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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example: ML regression on hyperspectral dataset with Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data\n",
    "\n",
    "Repository: https://github.com/felixriese/hyperspectral-soilmoisture-dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load dataframe\n",
    "path = \"https://raw.githubusercontent.com/felixriese/hyperspectral-soilmoisture-dataset/master/soilmoisture_dataset.csv\"\n",
    "df = pd.read_csv(path, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get hyperspectral bands:\n",
    "hypbands = []\n",
    "for col in df.columns:\n",
    "    try:\n",
    "        int(col)\n",
    "    except Exception:\n",
    "        continue\n",
    "    hypbands.append(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    df[hypbands], df[\"soil_moisture\"],\n",
    "    test_size=0.5, random_state=42, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lg = LinearRegression()\n",
    "lg.fit(X_train, y_train)\n",
    "lg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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