Read the Docs build information Build id: 1822393 Project: feature-labs-inc-evalml Version: latest Commit: 735ca67a3b5d39c71ba1dcb1eaf30e1877714ef9 Date: 2023-11-02T21:18:20.096830Z State: finished Success: False [rtd-command-info] start-time: 2023-11-02T21:18:21.354136Z, end-time: 2023-11-02T21:18:22.767593Z, duration: 1, exit-code: 0 git clone --depth 1 https://github.com/alteryx/evalml.git . Cloning into '.'... [rtd-command-info] start-time: 2023-11-02T21:18:22.859696Z, end-time: 2023-11-02T21:18:24.160311Z, duration: 1, exit-code: 0 git fetch origin --force --prune --prune-tags --depth 50 refs/heads/main:refs/remotes/origin/main From https://github.com/alteryx/evalml * [new tag] v0.80.0 -> v0.80.0 * [new tag] v0.81.0 -> v0.81.0 * [new tag] v0.81.1 -> v0.81.1 [rtd-command-info] start-time: 2023-11-02T21:18:24.354821Z, end-time: 2023-11-02T21:18:24.551750Z, 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 Or undo this operation with: git switch - Turn off this advice by setting config variable advice.detachedHead to false HEAD is now at 735ca67 Changed target name/series ID divider and added ability to return series ID column with predictions (#4357) [rtd-command-info] start-time: 2023-11-02T21:18:24.616498Z, end-time: 2023-11-02T21:18:24.675590Z, duration: 0, exit-code: 0 git clean -d -f -f [rtd-command-info] start-time: 2023-11-02T21:18:24.736200Z, end-time: 2023-11-02T21:18:24.797621Z, duration: 0, exit-code: 0 cat .readthedocs.yaml # Read the Docs configuration file # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details # Required version: 2 build: os: ubuntu-22.04 tools: python: "3.8" apt_packages: - graphviz # Build documentation in the docs/ directory with Sphinx sphinx: configuration: docs/source/conf.py fail_on_warning: false # Build documentation with MkDocs #mkdocs: # configuration: mkdocs.yml # Optionally build your docs in additional formats such as PDF and ePub formats: - pdf # Optionally set the version of Python and requirements required to build your docs python: install: - requirements: docs/readthedocs-requirements.txt - method: pip path: . extra_requirements: - dev - prophet [rtd-command-info] start-time: 2023-11-02T21:18:25.897517Z, end-time: 2023-11-02T21:18:36.546441Z, duration: 10, exit-code: 0 apt-get update --assume-yes --quiet Hit:1 http://archive.ubuntu.com/ubuntu jammy InRelease Get:2 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [119 kB] Get:3 http://archive.ubuntu.com/ubuntu jammy-backports InRelease [109 kB] Get:4 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [1455 kB] Get:5 http://security.ubuntu.com/ubuntu jammy-security InRelease [110 kB] Get:6 http://archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 Packages [1419 kB] Get:7 http://archive.ubuntu.com/ubuntu jammy-updates/multiverse amd64 Packages [49.9 kB] Get:8 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 Packages [1278 kB] Get:9 http://archive.ubuntu.com/ubuntu jammy-backports/universe amd64 Packages [32.6 kB] Get:10 http://archive.ubuntu.com/ubuntu jammy-backports/main amd64 Packages [78.3 kB] Get:11 http://security.ubuntu.com/ubuntu jammy-security/main amd64 Packages [1185 kB] Get:12 http://security.ubuntu.com/ubuntu jammy-security/universe amd64 Packages [1013 kB] Get:13 http://security.ubuntu.com/ubuntu jammy-security/restricted amd64 Packages [1389 kB] Get:14 http://security.ubuntu.com/ubuntu jammy-security/multiverse amd64 Packages [44.0 kB] Fetched 8282 kB in 2s (3320 kB/s) Reading package lists... [rtd-command-info] start-time: 2023-11-02T21:18:36.628487Z, end-time: 2023-11-02T21:19:18.518879Z, duration: 41, exit-code: 0 apt-get install --assume-yes --quiet -- graphviz Reading package lists... Building dependency tree... Reading state information... The following additional packages will be installed: fonts-liberation libann0 Suggested packages: graphviz-doc The following NEW packages will be installed: fonts-liberation graphviz libann0 0 upgraded, 3 newly installed, 0 to remove and 189 not upgraded. Need to get 1498 kB of archives. After this operation, 4810 kB of additional disk space will be used. Get:1 http://archive.ubuntu.com/ubuntu jammy/main amd64 fonts-liberation all 1:1.07.4-11 [822 kB] Get:2 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libann0 amd64 1.1.2+doc-7build1 [26.0 kB] Get:3 http://archive.ubuntu.com/ubuntu jammy/universe amd64 graphviz amd64 2.42.2-6 [650 kB] debconf: delaying package configuration, since apt-utils is not installed Fetched 1498 kB in 1s (2140 kB/s) Selecting previously unselected package fonts-liberation. (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 271716 files and directories currently installed.) Preparing to unpack .../fonts-liberation_1%3a1.07.4-11_all.deb ... Unpacking fonts-liberation (1:1.07.4-11) ... Selecting previously unselected package libann0. Preparing to unpack .../libann0_1.1.2+doc-7build1_amd64.deb ... Unpacking libann0 (1.1.2+doc-7build1) ... Selecting previously unselected package graphviz. Preparing to unpack .../graphviz_2.42.2-6_amd64.deb ... Unpacking graphviz (2.42.2-6) ... Setting up libann0 (1.1.2+doc-7build1) ... Setting up fonts-liberation (1:1.07.4-11) ... Setting up graphviz (2.42.2-6) ... Processing triggers for libc-bin (2.35-0ubuntu3) ... Processing triggers for fontconfig (2.13.1-4.2ubuntu5) ... 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/tmp/pip-ephem-wheel-cache-hxlznnfx/wheels/e6/a6/20/cc1e293fcdb67ede666fed293cb895395e7ecceb4467779546 Building wheel for evalml (pyproject.toml): started Building wheel for evalml (pyproject.toml): finished with status 'done' Created wheel for evalml: filename=evalml-0.81.1-py3-none-any.whl size=6586929 sha256=d34a86f17fa0b799254936b93940e38daf1131f1df11bb17f0dfc6a33ccc6603 Stored in directory: /tmp/pip-ephem-wheel-cache-hxlznnfx/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=731999 sha256=7516f9c2a630276cb2d6de456ba94be390f90c9e093847b426911f02e782fca8 Stored in directory: /tmp/pip-ephem-wheel-cache-hxlznnfx/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=75888 sha256=cbcf68a38fbd8b8c0a3b85acdec97f467564e19fab9a96e892b5aa613f488a40 Stored in directory: /tmp/pip-ephem-wheel-cache-hxlznnfx/wheels/5f/fd/9e/b6cf5890494cb8ef0b5eaff72e5d55a70fb56316007d6dfe73 Successfully built lime evalml PyMeeus wrapt Installing collected packages: wrapt, webencodings, wcwidth, texttable, sortedcontainers, PyMeeus, pure-eval, ptyprocess, pickleshare, korean-lunar-calendar, kaleido, fastjsonschema, ephem, distlib, backcall, zict, widgetsnbextension, vowpalwabbit, unidecode, tzdata, typing-extensions, traitlets, tqdm, tornado, toolz, tomli, tokenize-rt, tinycss2, threadpoolctl, tenacity, tblib, soupsieve, slicer, six, scikit-base, ruff, rpds-py, regex, pyzmq, pyyaml, pyparsing, py, psutil, prompt-toolkit, pluggy, platformdirs, pkgutil-resolve-name, pillow, pexpect, pathspec, parso, pandocfilters, numpy, nodeenv, networkx, nest-asyncio, mypy-extensions, msgpack, mistune, mdurl, locket, llvmlite, lazy-object-proxy, lazy_loader, kiwisolver, jupyterlab-widgets, jupyterlab-pygments, joblib, iniconfig, importlib-resources, identify, hijri-converter, graphviz, fsspec, fonttools, filelock, executing, execnet, docutils, defusedxml, decorator, debugpy, darglint, Cython, cycler, coverage, convertdate, colorama, cloudpickle, click, cfgv, attrs, accessible-pygments, virtualenv, tifffile, stanio, Sphinx, scipy, referencing, PyWavelets, python-dateutil, pytest, pyaml, plotly, patsy, partd, numba, nltk, matplotlib-inline, markdown-it-py, jupyter-core, jedi, imageio, contourpy, comm, bleach, black, beautifulsoup4, asttokens, astroid, xgboost, stack-data, sphinx-inline-tabs, sphinx-copybutton, sphinx-autoapi, scikit-learn, scikit-image, pytest-timeout, pytest-forked, pytest-cov, pydata-sphinx-theme, pre-commit, pandas, mdit-py-plugins, matplotlib, LunarCalendar, lightgbm, jupyter-client, jsonschema-specifications, holidays, dask, woodwork, statsmodels, sktime, shap, seaborn, scikit-optimize, pytest-xdist, myst-parser, lime, jsonschema, ipython, imbalanced-learn, distributed, cmdstanpy, catboost, prophet, pmdarima, nbformat, ipywidgets, ipykernel, featuretools, category-encoders, nlp-primitives, nbval, nbclient, nbconvert, evalml, nbsphinx Attempting uninstall: docutils Found existing installation: docutils 0.20.1 Uninstalling docutils-0.20.1: Successfully uninstalled docutils-0.20.1 Attempting uninstall: Sphinx Found existing installation: Sphinx 7.1.2 Uninstalling Sphinx-7.1.2: Successfully uninstalled Sphinx-7.1.2 Successfully installed Cython-3.0.5 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-2.4.1 attrs-23.1.0 backcall-0.2.0 beautifulsoup4-4.12.2 black-23.10.1 bleach-6.1.0 catboost-1.2.2 category-encoders-2.5.1.post0 cfgv-3.4.0 click-8.1.7 cloudpickle-3.0.0 cmdstanpy-1.2.0 colorama-0.4.6 comm-0.1.4 contourpy-1.1.1 convertdate-2.4.0 coverage-7.3.2 cycler-0.12.1 darglint-1.8.0 dask-2023.5.0 debugpy-1.8.0 decorator-5.1.1 defusedxml-0.7.1 distlib-0.3.7 distributed-2023.5.0 docutils-0.16 ephem-4.1.5 evalml-0.81.1 execnet-2.0.2 executing-2.0.1 fastjsonschema-2.18.1 featuretools-1.28.0 filelock-3.13.1 fonttools-4.43.1 fsspec-2023.10.0 graphviz-0.20.1 hijri-converter-2.3.1 holidays-0.20 identify-2.5.31 imageio-2.31.6 imbalanced-learn-0.11.0 importlib-resources-6.1.0 iniconfig-2.0.0 ipykernel-6.26.0 ipython-8.12.0 ipywidgets-8.1.1 jedi-0.19.1 joblib-1.3.2 jsonschema-4.19.2 jsonschema-specifications-2023.7.1 jupyter-client-8.5.0 jupyter-core-5.5.0 jupyterlab-pygments-0.2.2 jupyterlab-widgets-3.0.9 kaleido-0.2.1 kiwisolver-1.4.5 korean-lunar-calendar-0.3.1 lazy-object-proxy-1.9.0 lazy_loader-0.3 lightgbm-4.1.0 lime-0.2.0.1 llvmlite-0.41.1 locket-1.0.0 markdown-it-py-2.2.0 matplotlib-3.7.3 matplotlib-inline-0.1.6 mdit-py-plugins-0.3.5 mdurl-0.1.2 mistune-3.0.2 msgpack-1.0.7 mypy-extensions-1.0.0 myst-parser-0.16.1 nbclient-0.8.0 nbconvert-7.10.0 nbformat-5.9.2 nbsphinx-0.8.12 nbval-0.9.3 nest-asyncio-1.5.8 networkx-3.1 nlp-primitives-2.11.0 nltk-3.8.1 nodeenv-1.8.0 numba-0.58.1 numpy-1.24.4 pandas-2.0.3 pandocfilters-1.5.0 parso-0.8.3 partd-1.4.1 pathspec-0.11.2 patsy-0.5.3 pexpect-4.8.0 pickleshare-0.7.5 pillow-10.0.1 pkgutil-resolve-name-1.3.10 platformdirs-3.11.0 plotly-5.18.0 pluggy-1.3.0 pmdarima-2.0.4 pre-commit-3.5.0 prompt-toolkit-3.0.39 prophet-1.1.3 psutil-5.9.6 ptyprocess-0.7.0 pure-eval-0.2.2 py-1.11.0 pyaml-23.9.7 pydata-sphinx-theme-0.13.3 pyparsing-3.1.1 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-25.1.1 referencing-0.30.2 regex-2023.10.3 rpds-py-0.10.6 ruff-0.0.228 scikit-base-0.6.1 scikit-image-0.21.0 scikit-learn-1.3.2 scikit-optimize-0.9.0 scipy-1.10.1 seaborn-0.13.0 shap-0.43.0 six-1.16.0 sktime-0.24.0 slicer-0.0.7 sortedcontainers-2.4.0 soupsieve-2.5 sphinx-autoapi-1.8.3 sphinx-copybutton-0.4.0 sphinx-inline-tabs-2022.1.2b11 stack-data-0.6.3 stanio-0.3.0 statsmodels-0.14.0 tblib-3.0.0 tenacity-8.2.3 texttable-1.7.0 threadpoolctl-3.2.0 tifffile-2023.7.10 tinycss2-1.2.1 tokenize-rt-5.2.0 tomli-2.0.1 toolz-0.12.0 tornado-6.3.3 tqdm-4.66.1 traitlets-5.13.0 typing-extensions-4.8.0 tzdata-2023.3 unidecode-1.3.7 virtualenv-20.24.6 vowpalwabbit-9.9.0 wcwidth-0.2.9 webencodings-0.5.1 widgetsnbextension-4.0.9 woodwork-0.26.0 wrapt-1.12.1 xgboost-2.0.1 zict-3.0.0 [rtd-command-info] start-time: 2023-11-02T21:21:43.065555Z, end-time: 2023-11-02T21:21:43.125679Z, 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') # Define this variable in case it's not defined by the user. # It defaults to `alabaster` which is the default from Sphinx. # https://www.sphinx-doc.org/en/master/usage/configuration.html#confval-html_theme html_theme = globals().get('html_theme', 'alabaster') #Add project information to the template context. context = { '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/"), ("v0.81.1", "/en/v0.81.1/"), ("v0.81.0", "/en/v0.81.0/"), ("v0.80.0", "/en/v0.80.0/"), ("v0.79.0", "/en/v0.79.0/"), ("v0.78.0", "/en/v0.78.0/"), ("v0.77.0", 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"/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/latest/pdf/"), ], 'subprojects': [ ], 'slug': 'feature-labs-inc-evalml', 'name': u'EvalML', 'rtd_language': u'en', 'programming_language': u'py', 'canonical_url': '', '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': '735ca67a', } # 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(): for key in context: if key not in html_context: html_context[key] = context[key] 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/1822393/' 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: 2023-11-02T21:21:43.198439Z, end-time: 2023-11-02T21:38:13.532169Z, duration: 990, exit-code: 2 python -m sphinx -T -E -b html -d _build/doctrees -D language=en . $READTHEDOCS_OUTPUT/html Running Sphinx v4.5.0 Matplotlib is building the font cache; this may take a moment. loading translations [en]... done making output directory... 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[100%] evalml.objectives.lead_scoring [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) building [mo]: targets for 0 po files that are out of date building [html]: targets for 22 source files that are out of date updating environment: [new config] 229 added, 0 changed, 0 removed reading sources... 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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... 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[ 99%] user_guide/timeseries You are using an unsupported version of pandoc (2.9.2.1). Your version must be at least (2.14.2) but less than (4.0.0). Refer to https://pandoc.org/installing.html. Continuing with doubts... /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:464: 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/model_understanding/metrics/index.rst:51: ERROR: Unexpected indentation. /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/checkouts/latest/docs/source/autoapi/evalml/model_understanding/metrics/index.rst:52: WARNING: Block quote ends without a blank line; unexpected unindent. /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/checkouts/latest/docs/source/autoapi/evalml/pipelines/components/index.rst:11158: WARNING: Definition list ends without a blank line; unexpected unindent. /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/checkouts/latest/docs/source/autoapi/evalml/pipelines/components/transformers/index.rst:5492: WARNING: Definition list ends without a blank line; unexpected unindent. /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/checkouts/latest/docs/source/autoapi/evalml/pipelines/components/transformers/preprocessing/index.rst:2532: WARNING: Definition list ends without a blank line; unexpected unindent. /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/checkouts/latest/docs/source/autoapi/evalml/pipelines/components/transformers/preprocessing/stl_decomposer/index.rst:213: WARNING: Definition list ends without a blank line; unexpected unindent. /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/checkouts/latest/docs/source/release_notes.rst:11: WARNING: Definition list ends without a blank line; unexpected unindent. /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/checkouts/latest/docs/source/release_notes.rst:172: 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 102, 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 123, 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 173, in wrapped return loop.run_until_complete(inner) File "/home/docs/.asdf/installs/python/3.8.18/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: ------------------ problem_configuration = { "time_index": "date", "forecast_horizon": 5, "max_delay": 10, "gap": 0, "series_id": "series_id", } automl = AutoMLSearch( X_train, y_train, problem_type="multiseries time series regression", problem_configuration=problem_configuration, ) automl.search() ------------------ ----- stderr ----- /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/linalg/_solvers.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.10048e-56): result may not be accurate. ----- stderr ----- /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/linalg/_solvers.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.55654e-52): result may not be accurate. ----- stderr ----- /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/linalg/_solvers.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.63763e-51): result may not be accurate. ----- stderr ----- /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/linalg/_solvers.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.75288e-52): result may not be accurate. ------------------ --------------------------------------------------------------------------- LinAlgError Traceback (most recent call last) Cell In[56], line 15  1 problem_configuration = {  2 "time_index": "date",  3 "forecast_horizon": 5,  (...)  6 "series_id": "series_id",  7 }  9 automl = AutoMLSearch(  10 X_train,  11 y_train,  12 problem_type="multiseries time series regression",  13 problem_configuration=problem_configuration,  14 ) ---> 15 automl.search() File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/automl/automl_search.py:1288, in AutoMLSearch.search(self, interactive_plot)  1285 if self.timing is True:  1286 log_batch_times(self.logger, batch_times) -> 1288 self._find_best_pipeline()  1289 if self._best_pipeline is not None:  1290 best_pipeline = self.rankings.iloc[0] File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/automl/automl_search.py:1319, in AutoMLSearch._find_best_pipeline(self)  1317 X_train = self.X_train  1318 y_train = self.y_train -> 1319 best_pipeline = self._engine.submit_training_job(  1320  self.automl_config,  1321  best_pipeline,  1322  X_train,  1323  y_train,  1324  ).get_result()[0]  1326 self._best_pipeline = best_pipeline File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/automl/engine/sequential_engine.py:47, in SequentialComputation.get_result(self)  38 def get_result(self):  39  """Gets the computation result. Will block until the computation is finished.  40  41  Raises:  (...)  45  Computation results.  46  """ ---> 47 return self.work(**self.kwargs) File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/automl/engine/engine_base.py:164, in train_pipeline(pipeline, X, y, automl_config, schema, get_hashes)  158 X, X_threshold_tuning, y, y_threshold_tuning = resplit_training_data(  159 pipeline,  160 X,  161 y,  162 )  163 cv_pipeline = pipeline.clone() --> 164 cv_pipeline.fit(X, y)  165 tune_binary_threshold(  166 cv_pipeline,  167 threshold_tuning_objective,  (...)  172 y,  173 )  174 if not get_hashes: File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/utils/base_meta.py:25, in BaseMeta.set_fit.._set_fit(self, X, y)  23 @wraps(method)  24 def _set_fit(self, X, y=None): ---> 25 return_value = method(self, X, y)  26 self._is_fitted = True  27 return return_value File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/pipelines/multiseries_regression_pipeline.py:68, in MultiseriesRegressionPipeline.fit(self, X, y)  55 def fit(self, X, y):  56  """Fit a multiseries time series pipeline.  57  58  Args:  (...)  66  ValueError: If the target is not numeric.  67  """ ---> 68 self._fit(X, y)  69 return self File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/pipelines/multiseries_regression_pipeline.py:84, in MultiseriesRegressionPipeline._fit(self, X, y)  75 X_unstacked, y_unstacked = unstack_multiseries(  76 X,  77 y,  (...)  80 self.input_target_name,  81 )  82 self.frequency = infer_frequency(X_unstacked[self.time_index]) ---> 84 self.component_graph.fit(X_unstacked, y_unstacked)  85 self.input_feature_names = self.component_graph.input_feature_names File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/pipelines/component_graph.py:256, in ComponentGraph.fit(self, X, y)  254 X = infer_feature_types(X)  255 y = infer_feature_types(y) --> 256 self._transform_features(self.compute_order, X, y, fit=True)  257 self._feature_provenance = self._get_feature_provenance(X.columns)  258 return self File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/pipelines/component_graph.py:531, in ComponentGraph._transform_features(self, component_list, X, y, fit, evaluate_training_only_components)  529 else:  530 if fit and not component_instance._is_fitted: --> 531 component_instance.fit(x_inputs, y_input)  532 if fit and component_name == self.compute_order[-1]:  533 # Don't call predict on the final component during fit  534 output = None File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/utils/base_meta.py:25, in BaseMeta.set_fit.._set_fit(self, X, y)  23 @wraps(method)  24 def _set_fit(self, X, y=None): ---> 25 return_value = method(self, X, y)  26 self._is_fitted = True  27 return return_value File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/pipelines/components/estimators/regressors/varmax_regressor.py:151, in VARMAXRegressor.fit(self, X, y)  149 else:  150 self.last_X_index = y.index[-1] --> 151 self._component_obj.fit(y=y)  152 return self File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/sktime/forecasting/base/_base.py:383, in BaseForecaster.fit(self, y, X, fh)  381 # we call the ordinary _fit if no looping/vectorization needed  382 if not vectorization_needed: --> 383 self._fit(y=y_inner, X=X_inner, fh=fh)  384 else:  385 # otherwise we call the vectorized version of fit  386 self._vectorize("fit", y=y_inner, X=X_inner, fh=fh) File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/sktime/forecasting/base/adapters/_statsmodels.py:54, in _StatsModelsAdapter._fit(self, y, X, fh)  52 if isinstance(y, pd.Series) and pd.api.types.is_integer_dtype(y.index):  53 y, X = _coerce_int_to_range_index(y, X) ---> 54 self._fit_forecaster(y, X)  55 return self File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/sktime/forecasting/varmax.py:315, in VARMAX._fit_forecaster(self, y, X)  302 from statsmodels.tsa.statespace.varmax import VARMAX as _VARMAX  304 self._forecaster = _VARMAX(  305 endog=y,  306 exog=X,  (...)  313 trend_offset=self.trend_offset,  314 ) --> 315 self._fitted_forecaster = self._forecaster.fit(  316  start_params=self.start_params,  317  transformed=self.transformed,  318  includes_fixed=self.includes_fixed,  319  cov_type=self.cov_type,  320  cov_kwds=self.cov_kwds,  321  method=self.method,  322  maxiter=self.maxiter,  323  full_output=self.full_output,  324  disp=self.disp,  325  callback=self.callback,  326  return_params=self.return_params,  327  optim_score=self.optim_score,  328  optim_complex_step=self.optim_complex_step,  329  optim_hessian=self.optim_hessian,  330  flags=self.flags,  331  low_memory=self.low_memory,  332 )  333 return self File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/tsa/statespace/mlemodel.py:704, in MLEModel.fit(self, start_params, transformed, includes_fixed, cov_type, cov_kwds, method, maxiter, full_output, disp, callback, return_params, optim_score, optim_complex_step, optim_hessian, flags, low_memory, **kwargs)  702 flags['hessian_method'] = optim_hessian  703 fargs = (flags,) --> 704 mlefit = super(MLEModel, self).fit(start_params, method=method,  705  fargs=fargs,  706  maxiter=maxiter,  707  full_output=full_output,  708  disp=disp, callback=callback,  709  skip_hessian=True, **kwargs)  711 # Just return the fitted parameters if requested  712 if return_params: File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/base/model.py:566, in LikelihoodModel.fit(self, start_params, method, maxiter, full_output, disp, fargs, callback, retall, skip_hessian, **kwargs)  563 del kwargs["use_t"]  565 optimizer = Optimizer() --> 566 xopt, retvals, optim_settings = optimizer._fit(f, score, start_params,  567  fargs, kwargs,  568  hessian=hess,  569  method=method,  570  disp=disp,  571  maxiter=maxiter,  572  callback=callback,  573  retall=retall,  574  full_output=full_output)  575 # Restore cov_type, cov_kwds and use_t  576 optim_settings.update(kwds) File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/base/optimizer.py:242, in Optimizer._fit(self, objective, gradient, start_params, fargs, kwargs, hessian, method, maxiter, full_output, disp, callback, retall)  239 fit_funcs.update(extra_fit_funcs)  241 func = fit_funcs[method] --> 242 xopt, retvals = func(objective, gradient, start_params, fargs, kwargs,  243  disp=disp, maxiter=maxiter, callback=callback,  244  retall=retall, full_output=full_output,  245  hess=hessian)  247 optim_settings = {'optimizer': method, 'start_params': start_params,  248 'maxiter': maxiter, 'full_output': full_output,  249 'disp': disp, 'fargs': fargs, 'callback': callback,  250 'retall': retall, "extra_fit_funcs": extra_fit_funcs}  251 optim_settings.update(kwargs) File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/base/optimizer.py:659, in _fit_lbfgs(f, score, start_params, fargs, kwargs, disp, maxiter, callback, retall, full_output, hess)  656 elif approx_grad:  657 func = f --> 659 retvals = optimize.fmin_l_bfgs_b(func, start_params, maxiter=maxiter,  660  callback=callback, args=fargs,  661  bounds=bounds, disp=disp,  662  **extra_kwargs)  664 if full_output:  665 xopt, fopt, d = retvals File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/optimize/_lbfgsb_py.py:197, in fmin_l_bfgs_b(func, x0, fprime, args, approx_grad, bounds, m, factr, pgtol, epsilon, iprint, maxfun, maxiter, disp, callback, maxls)  185 # build options  186 opts = {'disp': disp,  187 'iprint': iprint,  188 'maxcor': m,  (...)  194 'callback': callback,  195 'maxls': maxls} --> 197 res = _minimize_lbfgsb(fun, x0, args=args, jac=jac, bounds=bounds,  198  **opts)  199 d = {'grad': res['jac'],  200 'task': res['message'],  201 'funcalls': res['nfev'],  202 'nit': res['nit'],  203 'warnflag': res['status']}  204 f = res['fun'] File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/optimize/_lbfgsb_py.py:359, in _minimize_lbfgsb(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, finite_diff_rel_step, **unknown_options)  353 task_str = task.tobytes()  354 if task_str.startswith(b'FG'):  355 # The minimization routine wants f and g at the current x.  356 # Note that interruptions due to maxfun are postponed  357 # until the completion of the current minimization iteration.  358 # Overwrite f and g: --> 359 f, g = func_and_grad(x)  360 elif task_str.startswith(b'NEW_X'):  361 # new iteration  362 n_iterations += 1 File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py:285, in ScalarFunction.fun_and_grad(self, x)  283 if not np.array_equal(x, self.x):  284 self._update_x_impl(x) --> 285 self._update_fun()  286 self._update_grad()  287 return self.f, self.g File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py:251, in ScalarFunction._update_fun(self)  249 def _update_fun(self):  250 if not self.f_updated: --> 251 self._update_fun_impl()  252 self.f_updated = True File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py:155, in ScalarFunction.__init__..update_fun()  154 def update_fun(): --> 155 self.f = fun_wrapped(self.x) File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py:137, in ScalarFunction.__init__..fun_wrapped(x)  133 self.nfev += 1  134 # Send a copy because the user may overwrite it.  135 # Overwriting results in undefined behaviour because  136 # fun(self.x) will change self.x, with the two no longer linked. --> 137 fx = fun(np.copy(x), *args)  138 # Make sure the function returns a true scalar  139 if not np.isscalar(fx): File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/base/model.py:534, in LikelihoodModel.fit..f(params, *args)  533 def f(params, *args): --> 534 return -self.loglike(params, *args) / nobs File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/tsa/statespace/mlemodel.py:939, in MLEModel.loglike(self, params, *args, **kwargs)  936 if complex_step:  937 kwargs['inversion_method'] = INVERT_UNIVARIATE | SOLVE_LU --> 939 loglike = self.ssm.loglike(complex_step=complex_step, **kwargs)  941 # Koopman, Shephard, and Doornik recommend maximizing the average  942 # likelihood to avoid scale issues, but the averaging is done  943 # automatically in the base model `fit` method  944 return loglike File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/tsa/statespace/kalman_filter.py:1001, in KalmanFilter.loglike(self, **kwargs)  985 r"""  986 Calculate the loglikelihood associated with the statespace model.  987  (...)  997  The joint loglikelihood.  998 """  999 kwargs.setdefault('conserve_memory',  1000 MEMORY_CONSERVE ^ MEMORY_NO_LIKELIHOOD) -> 1001 kfilter = self._filter(**kwargs)  1002 loglikelihood_burn = kwargs.get('loglikelihood_burn',  1003 self.loglikelihood_burn)  1004 if not (kwargs['conserve_memory'] & MEMORY_NO_LIKELIHOOD): File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/tsa/statespace/kalman_filter.py:921, in KalmanFilter._filter(self, filter_method, inversion_method, stability_method, conserve_memory, filter_timing, tolerance, loglikelihood_burn, complex_step)  918 kfilter = self._kalman_filters[prefix]  920 # Initialize the state --> 921 self._initialize_state(prefix=prefix, complex_step=complex_step)  923 # Run the filter  924 kfilter() File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/tsa/statespace/representation.py:1058, in Representation._initialize_state(self, prefix, complex_step)  1056 if not self.initialization.initialized:  1057 raise RuntimeError('Initialization is incomplete.') -> 1058 self._statespaces[prefix].initialize(self.initialization,  1059  complex_step=complex_step)  1060 else:  1061 raise RuntimeError('Statespace model not initialized.') File statsmodels/tsa/statespace/_representation.pyx:1362, in statsmodels.tsa.statespace._representation.dStatespace.initialize() File statsmodels/tsa/statespace/_initialization.pyx:288, in statsmodels.tsa.statespace._initialization.dInitialization.initialize() File statsmodels/tsa/statespace/_initialization.pyx:406, in statsmodels.tsa.statespace._initialization.dInitialization.initialize_stationary_stationary_cov() File statsmodels/tsa/statespace/_tools.pyx:1525, in statsmodels.tsa.statespace._tools._dsolve_discrete_lyapunov() LinAlgError: LU decomposition error. 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/timeseries.ipynb: ------------------ problem_configuration = { "time_index": "date", "forecast_horizon": 5, "max_delay": 10, "gap": 0, "series_id": "series_id", } automl = AutoMLSearch( X_train, y_train, problem_type="multiseries time series regression", problem_configuration=problem_configuration, ) automl.search() ------------------ ----- stderr ----- /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/linalg/_solvers.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.10048e-56): result may not be accurate. ----- stderr ----- /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/linalg/_solvers.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.55654e-52): result may not be accurate. ----- stderr ----- /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/linalg/_solvers.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.63763e-51): result may not be accurate. ----- stderr ----- /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/linalg/_solvers.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.75288e-52): result may not be accurate. ------------------ --------------------------------------------------------------------------- LinAlgError Traceback (most recent call last) Cell In[56], line 15  1 problem_configuration = {  2 "time_index": "date",  3 "forecast_horizon": 5,  (...)  6 "series_id": "series_id",  7 }  9 automl = AutoMLSearch(  10 X_train,  11 y_train,  12 problem_type="multiseries time series regression",  13 problem_configuration=problem_configuration,  14 ) ---> 15 automl.search() File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/automl/automl_search.py:1288, in AutoMLSearch.search(self, interactive_plot)  1285 if self.timing is True:  1286 log_batch_times(self.logger, batch_times) -> 1288 self._find_best_pipeline()  1289 if self._best_pipeline is not None:  1290 best_pipeline = self.rankings.iloc[0] File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/automl/automl_search.py:1319, in AutoMLSearch._find_best_pipeline(self)  1317 X_train = self.X_train  1318 y_train = self.y_train -> 1319 best_pipeline = self._engine.submit_training_job(  1320  self.automl_config,  1321  best_pipeline,  1322  X_train,  1323  y_train,  1324  ).get_result()[0]  1326 self._best_pipeline = best_pipeline File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/automl/engine/sequential_engine.py:47, in SequentialComputation.get_result(self)  38 def get_result(self):  39  """Gets the computation result. Will block until the computation is finished.  40  41  Raises:  (...)  45  Computation results.  46  """ ---> 47 return self.work(**self.kwargs) File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/automl/engine/engine_base.py:164, in train_pipeline(pipeline, X, y, automl_config, schema, get_hashes)  158 X, X_threshold_tuning, y, y_threshold_tuning = resplit_training_data(  159 pipeline,  160 X,  161 y,  162 )  163 cv_pipeline = pipeline.clone() --> 164 cv_pipeline.fit(X, y)  165 tune_binary_threshold(  166 cv_pipeline,  167 threshold_tuning_objective,  (...)  172 y,  173 )  174 if not get_hashes: File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/utils/base_meta.py:25, in BaseMeta.set_fit.._set_fit(self, X, y)  23 @wraps(method)  24 def _set_fit(self, X, y=None): ---> 25 return_value = method(self, X, y)  26 self._is_fitted = True  27 return return_value File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/pipelines/multiseries_regression_pipeline.py:68, in MultiseriesRegressionPipeline.fit(self, X, y)  55 def fit(self, X, y):  56  """Fit a multiseries time series pipeline.  57  58  Args:  (...)  66  ValueError: If the target is not numeric.  67  """ ---> 68 self._fit(X, y)  69 return self File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/pipelines/multiseries_regression_pipeline.py:84, in MultiseriesRegressionPipeline._fit(self, X, y)  75 X_unstacked, y_unstacked = unstack_multiseries(  76 X,  77 y,  (...)  80 self.input_target_name,  81 )  82 self.frequency = infer_frequency(X_unstacked[self.time_index]) ---> 84 self.component_graph.fit(X_unstacked, y_unstacked)  85 self.input_feature_names = self.component_graph.input_feature_names File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/pipelines/component_graph.py:256, in ComponentGraph.fit(self, X, y)  254 X = infer_feature_types(X)  255 y = infer_feature_types(y) --> 256 self._transform_features(self.compute_order, X, y, fit=True)  257 self._feature_provenance = self._get_feature_provenance(X.columns)  258 return self File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/pipelines/component_graph.py:531, in ComponentGraph._transform_features(self, component_list, X, y, fit, evaluate_training_only_components)  529 else:  530 if fit and not component_instance._is_fitted: --> 531 component_instance.fit(x_inputs, y_input)  532 if fit and component_name == self.compute_order[-1]:  533 # Don't call predict on the final component during fit  534 output = None File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/utils/base_meta.py:25, in BaseMeta.set_fit.._set_fit(self, X, y)  23 @wraps(method)  24 def _set_fit(self, X, y=None): ---> 25 return_value = method(self, X, y)  26 self._is_fitted = True  27 return return_value File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/pipelines/components/estimators/regressors/varmax_regressor.py:151, in VARMAXRegressor.fit(self, X, y)  149 else:  150 self.last_X_index = y.index[-1] --> 151 self._component_obj.fit(y=y)  152 return self File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/sktime/forecasting/base/_base.py:383, in BaseForecaster.fit(self, y, X, fh)  381 # we call the ordinary _fit if no looping/vectorization needed  382 if not vectorization_needed: --> 383 self._fit(y=y_inner, X=X_inner, fh=fh)  384 else:  385 # otherwise we call the vectorized version of fit  386 self._vectorize("fit", y=y_inner, X=X_inner, fh=fh) File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/sktime/forecasting/base/adapters/_statsmodels.py:54, in _StatsModelsAdapter._fit(self, y, X, fh)  52 if isinstance(y, pd.Series) and pd.api.types.is_integer_dtype(y.index):  53 y, X = _coerce_int_to_range_index(y, X) ---> 54 self._fit_forecaster(y, X)  55 return self File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/sktime/forecasting/varmax.py:315, in VARMAX._fit_forecaster(self, y, X)  302 from statsmodels.tsa.statespace.varmax import VARMAX as _VARMAX  304 self._forecaster = _VARMAX(  305 endog=y,  306 exog=X,  (...)  313 trend_offset=self.trend_offset,  314 ) --> 315 self._fitted_forecaster = self._forecaster.fit(  316  start_params=self.start_params,  317  transformed=self.transformed,  318  includes_fixed=self.includes_fixed,  319  cov_type=self.cov_type,  320  cov_kwds=self.cov_kwds,  321  method=self.method,  322  maxiter=self.maxiter,  323  full_output=self.full_output,  324  disp=self.disp,  325  callback=self.callback,  326  return_params=self.return_params,  327  optim_score=self.optim_score,  328  optim_complex_step=self.optim_complex_step,  329  optim_hessian=self.optim_hessian,  330  flags=self.flags,  331  low_memory=self.low_memory,  332 )  333 return self File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/tsa/statespace/mlemodel.py:704, in MLEModel.fit(self, start_params, transformed, includes_fixed, cov_type, cov_kwds, method, maxiter, full_output, disp, callback, return_params, optim_score, optim_complex_step, optim_hessian, flags, low_memory, **kwargs)  702 flags['hessian_method'] = optim_hessian  703 fargs = (flags,) --> 704 mlefit = super(MLEModel, self).fit(start_params, method=method,  705  fargs=fargs,  706  maxiter=maxiter,  707  full_output=full_output,  708  disp=disp, callback=callback,  709  skip_hessian=True, **kwargs)  711 # Just return the fitted parameters if requested  712 if return_params: File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/base/model.py:566, in LikelihoodModel.fit(self, start_params, method, maxiter, full_output, disp, fargs, callback, retall, skip_hessian, **kwargs)  563 del kwargs["use_t"]  565 optimizer = Optimizer() --> 566 xopt, retvals, optim_settings = optimizer._fit(f, score, start_params,  567  fargs, kwargs,  568  hessian=hess,  569  method=method,  570  disp=disp,  571  maxiter=maxiter,  572  callback=callback,  573  retall=retall,  574  full_output=full_output)  575 # Restore cov_type, cov_kwds and use_t  576 optim_settings.update(kwds) File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/base/optimizer.py:242, in Optimizer._fit(self, objective, gradient, start_params, fargs, kwargs, hessian, method, maxiter, full_output, disp, callback, retall)  239 fit_funcs.update(extra_fit_funcs)  241 func = fit_funcs[method] --> 242 xopt, retvals = func(objective, gradient, start_params, fargs, kwargs,  243  disp=disp, maxiter=maxiter, callback=callback,  244  retall=retall, full_output=full_output,  245  hess=hessian)  247 optim_settings = {'optimizer': method, 'start_params': start_params,  248 'maxiter': maxiter, 'full_output': full_output,  249 'disp': disp, 'fargs': fargs, 'callback': callback,  250 'retall': retall, "extra_fit_funcs": extra_fit_funcs}  251 optim_settings.update(kwargs) File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/base/optimizer.py:659, in _fit_lbfgs(f, score, start_params, fargs, kwargs, disp, maxiter, callback, retall, full_output, hess)  656 elif approx_grad:  657 func = f --> 659 retvals = optimize.fmin_l_bfgs_b(func, start_params, maxiter=maxiter,  660  callback=callback, args=fargs,  661  bounds=bounds, disp=disp,  662  **extra_kwargs)  664 if full_output:  665 xopt, fopt, d = retvals File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/optimize/_lbfgsb_py.py:197, in fmin_l_bfgs_b(func, x0, fprime, args, approx_grad, bounds, m, factr, pgtol, epsilon, iprint, maxfun, maxiter, disp, callback, maxls)  185 # build options  186 opts = {'disp': disp,  187 'iprint': iprint,  188 'maxcor': m,  (...)  194 'callback': callback,  195 'maxls': maxls} --> 197 res = _minimize_lbfgsb(fun, x0, args=args, jac=jac, bounds=bounds,  198  **opts)  199 d = {'grad': res['jac'],  200 'task': res['message'],  201 'funcalls': res['nfev'],  202 'nit': res['nit'],  203 'warnflag': res['status']}  204 f = res['fun'] File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/optimize/_lbfgsb_py.py:359, in _minimize_lbfgsb(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, finite_diff_rel_step, **unknown_options)  353 task_str = task.tobytes()  354 if task_str.startswith(b'FG'):  355 # The minimization routine wants f and g at the current x.  356 # Note that interruptions due to maxfun are postponed  357 # until the completion of the current minimization iteration.  358 # Overwrite f and g: --> 359 f, g = func_and_grad(x)  360 elif task_str.startswith(b'NEW_X'):  361 # new iteration  362 n_iterations += 1 File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py:285, in ScalarFunction.fun_and_grad(self, x)  283 if not np.array_equal(x, self.x):  284 self._update_x_impl(x) --> 285 self._update_fun()  286 self._update_grad()  287 return self.f, self.g File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py:251, in ScalarFunction._update_fun(self)  249 def _update_fun(self):  250 if not self.f_updated: --> 251 self._update_fun_impl()  252 self.f_updated = True File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py:155, in ScalarFunction.__init__..update_fun()  154 def update_fun(): --> 155 self.f = fun_wrapped(self.x) File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py:137, in ScalarFunction.__init__..fun_wrapped(x)  133 self.nfev += 1  134 # Send a copy because the user may overwrite it.  135 # Overwriting results in undefined behaviour because  136 # fun(self.x) will change self.x, with the two no longer linked. --> 137 fx = fun(np.copy(x), *args)  138 # Make sure the function returns a true scalar  139 if not np.isscalar(fx): File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/base/model.py:534, in LikelihoodModel.fit..f(params, *args)  533 def f(params, *args): --> 534 return -self.loglike(params, *args) / nobs File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/tsa/statespace/mlemodel.py:939, in MLEModel.loglike(self, params, *args, **kwargs)  936 if complex_step:  937 kwargs['inversion_method'] = INVERT_UNIVARIATE | SOLVE_LU --> 939 loglike = self.ssm.loglike(complex_step=complex_step, **kwargs)  941 # Koopman, Shephard, and Doornik recommend maximizing the average  942 # likelihood to avoid scale issues, but the averaging is done  943 # automatically in the base model `fit` method  944 return loglike File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/tsa/statespace/kalman_filter.py:1001, in KalmanFilter.loglike(self, **kwargs)  985 r"""  986 Calculate the loglikelihood associated with the statespace model.  987  (...)  997  The joint loglikelihood.  998 """  999 kwargs.setdefault('conserve_memory',  1000 MEMORY_CONSERVE ^ MEMORY_NO_LIKELIHOOD) -> 1001 kfilter = self._filter(**kwargs)  1002 loglikelihood_burn = kwargs.get('loglikelihood_burn',  1003 self.loglikelihood_burn)  1004 if not (kwargs['conserve_memory'] & MEMORY_NO_LIKELIHOOD): File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/tsa/statespace/kalman_filter.py:921, in KalmanFilter._filter(self, filter_method, inversion_method, stability_method, conserve_memory, filter_timing, tolerance, loglikelihood_burn, complex_step)  918 kfilter = self._kalman_filters[prefix]  920 # Initialize the state --> 921 self._initialize_state(prefix=prefix, complex_step=complex_step)  923 # Run the filter  924 kfilter() File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/tsa/statespace/representation.py:1058, in Representation._initialize_state(self, prefix, complex_step)  1056 if not self.initialization.initialized:  1057 raise RuntimeError('Initialization is incomplete.') -> 1058 self._statespaces[prefix].initialize(self.initialization,  1059  complex_step=complex_step)  1060 else:  1061 raise RuntimeError('Statespace model not initialized.') File statsmodels/tsa/statespace/_representation.pyx:1362, in statsmodels.tsa.statespace._representation.dStatespace.initialize() File statsmodels/tsa/statespace/_initialization.pyx:288, in statsmodels.tsa.statespace._initialization.dInitialization.initialize() File statsmodels/tsa/statespace/_initialization.pyx:406, in statsmodels.tsa.statespace._initialization.dInitialization.initialize_stationary_stationary_cov() File statsmodels/tsa/statespace/_tools.pyx:1525, in statsmodels.tsa.statespace._tools._dsolve_discrete_lyapunov() LinAlgError: LU decomposition error. You can ignore this error by setting the following in conf.py: nbsphinx_allow_errors = True Notebook error: CellExecutionError in user_guide/timeseries.ipynb: ------------------ problem_configuration = { "time_index": "date", "forecast_horizon": 5, "max_delay": 10, "gap": 0, "series_id": "series_id", } automl = AutoMLSearch( X_train, y_train, problem_type="multiseries time series regression", problem_configuration=problem_configuration, ) automl.search() ------------------ ----- stderr ----- /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/linalg/_solvers.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.10048e-56): result may not be accurate. ----- stderr ----- /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/linalg/_solvers.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.55654e-52): result may not be accurate. ----- stderr ----- /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/linalg/_solvers.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.63763e-51): result may not be accurate. ----- stderr ----- /home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/linalg/_solvers.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.75288e-52): result may not be accurate. ------------------ --------------------------------------------------------------------------- LinAlgError Traceback (most recent call last) Cell In[56], line 15  1 problem_configuration = {  2 "time_index": "date",  3 "forecast_horizon": 5,  (...)  6 "series_id": "series_id",  7 }  9 automl = AutoMLSearch(  10 X_train,  11 y_train,  12 problem_type="multiseries time series regression",  13 problem_configuration=problem_configuration,  14 ) ---> 15 automl.search() File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/automl/automl_search.py:1288, in AutoMLSearch.search(self, interactive_plot)  1285 if self.timing is True:  1286 log_batch_times(self.logger, batch_times) -> 1288 self._find_best_pipeline()  1289 if self._best_pipeline is not None:  1290 best_pipeline = self.rankings.iloc[0] File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/automl/automl_search.py:1319, in AutoMLSearch._find_best_pipeline(self)  1317 X_train = self.X_train  1318 y_train = self.y_train -> 1319 best_pipeline = self._engine.submit_training_job(  1320  self.automl_config,  1321  best_pipeline,  1322  X_train,  1323  y_train,  1324  ).get_result()[0]  1326 self._best_pipeline = best_pipeline File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/automl/engine/sequential_engine.py:47, in SequentialComputation.get_result(self)  38 def get_result(self):  39  """Gets the computation result. Will block until the computation is finished.  40  41  Raises:  (...)  45  Computation results.  46  """ ---> 47 return self.work(**self.kwargs) File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/automl/engine/engine_base.py:164, in train_pipeline(pipeline, X, y, automl_config, schema, get_hashes)  158 X, X_threshold_tuning, y, y_threshold_tuning = resplit_training_data(  159 pipeline,  160 X,  161 y,  162 )  163 cv_pipeline = pipeline.clone() --> 164 cv_pipeline.fit(X, y)  165 tune_binary_threshold(  166 cv_pipeline,  167 threshold_tuning_objective,  (...)  172 y,  173 )  174 if not get_hashes: File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/utils/base_meta.py:25, in BaseMeta.set_fit.._set_fit(self, X, y)  23 @wraps(method)  24 def _set_fit(self, X, y=None): ---> 25 return_value = method(self, X, y)  26 self._is_fitted = True  27 return return_value File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/pipelines/multiseries_regression_pipeline.py:68, in MultiseriesRegressionPipeline.fit(self, X, y)  55 def fit(self, X, y):  56  """Fit a multiseries time series pipeline.  57  58  Args:  (...)  66  ValueError: If the target is not numeric.  67  """ ---> 68 self._fit(X, y)  69 return self File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/pipelines/multiseries_regression_pipeline.py:84, in MultiseriesRegressionPipeline._fit(self, X, y)  75 X_unstacked, y_unstacked = unstack_multiseries(  76 X,  77 y,  (...)  80 self.input_target_name,  81 )  82 self.frequency = infer_frequency(X_unstacked[self.time_index]) ---> 84 self.component_graph.fit(X_unstacked, y_unstacked)  85 self.input_feature_names = self.component_graph.input_feature_names File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/pipelines/component_graph.py:256, in ComponentGraph.fit(self, X, y)  254 X = infer_feature_types(X)  255 y = infer_feature_types(y) --> 256 self._transform_features(self.compute_order, X, y, fit=True)  257 self._feature_provenance = self._get_feature_provenance(X.columns)  258 return self File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/pipelines/component_graph.py:531, in ComponentGraph._transform_features(self, component_list, X, y, fit, evaluate_training_only_components)  529 else:  530 if fit and not component_instance._is_fitted: --> 531 component_instance.fit(x_inputs, y_input)  532 if fit and component_name == self.compute_order[-1]:  533 # Don't call predict on the final component during fit  534 output = None File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/utils/base_meta.py:25, in BaseMeta.set_fit.._set_fit(self, X, y)  23 @wraps(method)  24 def _set_fit(self, X, y=None): ---> 25 return_value = method(self, X, y)  26 self._is_fitted = True  27 return return_value File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/evalml/pipelines/components/estimators/regressors/varmax_regressor.py:151, in VARMAXRegressor.fit(self, X, y)  149 else:  150 self.last_X_index = y.index[-1] --> 151 self._component_obj.fit(y=y)  152 return self File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/sktime/forecasting/base/_base.py:383, in BaseForecaster.fit(self, y, X, fh)  381 # we call the ordinary _fit if no looping/vectorization needed  382 if not vectorization_needed: --> 383 self._fit(y=y_inner, X=X_inner, fh=fh)  384 else:  385 # otherwise we call the vectorized version of fit  386 self._vectorize("fit", y=y_inner, X=X_inner, fh=fh) File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/sktime/forecasting/base/adapters/_statsmodels.py:54, in _StatsModelsAdapter._fit(self, y, X, fh)  52 if isinstance(y, pd.Series) and pd.api.types.is_integer_dtype(y.index):  53 y, X = _coerce_int_to_range_index(y, X) ---> 54 self._fit_forecaster(y, X)  55 return self File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/sktime/forecasting/varmax.py:315, in VARMAX._fit_forecaster(self, y, X)  302 from statsmodels.tsa.statespace.varmax import VARMAX as _VARMAX  304 self._forecaster = _VARMAX(  305 endog=y,  306 exog=X,  (...)  313 trend_offset=self.trend_offset,  314 ) --> 315 self._fitted_forecaster = self._forecaster.fit(  316  start_params=self.start_params,  317  transformed=self.transformed,  318  includes_fixed=self.includes_fixed,  319  cov_type=self.cov_type,  320  cov_kwds=self.cov_kwds,  321  method=self.method,  322  maxiter=self.maxiter,  323  full_output=self.full_output,  324  disp=self.disp,  325  callback=self.callback,  326  return_params=self.return_params,  327  optim_score=self.optim_score,  328  optim_complex_step=self.optim_complex_step,  329  optim_hessian=self.optim_hessian,  330  flags=self.flags,  331  low_memory=self.low_memory,  332 )  333 return self File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/tsa/statespace/mlemodel.py:704, in MLEModel.fit(self, start_params, transformed, includes_fixed, cov_type, cov_kwds, method, maxiter, full_output, disp, callback, return_params, optim_score, optim_complex_step, optim_hessian, flags, low_memory, **kwargs)  702 flags['hessian_method'] = optim_hessian  703 fargs = (flags,) --> 704 mlefit = super(MLEModel, self).fit(start_params, method=method,  705  fargs=fargs,  706  maxiter=maxiter,  707  full_output=full_output,  708  disp=disp, callback=callback,  709  skip_hessian=True, **kwargs)  711 # Just return the fitted parameters if requested  712 if return_params: File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/base/model.py:566, in LikelihoodModel.fit(self, start_params, method, maxiter, full_output, disp, fargs, callback, retall, skip_hessian, **kwargs)  563 del kwargs["use_t"]  565 optimizer = Optimizer() --> 566 xopt, retvals, optim_settings = optimizer._fit(f, score, start_params,  567  fargs, kwargs,  568  hessian=hess,  569  method=method,  570  disp=disp,  571  maxiter=maxiter,  572  callback=callback,  573  retall=retall,  574  full_output=full_output)  575 # Restore cov_type, cov_kwds and use_t  576 optim_settings.update(kwds) File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/base/optimizer.py:242, in Optimizer._fit(self, objective, gradient, start_params, fargs, kwargs, hessian, method, maxiter, full_output, disp, callback, retall)  239 fit_funcs.update(extra_fit_funcs)  241 func = fit_funcs[method] --> 242 xopt, retvals = func(objective, gradient, start_params, fargs, kwargs,  243  disp=disp, maxiter=maxiter, callback=callback,  244  retall=retall, full_output=full_output,  245  hess=hessian)  247 optim_settings = {'optimizer': method, 'start_params': start_params,  248 'maxiter': maxiter, 'full_output': full_output,  249 'disp': disp, 'fargs': fargs, 'callback': callback,  250 'retall': retall, "extra_fit_funcs": extra_fit_funcs}  251 optim_settings.update(kwargs) File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/base/optimizer.py:659, in _fit_lbfgs(f, score, start_params, fargs, kwargs, disp, maxiter, callback, retall, full_output, hess)  656 elif approx_grad:  657 func = f --> 659 retvals = optimize.fmin_l_bfgs_b(func, start_params, maxiter=maxiter,  660  callback=callback, args=fargs,  661  bounds=bounds, disp=disp,  662  **extra_kwargs)  664 if full_output:  665 xopt, fopt, d = retvals File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/optimize/_lbfgsb_py.py:197, in fmin_l_bfgs_b(func, x0, fprime, args, approx_grad, bounds, m, factr, pgtol, epsilon, iprint, maxfun, maxiter, disp, callback, maxls)  185 # build options  186 opts = {'disp': disp,  187 'iprint': iprint,  188 'maxcor': m,  (...)  194 'callback': callback,  195 'maxls': maxls} --> 197 res = _minimize_lbfgsb(fun, x0, args=args, jac=jac, bounds=bounds,  198  **opts)  199 d = {'grad': res['jac'],  200 'task': res['message'],  201 'funcalls': res['nfev'],  202 'nit': res['nit'],  203 'warnflag': res['status']}  204 f = res['fun'] File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/optimize/_lbfgsb_py.py:359, in _minimize_lbfgsb(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, finite_diff_rel_step, **unknown_options)  353 task_str = task.tobytes()  354 if task_str.startswith(b'FG'):  355 # The minimization routine wants f and g at the current x.  356 # Note that interruptions due to maxfun are postponed  357 # until the completion of the current minimization iteration.  358 # Overwrite f and g: --> 359 f, g = func_and_grad(x)  360 elif task_str.startswith(b'NEW_X'):  361 # new iteration  362 n_iterations += 1 File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py:285, in ScalarFunction.fun_and_grad(self, x)  283 if not np.array_equal(x, self.x):  284 self._update_x_impl(x) --> 285 self._update_fun()  286 self._update_grad()  287 return self.f, self.g File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py:251, in ScalarFunction._update_fun(self)  249 def _update_fun(self):  250 if not self.f_updated: --> 251 self._update_fun_impl()  252 self.f_updated = True File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py:155, in ScalarFunction.__init__..update_fun()  154 def update_fun(): --> 155 self.f = fun_wrapped(self.x) File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py:137, in ScalarFunction.__init__..fun_wrapped(x)  133 self.nfev += 1  134 # Send a copy because the user may overwrite it.  135 # Overwriting results in undefined behaviour because  136 # fun(self.x) will change self.x, with the two no longer linked. --> 137 fx = fun(np.copy(x), *args)  138 # Make sure the function returns a true scalar  139 if not np.isscalar(fx): File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/base/model.py:534, in LikelihoodModel.fit..f(params, *args)  533 def f(params, *args): --> 534 return -self.loglike(params, *args) / nobs File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/tsa/statespace/mlemodel.py:939, in MLEModel.loglike(self, params, *args, **kwargs)  936 if complex_step:  937 kwargs['inversion_method'] = INVERT_UNIVARIATE | SOLVE_LU --> 939 loglike = self.ssm.loglike(complex_step=complex_step, **kwargs)  941 # Koopman, Shephard, and Doornik recommend maximizing the average  942 # likelihood to avoid scale issues, but the averaging is done  943 # automatically in the base model `fit` method  944 return loglike File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/tsa/statespace/kalman_filter.py:1001, in KalmanFilter.loglike(self, **kwargs)  985 r"""  986 Calculate the loglikelihood associated with the statespace model.  987  (...)  997  The joint loglikelihood.  998 """  999 kwargs.setdefault('conserve_memory',  1000 MEMORY_CONSERVE ^ MEMORY_NO_LIKELIHOOD) -> 1001 kfilter = self._filter(**kwargs)  1002 loglikelihood_burn = kwargs.get('loglikelihood_burn',  1003 self.loglikelihood_burn)  1004 if not (kwargs['conserve_memory'] & MEMORY_NO_LIKELIHOOD): File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/tsa/statespace/kalman_filter.py:921, in KalmanFilter._filter(self, filter_method, inversion_method, stability_method, conserve_memory, filter_timing, tolerance, loglikelihood_burn, complex_step)  918 kfilter = self._kalman_filters[prefix]  920 # Initialize the state --> 921 self._initialize_state(prefix=prefix, complex_step=complex_step)  923 # Run the filter  924 kfilter() File ~/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.8/site-packages/statsmodels/tsa/statespace/representation.py:1058, in Representation._initialize_state(self, prefix, complex_step)  1056 if not self.initialization.initialized:  1057 raise RuntimeError('Initialization is incomplete.') -> 1058 self._statespaces[prefix].initialize(self.initialization,  1059  complex_step=complex_step)  1060 else:  1061 raise RuntimeError('Statespace model not initialized.') File statsmodels/tsa/statespace/_representation.pyx:1362, in statsmodels.tsa.statespace._representation.dStatespace.initialize() File statsmodels/tsa/statespace/_initialization.pyx:288, in statsmodels.tsa.statespace._initialization.dInitialization.initialize() File statsmodels/tsa/statespace/_initialization.pyx:406, in statsmodels.tsa.statespace._initialization.dInitialization.initialize_stationary_stationary_cov() File statsmodels/tsa/statespace/_tools.pyx:1525, in statsmodels.tsa.statespace._tools._dsolve_discrete_lyapunov() LinAlgError: LU decomposition error. You can ignore this error by setting the following in conf.py: nbsphinx_allow_errors = True