06a-ML-Solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML Lab Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can take a quick try ourselves using the UC Irvine ML repository's *Combined Cycle Power Plant Data Set* (https://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant)\n",
    "\n",
    "This dataset consists of about 10,000 records of measurements relating to peaker power plants.\n",
    "- Temperature (AT) in the range 1.81°C and 37.11°C,\n",
    "- Ambient Pressure (AP) in the range 992.89-1033.30 millibar,\n",
    "- Relative Humidity (RH) in the range 25.56% to 100.16%\n",
    "- Exhaust Vacuum (V) in the range 25.36-81.56 cm Hg\n",
    "- Net hourly electrical energy output (PE) 420.26-495.76 MW\n",
    "\n",
    "We want to model the power output as a function of the other parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask.distributed import Client\n",
    "\n",
    "client = Client(n_workers=2, threads_per_worker=1, memory_limit='1GB')\n",
    "\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask.dataframe\n",
    "\n",
    "ddf = dask.dataframe.read_csv('data/powerplant.csv', sample=512000, blocksize=4e4)\n",
    "ddf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = ddf.PE\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = ddf.drop(columns=['PE'])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.to_dask_array(lengths=True)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.to_dask_array(lengths=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask_ml.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n",
    "\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask_ml.linear_model import LinearRegression\n",
    "\n",
    "lr = LinearRegression(solver='lbfgs', max_iter=10)\n",
    "lr_model = lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = lr_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask_ml.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "\n",
    "sqrt(mean_squared_error(y_test, y_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}