{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Walkthrough\n",
    "\n",
    "This notebook contains the code for the [walkthrough in the quickstart guide](http://www.scikit-yb.org/en/latest/quickstart.html#walkthrough). We've purposefully omitted the text of the guide so that you can follow along in code using this notebook as a template! The scikit-yb developers also use this notebook to verify that the quickstart code is correct, so if this code doesn't match what's on the guide, please leave us a note on our [GitHub Issues](https://github.com/DistrictDataLabs/yellowbrick/issues)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from yellowbrick.datasets import load_bikeshare\n",
    "\n",
    "X, y = load_bikeshare()\n",
    "print(X.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from yellowbrick.features import Rank2D\n",
    "\n",
    "visualizer = Rank2D(algorithm=\"pearson\")\n",
    "visualizer.fit_transform(X)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from yellowbrick.features import JointPlotVisualizer\n",
    "\n",
    "visualizer = JointPlotVisualizer(feature='temp', target='feelslike')\n",
    "visualizer.fit_transform(X['temp'], X['feelslike'])\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from yellowbrick.regressor import ResidualsPlot\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Create training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.1\n",
    ")\n",
    "\n",
    "visualizer = ResidualsPlot(LinearRegression())\n",
    "visualizer.fit(X_train, y_train)\n",
    "visualizer.score(X_test, y_test)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from sklearn.linear_model import RidgeCV\n",
    "from yellowbrick.regressor import AlphaSelection\n",
    "\n",
    "alphas = np.logspace(-10, 1, 200)\n",
    "visualizer = AlphaSelection(RidgeCV(alphas=alphas))\n",
    "visualizer.fit(X, y)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "from yellowbrick.regressor import PredictionError\n",
    "\n",
    "visualizer = PredictionError(Ridge(alpha=3.181))\n",
    "visualizer.fit(X_train, y_train)\n",
    "visualizer.score(X_test, y_test)\n",
    "visualizer.show()"
   ]
  }
 ],
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