SCNet/03_model_evaluating.ipynb

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   "source": [
    "# Experiment 2: Model Evaluating"
   ]
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    "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"
   ]
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   "source": [
    "In this experiment, we load model weights from the experiment1 and evaluate them on test dataset."
   ]
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     "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}\")"
   ]
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   "outputs": [
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     "text": [
      "plain 5 mse :  0.05133910188824081\n",
      "plain 5 Dry matter content error 0.7758644362065223\n",
      "plain 5 r^2 : 0.902928516828363\n",
      "plain 11 mse :  0.05200769624271875\n",
      "plain 11 Dry matter content error 0.7859685978067217\n",
      "plain 11 r^2 : 0.9003837097594369\n",
      "shortcut 5 mse :  0.051382735052895194\n",
      "shortcut 5 Dry matter content error 0.7765238443272209\n",
      "shortcut 5 r^2 : 0.9027634443691182\n",
      "shortcut11 mse :  0.05078784364469306\n",
      "shortcut11 Dry matter content error 0.7675335217455442\n",
      "shortcut11 r^2 : 0.9050019525259844\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",
    "      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))"
   ]
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