SCNet/04_model_comparision.ipynb

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   "source": [
    "# Model comparison"
   ]
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    "## PLS"
   ]
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   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.svm import SVR\n",
    "import numpy as np\n",
    "from scipy.io import loadmat\n",
    "from sklearn.cross_decomposition import PLSRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "data = loadmat('./dataset/mango/mango_dm_split.mat')\n",
    "min_value, max_value = data['min_y'][-1][-1], data['max_y'][-1][-1]\n",
    "retransform = lambda x: x * (max_value - min_value)"
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     "text": [
      "shape of data:\n",
      "x_train: (8183, 102), y_train: (8183, 1),\n",
      "x_test: (3508, 102), y_test: (3508, 1)\n"
     ]
    }
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   "source": [
    "x_train, y_train, x_test, y_test = data['x_train'], data['y_train'], data['x_test'], data['y_test']\n",
    "print(f\"shape of data:\\n\"\n",
    "      f\"x_train: {x_train.shape}, y_train: {y_train.shape},\\n\"\n",
    "      f\"x_test: {x_test.shape}, y_test: {y_test.shape}\")"
   ]
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     "text": [
      "PLS RMSE:  0.05722520296881164\n",
      "PLS Dry matter content error 0.8648183977750965\n",
      "PLS R^2:  0.8793937498230511\n",
      "SVR RMSE:  0.1139650997574326\n",
      "SVR Dry matter content error 1.7223025845485895\n",
      "SVR R^2:  0.5216575965112935\n",
      "MLP RMSE:  0.15508626630172465\n",
      "MLP Dry matter content error 2.343748023280531\n",
      "MLP R^2:  0.11418748397100065\n"
     ]
    }
   ],
   "source": [
    "pls = PLSRegression(n_components=90)\n",
    "svr = SVR(kernel=\"rbf\", degree=30, gamma=\"scale\")\n",
    "mlp = MLPRegressor(hidden_layer_sizes=(60, 50, ))\n",
    "pls = pls.fit(x_train, y_train.ravel())\n",
    "svr = svr.fit(x_train, y_train.ravel())\n",
    "mlp = mlp.fit(x_train, y_train.ravel())\n",
    "\n",
    "models = {'PLS': pls, \"SVR\": svr, \"MLP\": mlp}\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, \"RMSE: \", 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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