app/services/data_exploration.ipynb

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   "source": [
    "import plotly.express as px\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn as sk\n",
    "from sklearn import linear_model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_squared_log_error, r2_score\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_train_path = '/home/nata-brain/Documents/tcc/web-eye-tracker/public/training/1685126241.2630084natanael/train_data.csv'\n",
    "dataset_session_path = '/home/nata-brain/Documents/tcc/web-eye-tracker/public/sessions/1685126241.2630084natanael/session_data.csv'\n",
    "\n",
    "raw_dataset = pd.read_csv(dataset_train_path)\n",
    "session_dataset = pd.read_csv(dataset_session_path)\n",
    "dataset_t = raw_dataset\n",
    "dataset_s = session_dataset.drop(['timestamp'], axis = 1)\n"
   ]
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            "gridwidth": 2,
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           "bgcolor": "#E5ECF6",
           "caxis": {
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            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
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           "zerolinewidth": 2
          },
          "yaxis": {
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           "ticks": "",
           "title": {
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        },
        "title": {
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        },
        "xaxis": {
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         "title": {
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     },
     "metadata": {},
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   ],
   "source": [
    "px.line(session_dataset, y=[\"left_iris_x\", \"left_iris_y\", \"right_iris_x\", \"right_iris_y\"], title=\"Left and Right Iris Position\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_for_mouse_x(X, Y1, model):\n",
    "    print('-----------------MODEL FOR X------------------')\n",
    "    # split dataset into train and test sets (80/20 where 20 is for test)\n",
    "    X_train, X_test, Y1_train, Y1_test = train_test_split(X, Y1, test_size=0.2)\n",
    "\n",
    "    model = model\n",
    "    model.fit(X_train, Y1_train)\n",
    "\n",
    "    Y1_pred_train = model.predict(X_train)\n",
    "    Y1_pred_test = model.predict(X_test)\n",
    "\n",
    "    Y1_test = normalizeData(Y1_test)\n",
    "    Y1_pred_test = normalizeData(Y1_pred_test)\n",
    "    \n",
    "    print(f'Mean absolute error MAE = {mean_absolute_error(Y1_train, Y1_pred_train)}')\n",
    "    print(f'Mean squared error MSE = {mean_squared_error(Y1_train, Y1_pred_train)}')\n",
    "    print(f'Mean squared log error MSLE = {mean_squared_log_error(Y1_train, Y1_pred_train)}')\n",
    "    print(f'MODEL X SCORE R2 = {model.score(X, Y1)}')\n",
    "    #print(f'TRAIN{Y1_pred_train}')\n",
    "    #print(f'TEST{Y1_pred_test}')\n",
    "    return model\n",
    "\n",
    "def model_for_mouse_y(X, Y2, model):\n",
    "    print('-----------------MODEL FOR Y------------------')\n",
    "     # split dataset into train and test sets (80/20 where 20 is for test)\n",
    "    X_train, X_test, Y2_train, Y2_test = train_test_split(X, Y2, test_size=0.2)\n",
    "\n",
    "    model = model\n",
    "    model.fit(X_train, Y2_train)\n",
    "\n",
    "    Y2_pred_train = model.predict(X_train)\n",
    "    Y2_pred_test = model.predict(X_test)\n",
    "\n",
    "\n",
    "    Y2_test = normalizeData(Y2_test)\n",
    "    Y2_pred_test = normalizeData(Y2_pred_test)\n",
    "\n",
    "    print(f'Mean absolute error MAE = {mean_absolute_error(Y2_train, Y2_pred_train)}')\n",
    "    print(f'Mean squared error MSE = {mean_squared_error(Y2_train, Y2_pred_train)}')\n",
    "    print(f'Mean squared log error MSLE = {mean_squared_log_error(Y2_train, Y2_pred_train)}')\n",
    "    print(f'MODEL Y SCORE R2 = {model.score(X, Y2)}')\n",
    "\n",
    "    #print(f'TRAIN{Y2_pred_train}')\n",
    "    #print(f'TEST{Y2_pred_test}')\n",
    "    return model\n",
    "\n",
    "def normalizeData(data):\n",
    "    return (data - np.min(data)) / (np.max(data) - np.min(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model):\n",
    "    # Drop the columns that will be predicted\n",
    "    X = dataset_t.drop(['timestamp', 'screen_x', 'screen_y'], axis=1)\n",
    "\n",
    "    Y1 = dataset_t.screen_x\n",
    "    Y2 = dataset_t.screen_y\n",
    "    # print('Y1 is the mouse_x column ->', Y1)\n",
    "    # print('Y2 is the mouse_y column ->', Y2)\n",
    "\n",
    "    MODEL_X = model_for_mouse_x(X, Y1, model)\n",
    "    MODEL_Y = model_for_mouse_y(X, Y2, model)\n",
    "\n",
    "    GAZE_X = MODEL_X.predict(dataset_s)\n",
    "    GAZE_Y = MODEL_Y.predict(dataset_s)\n",
    "\n",
    "    GAZE_X = np.abs(GAZE_X)\n",
    "    GAZE_Y = np.abs(GAZE_Y)\n",
    "\n",
    "    return GAZE_X, GAZE_Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def showSaccades(model):\n",
    "   plt.rcParams[\"figure.figsize\"] = [7.6, 5.5]\n",
    "   plt.rcParams[\"figure.autolayout\"] = True\n",
    "\n",
    "   x, y           = train(model)\n",
    "   datetime       = session_dataset.timestamp\n",
    "\n",
    "   plt.plot(x, y, '*')\n",
    "   \"\"\" i = 0\n",
    "\n",
    "   for xy in zip(x, y):\n",
    "      i = i+1\n",
    "      plt.annotate(f'{i}', xy) \"\"\"\n",
    "      \n",
    "   plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regressão Linear padrão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------MODEL FOR X------------------\n",
      "Mean absolute error MAE = 399.93432181006534\n",
      "Mean squared error MSE = 230157.63167955956\n",
      "Mean squared log error MSLE = 1.75272107741231\n",
      "MODEL X SCORE R2 = 0.30695553986889035\n",
      "-----------------MODEL FOR Y------------------\n",
      "Mean absolute error MAE = 111.49579179170625\n",
      "Mean squared error MSE = 20061.134542346776\n",
      "Mean squared log error MSLE = 0.4253719549646367\n",
      "MODEL Y SCORE R2 = 0.7582732467365366\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 760x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showSaccades(linear_model.LinearRegression())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regressão Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------MODEL FOR X------------------\n",
      "Mean absolute error MAE = 387.5482074666731\n",
      "Mean squared error MSE = 220760.8799531483\n",
      "Mean squared log error MSLE = 1.6600780219784645\n",
      "MODEL X SCORE R2 = 0.2989927731166705\n",
      "-----------------MODEL FOR Y------------------\n",
      "Mean absolute error MAE = 110.9217453159226\n",
      "Mean squared error MSE = 19278.12736071733\n",
      "Mean squared log error MSLE = 0.4546442654771556\n",
      "MODEL Y SCORE R2 = 0.7573473730421474\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 760x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showSaccades(linear_model.Ridge(alpha=.5))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regressão Ridge CV - utiliza um método para cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------MODEL FOR X------------------\n",
      "Mean absolute error MAE = 381.4537709661892\n",
      "Mean squared error MSE = 207860.40108829536\n",
      "Mean squared log error MSLE = 1.6934315452248683\n",
      "MODEL X SCORE R2 = 0.2872472569481368\n",
      "-----------------MODEL FOR Y------------------\n",
      "Mean absolute error MAE = 101.49170069063705\n",
      "Mean squared error MSE = 17714.184998417986\n",
      "Mean squared log error MSLE = 0.35102932502830103\n",
      "MODEL Y SCORE R2 = 0.7580421613174579\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 760x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showSaccades(linear_model.RidgeCV(alphas=np.logspace(-6, 6, 13)))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regressão utilizando Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------MODEL FOR X------------------\n",
      "Mean absolute error MAE = 385.73943656877026\n",
      "Mean squared error MSE = 217702.611505249\n",
      "Mean squared log error MSLE = 1.6573437151099277\n",
      "MODEL X SCORE R2 = 0.30767371418647227\n",
      "-----------------MODEL FOR Y------------------\n",
      "Mean absolute error MAE = 104.45635875736743\n",
      "Mean squared error MSE = 18265.35017428518\n",
      "Mean squared log error MSLE = 0.3035573774543131\n",
      "MODEL Y SCORE R2 = 0.7604279015213925\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 760x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showSaccades(linear_model.Lasso(alpha=0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "def processHetmap():\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------MODEL FOR X------------------\n",
      "Mean absolute error MAE = 391.7625874408479\n",
      "Mean squared error MSE = 221344.72009550178\n",
      "Mean squared log error MSLE = 1.8293058135952383\n",
      "MODEL X SCORE R2 = 0.3035564537840326\n",
      "-----------------MODEL FOR Y------------------\n",
      "Mean absolute error MAE = 100.23737398389389\n",
      "Mean squared error MSE = 16594.76876236327\n",
      "Mean squared log error MSLE = 0.30334750594637916\n",
      "MODEL Y SCORE R2 = 0.7585761541537358\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 760x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showSaccades(linear_model.LinearRegression())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(196, 4) (196, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test\n",
    "\n",
    "x_train = dataset_t.drop(['timestamp', 'mouse_x', 'mouse_y'], axis=1)\n",
    "y_train = dataset_t.mouse_x.values.reshape(-1, 1)\n",
    "\n",
    "print(x_train.shape, y_train.shape)\n",
    "\n",
    "sns.lmplot(x =\"left_iris_x\", y =\"mouse_x\", data = dataset_t, order = 2, ci = None)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.42687598996186726\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(x_train, y_train, test_size = 0.25, random_state = 1000)\n",
    "\n",
    "model = linear_model.LinearRegression()\n",
    "model.fit(X_train, Y_train)\n",
    "print(model.score(X_test, Y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.30872445106620683"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_left_x = linear_model.LinearRegression()\n",
    "model_left_x.fit(x_train, y_train)\n",
    "model_left_x.score(x_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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