ETR Model Submitted.ipynb

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    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "df = pd.read_csv('/kaggle/input/nonoiseoutliersrms/development_noNoise_noRMS_noOutliers.csv')\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
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    "\n",
    "def euclidean_distance_loss(y_true, y_pred):\n",
    "    squared_difference = np.square(y_true - y_pred)\n",
    "    summation = np.sum(squared_difference, axis=0)\n",
    "    sqrt_diff = np.sqrt(summation)\n",
    "    summation2 = np.sum(sqrt_diff, axis=0)\n",
    "    return summation2/squared_difference.shape[1]\n"
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     "text": [
      "Mean Squared Error (X): 10.115061535556707\n",
      "Mean Squared Error (Y): 14.47686018984684\n"
     ]
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   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import numpy as np\n",
    "from sklearn.multioutput import MultiOutputRegressor\n",
    "\n",
    "# Sample data loading (replace with your dataset)\n",
    "dfx = df.drop(['y'],axis=1)\n",
    "dfy = df.drop(['x'],axis=1)\n",
    "dfx1 , dfx2 = dfx.drop(['x'], axis=1), dfx['x']\n",
    "dfy1 , dfy2 = dfy.drop(['y'], axis=1), dfy['y']\n",
    "\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train1, X_test1, y_train1, y_test1 = train_test_split(dfx1, dfx2, test_size=0.25, random_state=42)\n",
    "X_train2, X_test2, y_train2, y_test2 = train_test_split(dfy1, dfy2, test_size=0.25, random_state=42)\n",
    "\n",
    "\n",
    "\n",
    "# Initialize the Extra Trees Regressor\n",
    "# Assuming you have a multi-output regression problem, ExtraTreesRegressor supports this by default.\n",
    "# You can adjust n_estimators and other parameters as needed.\n",
    "etrX = ExtraTreesRegressor(n_estimators=900, max_depth=None, random_state=42)\n",
    "etrY = ExtraTreesRegressor(n_estimators=900, max_depth=None, random_state=42)\n",
    "\n",
    "\n",
    "# Train the model\n",
    "etrX.fit(X_train1, y_train1)\n",
    "etrY.fit(X_train2, y_train2)\n",
    "\n",
    "# Predict on the test set\n",
    "prdX = etrX.predict(X_test1)\n",
    "prdY = etrY.predict(X_test2)\n",
    "\n",
    "# Evaluate the model\n",
    "mseX = mean_squared_error(y_test1, prdX)\n",
    "print(f'Mean Squared Error (X): {mseX}')\n",
    "\n",
    "mseY = mean_squared_error(y_test2, prdY)\n",
    "print(f'Mean Squared Error (Y): {mseY}')\n",
    "\n",
    "# print(f'Eucld:{euclidean_distance_loss(y_test1,prdX)}')\n",
    "# print(f'Eucld:{euclidean_distance_loss(y_test2,prdY)}')\n",
    "# If you have multiple outputs, mse will return an array of errors for each output.\n"
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    "df_test = pd.read_csv('/kaggle/input/evaluation/evaluation.csv')\n"
   ]
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   "source": [
    "ix = 0,7,12,15,16,17\n",
    "noise = [f\"rms[{i}]\" for i in range(18)]\n",
    "noise += [f\"pmax[{i}]\" for i in ix]\n",
    "noise += [f\"negpmax[{i}]\" for i in ix]\n",
    "noise += [f\"tmax[{i}]\" for i in ix]\n",
    "noise += [f\"area[{i}]\" for i in ix]\n",
    "df_test = df_test.drop(columns = noise)"
   ]
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    "ids = df_test['Id']\n",
    "features_df = df_test.drop(columns=['Id'])\n",
    "predX = etrX.predict(features_df)\n",
    "predY = etrY.predict(features_df)\n",
    "predictions_flattenedX = predX.flatten()\n",
    "predictions_flattenedY = predY.flatten()\n",
    "combined_predictions = [f\"{x}|{y}\" for x, y in zip(predictions_flattenedX, predictions_flattenedY)]\n",
    "result_df = pd.DataFrame({'Id': ids, 'Predicted': combined_predictions})\n",
    "result_df.to_csv('evalsu.csv', index=False)"
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