Kode til simpel træmodel

Simpel_træmodel_Philip.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function, division   # Ensures Python3 printing & division standard\n",
+    "import pandas as pd \n",
+    "from pandas import Series, DataFrame \n",
+    "from matplotlib import pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "SavePlots = False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bedmap_train data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         LON        LAT   THICK  \\\n",
+      "0  76.889142 -69.876749  1046.6   \n",
+      "1  76.893603 -69.876762  1058.5   \n",
+      "2  76.899026 -69.876753  1061.1   \n",
+      "3  76.904100 -69.876424  1063.7   \n",
+      "4  76.909194 -69.876374  1069.8   \n",
+      "\n",
+      "                                            geometry          EAST  \\\n",
+      "0  b'\\x01\\x01\\x00\\x00\\x00\\xf4\\r\\xbfW\\xa2h@A\\xe45\\...  2.150725e+06   \n",
+      "1  b'\\x01\\x01\\x00\\x00\\x00$\\xf8B\"\\xb5h@A\\x11-)\\xd5...  2.150762e+06   \n",
+      "2  b'\\x01\\x01\\x00\\x00\\x00\\xb8\\xdb\\x03T\\xcdh@A\\xb2...  2.150811e+06   \n",
+      "3  b'\\x01\\x01\\x00\\x00\\x00\\xe3\\xdcsj\\xf5h@A,8\\xae\\...  2.150891e+06   \n",
+      "4  b'\\x01\\x01\\x00\\x00\\x00\\xd5<+a\\x0ei@A\\xf3\\xef\\x...  2.150941e+06   \n",
+      "\n",
+      "           NORTH          v       ith_bm         smb            z         s  \n",
+      "0  500918.979443  20.060943  1007.947592  211.410147  1155.742697  0.019393  \n",
+      "1  500751.208165  19.999543  1006.453881  211.418109  1158.179379  0.018178  \n",
+      "2  500547.878047  19.941658  1004.313773  211.428499  1161.195846  0.016543  \n",
+      "3  500365.723321  19.862532  1008.705660  211.463026  1163.372114  0.015692  \n",
+      "4  500175.761230  19.754973  1015.940170  211.472767  1165.676239  0.014873  \n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Læs en Parquet-fil ind i en DataFrame\n",
+    "bedmap_train = pd.read_parquet(\"bedmap_train.parquet\")\n",
+    "\n",
+    "# Vis de første rækker\n",
+    "print(bedmap_train.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "# Tag 10% af dataen tilfældigt\n",
+    "sample_df = bedmap_train.sample(frac=0.1, random_state=42)\n",
+    "\n",
+    "# Fjern ikke-numeriske kolonner som 'geometry'\n",
+    "numeric_df = sample_df.select_dtypes(include='number')\n",
+    "\n",
+    "# Definér target og features\n",
+    "X = numeric_df.drop(columns=[\"THICK\"])\n",
+    "y = numeric_df[\"THICK\"]\n",
+    "\n",
+    "# Split i train/test\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Træn beslutningstræ-model\n",
+    "model = DecisionTreeRegressor(random_state=42)\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Forudsig på testdata\n",
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "# Evaluer\n",
+    "mse = mean_squared_error(y_test, y_pred)\n",
+    "r2 = r2_score(y_test, y_pred)\n",
+    "\n",
+    "print(f\"Mean Squared Error: {mse:.2f}\")\n",
+    "print(f\"R^2 Score: {r2:.2f}\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "appml",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}