Read the Docs build information
Build id: 1106155
Project: feature-labs-inc-evalml
Version: main
Commit: c4c629e7b4a4ddf5599a98d5efe021ba2204a92b
Date: 2022-09-20T21:30:27.349339Z
State: finished
Success: True


[rtd-command-info] start-time: 2022-09-20T21:30:29.475230Z, end-time: 2022-09-20T21:30:32.324400Z, duration: 2, exit-code: 0
git clone --no-single-branch --depth 50 https://github.com/alteryx/evalml.git .
Cloning into '.'...

[rtd-command-info] start-time: 2022-09-20T21:30:32.554068Z, end-time: 2022-09-20T21:30:32.762557Z, duration: 0, exit-code: 0
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 c4c629e7 Release v0.58.0 (#3724)

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git clean -d -f -f


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python3.8 -mvirtualenv 
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/main/bin/python3.8
Also creating executable in /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/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<2.2 jinja2<3.1.0
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Successfully installed MarkupSafe-2.1.1 Pygments-2.13.0 alabaster-0.7.12 babel-2.10.3 certifi-2022.9.14 charset-normalizer-2.1.1 commonmark-0.9.1 docutils-0.17.1 idna-3.4 imagesize-1.4.1 jinja2-3.0.3 mock-1.0.1 packaging-21.3 pillow-9.2.0 pyparsing-3.0.9 pytz-2022.2.1 readthedocs-sphinx-ext-2.1.9 recommonmark-0.5.0 requests-2.28.1 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 urllib3-1.26.12

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Installing collected packages: wrapt, webencodings, wcwidth, types-setuptools, texttable, sortedcontainers, setuptools-git, pymeeus, pure-eval, ptyprocess, pickleshare, mypy-extensions, msgpack, mistune, mccabe, korean-lunar-calendar, kaleido, iniconfig, heapdict, fastjsonschema, executing, ephem, distlib, backcall, zipp, zict, widgetsnbextension, vowpalwabbit, unidecode, ujson, typing-extensions, traitlets, tqdm, tornado, toolz, tomli, toml, tokenize-rt, tinycss2, threadpoolctl, tenacity, tblib, sphinxcontrib-qthelp, sphinxcontrib-jsmath, sphinxcontrib-htmlhelp, sphinxcontrib-devhelp, sphinxcontrib-applehelp, soupsieve, slicer, setuptools, requirements-parser, regex, pyzmq, PyYAML, python-dateutil, pyrsistent, pyflakes, pydocstyle, pycodestyle, py, psutil, prompt-toolkit, pluggy, platformdirs, pkgutil-resolve-name, pexpect, pathspec, parso, pandocfilters, numpy, nest-asyncio, mdurl, lxml, locket, llvmlite, lazy-object-proxy, kiwisolver, jupyterlab-widgets, jupyterlab-pygments, joblib, jinja2, isort, identify, hijri-converter, graphviz, fsspec, fonttools, filelock, execnet, entrypoints, docutils, deprecated, defusedxml, decorator, debugpy, darglint, Cython, cycler, coverage, convertdate, colorama, cloudpickle, click, cfgv, bleach, attrs, asttokens, virtualenv, tifffile, stack-data, scipy, PyWavelets, pytest, pyaml, plotly, patsy, partd, pandas, nodeenv, nltk, networkx, matplotlib-inline, markdown-it-py, LunarCalendar, jupyter-core, jedi, importlib-resources, importlib-metadata, imageio, holidays, flake8, contourpy, codecov, 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-prebuilt, pmdarima, nbformat, myst-parser, lime, lightgbm, ipykernel, imbalanced-learn, distributed, category-encoders, catboost, nbval, nbclient, ipywidgets, featuretools, nlp-primitives, nbconvert, nbsphinx, evalml
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[rtd-command-info] start-time: 2022-09-20T21:32:26.682253Z, end-time: 2022-09-20T21:32:26.746668Z, 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 subprocess
import sys
from pathlib import Path

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


from sphinx.domains.python import PythonDomain


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:
    theme = importlib.import_module('sphinx_rtd_theme')
    html_theme = 'sphinx_rtd_theme'
    html_style = None
    html_theme_options = {}
    if 'html_theme_path' in globals():
        html_theme_path.append(theme.get_html_theme_path())
    else:
        html_theme_path = [theme.get_html_theme_path()]

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': "main",
    'version_slug': "main",
    '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/"),
    ("v0.58.0", "/en/v0.58.0/"),
    ("v0.57.0", "/en/v0.57.0/"),
    ("v0.56.1", "/en/v0.56.1/"),
    ("v0.56.0", "/en/v0.56.0/"),
    ("v0.55.0", "/en/v0.55.0/"),
    ("v0.54.0", "/en/v0.54.0/"),
    ("v0.53.1", "/en/v0.53.1/"),
    ("v0.53.0", "/en/v0.53.0/"),
    ("v0.52.0", "/en/v0.52.0/"),
    ("v0.51.0", "/en/v0.51.0/"),
    ("v0.50.0", "/en/v0.50.0/"),
    ("v0.49.0", "/en/v0.49.0/"),
    ("v0.48.0", "/en/v0.48.0/"),
    ("v0.47.0", "/en/v0.47.0/"),
    ("v0.46.0", "/en/v0.46.0/"),
    ("v0.45.0", "/en/v0.45.0/"),
    ("v0.44.0", "/en/v0.44.0/"),
    ("v0.43.0", "/en/v0.43.0/"),
    ("v0.42.0", "/en/v0.42.0/"),
    ("v0.41.0", "/en/v0.41.0/"),
    ("v0.40.0", "/en/v0.40.0/"),
    ("v0.39.0", "/en/v0.39.0/"),
    ("v0.38.0", "/en/v0.38.0/"),
    ("v0.37.0", "/en/v0.37.0/"),
    ("v0.36.0", "/en/v0.36.0/"),
    ("v0.35.0", "/en/v0.35.0/"),
    ("v0.34.0", "/en/v0.34.0/"),
    ("v0.33.0", "/en/v0.33.0/"),
    ("v0.32.1", "/en/v0.32.1/"),
    ("v0.32.0", "/en/v0.32.0/"),
    ("v0.31.0", "/en/v0.31.0/"),
    ("v0.30.2", "/en/v0.30.2/"),
    ("v0.30.1", "/en/v0.30.1/"),
    ("v0.30.0", "/en/v0.30.0/"),
    ("v0.29.0", "/en/v0.29.0/"),
    ("v0.28.0", "/en/v0.28.0/"),
    ("v0.27.0", "/en/v0.27.0/"),
    ("v0.25.0", "/en/v0.25.0/"),
    ("v0.24.2", "/en/v0.24.2/"),
    ("v0.24.1", "/en/v0.24.1/"),
    ("v0.24.0", "/en/v0.24.0/"),
    ("v0.23.0", "/en/v0.23.0/"),
    ("v0.22.0", "/en/v0.22.0/"),
    ("v0.22.0_a", "/en/v0.22.0_a/"),
    ("v0.21.0", "/en/v0.21.0/"),
    ("v0.20.0", "/en/v0.20.0/"),
    ("v0.19.0", "/en/v0.19.0/"),
    ("v0.18.2", "/en/v0.18.2/"),
    ("v0.18.1", "/en/v0.18.1/"),
    ("v0.18.0", "/en/v0.18.0/"),
    ("v0.17.0", "/en/v0.17.0/"),
    ("v0.16.1", "/en/v0.16.1/"),
    ("v0.16.0", "/en/v0.16.0/"),
    ("v0.15.0", "/en/v0.15.0/"),
    ("v0.14.1", "/en/v0.14.1/"),
    ("v0.13.2", "/en/v0.13.2/"),
    ("v0.13.1", "/en/v0.13.1/"),
    ("v0.12.2", "/en/v0.12.2/"),
    ("v0.12.0", "/en/v0.12.0/"),
    ("v0.11.2", "/en/v0.11.2/"),
    ("v0.11.0", "/en/v0.11.0/"),
    ("v0.10.0", "/en/v0.10.0/"),
    ("v0.9.0", "/en/v0.9.0/"),
    ("v0.8.0", "/en/v0.8.0/"),
    ("v0.7.0", "/en/v0.7.0/"),
    ("v0.6.0", "/en/v0.6.0/"),
    ("v0.5.2", "/en/v0.5.2/"),
    ("v0.5.1", "/en/v0.5.1/"),
    ("main", "/en/main/"),
    ],
    'downloads': [ 
    ("pdf", "//evalml.alteryx.com/_/downloads/en/main/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',
    'commit': 'c4c629e7',
}

# 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/1106155/'

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: 2022-09-20T21:32:26.967203Z, end-time: 2022-09-20T21:48:44.642246Z, duration: 977, exit-code: 0
python -m sphinx -T -E -W --keep-going -b html -d _build/doctrees -D language=en . _build/html
Running Sphinx v4.5.0
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
myst v0.16.1: MdParserConfig(renderer='sphinx', commonmark_only=False, enable_extensions=['dollarmath'], linkify_fuzzy_links=True, dmath_allow_labels=True, dmath_allow_space=True, dmath_allow_digits=True, dmath_double_inline=False, update_mathjax=True, mathjax_classes='tex2jax_process|mathjax_process|math|output_area', disable_syntax=[], url_schemes=['http', 'https', 'mailto', 'ftp'], heading_anchors=None, heading_slug_func=None, html_meta=[], footnote_transition=True, substitutions=[], sub_delimiters=['{', '}'], words_per_minute=200)
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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.
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.
			CatBoost Classifier w/ Label Encoder + Select Columns By Type Transformer + Label Encoder + Replace Nullable Types Transformer + Drop Columns Transformer + DateTime Featurizer + Imputer + Select Columns Transformer + Select Columns Transformer + Label Encoder + Replace Nullable Types Transformer + Imputer + Oversampler fold 0: Encountered an error.
			CatBoost Classifier w/ Label Encoder + Select Columns By Type Transformer + Label Encoder + Replace Nullable Types Transformer + Drop Columns Transformer + DateTime Featurizer + Imputer + Select Columns Transformer + Select Columns Transformer + Label Encoder + Replace Nullable Types Transformer + Imputer + Oversampler fold 0: All scores will be replaced with nan.
			Fold 0: Exception during automl search: Cannot cast object dtype to float64
			Fold 0: Parameters:
	{'Label Encoder': {'positive_label': None}, 'Numeric Pipeline - Select Columns By Type Transformer': {'column_types': ['Categorical', 'EmailAddress', 'URL'], 'exclude': True}, 'Numeric Pipeline - Label Encoder': {'positive_label': None}, 'Numeric Pipeline - Drop Columns Transformer': {'columns': ['currency', 'expiration_date']}, 'Numeric Pipeline - DateTime Featurizer': {'features_to_extract': ['year', 'month', 'day_of_week', 'hour'], 'encode_as_categories': False, 'time_index': None}, 'Numeric Pipeline - Imputer': {'categorical_impute_strategy': 'most_frequent', 'numeric_impute_strategy': 'mean', 'boolean_impute_strategy': 'most_frequent', 'categorical_fill_value': None, 'numeric_fill_value': None, 'boolean_fill_value': None}, 'Numeric Pipeline - Select Columns Transformer': {'columns': ['card_id', 'store_id', 'amount', 'customer_present', 'lat', 'lng', 'datetime_month', 'datetime_day_of_week', 'datetime_hour']}, 'Categorical Pipeline - Select Columns Transformer': {'columns': ['provider', 'region', 'country']}, 'Categorical Pipeline - Label Encoder': {'positive_label': None}, 'Categorical Pipeline - Imputer': {'categorical_impute_strategy': 'most_frequent', 'numeric_impute_strategy': 'mean', 'boolean_impute_strategy': 'most_frequent', 'categorical_fill_value': None, 'numeric_fill_value': None, 'boolean_fill_value': None}, 'Oversampler': {'sampling_ratio': 0.25, 'k_neighbors_default': 5, 'n_jobs': -1, 'sampling_ratio_dict': None}, 'CatBoost Classifier': {'n_estimators': 10, 'eta': 0.03, 'max_depth': 6, 'bootstrap_type': None, 'silent': True, 'allow_writing_files': False, 'n_jobs': -1}}
			Fold 0: Traceback:
  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/automl/engine/engine_base.py", line 238, in _train_and_score
    fitted_pipeline, hashes = train_pipeline(

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/automl/engine/engine_base.py", line 176, in train_pipeline
    cv_pipeline.fit(X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/utils/base_meta.py", line 19, in _set_fit
    return_value = method(self, X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/pipelines/classification_pipeline.py", line 70, in fit
    self._fit(X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/pipelines/pipeline_base.py", line 277, in _fit
    self.component_graph.fit(X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/pipelines/component_graph.py", line 244, in fit
    self._transform_features(self.compute_order, X, y, fit=True)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/pipelines/component_graph.py", line 501, in _transform_features
    output = component_instance.fit_transform(x_inputs, y_input)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/utils/base_meta.py", line 19, in _set_fit
    return_value = method(self, X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/pipelines/components/transformers/samplers/base_sampler.py", line 169, in fit_transform
    return self.fit(X, y).transform(X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/pipelines/components/component_base_meta.py", line 42, in _check_for_fit
    return method(self, X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/pipelines/components/transformers/samplers/base_sampler.py", line 97, in transform
    X_new, y_new = self._component_obj.fit_resample(X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/imblearn/base.py", line 77, in fit_resample
    X, y, binarize_y = self._check_X_y(X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/imblearn/over_sampling/_smote/base.py", line 455, in _check_X_y
    X, y = self._validate_data(

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/sklearn/base.py", line 596, in _validate_data
    X, y = check_X_y(X, y, **check_params)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/sklearn/utils/validation.py", line 1074, in check_X_y
    X = check_array(

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/sklearn/utils/validation.py", line 791, in check_array
    array = array.astype(new_dtype)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/pandas/core/generic.py", line 5912, in astype
    new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 419, in astype
    return self.apply("astype", dtype=dtype, copy=copy, errors=errors)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 304, in apply
    applied = getattr(b, f)(**kwargs)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 580, in astype
    new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/pandas/core/dtypes/cast.py", line 1292, in astype_array_safe
    new_values = astype_array(values, dtype, copy=copy)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/pandas/core/dtypes/cast.py", line 1234, in astype_array
    values = values.astype(dtype, copy=copy)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/pandas/core/arrays/categorical.py", line 556, in astype
    raise ValueError(msg)

			CatBoost Classifier w/ Label Encoder + Select Columns By Type Transformer + Label Encoder + Replace Nullable Types Transformer + Drop Columns Transformer + DateTime Featurizer + Imputer + Select Columns Transformer + Select Columns Transformer + Label Encoder + Replace Nullable Types Transformer + Imputer + Oversampler fold 1: Encountered an error.
			CatBoost Classifier w/ Label Encoder + Select Columns By Type Transformer + Label Encoder + Replace Nullable Types Transformer + Drop Columns Transformer + DateTime Featurizer + Imputer + Select Columns Transformer + Select Columns Transformer + Label Encoder + Replace Nullable Types Transformer + Imputer + Oversampler fold 1: All scores will be replaced with nan.
			Fold 1: Exception during automl search: Cannot cast object dtype to float64
			Fold 1: Parameters:
	{'Label Encoder': {'positive_label': None}, 'Numeric Pipeline - Select Columns By Type Transformer': {'column_types': ['Categorical', 'EmailAddress', 'URL'], 'exclude': True}, 'Numeric Pipeline - Label Encoder': {'positive_label': None}, 'Numeric Pipeline - Drop Columns Transformer': {'columns': ['currency', 'expiration_date']}, 'Numeric Pipeline - DateTime Featurizer': {'features_to_extract': ['year', 'month', 'day_of_week', 'hour'], 'encode_as_categories': False, 'time_index': None}, 'Numeric Pipeline - Imputer': {'categorical_impute_strategy': 'most_frequent', 'numeric_impute_strategy': 'mean', 'boolean_impute_strategy': 'most_frequent', 'categorical_fill_value': None, 'numeric_fill_value': None, 'boolean_fill_value': None}, 'Numeric Pipeline - Select Columns Transformer': {'columns': ['card_id', 'store_id', 'amount', 'customer_present', 'lat', 'lng', 'datetime_month', 'datetime_day_of_week', 'datetime_hour']}, 'Categorical Pipeline - Select Columns Transformer': {'columns': ['provider', 'region', 'country']}, 'Categorical Pipeline - Label Encoder': {'positive_label': None}, 'Categorical Pipeline - Imputer': {'categorical_impute_strategy': 'most_frequent', 'numeric_impute_strategy': 'mean', 'boolean_impute_strategy': 'most_frequent', 'categorical_fill_value': None, 'numeric_fill_value': None, 'boolean_fill_value': None}, 'Oversampler': {'sampling_ratio': 0.25, 'k_neighbors_default': 5, 'n_jobs': -1, 'sampling_ratio_dict': None}, 'CatBoost Classifier': {'n_estimators': 10, 'eta': 0.03, 'max_depth': 6, 'bootstrap_type': None, 'silent': True, 'allow_writing_files': False, 'n_jobs': -1}}
			Fold 1: Traceback:
  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/automl/engine/engine_base.py", line 238, in _train_and_score
    fitted_pipeline, hashes = train_pipeline(

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/automl/engine/engine_base.py", line 176, in train_pipeline
    cv_pipeline.fit(X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/utils/base_meta.py", line 19, in _set_fit
    return_value = method(self, X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/pipelines/classification_pipeline.py", line 70, in fit
    self._fit(X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/pipelines/pipeline_base.py", line 277, in _fit
    self.component_graph.fit(X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/pipelines/component_graph.py", line 244, in fit
    self._transform_features(self.compute_order, X, y, fit=True)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/pipelines/component_graph.py", line 501, in _transform_features
    output = component_instance.fit_transform(x_inputs, y_input)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/utils/base_meta.py", line 19, in _set_fit
    return_value = method(self, X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/pipelines/components/transformers/samplers/base_sampler.py", line 169, in fit_transform
    return self.fit(X, y).transform(X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/pipelines/components/component_base_meta.py", line 42, in _check_for_fit
    return method(self, X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/pipelines/components/transformers/samplers/base_sampler.py", line 97, in transform
    X_new, y_new = self._component_obj.fit_resample(X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/imblearn/base.py", line 77, in fit_resample
    X, y, binarize_y = self._check_X_y(X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/imblearn/over_sampling/_smote/base.py", line 455, in _check_X_y
    X, y = self._validate_data(

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/sklearn/base.py", line 596, in _validate_data
    X, y = check_X_y(X, y, **check_params)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/sklearn/utils/validation.py", line 1074, in check_X_y
    X = check_array(

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/sklearn/utils/validation.py", line 791, in check_array
    array = array.astype(new_dtype)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/pandas/core/generic.py", line 5912, in astype
    new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 419, in astype
    return self.apply("astype", dtype=dtype, copy=copy, errors=errors)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 304, in apply
    applied = getattr(b, f)(**kwargs)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 580, in astype
    new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/pandas/core/dtypes/cast.py", line 1292, in astype_array_safe
    new_values = astype_array(values, dtype, copy=copy)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/pandas/core/dtypes/cast.py", line 1234, in astype_array
    values = values.astype(dtype, copy=copy)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/pandas/core/arrays/categorical.py", line 556, in astype
    raise ValueError(msg)

			CatBoost Classifier w/ Label Encoder + Select Columns By Type Transformer + Label Encoder + Replace Nullable Types Transformer + Drop Columns Transformer + DateTime Featurizer + Imputer + Select Columns Transformer + Select Columns Transformer + Label Encoder + Replace Nullable Types Transformer + Imputer + Oversampler fold 2: Encountered an error.
			CatBoost Classifier w/ Label Encoder + Select Columns By Type Transformer + Label Encoder + Replace Nullable Types Transformer + Drop Columns Transformer + DateTime Featurizer + Imputer + Select Columns Transformer + Select Columns Transformer + Label Encoder + Replace Nullable Types Transformer + Imputer + Oversampler fold 2: All scores will be replaced with nan.
			Fold 2: Exception during automl search: Cannot cast object dtype to float64
			Fold 2: Parameters:
	{'Label Encoder': {'positive_label': None}, 'Numeric Pipeline - Select Columns By Type Transformer': {'column_types': ['Categorical', 'EmailAddress', 'URL'], 'exclude': True}, 'Numeric Pipeline - Label Encoder': {'positive_label': None}, 'Numeric Pipeline - Drop Columns Transformer': {'columns': ['currency', 'expiration_date']}, 'Numeric Pipeline - DateTime Featurizer': {'features_to_extract': ['year', 'month', 'day_of_week', 'hour'], 'encode_as_categories': False, 'time_index': None}, 'Numeric Pipeline - Imputer': {'categorical_impute_strategy': 'most_frequent', 'numeric_impute_strategy': 'mean', 'boolean_impute_strategy': 'most_frequent', 'categorical_fill_value': None, 'numeric_fill_value': None, 'boolean_fill_value': None}, 'Numeric Pipeline - Select Columns Transformer': {'columns': ['card_id', 'store_id', 'amount', 'customer_present', 'lat', 'lng', 'datetime_month', 'datetime_day_of_week', 'datetime_hour']}, 'Categorical Pipeline - Select Columns Transformer': {'columns': ['provider', 'region', 'country']}, 'Categorical Pipeline - Label Encoder': {'positive_label': None}, 'Categorical Pipeline - Imputer': {'categorical_impute_strategy': 'most_frequent', 'numeric_impute_strategy': 'mean', 'boolean_impute_strategy': 'most_frequent', 'categorical_fill_value': None, 'numeric_fill_value': None, 'boolean_fill_value': None}, 'Oversampler': {'sampling_ratio': 0.25, 'k_neighbors_default': 5, 'n_jobs': -1, 'sampling_ratio_dict': None}, 'CatBoost Classifier': {'n_estimators': 10, 'eta': 0.03, 'max_depth': 6, 'bootstrap_type': None, 'silent': True, 'allow_writing_files': False, 'n_jobs': -1}}
			Fold 2: Traceback:
  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/automl/engine/engine_base.py", line 238, in _train_and_score
    fitted_pipeline, hashes = train_pipeline(

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/automl/engine/engine_base.py", line 176, in train_pipeline
    cv_pipeline.fit(X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/utils/base_meta.py", line 19, in _set_fit
    return_value = method(self, X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/pipelines/classification_pipeline.py", line 70, in fit
    self._fit(X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/pipelines/pipeline_base.py", line 277, in _fit
    self.component_graph.fit(X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/pipelines/component_graph.py", line 244, in fit
    self._transform_features(self.compute_order, X, y, fit=True)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/pipelines/component_graph.py", line 501, in _transform_features
    output = component_instance.fit_transform(x_inputs, y_input)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/utils/base_meta.py", line 19, in _set_fit
    return_value = method(self, X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/pipelines/components/transformers/samplers/base_sampler.py", line 169, in fit_transform
    return self.fit(X, y).transform(X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/pipelines/components/component_base_meta.py", line 42, in _check_for_fit
    return method(self, X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/evalml/pipelines/components/transformers/samplers/base_sampler.py", line 97, in transform
    X_new, y_new = self._component_obj.fit_resample(X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/imblearn/base.py", line 77, in fit_resample
    X, y, binarize_y = self._check_X_y(X, y)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/imblearn/over_sampling/_smote/base.py", line 455, in _check_X_y
    X, y = self._validate_data(

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/sklearn/base.py", line 596, in _validate_data
    X, y = check_X_y(X, y, **check_params)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/sklearn/utils/validation.py", line 1074, in check_X_y
    X = check_array(

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/sklearn/utils/validation.py", line 791, in check_array
    array = array.astype(new_dtype)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/pandas/core/generic.py", line 5912, in astype
    new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 419, in astype
    return self.apply("astype", dtype=dtype, copy=copy, errors=errors)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 304, in apply
    applied = getattr(b, f)(**kwargs)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 580, in astype
    new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/pandas/core/dtypes/cast.py", line 1292, in astype_array_safe
    new_values = astype_array(values, dtype, copy=copy)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/pandas/core/dtypes/cast.py", line 1234, in astype_array
    values = values.astype(dtype, copy=copy)

  File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/pandas/core/arrays/categorical.py", line 556, in astype
    raise ValueError(msg)

deleting tmpfiles dir: /tmp/tmp7txqc1qa
done
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.
deleting tmpfiles dir: /tmp/tmp98v0rh2f
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/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/main/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:444: 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(
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[autosummary] generating autosummary for: api_index.rst, autoapi/evalml/__main__/index.rst, autoapi/evalml/automl/automl_algorithm/automl_algorithm/index.rst, autoapi/evalml/automl/automl_algorithm/default_algorithm/index.rst, autoapi/evalml/automl/automl_algorithm/index.rst, autoapi/evalml/automl/automl_algorithm/iterative_algorithm/index.rst, autoapi/evalml/automl/automl_search/index.rst, autoapi/evalml/automl/callbacks/index.rst, autoapi/evalml/automl/engine/cf_engine/index.rst, autoapi/evalml/automl/engine/dask_engine/index.rst, ..., 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
myst v0.16.1: MdParserConfig(renderer='sphinx', commonmark_only=False, enable_extensions=['dollarmath'], linkify_fuzzy_links=True, dmath_allow_labels=True, dmath_allow_space=True, dmath_allow_digits=True, dmath_double_inline=False, update_mathjax=True, mathjax_classes='tex2jax_process|mathjax_process|math|output_area', disable_syntax=[], url_schemes=['http', 'https', 'mailto', 'ftp'], heading_anchors=None, heading_slug_func=None, html_meta=[], footnote_transition=True, substitutions=[], sub_delimiters=['{', '}'], words_per_minute=200)
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done
build succeeded.

The LaTeX files are in _build/latex.
Run 'make' in that directory to run these through (pdf)latex
(use `make latexpdf' here to do that automatically).
WARNING: Missing searchtools: /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/checkouts/main/docs/source/_build/latex/_static/searchtools.js

[rtd-command-info] start-time: 2022-09-20T21:50:49.341437Z, end-time: 2022-09-20T21:50:49.423712Z, duration: 0, exit-code: 0
cat latexmkrc
$latex = 'latex ' . $ENV{'LATEXOPTS'} . ' %O %S';
$pdflatex = 'pdflatex ' . $ENV{'LATEXOPTS'} . ' %O %S';
$lualatex = 'lualatex ' . $ENV{'LATEXOPTS'} . ' %O %S';
$xelatex = 'xelatex --no-pdf ' . $ENV{'LATEXOPTS'} . ' %O %S';
$makeindex = 'makeindex -s python.ist %O -o %D %S';
add_cus_dep( "glo", "gls", 0, "makeglo" );
sub makeglo {
 return system( "makeindex -s gglo.ist -o '$_[0].gls' '$_[0].glo'" );
}

[rtd-command-info] start-time: 2022-09-20T21:50:49.640667Z, end-time: 2022-09-20T21:51:40.340104Z, duration: 50, exit-code: 0
latexmk -r latexmkrc -pdf -f -dvi- -ps- -jobname=feature-labs-inc-evalml -interaction=nonstopmode


... (truncated) ...

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embleClassifier

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nes.components.estimators.regressors.ProphetRegressor

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nes.components.estimators.regressors.RandomForestRegressor

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nes.components.estimators.regressors.SVMRegressor

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nes.components.estimators.regressors.vowpal_wabbit_regressor.VowpalWabbitRegres
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nes.components.estimators.SVMClassifier

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nes.components.ExponentialSmoothingRegressor

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nes.components.ExtraTreesClassifier

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nes.components.ExtraTreesRegressor

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nes.components.FeatureSelector

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nes.components.Imputer

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nes.components.KNeighborsClassifier

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nes.components.LabelEncoder

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nes.components.LightGBMClassifier

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nes.components.LightGBMRegressor

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nes.components.LinearDiscriminantAnalysis

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nes.components.LinearRegressor

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nes.components.LogTransformer

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nes.components.LSA

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nes.components.NaturalLanguageFeaturizer

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nes.components.OneHotEncoder

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nes.components.Oversampler

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nes.components.PCA
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nes.components.PerColumnImputer

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nes.components.PolynomialDecomposer

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nes.components.ProphetRegressor

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nes.components.SimpleImputer

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nes.components.StandardScaler

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nes.components.SVMClassifier

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nes.components.TargetEncoder

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nes.components.Transformer

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nes.components.transformers.preprocessing.LSA

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nes.components.XGBoostClassifier

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nes.DecisionTreeClassifier

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nes.DecisionTreeRegressor

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nes.DFSTransformer

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nes.DropNaNRowsTransformer

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nes.ElasticNetClassifier

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nes.ElasticNetRegressor

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nes.ExponentialSmoothingRegressor

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nes.ExtraTreesClassifier

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nes.ExtraTreesRegressor

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nes.FeatureSelector

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nes.KNeighborsClassifier

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ives.PrecisionMicro

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ives.RecallMicro

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ives.RecallWeighted

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ives.standard_metrics.MAE

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ives.standard_metrics.MedianAE

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tBoostClassifier

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tBoostRegressor

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assification_pipeline.ClassificationPipeline

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assificationPipeline

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nes.components.estimators.Estimator

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nes.components.estimators.LinearRegressor

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nes.components.estimators.ProphetRegressor

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nes.components.estimators.RandomForestRegressor

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nes.components.estimators.regressors.arima_regressor.ARIMARegressor

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nes.components.estimators.regressors.baseline_regressor.BaselineRegressor

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nes.components.estimators.regressors.ElasticNetRegressor

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nes.components.estimators.regressors.et_regressor.ExtraTreesRegressor

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nes.components.estimators.regressors.exponential_smoothing_regressor.Exponentia
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nes.components.estimators.regressors.ExponentialSmoothingRegressor

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nes.components.estimators.regressors.ExtraTreesRegressor

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nes.components.estimators.regressors.lightgbm_regressor.LightGBMRegressor

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nes.components.estimators.regressors.linear_regressor.LinearRegressor

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nes.components.estimators.regressors.prophet_regressor.ProphetRegressor

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nes.components.estimators.regressors.RandomForestRegressor

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nes.components.estimators.regressors.rf_regressor.RandomForestRegressor

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nes.components.estimators.regressors.SVMRegressor

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nes.components.estimators.regressors.time_series_baseline_estimator.TimeSeriesB
aselineEstimator

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nes.components.estimators.regressors.vowpal_wabbit_regressor.VowpalWabbitRegres
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nes.components.estimators.regressors.xgboost_regressor.XGBoostRegressor

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nes.components.estimators.regressors.XGBoostRegressor

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nes.components.estimators.SVMClassifier

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nes.components.estimators.SVMRegressor

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nes.components.estimators.TimeSeriesBaselineEstimator

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nes.components.estimators.VowpalWabbitBinaryClassifier

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nes.components.estimators.VowpalWabbitMulticlassClassifier

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nes.components.estimators.XGBoostClassifier

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nes.components.estimators.XGBoostRegressor

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nes.components.ExponentialSmoothingRegressor

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nes.components.ExtraTreesClassifier

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nes.components.ExtraTreesRegressor

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nes.components.KNeighborsClassifier

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nes.components.LightGBMClassifier

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nes.components.LightGBMRegressor

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nes.components.LinearRegressor

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nes.components.LogisticRegressionClassifier

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nes.components.ProphetRegressor

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nes.components.RandomForestClassifier

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nes.components.RandomForestRegressor

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nes.components.StackedEnsembleClassifier

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nes.components.StackedEnsembleRegressor

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nes.components.SVMClassifier

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nes.components.SVMRegressor

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nes.components.utils.WrappedSKClassifier

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nes.components.VowpalWabbitBinaryClassifier

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nes.components.VowpalWabbitMulticlassClassifier

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nes.components.VowpalWabbitRegressor
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nes.components.XGBoostClassifier

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nes.components.XGBoostRegressor

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nes.DecisionTreeClassifier

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nes.DecisionTreeRegressor

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nes.ElasticNetClassifier

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nes.ElasticNetRegressor

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nes.ExponentialSmoothingRegressor

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nes.ExtraTreesClassifier

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nes.ExtraTreesRegressor

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nes.KNeighborsClassifier

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nes.LightGBMClassifier

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nes.LightGBMRegressor

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nes.LinearRegressor

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nes.LogisticRegressionClassifier

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BinaryClassificationPipeline

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ClassificationPipeline

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component_graph.ComponentGraph

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ComponentGraph

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components.DFSTransformer

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components.DropColumns

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components.FeatureSelector

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components.Imputer

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components.LabelEncoder

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components.LinearDiscriminantAnalysis

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components.LogTransformer

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components.LSA

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components.OneHotEncoder

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components.Oversampler
[1976]
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components.PCA

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components.PerColumnImputer

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components.PolynomialDecomposer

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components.ReplaceNullableTypes

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components.RFClassifierSelectFromModel

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components.SelectByType

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components.SelectColumns

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components.SimpleImputer

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components.StandardScaler

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components.TargetEncoder

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components.TimeSeriesRegularizer

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DFSTransformer

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DropNaNRowsTransformer

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FeatureSelector

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RegressionPipeline

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