{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Experiment 2: Model Evaluating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from keras.models import load_model\n",
    "from matplotlib import ticker\n",
    "from scipy.io import loadmat\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "In this experiment, we load model weights from the experiment1 and evaluate them on test dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data:\n",
      "x_train: (5728, 1, 102), y_train: (5728, 1),\n",
      "x_val: (2455, 1, 102), y_val: (2455, 1)\n",
      "x_test: (3508, 1, 102), y_test: (3508, 1)\n"
     ]
    }
   ],
   "source": [
    "data = loadmat('./dataset/mango/mango_dm_split.mat')\n",
    "\n",
    "min_value, max_value = data['min_y'][-1][-1], data['max_y'][-1][-1]\n",
    "retransform = lambda x: x * (max_value - min_value)\n",
    "x_train, y_train, x_test, y_test = data['x_train'], data['y_train'], data['x_test'], data['y_test']\n",
    "x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.3, random_state=12, shuffle=True)\n",
    "x_train, x_val, x_test = x_train[:, np.newaxis, :], x_val[:, np.newaxis, :], x_test[:, np.newaxis, :]\n",
    "print(f\"shape of data:\\n\"\n",
    "      f\"x_train: {x_train.shape}, y_train: {y_train.shape},\\n\"\n",
    "      f\"x_val: {x_val.shape}, y_val: {y_val.shape}\\n\"\n",
    "      f\"x_test: {x_test.shape}, y_test: {y_test.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plain 5 mse:  0.007162414257423199\n",
      "plain 5 rmse :  0.08463104783365971\n",
      "plain 5 Dry matter content error 1.278990434152287\n",
      "plain 5 r^2 : 0.7362122841198271\n",
      "plain 11 mse:  0.027140651722534437\n",
      "plain 11 rmse :  0.16474420087679698\n",
      "plain 11 Dry matter content error 2.4897039844954327\n",
      "plain 11 r^2 : 0.0004249589491984729\n",
      "shortcut 5 mse:  0.007429169596940547\n",
      "shortcut 5 rmse :  0.08619263075774254\n",
      "shortcut 5 Dry matter content error 1.3025899248021375\n",
      "shortcut 5 r^2 : 0.7263878339859644\n",
      "shortcut11 mse:  0.007825262774295792\n",
      "shortcut11 rmse :  0.08846051534043757\n",
      "shortcut11 Dry matter content error 1.3368634303450377\n",
      "shortcut11 r^2 : 0.7117999435379954\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "## Build model and load weights\n",
    "plain_5, plain_11 = load_model('./checkpoints/plain5.hdf5'), load_model('./checkpoints/plain11.hdf5')\n",
    "shortcut5, shortcut11 = load_model('./checkpoints/shortcut5.hdf5'), load_model('./checkpoints/shortcut11.hdf5')\n",
    "models = {'plain 5': plain_5, 'plain 11': plain_11, 'shortcut 5': shortcut5, 'shortcut11': shortcut11}\n",
    "results = {model_name: model.predict(x_test).reshape((-1, )) for model_name, model in models.items()}\n",
    "for model_name, model_result in results.items():\n",
    "      mse = mean_squared_error(y_test, model_result)\n",
    "      rmse = np.sqrt(mse)\n",
    "      print(model_name, \"mse: \", mse)\n",
    "      print(model_name, \"rmse : \", rmse)\n",
    "      print(model_name, \"Dry matter content error\", retransform(rmse))\n",
    "      print(model_name, \"r^2 :\", r2_score(y_test, model_result))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plain 5 nadam mse:  0.0025042022303828344\n",
      "plain 5 nadam rmse :  0.0500420046599138\n",
      "plain 5 nadam Dry matter content error 0.7562619972711523\n",
      "plain 5 nadam r^2 : 0.9077716308058986\n",
      "plain 11 nadam mse:  0.0027148425657891745\n",
      "plain 11 nadam rmse :  0.05210415113778531\n",
      "plain 11 nadam Dry matter content error 0.7874262766524306\n",
      "plain 11 nadam r^2 : 0.900013864925283\n",
      "shortcut 5 nadam mse:  0.0026949613632585197\n",
      "shortcut 5 nadam rmse :  0.051913017281396\n",
      "shortcut 5 nadam Dry matter content error 0.7845377578378575\n",
      "shortcut 5 nadam r^2 : 0.9007460785080253\n",
      "shortcut11 nadam mse:  0.002492666414101387\n",
      "shortcut11 nadam rmse :  0.049926610280504595\n",
      "shortcut11 nadam Dry matter content error 0.754518094634978\n",
      "shortcut11 nadam r^2 : 0.9081964884751603\n"
     ]
    }
   ],
   "source": [
    "plain_5, plain_11 = load_model('./checkpoints/plain5_nadam.hdf5'), load_model('./checkpoints/plain11_nadam.hdf5')\n",
    "shortcut5, shortcut11 = load_model('./checkpoints/shortcut5_nadam.hdf5'), load_model('./checkpoints/shortcut11_nadam.hdf5')\n",
    "models = {'plain 5 nadam': plain_5, 'plain 11 nadam': plain_11, 'shortcut 5 nadam': shortcut5, 'shortcut11 nadam': shortcut11}\n",
    "results = {model_name: model.predict(x_test).reshape((-1, )) for model_name, model in models.items()}\n",
    "for model_name, model_result in results.items():\n",
    "      mse = mean_squared_error(y_test, model_result)\n",
    "      rmse = np.sqrt(mse)\n",
    "      print(model_name, \"mse: \", mse)\n",
    "      print(model_name, \"rmse : \", rmse)\n",
    "      print(model_name, \"Dry matter content error\", retransform(rmse))\n",
    "      print(model_name, \"r^2 :\", r2_score(y_test, model_result))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plain 5 rmsprop mse:  0.0025762880890505646\n",
      "plain 5 rmsprop rmse :  0.05075714815718634\n",
      "plain 5 rmsprop Dry matter content error 0.7670696348399975\n",
      "plain 5 rmsprop r^2 : 0.905116748901307\n",
      "plain 11 rmsprop mse:  0.0026627965381971945\n",
      "plain 11 rmsprop rmse :  0.051602291985891426\n",
      "plain 11 rmsprop Dry matter content error 0.7798419081376315\n",
      "plain 11 rmsprop r^2 : 0.9019306910464329\n",
      "shortcut 5 rmsprop mse:  0.002591777512557128\n",
      "shortcut 5 rmsprop rmse :  0.0509095031654909\n",
      "shortcut 5 rmsprop Dry matter content error 0.7693721066066205\n",
      "shortcut 5 rmsprop r^2 : 0.9045462820865926\n",
      "shortcut11 rmsprop mse:  0.0025523285716201384\n",
      "shortcut11 rmsprop rmse :  0.050520575725343214\n",
      "shortcut11 rmsprop Dry matter content error 0.7634944235545812\n",
      "shortcut11 rmsprop r^2 : 0.9059991645434926\n"
     ]
    }
   ],
   "source": [
    "plain_5, plain_11 = load_model('./checkpoints/plain5_rmsprop.hdf5'), load_model('./checkpoints/plain11_rmsprop.hdf5')\n",
    "shortcut5, shortcut11 = load_model('./checkpoints/shortcut5_rmsprop.hdf5'), load_model('./checkpoints/shortcut11_rmsprop.hdf5')\n",
    "models = {'plain 5 rmsprop': plain_5, 'plain 11 rmsprop': plain_11, 'shortcut 5 rmsprop': shortcut5, 'shortcut11 rmsprop': shortcut11}\n",
    "results = {model_name: model.predict(x_test).reshape((-1, )) for model_name, model in models.items()}\n",
    "for model_name, model_result in results.items():\n",
    "      mse = mean_squared_error(y_test, model_result)\n",
    "      rmse = np.sqrt(mse)\n",
    "      print(model_name, \"mse: \", mse)\n",
    "      print(model_name, \"rmse : \", rmse)\n",
    "      print(model_name, \"Dry matter content error\", retransform(rmse))\n",
    "      print(model_name, \"r^2 :\", r2_score(y_test, model_result))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plain 5 sgd mse :  0.11134143767715826\n",
      "plain 5 sgd Dry matter content error 1.682652375919312\n",
      "plain 5 sgd r^2 : 0.543428518577538\n",
      "plain 11 sgd mse :  0.1281311162291065\n",
      "plain 11 sgd Dry matter content error 1.936387131781486\n",
      "plain 11 sgd r^2 : 0.3953495916351124\n",
      "shortcut 5 sgd mse :  0.07824195777158978\n",
      "shortcut 5 sgd Dry matter content error 1.1824350294692925\n",
      "shortcut 5 sgd r^2 : 0.7745373801958391\n",
      "shortcut11 sgd mse :  0.09167697720606416\n",
      "shortcut11 sgd Dry matter content error 1.3854723518136416\n",
      "shortcut11 sgd r^2 : 0.690460767243821\n"
     ]
    }
   ],
   "source": [
    "plain_5, plain_11 = load_model('./checkpoints/plain5_sgd.hdf5'), load_model('./checkpoints/plain11_sgd.hdf5')\n",
    "shortcut5, shortcut11 = load_model('./checkpoints/shortcut5_sgd.hdf5'), load_model('./checkpoints/shortcut11_sgd.hdf5')\n",
    "models = {'plain 5 sgd': plain_5, 'plain 11 sgd': plain_11, 'shortcut 5 sgd': shortcut5, 'shortcut11 sgd': shortcut11}\n",
    "results = {model_name: model.predict(x_test).reshape((-1, )) for model_name, model in models.items()}\n",
    "for model_name, model_result in results.items():\n",
    "      rmse = np.sqrt(mean_squared_error(y_test, model_result))\n",
    "      print(model_name, \"mse : \", rmse)\n",
    "      print(model_name, \"Dry matter content error\", retransform(rmse))\n",
    "      print(model_name, \"r^2 :\", r2_score(y_test, model_result))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plain 5 adagrad mse :  0.09175815616893311\n",
      "plain 5 adagrad Dry matter content error 1.3866991724618731\n",
      "plain 5 adagrad r^2 : 0.6899123373115493\n",
      "plain 11 adagrad mse :  0.10080308998502606\n",
      "plain 11 adagrad Dry matter content error 1.5233911327346656\n",
      "plain 11 adagrad r^2 : 0.6257663620335936\n",
      "shortcut 5 adagrad mse :  0.07867384574842859\n",
      "shortcut 5 adagrad Dry matter content error 1.18896195552234\n",
      "shortcut 5 adagrad r^2 : 0.7720414471534031\n",
      "shortcut11 adagrad mse :  0.08402036572248298\n",
      "shortcut11 adagrad Dry matter content error 1.2697614738771157\n",
      "shortcut11 adagrad r^2 : 0.7400054367232816\n"
     ]
    }
   ],
   "source": [
    "plain_5, plain_11 = load_model('./checkpoints/plain5_adagrad.hdf5'), load_model('./checkpoints/plain11_adagrad.hdf5')\n",
    "shortcut5, shortcut11 = load_model('./checkpoints/shortcut5_adagrad.hdf5'), load_model('./checkpoints/shortcut11_adagrad.hdf5')\n",
    "models = {'plain 5 adagrad': plain_5, 'plain 11 adagrad': plain_11, 'shortcut 5 adagrad': shortcut5, 'shortcut11 adagrad': shortcut11}\n",
    "results = {model_name: model.predict(x_test).reshape((-1, )) for model_name, model in models.items()}\n",
    "for model_name, model_result in results.items():\n",
    "      rmse = np.sqrt(mean_squared_error(y_test, model_result))\n",
    "      print(model_name, \"mse : \", rmse)\n",
    "      print(model_name, \"Dry matter content error\", retransform(rmse))\n",
    "      print(model_name, \"r^2 :\", r2_score(y_test, model_result))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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