1 - mean_ensem_accuracy[3, :], yerr=std_ensem_accuracy[3, :], capsize=4, label='Disagreement')\n",
    "\n",
    "plt.title(f\"Error Rate for Horizontal Ensemble Models\")\n",
    "# plt.ylim(0, 1)\n",
    "plt.ylim(0, np.max(1 - mean_ensem_accuracy + std_ensem_accuracy) + 0.05)\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.xlabel(\"Number of Models\")\n",
    "plt.ylabel(\"Error Rate\")\n",
    "plt.savefig(f'graphs/{exp2_testname}-error-rate-curves.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FSZq1mNiVZq_",
    "tags": [
     "ex3"
    ]
   },
   "source": [
    "# Experiment 3\n",
    "\n",
    "Repeat Exp 2) for cancer dataset with two different optimisers of your choice e.g. 'trainlm' and 'trainrp'. Comment and discuss the result and decide which is more appropriate training algorithm for the problem. In your discussion, include in your description a detailed account of how the training algorithms (optimisations) work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_optimisers(optimizers=[(lambda: 'sgd', 'sgd'), \n",
    "                                    (lambda: 'adam', 'adam'), \n",
    "                                    (lambda: 'rmsprop', 'rmsprop')],\n",
    "                        weight_init=lambda: 'glorot_uniform',\n",
    "                        print_params=True,\n",
    "                        **kwargs\n",
    "                       ):\n",
    "    for o in optimizers:\n",
    "        \n",
    "        if print_params:\n",
    "            print(f'Optimiser: {o[1]}')\n",
    "            \n",
    "        yield list(evaluate_ensemble_vote(optimizer=o[0],\n",
    "                                          weight_init=weight_init,\n",
    "                                          exp=f'3-{o[1]}',\n",
    "                                          print_params=print_params,\n",
    "                                          **kwargs\n",
    "                                         ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single Iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimiser: sgd\n",
      "Models: 1\n",
      "Models: 3\n",
      "Models: 5\n",
      "Optimiser: adam\n",
      "Models: 1\n",
      "Models: 3\n",
      "Models: 5\n",
      "Optimiser: rmsprop\n",
      "Models: 1\n",
      "Models: 3\n",
      "Models: 5\n"
     ]
    }
   ],
   "source": [
    "single_optim_results = list()\n",
    "for test in evaluate_optimisers(epochs=(5, 300), nmodels=[1, 3, 5]):\n",
    "    single_optim_results.append(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple Iterations\n",
    "\n",
    "### Pickle Results\n",
    "\n",
    "| test | optim1 | optim2 | optim3 | lr | momentum | epsilon | batch size | hidden nodes | epochs | models |\n",
    "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n",
    "| 1 | SGD | Adam | RMSprop | 0.1 | 0.0 | 1e7 | 35 | 16 | 1 - 100 | 1, 3, 9, 15, 25 |\n",
    "| 2 | SGD | Adam | RMSprop | 0.05 | 0.01 | 1e7 | 35 | 16 | 1 - 100 | 1, 3, 9, 15, 25 |\n",
    "| 3 | SGD | Adam | RMSprop | 0.1 | 0.01 | 1e7 | 35 | 1 - 400 | 20 | 1, 3, 9, 15, 25, 35, 45 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1/30\n",
      "Iteration 2/30\n",
      "Iteration 3/30\n",
      "Iteration 4/30\n",
      "Iteration 5/30\n",
      "Iteration 6/30\n",
      "Iteration 7/30\n",
      "Iteration 8/30\n",
      "Iteration 9/30\n",
      "Iteration 10/30\n",
      "Iteration 11/30\n",
      "Iteration 12/30\n",
      "Iteration 13/30\n",
      "Iteration 14/30\n",
      "Iteration 15/30\n",
      "Iteration 16/30\n",
      "Iteration 17/30\n",
      "Iteration 18/30\n",
      "Iteration 19/30\n",
      "Iteration 20/30\n",
      "Iteration 21/30\n",
      "Iteration 22/30\n",
      "Iteration 23/30\n",
      "Iteration 24/30\n",
      "Iteration 25/30\n",
      "Iteration 26/30\n",
      "Iteration 27/30\n",
      "Iteration 28/30\n",
      "Iteration 29/30\n",
      "Iteration 30/30\n"
     ]
    }
   ],
   "source": [
    "multi_optim_results = list()\n",
    "multi_optim_iterations = 30\n",
    "\n",
    "multi_optim_lr = 0.1\n",
    "multi_optim_mom = 0.01\n",
    "multi_optim_eps = 1e-07\n",
    "multi_optims = [(lambda: tf_optim.SGD(learning_rate=multi_optim_lr, \n",
    "                                      momentum=multi_optim_mom), 'sgd'), \n",
    "                (lambda: tf_optim.Adam(learning_rate=multi_optim_lr, \n",
    "                                       epsilon=multi_optim_eps), 'adam'), \n",
    "                (lambda: tf_optim.RMSprop(learning_rate=multi_optim_lr, \n",
    "                                          momentum=multi_optim_mom, \n",
    "                                          epsilon=multi_optim_eps), 'rmsprop')]\n",
    "\n",
    "for i in range(multi_optim_iterations):\n",
    "    print(f\"Iteration {i+1}/{multi_optim_iterations}\")\n",
    "    data_train, data_test, labels_train, labels_test = train_test_split(data, labels, test_size=0.5, stratify=labels)\n",
    "    multi_optim_results.append(list(evaluate_optimisers(epochs=20,\n",
    "                                                        hidden_nodes=(1, 400),\n",
    "                                                        nmodels=[1, 3, 9, 15, 25, 35, 45],\n",
    "                                                        optimizers=multi_optims,\n",
    "                                                        weight_init=lambda: 'random_uniform',\n",
    "                                                        batch_size=35,\n",
    "                                                        dtrain=data_train, \n",
    "                                                        dtest=data_test, \n",
    "                                                        ltrain=labels_train, \n",
    "                                                        ltest=labels_test,\n",
    "                                                        return_model=False,\n",
    "                                                        print_params=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy Tensor\n",
    "\n",
    "Create a tensor for holding the accuracy results\n",
    "\n",
    "(Iterations x Param x Number of models)\n",
    "\n",
    "#### Params\n",
    "0. Test Accuracy\n",
    "1. Train Accuracy\n",
    "2. Individual Accuracy\n",
    "3. Agreement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30 Tests\n",
      "Optimisers: ['SGD', 'Adam', 'RMSprop']\n",
      "Models: [1, 3, 9, 15, 25, 35, 45]\n",
      "\n",
      "Loss: categorical_crossentropy\n"
     ]
    }
   ],
   "source": [
    "multi_optim_results_dict = dict() # indexed by optimiser name\n",
    "multi_optim_iter = len(multi_optim_results) # number of iterations (30)\n",
    "\n",
    "#####################################\n",
    "## INDIVIDUAL RESULTS TO DICTIONARY\n",
    "#####################################\n",
    "for iter_idx, iteration in enumerate(multi_optim_results): # of 30 iterations\n",
    "    for model_idx, model_test in enumerate(iteration): # of 3 optimisers\n",
    "        for single_optim_test in model_test: # single tests for each optimisers\n",
    "            \n",
    "            single_optim_name = single_optim_test[\"optimizer\"][\"name\"]\n",
    "            if single_optim_name not in multi_optim_results_dict:\n",
    "                multi_optim_results_dict[single_optim_name] = list(list() for _ in range(multi_optim_iter))\n",
    "\n",
    "            multi_optim_results_dict[single_optim_name][iter_idx].append(single_optim_test)\n",
    "\n",
    "# list of numbers of models used in test\n",
    "multi_optim_models = sorted(list({i[\"num_models\"] for i in multi_optim_results[0][0]}))\n",
    "\n",
    "##################################\n",
    "## DICTIONARY TO RESULTS TENSORS\n",
    "##################################\n",
    "optim_tensors = dict()\n",
    "for optim, optim_results in multi_optim_results_dict.items():\n",
    "    \n",
    "    accuracy_optim_tensor = np.zeros((multi_optim_iter, 4, len(multi_optim_models)))\n",
    "    for iter_idx, iteration in enumerate(optim_results):\n",
    "        for single_test in iteration:\n",
    "\n",
    "            optim_models_idx = multi_optim_models.index(single_test['num_models'])\n",
    "            accuracy_optim_tensor[iter_idx, :, optim_models_idx] = test_tensor_data(single_test)\n",
    "            \n",
    "    optim_tensors[optim] = {\n",
    "        \"accuracy\": accuracy_optim_tensor,\n",
    "        \"mean\": np.mean(accuracy_optim_tensor, axis=0),\n",
    "        \"std\": np.std(accuracy_optim_tensor, axis=0)\n",
    "    }\n",
    "\n",
    "print(f'{multi_optim_iter} Tests')\n",
    "print(f'Optimisers: {list(multi_optim_results_dict.keys())}')\n",
    "print(f'Models: {multi_optim_models}')\n",
    "print()\n",
    "print(f'Loss: {multi_optim_results[0][0][0][\"loss\"]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Export/Import Test Sets\n",
    "\n",
    "Export mean and standard deviations for retrieval and visualisation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(multi_optim_results, open(\"results/exp3-test3.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp3_testname = 'exp3-test3'\n",
    "multi_optim_results = pickle.load(open(f\"results/{exp3_testname}.p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SGD: 35 Models, 90.4% Accurate\n",
      "Adam: 15 Models, 96.9% Accurate\n",
      "RMSprop: 35 Models, 96.8% Accurate\n"
     ]
    }
   ],
   "source": [
    "for optim, optim_results in optim_tensors.items():\n",
    "    best_optim_accuracy_idx = np.unravel_index(np.argmax(optim_results[\"mean\"][0, :]), optim_results[\"mean\"].shape)\n",
    "    best_optim_accuracy = optim_results[\"mean\"][best_optim_accuracy_idx]\n",
    "    best_optim_accuracy_models = multi_optim_models[best_optim_accuracy_idx[1]]\n",
    "\n",
    "    print(f'{optim}: {best_optim_accuracy_models} Models, {best_optim_accuracy * 100:.3}% Accurate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimiser Error Rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1680x350 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(24, 5))\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "for idx, ((optimiser_name, tensors_dict), ax) in enumerate(zip(optim_tensors.items(), axes.flatten())):\n",
    "    ax.plot(multi_optim_models, 1 - tensors_dict[\"mean\"][0, :], 'x-', label='Ensemble Test')\n",
    "    ax.plot(multi_optim_models, 1 - tensors_dict[\"mean\"][2, :], 'x-', label='Individual Test')\n",
    "    ax.plot(multi_optim_models, 1 - tensors_dict[\"mean\"][1, :], 'x-', label='Individual Train')\n",
    "    ax.plot(multi_optim_models, 1 - tensors_dict[\"mean\"][3, :], 'x-', label='Disagreement')\n",
    "\n",
    "#     ax.errorbar(multi_optim_models, 1 - tensors_dict[\"mean\"][0, :], yerr=tensors_dict[\"std\"][0, :], capsize=4, label='Ensemble Test')\n",
    "#     ax.errorbar(multi_optim_models, 1 - tensors_dict[\"mean\"][2, :], yerr=tensors_dict[\"std\"][2, :], capsize=4, label='Individual Test')\n",
    "#     ax.errorbar(multi_optim_models, 1 - tensors_dict[\"mean\"][1, :], yerr=tensors_dict[\"std\"][1, :], capsize=4, label='Individual Train')\n",
    "#     ax.errorbar(multi_optim_models, 1 - tensors_dict[\"mean\"][3, :], yerr=tensors_dict[\"std\"][3, :], capsize=4, label='Disagreement')\n",
    "\n",
    "    ax.set_title(f\"{optimiser_name} Error Rate for Ensemble Models\")\n",
    "#     ax.set_ylim(0, 1)\n",
    "    ax.set_ylim(0, np.max([np.max(1 - i[\"mean\"] + i[\"std\"]) for i in optim_tensors.values()]) + 0.03)\n",
    "    ax.grid()\n",
    "    ax.legend()\n",
    "    ax.set_xlabel(\"Number of Models\")\n",
    "    ax.set_ylabel(\"Error Rate\")\n",
    "\n",
    "# plt.savefig(f'graphs/{exp3_testname}-error-rate-curves.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "authorship_tag": "ABX9TyNAMGLKzaoWaq1wvQ+w0w8h",
   "collapsed_sections": [],
   "name": "nncw.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "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": "3.8.9"
  },
  "toc-autonumbering": false,
  "toc-showcode": false,
  "toc-showmarkdowntxt": false,
  "toc-showtags": false
 },
 "nbformat": 4,
 "nbformat_minor": 4
}