{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scikit-downscale: an open source Python package for scalable climate downscaling\n",
    "\n",
    "Joseph Hamman (jhamman@ucar.edu) and Julia Kent (jkent@ucar.edu)\n",
    "\n",
    "NCAR, Boulder, CO, USA\n",
    "\n",
    "-------\n",
    "\n",
    "This notebook was developed for the [2020 EarthCube All Hands Meeting](https://www.earthcube.org/EC2020). The development of Scikit-downscale done in conjunction with the development of the [Pangeo Project](http://pangeo.io/) and was supported by the following awards:\n",
    "\n",
    "- [NSF-GEO-AGS 1928374: EarthCube Data Capabilities: Collaborative Proposal: Jupyter meets the Earth: Enabling discovery in geoscience through interactive computing at scale](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1740633)\n",
    "- [NSF-OIA 1937136: Convergence Accelerator Phase I (RAISE): Knowledge Open Network Queries for Research (KONQUER)](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1937136)\n",
    "\n",
    "ECAHM 2020 ID: 143\n",
    "\n",
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction\n",
    "\n",
    "Climate data from Earth System Models (ESMs) are increasingly being used to study the impacts of climate change on a broad range of biogeophysical systems (forest fire, flood, fisheries, etc.) and human systems (water resources, power grids, etc.). Before this data can be used to study many of these systems, post-processing steps commonly referred to as bias correction and statistical downscaling must be performed. “Bias correction” is used to correct persistent biases in climate model output and “statistical downscaling” is used to increase the spatiotemporal resolution of the model output (i.e. from 1 deg to 1/16th deg grid boxes). For our purposes, we’ll refer to both parts as “downscaling”.\n",
    "\n",
    "In the past few decades, the applications community has developed a plethora of downscaling methods. Many of these methods are ad-hoc collections of processing routines while others target very specific applications. The proliferation of downscaling methods has left the climate applications community with an overwhelming body of research to sort through without much in the form of synthesis guilding method selection or applicability.\n",
    "\n",
    "Motivated by the pressing socio-environmental challenges of climate change – and with the learnings from previous downscaling efforts (e.g. Gutmann et al. 2014, Lanzante et al. 2018) in mind – we have begun working on a community-centered open framework for climate downscaling: [scikit-downscale](https://scikit-downscale.readthedocs.io/en/latest/). We believe that the community will benefit from the presence of a well-designed open source downscaling toolbox with standard interfaces alongside a repository of benchmark data to test and evaluate new and existing downscaling methods.\n",
    "\n",
    "In this notebook, we provide an overview of the scikit-downscale project, detailing how it can be used to downscale a range of surface climate variables such as surface air temperature and precipitation. We also highlight how scikit-downscale framework is being used to compare exisiting methods and how it can be extended to support the development of new downscaling methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Scikit-downscale\n",
    "Scikit-downscale is a new open source Python project. Within Scikit-downscale, we are been curating a collection of new and existing downscaling methods within a common framework. Key features of Scikit-downscale are:\n",
    "\n",
    "- A high-level interface modeled after the popular `fit` / `predict` pattern found in many machine learning packages ([Scikit-learn](https://scikit-learn.org/stable/index.html), [Tensorflow](https://www.tensorflow.org/guide/keras), etc.),\n",
    "- Uses [Xarray](http://xarray.pydata.org/en/stable/) and [Pandas](https://pandas.pydata.org/) data structures and utilities for handling of labeled datasets,\n",
    "- Utilities for automatic parallelization of pointwisde downscaling models,\n",
    "- Common interface for pointwise and spatial (or global) downscaling models, and\n",
    "- Extensible, allowing the creation of new downscaling methods through composition.\n",
    "\n",
    "Scikit-downscale's source code is available on [GitHub](https://github.com/jhamman/scikit-downscale)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Pointwise Models\n",
    "We define pointwise methods as those that only use local information during the downscaling process. They can be often represented as a general linear model and fit independently across each point in the study domain. Examples of existing pointwise methods are:\n",
    "\n",
    "- BCSD_[Temperature, Precipitation]: Wood et al. (2004)\n",
    "- ARRM: Stoner et al. (2012)\n",
    "- (Hybrid) Delta Method (e.g. Hamlet et al. (2010)\n",
    "- [GARD](https://github.com/NCAR/GARD): Gutmann et al (in prep). \n",
    "\n",
    "Because pointwise methods can be written as a stand-alone linear model, Scikit-downscale implements these models as a Scikit-learn [LinearModel](https://scikit-learn.org/stable/modules/linear_model.html) or [Pipeline](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html). By building directly on Scikit-learn, we inherit a well defined model API and the ability to interoperate with a robust ecosystem utilities for model evaluation and optimization (e.g. grid-search). Perhaps more importantly, this structure also allows us to compare methods at a high-level of granularity (single spatial point) before deploying them on large domain problems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------\n",
    "\n",
    "***Begin interactive demo***\n",
    "\n",
    "-------\n",
    "\n",
    "From here forward in this notebook, we'll jump back and forth between Python and text cells to describe how scikit-downscale works.\n",
    "\n",
    "This first cell just imports some libraries and get's things setup for our analysis to come."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from utils import get_sample_data\n",
    "\n",
    "sns.set(style='darkgrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've imported a few libraries, let's open a sample dataset from a single point in North America. We'll use this data to explore Scikit-downscale and its existing functionality. You'll notice there are two groups of data, `training` and `targets`. The `training` data is meant to represent data from a typical climate model and the `targets` data is meant to represent our \"observations\". For the purpose of this demonstration, we've choosen training data sampled from a regional climate model (WRF) run at 50km resolution over North America. The observations are sampled from the nearest 1/16th grid cell in Livneh et al, 2013.\n",
    "\n",
    "We have choosen to use the `tmax` variable (daily maximum surface air temperature) for demonstration purposes. With a small amount of effort, an interested reader could swap `tmax` for `pcp` and test these methods on precipitation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "             training               targets          \n",
       "                 tmax           pcp    tmax       pcp\n",
       "time                                                 \n",
       "1950-01-01        NaN           NaN   -0.22  5.608394\n",
       "1950-01-02        NaN           NaN   -4.54  2.919726\n",
       "1950-01-03        NaN           NaN   -7.87  3.066762\n",
       "1950-01-04        NaN           NaN   -5.08  4.684164\n",
       "1950-01-05        NaN           NaN   -0.79  4.295568\n",
       "...               ...           ...     ...       ...\n",
       "2015-11-26   7.657013  0.000000e+00     NaN       NaN\n",
       "2015-11-27   7.687256  0.000000e+00     NaN       NaN\n",
       "2015-11-28  10.480835  0.000000e+00     NaN       NaN\n",
       "2015-11-29  11.728516  0.000000e+00     NaN       NaN\n",
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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load sample data\n",
    "training = get_sample_data('training')\n",
    "targets = get_sample_data('targets')\n",
    "\n",
    "# print a table of the training/targets data\n",
    "display(pd.concat({'training': training, 'targets': targets}, axis=1))\n",
    "\n",
    "# make a plot of the temperature and precipitation data\n",
    "fig, axes = plt.subplots(ncols=1, nrows=2, figsize=(8, 6), sharex=True)\n",
    "time_slice = slice('1990-01-01', '1990-12-31')\n",
    "\n",
    "# plot-temperature\n",
    "training[time_slice]['tmax'].plot(ax=axes[0], label='training')\n",
    "targets[time_slice]['tmax'].plot(ax=axes[0], label='targets')\n",
    "axes[0].legend()\n",
    "axes[0].set_ylabel('Temperature [C]')\n",
    "\n",
    "# plot-precipitation\n",
    "training[time_slice]['pcp'].plot(ax=axes[1])\n",
    "targets[time_slice]['pcp'].plot(ax=axes[1])\n",
    "_ = axes[1].set_ylabel('Precipitation [mm/day]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/andersy005/devel/pangeo-data/scikit-downscale/.venv/lib/python3.12/site-packages/sklearn/preprocessing/_data.py:2846: UserWarning: n_quantiles (1000000) is greater than the total number of samples (5753). n_quantiles is set to n_samples.\n",
      "  warnings.warn(\n",
      "/Users/andersy005/devel/pangeo-data/scikit-downscale/.venv/lib/python3.12/site-packages/sklearn/preprocessing/_data.py:2846: UserWarning: n_quantiles (1000000) is greater than the total number of samples (5753). n_quantiles is set to n_samples.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7671, 1) (7671, 1) (5753, 1) (1918, 1) (5753, 1) (1918, 1)\n",
      "kmeans 0.9040673467681113\n",
      "uniform 0.9040532988665346\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/andersy005/devel/pangeo-data/scikit-downscale/.venv/lib/python3.12/site-packages/sklearn/preprocessing/_discretization.py:296: FutureWarning: The current default behavior, quantile_method='linear', will be changed to quantile_method='averaged_inverted_cdf' in scikit-learn version 1.9 to naturally support sample weight equivalence properties by default. Pass quantile_method='averaged_inverted_cdf' explicitly to silence this warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quantile 0.9039019265951712\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x137a0f290>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# exploratory data analysis for arrm model\n",
    "\n",
    "import numpy as np\n",
    "from mlinsights.mlmodel import PiecewiseRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import KBinsDiscretizer, QuantileTransformer\n",
    "\n",
    "\n",
    "def ARRM(n_bins=7):\n",
    "    return Pipeline([('')])\n",
    "\n",
    "\n",
    "sns.set(style='whitegrid')\n",
    "c = {'train': 'black', 'predict': 'blue', 'test': 'grey'}\n",
    "\n",
    "qqwargs = {'n_quantiles': int(1e6), 'copy': True, 'subsample': int(1e6)}\n",
    "n_bins = 7\n",
    "\n",
    "X = training[['tmax']]['1980':'2000'].values\n",
    "y = targets[['tmax']]['1980':'2000'].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "xqt = QuantileTransformer(**qqwargs).fit(X_train)\n",
    "Xq_train = xqt.transform(X_train)\n",
    "Xq_test = xqt.transform(X_test)\n",
    "\n",
    "yqt = QuantileTransformer(**qqwargs).fit(y_train)\n",
    "yq_train = xqt.transform(y_train)[:, 0]\n",
    "yq_test = yqt.transform(y_test)[:, 0]\n",
    "\n",
    "\n",
    "print(X.shape, y.shape, X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n",
    "\n",
    "# model = PiecewiseRegressor(binner=KBinsDiscretizer(n_bins=n_bins, strategy='quantile'))\n",
    "# model.fit(Xq_train, yq_train)\n",
    "# predq = model.predict(Xq_test)\n",
    "# pred = qt.inverse_transform(predq.reshape(-1, 1))\n",
    "\n",
    "y_train = y_train[:, 0]\n",
    "for strat in ['kmeans', 'uniform', 'quantile']:\n",
    "    model = PiecewiseRegressor(binner=KBinsDiscretizer(n_bins=n_bins, strategy=strat))\n",
    "\n",
    "    model.fit(X_train, y_train)\n",
    "    pred = model.predict(X_test)\n",
    "    print(strat, model.score(X_test, y_test))\n",
    "\n",
    "model = PiecewiseRegressor(binner=KBinsDiscretizer(n_bins=n_bins, strategy='kmeans'))\n",
    "model.fit(X_train, y_train)\n",
    "pred = model.predict(X_test)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "plt.scatter(X_train, y_train, c=c['train'], s=5, label='train')\n",
    "plt.scatter(X_test, y_test, c=c['test'], s=5, label='test')\n",
    "ax.legend()\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "plt.scatter(np.sort(X_train, axis=0), np.sort(y_train, axis=0), c=c['train'], s=5, label='train')\n",
    "plt.scatter(np.sort(X_test, axis=0), np.sort(y_test, axis=0), c=c['test'], s=5, label='test')\n",
    "plt.plot(np.sort(X_test, axis=0), np.sort(pred, axis=0), c=c['predict'], lw=2, label='predictions')\n",
    "ax.legend()\n",
    "\n",
    "# fig, ax = plt.subplots(1, 1)\n",
    "# ax.plot(Xq_test[:, 0], yq_test, \".\", label='data', c=c['test'])\n",
    "# ax.plot(Xq_test[:, 0], predq, \".\", label=\"predictions\", c=c['predict'])\n",
    "# ax.set_title(f\"Piecewise Linear Regression\\n{n_bins} buckets\")\n",
    "# ax.legend()\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "ax.plot(X_test[:, 0], y_test, '.', label='data', c=c['test'])\n",
    "ax.plot(X_test[:, 0], pred, '.', label='predictions', c=c['predict'])\n",
    "ax.set_title(f'Piecewise Linear Regression\\n{n_bins} buckets')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Models as cattle, not pets\n",
    "\n",
    "As we mentioned above, Scikit-downscale utilizes a similiar API to that of Scikit-learn for its pointwise models. This means we can build collections of models that may be quite different internally, but operate the same at the API level. Importantly, this means that all downscaling methods have two common API methods: `fit`, which trains the model given _training_ and _targets_ data, and `predict` which uses the fit model to perform the downscaling opperation. This is perhaps the most important feature of Scikit-downscale, the ability to test and compare arbitrary combinations of models under a common interface. This allows us to try many combinations of models and parameters, choosing only the best combinations. The following pseudo-code block describe the workflow common to all scikit-downscale models:\n",
    "\n",
    "```python\n",
    "from skdownscale.pointwise_models import MyModel\n",
    "\n",
    "...\n",
    "# load and pre-process input data (X and y)\n",
    "...\n",
    "\n",
    "model = MyModel(**parameters)\n",
    "model.fit(X_train, y)\n",
    "predictions = model.predict(X_predict)\n",
    "\n",
    "...\n",
    "# evaluate and/or save predictions\n",
    "...\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "In the cell below, we'll create nine different downscaling models, some from Scikit-downscale and some from Scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "from skdownscale.pointwise_models import (\n",
    "    AnalogRegression,\n",
    "    BcsdTemperature,\n",
    "    PureAnalog,\n",
    ")\n",
    "\n",
    "models = {\n",
    "    'GARD: PureAnalog-best-1': PureAnalog(kind='best_analog', n_analogs=1),\n",
    "    'GARD: PureAnalog-sample-10': PureAnalog(kind='sample_analogs', n_analogs=10),\n",
    "    'GARD: PureAnalog-weight-10': PureAnalog(kind='weight_analogs', n_analogs=10),\n",
    "    'GARD: PureAnalog-weight-100': PureAnalog(kind='weight_analogs', n_analogs=100),\n",
    "    'GARD: PureAnalog-mean-10': PureAnalog(kind='mean_analogs', n_analogs=10),\n",
    "    'GARD: AnalogRegression-100': AnalogRegression(n_analogs=100),\n",
    "    'GARD: LinearRegression': LinearRegression(),\n",
    "    'BCSD: BcsdTemperature': BcsdTemperature(return_anoms=False),\n",
    "    'Sklearn: RandomForestRegressor': RandomForestRegressor(random_state=0),\n",
    "}\n",
    "\n",
    "train_slice = slice('1980-01-01', '1989-12-31')\n",
    "predict_slice = slice('1990-01-01', '1999-12-31')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've created a collection of models, we want to train the models on the same input data. We do this by looping through our dictionary of models and calling the `fit` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/andersy005/devel/pangeo-data/scikit-downscale/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:1406: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/andersy005/devel/pangeo-data/scikit-downscale/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:1406: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/andersy005/devel/pangeo-data/scikit-downscale/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:1406: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/andersy005/devel/pangeo-data/scikit-downscale/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:1406: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/andersy005/devel/pangeo-data/scikit-downscale/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:1406: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/andersy005/devel/pangeo-data/scikit-downscale/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:1406: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/andersy005/devel/pangeo-data/scikit-downscale/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:1406: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/andersy005/devel/pangeo-data/scikit-downscale/.venv/lib/python3.12/site-packages/sklearn/base.py:1365: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# extract training / prediction data\n",
    "X_train = training[['tmax']][train_slice]\n",
    "y_train = targets[['tmax']][train_slice]\n",
    "X_predict = training[['tmax']][predict_slice]\n",
    "\n",
    "# Fit all models\n",
    "for key, model in models.items():\n",
    "    model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just like that, we fit nine downscaling models. Now we want to use those models to downscale/bias-correct our data. For the sake of easy comparison, we'll use a different part of the training data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "      <th>exceedance_prob</th>\n",
       "      <th>prediction_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.46</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.57</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.67</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647</th>\n",
       "      <td>11.37</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3648</th>\n",
       "      <td>14.55</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3649</th>\n",
       "      <td>14.47</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3650</th>\n",
       "      <td>9.11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>1.42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>3652 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       pred  exceedance_prob  prediction_error\n",
       "0      4.50              1.0               0.0\n",
       "1      6.13              1.0               0.0\n",
       "2      5.46              1.0               0.0\n",
       "3      8.57              1.0               0.0\n",
       "4      5.67              1.0               0.0\n",
       "...     ...              ...               ...\n",
       "3647  11.37              1.0               0.0\n",
       "3648  14.55              1.0               0.0\n",
       "3649  14.47              1.0               0.0\n",
       "3650   9.11              1.0               0.0\n",
       "3651   1.42              1.0               0.0\n",
       "\n",
       "[3652 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'GARD: PureAnalog-best-1'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-10 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-10 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-10 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-10 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-10 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-10 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-10 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-10 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-10 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PureAnalog(n_analogs=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>PureAnalog</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_analogs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_analogs&nbsp;</td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('kind',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">kind&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;best_analog&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('thresh',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">thresh&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('kdtree_kwargs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">kdtree_kwargs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('query_kwargs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">query_kwargs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "PureAnalog(n_analogs=1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "      <th>exceedance_prob</th>\n",
       "      <th>prediction_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.670000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.546624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.410000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.199347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.100000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.920702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.740000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.424612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.257579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647</th>\n",
       "      <td>17.870001</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.084592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3648</th>\n",
       "      <td>15.910000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.660928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3649</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.297076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3650</th>\n",
       "      <td>9.100000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.391747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>3.750000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.079105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3652 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           pred  exceedance_prob  prediction_error\n",
       "0      5.670000              1.0          1.546624\n",
       "1      3.410000              1.0          2.199347\n",
       "2      3.100000              1.0          1.920702\n",
       "3      5.740000              1.0          1.424612\n",
       "4      7.500000              1.0          1.257579\n",
       "...         ...              ...               ...\n",
       "3647  17.870001              1.0          2.084592\n",
       "3648  15.910000              1.0          1.660928\n",
       "3649   6.000000              1.0          2.297076\n",
       "3650   9.100000              1.0          1.391747\n",
       "3651   3.750000              1.0          2.079105\n",
       "\n",
       "[3652 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'GARD: PureAnalog-sample-10'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-11 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-11 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-11 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-11 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-11 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-11 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-11 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-11 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-11 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-11 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PureAnalog(kind=&#x27;sample_analogs&#x27;, n_analogs=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>PureAnalog</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_analogs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_analogs&nbsp;</td>\n",
       "            <td class=\"value\">10</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('kind',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">kind&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;sample_analogs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('thresh',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">thresh&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('kdtree_kwargs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">kdtree_kwargs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('query_kwargs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">query_kwargs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "PureAnalog(kind='sample_analogs', n_analogs=10)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "      <th>exceedance_prob</th>\n",
       "      <th>prediction_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.375299</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.546624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.543398</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.199347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.963575</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.920702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.369125</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.424612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.424970</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.257579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647</th>\n",
       "      <td>15.220733</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.084592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3648</th>\n",
       "      <td>14.594578</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.660928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3649</th>\n",
       "      <td>13.270018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.297076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3650</th>\n",
       "      <td>8.962508</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.391747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>3.740477</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.079105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3652 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           pred  exceedance_prob  prediction_error\n",
       "0      5.375299              1.0          1.546624\n",
       "1      3.543398              1.0          2.199347\n",
       "2      4.963575              1.0          1.920702\n",
       "3      8.369125              1.0          1.424612\n",
       "4      7.424970              1.0          1.257579\n",
       "...         ...              ...               ...\n",
       "3647  15.220733              1.0          2.084592\n",
       "3648  14.594578              1.0          1.660928\n",
       "3649  13.270018              1.0          2.297076\n",
       "3650   8.962508              1.0          1.391747\n",
       "3651   3.740477              1.0          2.079105\n",
       "\n",
       "[3652 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'GARD: PureAnalog-weight-10'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-12 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-12 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-12 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-12 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-12 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-12 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-12 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-12 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-12 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-12 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-12 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-12 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PureAnalog(kind=&#x27;weight_analogs&#x27;, n_analogs=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>PureAnalog</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_analogs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_analogs&nbsp;</td>\n",
       "            <td class=\"value\">10</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('kind',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">kind&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;weight_analogs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('thresh',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">thresh&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('kdtree_kwargs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">kdtree_kwargs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('query_kwargs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">query_kwargs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "PureAnalog(kind='weight_analogs', n_analogs=10)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "      <th>exceedance_prob</th>\n",
       "      <th>prediction_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.697786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.778671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.264698</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.875078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.933534</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.731955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.239455</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.980419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.583703</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.169141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647</th>\n",
       "      <td>15.343481</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.066250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3648</th>\n",
       "      <td>14.328917</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.727990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3649</th>\n",
       "      <td>12.735724</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.172175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3650</th>\n",
       "      <td>8.718857</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.687118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>4.087544</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.760901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3652 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           pred  exceedance_prob  prediction_error\n",
       "0      5.697786              1.0          1.778671\n",
       "1      3.264698              1.0          1.875078\n",
       "2      4.933534              1.0          1.731955\n",
       "3      8.239455              1.0          1.980419\n",
       "4      7.583703              1.0          2.169141\n",
       "...         ...              ...               ...\n",
       "3647  15.343481              1.0          2.066250\n",
       "3648  14.328917              1.0          2.727990\n",
       "3649  12.735724              1.0          2.172175\n",
       "3650   8.718857              1.0          1.687118\n",
       "3651   4.087544              1.0          1.760901\n",
       "\n",
       "[3652 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'GARD: PureAnalog-weight-100'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-13 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-13 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-13 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-13 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-13 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-13 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-13 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-13 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-13 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-13 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-13 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-13 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-13 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PureAnalog(kind=&#x27;weight_analogs&#x27;, n_analogs=100)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>PureAnalog</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_analogs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_analogs&nbsp;</td>\n",
       "            <td class=\"value\">100</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('kind',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">kind&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;weight_analogs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('thresh',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">thresh&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('kdtree_kwargs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">kdtree_kwargs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('query_kwargs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">query_kwargs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "PureAnalog(kind='weight_analogs', n_analogs=100)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "      <th>exceedance_prob</th>\n",
       "      <th>prediction_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.895000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.546624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.561000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.199347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.692000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.920702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.340000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.424612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.705000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.257579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647</th>\n",
       "      <td>15.436000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.084592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3648</th>\n",
       "      <td>14.257001</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.660928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3649</th>\n",
       "      <td>10.417001</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.297076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3650</th>\n",
       "      <td>7.543000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.391747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>5.528000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.079105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3652 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           pred  exceedance_prob  prediction_error\n",
       "0      5.895000              1.0          1.546624\n",
       "1      2.561000              1.0          2.199347\n",
       "2      4.692000              1.0          1.920702\n",
       "3      7.340000              1.0          1.424612\n",
       "4      7.705000              1.0          1.257579\n",
       "...         ...              ...               ...\n",
       "3647  15.436000              1.0          2.084592\n",
       "3648  14.257001              1.0          1.660928\n",
       "3649  10.417001              1.0          2.297076\n",
       "3650   7.543000              1.0          1.391747\n",
       "3651   5.528000              1.0          2.079105\n",
       "\n",
       "[3652 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'GARD: PureAnalog-mean-10'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-14 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-14 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-14 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-14 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-14 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-14 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-14 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-14 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-14 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-14 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-14 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-14 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-14 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-14 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PureAnalog(kind=&#x27;mean_analogs&#x27;, n_analogs=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>PureAnalog</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_analogs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_analogs&nbsp;</td>\n",
       "            <td class=\"value\">10</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('kind',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">kind&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;mean_analogs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('thresh',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">thresh&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('kdtree_kwargs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">kdtree_kwargs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('query_kwargs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">query_kwargs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "PureAnalog(kind='mean_analogs', n_analogs=10)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "      <th>exceedance_prob</th>\n",
       "      <th>prediction_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.931445</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.778564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.515919</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.816021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.862730</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.678216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.255379</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.972617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.711861</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.165631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647</th>\n",
       "      <td>15.496596</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.066250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3648</th>\n",
       "      <td>14.133076</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.721729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3649</th>\n",
       "      <td>10.204446</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.171535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3650</th>\n",
       "      <td>5.816015</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.685203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>5.289299</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.577873</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3652 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           pred  exceedance_prob  prediction_error\n",
       "0      5.931445              1.0          1.778564\n",
       "1      2.515919              1.0          1.816021\n",
       "2      4.862730              1.0          1.678216\n",
       "3      7.255379              1.0          1.972617\n",
       "4      7.711861              1.0          2.165631\n",
       "...         ...              ...               ...\n",
       "3647  15.496596              1.0          2.066250\n",
       "3648  14.133076              1.0          2.721729\n",
       "3649  10.204446              1.0          2.171535\n",
       "3650   5.816015              1.0          1.685203\n",
       "3651   5.289299              1.0          1.577873\n",
       "\n",
       "[3652 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'GARD: AnalogRegression-100'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-15 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-15 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-15 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-15 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-15 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-15 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-15 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-15 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-15 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-15 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-15 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-15 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-15 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-15 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AnalogRegression(n_analogs=100)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>AnalogRegression</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_analogs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_analogs&nbsp;</td>\n",
       "            <td class=\"value\">100</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('thresh',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">thresh&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('kdtree_kwargs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">kdtree_kwargs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('query_kwargs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">query_kwargs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('logistic_kwargs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">logistic_kwargs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('lr_kwargs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">lr_kwargs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "AnalogRegression(n_analogs=100)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 5.7814717],\n",
       "       [ 2.5243216],\n",
       "       [ 4.944167 ],\n",
       "       ...,\n",
       "       [10.457229 ],\n",
       "       [ 5.663402 ],\n",
       "       [ 5.20131  ]], shape=(3652, 1), dtype=float32)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'GARD: LinearRegression'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-16 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-16 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-16 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-16 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-16 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-16 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-16 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-16 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-16 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-16 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-16 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-16 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-16 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-16 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-16 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-16 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-16 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-16 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-16 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-16 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-16 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-16 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-16 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-16 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-16 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-16 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-16 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-16 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-16 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-16 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-16 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-16 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-16 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-16 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-16 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-16 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-16 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy_X',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">copy_X&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">1e-06</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('positive',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">positive&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tmax</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>4.528703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>-1.584750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>2.848937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>6.687825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>8.296424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-12-27</th>\n",
       "      <td>19.128365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-12-28</th>\n",
       "      <td>16.533390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-12-29</th>\n",
       "      <td>8.740290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-12-30</th>\n",
       "      <td>3.565762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-12-31</th>\n",
       "      <td>4.326358</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3652 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 tmax\n",
       "time                 \n",
       "1990-01-01   4.528703\n",
       "1990-01-02  -1.584750\n",
       "1990-01-03   2.848937\n",
       "1990-01-04   6.687825\n",
       "1990-01-05   8.296424\n",
       "...               ...\n",
       "1999-12-27  19.128365\n",
       "1999-12-28  16.533390\n",
       "1999-12-29   8.740290\n",
       "1999-12-30   3.565762\n",
       "1999-12-31   4.326358\n",
       "\n",
       "[3652 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'BCSD: BcsdTemperature'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-17 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-17 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-17 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-17 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-17 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-17 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-17 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-17 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-17 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-17 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-17 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-17 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-17 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-17 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BcsdTemperature(return_anoms=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" checked><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>BcsdTemperature</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('time_grouper',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">time_grouper&nbsp;</td>\n",
       "            <td class=\"value\">&lt;function MON...t 0x137b27740&gt;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('climate_trend_grouper',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">climate_trend_grouper&nbsp;</td>\n",
       "            <td class=\"value\">&lt;function DAY...t 0x137b277e0&gt;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('climate_trend',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">climate_trend&nbsp;</td>\n",
       "            <td class=\"value\">&lt;function MON...t 0x137b27740&gt;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('return_anoms',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">return_anoms&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('qm_kwargs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">qm_kwargs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "BcsdTemperature(return_anoms=False)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([ 5.20240005,  4.69350009,  4.67359999, ..., 13.04250015,\n",
       "        8.69379969,  2.98729996], shape=(3652,))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Sklearn: RandomForestRegressor'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-18 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-18 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-18 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-18 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-18 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-18 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-18 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-18 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-18 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-18 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-18 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-18 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-18 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-18 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
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       "}\n",
       "\n",
       ".default td {\n",
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       "}\n",
       "\n",
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       "    color: black;\n",
       "}\n",
       "\n",
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       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" checked><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_estimators',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
       "            <td class=\"value\">100</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('criterion',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">criterion&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;squared_error&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_depth',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_depth&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples_split',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
       "            <td class=\"value\">2</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_features',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_features&nbsp;</td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('bootstrap',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">bootstrap&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('oob_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">oob_score&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">warm_start&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_samples',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_samples&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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       "</script></body>"
      ],
      "text/plain": [
       "RandomForestRegressor(random_state=0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GARD: PureAnalog-best-1</th>\n",
       "      <th>GARD: PureAnalog-sample-10</th>\n",
       "      <th>GARD: PureAnalog-weight-10</th>\n",
       "      <th>GARD: PureAnalog-weight-100</th>\n",
       "      <th>GARD: PureAnalog-mean-10</th>\n",
       "      <th>GARD: AnalogRegression-100</th>\n",
       "      <th>GARD: LinearRegression</th>\n",
       "      <th>BCSD: BcsdTemperature</th>\n",
       "      <th>Sklearn: RandomForestRegressor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>4.50</td>\n",
       "      <td>5.67</td>\n",
       "      <td>5.375299</td>\n",
       "      <td>5.697786</td>\n",
       "      <td>5.895</td>\n",
       "      <td>5.931445</td>\n",
       "      <td>5.781472</td>\n",
       "      <td>4.528703</td>\n",
       "      <td>5.2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>6.13</td>\n",
       "      <td>3.41</td>\n",
       "      <td>3.543398</td>\n",
       "      <td>3.264698</td>\n",
       "      <td>2.561</td>\n",
       "      <td>2.515919</td>\n",
       "      <td>2.524322</td>\n",
       "      <td>-1.584750</td>\n",
       "      <td>4.6935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>5.46</td>\n",
       "      <td>3.10</td>\n",
       "      <td>4.963575</td>\n",
       "      <td>4.933534</td>\n",
       "      <td>4.692</td>\n",
       "      <td>4.862730</td>\n",
       "      <td>4.944167</td>\n",
       "      <td>2.848937</td>\n",
       "      <td>4.6736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>8.57</td>\n",
       "      <td>5.74</td>\n",
       "      <td>8.369125</td>\n",
       "      <td>8.239455</td>\n",
       "      <td>7.340</td>\n",
       "      <td>7.255379</td>\n",
       "      <td>7.107427</td>\n",
       "      <td>6.687825</td>\n",
       "      <td>8.4134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>5.67</td>\n",
       "      <td>7.50</td>\n",
       "      <td>7.424970</td>\n",
       "      <td>7.583703</td>\n",
       "      <td>7.705</td>\n",
       "      <td>7.711861</td>\n",
       "      <td>7.878299</td>\n",
       "      <td>8.296424</td>\n",
       "      <td>6.5789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            GARD: PureAnalog-best-1  GARD: PureAnalog-sample-10  \\\n",
       "time                                                              \n",
       "1990-01-01                     4.50                        5.67   \n",
       "1990-01-02                     6.13                        3.41   \n",
       "1990-01-03                     5.46                        3.10   \n",
       "1990-01-04                     8.57                        5.74   \n",
       "1990-01-05                     5.67                        7.50   \n",
       "\n",
       "            GARD: PureAnalog-weight-10  GARD: PureAnalog-weight-100  \\\n",
       "time                                                                  \n",
       "1990-01-01                    5.375299                     5.697786   \n",
       "1990-01-02                    3.543398                     3.264698   \n",
       "1990-01-03                    4.963575                     4.933534   \n",
       "1990-01-04                    8.369125                     8.239455   \n",
       "1990-01-05                    7.424970                     7.583703   \n",
       "\n",
       "            GARD: PureAnalog-mean-10  GARD: AnalogRegression-100  \\\n",
       "time                                                               \n",
       "1990-01-01                     5.895                    5.931445   \n",
       "1990-01-02                     2.561                    2.515919   \n",
       "1990-01-03                     4.692                    4.862730   \n",
       "1990-01-04                     7.340                    7.255379   \n",
       "1990-01-05                     7.705                    7.711861   \n",
       "\n",
       "            GARD: LinearRegression  BCSD: BcsdTemperature  \\\n",
       "time                                                        \n",
       "1990-01-01                5.781472               4.528703   \n",
       "1990-01-02                2.524322              -1.584750   \n",
       "1990-01-03                4.944167               2.848937   \n",
       "1990-01-04                7.107427               6.687825   \n",
       "1990-01-05                7.878299               8.296424   \n",
       "\n",
       "            Sklearn: RandomForestRegressor  \n",
       "time                                        \n",
       "1990-01-01                          5.2024  \n",
       "1990-01-02                          4.6935  \n",
       "1990-01-03                          4.6736  \n",
       "1990-01-04                          8.4134  \n",
       "1990-01-05                          6.5789  "
      ]
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    }
   ],
   "source": [
    "# store predicted results in this dataframe\n",
    "predict_df = pd.DataFrame(index=X_predict.index)\n",
    "\n",
    "for key, model in models.items():\n",
    "    result = model.predict(X_predict)\n",
    "    display(result, key, model)\n",
    "    if isinstance(result, pd.DataFrame) and 'gard' in key.lower():\n",
    "        predict_df[key] = result['pred'].values\n",
    "    else:\n",
    "        predict_df[key] = result\n",
    "\n",
    "# show a table of the predicted data\n",
    "display(predict_df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's do some sample analysis on our predicted data. First, we'll look at a timeseries of all the downscaled timeseries for the first year of the prediction period. In the figure below, the `target` (truth) data is shown in black, the original (pre-correction) data is shown in grey, and each of the downscaled data timeseries is shown in a different color."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 3.5))\n",
    "targets['tmax'][time_slice].plot(\n",
    "    ax=ax, label='target', c='k', lw=1, alpha=0.75, legend=True, zorder=10\n",
    ")\n",
    "X_predict['tmax'][time_slice].plot(label='original', c='grey', ax=ax, alpha=0.75, legend=True)\n",
    "predict_df[time_slice].plot(ax=ax, lw=0.75)\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "_ = ax.set_ylabel('Temperature [C]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, its difficult to tell which of the nine downscaling methods performed best from our plot above. We may want to evaluate our predictions using a standard statistical score, such as $r^2$. Those results are easily computed below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r2_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GARD: PureAnalog-best-1</th>\n",
       "      <td>0.820414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GARD: PureAnalog-sample-10</th>\n",
       "      <td>0.823788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BCSD: BcsdTemperature</th>\n",
       "      <td>0.858260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sklearn: RandomForestRegressor</th>\n",
       "      <td>0.864160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GARD: PureAnalog-weight-10</th>\n",
       "      <td>0.881289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GARD: PureAnalog-weight-100</th>\n",
       "      <td>0.892049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GARD: PureAnalog-mean-10</th>\n",
       "      <td>0.899296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GARD: AnalogRegression-100</th>\n",
       "      <td>0.906216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GARD: LinearRegression</th>\n",
       "      <td>0.906316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                r2_score\n",
       "GARD: PureAnalog-best-1         0.820414\n",
       "GARD: PureAnalog-sample-10      0.823788\n",
       "BCSD: BcsdTemperature           0.858260\n",
       "Sklearn: RandomForestRegressor  0.864160\n",
       "GARD: PureAnalog-weight-10      0.881289\n",
       "GARD: PureAnalog-weight-100     0.892049\n",
       "GARD: PureAnalog-mean-10        0.899296\n",
       "GARD: AnalogRegression-100      0.906216\n",
       "GARD: LinearRegression          0.906316"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate r2\n",
    "score = (predict_df.corrwith(targets.tmax[predict_slice]) ** 2).sort_values().to_frame('r2_score')\n",
    "display(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of our downscaling methods seem to be doing fairly well. The timeseries and statistics above shows that all our methods are producing generally resonable results. However, we are often interested in how our models do at predicting extreme events. We can quickly look into those aspects of our results using the `qq` plots below. There you'll see that the models diverge in some interesting ways. For example, while the `LinearRegression` method has the highest $r^2$ score, it seems to have trouble capturing extreme heat events. Whereas many of the analog methods, as well as the `RandomForestRegressor`, perform much better on the tails of the distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from utils import prob_plots\n",
    "\n",
    "fig = prob_plots(\n",
    "    X_predict, targets['tmax'], predict_df[score.index.values], shape=(3, 3), figsize=(12, 12)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we've shown how easy it is to fit, predict, and evaluate scikit-downscale models. The seamless interoperability of these models clearly facilitates a workflow that enables a deeper level of model evaluation that is otherwise possible in the downscaling world. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Tailor-made methods in a common framework\n",
    "\n",
    "In the section above, we showed how it is possible to use scikit-downscale to bias-correct a timeseries of daily maximum air temperature using an arbitrary collection of linear models. Some of those models were general machine learning methods (e.g. `LinearRegression` or `RandomForestRegressor`) while others were tailor-made methods developed specifically for downscaling (e.g. `BCSDTemperature`). In this section, we walk through how new pointwise methods can be added to the scikit-downscale framework, highlighting the Z-Score method along the way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3.1 Z-Score Method\n",
    "\n",
    "Z-Score bias correction is a good technique for target variables with Gaussian probability distributions, such as zonal wind speed.\n",
    "\n",
    "In essence the technique:\n",
    "\n",
    "1. Finds the mean  \n",
    "$$\\overline{x} = \\sum_{i=0}^N \\frac{x_i}{N}$$ \n",
    "and standard deviation \n",
    "$$\\sigma = \\sqrt{\\frac{\\sum_{i=0}^N |x_i - \\overline{x}|^2}{N-1}}$$ \n",
    "of target (measured) data and training (historical modeled) data. \n",
    "\n",
    "2. Compares the difference between the statistical values to produce a shift \n",
    "$$shift = \\overline{x_{target}} - \\overline{x_{training}}$$ \n",
    "and scale parameter \n",
    "$$scale = \\sigma_{target} \\div \\sigma_{training}$$ \n",
    "\n",
    "3. Applies these paramaters to the future model data to be corrected to get a new mean\n",
    "$$\\overline{x_{corrected}} = \\overline{x_{future}} + shift$$\n",
    "and new standard deviation\n",
    "$$\\sigma_{corrected} = \\sigma_{future} \\times scale$$\n",
    "\n",
    "4. Calculates the corrected values\n",
    "$$x_{corrected_{i}} = z_i \\times \\sigma_{corrected} + \\overline{x_{corrected}}$$\n",
    "from the future model's z-score values\n",
    "$$z_i = \\frac{x_i-\\overline{x}}{\\sigma}$$\n",
    "\n",
    "In practice, if the wind was on average 3 m/s faster on the first of July in the models compared to the measurements, we would adjust the modeled data for all July 1sts in the future modeled dataset to be 3 m/s faster. And similarly for scaling the standard deviation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3.2 Building the ZScoreRegressor Class\n",
    "\n",
    "Scikit-downscale's pointwise all implement Scikit-learn's `fit`/`predict` API. Each new downscaler must implement a minimum of three class methods: `__init__`, `fit`, `predict`. \n",
    "\n",
    "```python\n",
    "class AbstractDownscaler(object):\n",
    "    \n",
    "    def __init__(self):\n",
    "        ...\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        ...\n",
    "        return self\n",
    "    \n",
    "    def predict(X):\n",
    "        ...\n",
    "        return y_hat\n",
    "```\n",
    "\n",
    "Ommitting some of the complexity in the full implementation (which can be found in the [full implementation on GitHub](https://github.com/jhamman/scikit-downscale/blob/master/skdownscale/pointwise_models/zscore.py)), we demonstrate how the `ZScoreRegressor` was built:\n",
    "\n",
    "First, we define our `__init__` method, allowing users to specify specific options (in this case `window_width`):\n",
    "```python\n",
    "class ZScoreRegressor(object):\n",
    "    \n",
    "    def __init__(self, window_width=31):\n",
    "        self.window_width = window_width\n",
    "```\n",
    "\n",
    "Next, we define our `fit` method, \n",
    "\n",
    "```python\n",
    "    def fit(self, X, y):\n",
    "        X_mean, X_std = _calc_stats(X.squeeze(), self.window_width)\n",
    "        y_mean, y_std = _calc_stats(y.squeeze(), self.window_width)\n",
    "        \n",
    "        self.stats_dict_ = {\n",
    "            \"X_mean\": X_mean,\n",
    "            \"X_std\": X_std,\n",
    "            \"y_mean\": y_mean,\n",
    "            \"y_std\": y_std,\n",
    "        }\n",
    "\n",
    "        shift, scale = _get_params(X_mean, X_std, y_mean, y_std)\n",
    "\n",
    "        self.shift_ = shift\n",
    "        self.scale_ = scale\n",
    "        return self\n",
    "```\n",
    "\n",
    "Finally, we define our `predict` method,\n",
    "\n",
    "```python\n",
    "    def predict(self, X):\n",
    "\n",
    "        fut_mean, fut_std, fut_zscore = _get_fut_stats(X.squeeze(), self.window_width)\n",
    "        shift_expanded, scale_expanded = _expand_params(X.squeeze(), self.shift_, self.scale_)\n",
    "\n",
    "        fut_mean_corrected, fut_std_corrected = _correct_fut_stats(\n",
    "            fut_mean, fut_std, shift_expanded, scale_expanded\n",
    "        )\n",
    "\n",
    "        self.fut_stats_dict_ = {\n",
    "            \"meani\": fut_mean,\n",
    "            \"stdi\": fut_std,\n",
    "            \"meanf\": fut_mean_corrected,\n",
    "            \"stdf\": fut_std_corrected,\n",
    "        }\n",
    "\n",
    "        fut_corrected = (fut_zscore * fut_std_corrected) + fut_mean_corrected\n",
    "\n",
    "        return fut_corrected.to_frame(name)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skdownscale.pointwise_models import ZScoreRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3650, 1), (3653, 1), (3650, 1))"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# open a small dataset\n",
    "from utils import get_sample_data\n",
    "\n",
    "training = get_sample_data('wind-hist')\n",
    "target = get_sample_data('wind-obs')\n",
    "future = get_sample_data('wind-rcp')\n",
    "\n",
    "training.shape, target.shape, future.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "Index are different\n\nIndex classes are different\n[left]:  CFTimeIndex([1980-01-01 12:00:00, 1980-01-02 12:00:00, 1980-01-03 12:00:00,\n             1980-01-04 12:00:00, 1980-01-05 12:00:00, 1980-01-06 12:00:00,\n             1980-01-07 12:00:00, 1980-01-08 12:00:00, 1980-01-09 12:00:00,\n             1980-01-10 12:00:00,\n             ...\n             1989-12-22 12:00:00, 1989-12-23 12:00:00, 1989-12-24 12:00:00,\n             1989-12-25 12:00:00, 1989-12-26 12:00:00, 1989-12-27 12:00:00,\n             1989-12-28 12:00:00, 1989-12-29 12:00:00, 1989-12-30 12:00:00,\n             1989-12-31 12:00:00],\n            dtype='object', length=3650, calendar='noleap', freq='D')\n[right]: DatetimeIndex(['1980-01-01 19:00:00', '1980-01-02 19:00:00',\n               '1980-01-03 19:00:00', '1980-01-04 19:00:00',\n               '1980-01-05 19:00:00', '1980-01-06 19:00:00',\n               '1980-01-07 19:00:00', '1980-01-08 19:00:00',\n               '1980-01-09 19:00:00', '1980-01-10 19:00:00',\n               ...\n               '1989-12-22 19:00:00', '1989-12-23 19:00:00',\n               '1989-12-24 19:00:00', '1989-12-25 19:00:00',\n               '1989-12-26 19:00:00', '1989-12-27 19:00:00',\n               '1989-12-28 19:00:00', '1989-12-29 19:00:00',\n               '1989-12-30 19:00:00', '1989-12-31 19:00:00'],\n              dtype='datetime64[ns]', name='time', length=3653, freq=None)",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mAssertionError\u001b[39m                            Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# bias correction using ZScoreRegresssor\u001b[39;00m\n\u001b[32m      2\u001b[39m zscore = ZScoreRegressor()\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[43mzscore\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraining\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m      4\u001b[39m fit_stats = zscore.fit_stats_dict_\n\u001b[32m      5\u001b[39m out = zscore.predict(future)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/devel/pangeo-data/scikit-downscale/skdownscale/pointwise_models/zscore.py:45\u001b[39m, in \u001b[36mZScoreRegressor.fit\u001b[39m\u001b[34m(self, X, y)\u001b[39m\n\u001b[32m     30\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m, X: pd.Series | pd.DataFrame, y: pd.Series | pd.DataFrame):\n\u001b[32m     31\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"Fit Z-Score Model finds the shift and scale parameters\u001b[39;00m\n\u001b[32m     32\u001b[39m \u001b[33;03m    to inform bias correction.\u001b[39;00m\n\u001b[32m     33\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m     43\u001b[39m \u001b[33;03m    self : returns an instance of self.\u001b[39;00m\n\u001b[32m     44\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m45\u001b[39m     X, y = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_numeric\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m     46\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.n_features_in_ != \u001b[32m1\u001b[39m:\n\u001b[32m     47\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[33mZscore only supports 1 feature, found \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.n_features_in_\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/devel/pangeo-data/scikit-downscale/skdownscale/pointwise_models/base.py:119\u001b[39m, in \u001b[36mTimeSynchronousDownscaler._validate_data\u001b[39m\u001b[34m(self, X, y, reset, validate_separately, **check_params)\u001b[39m\n\u001b[32m    117\u001b[39m         y = \u001b[38;5;28mself\u001b[39m._check_array(y, **check_y_params)\n\u001b[32m    118\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m119\u001b[39m         X, y = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_check_X_y\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mcheck_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    120\u001b[39m     out = X, y\n\u001b[32m    122\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m check_params.get(\u001b[33m'\u001b[39m\u001b[33mensure_2d\u001b[39m\u001b[33m'\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/devel/pangeo-data/scikit-downscale/skdownscale/pointwise_models/base.py:11\u001b[39m, in \u001b[36mTimeSynchronousDownscaler._check_X_y\u001b[39m\u001b[34m(self, X, y, **kwargs)\u001b[39m\n\u001b[32m      9\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_check_X_y\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y, **kwargs):\n\u001b[32m     10\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(X, pd.DataFrame) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(y, pd.DataFrame):\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m         \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtesting\u001b[49m\u001b[43m.\u001b[49m\u001b[43massert_index_equal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m.\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m.\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     12\u001b[39m         check_X_y(X, y)  \u001b[38;5;66;03m# this may be inefficient\u001b[39;00m\n\u001b[32m     13\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "    \u001b[31m[... skipping hidden 1 frame]\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/devel/pangeo-data/scikit-downscale/.venv/lib/python3.12/site-packages/pandas/_testing/asserters.py:235\u001b[39m, in \u001b[36massert_index_equal.<locals>._check_types\u001b[39m\u001b[34m(left, right, obj)\u001b[39m\n\u001b[32m    232\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m exact:\n\u001b[32m    233\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m235\u001b[39m \u001b[43massert_class_equal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mleft\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexact\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexact\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m=\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    236\u001b[39m assert_attr_equal(\u001b[33m\"\u001b[39m\u001b[33minferred_type\u001b[39m\u001b[33m\"\u001b[39m, left, right, obj=obj)\n\u001b[32m    238\u001b[39m \u001b[38;5;66;03m# Skip exact dtype checking when `check_categorical` is False\u001b[39;00m\n",
      "    \u001b[31m[... skipping hidden 1 frame]\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/devel/pangeo-data/scikit-downscale/.venv/lib/python3.12/site-packages/pandas/_testing/asserters.py:620\u001b[39m, in \u001b[36mraise_assert_detail\u001b[39m\u001b[34m(obj, message, left, right, diff, first_diff, index_values)\u001b[39m\n\u001b[32m    617\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m first_diff \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    618\u001b[39m     msg += \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mfirst_diff\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m620\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAssertionError\u001b[39;00m(msg)\n",
      "\u001b[31mAssertionError\u001b[39m: Index are different\n\nIndex classes are different\n[left]:  CFTimeIndex([1980-01-01 12:00:00, 1980-01-02 12:00:00, 1980-01-03 12:00:00,\n             1980-01-04 12:00:00, 1980-01-05 12:00:00, 1980-01-06 12:00:00,\n             1980-01-07 12:00:00, 1980-01-08 12:00:00, 1980-01-09 12:00:00,\n             1980-01-10 12:00:00,\n             ...\n             1989-12-22 12:00:00, 1989-12-23 12:00:00, 1989-12-24 12:00:00,\n             1989-12-25 12:00:00, 1989-12-26 12:00:00, 1989-12-27 12:00:00,\n             1989-12-28 12:00:00, 1989-12-29 12:00:00, 1989-12-30 12:00:00,\n             1989-12-31 12:00:00],\n            dtype='object', length=3650, calendar='noleap', freq='D')\n[right]: DatetimeIndex(['1980-01-01 19:00:00', '1980-01-02 19:00:00',\n               '1980-01-03 19:00:00', '1980-01-04 19:00:00',\n               '1980-01-05 19:00:00', '1980-01-06 19:00:00',\n               '1980-01-07 19:00:00', '1980-01-08 19:00:00',\n               '1980-01-09 19:00:00', '1980-01-10 19:00:00',\n               ...\n               '1989-12-22 19:00:00', '1989-12-23 19:00:00',\n               '1989-12-24 19:00:00', '1989-12-25 19:00:00',\n               '1989-12-26 19:00:00', '1989-12-27 19:00:00',\n               '1989-12-28 19:00:00', '1989-12-29 19:00:00',\n               '1989-12-30 19:00:00', '1989-12-31 19:00:00'],\n              dtype='datetime64[ns]', name='time', length=3653, freq=None)"
     ]
    }
   ],
   "source": [
    "# bias correction using ZScoreRegresssor\n",
    "zscore = ZScoreRegressor()\n",
    "zscore.fit(training, target)\n",
    "fit_stats = zscore.fit_stats_dict_\n",
    "out = zscore.predict(future)\n",
    "predict_stats = zscore.predict_stats_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualize the datasets\n",
    "from utils import zscore_ds_plot\n",
    "\n",
    "zscore_ds_plot(training, target, future, out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import zscore_correction_plot\n",
    "\n",
    "zscore_correction_plot(zscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Automatic Parallelization\n",
    "\n",
    "In the examples above, we have performed downscaling on sample data sourced from individual points. In many downscaling workflows, however, users will want to apply pointwise methods at all points in their study domain. For this use case, scikit-downscale provides a high-level wrapper class: `PointWiseDownscaler`.\n",
    "\n",
    "In the example below, we'll use the `BCSDTemperature` model, along with the `PointWiseDownscaler` wrapper, to downscale daily maximum surface air temperature from CMIP6 for all point in a subset of the Pacific Norwest. We'll use a local [Dask Cluster](https://dask.org/) to distribute the computation among our available processors. Though not the point of this example, we also use [intake-esm](https://intake-esm.readthedocs.io/en/latest/) to access [CMIP6](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) data stored on [Google Cloud Storage](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6).\n",
    "\n",
    "**Data:**\n",
    "  - Training / Prediction data: NASA-GISS-E2 historical data from CMIP6\n",
    "  - Targets: [GridMet](http://www.climatologylab.org/gridmet.html) daily maximum surface air temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameters\n",
    "train_slice = slice('1980', '1982')  # train time range\n",
    "holdout_slice = slice('1990', '1991')  # prediction time range\n",
    "\n",
    "# bounding box of downscaling region\n",
    "lon_slice = slice(-124.8, -120.0)\n",
    "lat_slice = slice(50, 45)\n",
    "\n",
    "# chunk shape for dask execution (time must be contiguous, ie -1)\n",
    "chunks = {'lat': 10, 'lon': 10, 'time': -1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 1: Start a Dask Cluster. Xarray and the `PointWiseDownscaler` will make use of this cluster when it comes time to load input data and train/predict downscaling models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask.distributed import Client\n",
    "\n",
    "client = Client()\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 2. Load our target data. \n",
    "\n",
    "Here we use xarray directly to load a collection of OpenDAP endpoints. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "\n",
    "fnames = [\n",
    "    f'http://thredds.northwestknowledge.net:8080/thredds/dodsC/MET/tmmx/tmmx_{year}.nc'\n",
    "    for year in range(int(train_slice.start), int(train_slice.stop) + 1)\n",
    "]\n",
    "# open the data and cleanup a bit of metadata\n",
    "obs = (\n",
    "    xr.open_mfdataset(fnames, engine='pydap', concat_dim='day').rename({'day': 'time'}).drop('crs')\n",
    ")\n",
    "\n",
    "obs_subset = (\n",
    "    obs['air_temperature']\n",
    "    .sel(time=train_slice, lon=lon_slice, lat=lat_slice)\n",
    "    .resample(time='1d')\n",
    "    .mean()\n",
    "    .load(scheduler='threads')\n",
    "    .chunk(chunks)\n",
    ")\n",
    "\n",
    "# display\n",
    "display(obs_subset)\n",
    "obs_subset.isel(time=0).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 3: Load our training/prediction data.\n",
    "\n",
    "Here we use `intake-esm` to access a single Xarray dataset from the Pangeo's Google Cloud CMIP6 data catalog. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import intake_esm\n",
    "\n",
    "intake_esm.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import intake\n",
    "\n",
    "# search the cmip6 catalog\n",
    "col = intake.open_esm_datastore('https://storage.googleapis.com/cmip6/pangeo-cmip6.json')\n",
    "cat = col.search(\n",
    "    experiment_id=['historical', 'ssp585'], table_id='day', variable_id='tasmax', grid_label='gn'\n",
    ")\n",
    "\n",
    "# access the data and do some cleanup\n",
    "ds_model = (\n",
    "    cat['CMIP.NASA-GISS.GISS-E2-1-G.historical.day.gn']\n",
    "    .to_dask()\n",
    "    .squeeze(drop=True)\n",
    "    .drop(['height', 'lat_bnds', 'lon_bnds', 'time_bnds'])\n",
    ")\n",
    "ds_model.lon.values[ds_model.lon.values > 180] -= 360\n",
    "ds_model = ds_model.roll(lon=72, roll_coords=True)\n",
    "\n",
    "# regional subsets, ready for downscaling\n",
    "train_subset = (\n",
    "    ds_model['tasmax']\n",
    "    .sel(time=train_slice)\n",
    "    .interp_like(obs_subset.isel(time=0, drop=True), method='linear')\n",
    ")\n",
    "train_subset['time'] = train_subset.indexes['time'].to_datetimeindex()\n",
    "train_subset = train_subset.resample(time='1d').mean().load(scheduler='threads').chunk(chunks)\n",
    "\n",
    "holdout_subset = (\n",
    "    ds_model['tasmax']\n",
    "    .sel(time=holdout_slice)\n",
    "    .interp_like(obs_subset.isel(time=0, drop=True), method='linear')\n",
    ")\n",
    "holdout_subset['time'] = holdout_subset.indexes['time'].to_datetimeindex()\n",
    "holdout_subset = holdout_subset.resample(time='1d').mean().load(scheduler='threads').chunk(chunks)\n",
    "\n",
    "# display\n",
    "display(train_subset)\n",
    "train_subset.isel(time=0).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 4. Now that we have loaded our training and target data, we can move on to fit our BcsdTemperature model at each x/y point in our domain. This is where the `PointWiseDownscaler` comes in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skdownscale.pointwise_models import PointWiseDownscaler\n",
    "\n",
    "model = PointWiseDownscaler(BcsdTemperature(return_anoms=False))\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 5. We fit the `PointWiseDownscaler`, passing it data in Xarray data structures (our regional subsets from above). This opperation is lazy and return immediately. Under the hood, we can see that `PointWiseDownscaler._models` is an `Xarray.DataArray` of `BcsdTemperature` models.\n",
    "\n",
    "<div class=\"alert-warning\">\n",
    "Note: The following two cells may take a few minutes, or longer, to complete depending on how many cores your computer has, and your internet connection.     \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(train_subset, obs_subset)\n",
    "display(model, model._models)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 6. Finally, we can use our model to complete the downscaling workflow using the `predict` method along with our `holdout_subset` of CMIP6 data. We call the `.load()` method to eagerly compute the data. We end by plotting a map of downscaled data over our study area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted = model.predict(holdout_subset).load()\n",
    "display(predicted)\n",
    "predicted.isel(time=0).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Spatial Models\n",
    "Spatial models is a second class of downscaling methods that use information from the full study domain to form relationships between observations and ESM data. Scikit-downscale implements these models as as SpatialDownscaler. Beyond providing fit and predict methods that accept Xarray objects, the internal layout of these methods is intentionally unspecified. We are currently working on wrapping a few popular spatial downscaling models such as:\n",
    "\n",
    "- [MACA: Multivariate Adaptive Constructed Analogs](http://www.climatologylab.org/maca.html), Abatzoglou and Brown (2012)\n",
    "- [LOCA: Localized Constructed Analogs](http://loca.ucsd.edu/), Pierce, Cayan, and Thrasher (2014)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Discussion\n",
    "\n",
    "### 3.1 Benchmark Applications\n",
    "Its likely that one of the reasons we haven’t seen strong consensus develop around particularl downscaling methodologies is the abscense of widely available benchamrk applications to test methods against eachother. While Scikit-downscale will not solve this problem on its own, we hope the ability to implemnt downscaling applications within a common framework will enable a more robust benchmarking inititive that previously possible.\n",
    "\n",
    "### 3.2 Call for Participation\n",
    "The Scikit-downscale effort is just getting started. With the recent release of [CMIP6](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6), we expect a surge of interest in downscaled climate data. There are clear opportunities for involvement from climate impacts practicioneers, computer scientists with an interest in machine learning for climate applications, and climate scientists alike. Please reach out if you are interested in participating in any way.\n",
    "\n",
    "## References\n",
    "\n",
    "1. Abatzoglou, J. T. (2013), Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol., 33: 121–131.\n",
    "1. Abatzoglou J.T. and Brown T.J. A comparison of statistical downscaling methods suited for wildfire applications, International Journal of Climatology (2012), 32, 772-780\n",
    "1. Gutmann, E., Pruitt, T., Clark, M. P., Brekke, L., Arnold, J. R., Raff, D. A., and Rasmussen, R. M. ( 2014), An intercomparison of statistical downscaling methods used for water resource assessments in the United States, Water Resour. Res., 50, 7167– 7186, doi:10.1002/2014WR015559.\n",
    "1. Gutmann, E., J. Hamman, M. Clark, T. Eidhammer, A. Wood, J. Arnold, K. Nowak (in prep), Evaluating the effect of statistical downscaling methodological choices in a common framework. To be submitted to JGR-Atomspheres.\n",
    "1. Hamlet, A.F., Salathé, E.P., and Carrasco, P., 2010. Statistical downscaling techniques for global climate model simulations of temperature and precipitation with application to water resources planning studies. A report prepared by the Climate Impact Group for Columbia Basin Climate Change Scenario Project, University of Washington, Seattle, WA.\n",
    "http://www.hydro.washington.edu/2860/products/sites/r7climate/study_report/CBCCSP_chap4_gcm_draft_20100111.pdf\n",
    "1. Lanzante JR, KW Dixon, MJ Nath, CE Whitlock, and D Adams-Smith (2018): Some Pitfalls in Statistical Downscaling of Future Climate. Bulletin of the American Meteorological Society. DOI: 0.1175/BAMS-D-17-0046.1.\n",
    "1. Livneh B., E.A. Rosenberg, C. Lin, B. Nijssen, V. Mishra, K.M. Andreadis, E.P. Maurer, and D.P. Lettenmaier, 2013: A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States: Update and Extensions, Journal of Climate, 26, 9384–9392.\n",
    "1. Pierce, D. W., D. R. Cayan, and B. L. Thrasher, 2014: Statistical downscaling using Localized Constructed Analogs (LOCA). Journal of Hydrometeorology, volume 15, page 2558-2585\n",
    "1. Stoner, A., K. Hayhoe, and X. Yang (2012), An asynchronous regional regression model for statistical downscaling of daily climate variables, Int. J. Climatol., 33, 2473–2494, doi:10.1002/joc.3603.\n",
    "1. Wood, A., L. Leung, V. Sridhar, and D. Lettenmaier (2004), Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs, Clim. Change, 62, 189–216.\n",
    "\n",
    "## License\n",
    "\n",
    "This notebook is licensed under [CC-BY](https://creativecommons.org/licenses/by/4.0/). Scikit-downscale is licensed under [Apache License 2.0](https://github.com/jhamman/scikit-downscale/blob/master/LICENSE)."
   ]
  }
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