{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "neural-welsh",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Interfacing PyTorch models with collections of AnnData objects via `annbatch`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "480364bc-813f-4834-a5b4-d15ef128171e",
   "metadata": {},
   "source": [
    "**Author:** [Sergei Rybakov](https://github.com/koncopd)\n",
    "**Author:** [Ilan Gold](https://github.com/ilan-gold)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "given-belarus",
   "metadata": {},
   "source": [
    "This tutorial demonstrates how to set up a {class}`annbatch.Loader` for an in-memory {class}`~anndata.AnnData` object to interface with a  PyTorch model and lay out complex covariates."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "annbatch-intro",
   "metadata": {},
   "source": [
    "Note, however, that when training models on many large `AnnData` objects, loading them all into memory at once is impractical as this notebook does.\n",
    "\n",
    "`annbatch.Loader` solves this by allowing users to streams batches formed of contiguous on-memory runs of the training data across multiple AnnData objects without requiring a full in-memory concatenation — compatible with PyTorch, JAX, and other frameworks while being extremely fast.\n",
    "Preshuffling is a crucial part of making this “contiguous fetching” strategy work – see {class}`annbatch.DatasetCollection` for more information on performing the on-disk, lazy preshuffle."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60ed6b48-175b-4f67-94d5-60b3376d9c4b",
   "metadata": {},
   "source": [
    "To install `annbatch` for in-memory sparse data (which is most common in single-cell RNA-seq and will be used in this tutorial), use `pip install \"annbatch[numba]\"` – note that the `numba` extra is required for in-memory sparse data.\n",
    "To use the `preload_to_gpu` option in {class}`annbatch.Loader`, install the `cupy-cudaxx` extra as well where `xx` is your `cuda` version.\n",
    "See the [docs](https://annbatch.readthedocs.io/en/stable/installation.html) for more info on installation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "possible-possession",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "from typing import TYPE_CHECKING\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pooch\n",
    "import pyro\n",
    "import pyro.distributions as dist\n",
    "import scanpy as sc\n",
    "import torch\n",
    "from annbatch import Loader\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from torch import nn\n",
    "\n",
    "import anndata as ad\n",
    "\n",
    "if TYPE_CHECKING:\n",
    "    from collections.abc import Sequence\n",
    "\n",
    "\n",
    "pyro.clear_param_store()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "inner-piano",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## VAE model definition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "semisup-motivation",
   "metadata": {},
   "source": [
    "The task is semi-supervised label transfer: `pbmc` cells have annotated cell types, while `covid` cells do not.\n",
    "The model learns a shared latent space across both datasets and uses the labeled pbmc cells to predict cell types for the unlabeled covid cells.\n",
    "Batch labels (study of origin) are included as covariates to correct for dataset-specific effects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "incorporated-cargo",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "class MLP(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dims, out_dim):\n",
    "        super().__init__()\n",
    "\n",
    "        modules = []\n",
    "        for in_size, out_size in zip(\n",
    "            [input_dim, *hidden_dims], hidden_dims, strict=True\n",
    "        ):\n",
    "            modules.append(nn.Linear(in_size, out_size))\n",
    "            modules.append(nn.LayerNorm(out_size))\n",
    "            modules.append(nn.ReLU())\n",
    "            modules.append(nn.Dropout(p=0.05))\n",
    "        modules.append(nn.Linear(hidden_dims[-1], out_dim))\n",
    "        self.fc = nn.Sequential(*modules)\n",
    "\n",
    "    def forward(self, *inputs):\n",
    "        shape = (*dist.util.broadcast_shape(*[s.shape[:-1] for s in inputs]), -1)\n",
    "        inputs = [s.expand(shape) for s in inputs]\n",
    "\n",
    "        input_cat = torch.cat(inputs, dim=-1)\n",
    "        return self.fc(input_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "victorian-brain",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "# The code is based on the scArches trVAE model: https://github.com/theislab/scarches/blob/v0.3.5/scarches/models/trvae/trvae.py\n",
    "# and on the pyro.ai Variational Autoencoders tutorial: http://pyro.ai/examples/vae.html\n",
    "# and the Semi-Supervised VAE tutorial: http://pyro.ai/examples/ss-vae.html\n",
    "class CVAE(nn.Module):\n",
    "    def __init__(\n",
    "        self,\n",
    "        input_dim,\n",
    "        *,\n",
    "        n_conds: int,\n",
    "        n_classes: int,\n",
    "        hidden_dims: Sequence[int],\n",
    "        latent_dim: int,\n",
    "        classifier_dims: Sequence[int] = (128,),\n",
    "    ) -> None:\n",
    "        super().__init__()\n",
    "        # output - mean and logvar of z\n",
    "        self.encoder = MLP(input_dim + n_conds, hidden_dims, 2 * latent_dim)\n",
    "        self.decoder = MLP(\n",
    "            latent_dim + n_conds + n_classes, hidden_dims[::-1], input_dim\n",
    "        )\n",
    "        self.theta = nn.Linear(n_conds, input_dim, bias=False)\n",
    "        self.classifier = MLP(latent_dim, classifier_dims, n_classes)\n",
    "        self.latent_dim = latent_dim\n",
    "\n",
    "    def model(self, x, batches, classes, size_factors, supervised):\n",
    "        pyro.module(\"cvae\", self)\n",
    "\n",
    "        batch_size = x.shape[0]\n",
    "\n",
    "        with pyro.plate(\"data\", batch_size):\n",
    "            z_loc = x.new_zeros((batch_size, self.latent_dim))\n",
    "            z_scale = x.new_ones((batch_size, self.latent_dim))\n",
    "            z = pyro.sample(\"Z\", dist.Normal(z_loc, z_scale).to_event(1))\n",
    "\n",
    "            classes_probs = self.classifier(z).softmax(dim=-1)\n",
    "            obs = classes if supervised else None\n",
    "            classes = pyro.sample(\n",
    "                \"Class\", dist.OneHotCategorical(probs=classes_probs), obs=obs\n",
    "            )\n",
    "\n",
    "            dec_mu = (\n",
    "                self.decoder(z, batches, classes).softmax(dim=-1)\n",
    "                * size_factors[:, None]\n",
    "            )\n",
    "            dec_theta = torch.exp(self.theta(batches))\n",
    "\n",
    "            logits = (dec_mu + 1e-6).log() - (dec_theta + 1e-6).log()\n",
    "\n",
    "            pyro.sample(\n",
    "                \"X\",\n",
    "                dist.NegativeBinomial(total_count=dec_theta, logits=logits).to_event(1),\n",
    "                obs=x.int(),\n",
    "            )\n",
    "\n",
    "    def guide(self, x, batches, classes, size_factors, supervised):\n",
    "        batch_size = x.shape[0]\n",
    "\n",
    "        with pyro.plate(\"data\", batch_size):\n",
    "            z_loc_scale = self.encoder(x, batches)\n",
    "\n",
    "            z_mu = z_loc_scale[:, : self.latent_dim]\n",
    "            z_var = torch.sqrt(torch.exp(z_loc_scale[:, self.latent_dim :]) + 1e-4)\n",
    "\n",
    "            z = pyro.sample(\"Z\", dist.Normal(z_mu, z_var).to_event(1))\n",
    "\n",
    "            if not supervised:\n",
    "                classes_probs = self.classifier(z).softmax(dim=-1)\n",
    "                pyro.sample(\"Class\", dist.OneHotCategorical(probs=classes_probs))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "floral-relation",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Create a concatenated `AnnData` from two `AnnData` objects"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eb9d012-a6a2-4854-89d2-99251286dc5e",
   "metadata": {
    "editable": true,
    "jp-MarkdownHeadingCollapsed": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "The data is from this [scvi reproducibility notebook](https://yoseflab.github.io/scvi-tools-reproducibility/scarches_totalvi_seurat_data/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "190da95b",
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "def download(url, fname, sha_hash):\n",
    "    pooch.retrieve(\n",
    "        url=url,\n",
    "        known_hash=sha_hash,\n",
    "        fname=fname,\n",
    "        path=\".\",\n",
    "        downloader=pooch.HTTPDownloader(\n",
    "            progressbar=True,\n",
    "            chunk_size=1024,\n",
    "            timeout=120,\n",
    "            headers={\n",
    "                \"User-Agent\": \"an/ndata/1.0.0 (https://github.com/scverse/anndata)\"\n",
    "            },\n",
    "        ),\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "interstate-crack",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading data from 'https://scverse-exampledata.s3.eu-west-1.amazonaws.com/anndata/cite_covid_full.h5ad' to file '/localscratch/ilan.gold/anndata-tutorials/covid_cite.h5ad'.\n",
      "100%|████████████████████████████████████████| 304M/304M [00:00<00:00, 491GB/s]\n",
      "Downloading data from 'https://scverse-exampledata.s3.eu-west-1.amazonaws.com/anndata/pbmc_seurat_v4.h5ad' to file '/localscratch/ilan.gold/anndata-tutorials/pbmc_seurat_v4.h5ad'.\n",
      "100%|█████████████████████████████████████| 1.00G/1.00G [00:00<00:00, 1.50TB/s]\n"
     ]
    }
   ],
   "source": [
    "download(\n",
    "    \"https://scverse-exampledata.s3.eu-west-1.amazonaws.com/anndata/cite_covid_full.h5ad\",\n",
    "    \"covid_cite.h5ad\",\n",
    "    \"cb9745b2d642459926194961f34110a17c16ef6b11777304a3478e12bd682657\",\n",
    ")\n",
    "download(\n",
    "    \"https://scverse-exampledata.s3.eu-west-1.amazonaws.com/anndata/pbmc_seurat_v4.h5ad\",\n",
    "    \"pbmc_seurat_v4.h5ad\",\n",
    "    \"c3b0100a6ce27beb64eff53692e09f98da2a58cfdfea08d15ff204f834b41396\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "listed-fairy",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "covid = ad.read_h5ad(\"covid_cite.h5ad\")\n",
    "pbmc = ad.read_h5ad(\"pbmc_seurat_v4.h5ad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e1380244-39e9-45a1-bf30-1acc6224361b",
   "metadata": {},
   "outputs": [],
   "source": [
    "covid.obs[\"size_factors\"] = np.asarray(covid.X.sum(1)).ravel()\n",
    "pbmc.obs[\"size_factors\"] = np.asarray(pbmc.X.sum(1)).ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hvg-explanation",
   "metadata": {},
   "source": [
    "We select highly variable genes using pbmc as the reference, since it has rich cell type annotations and multiple batches to compute variability across.\n",
    "Concatenating with `join='inner'` then restricts covid to the same gene set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "closed-greeting",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.highly_variable_genes(\n",
    "    pbmc,\n",
    "    n_top_genes=4000,\n",
    "    flavor=\"seurat_v3\",\n",
    "    batch_key=\"orig.ident\",\n",
    "    subset=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2e91bbe4-4cc1-4f82-b2b0-cde78eb17e5f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 57669 × 33538\n",
       "    obs: 'orig.ident', 'nCount_RNA', 'nFeature_RNA', 'RNA_snn_res.0.4', 'seurat_clusters', 'set', 'Resp', 'disease', 'subj_code', 'covidpt_orhealth', 'mito', 'ncount', 'nfeat', 'bust_21', 'og_clust', 'severmod_other', 'og_clusts', 'nCount_ADT', 'nFeature_ADT', 'UMAP1', 'UMAP2', 'final_clust', 'final_clust_v2', 'new_pt_id', 'Resp_og', 'final_clust_withnum', 'final_clust_review', 'Age', 'Gender', 'Gender_num', 'size_factors'\n",
       "    obsm: 'pro_exp'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1f101ae4-d68a-4e36-8349-4ac867fa8f61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 161764 × 4000\n",
       "    obs: 'nCount_ADT', 'nFeature_ADT', 'nCount_RNA', 'nFeature_RNA', 'orig.ident', 'lane', 'donor', 'time', 'celltype.l1', 'celltype.l2', 'celltype.l3', 'Phase', 'nCount_SCT', 'nFeature_SCT', 'X_index', 'size_factors'\n",
       "    var: 'highly_variable', 'highly_variable_rank', 'means', 'variances', 'variances_norm', 'highly_variable_nbatches'\n",
       "    uns: 'hvg'\n",
       "    obsm: 'protein_counts'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pbmc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "straight-license",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = ad.concat(\n",
    "    {\"covid\": covid, \"pbmc\": pbmc},\n",
    "    join=\"inner\",\n",
    "    label=\"dataset\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "chief-heading",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 219433 × 4000\n",
       "    obs: 'orig.ident', 'nCount_RNA', 'nFeature_RNA', 'nCount_ADT', 'nFeature_ADT', 'size_factors', 'dataset'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6fddc99-f389-4e33-88aa-a92a0806d42a",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "Setup important covariates in the concatenated object as well as one-hot encoders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "friendly-lightweight",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.obs[\"orig.ident\"] = dataset.obs[\"orig.ident\"].astype(str)\n",
    "dataset.obs.loc[covid.obs_names, \"orig.ident\"] = covid.obs[\"set\"]\n",
    "dataset.obs[\"orig.ident\"] = dataset.obs[\"orig.ident\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "restricted-interstate",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.obs[\"size_factors\"] = dataset.X.sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "satisfied-slovenia",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = \"celltype.l1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6e4edca9-493f-488e-9a24-1a29f8778740",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.obs[labels] = pd.concat([\n",
    "    pd.Series(index=covid.obs_names, data=[\"not-annotated\"] * covid.shape[0]),\n",
    "    pbmc.obs[\"celltype.l1\"].astype(\"str\"),\n",
    "]).astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d0d21b68-4320-4571-88aa-6f3c3aca7211",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAACCCACACCAGCGT-1       not-annotated\n",
       "AAACCCACATCTCAAG-1       not-annotated\n",
       "AAACGAAAGACCTGGA-1       not-annotated\n",
       "AAACGCTCAGTGGGTA-1       not-annotated\n",
       "AAACGCTGTAGCTTGT-1       not-annotated\n",
       "                             ...      \n",
       "E2L8_TTTGTTGGTCGTGATT            CD8 T\n",
       "E2L8_TTTGTTGGTGTGCCTG             Mono\n",
       "E2L8_TTTGTTGGTTAGTTCG                B\n",
       "E2L8_TTTGTTGGTTGGCTAT             Mono\n",
       "E2L8_TTTGTTGTCTCATGGA             Mono\n",
       "Name: celltype.l1, Length: 219433, dtype: category\n",
       "Categories (9, object): ['B', 'CD4 T', 'CD8 T', 'DC', ..., 'NK', 'not-annotated', 'other', 'other T']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.obs[labels]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "loose-czech",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "use_cuda = torch.cuda.is_available()\n",
    "use_cuda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "06281003-52b1-44f7-841d-673874991945",
   "metadata": {
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.sk-global {\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",
       "\n",
       ".sk-global.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       ".sk-global.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       ".sk-global {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".sk-global pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip-path: inset(100%);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       ".sk-global 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-global 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-global 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-global 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-global 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-global div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       ".sk-global 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-global 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-global 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: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global 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-global 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-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global 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-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global 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-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global 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-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global 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-global 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-global 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-global 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-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global 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-global 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-global 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-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global 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-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global 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-unfitted-level-0);\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-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\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",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\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",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\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",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\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-global 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-unfitted-level-0);\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-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\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-global 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-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\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",
       "    margin-top: 0;\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 td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "@supports(anchor-name: --doc-link) {\n",
       "    a.param-doc-link,\n",
       "    a.param-doc-link:link,\n",
       "    a.param-doc-link:visited {\n",
       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "@supports(position-area: center right) {\n",
       "    .param-doc-description {\n",
       "    position-area: center right;\n",
       "    position: fixed;\n",
       "    margin-left: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\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",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
       "  cursor: pointer;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: .20em;\n",
       "  margin-bottom: 0.5em;\n",
       "  font-size: inherit; /* Needed for jupyter */\n",
       "}\n",
       "\n",
       ".features.fitted {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features summary {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  margin-bottom: 0;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features.fitted details[open] > summary {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features details > summary .arrow::before {\n",
       "  content: \"▸\";\n",
       "  color: grey;\n",
       "}\n",
       "\n",
       ".features details[open] > summary .arrow::before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       ".features details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".features.fitted details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".features .features-container {\n",
       "  max-width: 15em;\n",
       "  max-height: 10em;\n",
       "  overflow: auto;\n",
       "  scrollbar-width: thin;\n",
       "  padding: .25em 0.1rem;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 0 0 .5em .5em;\n",
       "}\n",
       "\n",
       ".features.fitted .features-container {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features .image-container {\n",
       "  block-size: 1em;\n",
       "  inline-size: 1em;\n",
       "  padding: 0;\n",
       "  margin: 0%;\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  align-items: center;\n",
       "}\n",
       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
       "  width: 1em;\n",
       "  height: 1em;\n",
       "  filter: grayscale(100%) opacity(60%);\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  width: 100%;\n",
       "  margin: 0.01em;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(odd) {\n",
       "  background-color: #fff;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(even) {\n",
       "  background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:hover {\n",
       "  background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  table-layout: inherit;\n",
       "}\n",
       "\n",
       ".features .features-container table td {\n",
       "  text-align: left;\n",
       "  padding: 0 0.5em;\n",
       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "  white-space: nowrap;\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>OneHotEncoder(dtype=&lt;class &#x27;numpy.float32&#x27;&gt;, sparse_output=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 sk-global\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</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=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('sparse_output',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-sparse_output;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=sparse_output,-bool%2C%20default%3DTrue\">\n",
       "            sparse_output\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-sparse_output;\">\n",
       "            sparse_output: bool, default=True<br><br>When ``True``, it returns a SciPy sparse matrix/array<br>in &quot;Compressed Sparse Row&quot; (CSR) format.<br><br>.. versionadded:: 1.2<br>   `sparse` was renamed to `sparse_output`</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dtype',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-dtype;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=dtype,-number%20type%2C%20default%3Dnp.float64\">\n",
       "            dtype\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-dtype;\">\n",
       "            dtype: number type, default=np.float64<br><br>Desired dtype of output.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&lt;class &#x27;numpy.float32&#x27;&gt;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('categories',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-categories;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=categories,-%27auto%27%20or%20a%20list%20of%20array-like%2C%20default%3D%27auto%27\">\n",
       "            categories\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-categories;\">\n",
       "            categories: &#x27;auto&#x27; or a list of array-like, default=&#x27;auto&#x27;<br><br>Categories (unique values) per feature:<br><br>- &#x27;auto&#x27; : Determine categories automatically from the training data.<br>- list : ``categories[i]`` holds the categories expected in the ith<br>  column. The passed categories should not mix strings and numeric<br>  values within a single feature, and should be sorted in case of<br>  numeric values.<br><br>The used categories can be found in the ``categories_`` attribute.<br><br>.. versionadded:: 0.20</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('drop',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-drop;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=drop,-%7B%27first%27%2C%20%27if_binary%27%7D%20or%20an%20array-like%20of%20shape%20%28n_features%2C%29%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
       "            drop\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-drop;\">\n",
       "            drop: {&#x27;first&#x27;, &#x27;if_binary&#x27;} or an array-like of shape (n_features,),             default=None<br><br>Specifies a methodology to use to drop one of the categories per<br>feature. This is useful in situations where perfectly collinear<br>features cause problems, such as when feeding the resulting data<br>into an unregularized linear regression model.<br><br>However, dropping one category breaks the symmetry of the original<br>representation and can therefore induce a bias in downstream models,<br>for instance for penalized linear classification or regression models.<br><br>- None : retain all features (the default).<br>- &#x27;first&#x27; : drop the first category in each feature. If only one<br>  category is present, the feature will be dropped entirely.<br>- &#x27;if_binary&#x27; : drop the first category in each feature with two<br>  categories. Features with 1 or more than 2 categories are<br>  left intact.<br>- array : ``drop[i]`` is the category in feature ``X[:, i]`` that<br>  should be dropped.<br><br>When `max_categories` or `min_frequency` is configured to group<br>infrequent categories, the dropping behavior is handled after the<br>grouping.<br><br>.. versionadded:: 0.21<br>   The parameter `drop` was added in 0.21.<br><br>.. versionchanged:: 0.23<br>   The option `drop=&#x27;if_binary&#x27;` was added in 0.23.<br><br>.. versionchanged:: 1.1<br>    Support for dropping infrequent categories.</span>\n",
       "        </a>\n",
       "    </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('handle_unknown',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-handle_unknown;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=handle_unknown,-%7B%27error%27%2C%20%27ignore%27%2C%20%27infrequent_if_exist%27%2C%20%27warn%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27error%27\">\n",
       "            handle_unknown\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-handle_unknown;\">\n",
       "            handle_unknown: {&#x27;error&#x27;, &#x27;ignore&#x27;, &#x27;infrequent_if_exist&#x27;, &#x27;warn&#x27;},                      default=&#x27;error&#x27;<br><br>Specifies the way unknown categories are handled during :meth:`transform`.<br><br>- &#x27;error&#x27; : Raise an error if an unknown category is present during transform.<br>- &#x27;ignore&#x27; : When an unknown category is encountered during<br>  transform, the resulting one-hot encoded columns for this feature<br>  will be all zeros. In the inverse transform, an unknown category<br>  will be denoted as None.<br>- &#x27;infrequent_if_exist&#x27; : When an unknown category is encountered<br>  during transform, the resulting one-hot encoded columns for this<br>  feature will map to the infrequent category if it exists. The<br>  infrequent category will be mapped to the last position in the<br>  encoding. During inverse transform, an unknown category will be<br>  mapped to the category denoted `&#x27;infrequent&#x27;` if it exists. If the<br>  `&#x27;infrequent&#x27;` category does not exist, then :meth:`transform` and<br>  :meth:`inverse_transform` will handle an unknown category as with<br>  `handle_unknown=&#x27;ignore&#x27;`. Infrequent categories exist based on<br>  `min_frequency` and `max_categories`. Read more in the<br>  :ref:`User Guide &lt;encoder_infrequent_categories&gt;`.<br>- &#x27;warn&#x27; : When an unknown category is encountered during transform<br>  a warning is issued, and the encoding then proceeds as described for<br>  `handle_unknown=&quot;infrequent_if_exist&quot;`.<br><br>.. versionchanged:: 1.1<br>    `&#x27;infrequent_if_exist&#x27;` was added to automatically handle unknown<br>    categories and infrequent categories.<br><br>.. versionadded:: 1.6<br>   The option `&quot;warn&quot;` was added in 1.6.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;error&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_frequency',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-min_frequency;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=min_frequency,-int%20or%20float%2C%20default%3DNone\">\n",
       "            min_frequency\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-min_frequency;\">\n",
       "            min_frequency: int or float, default=None<br><br>Specifies the minimum frequency below which a category will be<br>considered infrequent.<br><br>- If `int`, categories with a smaller cardinality will be considered<br>  infrequent.<br><br>- If `float`, categories with a smaller cardinality than<br>  `min_frequency * n_samples`  will be considered infrequent.<br><br>.. versionadded:: 1.1<br>    Read more in the :ref:`User Guide &lt;encoder_infrequent_categories&gt;`.</span>\n",
       "        </a>\n",
       "    </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('max_categories',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_categories;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=max_categories,-int%2C%20default%3DNone\">\n",
       "            max_categories\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_categories;\">\n",
       "            max_categories: int, default=None<br><br>Specifies an upper limit to the number of output features for each input<br>feature when considering infrequent categories. If there are infrequent<br>categories, `max_categories` includes the category representing the<br>infrequent categories along with the frequent categories. If `None`,<br>there is no limit to the number of output features.<br><br>.. versionadded:: 1.1<br>    Read more in the :ref:`User Guide &lt;encoder_infrequent_categories&gt;`.</span>\n",
       "        </a>\n",
       "    </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('feature_name_combiner',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-feature_name_combiner;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=feature_name_combiner,-%22concat%22%20or%20callable%2C%20default%3D%22concat%22\">\n",
       "            feature_name_combiner\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-feature_name_combiner;\">\n",
       "            feature_name_combiner: &quot;concat&quot; or callable, default=&quot;concat&quot;<br><br>Callable with signature `def callable(input_feature, category)` that returns a<br>string. This is used to create feature names to be returned by<br>:meth:`get_feature_names_out`.<br><br>`&quot;concat&quot;` concatenates encoded feature name and category with<br>`feature + &quot;_&quot; + str(category)`.E.g. feature X with values 1, 6, 7 create<br>feature names `X_1, X_6, X_7`.<br><br>.. versionadded:: 1.3</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;concat&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-categories_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=categories_,-list%20of%20arrays\">\n",
       "            categories_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-categories_;\">\n",
       "            categories_: list of arrays<br><br>The categories of each feature determined during fitting<br>(in order of the features in X and corresponding with the output<br>of ``transform``). This includes the category specified in ``drop``<br>(if any).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">list</td>\n",
       "           <td>[array([&#x27;B&#x27;, &#x27;... dtype=object)]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-drop_idx_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=drop_idx_,-array%20of%20shape%20%28n_features%2C%29\">\n",
       "            drop_idx_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-drop_idx_;\">\n",
       "            drop_idx_: array of shape (n_features,)<br><br>- ``drop_idx_[i]`` is the index in ``categories_[i]`` of the category<br>  to be dropped for each feature.<br>- ``drop_idx_[i] = None`` if no category is to be dropped from the<br>  feature with index ``i``, e.g. when `drop=&#x27;if_binary&#x27;` and the<br>  feature isn&#x27;t binary.<br>- ``drop_idx_ = None`` if all the transformed features will be<br>  retained.<br><br>If infrequent categories are enabled by setting `min_frequency` or<br>`max_categories` to a non-default value and `drop_idx[i]` corresponds<br>to an infrequent category, then the entire infrequent category is<br>dropped.<br><br>.. versionchanged:: 0.23<br>   Added the possibility to contain `None` values.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">NoneType</td>\n",
       "           <td>None</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 1.0</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>1</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div>\n",
       "        <div class=\"features fitted\">\n",
       "          <details>\n",
       "            <summary>\n",
       "              <div class=\"arrow\"></div>\n",
       "              <div>8 features</div>\n",
       "              <div class=\"image-container\" title=\"Copy all output features\">\n",
       "                <i class=\"copy-paste-icon\"\n",
       "                  onclick=\"\n",
       "                  event.stopPropagation();\n",
       "                  event.preventDefault();\n",
       "                  copyFeatureNamesToClipboard(this);\n",
       "                  \"\n",
       "                >\n",
       "                </i>\n",
       "              </div>\n",
       "            </summary>\n",
       "            <div class=\"features-container\">\n",
       "                <table class=\"features-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr>\n",
       "          <td>x0_B</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_CD4 T</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_CD8 T</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_DC</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Mono</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_NK</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_other</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_other T</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </div>\n",
       "          </details>\n",
       "        </div>\n",
       "    </div></div><script>/*  Authors: The scikit-learn developers\n",
       " SPDX-License-Identifier: BSD-3-Clause\n",
       "*/\n",
       "\n",
       "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('.copy-paste-icon').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "\n",
       "    const parent = element.parentElement;\n",
       "    if (!parent || !parent.nextElementSibling) {\n",
       "        console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    const paramName = element.parentElement.nextElementSibling\n",
       "        .textContent.trim().split(' ')[0];\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "\n",
       "/**\n",
       " * Copy the list of feature names formatted as a Python list.\n",
       " *\n",
       " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
       " *   contain a `details` element and a table containing feature named.\n",
       " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
       " */\n",
       "function copyFeatureNamesToClipboard(element) {\n",
       "    var detailsElem = element.closest('.features').querySelector('details');\n",
       "    var wasOpen = detailsElem.open;\n",
       "    detailsElem.open = true;\n",
       "    var content = element.closest('.features').querySelector('tbody')\n",
       "                  .innerText.trim();\n",
       "    if (!wasOpen) detailsElem.open = false;\n",
       "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
       "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
       "    const originalStyle = element.style;\n",
       "    const copyMark = document.createElement('span');\n",
       "    copyMark.innerHTML = '✔';\n",
       "    copyMark.style.color = 'blue';\n",
       "    copyMark.style.fontSize = '1em';\n",
       "\n",
       "    navigator.clipboard.writeText(formattedText)\n",
       "        .then(() => {\n",
       "            element.style.display = 'none';\n",
       "            element.parentElement.appendChild(copyMark);\n",
       "\n",
       "            setTimeout(() => {\n",
       "                copyMark.remove();\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 1000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'orange';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 1000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "/**\n",
       " * Adapted from Skrub\n",
       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
       " * @returns \"light\" or \"dark\"\n",
       " */\n",
       "function detectTheme(element) {\n",
       "    const body = document.querySelector('body');\n",
       "\n",
       "    // Check VSCode theme\n",
       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
       "\n",
       "    if (themeKindAttr && themeNameAttr) {\n",
       "        const themeKind = themeKindAttr.toLowerCase();\n",
       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Check Jupyter theme\n",
       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-1');</script></body>"
      ],
      "text/plain": [
       "OneHotEncoder(dtype=<class 'numpy.float32'>, sparse_output=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder_study = OneHotEncoder(sparse_output=False, dtype=np.float32)\n",
    "encoder_study.fit(dataset.obs[\"orig.ident\"].to_numpy()[:, None])\n",
    "\n",
    "encoder_celltype = OneHotEncoder(sparse_output=False, dtype=np.float32)\n",
    "encoder_celltype.fit(pbmc.obs[labels].to_numpy()[:, None])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "miniature-oasis",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Loader and model initialization\n",
    "\n",
    "We initialize a {class}`annbatch.Loader` from the concatenated dataset.\n",
    "\n",
    "- **`chunk_size`**: number of AnnData chunks streamed at a time.\n",
    "  For in-memory data you can safely choose `chunk_size=1` for perfect random sampling; for on-disk data, larger values with pre-shuffled data are important for maximizing both training quality and speed.\n",
    "- **`preload_nchunks`**: how many chunks to buffer before yielding batches – here it can be the same as `batch_size` because we have `chunk_size=1`.\n",
    "- **`batch_size`**: number of cells per training batch.\n",
    "  Typical values are 256–8192; larger batches train faster but require more GPU memory.\n",
    "- **`preload_to_gpu`**: reads directly into pinned GPU memory for a speed boost; requires the `cupy-cudaxx` extra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "traditional-rebate",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "loader = Loader(\n",
    "    chunk_size=1,\n",
    "    preload_nchunks=8192,\n",
    "    batch_size=8192,\n",
    "    shuffle=True,\n",
    "    preload_to_gpu=True,\n",
    ").add_adata(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "oriental-puzzle",
   "metadata": {},
   "outputs": [],
   "source": [
    "pyro.clear_param_store()\n",
    "torch.set_float32_matmul_precision(\"high\")\n",
    "\n",
    "n_conds = len(dataset.obs[\"orig.ident\"].cat.categories)\n",
    "n_classes = len(dataset.obs[labels].cat.categories) - 1\n",
    "\n",
    "latent_dim = 20\n",
    "\n",
    "cvae = CVAE(\n",
    "    dataset.shape[1],\n",
    "    n_conds=n_conds,\n",
    "    n_classes=n_classes,\n",
    "    hidden_dims=[1280, 256],\n",
    "    latent_dim=latent_dim,\n",
    ")\n",
    "\n",
    "if use_cuda:\n",
    "    cvae.cuda()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pressed-converter",
   "metadata": {},
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "narrative-stomach",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = pyro.optim.Adam({\"lr\": 1e-3})\n",
    "\n",
    "svi_sup = pyro.infer.SVI(\n",
    "    cvae.model, cvae.guide, optimizer, loss=pyro.infer.TraceMeanField_ELBO()\n",
    ")\n",
    "\n",
    "# very slow for sequential\n",
    "svi_unsup = pyro.infer.SVI(\n",
    "    cvae.model,\n",
    "    pyro.infer.config_enumerate(cvae.guide, \"parallel\", expand=True),\n",
    "    optimizer,\n",
    "    loss=pyro.infer.TraceEnum_ELBO(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acquired-demonstration",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(svi_sup, svi_unsup, train_loader):\n",
    "    epoch_loss = 0.0\n",
    "\n",
    "    for batch in train_loader:\n",
    "        # select indices of labelled cells\n",
    "        sup_idx = (batch[\"obs\"][\"dataset\"] == \"pbmc\").to_numpy()\n",
    "        X = batch[\"X\"].to_dense().int()\n",
    "        cols = [\"orig.ident\", labels, \"size_factors\"]\n",
    "        obs_dict = batch[\"obs\"][cols].to_dict(\"series\")\n",
    "        obs_dict[\"size_factors\"] = torch.Tensor(\n",
    "            obs_dict[\"size_factors\"].to_numpy()\n",
    "        ).cuda()\n",
    "        obs_dict[\"orig.ident\"] = torch.Tensor(\n",
    "            encoder_study.transform(obs_dict[\"orig.ident\"].to_numpy()[:, None])\n",
    "        ).cuda()\n",
    "        # do supervised step for the labelled cells\n",
    "        sup_data = {\n",
    "            \"orig.ident\": obs_dict[\"orig.ident\"][sup_idx],\n",
    "            labels: torch.Tensor(\n",
    "                encoder_celltype.transform(\n",
    "                    obs_dict[labels].loc[sup_idx].to_numpy()[:, None]\n",
    "                )\n",
    "            ).cuda(),\n",
    "            \"size_factors\": obs_dict[\"size_factors\"][sup_idx],\n",
    "        }\n",
    "\n",
    "        sup_data[\"supervised\"] = True\n",
    "        epoch_loss += svi_sup.step(X[sup_idx], *sup_data.values())\n",
    "\n",
    "        # do unsupervised step for unlabelled cells\n",
    "        unsup_data = {\n",
    "            \"orig.ident\": obs_dict[\"orig.ident\"][~sup_idx],\n",
    "            labels: torch.empty(((~sup_idx).sum(), n_classes)).cuda(),\n",
    "            \"size_factors\": obs_dict[\"size_factors\"][~sup_idx],\n",
    "        }\n",
    "\n",
    "        unsup_data[\"supervised\"] = False\n",
    "        epoch_loss += svi_unsup.step(X[~sup_idx], *unsup_data.values())\n",
    "    normalizer_train = train_loader.n_obs\n",
    "    total_epoch_loss_train = epoch_loss / normalizer_train\n",
    "\n",
    "    return total_epoch_loss_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "played-strike",
   "metadata": {
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[epoch 000]  average training loss: 1255.0783\n",
      "[epoch 040]  average training loss: 919.0868\n",
      "[epoch 080]  average training loss: 903.4886\n",
      "[epoch 120]  average training loss: 898.2071\n",
      "[epoch 160]  average training loss: 895.3621\n",
      "[epoch 200]  average training loss: 894.0363\n",
      "[epoch 240]  average training loss: 893.1848\n",
      "[epoch 249]  average training loss: 893.0040\n"
     ]
    }
   ],
   "source": [
    "NUM_EPOCHS = 250\n",
    "\n",
    "for epoch in range(NUM_EPOCHS):\n",
    "    total_epoch_loss_train = train(svi_sup, svi_unsup, loader)\n",
    "    if epoch % 40 == 0 or epoch == NUM_EPOCHS - 1:\n",
    "        print(f\"[epoch {epoch:03}]  average training loss: {total_epoch_loss_train:.4}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "inference-transition",
   "metadata": {},
   "source": [
    "Training is complete.\n",
    "We now run inference over the full dataset to extract latent representations and transfer cell type labels from pbmc to the unlabeled covid cells."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "changing-lewis",
   "metadata": {},
   "source": [
    "## Check the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "endless-summit",
   "metadata": {},
   "source": [
    "Get means of latent variables and cell labels predictions for the unlabelled data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "greatest-dating",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.obsm[\"X_cvae\"] = np.empty((dataset.shape[0], latent_dim), dtype=\"float32\")\n",
    "dataset.obs[\"cell_type_pred\"] = pd.Series(dtype=pbmc.obs[labels].dtype)\n",
    "loader = Loader(\n",
    "    chunk_size=512,\n",
    "    preload_nchunks=8192,\n",
    "    batch_size=8192,\n",
    "    shuffle=False,\n",
    "    preload_to_gpu=True,\n",
    "    return_index=True,\n",
    ").add_adata(dataset)\n",
    "\n",
    "for batch in loader:\n",
    "    latents = cvae.encoder(\n",
    "        batch[\"X\"].to_dense(),\n",
    "        torch.Tensor(\n",
    "            encoder_study.transform(batch[\"obs\"][\"orig.ident\"].to_numpy()[:, None])\n",
    "        ).cuda(),\n",
    "    )[:, :latent_dim].detach()\n",
    "\n",
    "    predict = cvae.classifier(latents).detach()\n",
    "    one_hot = torch.zeros(predict.shape[0], predict.shape[1], device=predict.device)\n",
    "    one_hot = (\n",
    "        one_hot.scatter_(1, predict.argmax(dim=-1, keepdim=True), 1.0).cpu().numpy()\n",
    "    )\n",
    "\n",
    "    dataset.obsm[\"X_cvae\"][batch[\"index\"]] = latents.cpu().numpy()\n",
    "    dataset.obs.iloc[batch[\"index\"], 8] = np.ravel(\n",
    "        encoder_celltype.inverse_transform(one_hot)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "optional-shell",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.obs[\"cell_type_joint\"] = dataset.obs[\"cell_type_pred\"]\n",
    "dataset.obs.loc[pbmc.obs_names, \"cell_type_joint\"] = pbmc.obs[labels]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "transparent-ready",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.neighbors(dataset, use_rep=\"X_cvae\")\n",
    "sc.tl.umap(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "pressing-milton",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(dataset, color=[\"dataset\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dependent-permit",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9397764644791178"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = (\n",
    "    dataset.obs[\"cell_type_pred\"].loc[pbmc.obs_names] == pbmc.obs[labels]\n",
    ").sum().item() / pbmc.n_obs\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accuracy-context",
   "metadata": {},
   "source": [
    "The model achieves ~94% accuracy on pbmc cell types, confirming that the shared latent space captures biologically meaningful structure.\n",
    "Below we visualize the joint UMAP colored by predicted cell type, which also covers the unlabeled covid cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "affecting-miami",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(dataset, color=\"cell_type_joint\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}