5_hyper_parameters_lstm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0cef3ed3",
   "metadata": {},
   "source": [
    "## Imports "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "68da50b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "98d667ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import keras modules\n",
    "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, LSTM\n",
    "from tensorflow.keras.callbacks import TensorBoard\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0ad4cd16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import scikit-optimize modules\n",
    "import skopt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from skopt import gp_minimize, forest_minimize\n",
    "from skopt.space import Real, Integer \n",
    "from skopt.plots import plot_convergence, plot_objective, plot_evaluations, plot_histogram, plot_objective_2D \n",
    "from skopt.utils import use_named_args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "33019266",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorflow: 2.4.1\n",
      "Keras: 2.4.0\n",
      "Skop: 0.8.1\n"
     ]
    }
   ],
   "source": [
    "# check versions of libraries\n",
    "print(\"Tensorflow: \" + tf.__version__)\n",
    "print(\"Keras: \" + tf.keras.__version__)\n",
    "print(\"Skop: \" + skopt.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cfecd85",
   "metadata": {},
   "source": [
    "## Hyper parameter optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "570347ca",
   "metadata": {},
   "source": [
    "Hyperparameter optimization refers to performing a search to discover the set of specific model configuration arguments that result in the best performance of the model on a specific dataset.\n",
    "\n",
    "There are many ways to perform hyperparameter optimization, although modern methods, such as Bayesian optimization, are fast and effective. The Scikit-Optimize (skop) library is an opensource Python library that provides an implementation of Bayesian Optimization that can be used to tune the hyperparameters of machine learning models from the scikit-learn Python library. Bayesian optimization is an efficient optimization algorithm based on Bayes Theorem capable of finding the minimum or maximum of arbitrary cost functions.\n",
    "\n",
    "In our case, the goal was to minimize the cost function of the LSTM model. The selected LSTM network's parameters to optimize were the learning rate, the number of LSTM units, the dimension of epochs and the batch size. The idea is that we have a true fitness function (the performance of a neural network on some dataset) and we want to find the minimum. To find out how the NN performs with a given set of hyperparameters, we need to train the network. A model called the Gaussain Process is built. The search starts with the initial values of the hyperparameters. At the early optimization phase, the search is explored more and in the later phase, the search is focused on the best regions found. At the end of the process, we would get a set of hyperparameters corresponding to the minimal validation loss."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66c1ef06",
   "metadata": {},
   "source": [
    "![Hyperparameter optimization process](./images/bayesian_optimization_lstm.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cddcbe84",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set hyper-parameters\n",
    "# learning rate\n",
    "dim_learning_rate = Real(low=1e-3, high=1, prior='log-uniform', name='learning_rate')\n",
    "\n",
    "# number of lstm layers\n",
    "dim_num_lstm_units = Integer(low=1, high=500, name='num_lstm_units')\n",
    "\n",
    "# number of epochs\n",
    "dim_num_epochs = Integer(low=1, high=500, name='num_epochs')\n",
    "\n",
    "# number of batch size\n",
    "dim_num_batch_size = Integer(low=1, high=500, name='num_batch_size')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e1a1f38b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine hyper-parameters\n",
    "dimensions = [dim_learning_rate, dim_num_lstm_units, dim_num_epochs, dim_num_batch_size]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6c9d08dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set default parameter values\n",
    "default_parameters = [1e-2, 100, 100, 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a4ac7eae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a helper-function for log-dir-name\n",
    "def log_dir_name(learning_rate, num_lstm_units,\n",
    "                 num_epochs, num_batch_size):\n",
    "\n",
    "    # the dir-name for the TensorBoard log-dir\n",
    "    s = \"./5_logs/lr_{0:.0e}_lstm_{1}_epochs_{2}_batch_{3}/\"\n",
    "\n",
    "    # insert all the hyper-parameters in the dir-name\n",
    "    log_dir = s.format(learning_rate,\n",
    "                       num_lstm_units,\n",
    "                       num_epochs,\n",
    "                       num_batch_size)\n",
    "\n",
    "    return log_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4ba9696c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "df = pd.read_csv('./model_data/model_data_interpolated.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "70048e53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop time column\n",
    "date_time = pd.to_datetime(df.pop('Date'), format='%Y-%m-%d') #or infer_datetime_format=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "94ebc7ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select values of dataframe\n",
    "values = df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "174079c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rescale data between 0 and 1\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled = scaler.fit_transform(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a20dc455",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to transform time series to supervised learning series\n",
    "from pandas import DataFrame\n",
    "from pandas import concat\n",
    "\n",
    "def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n",
    "    \"\"\"\n",
    "    Frame a time series as a supervised learning dataset.\n",
    "    Arguments:\n",
    "        data: Sequence of observations as a list or NumPy array.\n",
    "        n_in: Number of lag observations as input (X).\n",
    "        n_out: Number of observations as output (y).\n",
    "        dropnan: Boolean whether or not to drop rows with NaN values.\n",
    "    Returns:\n",
    "        Pandas DataFrame of series framed for supervised learning.\n",
    "    \"\"\"\n",
    "    n_vars = 1 if type(data) is list else data.shape[1]\n",
    "    df = DataFrame(data)\n",
    "    cols, names = list(), list()\n",
    "    # input sequence (t-n, ... t-1)\n",
    "    for i in range(n_in, 0, -1):\n",
    "        cols.append(df.shift(i))\n",
    "        names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # forecast sequence (t, t+1, ... t+n)\n",
    "    for i in range(0, n_out):\n",
    "        cols.append(df.shift(-i))\n",
    "        if i == 0:\n",
    "            names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n",
    "        else:\n",
    "            names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # put it all together\n",
    "    agg = concat(cols, axis=1)\n",
    "    agg.columns = names\n",
    "    # drop rows with NaN values\n",
    "    if dropnan:\n",
    "        agg.dropna(inplace=True)\n",
    "    return agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6bf558ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# using function above\n",
    "reframed = series_to_supervised(scaled, 1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3968199c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop columns we don't want to predict\n",
    "reframed.drop(reframed.columns[[5,6,7,8,9,10]], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3cc265c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into train, val and test sets\n",
    "values = reframed.values\n",
    "\n",
    "n_train_years = 7305 # days in 20 years for training\n",
    "n_val_years = 9131 - n_train_years # days in 5 years for validating\n",
    "n_val_sum = n_train_years + n_val_years\n",
    "\n",
    "train = values[:n_train_years, :]\n",
    "val = values[n_train_years:n_val_sum, :]\n",
    "test = values[n_val_sum:, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f1e99d50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into input and outputs\n",
    "x_train, y_train = train[:, :-1], train[:, -1]\n",
    "x_val, y_val = val[:, :-1], val[:, -1]\n",
    "x_test, y_test = test[:, :-1], test[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f07a5a8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7305, 1, 5) (7305,) (1826, 1, 5) (1826,) (1674, 1, 5) (1674,)\n"
     ]
    }
   ],
   "source": [
    "# reshape input to be 3D [samples, timesteps, features]\n",
    "x_train = x_train.reshape((x_train.shape[0], 1, x_train.shape[1]))\n",
    "x_val = x_val.reshape((x_val.shape[0], 1, x_val.shape[1]))\n",
    "x_test = x_test.reshape((x_test.shape[0], 1, x_test.shape[1]))\n",
    "print(x_train.shape, y_train.shape, x_val.shape, y_val.shape, x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8876cc10",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model(learning_rate, num_lstm_units, num_batch_size, num_epochs):\n",
    "    \n",
    "    \"\"\"\n",
    "    Hyper-parameters:\n",
    "    learning_rate:     Learning-rate for the optimizer.\n",
    "    num_lstm_units:    Number of lstm units.\n",
    "    num_epochs:        Number of epochs.\n",
    "    num_batch_size:    Batch size of model.\n",
    "    \"\"\"\n",
    "                       \n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(LSTM(units=num_lstm_units, input_shape=(x_train.shape[1], x_train.shape[2])))\n",
    "    model.add(Dense(units=1))\n",
    "\n",
    "    optimizer = Adam(lr=learning_rate)\n",
    "    model.compile(optimizer=optimizer, loss='mse', metrics=['mae'])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b034c411",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save network with best hyper-parameters\n",
    "path_best_model = '5_best_model.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "df192da1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set regression loss\n",
    "best_loss = float('inf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f470b717",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function that creates and trains a neural network with the given hyper-parameters\n",
    "# and then evaluates its performance on the validation-set\n",
    "@use_named_args(dimensions=dimensions)\n",
    "def fitness(learning_rate, num_lstm_units, num_epochs, num_batch_size):\n",
    "    \n",
    "    \"\"\"\n",
    "    Hyper-parameters:\n",
    "    learning_rate:     Learning-rate for the optimizer.\n",
    "    num_lstm_units:    Number of lstm units.\n",
    "    num_epochs:        Number of epochs.\n",
    "    num_batch_size:    Batch size of model.\n",
    "    \"\"\"\n",
    "\n",
    "    # Print the hyper-parameters.\n",
    "    print('learning rate: {0:.1e}'.format(learning_rate))\n",
    "    print('num_lstm_units:', num_lstm_units)\n",
    "    print('num_epochs:', num_epochs)\n",
    "    print('num_batch_size:', num_batch_size)\n",
    "    print()\n",
    "    \n",
    "    # Create the neural network with these hyper-parameters.\n",
    "    model = create_model(learning_rate=learning_rate,\n",
    "                         num_lstm_units=num_lstm_units,\n",
    "                         num_epochs=num_epochs,\n",
    "                         num_batch_size=num_batch_size)\n",
    "\n",
    "    # Dir-name for the TensorBoard log-files.\n",
    "    log_dir = log_dir_name(learning_rate, num_lstm_units,\n",
    "                           num_epochs, num_batch_size)\n",
    "    \n",
    "    # Create a callback-function for Keras which will be\n",
    "    # run after each epoch has ended during training.\n",
    "    # This saves the log-files for TensorBoard.\n",
    "    # Note that there are complications when histogram_freq=1.\n",
    "    # It might give strange errors and it also does not properly\n",
    "    # support Keras data-generators for the validation-set.\n",
    "    callback_log = TensorBoard(\n",
    "        log_dir=log_dir,\n",
    "        histogram_freq=0,\n",
    "        write_graph=True,\n",
    "        write_grads=False,\n",
    "        write_images=False)\n",
    "   \n",
    "    # Use Keras to train the model.\n",
    "    history = model.fit(x=x_train,\n",
    "                        y=y_train,\n",
    "                        epochs=num_epochs,\n",
    "                        batch_size=num_batch_size,\n",
    "                        validation_data=(x_val, y_val),\n",
    "                        verbose=2, \n",
    "                        shuffle=False,\n",
    "                        callbacks=[callback_log])\n",
    "\n",
    "    # Get the loss on the validation-set\n",
    "    # after the last training-epoch.\n",
    "    val_loss = history.history['val_loss'][-1]\n",
    "\n",
    "    # print the classification accuracy.\n",
    "    print()\n",
    "    print(\"Loss: {0:.2%}\".format(val_loss))\n",
    "    print()\n",
    "\n",
    "    # Save the model if it improves on the best-found performance.\n",
    "    # We use the global keyword so we update the variable outside\n",
    "    # of this function.\n",
    "    global best_loss\n",
    "\n",
    "    # if the loss of the saved model is improved ...\n",
    "    if val_loss < best_loss:\n",
    "        # Save the new model to harddisk.\n",
    "        model.save(path_best_model)\n",
    "        \n",
    "        # Update the loss.\n",
    "        best_loss = val_loss\n",
    "\n",
    "    # Delete the Keras model with these hyper-parameters from memory.\n",
    "    del model\n",
    "    \n",
    "    # Clear the Keras session, otherwise it will keep adding new\n",
    "    # models to the same TensorFlow graph each time we create\n",
    "    # a model with a different set of hyper-parameters.\n",
    "    K.clear_session()\n",
    "    \n",
    "    # find a set of hyper-parameters with the LOWEST fitness-value\n",
    "    return val_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1e0b2db7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "learning rate: 1.0e-02\n",
      "num_lstm_units: 100\n",
      "num_epochs: 100\n",
      "num_batch_size: 100\n",
      "\n",
      "Epoch 1/100\n",
      "74/74 - 5s - loss: 0.0250 - mae: 0.1074 - val_loss: 0.0110 - val_mae: 0.0880\n",
      "Epoch 2/100\n",
      "74/74 - 0s - loss: 0.0066 - mae: 0.0581 - val_loss: 0.0075 - val_mae: 0.0642\n",
      "Epoch 3/100\n",
      "74/74 - 0s - loss: 0.0052 - mae: 0.0506 - val_loss: 0.0070 - val_mae: 0.0587\n",
      "Epoch 4/100\n",
      "74/74 - 1s - loss: 0.0045 - mae: 0.0459 - val_loss: 0.0071 - val_mae: 0.0601\n",
      "Epoch 5/100\n",
      "74/74 - 0s - loss: 0.0040 - mae: 0.0427 - val_loss: 0.0073 - val_mae: 0.0615\n",
      "Epoch 6/100\n",
      "74/74 - 0s - loss: 0.0037 - mae: 0.0409 - val_loss: 0.0074 - val_mae: 0.0622\n",
      "Epoch 7/100\n",
      "74/74 - 0s - loss: 0.0035 - mae: 0.0399 - val_loss: 0.0074 - val_mae: 0.0623\n",
      "Epoch 8/100\n",
      "74/74 - 0s - loss: 0.0034 - mae: 0.0392 - val_loss: 0.0073 - val_mae: 0.0621\n",
      "Epoch 9/100\n",
      "74/74 - 0s - loss: 0.0033 - mae: 0.0387 - val_loss: 0.0073 - val_mae: 0.0619\n",
      "Epoch 10/100\n",
      "74/74 - 0s - loss: 0.0032 - mae: 0.0383 - val_loss: 0.0072 - val_mae: 0.0616\n",
      "Epoch 11/100\n",
      "74/74 - 0s - loss: 0.0032 - mae: 0.0380 - val_loss: 0.0072 - val_mae: 0.0612\n",
      "Epoch 12/100\n",
      "74/74 - 0s - loss: 0.0032 - mae: 0.0377 - val_loss: 0.0071 - val_mae: 0.0609\n",
      "Epoch 13/100\n",
      "74/74 - 0s - loss: 0.0031 - mae: 0.0375 - val_loss: 0.0070 - val_mae: 0.0604\n",
      "Epoch 14/100\n",
      "74/74 - 0s - loss: 0.0031 - mae: 0.0373 - val_loss: 0.0069 - val_mae: 0.0600\n",
      "Epoch 15/100\n",
      "74/74 - 0s - loss: 0.0031 - mae: 0.0372 - val_loss: 0.0068 - val_mae: 0.0594\n",
      "Epoch 16/100\n",
      "74/74 - 0s - loss: 0.0031 - mae: 0.0371 - val_loss: 0.0067 - val_mae: 0.0588\n",
      "Epoch 17/100\n",
      "74/74 - 0s - loss: 0.0031 - mae: 0.0369 - val_loss: 0.0066 - val_mae: 0.0581\n",
      "Epoch 18/100\n",
      "74/74 - 0s - loss: 0.0031 - mae: 0.0368 - val_loss: 0.0064 - val_mae: 0.0573\n",
      "Epoch 19/100\n",
      "74/74 - 0s - loss: 0.0031 - mae: 0.0367 - val_loss: 0.0063 - val_mae: 0.0565\n",
      "Epoch 20/100\n",
      "74/74 - 0s - loss: 0.0030 - mae: 0.0366 - val_loss: 0.0062 - val_mae: 0.0556\n",
      "Epoch 21/100\n",
      "74/74 - 0s - loss: 0.0030 - mae: 0.0365 - val_loss: 0.0060 - val_mae: 0.0547\n",
      "Epoch 22/100\n",
      "74/74 - 0s - loss: 0.0030 - mae: 0.0364 - val_loss: 0.0059 - val_mae: 0.0537\n",
      "Epoch 23/100\n",
      "74/74 - 0s - loss: 0.0030 - mae: 0.0363 - val_loss: 0.0057 - val_mae: 0.0526\n",
      "Epoch 24/100\n",
      "74/74 - 0s - loss: 0.0030 - mae: 0.0362 - val_loss: 0.0056 - val_mae: 0.0516\n",
      "Epoch 25/100\n",
      "74/74 - 0s - loss: 0.0030 - mae: 0.0361 - val_loss: 0.0054 - val_mae: 0.0506\n",
      "Epoch 26/100\n",
      "74/74 - 0s - loss: 0.0030 - mae: 0.0360 - val_loss: 0.0053 - val_mae: 0.0496\n",
      "Epoch 27/100\n",
      "74/74 - 0s - loss: 0.0030 - mae: 0.0359 - val_loss: 0.0051 - val_mae: 0.0487\n",
      "Epoch 28/100\n",
      "74/74 - 0s - loss: 0.0030 - mae: 0.0357 - val_loss: 0.0050 - val_mae: 0.0478\n",
      "Epoch 29/100\n",
      "74/74 - 0s - loss: 0.0029 - mae: 0.0356 - val_loss: 0.0049 - val_mae: 0.0471\n",
      "Epoch 30/100\n",
      "74/74 - 0s - loss: 0.0029 - mae: 0.0355 - val_loss: 0.0048 - val_mae: 0.0464\n",
      "Epoch 31/100\n",
      "74/74 - 0s - loss: 0.0029 - mae: 0.0354 - val_loss: 0.0048 - val_mae: 0.0459\n",
      "Epoch 32/100\n",
      "74/74 - 0s - loss: 0.0029 - mae: 0.0352 - val_loss: 0.0047 - val_mae: 0.0455\n",
      "Epoch 33/100\n",
      "74/74 - 0s - loss: 0.0029 - mae: 0.0351 - val_loss: 0.0046 - val_mae: 0.0453\n",
      "Epoch 34/100\n",
      "74/74 - 0s - loss: 0.0029 - mae: 0.0349 - val_loss: 0.0046 - val_mae: 0.0451\n",
      "Epoch 35/100\n",
      "74/74 - 0s - loss: 0.0028 - mae: 0.0348 - val_loss: 0.0045 - val_mae: 0.0450\n",
      "Epoch 36/100\n",
      "74/74 - 0s - loss: 0.0028 - mae: 0.0347 - val_loss: 0.0045 - val_mae: 0.0449\n",
      "Epoch 37/100\n",
      "74/74 - 0s - loss: 0.0028 - mae: 0.0345 - val_loss: 0.0045 - val_mae: 0.0449\n",
      "Epoch 38/100\n",
      "74/74 - 0s - loss: 0.0028 - mae: 0.0344 - val_loss: 0.0044 - val_mae: 0.0450\n",
      "Epoch 39/100\n",
      "74/74 - 0s - loss: 0.0028 - mae: 0.0343 - val_loss: 0.0044 - val_mae: 0.0450\n",
      "Epoch 40/100\n",
      "74/74 - 0s - loss: 0.0028 - mae: 0.0342 - val_loss: 0.0044 - val_mae: 0.0451\n",
      "Epoch 41/100\n",
      "74/74 - 0s - loss: 0.0028 - mae: 0.0341 - val_loss: 0.0044 - val_mae: 0.0451\n",
      "Epoch 42/100\n",
      "74/74 - 0s - loss: 0.0027 - mae: 0.0340 - val_loss: 0.0044 - val_mae: 0.0452\n",
      "Epoch 43/100\n",
      "74/74 - 0s - loss: 0.0027 - mae: 0.0339 - val_loss: 0.0044 - val_mae: 0.0453\n",
      "Epoch 44/100\n",
      "74/74 - 0s - loss: 0.0027 - mae: 0.0338 - val_loss: 0.0043 - val_mae: 0.0453\n",
      "Epoch 45/100\n",
      "74/74 - 0s - loss: 0.0027 - mae: 0.0337 - val_loss: 0.0043 - val_mae: 0.0454\n",
      "Epoch 46/100\n",
      "74/74 - 0s - loss: 0.0027 - mae: 0.0336 - val_loss: 0.0043 - val_mae: 0.0454\n",
      "Epoch 47/100\n",
      "74/74 - 0s - loss: 0.0027 - mae: 0.0335 - val_loss: 0.0043 - val_mae: 0.0454\n",
      "Epoch 48/100\n",
      "74/74 - 0s - loss: 0.0027 - mae: 0.0334 - val_loss: 0.0043 - val_mae: 0.0454\n",
      "Epoch 49/100\n",
      "74/74 - 0s - loss: 0.0027 - mae: 0.0333 - val_loss: 0.0043 - val_mae: 0.0455\n",
      "Epoch 50/100\n",
      "74/74 - 0s - loss: 0.0026 - mae: 0.0332 - val_loss: 0.0043 - val_mae: 0.0455\n",
      "Epoch 51/100\n",
      "74/74 - 0s - loss: 0.0026 - mae: 0.0332 - val_loss: 0.0043 - val_mae: 0.0455\n",
      "Epoch 52/100\n",
      "74/74 - 0s - loss: 0.0026 - mae: 0.0331 - val_loss: 0.0043 - val_mae: 0.0454\n",
      "Epoch 53/100\n",
      "74/74 - 0s - loss: 0.0026 - mae: 0.0330 - val_loss: 0.0043 - val_mae: 0.0454\n",
      "Epoch 54/100\n",
      "74/74 - 0s - loss: 0.0026 - mae: 0.0330 - val_loss: 0.0043 - val_mae: 0.0453\n",
      "Epoch 55/100\n",
      "74/74 - 0s - loss: 0.0026 - mae: 0.0329 - val_loss: 0.0043 - val_mae: 0.0453\n",
      "Epoch 56/100\n",
      "74/74 - 0s - loss: 0.0026 - mae: 0.0329 - val_loss: 0.0043 - val_mae: 0.0452\n",
      "Epoch 57/100\n",
      "74/74 - 0s - loss: 0.0026 - mae: 0.0328 - val_loss: 0.0043 - val_mae: 0.0452\n",
      "Epoch 58/100\n",
      "74/74 - 0s - loss: 0.0026 - mae: 0.0327 - val_loss: 0.0043 - val_mae: 0.0452\n",
      "Epoch 59/100\n",
      "74/74 - 0s - loss: 0.0025 - mae: 0.0326 - val_loss: 0.0043 - val_mae: 0.0451\n",
      "Epoch 60/100\n",
      "74/74 - 0s - loss: 0.0025 - mae: 0.0326 - val_loss: 0.0042 - val_mae: 0.0451\n",
      "Epoch 61/100\n",
      "74/74 - 0s - loss: 0.0025 - mae: 0.0325 - val_loss: 0.0042 - val_mae: 0.0450\n",
      "Epoch 62/100\n",
      "74/74 - 0s - loss: 0.0025 - mae: 0.0324 - val_loss: 0.0042 - val_mae: 0.0449\n",
      "Epoch 63/100\n",
      "74/74 - 0s - loss: 0.0025 - mae: 0.0323 - val_loss: 0.0042 - val_mae: 0.0448\n",
      "Epoch 64/100\n",
      "74/74 - 0s - loss: 0.0025 - mae: 0.0323 - val_loss: 0.0042 - val_mae: 0.0448\n",
      "Epoch 65/100\n",
      "74/74 - 0s - loss: 0.0025 - mae: 0.0322 - val_loss: 0.0042 - val_mae: 0.0447\n",
      "Epoch 66/100\n",
      "74/74 - 0s - loss: 0.0025 - mae: 0.0322 - val_loss: 0.0042 - val_mae: 0.0446\n",
      "Epoch 67/100\n",
      "74/74 - 0s - loss: 0.0025 - mae: 0.0321 - val_loss: 0.0042 - val_mae: 0.0445\n",
      "Epoch 68/100\n",
      "74/74 - 0s - loss: 0.0025 - mae: 0.0321 - val_loss: 0.0042 - val_mae: 0.0445\n",
      "Epoch 69/100\n",
      "74/74 - 0s - loss: 0.0024 - mae: 0.0320 - val_loss: 0.0042 - val_mae: 0.0444\n",
      "Epoch 70/100\n",
      "74/74 - 0s - loss: 0.0024 - mae: 0.0319 - val_loss: 0.0042 - val_mae: 0.0444\n",
      "Epoch 71/100\n",
      "74/74 - 0s - loss: 0.0024 - mae: 0.0319 - val_loss: 0.0042 - val_mae: 0.0443\n",
      "Epoch 72/100\n",
      "74/74 - 0s - loss: 0.0024 - mae: 0.0318 - val_loss: 0.0042 - val_mae: 0.0442\n",
      "Epoch 73/100\n",
      "74/74 - 0s - loss: 0.0024 - mae: 0.0317 - val_loss: 0.0042 - val_mae: 0.0441\n",
      "Epoch 74/100\n",
      "74/74 - 0s - loss: 0.0024 - mae: 0.0317 - val_loss: 0.0042 - val_mae: 0.0441\n",
      "Epoch 75/100\n",
      "74/74 - 0s - loss: 0.0024 - mae: 0.0316 - val_loss: 0.0042 - val_mae: 0.0440\n",
      "Epoch 76/100\n",
      "74/74 - 1s - loss: 0.0024 - mae: 0.0316 - val_loss: 0.0042 - val_mae: 0.0439\n",
      "Epoch 77/100\n",
      "74/74 - 1s - loss: 0.0024 - mae: 0.0316 - val_loss: 0.0042 - val_mae: 0.0439\n",
      "Epoch 78/100\n",
      "74/74 - 1s - loss: 0.0024 - mae: 0.0315 - val_loss: 0.0042 - val_mae: 0.0438\n",
      "Epoch 79/100\n",
      "74/74 - 0s - loss: 0.0024 - mae: 0.0315 - val_loss: 0.0042 - val_mae: 0.0437\n",
      "Epoch 80/100\n",
      "74/74 - 0s - loss: 0.0024 - mae: 0.0314 - val_loss: 0.0041 - val_mae: 0.0437\n",
      "Epoch 81/100\n",
      "74/74 - 0s - loss: 0.0024 - mae: 0.0314 - val_loss: 0.0041 - val_mae: 0.0436\n",
      "Epoch 82/100\n",
      "74/74 - 0s - loss: 0.0023 - mae: 0.0313 - val_loss: 0.0041 - val_mae: 0.0436\n",
      "Epoch 83/100\n",
      "74/74 - 0s - loss: 0.0023 - mae: 0.0313 - val_loss: 0.0041 - val_mae: 0.0435\n",
      "Epoch 84/100\n",
      "74/74 - 0s - loss: 0.0023 - mae: 0.0312 - val_loss: 0.0041 - val_mae: 0.0435\n",
      "Epoch 85/100\n",
      "74/74 - 0s - loss: 0.0023 - mae: 0.0312 - val_loss: 0.0041 - val_mae: 0.0434\n",
      "Epoch 86/100\n",
      "74/74 - 1s - loss: 0.0023 - mae: 0.0312 - val_loss: 0.0041 - val_mae: 0.0434\n",
      "Epoch 87/100\n",
      "74/74 - 0s - loss: 0.0023 - mae: 0.0311 - val_loss: 0.0041 - val_mae: 0.0433\n",
      "Epoch 88/100\n",
      "74/74 - 0s - loss: 0.0023 - mae: 0.0311 - val_loss: 0.0041 - val_mae: 0.0433\n",
      "Epoch 89/100\n",
      "74/74 - 0s - loss: 0.0023 - mae: 0.0311 - val_loss: 0.0041 - val_mae: 0.0432\n",
      "Epoch 90/100\n",
      "74/74 - 0s - loss: 0.0023 - mae: 0.0310 - val_loss: 0.0041 - val_mae: 0.0432\n",
      "Epoch 91/100\n",
      "74/74 - 0s - loss: 0.0023 - mae: 0.0310 - val_loss: 0.0041 - val_mae: 0.0432\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 92/100\n",
      "74/74 - 0s - loss: 0.0023 - mae: 0.0310 - val_loss: 0.0041 - val_mae: 0.0431\n",
      "Epoch 93/100\n",
      "74/74 - 0s - loss: 0.0023 - mae: 0.0309 - val_loss: 0.0041 - val_mae: 0.0431\n",
      "Epoch 94/100\n",
      "74/74 - 0s - loss: 0.0023 - mae: 0.0309 - val_loss: 0.0041 - val_mae: 0.0430\n",
      "Epoch 95/100\n",
      "74/74 - 0s - loss: 0.0023 - mae: 0.0309 - val_loss: 0.0041 - val_mae: 0.0430\n",
      "Epoch 96/100\n",
      "74/74 - 0s - loss: 0.0023 - mae: 0.0309 - val_loss: 0.0041 - val_mae: 0.0430\n",
      "Epoch 97/100\n",
      "74/74 - 0s - loss: 0.0023 - mae: 0.0308 - val_loss: 0.0041 - val_mae: 0.0429\n",
      "Epoch 98/100\n",
      "74/74 - 0s - loss: 0.0023 - mae: 0.0308 - val_loss: 0.0041 - val_mae: 0.0429\n",
      "Epoch 99/100\n",
      "74/74 - 0s - loss: 0.0023 - mae: 0.0308 - val_loss: 0.0041 - val_mae: 0.0429\n",
      "Epoch 100/100\n",
      "74/74 - 0s - loss: 0.0023 - mae: 0.0308 - val_loss: 0.0041 - val_mae: 0.0428\n",
      "\n",
      "Loss: 0.41%\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.004121147561818361"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check run\n",
    "fitness(x=default_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8283205",
   "metadata": {},
   "outputs": [],
   "source": [
    "##### run the hyper-parameter optimization\n",
    "search_result = gp_minimize(func=fitness,\n",
    "                            dimensions=dimensions,\n",
    "                            acq_func='EI',\n",
    "                            n_calls=40,\n",
    "                            x0=default_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2decfd5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Convergence plot'}, xlabel='Number of calls $n$', ylabel='$\\\\min f(x)$ after $n$ calls'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# optimization progress\n",
    "plot_convergence(search_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd2d5405",
   "metadata": {},
   "source": [
    "The convergence plot shows that the loss function of the LSTM model is already pretty low from the beginning of the process. At the 22nd search, the loss function drops by around $2.10^{-5}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "69849c13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "learning_rate     0.01561601307656165\n",
      "num_lstm_units    30\n",
      "num_epochs        500\n",
      "num_batch_size    128\n"
     ]
    }
   ],
   "source": [
    "# best hyper-parameters\n",
    "print('learning_rate     {}'.format(search_result.x[0]))\n",
    "print('num_lstm_units    {}'.format(search_result.x[1]))\n",
    "print('num_epochs        {}'.format(search_result.x[2]))\n",
    "print('num_batch_size    {}'.format(search_result.x[3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "155fb13b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "learning_rate     Real(low=0.001, high=1, prior='log-uniform', transform='identity')\n",
      "num_lstm_units    Integer(low=1, high=500, prior='uniform', transform='identity')\n",
      "num_epochs        Integer(low=1, high=500, prior='uniform', transform='identity')\n",
      "num_batch_size    Integer(low=1, high=500, prior='uniform', transform='identity')\n"
     ]
    }
   ],
   "source": [
    "print('learning_rate     {}'.format(dimensions[0]))\n",
    "print('num_lstm_units    {}'.format(dimensions[1]))\n",
    "print('num_epochs        {}'.format(dimensions[2]))\n",
    "print('num_batch_size    {}'.format(dimensions[3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a18cf9f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.004136530216783285"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_result.fun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "137faccc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.004136530216783285, [0.01561601307656165, 30, 500, 128]),\n",
       " (0.004155533853918314, [0.01, 100, 100, 100]),\n",
       " (0.004326516762375832, [0.4334862687432915, 115, 92, 379]),\n",
       " (0.004370750393718481, [0.02958197314641175, 488, 60, 192]),\n",
       " (0.004542979411780834, [0.1277294961686797, 7, 458, 305]),\n",
       " (0.004700458608567715, [0.0018950585350399308, 189, 351, 70]),\n",
       " (0.0047556255012750626, [0.001, 407, 258, 76]),\n",
       " (0.004877073690295219, [0.001, 1, 500, 1]),\n",
       " (0.004932855721563101, [0.001, 425, 428, 26]),\n",
       " (0.004972529131919146, [1.0, 500, 500, 500]),\n",
       " (0.005517391487956047, [0.4919967434016415, 441, 500, 182]),\n",
       " (0.006089142058044672, [0.032242457725426804, 89, 278, 463]),\n",
       " (0.006123912986367941, [0.017697684634001537, 276, 107, 307]),\n",
       " (0.006264571100473404, [0.0011624505344010765, 236, 500, 500]),\n",
       " (0.0064185503870248795, [0.02201991410428172, 41, 213, 366]),\n",
       " (0.006625488866120577, [0.001, 480, 500, 150]),\n",
       " (0.006798347923904657, [0.001, 449, 440, 418]),\n",
       " (0.007092648185789585, [0.12159090639428773, 122, 394, 286]),\n",
       " (0.007519216276705265, [0.001, 1, 199, 486]),\n",
       " (0.00759211415424943, [0.001, 357, 500, 194]),\n",
       " (0.007792661432176828, [0.0027274729876038523, 119, 487, 238]),\n",
       " (0.00807427242398262, [0.001, 384, 58, 387]),\n",
       " (0.008278263732790947, [0.31576237166334437, 318, 235, 140]),\n",
       " (0.00930437259376049, [0.21031953265427586, 232, 239, 473]),\n",
       " (0.009309734217822552, [0.01944153922922999, 55, 442, 450]),\n",
       " (0.009427351877093315, [0.02678837355906454, 203, 372, 493]),\n",
       " (0.012887410819530487, [0.004376482956726247, 496, 32, 470]),\n",
       " (0.014559315517544746, [1.0, 209, 149, 246]),\n",
       " (0.0433134064078331, [0.0027248552600231493, 20, 1, 143]),\n",
       " (0.05107715725898743, [0.005029621261430646, 1, 253, 1]),\n",
       " (0.08007945865392685, [0.01438861733940544, 455, 151, 1]),\n",
       " (0.08410190790891647, [0.001, 170, 1, 483]),\n",
       " (0.10518210381269455, [1.0, 331, 500, 89]),\n",
       " (0.17476697266101837, [0.7165448743509659, 14, 3, 18]),\n",
       " (0.181575745344162, [1.0, 176, 1, 500]),\n",
       " (0.22631193697452545, [0.24275089163011399, 340, 439, 1]),\n",
       " (0.32057997584342957, [1.0, 99, 329, 43]),\n",
       " (0.3514423668384552, [0.0350179548277067, 500, 500, 1]),\n",
       " (0.4032553434371948, [0.038801277679289814, 1, 1, 1]),\n",
       " (1.712756872177124, [1.0, 475, 1, 337])]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(zip(search_result.func_vals, search_result.x_iters))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94720961",
   "metadata": {},
   "source": [
    "The following figure shows a matrix plot of all combinations of dimensions (hyperparameters). The verticals show the influence of a single dimension on fitness. This is a so-called “Partial Dependence plot” for that dimension. It shows how the approximated fitness value changes with different values in that dimension. The plots below the diagonal show the Partial Dependence for 3 dimensions. This shows how the approximated fitness value changes when we are varying 3 dimensions simultaneously."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88397a4c",
   "metadata": {},
   "source": [
    "The yellow regions show areas where the loss on the validation set is the lowest as contrary to the darker regions. The star in the plot represents the location where the optimal hyperparameters were found. From the curves shown in the figure below, the loss improves when the LSTM model’s learning rate is lower (the model learns better at smaller time steps), its number of LSTM units is not more than 350 and its number of epochs is bigger than 100 epochs. The batch size does not seem to affect a lot the loss function. The partial dimension plot of the batch size is flat which indicates that the performance might change with the batch size but there is then another choice of the number of epochs, the number of LSTM units and the learning rate in the layer that compensate for that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "28df8a38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plot_objective(result=search_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28252e69",
   "metadata": {},
   "source": [
    "We also displayed another type of matrix plot: the evaluation plot. Here, the verticals show histograms of the sample distributions for each of the hyperparameters during the Bayesian optimization. The plots below the diagonal show the location of samples in the search space and the color-coding shows the order in which the samples were taken. The early samples are darker while the late samples are yellow. As excepted, the earlier samples were more random and the later samples were more in clusters because a certain pattern started to form, the search was more structured. The hyperparameters were mostly sampled at the limits of the search range. If we had done more iterations, the results would have been even better and the clustering pattern would have been easier to identify."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b4aac012",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plot_evaluations(result=search_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b8b05870",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model(path_best_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "32dc9d9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "58/58 [==============================] - 1s 2ms/step - loss: 0.0041 - mae: 0.0428\n"
     ]
    }
   ],
   "source": [
    "result = model.evaluate(x=x_val,\n",
    "                        y=y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dcae0524",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss 0.004121147096157074\n",
      "mae 0.04284857586026192\n"
     ]
    }
   ],
   "source": [
    "for name, value in zip(model.metrics_names, result):\n",
    "    print(name, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c8f4ccc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 0.41%\n"
     ]
    }
   ],
   "source": [
    "print(\"{0}: {1:.2%}\".format(model.metrics_names[0], result[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37fb8acd",
   "metadata": {},
   "source": [
    "**Reference:**\n",
    "\n",
    "Most of the codes from thi notebook was inspired by Magnus Erik Hvass Pedersen work on [Hyper-Parameter Optimization of CNN](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/19_Hyper-Parameters.ipynb)."
   ]
  }
 ],
 "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}