examples/bike_sharing_dac.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from itertools import combinations\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import sys\n",
    "sys.path.append('../dac')\n",
    "import dac\n",
    "import seaborn as sns\n",
    "\n",
    "from copy import deepcopy\n",
    "from os.path import join as oj\n",
    "from tqdm import tqdm\n",
    "import pickle as pkl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# load data + fit a model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**load data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../data/Bike-Sharing-Dataset/hour.csv\", sep=',',header=None)\n",
    "raw_data = np.genfromtxt(\"../data/Bike-Sharing-Dataset/hour.csv\", delimiter=',')\n",
    "labels = df.values[0, :]\n",
    "raw_data = raw_data[1:, :]\n",
    "raw_y = raw_data[:, 16]\n",
    "raw_data = raw_data[:, 2:14]\n",
    "\n",
    "feature_names = labels[2:14]\n",
    "X, X_test, y, y_test = train_test_split(raw_data, raw_y, test_size=.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**fit model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest = RandomForestRegressor(n_estimators=4)\n",
    "forest.fit(X, y)\n",
    "preds = forest.predict(X_test)\n",
    "rmse = np.sqrt(np.mean((y_test - preds) ** 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# look at dac curves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**names of features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['season' 'yr' 'mnth' 'hr' 'holiday' 'weekday' 'workingday' 'weathersit'\n",
      " 'temp' 'atemp' 'hum' 'windspeed']\n"
     ]
    }
   ],
   "source": [
    "print(feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_intervals = [(1, 4.5), (0, 1.25), (1, 13), (0, 25), (0, 1.25), (0, 1.5), (0, 1.5), (1, 3.5), (0, 1), (0, 1), (0, 1), (0, 1)]\n",
    "feat_di = [.5, .25, 1, 1, .25, .5, .5, .5, .1, .1, .1, .1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1D dac curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature = 'mnth'\n",
    "idx = np.argmax(feature_names == feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "S = np.zeros(len(feature_names))\n",
    "S[idx] = 1\n",
    "dac_1D = make_curve_forest(forest, X, y, S, feat_intervals[idx], feat_di[idx], C=1, continuous_y=True)\n",
    "x_axis = np.arange(feat_intervals[idx][0], feat_intervals[idx][1], feat_di[idx])\n",
    "plt.plot(x_axis, dac_1D, label=feature_names[idx])\n",
    "plt.xlabel(feature)\n",
    "plt.ylabel(f'DAC({feature})')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2D dac curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_ind = 0\n",
    "y_ind = 1\n",
    "inds = np.arange(12)\n",
    "\n",
    "S = np.zeros(12)\n",
    "S[x_ind] = 1\n",
    "S[y_ind] = 1\n",
    "grid = make_map_forest(forest, X, y, S, feat_intervals[x_ind], feat_intervals[y_ind], feat_di[x_ind], feat_di[y_ind], C=1, continuous_y=True)\n",
    "\n",
    "x_rng = np.arange(feat_intervals[x_ind][0], feat_intervals[x_ind][1], feat_di[x_ind])\n",
    "y_rng = np.arange(feat_intervals[y_ind][0], feat_intervals[y_ind][1], feat_di[y_ind])\n",
    "\n",
    "\n",
    "plt.imshow(grid)\n",
    "plt.xlabel(features[x_ind])\n",
    "plt.ylabel(features[y_ind])\n",
    "plt.xticks(range(x_rng.size), labels=x_rng)\n",
    "plt.yticks(range(y_rng.size), labels=y_rng)\n",
    "plt.title(f'DAC({features[x_ind]}, {features[y_ind]})')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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