{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA Preprocessing\n",
    "\n",
    "PCA is a standard step in many RNA-seq analysis pipelines, where it serves as a denoising and dimensionality-reduction layer ahead of downstream tasks like clustering and visualization. Glass Box UMAP exposes a `pca_components` kwarg that folds this step into `fit` / `transform`, so raw features can be passed through directly.\n",
    "\n",
    "This notebook walks through what `pca_components` does to feature contributions, and how to reproduce its behavior by hand if you'd rather control the PCA step yourself.\n",
    "\n",
    "We'll use the [Bhattacharjee 2001 lung cancer microarray dataset](https://www.pnas.org/doi/abs/10.1073/pnas.191502998) for the demo. It's hosted on OpenML so loading is a one-liner.\n",
    "\n",
    ":::{warning}\n",
    "We do not take a position on whether PCA preprocessing is the right choice for your data. We simply recognize that it is a standard, field-specific step that many pipelines already include, and show how Glass Box UMAP fits into that workflow either way.\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the data\n",
    "\n",
    "`fetch_openml` downloads and caches the dataset. Class labels come back as integer codes. We map them to the lung subtype names from the original paper, inferred by matching class sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-11T22:17:21.685189Z",
     "iopub.status.busy": "2026-05-11T22:17:21.685110Z",
     "iopub.status.idle": "2026-05-11T22:17:22.698903Z",
     "shell.execute_reply": "2026-05-11T22:17:22.698430Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape: (203, 12600)\n",
      "classes: ['Adenocarcinoma', 'Carcinoid', 'Normal lung', 'Small cell', 'Squamous cell']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import fetch_openml\n",
    "\n",
    "CLASS_NAME_BY_SIZE = {\n",
    "    139: \"Adenocarcinoma\",\n",
    "    21: \"Squamous cell\",\n",
    "    20: \"Carcinoid\",\n",
    "    17: \"Normal lung\",\n",
    "    6: \"Small cell\",\n",
    "}\n",
    "\n",
    "lung = fetch_openml(data_id=45093, as_frame=True, parser=\"auto\")\n",
    "df = lung.frame\n",
    "y_int = df[\"type\"].astype(int).to_numpy()\n",
    "counts = pd.Series(y_int).value_counts().to_dict()\n",
    "int_to_name = {code: CLASS_NAME_BY_SIZE[n] for code, n in counts.items()}\n",
    "labels = np.array([int_to_name[c] for c in y_int])\n",
    "\n",
    "features = df.drop(columns=\"type\")\n",
    "feature_names = features.columns.tolist()\n",
    "X = np.log2(np.maximum(features.to_numpy(dtype=np.float32), 1.0))\n",
    "\n",
    "print(f\"X shape: {X.shape}\")\n",
    "print(f\"classes: {sorted(set(labels))}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approach 1: let `pca_components` handle it\n",
    "\n",
    "Set `pca_components` and pass the raw input straight in. Glass Box UMAP fits its own PCA inside `fit`, projects the input down, and trains the encoder on the reduced features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-11T22:17:22.700238Z",
     "iopub.status.busy": "2026-05-11T22:17:22.700137Z",
     "iopub.status.idle": "2026-05-11T22:17:37.899981Z",
     "shell.execute_reply": "2026-05-11T22:17:37.899582Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "embedding shape: (203, 2)\n"
     ]
    }
   ],
   "source": [
    "from glass_box_umap import GlassBoxUMAP\n",
    "\n",
    "reducer_a = GlassBoxUMAP(\n",
    "    pca_components=50,\n",
    "    random_state=42,\n",
    "    quiet=True,\n",
    ")\n",
    "reducer_a.fit(X)\n",
    "embedding_a = reducer_a.transform(X)\n",
    "print(f\"embedding shape: {embedding_a.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Where do contributions live?\n",
    "\n",
    "The encoder works in PCA space, but `compute_contributions` returns contributions in the **original feature space**. Glass Box UMAP projects the encoder's Jacobian back through the PCA basis before forming the gradient-times-input product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-11T22:17:37.901418Z",
     "iopub.status.busy": "2026-05-11T22:17:37.901312Z",
     "iopub.status.idle": "2026-05-11T22:17:37.997047Z",
     "shell.execute_reply": "2026-05-11T22:17:37.996688Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contributions shape: (203, 2, 12600)\n"
     ]
    }
   ],
   "source": [
    "contributions_a = reducer_a.compute_contributions(X)\n",
    "print(f\"contributions shape: {contributions_a.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the bokeh plotting helper bundled with Glass Box UMAP to inspect the embedding interactively.\n",
    "\n",
    ":::{admonition} Install the plotting extras\n",
    ":class: tip dropdown\n",
    "\n",
    "`glass_box_umap.plotting` is an optional dependency, that can be installed like so:\n",
    "\n",
    "```bash\n",
    "pip install \"glass-box-umap[plotting]\"\n",
    "# or\n",
    "uv pip install \"glass-box-umap[plotting]\"\n",
    "```\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-11T22:17:37.998379Z",
     "iopub.status.busy": "2026-05-11T22:17:37.998290Z",
     "iopub.status.idle": "2026-05-11T22:17:38.315185Z",
     "shell.execute_reply": "2026-05-11T22:17:38.314808Z"
    }
   },
   "outputs": [
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       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
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       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
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       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
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       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
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       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
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       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
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       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
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       "\n",
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       "\n",
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       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (let i = 0; i < js_urls.length; i++) {\n",
       "      const url = js_urls[i];\n",
       "      const element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.9.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.9.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.9.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.9.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.9.0.min.js\"];\n",
       "  const css_urls = [];\n",
       "\n",
       "  const inline_js = [    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "function(Bokeh) {\n",
       "    }\n",
       "  ];\n",
       "\n",
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       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      try {\n",
       "            for (let i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
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       "\n",
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       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "'use strict';\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded(error = null) {\n    const el = document.getElementById(null);\n    if (el != null) {\n      const html = (() => {\n        if (typeof root.Bokeh === \"undefined\") {\n          if (error == null) {\n            return \"BokehJS is loading ...\";\n          } else {\n            return \"BokehJS failed to load.\";\n          }\n        } else {\n          const prefix = `BokehJS ${root.Bokeh.version}`;\n          if (error == null) {\n            return `${prefix} successfully loaded.`;\n          } else {\n            return `${prefix} <b>encountered errors</b> while loading and may not function as expected.`;\n          }\n        }\n      })();\n      el.innerHTML = html;\n\n      if (error != null) {\n        const wrapper = document.createElement(\"div\");\n        wrapper.style.overflow = \"auto\";\n        wrapper.style.height = \"5em\";\n        wrapper.style.resize = \"vertical\";\n        const content = document.createElement(\"div\");\n        content.style.fontFamily = \"monospace\";\n        content.style.whiteSpace = \"pre-wrap\";\n        content.style.backgroundColor = \"rgb(255, 221, 221)\";\n        content.textContent = error.stack ?? error.toString();\n        wrapper.append(content);\n        el.append(wrapper);\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(() => display_loaded(error), 100);\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.9.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.9.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.9.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.9.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.9.0.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n      try {\n            for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n\n      } catch (error) {throw error;\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
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     "output_type": "display_data"
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    {
     "data": {
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idx = feature_names.indexOf(cb_obj.value);\\nif (idx < 0) { return; }\\nconst col = reduced_source.data[\\\"c\\\" + idx];\\nconst copy = new Float64Array(col.length);\\nlet lo = Infinity, hi = -Infinity;\\nfor (let i = 0; i < col.length; i++) {\\n    const v = col[i];\\n    copy[i] = v;\\n    if (v < lo) lo = v;\\n    if (v > hi) hi = v;\\n}\\nif (hi - lo < degenerate_eps) {\\n    const mid = (lo + hi) / 2;\\n    const span = Math.max(Math.abs(mid) * degenerate_frac, degenerate_min_span);\\n    lo = mid - span;\\n    hi = mid + span;\\n}\\nscatter_source.data[\\\"color_value\\\"] = copy;\\nif (values_source !== null) {\\n    const vcol = values_source.data[\\\"c\\\" + idx];\\n    const vcopy = new Float64Array(vcol.length);\\n    for (let i = 0; i < vcol.length; i++) vcopy[i] = vcol[i];\\n    scatter_source.data[\\\"picker_data_value\\\"] = vcopy;\\n}\\nscatter_source.change.emit();\\nmapper.low = lo;\\nmapper.high = hi;\\n\"}}]]]},\"styles\":{\"type\":\"map\",\"entries\":[[\"color\",\"#444444\"]]},\"stylesheets\":[{\"type\":\"object\",\"name\":\"InlineStyleSheet\",\"id\":\"p1138\",\"attributes\":{\"css\":\".bk-input { color: #444444; }\"}}],\"visible\":false,\"width\":260,\"title\":\"Search for feature\",\"value\":\"613_at\",\"placeholder\":\"start 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t = cb_obj.value;\\nconst tfg = scatter_source.data[\\\"top_feature_group\\\"];\\nconst ranks = scatter_source.data[\\\"sample_rank\\\"];\\nconst n = ranks.length;\\nfor (let i = 0; i < n; i++) {\\n    tfg[i] = ranks[i] < t ? names_by_rank[ranks[i]] : \\\"(other)\\\";\\n}\\nscatter_source.change.emit();\\n\"}}]]]},\"styles\":{\"type\":\"map\",\"entries\":[[\"color\",\"#444444\"]]},\"visible\":false,\"width\":260,\"title\":\"Top features\",\"start\":1,\"end\":61,\"value\":20}}]]},\"code\":\"const mode = color_modes[cb_obj.active];\\nconst is_group = (mode === \\\"Group\\\");\\nconst is_feature = (mode === \\\"Feature\\\");\\nconst is_top = (mode === \\\"Top feature\\\");\\nfor (const g of group_glyphs) g.visible = is_group;\\ntop_other_glyph.visible = is_top;\\ntop_named_glyph.visible = is_top;\\ngradient_glyph.visible = is_feature;\\ncolor_bar.visible = is_feature;\\nfeature_picker.visible = is_feature;\\ntop_n_slider.visible = is_top;\\n\"}}]]]},\"labels\":[\"Group\",\"Feature\",\"Top feature\"],\"active\":0}}]}},{\"id\":\"p1139\"},{\"id\":\"p1140\"},{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1017\",\"attributes\":{\"sizing_mode\":\"stretch_both\",\"x_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1018\"},\"y_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1019\"},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1027\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1028\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1020\",\"attributes\":{\"text\":\"Embedding \\u2014 lasso or box-select to filter\"}},\"renderers\":[{\"id\":\"p1066\"},{\"id\":\"p1076\"},{\"id\":\"p1085\"},{\"id\":\"p1094\"},{\"id\":\"p1103\"},{\"id\":\"p1112\"},{\"id\":\"p1121\"},{\"id\":\"p1128\"}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1026\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p1039\"},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p1040\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p1041\",\"attributes\":{\"dimensions\":\"both\",\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p1042\",\"attributes\":{\"syncable\":false,\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5,\"level\":\"overlay\",\"visible\":false,\"left\":{\"type\":\"number\",\"value\":\"nan\"},\"right\":{\"type\":\"number\",\"value\":\"nan\"},\"top\":{\"type\":\"number\",\"value\":\"nan\"},\"bottom\":{\"type\":\"number\",\"value\":\"nan\"},\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"top_units\":\"canvas\",\"bottom_units\":\"canvas\",\"handles\":{\"type\":\"object\",\"name\":\"BoxInteractionHandles\",\"id\":\"p1048\",\"attributes\":{\"all\":{\"type\":\"object\",\"name\":\"AreaVisuals\",\"id\":\"p1047\",\"attributes\":{\"fill_color\":\"white\",\"hover_fill_color\":\"lightgray\"}}}}}}}},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p1049\"},{\"type\":\"object\",\"name\":\"LassoSelectTool\",\"id\":\"p1050\",\"attributes\":{\"renderers\":\"auto\",\"overlay\":{\"type\":\"object\",\"name\":\"PolyAnnotation\",\"id\":\"p1051\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"xs\":[],\"ys\":[],\"editable\":true,\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"BoxSelectTool\",\"id\":\"p1052\",\"attributes\":{\"renderers\":\"auto\",\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p1053\",\"attributes\":{\"syncable\":false,\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5,\"level\":\"overlay\",\"visible\":false,\"left\":{\"type\":\"number\",\"value\":\"nan\"},\"right\":{\"type\":\"number\",\"value\":\"nan\"},\"top\":{\"type\":\"number\",\"value\":\"nan\"},\"bottom\":{\"type\":\"number\",\"value\":\"nan\"},\"editable\":true,\"handles\":{\"type\":\"object\",\"name\":\"BoxInteractionHandles\",\"id\":\"p1059\",\"attributes\":{\"all\":{\"type\":\"object\",\"name\":\"AreaVisuals\",\"id\":\"p1058\",\"attributes\":{\"fill_color\":\"white\",\"hover_fill_color\":\"lightgray\"}}}}}}}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p1133\",\"attributes\":{\"renderers\":[{\"id\":\"p1128\"}],\"tooltips\":\"<div>index: @index &nbsp;&middot;&nbsp; group: @group &nbsp;&middot;&nbsp; value: @picker_data_value{0.000}</div>\",\"sort_by\":null}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p1134\",\"attributes\":{\"renderers\":[{\"id\":\"p1076\"},{\"id\":\"p1066\"}],\"tooltips\":\"<div>index: @index &nbsp;&middot;&nbsp; group: @group &nbsp;&middot;&nbsp; value: @top_data_value{0.000} &nbsp;&middot;&nbsp; feature: @top_feature_name</div>\",\"sort_by\":null}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p1135\",\"attributes\":{\"renderers\":[{\"id\":\"p1085\"},{\"id\":\"p1094\"},{\"id\":\"p1103\"},{\"id\":\"p1112\"},{\"id\":\"p1121\"}],\"tooltips\":\"<div>index: @index &nbsp;&middot;&nbsp; group: @group</div>\",\"sort_by\":null}}]}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1034\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1035\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1036\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1037\"}}}],\"right\":[{\"id\":\"p1131\"}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1029\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1030\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1031\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1032\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1033\",\"attributes\":{\"axis\":{\"id\":\"p1029\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1038\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1034\"}}}],\"output_backend\":\"webgl\"}}]}},{\"type\":\"object\",\"name\":\"Column\",\"id\":\"p1184\",\"attributes\":{\"styles\":{\"type\":\"map\",\"entries\":[[\"background-color\",\"white\"],[\"flex\",\"0 0 40%\"],[\"min-width\",\"0\"]]},\"sizing_mode\":\"stretch_both\",\"children\":[{\"id\":\"p1182\"},{\"id\":\"p1181\"},{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1154\",\"attributes\":{\"sizing_mode\":\"stretch_both\",\"x_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1156\"},\"y_range\":{\"id\":\"p1153\"},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1163\"},\"y_scale\":{\"type\":\"object\",\"name\":\"CategoricalScale\",\"id\":\"p1164\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1161\"},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1178\",\"attributes\":{\"data_source\":{\"id\":\"p1150\"},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1179\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1180\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"HBar\",\"id\":\"p1175\",\"attributes\":{\"y\":{\"type\":\"field\",\"field\":\"feature\"},\"height\":{\"type\":\"value\",\"value\":0.8},\"right\":{\"type\":\"field\",\"field\":\"mean\"},\"line_color\":{\"type\":\"value\",\"value\":\"#756bb1\"},\"fill_color\":{\"type\":\"value\",\"value\":\"#756bb1\"},\"hatch_color\":{\"type\":\"value\",\"value\":\"#756bb1\"}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"HBar\",\"id\":\"p1176\",\"attributes\":{\"y\":{\"type\":\"field\",\"field\":\"feature\"},\"height\":{\"type\":\"value\",\"value\":0.8},\"right\":{\"type\":\"field\",\"field\":\"mean\"},\"line_color\":{\"type\":\"value\",\"value\":\"#756bb1\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"value\",\"value\":\"#756bb1\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_color\":{\"type\":\"value\",\"value\":\"#756bb1\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"HBar\",\"id\":\"p1177\",\"attributes\":{\"y\":{\"type\":\"field\",\"field\":\"feature\"},\"height\":{\"type\":\"value\",\"value\":0.8},\"right\":{\"type\":\"field\",\"field\":\"mean\"},\"line_color\":{\"type\":\"value\",\"value\":\"#756bb1\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"value\",\"value\":\"#756bb1\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_color\":{\"type\":\"value\",\"value\":\"#756bb1\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1162\"},\"toolbar_location\":null,\"left\":[{\"type\":\"object\",\"name\":\"CategoricalAxis\",\"id\":\"p1170\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"CategoricalTicker\",\"id\":\"p1171\"},\"formatter\":{\"type\":\"object\",\"name\":\"CategoricalTickFormatter\",\"id\":\"p1172\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1173\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1165\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1166\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1167\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1168\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1169\",\"attributes\":{\"axis\":{\"id\":\"p1165\"},\"grid_line_color\":null}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1174\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1170\"}}}]}}]}}]}}]}};\n",
       "  const render_items = [{\"docid\":\"f2bfa2cc-b2ef-4f60-900f-775fb085ddae\",\"roots\":{\"p1187\":\"af98c8a6-8576-4cba-b8b0-909114adca2f\"},\"root_ids\":[\"p1187\"]}];\n",
       "  void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1187"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from glass_box_umap.plotting import output_notebook, plot_embedding, show\n",
    "\n",
    "output_notebook(hide_banner=True)\n",
    "\n",
    "show(\n",
    "    plot_embedding(\n",
    "        Z=embedding_a,\n",
    "        contributions=contributions_a,\n",
    "        group_names=labels,\n",
    "        feature_names=feature_names,\n",
    "        feature_values=X,\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approach 2: do PCA yourself, then use `project_jacobian`\n",
    "\n",
    "If you already PCA-reduce upstream of UMAP (a common shape for RNA-seq pipelines), you can skip `pca_components` and pass the reduced features in directly. The encoder is then trained on those features, and `compute_contributions` returns contributions in **PCA component space** instead of the original feature space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-11T22:17:38.317873Z",
     "iopub.status.busy": "2026-05-11T22:17:38.317714Z",
     "iopub.status.idle": "2026-05-11T22:17:44.203785Z",
     "shell.execute_reply": "2026-05-11T22:17:44.203389Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_pca shape: (203, 50)\n",
      "contributions shape: (203, 2, 50)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=50, random_state=42)\n",
    "X_pca = pca.fit_transform(X).astype(np.float32)\n",
    "\n",
    "reducer_b = GlassBoxUMAP(\n",
    "    pca_components=None,\n",
    "    random_state=42,\n",
    "    quiet=True,\n",
    ")\n",
    "reducer_b.fit(X_pca)\n",
    "embedding_b = reducer_b.transform(X_pca)\n",
    "\n",
    "contributions_b = reducer_b.compute_contributions(X_pca)\n",
    "print(f\"X_pca shape: {X_pca.shape}\")\n",
    "print(f\"contributions shape: {contributions_b.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get back to the original feature space, replicate what `pca_components` does internally. The encoder was trained on mean-centered PCA features. Sklearn's `PCA` mean-centers its input by construction, so `X_pca` is already near-zero-mean, and Glass Box UMAP applies its own centering pass on top of whatever you hand it. We mirror that here, subtracting `X_pca.mean(axis=0)` before computing the Jacobian so we're querying the encoder at the same point it saw during training. From there, `project_jacobian` chains the PCA components matrix onto the resulting Jacobian, taking it back to the original feature space. Multiplying by the mean-centered input then gives feature-space contributions.\n",
    "\n",
    ":::{admonition} compute_jacobian / project_jacobian API\n",
    ":class: api, dropdown\n",
    "\n",
    "From the API docs:\n",
    "\n",
    "```{eval-rst}\n",
    ".. autofunction:: glass_box_umap.jacobian.compute_jacobian\n",
    "    :noindex:\n",
    "\n",
    ".. autofunction:: glass_box_umap.jacobian.project_jacobian\n",
    "    :noindex:\n",
    "```\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-11T22:17:44.205103Z",
     "iopub.status.busy": "2026-05-11T22:17:44.205022Z",
     "iopub.status.idle": "2026-05-11T22:17:44.236659Z",
     "shell.execute_reply": "2026-05-11T22:17:44.236178Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "recovered contributions shape: (203, 2, 12600)\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from glass_box_umap.jacobian import project_jacobian\n",
    "\n",
    "X_pca_centered = torch.from_numpy(X_pca - X_pca.mean(axis=0)).to(reducer_b._device)\n",
    "J_pca = reducer_b.compute_jacobian(X_pca_centered)\n",
    "\n",
    "P = torch.tensor(pca.components_, dtype=torch.float32, device=reducer_b._device)\n",
    "J_features = project_jacobian(J_pca, P)\n",
    "\n",
    "X_centered = torch.from_numpy(X - pca.mean_).to(reducer_b._device).unsqueeze(1)\n",
    "contributions_b_features = (J_features * X_centered).cpu().numpy()\n",
    "print(f\"recovered contributions shape: {contributions_b_features.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The recovered contributions are nearly identical to the ones approach 1 produced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-11T22:17:44.237836Z",
     "iopub.status.busy": "2026-05-11T22:17:44.237760Z",
     "iopub.status.idle": "2026-05-11T22:17:44.245411Z",
     "shell.execute_reply": "2026-05-11T22:17:44.244856Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean abs diff: 0.0000182\n"
     ]
    }
   ],
   "source": [
    "mean_abs_diff = np.mean(np.abs(contributions_a - contributions_b_features))\n",
    "print(f\"mean abs diff: {mean_abs_diff:.7f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Takeaways\n",
    "\n",
    "`pca_components` is the convenient default. Pass raw features in, and `compute_contributions` returns contributions in the same raw feature space. If you'd rather run PCA yourself, Glass Box UMAP will train on the PCA features as is, and contributions will live in PCA component space. To get back to feature-space contributions in that case, project from PCA space yourself with `compute_jacobian` and `project_jacobian`. That's the same recipe Glass Box UMAP runs internally when `pca_components` is set."
   ]
  }
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