examples/sample_notebooks/regression_performance_bike_sharing_demand.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression Performance Dashboard for Bike Sharing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import requests\n",
    "import zipfile\n",
    "import io\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "from evidently import ColumnMapping\n",
    "from evidently.dashboard import Dashboard\n",
    "from evidently.dashboard.tabs import RegressionPerformanceTab\n",
    "\n",
    "from evidently.model_profile import Profile\n",
    "from evidently.model_profile.sections import RegressionPerformanceProfileSection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bike Sharing Demand Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More information about the dataset can be found in UCI machine learning repository: https://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset\n",
    "\n",
    "Acknowledgement: Fanaee-T, Hadi, and Gama, Joao, 'Event labeling combining ensemble detectors and background knowledge', Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "content = requests.get(\"https://archive.ics.uci.edu/ml/machine-learning-databases/00275/Bike-Sharing-Dataset.zip\").content\n",
    "with zipfile.ZipFile(io.BytesIO(content)) as arc:\n",
    "    raw_data = pd.read_csv(arc.open(\"day.csv\"), header=0, sep=',', parse_dates=['dteday'], index_col='dteday')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_data = raw_data[:120]\n",
    "prod_data = raw_data[120:150]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'cnt'\n",
    "prediction = 'prediction'\n",
    "datetime = 'dteday'\n",
    "\n",
    "numerical_features = ['temp', 'atemp', 'hum', 'windspeed', 'weekday']\n",
    "categorical_features = ['season', 'holiday', 'workingday', 'weathersit']\n",
    "\n",
    "features = numerical_features + categorical_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestRegressor(random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(ref_data[features], ref_data[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_data['prediction']  = model.predict(ref_data[features])\n",
    "prod_data['prediction'] = model.predict(prod_data[features])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Perfomance Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_mapping = ColumnMapping(target,\n",
    "                               'prediction',\n",
    "                               numerical_features=numerical_features,\n",
    "                               categorical_features=categorical_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression Perfomance Report full (verbose_level=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dashboard = Dashboard(tabs=[RegressionPerformanceTab(verbose_level=1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dashboard.calculate(ref_data, prod_data, column_mapping=column_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "dashboard.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dashboard.save('bike_sharing_demand_model_perfomance.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression Perfomance Report short (verbose_level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dashboard = Dashboard(tabs=[RegressionPerformanceTab(verbose_level=0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dashboard.calculate(ref_data, prod_data, column_mapping=column_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dashboard.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dashboard.save('bike_sharing_demand_model_perfomance.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Model Profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bike_regression_performance_profile = Profile(sections=[RegressionPerformanceProfileSection()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bike_regression_performance_profile.calculate(ref_data, prod_data, column_mapping=column_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "regression_profile = bike_regression_performance_profile.json() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "regression_profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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