Read the Docs build information
Build id: 1448394
Project: feature-labs-inc-evalml
Version: latest
Commit: 5b4a1ca9ceb8da1120f67af28e76266f37462ef2
Date: 2023-05-02T17:15:44.122676Z
State: finished
Success: False


[rtd-command-info] start-time: 2023-05-02T17:15:46.413563Z, end-time: 2023-05-02T17:15:49.843650Z, duration: 3, exit-code: 0
git clone --no-single-branch --depth 50 https://github.com/alteryx/evalml.git .
Cloning into '.'...

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git checkout --force origin/main
Note: switching to 'origin/main'.

You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by switching back to a branch.

If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -c with the switch command. Example:

  git switch -c <new-branch-name>

Or undo this operation with:

  git switch -

Turn off this advice by setting config variable advice.detachedHead to false

HEAD is now at 5b4a1ca9 Release v0.75.0 (#4167)

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git clean -d -f -f


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python3.8 -mvirtualenv $READTHEDOCS_VIRTUALENV_PATH
Using base prefix '/home/docs/.pyenv/versions/3.8.6'
New python executable in /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/bin/python3.8
Also creating executable in /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/bin/python
Installing setuptools, pip, wheel...
done.

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python -m pip install --upgrade --no-cache-dir pip setuptools<58.3.0
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python -m pip install --upgrade --no-cache-dir pillow mock==1.0.1 alabaster>=0.7,<0.8,!=0.7.5 commonmark==0.9.1 recommonmark==0.5.0 sphinx<2 sphinx-rtd-theme<0.5 readthedocs-sphinx-ext<2.3 jinja2<3.1.0
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Successfully installed MarkupSafe-2.1.2 Pygments-2.15.1 alabaster-0.7.13 babel-2.12.1 certifi-2022.12.7 charset-normalizer-3.1.0 commonmark-0.9.1 docutils-0.17.1 idna-3.4 imagesize-1.4.1 jinja2-3.0.3 mock-1.0.1 packaging-23.1 pillow-9.5.0 pytz-2023.3 readthedocs-sphinx-ext-2.2.0 recommonmark-0.5.0 requests-2.29.0 six-1.16.0 snowballstemmer-2.2.0 sphinx-1.8.6 sphinx-rtd-theme-0.4.3 sphinxcontrib-serializinghtml-1.1.5 sphinxcontrib-websupport-1.2.4 urllib3-1.26.15

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  Building wheel for evalml (pyproject.toml): started
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  Created wheel for evalml: filename=evalml-0.75.0-py3-none-any.whl size=6544182 sha256=cb258317f7b94ce012b988aac019429b2b4ba1ab16651af6ff988653e045c44a
  Stored in directory: /tmp/pip-ephem-wheel-cache-ppaihmxd/wheels/46/d8/38/4b637ddfe642e0ae69adb3c86758f65c78fbb617611ecc4c0f
  Building wheel for PyMeeus (setup.py): started
  Building wheel for PyMeeus (setup.py): finished with status 'done'
  Created wheel for PyMeeus: filename=PyMeeus-0.5.12-py3-none-any.whl size=732017 sha256=e698cf84c5befdf617c8c5d925a11ad1b370d99d26e787cdcc380a23de0eabd5
  Stored in directory: /tmp/pip-ephem-wheel-cache-ppaihmxd/wheels/c2/3a/3d/11734e652782d3f823a08aae1c452e887eb16349750cca3f8a
  Building wheel for wrapt (setup.py): started
  Building wheel for wrapt (setup.py): finished with status 'done'
  Created wheel for wrapt: filename=wrapt-1.12.1-cp38-cp38-linux_x86_64.whl size=78118 sha256=b96dc245195af1a588b53622ebd5004a12cb4262e878ea312330818e0017e693
  Stored in directory: /tmp/pip-ephem-wheel-cache-ppaihmxd/wheels/5f/fd/9e/b6cf5890494cb8ef0b5eaff72e5d55a70fb56316007d6dfe73
Successfully built lime evalml PyMeeus wrapt
Installing collected packages: wrapt, webencodings, wcwidth, texttable, sortedcontainers, PyMeeus, pure-eval, ptyprocess, pickleshare, msgpack, mistune, korean-lunar-calendar, kaleido, fastjsonschema, executing, ephem, distlib, backcall, zipp, zict, widgetsnbextension, vowpalwabbit, unidecode, typing-extensions, traitlets, tqdm, tornado, toolz, tomli, tokenize-rt, tinycss2, threadpoolctl, tenacity, tblib, sphinxcontrib-qthelp, sphinxcontrib-jsmath, sphinxcontrib-htmlhelp, sphinxcontrib-devhelp, sphinxcontrib-applehelp, soupsieve, slicer, setuptools, ruff, regex, pyzmq, pyyaml, python-dateutil, pyrsistent, pyparsing, py, psutil, prompt-toolkit, pluggy, platformdirs, pkgutil-resolve-name, pexpect, pathspec, parso, pandocfilters, numpy, networkx, nest-asyncio, mypy-extensions, mdurl, locket, llvmlite, lazy-object-proxy, lazy_loader, kiwisolver, jupyterlab-widgets, jupyterlab-pygments, joblib, jinja2, iniconfig, identify, hijri-converter, graphviz, fsspec, fonttools, filelock, execnet, docutils, deprecated, defusedxml, decorator, debugpy, darglint, Cython, cycler, coverage, convertdate, colorama, cloudpickle, click, cfgv, bleach, attrs, asttokens, accessible-pygments, virtualenv, tifffile, stack-data, scipy, PyWavelets, pytest, pyaml, plotly, patsy, partd, pandas, nodeenv, nltk, matplotlib-inline, markdown-it-py, LunarCalendar, jupyter-core, jedi, importlib-resources, importlib-metadata, imageio, holidays, contourpy, comm, black, beautifulsoup4, astroid, xgboost, statsmodels, Sphinx, scikit-learn, scikit-image, pytest-timeout, pytest-forked, pytest-cov, pre-commit, numba, mdit-py-plugins, matplotlib, jupyter-client, jsonschema, ipython, dask, cmdstanpy, woodwork, sphinx-inline-tabs, sphinx-copybutton, sphinx-autoapi, sktime, shap, seaborn, scikit-optimize, pytest-xdist, pydata-sphinx-theme, prophet, pmdarima, nbformat, myst-parser, lime, lightgbm, ipykernel, imbalanced-learn, distributed, category-encoders, catboost, nbval, nbclient, ipywidgets, featuretools, nlp-primitives, nbconvert, nbsphinx, evalml
  Attempting uninstall: setuptools
    Found existing installation: setuptools 58.2.0
    Uninstalling setuptools-58.2.0:
      Successfully uninstalled setuptools-58.2.0
  Attempting uninstall: jinja2
    Found existing installation: Jinja2 3.0.3
    Uninstalling Jinja2-3.0.3:
      Successfully uninstalled Jinja2-3.0.3
  Attempting uninstall: docutils
    Found existing installation: docutils 0.17.1
    Uninstalling docutils-0.17.1:
      Successfully uninstalled docutils-0.17.1
  Attempting uninstall: Sphinx
    Found existing installation: Sphinx 1.8.6
    Uninstalling Sphinx-1.8.6:
      Successfully uninstalled Sphinx-1.8.6
Successfully installed Cython-0.29.34 LunarCalendar-0.0.9 PyMeeus-0.5.12 PyWavelets-1.4.1 Sphinx-4.5.0 accessible-pygments-0.0.4 astroid-2.6.6 asttokens-2.2.1 attrs-23.1.0 backcall-0.2.0 beautifulsoup4-4.12.2 black-23.3.0 bleach-6.0.0 catboost-1.2 category-encoders-2.5.1.post0 cfgv-3.3.1 click-8.1.3 cloudpickle-2.2.1 cmdstanpy-1.1.0 colorama-0.4.6 comm-0.1.3 contourpy-1.0.7 convertdate-2.4.0 coverage-7.2.5 cycler-0.11.0 darglint-1.8.0 dask-2023.3.2 debugpy-1.6.7 decorator-5.1.1 defusedxml-0.7.1 deprecated-1.2.13 distlib-0.3.6 distributed-2023.3.2.1 docutils-0.16 ephem-4.1.4 evalml-0.75.0 execnet-1.9.0 executing-1.2.0 fastjsonschema-2.16.3 featuretools-1.26.0 filelock-3.12.0 fonttools-4.39.3 fsspec-2023.4.0 graphviz-0.20.1 hijri-converter-2.3.1 holidays-0.20 identify-2.5.23 imageio-2.28.1 imbalanced-learn-0.10.1 importlib-metadata-6.6.0 importlib-resources-5.12.0 iniconfig-2.0.0 ipykernel-6.22.0 ipython-8.12.0 ipywidgets-8.0.4 jedi-0.18.2 jinja2-3.1.2 joblib-1.2.0 jsonschema-4.17.3 jupyter-client-8.2.0 jupyter-core-5.3.0 jupyterlab-pygments-0.2.2 jupyterlab-widgets-3.0.7 kaleido-0.2.1 kiwisolver-1.4.4 korean-lunar-calendar-0.3.1 lazy-object-proxy-1.9.0 lazy_loader-0.2 lightgbm-3.3.5 lime-0.2.0.1 llvmlite-0.40.0 locket-1.0.0 markdown-it-py-2.2.0 matplotlib-3.7.1 matplotlib-inline-0.1.6 mdit-py-plugins-0.3.5 mdurl-0.1.2 mistune-2.0.5 msgpack-1.0.5 mypy-extensions-1.0.0 myst-parser-0.16.1 nbclient-0.7.4 nbconvert-7.3.1 nbformat-5.8.0 nbsphinx-0.8.12 nbval-0.9.3 nest-asyncio-1.5.6 networkx-3.1 nlp-primitives-2.11.0 nltk-3.8.1 nodeenv-1.7.0 numba-0.57.0 numpy-1.24.3 pandas-1.5.3 pandocfilters-1.5.0 parso-0.8.3 partd-1.4.0 pathspec-0.11.1 patsy-0.5.3 pexpect-4.8.0 pickleshare-0.7.5 pkgutil-resolve-name-1.3.10 platformdirs-3.5.0 plotly-5.14.1 pluggy-1.0.0 pmdarima-2.0.3 pre-commit-3.3.1 prompt-toolkit-3.0.38 prophet-1.1.2 psutil-5.9.5 ptyprocess-0.7.0 pure-eval-0.2.2 py-1.11.0 pyaml-21.10.1 pydata-sphinx-theme-0.13.3 pyparsing-3.0.9 pyrsistent-0.19.3 pytest-7.1.2 pytest-cov-2.10.1 pytest-forked-1.6.0 pytest-timeout-1.4.2 pytest-xdist-2.1.0 python-dateutil-2.8.2 pyyaml-5.4 pyzmq-25.0.2 regex-2023.5.2 ruff-0.0.228 scikit-image-0.20.0 scikit-learn-1.2.2 scikit-optimize-0.9.0 scipy-1.9.1 seaborn-0.12.2 setuptools-67.7.2 shap-0.41.0 sktime-0.17.0 slicer-0.0.7 sortedcontainers-2.4.0 soupsieve-2.4.1 sphinx-autoapi-1.8.3 sphinx-copybutton-0.4.0 sphinx-inline-tabs-2022.1.2b11 sphinxcontrib-applehelp-1.0.4 sphinxcontrib-devhelp-1.0.2 sphinxcontrib-htmlhelp-2.0.1 sphinxcontrib-jsmath-1.0.1 sphinxcontrib-qthelp-1.0.3 stack-data-0.6.2 statsmodels-0.13.5 tblib-1.7.0 tenacity-8.2.2 texttable-1.6.7 threadpoolctl-3.1.0 tifffile-2023.4.12 tinycss2-1.2.1 tokenize-rt-5.0.0 tomli-2.0.1 toolz-0.12.0 tornado-6.3.1 tqdm-4.65.0 traitlets-5.9.0 typing-extensions-4.5.0 unidecode-1.3.6 virtualenv-20.23.0 vowpalwabbit-9.8.0 wcwidth-0.2.6 webencodings-0.5.1 widgetsnbextension-4.0.7 woodwork-0.23.0 wrapt-1.12.1 xgboost-1.7.5 zict-3.0.0 zipp-3.15.0

[rtd-command-info] start-time: 2023-05-02T17:17:47.653890Z, end-time: 2023-05-02T17:17:47.716874Z, duration: 0, exit-code: 0
cat docs/source/conf.py
# -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config

# -- Path setup --------------------------------------------------------------

# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.

import os
import shutil
import sys
from pathlib import Path

from sphinx.domains.python import PythonDomain
from sphinx.ext.autodoc import Documenter, MethodDocumenter

import evalml

path = os.path.join("..", "..")
sys.path.insert(0, os.path.abspath(path))


# -- Project information -----------------------------------------------------

project = "EvalML"
copyright = "2020, Alteryx, Inc."
author = "Alteryx, Inc."

# The short X.Y version
version = evalml.__version__
# The full version, including alpha/beta/rc tags
release = evalml.__version__


# -- General configuration ---------------------------------------------------

# If your documentation needs a minimal Sphinx version, state it here.
#
# needs_sphinx = '1.0'

# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
    "nbsphinx",
    "autoapi.extension",
    "sphinx.ext.napoleon",
    "sphinx.ext.viewcode",
    "sphinx.ext.extlinks",
    "sphinx.ext.graphviz",
    "sphinx.ext.inheritance_diagram",
    "sphinx_inline_tabs",
    "sphinx_copybutton",
    "myst_parser",
]

autoapi_dirs = ["../../evalml"]
autoapi_ignore = ["*/evalml/tests/*"]
autoapi_options = [
    "members",
    "undoc-members",
    "show-module-summary",
    "imported-members",
    "inherited-members",
]
autoapi_add_toctree_entry = False
autoapi_template_dir = "_auto_api_templates"

# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
#
source_suffix = ".rst"

# The main toctree document.
master_doc = "index"

# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = None

# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = ["**.ipynb_checkpoints", "_templates", "_auto_api_templates"]

suppress_warnings = ["autoapi"]


# The name of the Pygments (syntax highlighting) style to use.
pygments_style = None


# -- Options for HTML output -------------------------------------------------

# The theme to use for HTML and HTML Help pages.  See the documentation for
# a list of builtin themes.
#
html_theme = "pydata_sphinx_theme"

# Theme options are theme-specific and customize the look and feel of a theme
# further.  For a list of options available for each theme, see the
# documentation.
#
html_theme_options = {
    "icon_links": [
        {
            "name": "GitHub",
            "url": "https://github.com/alteryx/evalml",
            "icon": "fab fa-github-square",
            "type": "fontawesome",
        },
        {
            "name": "Twitter",
            "url": "https://twitter.com/AlteryxOSS",
            "icon": "fab fa-twitter-square",
            "type": "fontawesome",
        },
        {
            "name": "Slack",
            "url": "https://join.slack.com/t/alteryx-oss/shared_invite/zt-182tyvuxv-NzIn6eiCEf8TBziuKp0bNA",
            "icon": "fab fa-slack",
            "type": "fontawesome",
        },
        {
            "name": "StackOverflow",
            "url": "https://stackoverflow.com/questions/tagged/evalml",
            "icon": "fab fa-stack-overflow",
            "type": "fontawesome",
        },
    ],
    "collapse_navigation": False,
    "navigation_depth": 2,
}

# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
html_logo = "images/evalml_horizontal.svg"

# The name of an image file (relative to this directory) to use as a favicon of
# the docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
html_favicon = "images/favicon.ico"

# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ["_static"]

# Custom sidebar templates, must be a dictionary that maps document names
# to template names.
#
# The default sidebars (for documents that don't match any pattern) are
# defined by theme itself.  Builtin themes are using these templates by
# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
# 'searchbox.html']``.
#
# html_sidebars = {}
html_js_files = [
    "https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG.js",
]


# Downgrading MathJax to version 2 to fix plot rendering. For some reason, not fixed in latest plotly.
# See https://github.com/spatialaudio/nbsphinx/issues/572 for more details.
mathjax_path = "https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG.js"
mathjax2_config = {
    "tex2jax": {
        "inlineMath": [["$", "$"], ["\\(", "\\)"]],
        "processEscapes": True,
        "ignoreClass": "document",
        "processClass": "math|output_area",
    },
}

# -- Options for HTMLHelp output ---------------------------------------------

# Output file base name for HTML help builder.
htmlhelp_basename = "EvalMLdoc"


# -- Options for LaTeX output ------------------------------------------------

latex_elements = {
    # The paper size ('letterpaper' or 'a4paper').
    #
    # 'papersize': 'letterpaper',
    # The font size ('10pt', '11pt' or '12pt').
    #
    # 'pointsize': '10pt',
    # Additional stuff for the LaTeX preamble.
    #
    # 'preamble': '',
    # Latex figure (float) alignment
    #
    # 'figure_align': 'htbp',
}

# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
#  author, documentclass [howto, manual, or own class]).
latex_documents = [
    (master_doc, "EvalML.tex", "EvalML Documentation", author, "manual"),
]


# -- Options for manual page output ------------------------------------------

# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [(master_doc, "evalml", "EvalML Documentation", [author], 1)]


# -- Options for Texinfo output ----------------------------------------------

# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
#  dir menu entry, description, category)
texinfo_documents = [
    (
        master_doc,
        "EvalML",
        "EvalML Documentation",
        author,
        "EvalML",
        "One line description of project.",
        "Miscellaneous",
    ),
]

# -- Options for Epub output -------------------------------------------------

# Bibliographic Dublin Core info.
epub_title = project

# The unique identifier of the text. This can be a ISBN number
# or the project homepage.
#
# epub_identifier = ''

# A unique identification for the text.
#
# epub_uid = ''

# A list of files that should not be packed into the epub file.
epub_exclude_files = ["search.html"]


# -- Extension configuration -------------------------------------------------

# If evalml is open-sourced: replace github specific style.css
extlinks = {
    "issue": ("https://github.com/alteryx/evalml/issues/%s", "#"),
    "pr": ("https://github.com/alteryx/evalml/pull/%s", "#"),
    "user": ("https://github.com/%s", "@"),
}

html_show_sphinx = False
nbsphinx_execute = "always"
nbsphinx_timeout = (
    600  # sphinx defaults each cell to 30 seconds so we need to override here
)

inheritance_graph_attrs = dict(rankdir="TB", dpi=192.0, ranksep=0.02)
inheritance_node_attrs = dict(fontsize=6, height=0.15, width=5, fixedsize=True)
inheritance_edge_attrs = dict(arrowsize=0.1)


class AccessorLevelDocumenter(Documenter):
    """Documenter subclass for objects on accessor level (methods, attributes).

    Referenced pandas-sphinx-theme (https://github.com/pandas-dev/pandas-sphinx-theme)
    and sphinx-doc (https://github.com/sphinx-doc/sphinx/blob/8c7faed6fcbc6b7d40f497698cb80fc10aee1ab3/sphinx/ext/autodoc/__init__.py#L846)
    """

    def resolve_name(self, modname, parents, path, base):
        modname = "evalml"
        mod_cls = path.rstrip(".")
        mod_cls = mod_cls.split(".")
        return modname, mod_cls + [base]


class AccessorCallableDocumenter(AccessorLevelDocumenter, MethodDocumenter):
    """This documenter lets us removes .__call__ from the method signature for callable accessors like Series.plot."""

    objtype = "accessorcallable"
    directivetype = "method"

    # lower than MethodDocumenter; otherwise the doc build prints warnings
    priority = 0.5

    def format_name(self):
        return MethodDocumenter.format_name(self).rstrip(".__call__")


class AccessorMethodDocumenter(AccessorLevelDocumenter, MethodDocumenter):
    objtype = "accessormethod"
    directivetype = "method"

    # lower than MethodDocumenter so this is not chosen for normal methods
    priority = 0.6


class PatchedPythonDomain(PythonDomain):
    """To disable cross-reference warning: https://github.com/sphinx-doc/sphinx/issues/3866."""

    def resolve_xref(self, env, fromdocname, builder, typ, target, node, contnode):
        if "refspecific" in node:
            del node["refspecific"]
        return super(PatchedPythonDomain, self).resolve_xref(
            env,
            fromdocname,
            builder,
            typ,
            target,
            node,
            contnode,
        )


def setup(app):
    p = Path("/home/docs/.ipython/profile_default/startup")
    if p.exists():
        print(f"Adding disable-warnings.py and set-headers.py to {str(p)}")
        p.mkdir(parents=True, exist_ok=True)
        shutil.copy(
            "disable-warnings.py",
            "/home/docs/.ipython/profile_default/startup/",
        )
        shutil.copy("set-headers.py", "/home/docs/.ipython/profile_default/startup")
    app.add_domain(PatchedPythonDomain, override=True)
    app.add_js_file(
        "https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js",
    )
    app.add_css_file("style.css")
    app.add_autodocumenter(AccessorCallableDocumenter)
    app.add_autodocumenter(AccessorMethodDocumenter)




###########################################################################
#          auto-created readthedocs.org specific configuration            #
###########################################################################


#
# The following code was added during an automated build on readthedocs.org
# It is auto created and injected for every build. The result is based on the
# conf.py.tmpl file found in the readthedocs.org codebase:
# https://github.com/rtfd/readthedocs.org/blob/main/readthedocs/doc_builder/templates/doc_builder/conf.py.tmpl
#
# Note: this file shouldn't rely on extra dependencies.

import importlib
import sys
import os.path

# Borrowed from six.
PY3 = sys.version_info[0] == 3
string_types = str if PY3 else basestring

from sphinx import version_info

# Get suffix for proper linking to GitHub
# This is deprecated in Sphinx 1.3+,
# as each page can have its own suffix
if globals().get('source_suffix', False):
    if isinstance(source_suffix, string_types):
        SUFFIX = source_suffix
    elif isinstance(source_suffix, (list, tuple)):
        # Sphinx >= 1.3 supports list/tuple to define multiple suffixes
        SUFFIX = source_suffix[0]
    elif isinstance(source_suffix, dict):
        # Sphinx >= 1.8 supports a mapping dictionary for multiple suffixes
        SUFFIX = list(source_suffix.keys())[0]  # make a ``list()`` for py2/py3 compatibility
    else:
        # default to .rst
        SUFFIX = '.rst'
else:
    SUFFIX = '.rst'

# Add RTD Static Path. Add to the end because it overwrites previous files.
if not 'html_static_path' in globals():
    html_static_path = []
if os.path.exists('_static'):
    html_static_path.append('_static')

# Add RTD Theme only if they aren't overriding it already
using_rtd_theme = (
    (
        'html_theme' in globals() and
        html_theme in ['default'] and
        # Allow people to bail with a hack of having an html_style
        'html_style' not in globals()
    ) or 'html_theme' not in globals()
)
if using_rtd_theme:
    html_theme = 'sphinx_rtd_theme'
    html_style = None
    html_theme_options = {}


# This following legacy behavior will gradually be sliced out until its deprecated and removed.
# Skipped for Sphinx 6+
# Skipped by internal Feature flag SKIP_SPHINX_HTML_THEME_PATH
# Skipped by all new projects since SKIP_SPHINX_HTML_THEME_PATH's introduction (jan 2023)
if (
        using_rtd_theme
        and version_info < (6,0)
        and not False
    ):
    theme = importlib.import_module('sphinx_rtd_theme')
    if 'html_theme_path' in globals():
        html_theme_path.append(theme.get_html_theme_path())
    else:
        html_theme_path = [theme.get_html_theme_path()]

# Define websupport2_base_url and websupport2_static_url
if globals().get('websupport2_base_url', False):
    websupport2_base_url = 'https://readthedocs.com/websupport'
    websupport2_static_url = 'https://assets.readthedocs.com/'


#Add project information to the template context.
context = {
    'using_theme': using_rtd_theme,
    'html_theme': html_theme,
    'current_version': "latest",
    'version_slug': "latest",
    'MEDIA_URL': "https://media.readthedocs.com/media/",
    'STATIC_URL': "https://assets.readthedocs.com/",
    'PRODUCTION_DOMAIN': "readthedocs.com",
    'proxied_static_path': "/_/static/",
    'versions': [
    ("latest", "/en/latest/"),
    ("stable", "/en/stable/"),
    ("v0.74.0", "/en/v0.74.0/"),
    ("v0.73.0", "/en/v0.73.0/"),
    ("v0.72.0", "/en/v0.72.0/"),
    ("v0.71.0", "/en/v0.71.0/"),
    ("v0.70.0", "/en/v0.70.0/"),
    ("v0.69.0", "/en/v0.69.0/"),
    ("v0.68.0", "/en/v0.68.0/"),
    ("v0.67.0", "/en/v0.67.0/"),
    ("v0.66.1", "/en/v0.66.1/"),
    ("v0.66.0", "/en/v0.66.0/"),
    ("v0.65.0", "/en/v0.65.0/"),
    ("v0.64.0", "/en/v0.64.0/"),
    ("v0.63.0", "/en/v0.63.0/"),
    ("v0.62.0", "/en/v0.62.0/"),
    ("v0.61.1", "/en/v0.61.1/"),
    ("v0.61.0", "/en/v0.61.0/"),
    ("v0.60.0", "/en/v0.60.0/"),
    ("v0.59.0", "/en/v0.59.0/"),
    ("v0.58.0", "/en/v0.58.0/"),
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    ("main", "/en/main/"),
    ],
    'downloads': [ 
    ("pdf", "//evalml.alteryx.com/_/downloads/en/latest/pdf/"),
    ],
    'subprojects': [ 
    ],
    'slug': 'feature-labs-inc-evalml',
    'name': u'EvalML',
    'rtd_language': u'en',
    'programming_language': u'py',
    'canonical_url': 'https://evalml.alteryx.com/en/stable/',
    'analytics_code': 'UA-64679566-12',
    'single_version': False,
    'conf_py_path': '/docs/source/',
    'api_host': 'https://readthedocs.com',
    'github_user': 'alteryx',
    'proxied_api_host': '/_',
    'github_repo': 'evalml',
    'github_version': 'main',
    'display_github': True,
    'bitbucket_user': 'None',
    'bitbucket_repo': 'None',
    'bitbucket_version': 'main',
    'display_bitbucket': False,
    'gitlab_user': 'None',
    'gitlab_repo': 'None',
    'gitlab_version': 'main',
    'display_gitlab': False,
    'READTHEDOCS': True,
    'using_theme': (html_theme == "default"),
    'new_theme': (html_theme == "sphinx_rtd_theme"),
    'source_suffix': SUFFIX,
    'ad_free': False,
    'docsearch_disabled': False,
    'user_analytics_code': 'UA-64679566-12',
    'global_analytics_code': 'UA-17997319-2',
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}

# For sphinx >=1.8 we can use html_baseurl to set the canonical URL.
# https://www.sphinx-doc.org/en/master/usage/configuration.html#confval-html_baseurl
if version_info >= (1, 8):
    if not globals().get('html_baseurl'):
        html_baseurl = context['canonical_url']
    context['canonical_url'] = None





if 'html_context' in globals():
    
    html_context.update(context)
    
else:
    html_context = context

# Add custom RTD extension
if 'extensions' in globals():
    # Insert at the beginning because it can interfere
    # with other extensions.
    # See https://github.com/rtfd/readthedocs.org/pull/4054
    extensions.insert(0, "readthedocs_ext.readthedocs")
else:
    extensions = ["readthedocs_ext.readthedocs"]

# Add External version warning banner to the external version documentation
if 'branch' == 'external':
    extensions.insert(1, "readthedocs_ext.external_version_warning")
    readthedocs_vcs_url = 'None'
    readthedocs_build_url = 'https://readthedocs.com/projects/feature-labs-inc-evalml/builds/1448394/'

project_language = 'en'

# User's Sphinx configurations
language_user = globals().get('language', None)
latex_engine_user = globals().get('latex_engine', None)
latex_elements_user = globals().get('latex_elements', None)

# Remove this once xindy gets installed in Docker image and XINDYOPS
# env variable is supported
# https://github.com/rtfd/readthedocs-docker-images/pull/98
latex_use_xindy = False

chinese = any([
    language_user in ('zh_CN', 'zh_TW'),
    project_language in ('zh_CN', 'zh_TW'),
])

japanese = any([
    language_user == 'ja',
    project_language == 'ja',
])

if chinese:
    latex_engine = latex_engine_user or 'xelatex'

    latex_elements_rtd = {
        'preamble': '\\usepackage[UTF8]{ctex}\n',
    }
    latex_elements = latex_elements_user or latex_elements_rtd
elif japanese:
    latex_engine = latex_engine_user or 'platex'

# Make sure our build directory is always excluded
exclude_patterns = globals().get('exclude_patterns', [])
exclude_patterns.extend(['_build'])

[rtd-command-info] start-time: 2023-05-02T17:17:47.918928Z, end-time: 2023-05-02T17:30:54.136164Z, duration: 786, exit-code: 2
python -m sphinx -T -E -b html -d _build/doctrees -D language=en . $READTHEDOCS_OUTPUT/html
Running Sphinx v4.5.0
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_clustering.py:35: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _pt_shuffle_rec(i, indexes, index_mask, partition_tree, M, pos):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_clustering.py:54: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def delta_minimization_order(all_masks, max_swap_size=100, num_passes=2):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_clustering.py:63: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _reverse_window(order, start, length):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_clustering.py:69: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _reverse_window_score_gain(masks, order, start, length):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_clustering.py:77: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _mask_delta_score(m1, m2):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/links.py:5: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def identity(x):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/links.py:10: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _identity_inverse(x):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/links.py:15: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def logit(x):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/links.py:20: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _logit_inverse(x):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_masked_model.py:363: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _build_fixed_single_output(averaged_outs, last_outs, outputs, batch_positions, varying_rows, num_varying_rows, link, linearizing_weights):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_masked_model.py:385: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _build_fixed_multi_output(averaged_outs, last_outs, outputs, batch_positions, varying_rows, num_varying_rows, link, linearizing_weights):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_masked_model.py:428: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _init_masks(cluster_matrix, M, indices_row_pos, indptr):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_masked_model.py:439: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _rec_fill_masks(cluster_matrix, indices_row_pos, indptr, indices, M, ind):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/maskers/_tabular.py:186: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _single_delta_mask(dind, masked_inputs, last_mask, data, x, noop_code):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/maskers/_tabular.py:197: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _delta_masking(masks, x, curr_delta_inds, varying_rows_out,
loading translations [en]... done
making output directory... done
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[autosummary] generating autosummary for: api_index.rst, demos/cost_benefit_matrix.ipynb, demos/fraud.ipynb, demos/lead_scoring.ipynb, demos/text_input.ipynb, index.ipynb, install.md, release_notes.rst, start.ipynb, tutorials.ipynb, ..., user_guide/component_graphs.ipynb, user_guide/components.ipynb, user_guide/data_check_actions.ipynb, user_guide/data_checks.ipynb, user_guide/faq.ipynb, user_guide/model_understanding.ipynb, user_guide/objectives.ipynb, user_guide/pipelines.ipynb, user_guide/timeseries.ipynb, user_guide/utilities.ipynb
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/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_clustering.py:35: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _pt_shuffle_rec(i, indexes, index_mask, partition_tree, M, pos):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_clustering.py:54: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def delta_minimization_order(all_masks, max_swap_size=100, num_passes=2):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_clustering.py:63: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _reverse_window(order, start, length):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_clustering.py:69: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _reverse_window_score_gain(masks, order, start, length):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_clustering.py:77: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _mask_delta_score(m1, m2):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/links.py:5: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def identity(x):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/links.py:10: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _identity_inverse(x):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/links.py:15: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def logit(x):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/links.py:20: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _logit_inverse(x):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_masked_model.py:363: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _build_fixed_single_output(averaged_outs, last_outs, outputs, batch_positions, varying_rows, num_varying_rows, link, linearizing_weights):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_masked_model.py:385: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _build_fixed_multi_output(averaged_outs, last_outs, outputs, batch_positions, varying_rows, num_varying_rows, link, linearizing_weights):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_masked_model.py:428: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _init_masks(cluster_matrix, M, indices_row_pos, indptr):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_masked_model.py:439: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _rec_fill_masks(cluster_matrix, indices_row_pos, indptr, indices, M, ind):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/maskers/_tabular.py:186: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _single_delta_mask(dind, masked_inputs, last_mask, data, x, noop_code):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/maskers/_tabular.py:197: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _delta_masking(masks, x, curr_delta_inds, varying_rows_out,
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_clustering.py:35: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _pt_shuffle_rec(i, indexes, index_mask, partition_tree, M, pos):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_clustering.py:54: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def delta_minimization_order(all_masks, max_swap_size=100, num_passes=2):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_clustering.py:63: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _reverse_window(order, start, length):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_clustering.py:69: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _reverse_window_score_gain(masks, order, start, length):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_clustering.py:77: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _mask_delta_score(m1, m2):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/links.py:5: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def identity(x):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/links.py:10: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _identity_inverse(x):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/links.py:15: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def logit(x):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/links.py:20: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _logit_inverse(x):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_masked_model.py:363: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _build_fixed_single_output(averaged_outs, last_outs, outputs, batch_positions, varying_rows, num_varying_rows, link, linearizing_weights):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_masked_model.py:385: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _build_fixed_multi_output(averaged_outs, last_outs, outputs, batch_positions, varying_rows, num_varying_rows, link, linearizing_weights):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_masked_model.py:428: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _init_masks(cluster_matrix, M, indices_row_pos, indptr):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/utils/_masked_model.py:439: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _rec_fill_masks(cluster_matrix, indices_row_pos, indptr, indices, M, ind):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/maskers/_tabular.py:186: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _single_delta_mask(dind, masked_inputs, last_mask, data, x, noop_code):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/maskers/_tabular.py:197: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _delta_masking(masks, x, curr_delta_inds, varying_rows_out,
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/maskers/_image.py:175: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _jit_build_partition_tree(xmin, xmax, ymin, ymax, zmin, zmax, total_ywidth, total_zwidth, M, clustering, q):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/explainers/_partition.py:676: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def lower_credit(i, value, M, values, clustering):
The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/maskers/_image.py:175: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _jit_build_partition_tree(xmin, xmax, ymin, ymax, zmin, zmax, total_ywidth, total_zwidth, M, clustering, q):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/explainers/_partition.py:676: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def lower_credit(i, value, M, values, clustering):
The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
Inside a Dask worker with daemon=True, setting n_jobs=1.
Possible work-arounds:
- dask.config.set({'distributed.worker.daemon': False})
- set the environment variable DASK_DISTRIBUTED__WORKER__DAEMON=False
before creating your Dask cluster.
reading sources... [ 96%] user_guide/component_graphs
reading sources... [ 96%] user_guide/components
reading sources... [ 96%] user_guide/data_check_actions
reading sources... [ 97%] user_guide/data_checks
reading sources... [ 97%] user_guide/faq
reading sources... [ 98%] user_guide/model_understanding
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/maskers/_image.py:175: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _jit_build_partition_tree(xmin, xmax, ymin, ymax, zmin, zmax, total_ywidth, total_zwidth, M, clustering, q):
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/explainers/_partition.py:676: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def lower_credit(i, value, M, values, clustering):
The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/checkouts/latest/docs/source/autoapi/evalml/data_checks/data_check_message/index.rst:72: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/checkouts/latest/docs/source/autoapi/evalml/data_checks/index.rst:462: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/checkouts/latest/docs/source/release_notes.rst:41: WARNING: Inline interpreted text or phrase reference start-string without end-string.

Traceback (most recent call last):
  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/nbsphinx.py", line 1100, in parse
    rststring, resources = exporter.from_notebook_node(nb, resources)
  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/nbsphinx.py", line 910, in from_notebook_node
    nb, resources = pp.preprocess(nb, resources)
  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/nbconvert/preprocessors/execute.py", line 100, in preprocess
    self.preprocess_cell(cell, resources, index)
  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/nbconvert/preprocessors/execute.py", line 121, in preprocess_cell
    cell = self.execute_cell(cell, index, store_history=True)
  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/jupyter_core/utils/__init__.py", line 166, in wrapped
    return loop.run_until_complete(inner)
  File "/home/docs/.pyenv/versions/3.8.6/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/nbclient/client.py", line 1058, in async_execute_cell
    await self._check_raise_for_error(cell, cell_index, exec_reply)
  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/nbclient/client.py", line 914, in _check_raise_for_error
    raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
------------------
from evalml.model_understanding.prediction_explanations import explain_predictions

table = explain_predictions(
    pipeline=pipeline_binary,
    input_features=X_holdout,
    y=None,
    indices_to_explain=[3],
    top_k_features=6,
    include_explainer_values=True,
)
print(table)
------------------


---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[28], line 3
      1 from evalml.model_understanding.prediction_explanations import explain_predictions
----> 3 table = explain_predictions(
      4     pipeline=pipeline_binary,
      5     input_features=X_holdout,
      6     y=None,
      7     indices_to_explain=[3],
      8     top_k_features=6,
      9     include_explainer_values=True,
     10 )
     11 print(table)

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/model_understanding/prediction_explanations/explainers.py:138, in explain_predictions(pipeline, input_features, y, indices_to_explain, top_k_features, include_explainer_values, include_expected_value, output_format, training_data, training_target, algorithm)
    117 data = _ReportData(
    118     pipeline,
    119     pipeline_features,
   (...)
    126     metric=None,
    127 )
    129 report_creator = _report_creator_factory(
    130     data,
    131     report_type="explain_predictions",
   (...)
    136     algorithm=algorithm,
    137 )
--> 138 return report_creator(data)

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/model_understanding/prediction_explanations/_user_interface.py:933, in _ReportMaker.make_text(self, data)
    930     else:
    931         report.extend([""])
    932     report.extend(
--> 933         self.table_maker.make_text(
    934             index,
    935             data.pipeline,
    936             data.pipeline_features,
    937             data.input_features,
    938         ),
    939     )
    940 return "".join(report)

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/model_understanding/prediction_explanations/_user_interface.py:845, in _ExplanationTable.make_text(self, index, pipeline, pipeline_features, input_features)
    827 def make_text(self, index, pipeline, pipeline_features, input_features):
    828     """Makes the explanation table section for reports formatted as text.
    829 
    830     The table is the same whether the user requests a best/worst report or they manually specified the
   (...)
    843         The explanation table section for reports formatted as text.
    844     """
--> 845     table = _make_single_prediction_explanation_table(
    846         pipeline,
    847         pipeline_features,
    848         input_features,
    849         index_to_explain=index,
    850         top_k=self.top_k_features,
    851         include_explainer_values=self.include_explainer_values,
    852         output_format="text",
    853         algorithm=self.algorithm,
    854     )
    855     table = table.splitlines()
    856     # Indent the rows of the table to match the indentation of the entire report.

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/model_understanding/prediction_explanations/_user_interface.py:548, in _make_single_prediction_explanation_table(pipeline, pipeline_features, input_features, index_to_explain, top_k, include_explainer_values, include_expected_value, output_format, algorithm)
    545 input_features_row = input_features.iloc[[index_to_explain]]
    547 if algorithm == "shap":
--> 548     explainer_values, expected_value = _compute_shap_values(
    549         pipeline,
    550         pipeline_features_row,
    551         training_data=pipeline_features.dropna(axis=0),
    552     )
    553 elif algorithm == "lime":
    554     explainer_values = _compute_lime_values(
    555         pipeline,
    556         pipeline_features,
    557         index_to_explain,
    558     )

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/model_understanding/prediction_explanations/_algorithms.py:146, in _compute_shap_values(pipeline, features, training_data)
    144 if ws:
    145     logger.debug(f"_compute_shap_values TreeExplainer: {ws[0].message}")
--> 146 shap_values = explainer.shap_values(features, check_additivity=False)
    147 # shap only outputs values for positive class for Catboost/Xgboost binary estimators.
    148 # this modifies the output to match the output format of other binary estimators.
    149 # Ok to fill values of negative class with zeros since the negative class will get dropped
    150 # in the UI anyways.
    151 if estimator.model_family in {
    152     ModelFamily.CATBOOST,
    153     ModelFamily.XGBOOST,
    154 } and is_binary(pipeline.problem_type):

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/explainers/_tree.py:384, in Tree.shap_values(self, X, y, tree_limit, approximate, check_additivity, from_call)
    380             self.assert_additivity(out, model_output_vals)
    382         return out
--> 384 X, y, X_missing, flat_output, tree_limit, check_additivity = self._validate_inputs(X, y,
    385                                                                                    tree_limit,
    386                                                                                    check_additivity)
    387 transform = self.model.get_transform()
    389 # run the core algorithm using the C extension

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/explainers/_tree.py:250, in Tree._validate_inputs(self, X, y, tree_limit, check_additivity)
    248 if X.dtype != self.model.input_dtype:
    249     X = X.astype(self.model.input_dtype)
--> 250 X_missing = np.isnan(X, dtype=np.bool)
    251 assert isinstance(X, np.ndarray), "Unknown instance type: " + str(type(X))
    252 assert len(X.shape) == 2, "Passed input data matrix X must have 1 or 2 dimensions!"

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/numpy/__init__.py:305, in __getattr__(attr)
    300     warnings.warn(
    301         f"In the future `np.{attr}` will be defined as the "
    302         "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    304 if attr in __former_attrs__:
--> 305     raise AttributeError(__former_attrs__[attr])
    307 # Importing Tester requires importing all of UnitTest which is not a
    308 # cheap import Since it is mainly used in test suits, we lazy import it
    309 # here to save on the order of 10 ms of import time for most users
    310 #
    311 # The previous way Tester was imported also had a side effect of adding
    312 # the full `numpy.testing` namespace
    313 if attr == 'testing':

AttributeError: module 'numpy' has no attribute 'bool'.
`np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations


During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/sphinx/cmd/build.py", line 276, in build_main
    app.build(args.force_all, filenames)
  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/sphinx/application.py", line 330, in build
    self.builder.build_update()
  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/sphinx/builders/__init__.py", line 286, in build_update
    self.build(to_build,
  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/sphinx/builders/__init__.py", line 300, in build
    updated_docnames = set(self.read())
  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/sphinx/builders/__init__.py", line 407, in read
    self._read_serial(docnames)
  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/sphinx/builders/__init__.py", line 428, in _read_serial
    self.read_doc(docname)
  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/sphinx/builders/__init__.py", line 468, in read_doc
    doctree = read_doc(self.app, self.env, self.env.doc2path(docname))
  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/sphinx/io.py", line 181, in read_doc
    pub.publish()
  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/docutils/core.py", line 217, in publish
    self.document = self.reader.read(self.source, self.parser,
  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/sphinx/io.py", line 101, in read
    self.parse()
  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/docutils/readers/__init__.py", line 77, in parse
    self.parser.parse(self.input, document)
  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/nbsphinx.py", line 1107, in parse
    raise NotebookError('\n'.join(lines))
nbsphinx.NotebookError: CellExecutionError in user_guide/model_understanding.ipynb:
------------------
from evalml.model_understanding.prediction_explanations import explain_predictions

table = explain_predictions(
    pipeline=pipeline_binary,
    input_features=X_holdout,
    y=None,
    indices_to_explain=[3],
    top_k_features=6,
    include_explainer_values=True,
)
print(table)
------------------


---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[28], line 3
      1 from evalml.model_understanding.prediction_explanations import explain_predictions
----> 3 table = explain_predictions(
      4     pipeline=pipeline_binary,
      5     input_features=X_holdout,
      6     y=None,
      7     indices_to_explain=[3],
      8     top_k_features=6,
      9     include_explainer_values=True,
     10 )
     11 print(table)

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/model_understanding/prediction_explanations/explainers.py:138, in explain_predictions(pipeline, input_features, y, indices_to_explain, top_k_features, include_explainer_values, include_expected_value, output_format, training_data, training_target, algorithm)
    117 data = _ReportData(
    118     pipeline,
    119     pipeline_features,
   (...)
    126     metric=None,
    127 )
    129 report_creator = _report_creator_factory(
    130     data,
    131     report_type="explain_predictions",
   (...)
    136     algorithm=algorithm,
    137 )
--> 138 return report_creator(data)

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/model_understanding/prediction_explanations/_user_interface.py:933, in _ReportMaker.make_text(self, data)
    930     else:
    931         report.extend([""])
    932     report.extend(
--> 933         self.table_maker.make_text(
    934             index,
    935             data.pipeline,
    936             data.pipeline_features,
    937             data.input_features,
    938         ),
    939     )
    940 return "".join(report)

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/model_understanding/prediction_explanations/_user_interface.py:845, in _ExplanationTable.make_text(self, index, pipeline, pipeline_features, input_features)
    827 def make_text(self, index, pipeline, pipeline_features, input_features):
    828     """Makes the explanation table section for reports formatted as text.
    829 
    830     The table is the same whether the user requests a best/worst report or they manually specified the
   (...)
    843         The explanation table section for reports formatted as text.
    844     """
--> 845     table = _make_single_prediction_explanation_table(
    846         pipeline,
    847         pipeline_features,
    848         input_features,
    849         index_to_explain=index,
    850         top_k=self.top_k_features,
    851         include_explainer_values=self.include_explainer_values,
    852         output_format="text",
    853         algorithm=self.algorithm,
    854     )
    855     table = table.splitlines()
    856     # Indent the rows of the table to match the indentation of the entire report.

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/model_understanding/prediction_explanations/_user_interface.py:548, in _make_single_prediction_explanation_table(pipeline, pipeline_features, input_features, index_to_explain, top_k, include_explainer_values, include_expected_value, output_format, algorithm)
    545 input_features_row = input_features.iloc[[index_to_explain]]
    547 if algorithm == "shap":
--> 548     explainer_values, expected_value = _compute_shap_values(
    549         pipeline,
    550         pipeline_features_row,
    551         training_data=pipeline_features.dropna(axis=0),
    552     )
    553 elif algorithm == "lime":
    554     explainer_values = _compute_lime_values(
    555         pipeline,
    556         pipeline_features,
    557         index_to_explain,
    558     )

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/model_understanding/prediction_explanations/_algorithms.py:146, in _compute_shap_values(pipeline, features, training_data)
    144 if ws:
    145     logger.debug(f"_compute_shap_values TreeExplainer: {ws[0].message}")
--> 146 shap_values = explainer.shap_values(features, check_additivity=False)
    147 # shap only outputs values for positive class for Catboost/Xgboost binary estimators.
    148 # this modifies the output to match the output format of other binary estimators.
    149 # Ok to fill values of negative class with zeros since the negative class will get dropped
    150 # in the UI anyways.
    151 if estimator.model_family in {
    152     ModelFamily.CATBOOST,
    153     ModelFamily.XGBOOST,
    154 } and is_binary(pipeline.problem_type):

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/explainers/_tree.py:384, in Tree.shap_values(self, X, y, tree_limit, approximate, check_additivity, from_call)
    380             self.assert_additivity(out, model_output_vals)
    382         return out
--> 384 X, y, X_missing, flat_output, tree_limit, check_additivity = self._validate_inputs(X, y,
    385                                                                                    tree_limit,
    386                                                                                    check_additivity)
    387 transform = self.model.get_transform()
    389 # run the core algorithm using the C extension

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/explainers/_tree.py:250, in Tree._validate_inputs(self, X, y, tree_limit, check_additivity)
    248 if X.dtype != self.model.input_dtype:
    249     X = X.astype(self.model.input_dtype)
--> 250 X_missing = np.isnan(X, dtype=np.bool)
    251 assert isinstance(X, np.ndarray), "Unknown instance type: " + str(type(X))
    252 assert len(X.shape) == 2, "Passed input data matrix X must have 1 or 2 dimensions!"

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/numpy/__init__.py:305, in __getattr__(attr)
    300     warnings.warn(
    301         f"In the future `np.{attr}` will be defined as the "
    302         "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    304 if attr in __former_attrs__:
--> 305     raise AttributeError(__former_attrs__[attr])
    307 # Importing Tester requires importing all of UnitTest which is not a
    308 # cheap import Since it is mainly used in test suits, we lazy import it
    309 # here to save on the order of 10 ms of import time for most users
    310 #
    311 # The previous way Tester was imported also had a side effect of adding
    312 # the full `numpy.testing` namespace
    313 if attr == 'testing':

AttributeError: module 'numpy' has no attribute 'bool'.
`np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

You can ignore this error by setting the following in conf.py:

    nbsphinx_allow_errors = True


Notebook error:
CellExecutionError in user_guide/model_understanding.ipynb:
------------------
from evalml.model_understanding.prediction_explanations import explain_predictions

table = explain_predictions(
    pipeline=pipeline_binary,
    input_features=X_holdout,
    y=None,
    indices_to_explain=[3],
    top_k_features=6,
    include_explainer_values=True,
)
print(table)
------------------


---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[28], line 3
      1 from evalml.model_understanding.prediction_explanations import explain_predictions
----> 3 table = explain_predictions(
      4     pipeline=pipeline_binary,
      5     input_features=X_holdout,
      6     y=None,
      7     indices_to_explain=[3],
      8     top_k_features=6,
      9     include_explainer_values=True,
     10 )
     11 print(table)

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/model_understanding/prediction_explanations/explainers.py:138, in explain_predictions(pipeline, input_features, y, indices_to_explain, top_k_features, include_explainer_values, include_expected_value, output_format, training_data, training_target, algorithm)
    117 data = _ReportData(
    118     pipeline,
    119     pipeline_features,
   (...)
    126     metric=None,
    127 )
    129 report_creator = _report_creator_factory(
    130     data,
    131     report_type="explain_predictions",
   (...)
    136     algorithm=algorithm,
    137 )
--> 138 return report_creator(data)

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/model_understanding/prediction_explanations/_user_interface.py:933, in _ReportMaker.make_text(self, data)
    930     else:
    931         report.extend([""])
    932     report.extend(
--> 933         self.table_maker.make_text(
    934             index,
    935             data.pipeline,
    936             data.pipeline_features,
    937             data.input_features,
    938         ),
    939     )
    940 return "".join(report)

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/model_understanding/prediction_explanations/_user_interface.py:845, in _ExplanationTable.make_text(self, index, pipeline, pipeline_features, input_features)
    827 def make_text(self, index, pipeline, pipeline_features, input_features):
    828     """Makes the explanation table section for reports formatted as text.
    829 
    830     The table is the same whether the user requests a best/worst report or they manually specified the
   (...)
    843         The explanation table section for reports formatted as text.
    844     """
--> 845     table = _make_single_prediction_explanation_table(
    846         pipeline,
    847         pipeline_features,
    848         input_features,
    849         index_to_explain=index,
    850         top_k=self.top_k_features,
    851         include_explainer_values=self.include_explainer_values,
    852         output_format="text",
    853         algorithm=self.algorithm,
    854     )
    855     table = table.splitlines()
    856     # Indent the rows of the table to match the indentation of the entire report.

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/model_understanding/prediction_explanations/_user_interface.py:548, in _make_single_prediction_explanation_table(pipeline, pipeline_features, input_features, index_to_explain, top_k, include_explainer_values, include_expected_value, output_format, algorithm)
    545 input_features_row = input_features.iloc[[index_to_explain]]
    547 if algorithm == "shap":
--> 548     explainer_values, expected_value = _compute_shap_values(
    549         pipeline,
    550         pipeline_features_row,
    551         training_data=pipeline_features.dropna(axis=0),
    552     )
    553 elif algorithm == "lime":
    554     explainer_values = _compute_lime_values(
    555         pipeline,
    556         pipeline_features,
    557         index_to_explain,
    558     )

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/model_understanding/prediction_explanations/_algorithms.py:146, in _compute_shap_values(pipeline, features, training_data)
    144 if ws:
    145     logger.debug(f"_compute_shap_values TreeExplainer: {ws[0].message}")
--> 146 shap_values = explainer.shap_values(features, check_additivity=False)
    147 # shap only outputs values for positive class for Catboost/Xgboost binary estimators.
    148 # this modifies the output to match the output format of other binary estimators.
    149 # Ok to fill values of negative class with zeros since the negative class will get dropped
    150 # in the UI anyways.
    151 if estimator.model_family in {
    152     ModelFamily.CATBOOST,
    153     ModelFamily.XGBOOST,
    154 } and is_binary(pipeline.problem_type):

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/explainers/_tree.py:384, in Tree.shap_values(self, X, y, tree_limit, approximate, check_additivity, from_call)
    380             self.assert_additivity(out, model_output_vals)
    382         return out
--> 384 X, y, X_missing, flat_output, tree_limit, check_additivity = self._validate_inputs(X, y,
    385                                                                                    tree_limit,
    386                                                                                    check_additivity)
    387 transform = self.model.get_transform()
    389 # run the core algorithm using the C extension

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/shap/explainers/_tree.py:250, in Tree._validate_inputs(self, X, y, tree_limit, check_additivity)
    248 if X.dtype != self.model.input_dtype:
    249     X = X.astype(self.model.input_dtype)
--> 250 X_missing = np.isnan(X, dtype=np.bool)
    251 assert isinstance(X, np.ndarray), "Unknown instance type: " + str(type(X))
    252 assert len(X.shape) == 2, "Passed input data matrix X must have 1 or 2 dimensions!"

File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/numpy/__init__.py:305, in __getattr__(attr)
    300     warnings.warn(
    301         f"In the future `np.{attr}` will be defined as the "
    302         "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    304 if attr in __former_attrs__:
--> 305     raise AttributeError(__former_attrs__[attr])
    307 # Importing Tester requires importing all of UnitTest which is not a
    308 # cheap import Since it is mainly used in test suits, we lazy import it
    309 # here to save on the order of 10 ms of import time for most users
    310 #
    311 # The previous way Tester was imported also had a side effect of adding
    312 # the full `numpy.testing` namespace
    313 if attr == 'testing':

AttributeError: module 'numpy' has no attribute 'bool'.
`np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

You can ignore this error by setting the following in conf.py:

    nbsphinx_allow_errors = True