diff --git a/.gitattributes b/.gitattributes index de1fb2e83c1295e6b87328c165a40251c599b513..cc8ea29bdc86dc218137ecddce10a4d9083cca75 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1951,3 +1951,5 @@ parrot/lib/python3.10/site-packages/matplotlib/_ttconv.cpython-310-x86_64-linux- parrot/lib/python3.10/site-packages/matplotlib/axes/__pycache__/_base.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text parrot/lib/python3.10/site-packages/altair/vegalite/v5/schema/__pycache__/core.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text parrot/lib/python3.10/site-packages/matplotlib/_path.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text +videollama2/lib/python3.10/site-packages/sklearn/preprocessing/_csr_polynomial_expansion.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text +llava_next/lib/python3.10/site-packages/scipy.libs/libscipy_openblas-c128ec02.so filter=lfs diff=lfs merge=lfs -text diff --git a/llava_next/lib/python3.10/site-packages/scipy.libs/libscipy_openblas-c128ec02.so b/llava_next/lib/python3.10/site-packages/scipy.libs/libscipy_openblas-c128ec02.so new file mode 100644 index 0000000000000000000000000000000000000000..511062bfa85adb0969806c96ec72cccb7c469bac --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/scipy.libs/libscipy_openblas-c128ec02.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:95121ba2173f1838ca08d988af0cc03a4959d50d9f8adea42d10d8fe8f3be61d +size 36060905 diff --git a/parrot/lib/python3.10/site-packages/matplotlib/__pycache__/_color_data.cpython-310.pyc b/parrot/lib/python3.10/site-packages/matplotlib/__pycache__/_color_data.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d220cea6766530d375611af7cc9d085aaaa963df Binary files /dev/null and b/parrot/lib/python3.10/site-packages/matplotlib/__pycache__/_color_data.cpython-310.pyc differ diff --git a/parrot/lib/python3.10/site-packages/matplotlib/__pycache__/_text_helpers.cpython-310.pyc b/parrot/lib/python3.10/site-packages/matplotlib/__pycache__/_text_helpers.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3098d7f910a4243a341b3e8ce09e459a9e0acea6 Binary files /dev/null and b/parrot/lib/python3.10/site-packages/matplotlib/__pycache__/_text_helpers.cpython-310.pyc differ diff --git a/parrot/lib/python3.10/site-packages/matplotlib/__pycache__/gridspec.cpython-310.pyc b/parrot/lib/python3.10/site-packages/matplotlib/__pycache__/gridspec.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3342a108d9aa3746b59e7c8b24cffbf1e581c8c5 Binary files /dev/null and b/parrot/lib/python3.10/site-packages/matplotlib/__pycache__/gridspec.cpython-310.pyc differ diff --git a/videollama2/lib/python3.10/site-packages/cmake_modules/FindAzure.cmake b/videollama2/lib/python3.10/site-packages/cmake_modules/FindAzure.cmake new file mode 100644 index 0000000000000000000000000000000000000000..fdf354b724e776bf0927386cd64dc870b00749e4 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/cmake_modules/FindAzure.cmake @@ -0,0 +1,45 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +if(Azure_FOUND) + return() +endif() + +set(find_package_args) +list(APPEND find_package_args CONFIG) +if(Azure_FIND_QUIETLY) + list(APPEND find_package_args QUIET) +endif() + +if(Azure_FIND_REQUIRED) + list(APPEND find_package_args REQUIRED) +endif() + +find_package(azure-core-cpp ${find_package_args}) +find_package(azure-identity-cpp ${find_package_args}) +find_package(azure-storage-blobs-cpp ${find_package_args}) +find_package(azure-storage-common-cpp ${find_package_args}) +find_package(azure-storage-files-datalake-cpp ${find_package_args}) + +find_package_handle_standard_args( + Azure + REQUIRED_VARS azure-core-cpp_FOUND + azure-identity-cpp_FOUND + azure-storage-blobs-cpp_FOUND + azure-storage-common-cpp_FOUND + azure-storage-files-datalake-cpp_FOUND + VERSION_VAR azure-core-cpp_VERSION) diff --git a/videollama2/lib/python3.10/site-packages/cmake_modules/UseCython.cmake b/videollama2/lib/python3.10/site-packages/cmake_modules/UseCython.cmake new file mode 100644 index 0000000000000000000000000000000000000000..e15ac59490c6e6bd6af9bed5987e0226f34513b3 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/cmake_modules/UseCython.cmake @@ -0,0 +1,187 @@ +# Define a function to create Cython modules. +# +# For more information on the Cython project, see http://cython.org/. +# "Cython is a language that makes writing C extensions for the Python language +# as easy as Python itself." +# +# This file defines a CMake function to build a Cython Python module. +# To use it, first include this file. +# +# include( UseCython ) +# +# Then call cython_add_module to create a module. +# +# cython_add_module( ... ) +# +# Where is the desired name of the target for the resulting Python module, +# is the desired name of the target that runs the Cython compiler +# to generate the needed C or C++ files, is a variable to hold the +# files generated by Cython, and ... are source files +# to be compiled into the module, e.g. *.pyx, *.c, *.cxx, etc. +# only one .pyx file may be present for each target +# (this is an inherent limitation of Cython). +# +# The sample paths set with the CMake include_directories() command will be used +# for include directories to search for *.pxd when running the Cython compiler. +# +# Cache variables that effect the behavior include: +# +# CYTHON_ANNOTATE +# CYTHON_NO_DOCSTRINGS +# CYTHON_FLAGS +# +# Source file properties that effect the build process are +# +# CYTHON_IS_CXX +# CYTHON_IS_PUBLIC +# CYTHON_IS_API +# +# If this is set of a *.pyx file with CMake set_source_files_properties() +# command, the file will be compiled as a C++ file. +# +# See also FindCython.cmake + +#============================================================================= +# Copyright 2011 Kitware, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +#============================================================================= + +# Configuration options. +set(CYTHON_ANNOTATE OFF CACHE BOOL "Create an annotated .html file when compiling *.pyx.") +set(CYTHON_NO_DOCSTRINGS OFF CACHE BOOL "Strip docstrings from the compiled module.") +set(CYTHON_FLAGS "" CACHE STRING "Extra flags to the cython compiler.") +mark_as_advanced(CYTHON_ANNOTATE CYTHON_NO_DOCSTRINGS CYTHON_FLAGS) + +find_package(Python3Alt REQUIRED) + +# (using another C++ extension breaks coverage) +set(CYTHON_CXX_EXTENSION "cpp") +set(CYTHON_C_EXTENSION "c") + +# Create a *.c or *.cpp file from a *.pyx file. +# Input the generated file basename. The generate files will put into the variable +# placed in the "generated_files" argument. Finally all the *.py and *.pyx files. +function(compile_pyx + _name + pyx_target_name + generated_files + pyx_file) + # Default to assuming all files are C. + set(cxx_arg "") + set(extension ${CYTHON_C_EXTENSION}) + set(pyx_lang "C") + set(comment "Compiling Cython C source for ${_name}...") + + get_filename_component(pyx_file_basename "${pyx_file}" NAME_WE) + + # Determine if it is a C or C++ file. + get_source_file_property(property_is_cxx ${pyx_file} CYTHON_IS_CXX) + if(${property_is_cxx}) + set(cxx_arg "--cplus") + set(extension ${CYTHON_CXX_EXTENSION}) + set(pyx_lang "CXX") + set(comment "Compiling Cython CXX source for ${_name}...") + endif() + get_source_file_property(pyx_location ${pyx_file} LOCATION) + + set(output_file "${_name}.${extension}") + + # Set additional flags. + if(CYTHON_ANNOTATE) + set(annotate_arg "--annotate") + endif() + + if(CYTHON_NO_DOCSTRINGS) + set(no_docstrings_arg "--no-docstrings") + endif() + + if(NOT WIN32) + string( TOLOWER "${CMAKE_BUILD_TYPE}" build_type ) + if("${build_type}" STREQUAL "debug" + OR "${build_type}" STREQUAL "relwithdebinfo") + set(cython_debug_arg "--gdb") + endif() + endif() + + # Determining generated file names. + get_source_file_property(property_is_public ${pyx_file} CYTHON_PUBLIC) + get_source_file_property(property_is_api ${pyx_file} CYTHON_API) + if(${property_is_api}) + set(_generated_files "${output_file}" "${_name}.h" "${_name}_api.h") + elseif(${property_is_public}) + set(_generated_files "${output_file}" "${_name}.h") + else() + set(_generated_files "${output_file}") + endif() + set_source_files_properties(${_generated_files} PROPERTIES GENERATED TRUE) + + if(NOT WIN32) + # Cython creates a lot of compiler warning detritus on clang + set_source_files_properties(${_generated_files} PROPERTIES COMPILE_FLAGS + -Wno-unused-function) + endif() + + set(${generated_files} ${_generated_files} PARENT_SCOPE) + + # Add the command to run the compiler. + add_custom_target( + ${pyx_target_name} + COMMAND ${PYTHON_EXECUTABLE} + -m + cython + ${cxx_arg} + ${annotate_arg} + ${no_docstrings_arg} + ${cython_debug_arg} + ${CYTHON_FLAGS} + # Necessary for autodoc of function arguments + --directive embedsignature=True + # Necessary for Cython code coverage + --working + ${CMAKE_CURRENT_SOURCE_DIR} + --output-file + "${CMAKE_CURRENT_BINARY_DIR}/${output_file}" + "${CMAKE_CURRENT_SOURCE_DIR}/${pyx_file}" + DEPENDS ${pyx_location} + # Do not specify byproducts for now since they don't work with the older + # version of cmake available in the apt repositories. + #BYPRODUCTS ${_generated_files} + COMMENT ${comment}) + + # Remove their visibility to the user. + set(corresponding_pxd_file "" CACHE INTERNAL "") + set(header_location "" CACHE INTERNAL "") + set(pxd_location "" CACHE INTERNAL "") +endfunction() + +# cython_add_module( src1 src2 ... srcN ) +# Build the Cython Python module. +function(cython_add_module _name pyx_target_name generated_files) + set(pyx_module_source "") + set(other_module_sources "") + foreach(_file ${ARGN}) + if(${_file} MATCHES ".*\\.py[x]?$") + list(APPEND pyx_module_source ${_file}) + else() + list(APPEND other_module_sources ${_file}) + endif() + endforeach() + compile_pyx(${_name} ${pyx_target_name} _generated_files ${pyx_module_source}) + set(${generated_files} ${_generated_files} PARENT_SCOPE) + include_directories(${PYTHON_INCLUDE_DIRS}) + python_add_module(${_name} ${_generated_files} ${other_module_sources}) + add_dependencies(${_name} ${pyx_target_name}) +endfunction() + +include(CMakeParseArguments) diff --git a/videollama2/lib/python3.10/site-packages/fonttools-4.53.1.dist-info/INSTALLER b/videollama2/lib/python3.10/site-packages/fonttools-4.53.1.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/fonttools-4.53.1.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/videollama2/lib/python3.10/site-packages/fonttools-4.53.1.dist-info/LICENSE b/videollama2/lib/python3.10/site-packages/fonttools-4.53.1.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..cc633905d333c4b42c1a0c8b34e9f734adeb6e1e --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/fonttools-4.53.1.dist-info/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2017 Just van Rossum + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/videollama2/lib/python3.10/site-packages/fonttools-4.53.1.dist-info/METADATA b/videollama2/lib/python3.10/site-packages/fonttools-4.53.1.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..6694a8733c156d474124e24d98ed52918f5c3708 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/fonttools-4.53.1.dist-info/METADATA @@ -0,0 +1,3321 @@ +Metadata-Version: 2.1 +Name: fonttools +Version: 4.53.1 +Summary: Tools to manipulate font files +Home-page: http://github.com/fonttools/fonttools +Author: Just van Rossum +Author-email: just@letterror.com +Maintainer: Behdad Esfahbod +Maintainer-email: behdad@behdad.org +License: MIT +Platform: Any +Classifier: Development Status :: 5 - Production/Stable +Classifier: Environment :: Console +Classifier: Environment :: Other Environment +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: End Users/Desktop +Classifier: License :: OSI Approved :: MIT License +Classifier: Natural Language :: English +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3 +Classifier: Topic :: Text Processing :: Fonts +Classifier: Topic :: Multimedia :: Graphics +Classifier: Topic :: Multimedia :: Graphics :: Graphics Conversion +Requires-Python: >=3.8 +License-File: LICENSE +Provides-Extra: all +Requires-Dist: fs <3,>=2.2.0 ; extra == 'all' +Requires-Dist: lxml >=4.0 ; extra == 'all' +Requires-Dist: zopfli >=0.1.4 ; extra == 'all' +Requires-Dist: lz4 >=1.7.4.2 ; extra == 'all' +Requires-Dist: pycairo ; extra == 'all' +Requires-Dist: matplotlib ; extra == 'all' +Requires-Dist: sympy ; extra == 'all' +Requires-Dist: skia-pathops >=0.5.0 ; extra == 'all' +Requires-Dist: uharfbuzz >=0.23.0 ; extra == 'all' +Requires-Dist: brotlicffi >=0.8.0 ; (platform_python_implementation != "CPython") and extra == 'all' +Requires-Dist: scipy ; (platform_python_implementation != "PyPy") and extra == 'all' +Requires-Dist: brotli >=1.0.1 ; (platform_python_implementation == "CPython") and extra == 'all' +Requires-Dist: munkres ; (platform_python_implementation == "PyPy") and extra == 'all' +Requires-Dist: unicodedata2 >=15.1.0 ; (python_version <= "3.12") and extra == 'all' +Requires-Dist: xattr ; (sys_platform == "darwin") and extra == 'all' +Provides-Extra: graphite +Requires-Dist: lz4 >=1.7.4.2 ; extra == 'graphite' +Provides-Extra: interpolatable +Requires-Dist: pycairo ; extra == 'interpolatable' +Requires-Dist: scipy ; (platform_python_implementation != "PyPy") and extra == 'interpolatable' +Requires-Dist: munkres ; (platform_python_implementation == "PyPy") and extra == 'interpolatable' +Provides-Extra: lxml +Requires-Dist: lxml >=4.0 ; extra == 'lxml' +Provides-Extra: pathops +Requires-Dist: skia-pathops >=0.5.0 ; extra == 'pathops' +Provides-Extra: plot +Requires-Dist: matplotlib ; extra == 'plot' +Provides-Extra: repacker +Requires-Dist: uharfbuzz >=0.23.0 ; extra == 'repacker' +Provides-Extra: symfont +Requires-Dist: sympy ; extra == 'symfont' +Provides-Extra: type1 +Requires-Dist: xattr ; (sys_platform == "darwin") and extra == 'type1' +Provides-Extra: ufo +Requires-Dist: fs <3,>=2.2.0 ; extra == 'ufo' +Provides-Extra: unicode +Requires-Dist: unicodedata2 >=15.1.0 ; (python_version <= "3.12") and extra == 'unicode' +Provides-Extra: woff +Requires-Dist: zopfli >=0.1.4 ; extra == 'woff' +Requires-Dist: brotlicffi >=0.8.0 ; (platform_python_implementation != "CPython") and extra == 'woff' +Requires-Dist: brotli >=1.0.1 ; (platform_python_implementation == "CPython") and extra == 'woff' + +|CI Build Status| |Coverage Status| |PyPI| |Gitter Chat| + +What is this? +~~~~~~~~~~~~~ + +| fontTools is a library for manipulating fonts, written in Python. The + project includes the TTX tool, that can convert TrueType and OpenType + fonts to and from an XML text format, which is also called TTX. It + supports TrueType, OpenType, AFM and to an extent Type 1 and some + Mac-specific formats. The project has an `MIT open-source + licence `__. +| Among other things this means you can use it free of charge. + +`User documentation `_ and +`developer documentation `_ +are available at `Read the Docs `_. + +Installation +~~~~~~~~~~~~ + +FontTools requires `Python `__ 3.8 +or later. We try to follow the same schedule of minimum Python version support as +NumPy (see `NEP 29 `__). + +The package is listed in the Python Package Index (PyPI), so you can +install it with `pip `__: + +.. code:: sh + + pip install fonttools + +If you would like to contribute to its development, you can clone the +repository from GitHub, install the package in 'editable' mode and +modify the source code in place. We recommend creating a virtual +environment, using `virtualenv `__ or +Python 3 `venv `__ module. + +.. code:: sh + + # download the source code to 'fonttools' folder + git clone https://github.com/fonttools/fonttools.git + cd fonttools + + # create new virtual environment called e.g. 'fonttools-venv', or anything you like + python -m virtualenv fonttools-venv + + # source the `activate` shell script to enter the environment (Unix-like); to exit, just type `deactivate` + . fonttools-venv/bin/activate + + # to activate the virtual environment in Windows `cmd.exe`, do + fonttools-venv\Scripts\activate.bat + + # install in 'editable' mode + pip install -e . + +Optional Requirements +--------------------- + +The ``fontTools`` package currently has no (required) external dependencies +besides the modules included in the Python Standard Library. +However, a few extra dependencies are required by some of its modules, which +are needed to unlock optional features. +The ``fonttools`` PyPI distribution also supports so-called "extras", i.e. a +set of keywords that describe a group of additional dependencies, which can be +used when installing via pip, or when specifying a requirement. +For example: + +.. code:: sh + + pip install fonttools[ufo,lxml,woff,unicode] + +This command will install fonttools, as well as the optional dependencies that +are required to unlock the extra features named "ufo", etc. + +- ``Lib/fontTools/misc/etree.py`` + + The module exports a ElementTree-like API for reading/writing XML files, and + allows to use as the backend either the built-in ``xml.etree`` module or + `lxml `__. The latter is preferred whenever present, + as it is generally faster and more secure. + + *Extra:* ``lxml`` + +- ``Lib/fontTools/ufoLib`` + + Package for reading and writing UFO source files; it requires: + + * `fs `__: (aka ``pyfilesystem2``) filesystem + abstraction layer. + + * `enum34 `__: backport for the built-in ``enum`` + module (only required on Python < 3.4). + + *Extra:* ``ufo`` + +- ``Lib/fontTools/ttLib/woff2.py`` + + Module to compress/decompress WOFF 2.0 web fonts; it requires: + + * `brotli `__: Python bindings of + the Brotli compression library. + + *Extra:* ``woff`` + +- ``Lib/fontTools/ttLib/sfnt.py`` + + To better compress WOFF 1.0 web fonts, the following module can be used + instead of the built-in ``zlib`` library: + + * `zopfli `__: Python bindings of + the Zopfli compression library. + + *Extra:* ``woff`` + +- ``Lib/fontTools/unicode.py`` + + To display the Unicode character names when dumping the ``cmap`` table + with ``ttx`` we use the ``unicodedata`` module in the Standard Library. + The version included in there varies between different Python versions. + To use the latest available data, you can install: + + * `unicodedata2 `__: + ``unicodedata`` backport for Python 3.x updated to the latest Unicode + version 15.0. + + *Extra:* ``unicode`` + +- ``Lib/fontTools/varLib/interpolatable.py`` + + Module for finding wrong contour/component order between different masters. + It requires one of the following packages in order to solve the so-called + "minimum weight perfect matching problem in bipartite graphs", or + the Assignment problem: + + * `scipy `__: the Scientific Library + for Python, which internally uses `NumPy `__ + arrays and hence is very fast; + * `munkres `__: a pure-Python + module that implements the Hungarian or Kuhn-Munkres algorithm. + + To plot the results to a PDF or HTML format, you also need to install: + + * `pycairo `__: Python bindings for the + Cairo graphics library. Note that wheels are currently only available for + Windows, for other platforms see pycairo's `installation instructions + `__. + + *Extra:* ``interpolatable`` + +- ``Lib/fontTools/varLib/plot.py`` + + Module for visualizing DesignSpaceDocument and resulting VariationModel. + + * `matplotlib `__: 2D plotting library. + + *Extra:* ``plot`` + +- ``Lib/fontTools/misc/symfont.py`` + + Advanced module for symbolic font statistics analysis; it requires: + + * `sympy `__: the Python library for + symbolic mathematics. + + *Extra:* ``symfont`` + +- ``Lib/fontTools/t1Lib.py`` + + To get the file creator and type of Macintosh PostScript Type 1 fonts + on Python 3 you need to install the following module, as the old ``MacOS`` + module is no longer included in Mac Python: + + * `xattr `__: Python wrapper for + extended filesystem attributes (macOS platform only). + + *Extra:* ``type1`` + +- ``Lib/fontTools/ttLib/removeOverlaps.py`` + + Simplify TrueType glyphs by merging overlapping contours and components. + + * `skia-pathops `__: Python + bindings for the Skia library's PathOps module, performing boolean + operations on paths (union, intersection, etc.). + + *Extra:* ``pathops`` + +- ``Lib/fontTools/pens/cocoaPen.py`` and ``Lib/fontTools/pens/quartzPen.py`` + + Pens for drawing glyphs with Cocoa ``NSBezierPath`` or ``CGPath`` require: + + * `PyObjC `__: the bridge between + Python and the Objective-C runtime (macOS platform only). + +- ``Lib/fontTools/pens/qtPen.py`` + + Pen for drawing glyphs with Qt's ``QPainterPath``, requires: + + * `PyQt5 `__: Python bindings for + the Qt cross platform UI and application toolkit. + +- ``Lib/fontTools/pens/reportLabPen.py`` + + Pen to drawing glyphs as PNG images, requires: + + * `reportlab `__: Python toolkit + for generating PDFs and graphics. + +- ``Lib/fontTools/pens/freetypePen.py`` + + Pen to drawing glyphs with FreeType as raster images, requires: + + * `freetype-py `__: Python binding + for the FreeType library. + +- ``Lib/fontTools/ttLib/tables/otBase.py`` + + Use the Harfbuzz library to serialize GPOS/GSUB using ``hb_repack`` method, requires: + + * `uharfbuzz `__: Streamlined Cython + bindings for the harfbuzz shaping engine + + *Extra:* ``repacker`` + +How to make a new release +~~~~~~~~~~~~~~~~~~~~~~~~~ + +1) Update ``NEWS.rst`` with all the changes since the last release. Write a + changelog entry for each PR, with one or two short sentences summarizing it, + as well as links to the PR and relevant issues addressed by the PR. Do not + put a new title, the next command will do it for you. +2) Use semantic versioning to decide whether the new release will be a 'major', + 'minor' or 'patch' release. It's usually one of the latter two, depending on + whether new backward compatible APIs were added, or simply some bugs were fixed. +3) From inside a venv, first do ``pip install -r dev-requirements.txt``, then run + the ``python setup.py release`` command from the tip of the ``main`` branch. + By default this bumps the third or 'patch' digit only, unless you pass ``--major`` + or ``--minor`` to bump respectively the first or second digit. + This bumps the package version string, extracts the changes since the latest + version from ``NEWS.rst``, and uses that text to create an annotated git tag + (or a signed git tag if you pass the ``--sign`` option and your git and Github + account are configured for `signing commits `__ + using a GPG key). + It also commits an additional version bump which opens the main branch for + the subsequent developmental cycle +4) Push both the tag and commit to the upstream repository, by running the command + ``git push --follow-tags``. Note: it may push other local tags as well, be + careful. +5) Let the CI build the wheel and source distribution packages and verify both + get uploaded to the Python Package Index (PyPI). +6) [Optional] Go to fonttools `Github Releases `__ + page and create a new release, copy-pasting the content of the git tag + message. This way, the release notes are nicely formatted as markdown, and + users watching the repo will get an email notification. One day we shall + automate that too. + + +Acknowledgements +~~~~~~~~~~~~~~~~ + +In alphabetical order: + +aschmitz, Olivier Berten, Samyak Bhuta, Erik van Blokland, Petr van Blokland, +Jelle Bosma, Sascha Brawer, Tom Byrer, Antonio Cavedoni, Frédéric Coiffier, +Vincent Connare, David Corbett, Simon Cozens, Dave Crossland, Simon Daniels, +Peter Dekkers, Behdad Esfahbod, Behnam Esfahbod, Hannes Famira, Sam Fishman, +Matt Fontaine, Takaaki Fuji, Rob Hagemans, Yannis Haralambous, Greg Hitchcock, +Jeremie Hornus, Khaled Hosny, John Hudson, Denis Moyogo Jacquerye, Jack Jansen, +Tom Kacvinsky, Jens Kutilek, Antoine Leca, Werner Lemberg, Tal Leming, Peter +Lofting, Cosimo Lupo, Olli Meier, Masaya Nakamura, Dave Opstad, Laurence Penney, +Roozbeh Pournader, Garret Rieger, Read Roberts, Colin Rofls, Guido van Rossum, +Just van Rossum, Andreas Seidel, Georg Seifert, Chris Simpkins, Miguel Sousa, +Adam Twardoch, Adrien Tétar, Vitaly Volkov, Paul Wise. + +Copyrights +~~~~~~~~~~ + +| Copyright (c) 1999-2004 Just van Rossum, LettError + (just@letterror.com) +| See `LICENSE `__ for the full license. + +Copyright (c) 2000 BeOpen.com. All Rights Reserved. + +Copyright (c) 1995-2001 Corporation for National Research Initiatives. +All Rights Reserved. + +Copyright (c) 1991-1995 Stichting Mathematisch Centrum, Amsterdam. All +Rights Reserved. + +Have fun! + +.. |CI Build Status| image:: https://github.com/fonttools/fonttools/workflows/Test/badge.svg + :target: https://github.com/fonttools/fonttools/actions?query=workflow%3ATest +.. |Coverage Status| image:: https://codecov.io/gh/fonttools/fonttools/branch/main/graph/badge.svg + :target: https://codecov.io/gh/fonttools/fonttools +.. |PyPI| image:: https://img.shields.io/pypi/v/fonttools.svg + :target: https://pypi.org/project/FontTools +.. |Gitter Chat| image:: https://badges.gitter.im/fonttools-dev/Lobby.svg + :alt: Join the chat at https://gitter.im/fonttools-dev/Lobby + :target: https://gitter.im/fonttools-dev/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge + +Changelog +~~~~~~~~~ + +4.53.1 (released 2024-07-05) +---------------------------- + +- [feaLib] Improve the sharing of inline chained lookups (#3559) +- [otlLib] Correct the calculation of OS/2.usMaxContext with reversed chaining contextual single substitutions (#3569) +- [misc.visitor] Visitors search the inheritance chain of objects they are visiting (#3581) + +4.53.0 (released 2024-05-31) +---------------------------- + +- [ttLib.removeOverlaps] Support CFF table to aid in downconverting CFF2 fonts (#3528) +- [avar] Fix crash when accessing not-yet-existing attribute (#3550) +- [docs] Add buildMathTable to otlLib.builder documentation (#3540) +- [feaLib] Allow UTF-8 with BOM when reading features (#3495) +- [SVGPathPen] Revert rounding coordinates to two decimal places by default (#3543) +- [varLib.instancer] Refix output filename decision-making (#3545, #3544, #3548) + +4.52.4 (released 2024-05-27) +---------------------------- + +- [varLib.cff] Restore and deprecate convertCFFtoCFF2 that was removed in 4.52.0 + release as it is used by downstream projects (#3535). + +4.52.3 (released 2024-05-27) +---------------------------- + +- Fixed a small syntax error in the reStructuredText-formatted NEWS.rst file + which caused the upload to PyPI to fail for 4.52.2. No other code changes. + +4.52.2 (released 2024-05-27) +---------------------------- + +- [varLib.interpolatable] Ensure that scipy/numpy output is JSON-serializable + (#3522, #3526). +- [housekeeping] Regenerate table lists, to fix pyinstaller packaging of the new + ``VARC`` table (#3531, #3529). +- [cffLib] Make CFFToCFF2 and CFF2ToCFF more robust (#3521, #3525). + +4.52.1 (released 2024-05-24) +---------------------------- + +- Fixed a small syntax error in the reStructuredText-formatted NEWS.rst file + which caused the upload to PyPI to fail for 4.52.0. No other code changes. + +4.52.0 (released 2024-05-24) +---------------------------- + +- Added support for the new ``VARC`` (Variable Composite) table that is being + proposed to OpenType spec (#3395). For more info: + https://github.com/harfbuzz/boring-expansion-spec/blob/main/VARC.md +- [ttLib.__main__] Fixed decompiling all tables (90fed08). +- [feaLib] Don't reference the same lookup index multiple times within the same + feature record, it is only applied once anyway (#3520). +- [cffLib] Moved methods to desubroutinize, remove hints and unused subroutines + from subset module to cffLib (#3517). +- [varLib.instancer] Added support for partial-instancing CFF2 tables! Also, added + method to down-convert from CFF2 to CFF 1.0, and CLI entry points to convert + CFF<->CFF2 (#3506). +- [subset] Prune unused user name IDs even with --name-IDs='*' (#3410). +- [ttx] use GNU-style getopt to intermix options and positional arguments (#3509). +- [feaLib.variableScalar] Fixed ``value_at_location()`` method (#3491) +- [psCharStrings] Shorten output of ``encodeFloat`` (#3492). +- [bezierTools] Fix infinite-recursion in ``calcCubicArcLength`` (#3502). +- [avar2] Implement ``avar2`` support in ``TTFont.getGlyphSet()`` (#3473). + +4.51.0 (released 2024-04-05) +---------------------------- + +- [ttLib] Optimization on loading aux fields (#3464). +- [ttFont] Add reorderGlyphs (#3468). + +4.50.0 (released 2024-03-15) +---------------------------- + +- [pens] Added decomposing filter pens that draw components as regular contours (#3460). +- [instancer] Drop explicit no-op axes from TupleVariations (#3457). +- [cu2qu/ufo] Return set of modified glyph names from fonts_to_quadratic (#3456). + +4.49.0 (released 2024-02-15) +---------------------------- + +- [otlLib] Add API for building ``MATH`` table (#3446) + +4.48.1 (released 2024-02-06) +---------------------------- + +- Fixed uploading wheels to PyPI, no code changes since v4.48.0. + +4.48.0 (released 2024-02-06) +---------------------------- + +- [varLib] Do not log when there are no OTL tables to be merged. +- [setup.py] Do not restrict lxml<5 any more, tests pass just fine with lxml>=5. +- [feaLib] Remove glyph and class names length restrictions in FEA (#3424). +- [roundingPens] Added ``transformRoundFunc`` parameter to the rounding pens to allow + for custom rounding of the components' transforms (#3426). +- [feaLib] Keep declaration order of ligature components within a ligature set, instead + of sorting by glyph name (#3429). +- [feaLib] Fixed ordering of alternates in ``aalt`` lookups, following the declaration + order of feature references within the ``aalt`` feature block (#3430). +- [varLib.instancer] Fixed a bug in the instancer's IUP optimization (#3432). +- [sbix] Support sbix glyphs with new graphicType "flip" (#3433). +- [svgPathPen] Added ``--glyphs`` option to dump the SVG paths for the named glyphs + in the font (0572f78). +- [designspaceLib] Added "description" attribute to ```` and ```` + elements, and allow multiple ```` elements to group ```` elements + that are logically related (#3435, #3437). +- [otlLib] Correctly choose the most compact GSUB contextual lookup format (#3439). + +4.47.2 (released 2024-01-11) +---------------------------- + +Minor release to fix uploading wheels to PyPI. + +4.47.1 (released 2024-01-11) +---------------------------- + +- [merge] Improve help message and add standard command line options (#3408) +- [otlLib] Pass ``ttFont`` to ``name.addName`` in ``buildStatTable`` (#3406) +- [featureVars] Re-use ``FeatureVariationRecord``'s when possible (#3413) + +4.47.0 (released 2023-12-18) +---------------------------- + +- [varLib.models] New API for VariationModel: ``getMasterScalars`` and + ``interpolateFromValuesAndScalars``. +- [varLib.interpolatable] Various bugfixes and rendering improvements. In particular, + add a Summary page in the front, and an Index and Table-of-Contents in the back. + Change the page size to Letter. +- [Docs/designspaceLib] Defined a new ``public.fontInfo`` lib key, not used anywhere yet (#3358). + +4.46.0 (released 2023-12-02) +---------------------------- + +- [featureVars] Allow to register the same set of substitution rules to multiple features. + The ``addFeatureVariations`` function can now take a list of featureTags; similarly, the + lib key 'com.github.fonttools.varLib.featureVarsFeatureTag' can now take a + comma-separateed string of feature tags (e.g. "salt,ss01") instead of a single tag (#3360). +- [featureVars] Don't overwrite GSUB FeatureVariations, but append new records to it + for features which are not already there. But raise ``VarLibError`` if the feature tag + already has feature variations associated with it (#3363). +- [varLib] Added ``addGSUBFeatureVariations`` function to add GSUB Feature Variations + to an existing variable font from rules defined in a DesignSpace document (#3362). +- [varLib.interpolatable] Various bugfixes and rendering improvements. In particular, + a new test for "underweight" glyphs. The new test reports quite a few false-positives + though. Please send feedback. + +4.45.1 (released 2023-11-23) +---------------------------- + +- [varLib.interpolatable] Various bugfixes and improvements, better reporting, reduced + false positives. +- [ttGlyphSet] Added option to not recalculate glyf bounds (#3348). + +4.45.0 (released 2023-11-20) +---------------------------- + +- [varLib.interpolatable] Vastly improved algorithms. Also available now is ``--pdf`` + and ``--html`` options to generate a PDF or HTML report of the interpolation issues. + The PDF/HTML report showcases the problematic masters, the interpolated broken + glyph, as well as the proposed fixed version. + +4.44.3 (released 2023-11-15) +---------------------------- + +- [subset] Only prune codepage ranges for OS/2.version >= 1, ignore otherwise (#3334). +- [instancer] Ensure hhea vertical metrics stay in sync with OS/2 ones after instancing + MVAR table containing 'hasc', 'hdsc' or 'hlgp' tags (#3297). + +4.44.2 (released 2023-11-14) +---------------------------- + +- [glyf] Have ``Glyph.recalcBounds`` skip empty components (base glyph with no contours) + when computing the bounding box of composite glyphs. This simply restores the existing + behavior before some changes were introduced in fonttools 4.44.0 (#3333). + +4.44.1 (released 2023-11-14) +---------------------------- + +- [feaLib] Ensure variable mark anchors are deep-copied while building since they + get modified in-place and later reused (#3330). +- [OS/2|subset] Added method to ``recalcCodePageRanges`` to OS/2 table class; added + ``--prune-codepage-ranges`` to `fonttools subset` command (#3328, #2607). + +4.44.0 (released 2023-11-03) +---------------------------- + +- [instancer] Recalc OS/2 AvgCharWidth after instancing if default changes (#3317). +- [otlLib] Make ClassDefBuilder class order match varLib.merger's, i.e. large + classes first, then glyph lexicographic order (#3321, #3324). +- [instancer] Allow not specifying any of min:default:max values and let be filled + up with fvar's values (#3322, #3323). +- [instancer] When running --update-name-table ignore axes that have no STAT axis + values (#3318, #3319). +- [Debg] When dumping to ttx, write the embedded JSON as multi-line string with + indentation (92cbfee0d). +- [varStore] Handle > 65535 items per encoding by splitting VarData subtable (#3310). +- [subset] Handle null-offsets in MarkLigPos subtables. +- [subset] Keep East Asian spacing fatures vhal, halt, chws, vchw by default (#3305). +- [instancer.solver] Fixed case where axisDef < lower and upper < axisMax (#3304). +- [glyf] Speed up compilation, mostly around ``recalcBounds`` (#3301). +- [varLib.interpolatable] Speed it up when working on variable fonts, plus various + micro-optimizations (#3300). +- Require unicodedata2 >= 15.1.0 when installed with 'unicode' extra, contains UCD 15.1. + +4.43.1 (released 2023-10-06) +---------------------------- + +- [EBDT] Fixed TypeError exception in `_reverseBytes` method triggered when dumping + some bitmap fonts with `ttx -z bitwise` option (#3162). +- [v/hhea] Fixed UnboundLocalError exception in ``recalc`` method when no vmtx or hmtx + tables are present (#3290). +- [bezierTools] Fixed incorrectly typed cython local variable leading to TypeError when + calling ``calcQuadraticArcLength`` (#3288). +- [feaLib/otlLib] Better error message when building Coverage table with missing glyph (#3286). + +4.43.0 (released 2023-09-29) +---------------------------- + +- [subset] Set up lxml ``XMLParser(resolve_entities=False)`` when parsing OT-SVG documents + to prevent XML External Entity (XXE) attacks (9f61271dc): + https://codeql.github.com/codeql-query-help/python/py-xxe/ +- [varLib.iup] Added workaround for a Cython bug in ``iup_delta_optimize`` that was + leading to IUP tolerance being incorrectly initialised, resulting in sub-optimal deltas + (60126435d, cython/cython#5732). +- [varLib] Added new command-line entry point ``fonttools varLib.avar`` to add an + ``avar`` table to an existing VF from axes mappings in a .designspace file (0a3360e52). +- [instancer] Fixed bug whereby no longer used variation regions were not correctly pruned + after VarData optimization (#3268). +- Added support for Python 3.12 (#3283). + +4.42.1 (released 2023-08-20) +---------------------------- + +- [t1Lib] Fixed several Type 1 issues (#3238, #3240). +- [otBase/packer] Allow sharing tables reached by different offset sizes (#3241, #3236). +- [varLib/merger] Fix Cursive attachment merging error when all anchors are NULL (#3248, #3247). +- [ttLib] Fixed warning when calling ``addMultilingualName`` and ``ttFont`` parameter was not + passed on to ``findMultilingualName`` (#3253). + +4.42.0 (released 2023-08-02) +---------------------------- + +- [varLib] Use sentinel value 0xFFFF to mark a glyph advance in hmtx/vmtx as non + participating, allowing sparse masters to contain glyphs for variation purposes other + than {H,V}VAR (#3235). +- [varLib/cff] Treat empty glyphs in non-default masters as missing, thus not participating + in CFF2 delta computation, similarly to how varLib already treats them for gvar (#3234). +- Added varLib.avarPlanner script to deduce 'correct' avar v1 axis mappings based on + glyph average weights (#3223). + +4.41.1 (released 2023-07-21) +---------------------------- + +- [subset] Fixed perf regression in v4.41.0 by making ``NameRecordVisitor`` only visit + tables that do contain nameID references (#3213, #3214). +- [varLib.instancer] Support instancing fonts containing null ConditionSet offsets in + FeatureVariationRecords (#3211, #3212). +- [statisticsPen] Report font glyph-average weight/width and font-wide slant. +- [fontBuilder] Fixed head.created date incorrectly set to 0 instead of the current + timestamp, regression introduced in v4.40.0 (#3210). +- [varLib.merger] Support sparse ``CursivePos`` masters (#3209). + +4.41.0 (released 2023-07-12) +---------------------------- + +- [fontBuilder] Fixed bug in setupOS2 with default panose attribute incorrectly being + set to a dict instead of a Panose object (#3201). +- [name] Added method to ``removeUnusedNameRecords`` in the user range (#3185). +- [varLib.instancer] Fixed issue with L4 instancing (moving default) (#3179). +- [cffLib] Use latin1 so we can roundtrip non-ASCII in {Full,Font,Family}Name (#3202). +- [designspaceLib] Mark as optional in docs (as it is in the code). +- [glyf-1] Fixed drawPoints() bug whereby last cubic segment becomes quadratic (#3189, #3190). +- [fontBuilder] Propagate the 'hidden' flag to the fvar Axis instance (#3184). +- [fontBuilder] Update setupAvar() to also support avar 2, fixing ``_add_avar()`` call + site (#3183). +- Added new ``voltLib.voltToFea`` submodule (originally Tiro Typeworks' "Volto") for + converting VOLT OpenType Layout sources to FEA format (#3164). + +4.40.0 (released 2023-06-12) +---------------------------- + +- Published native binary wheels to PyPI for all the python minor versions and platform + and architectures currently supported that would benefit from this. They will include + precompiled Cython-accelerated modules (e.g. cu2qu) without requiring to compile them + from source. The pure-python wheel and source distribution will continue to be + published as always (pip will automatically chose them when no binary wheel is + available for the given platform, e.g. pypy). Use ``pip install --no-binary=fonttools fonttools`` + to expliclity request pip to install from the pure-python source. +- [designspaceLib|varLib] Add initial support for specifying axis mappings and build + ``avar2`` table from those (#3123). +- [feaLib] Support variable ligature caret position (#3130). +- [varLib|glyf] Added option to --drop-implied-oncurves; test for impliable oncurve + points either before or after rounding (#3146, #3147, #3155, #3156). +- [TTGlyphPointPen] Don't error with empty contours, simply ignore them (#3145). +- [sfnt] Fixed str vs bytes remnant of py3 transition in code dealing with de/compiling + WOFF metadata (#3129). +- [instancer-solver] Fixed bug when moving default instance with sparse masters (#3139, #3140). +- [feaLib] Simplify variable scalars that don’t vary (#3132). +- [pens] Added filter pen that explicitly emits closing line when lastPt != movePt (#3100). +- [varStore] Improve optimize algorithm and better document the algorithm (#3124, #3127). + Added ``quantization`` option (#3126). +- Added CI workflow config file for building native binary wheels (#3121). +- [fontBuilder] Added glyphDataFormat=0 option; raise error when glyphs contain cubic + outlines but glyphDataFormat was not explicitly set to 1 (#3113, #3119). +- [subset] Prune emptied GDEF.MarkGlyphSetsDef and remap indices; ensure GDEF is + subsetted before GSUB and GPOS (#3114, #3118). +- [xmlReader] Fixed issue whereby DSIG table data was incorrectly parsed (#3115, #2614). +- [varLib/merger] Fixed merging of SinglePos with pos=0 (#3111, #3112). +- [feaLib] Demote "Feature has not been defined" error to a warning when building aalt + and referenced feature is empty (#3110). +- [feaLib] Dedupe multiple substitutions with classes (#3105). + +4.39.4 (released 2023-05-10) +---------------------------- + +- [varLib.interpolatable] Allow for sparse masters (#3075) +- [merge] Handle differing default/nominalWidthX in CFF (#3070) +- [ttLib] Add missing main.py file to ttLib package (#3088) +- [ttx] Fix missing composite instructions in XML (#3092) +- [ttx] Fix split tables option to work on filenames containing '%' (#3096) +- [featureVars] Process lookups for features other than rvrn last (#3099) +- [feaLib] support multiple substitution with classes (#3103) + +4.39.3 (released 2023-03-28) +---------------------------- + +- [sbix] Fixed TypeError when compiling empty glyphs whose imageData is None, regression + was introduced in v4.39 (#3059). +- [ttFont] Fixed AttributeError on python <= 3.10 when opening a TTFont from a tempfile + SpooledTemporaryFile, seekable method only added on python 3.11 (#3052). + +4.39.2 (released 2023-03-16) +---------------------------- + +- [varLib] Fixed regression introduced in 4.39.1 whereby an incomplete 'STAT' table + would be built even though a DesignSpace v5 did contain 'STAT' definitions (#3045, #3046). + +4.39.1 (released 2023-03-16) +---------------------------- + +- [avar2] Added experimental support for reading/writing avar version 2 as specified in + this draft proposal: https://github.com/harfbuzz/boring-expansion-spec/blob/main/avar2.md +- [glifLib] Wrap underlying XML library exceptions with GlifLibError when parsing GLIFs, + and also print the name and path of the glyph that fails to be parsed (#3042). +- [feaLib] Consult avar for normalizing user-space values in ConditionSets and in + VariableScalars (#3042, #3043). +- [ttProgram] Handle string input to Program.fromAssembly() (#3038). +- [otlLib] Added a config option to emit GPOS 7 lookups, currently disabled by default + because of a macOS bug (#3034). +- [COLRv1] Added method to automatically compute ClipBoxes (#3027). +- [ttFont] Fixed getGlyphID to raise KeyError on missing glyphs instead of returning + None. The regression was introduced in v4.27.0 (#3032). +- [sbix] Fixed UnboundLocalError: cannot access local variable 'rawdata' (#3031). +- [varLib] When building VF, do not overwrite a pre-existing ``STAT`` table that was built + with feaLib from FEA feature file. Also, added support for building multiple VFs + defined in Designspace v5 from ``fonttools varLib`` script (#3024). +- [mtiLib] Only add ``Debg`` table with lookup names when ``FONTTOOLS_LOOKUP_DEBUGGING`` + env variable is set (#3023). + +4.39.0 (released 2023-03-06) +---------------------------- + +- [mtiLib] Optionally add `Debg` debug info for MTI feature builds (#3018). +- [ttx] Support reading input file from standard input using special `-` character, + similar to existing `-o -` option to write output to standard output (#3020). +- [cython] Prevent ``cython.compiled`` raise AttributeError if cython not installed + properly (#3017). +- [OS/2] Guard against ZeroDivisionError when calculating xAvgCharWidth in the unlikely + scenario no glyph has non-zero advance (#3015). +- [subset] Recompute xAvgCharWidth independently of --no-prune-unicode-ranges, + previously the two options were involuntarily bundled together (#3012). +- [fontBuilder] Add ``debug`` parameter to addOpenTypeFeatures method to add source + debugging information to the font in the ``Debg`` private table (#3008). +- [name] Make NameRecord `__lt__` comparison not fail on Unicode encoding errors (#3006). +- [featureVars] Fixed bug in ``overlayBox`` (#3003, #3005). +- [glyf] Added experimental support for cubic bezier curves in TrueType glyf table, as + outlined in glyf v1 proposal (#2988): + https://github.com/harfbuzz/boring-expansion-spec/blob/main/glyf1-cubicOutlines.md +- Added new qu2cu module and related qu2cuPen, the reverse of cu2qu for converting + TrueType quadratic splines to cubic bezier curves (#2993). +- [glyf] Added experimental support for reading and writing Variable Composites/Components + as defined in glyf v1 spec proposal (#2958): + https://github.com/harfbuzz/boring-expansion-spec/blob/main/glyf1-varComposites.md. +- [pens]: Added `addVarComponent` method to pen protocols' base classes, which pens can implement + to handle varcomponents (by default they get decomposed) (#2958). +- [misc.transform] Added DecomposedTransform class which implements an affine transformation + with separate translate, rotation, scale, skew, and transformation-center components (#2598) +- [sbix] Ensure Glyph.referenceGlyphName is set; fixes error after dumping and + re-compiling sbix table with 'dupe' glyphs (#2984). +- [feaLib] Be cleverer when merging chained single substitutions into same lookup + when they are specified using the inline notation (#2150, #2974). +- [instancer] Clamp user-inputted axis ranges to those of fvar (#2959). +- [otBase/subset] Define ``__getstate__`` for BaseTable so that a copied/pickled 'lazy' + object gets its own OTTableReader to read from; incidentally fixes a bug while + subsetting COLRv1 table containing ClipBoxes on python 3.11 (#2965, #2968). +- [sbix] Handle glyphs with "dupe" graphic type on compile correctly (#2963). +- [glyf] ``endPointsOfContours`` field should be unsigned! Kudos to behdad for + spotting one of the oldest bugs in FT. Probably nobody has ever dared to make + glyphs with more than 32767 points... (#2957). +- [feaLib] Fixed handling of ``ignore`` statements with unmarked glyphs to match + makeotf behavior, which assumes the first glyph is marked (#2950). +- Reformatted code with ``black`` and enforce new code style via CI check (#2925). +- [feaLib] Sort name table entries following OT spec prescribed order in the builder (#2927). +- [cu2quPen] Add Cu2QuMultiPen that converts multiple outlines at a time in + interpolation compatible way; its methods take a list of tuples arguments + that would normally be passed to individual segment pens, and at the end it + dispatches the converted outlines to each pen (#2912). +- [reverseContourPen/ttGlyphPen] Add outputImpliedClosingLine option (#2913, #2914, + #2921, #2922, #2995). +- [gvar] Avoid expanding all glyphs unnecessarily upon compile (#2918). +- [scaleUpem] Fixed bug whereby CFF2 vsindex was scaled; it should not (#2893, #2894). +- [designspaceLib] Add DS.getAxisByTag and refactor getAxis (#2891). +- [unicodedata] map Zmth<->math in ot_tag_{to,from}_script (#1737, #2889). +- [woff2] Support encoding/decoding OVERLAP_SIMPLE glyf flags (#2576, #2884). +- [instancer] Update OS/2 class and post.italicAngle when default moved (L4) +- Dropped support for Python 3.7 which reached EOL, fontTools requires 3.8+. +- [instancer] Fixed instantiateFeatureVariations logic when a rule range becomes + default-applicable (#2737, #2880). +- [ttLib] Add main to ttFont and ttCollection that just decompile and re-compile the + input font (#2869). +- [featureVars] Insert 'rvrn' lookup at the beginning of LookupList, to work around bug + in Apple implementation of 'rvrn' feature which the spec says it should be processed + early whereas on macOS 10.15 it follows lookup order (#2140, #2867). +- [instancer/mutator] Remove 'DSIG' table if present. +- [svgPathPen] Don't close path in endPath(), assume open unless closePath() (#2089, #2865). + +4.38.0 (released 2022-10-21) +---------------------------- + +- [varLib.instancer] Added support for L4 instancing, i.e. moving the default value of + an axis while keeping it variable. Thanks Behdad! (#2728, #2861). + It's now also possible to restrict an axis min/max values beyond the current default + value, e.g. a font wght has min=100, def=400, max=900 and you want a partial VF that + only varies between 500 and 700, you can now do that. + You can either specify two min/max values (wght=500:700), and the new default will be + set to either the minimum or maximum, depending on which one is closer to the current + default (e.g. 500 in this case). Or you can specify three values (e.g. wght=500:600:700) + to specify the new default value explicitly. +- [otlLib/featureVars] Set a few Count values so one doesn't need to compile the font + to update them (#2860). +- [varLib.models] Make extrapolation work for 2-master models as well where one master + is at the default location (#2843, #2846). + Add optional extrapolate=False to normalizeLocation() (#2847, #2849). +- [varLib.cff] Fixed sub-optimal packing of CFF2 deltas by no longer rounding them to + integer (#2838). +- [scaleUpem] Calculate numShorts in VarData after scale; handle CFF hintmasks (#2840). + +4.37.4 (released 2022-09-30) +---------------------------- + +- [subset] Keep nameIDs used by CPAL palette entry labels (#2837). +- [varLib] Avoid negative hmtx values when creating font from variable CFF2 font (#2827). +- [instancer] Don't prune stat.ElidedFallbackNameID (#2828). +- [unicodedata] Update Scripts/Blocks to Unicode 15.0 (#2833). + +4.37.3 (released 2022-09-20) +---------------------------- + +- Fix arguments in calls to (glyf) glyph.draw() and drawPoints(), whereby offset wasn't + correctly passed down; this fix also exposed a second bug, where lsb and tsb were not + set (#2824, #2825, adobe-type-tools/afdko#1560). + +4.37.2 (released 2022-09-15) +---------------------------- + +- [subset] Keep CPAL table and don't attempt to prune unused color indices if OT-SVG + table is present even if COLR table was subsetted away; OT-SVG may be referencing the + CPAL table; for now we assume that's the case (#2814, #2815). +- [varLib.instancer] Downgrade GPOS/GSUB version if there are no more FeatureVariations + after instancing (#2812). +- [subset] Added ``--no-lazy`` to optionally load fonts eagerly (mostly to ease + debugging of table lazy loading, no practical effects) (#2807). +- [varLib] Avoid building empty COLR.DeltaSetIndexMap with only identity mappings (#2803). +- [feaLib] Allow multiple value record types (by promoting to the most general format) + within the same PairPos subtable; e.g. this allows variable and non variable kerning + rules to share the same subtable. This also fixes a bug whereby some kerning pairs + would become unreachable while shapiong because of premature subtable splitting (#2772, #2776). +- [feaLib] Speed up ``VarScalar`` by caching models for recurring master locations (#2798). +- [feaLib] Optionally cythonize ``feaLib.lexer``, speeds up parsing FEA a bit (#2799). +- [designspaceLib] Avoid crash when handling unbounded rule conditions (#2797). +- [post] Don't crash if ``post`` legacy format 1 is malformed/improperly used (#2786) +- [gvar] Don't be "lazy" (load all glyph variations up front) when TTFont.lazy=False (#2771). +- [TTFont] Added ``normalizeLocation`` method to normalize a location dict from the + font's defined axes space (also known as "user space") into the normalized (-1..+1) + space. It applies ``avar`` mapping if the font contains an ``avar`` table (#2789). +- [TTVarGlyphSet] Support drawing glyph instances from CFF2 variable glyph set (#2784). +- [fontBuilder] Do not error when building cmap if there are zero code points (#2785). +- [varLib.plot] Added ability to plot a variation model and set of accompaning master + values corresponding to the model's master locations into a pyplot figure (#2767). +- [Snippets] Added ``statShape.py`` script to draw statistical shape of a glyph as an + ellips (requires pycairo) (baecd88). +- [TTVarGlyphSet] implement drawPoints natively, avoiding going through + SegmentToPointPen (#2778). +- [TTVarGlyphSet] Fixed bug whereby drawing a composite glyph multiple times, its + components would shif; needed an extra copy (#2774). + +4.37.1 (released 2022-08-24) +---------------------------- + +- [subset] Fixed regression introduced with v4.37.0 while subsetting the VarStore of + ``HVAR`` and ``VVAR`` tables, whereby an ``AttributeError: subset_varidxes`` was + thrown because an apparently unused import statement (with the side-effect of + dynamically binding that ``subset_varidxes`` method to the VarStore class) had been + accidentally deleted in an unrelated PR (#2679, #2773). +- [pens] Added ``cairoPen`` (#2678). +- [gvar] Read ``gvar`` more lazily by not parsing all of the ``glyf`` table (#2771). +- [ttGlyphSet] Make ``drawPoints(pointPen)`` method work for CFF fonts as well via + adapter pen (#2770). + +4.37.0 (released 2022-08-23) +---------------------------- + +- [varLib.models] Reverted PR #2717 which added support for "narrow tents" in v4.36.0, + as it introduced a regression (#2764, #2765). It will be restored in upcoming release + once we found a solution to the bug. +- [cff.specializer] Fixed issue in charstring generalizer with the ``blend`` operator + (#2750, #1975). +- [varLib.models] Added support for extrapolation (#2757). +- [ttGlyphSet] Ensure the newly added ``_TTVarGlyphSet`` inherits from ``_TTGlyphSet`` + to keep backward compatibility with existing API (#2762). +- [kern] Allow compiling legacy kern tables with more than 64k entries (d21cfdede). +- [visitor] Added new visitor API to traverse tree of objects and dispatch based + on the attribute type: cf. ``fontTools.misc.visitor`` and ``fontTools.ttLib.ttVisitor``. Added ``fontTools.ttLib.scaleUpem`` module that uses the latter to + change a font's units-per-em and scale all the related fields accordingly (#2718, + #2755). + +4.36.0 (released 2022-08-17) +---------------------------- + +- [varLib.models] Use a simpler model that generates narrower "tents" (regions, master + supports) whenever possible: specifically when any two axes that actively "cooperate" + (have masters at non-zero positions for both axes) have a complete set of intermediates. + The simpler algorithm produces fewer overlapping regions and behaves better with + respect to rounding at the peak positions than the generic solver, always matching + intermediate masters exactly, instead of maximally 0.5 units off. This may be useful + when 100% metrics compatibility is desired (#2218, #2717). +- [feaLib] Remove warning when about ``GDEF`` not being built when explicitly not + requested; don't build one unconditonally even when not requested (#2744, also works + around #2747). +- [ttFont] ``TTFont.getGlyphSet`` method now supports selecting a location that + represents an instance of a variable font (supports both user-scale and normalized + axes coordinates via the ``normalized=False`` parameter). Currently this only works + for TrueType-flavored variable fonts (#2738). + +4.35.0 (released 2022-08-15) +---------------------------- + +- [otData/otConverters] Added support for 'biased' PaintSweepGradient start/end angles + to match latest COLRv1 spec (#2743). +- [varLib.instancer] Fixed bug in ``_instantiateFeatureVariations`` when at the same + time pinning one axis and restricting the range of a subsequent axis; the wrong axis + tag was being used in the latter step (as the records' axisIdx was updated in the + preceding step but looked up using the old axes order in the following step) (#2733, + #2734). +- [mtiLib] Pad script tags with space when less than 4 char long (#1727). +- [merge] Use ``'.'`` instead of ``'#'`` in duplicate glyph names (#2742). +- [gvar] Added support for lazily loading glyph variations (#2741). +- [varLib] In ``build_many``, we forgot to pass on ``colr_layer_reuse`` parameter to + the ``build`` method (#2730). +- [svgPathPen] Add a main that prints SVG for input text (6df779fd). +- [cffLib.width] Fixed off-by-one in optimized values; previous code didn't match the + code block above it (2963fa50). +- [varLib.interpolatable] Support reading .designspace and .glyphs files (via optional + ``glyphsLib``). +- Compile some modules with Cython when available and building/installing fonttools + from source: ``varLib.iup`` (35% faster), ``pens.momentsPen`` (makes + ``varLib.interpolatable`` 3x faster). +- [feaLib] Allow features to be built for VF without also building a GDEF table (e.g. + only build GSUB); warn when GDEF would be needed but isn't requested (#2705, 2694). +- [otBase] Fixed ``AttributeError`` when uharfbuzz < 0.23.0 and 'repack' method is + missing (32aa8eaf). Use new ``uharfbuzz.repack_with_tag`` when available (since + uharfbuzz>=0.30.0), enables table-specific optimizations to be performed during + repacking (#2724). +- [statisticsPen] By default report all glyphs (4139d891). Avoid division-by-zero + (52b28f90). +- [feaLib] Added missing required argument to FeatureLibError exception (#2693) +- [varLib.merge] Fixed error during error reporting (#2689). Fixed undefined + ``NotANone`` variable (#2714). + +4.34.4 (released 2022-07-07) +---------------------------- + +- Fixed typo in varLib/merger.py that causes NameError merging COLR glyphs + containing more than 255 layers (#2685). + +4.34.3 (released 2022-07-07) +---------------------------- + +- [designspaceLib] Don't make up bad PS names when no STAT data (#2684) + +4.34.2 (released 2022-07-06) +---------------------------- + +- [varStore/subset] fixed KeyError exception to do with NO_VARIATION_INDEX while + subsetting varidxes in GPOS/GDEF (a08140d). + +4.34.1 (released 2022-07-06) +---------------------------- + +- [instancer] When optimizing HVAR/VVAR VarStore, use_NO_VARIATION_INDEX=False to avoid + including NO_VARIATION_INDEX in AdvWidthMap, RsbMap, LsbMap mappings, which would + push the VarIdx width to maximum (4bytes), which is not desirable. This also fixes + a hard crash when attempting to subset a varfont after it had been partially instanced + with use_NO_VARIATION_INDEX=True. + +4.34.0 (released 2022-07-06) +---------------------------- + +- [instancer] Set RIBBI bits in head and OS/2 table when cutting instances and the + subfamily nameID=2 contains strings like 'Italic' or 'Bold' (#2673). +- [otTraverse] Addded module containing methods for traversing trees of otData tables + (#2660). +- [otTables] Made DeltaSetIndexMap TTX dump less verbose by omitting no-op entries + (#2660). +- [colorLib.builder] Added option to disable PaintColrLayers's reuse of layers from + LayerList (#2660). +- [varLib] Added support for merging multiple master COLRv1 tables into a variable + COLR table (#2660, #2328). Base color glyphs of same name in different masters must have + identical paint graph structure (incl. number of layers, palette indices, number + of color line stops, corresponding paint formats at each level of the graph), + but can differ in the variable fields (e.g. PaintSolid.Alpha). PaintVar* tables + are produced when this happens and a VarStore/DeltaSetIndexMap is added to the + variable COLR table. It is possible for non-default masters to be 'sparse', i.e. + omit some of the color glyphs present in the default master. +- [feaLib] Let the Parser set nameIDs 1 through 6 that were previously reserved (#2675). +- [varLib.varStore] Support NO_VARIATION_INDEX in optimizer and instancer. +- [feaLib] Show all missing glyphs at once at end of parsing (#2665). +- [varLib.iup] Rewrite force-set conditions and limit DP loopback length (#2651). + For Noto Sans, IUP time drops from 23s down to 9s, with only a slight size increase + in the final font. This basically turns the algorithm from O(n^3) into O(n). +- [featureVars] Report about missing glyphs in substitution rules (#2654). +- [mutator/instancer] Added CLI flag to --no-recalc-timestamp (#2649). +- [SVG] Allow individual SVG documents in SVG OT table to be compressed on uncompressed, + and remember that when roundtripping to/from ttx. The SVG.docList is now a list + of SVGDocument namedtuple-like dataclass containing an extra ``compressed`` field, + and no longer a bare 3-tuple (#2645). +- [designspaceLib] Check for descriptor types with hasattr() to allow custom classes + that don't inherit the default descriptors (#2634). +- [subset] Enable sharing across subtables of extension lookups for harfbuzz packing + (#2626). Updated how table packing falls back to fontTools from harfbuzz (#2668). +- [subset] Updated default feature tags following current Harfbuzz (#2637). +- [svgLib] Fixed regex for real number to support e.g. 1e-4 in addition to 1.0e-4. + Support parsing negative rx, ry on arc commands (#2596, #2611). +- [subset] Fixed subsetting SinglePosFormat2 when ValueFormat=0 (#2603). + +4.33.3 (released 2022-04-26) +---------------------------- + +- [designspaceLib] Fixed typo in ``deepcopyExceptFonts`` method, preventing font + references to be transferred (#2600). Fixed another typo in the name of ``Range`` + dataclass's ``__post_init__`` magic method (#2597). + +4.33.2 (released 2022-04-22) +---------------------------- + +- [otBase] Make logging less verbose when harfbuzz fails to serialize. Do not exit + at the first failure but continue attempting to fix offset overflow error using + the pure-python serializer even when the ``USE_HARFBUZZ_REPACKER`` option was + explicitly set to ``True``. This is normal with fonts with relatively large + tables, at least until hb.repack implements proper table splitting. + +4.33.1 (released 2022-04-22) +---------------------------- + +- [otlLib] Put back the ``FONTTOOLS_GPOS_COMPACT_MODE`` environment variable to fix + regression in ufo2ft (and thus fontmake) introduced with v4.33.0 (#2592, #2593). + This is deprecated and will be removed one ufo2ft gets updated to use the new + config setup. + +4.33.0 (released 2022-04-21) +---------------------------- + +- [OS/2 / merge] Automatically recalculate ``OS/2.xAvgCharWidth`` after merging + fonts with ``fontTools.merge`` (#2591, #2538). +- [misc/config] Added ``fontTools.misc.configTools`` module, a generic configuration + system (#2416, #2439). + Added ``fontTools.config`` module, a fontTools-specific configuration + system using ``configTools`` above. + Attached a ``Config`` object to ``TTFont``. +- [otlLib] Replaced environment variable for GPOS compression level with an + equivalent option using the new config system. +- [designspaceLib] Incremented format version to 5.0 (#2436). + Added discrete axes, variable fonts, STAT information, either design- or + user-space location on instances. + Added ``fontTools.designspaceLib.split`` module to split a designspace + into sub-spaces that interpolate and that represent the variable fonts + listed in the document. + Made instance names optional and allow computing them from STAT data instead. + Added ``fontTools.designspaceLib.statNames`` module. + Allow instances to have the same location as a previously defined STAT label. + Deprecated some attributes: + ``SourceDescriptor``: ``copyLib``, ``copyInfo``, ``copyGroups``, ``copyFeatures``. + ``InstanceDescriptor``: ``kerning``, ``info``; ``glyphs``: use rules or sparse + sources. + For both, ``location``: use the more explicit designLocation. + Note: all are soft deprecations and existing code should keep working. + Updated documentation for Python methods and the XML format. +- [varLib] Added ``build_many`` to build several variable fonts from a single + designspace document (#2436). + Added ``fontTools.varLib.stat`` module to build STAT tables from a designspace + document. +- [otBase] Try to use the Harfbuzz Repacker for packing GSUB/GPOS tables when + ``uharfbuzz`` python bindings are available (#2552). Disable it by setting the + "fontTools.ttLib.tables.otBase:USE_HARFBUZZ_REPACKER" config option to ``False``. + If the option is set explicitly to ``True`` but ``uharfbuzz`` can't be imported + or fails to serialize for any reasons, an error will be raised (ImportError or + uharfbuzz errors). +- [CFF/T2] Ensure that ``pen.closePath()`` gets called for CFF2 charstrings (#2577). + Handle implicit CFF2 closePath within ``T2OutlineExtractor`` (#2580). + +4.32.0 (released 2022-04-08) +---------------------------- + +- [otlLib] Disable GPOS7 optimization to work around bug in Apple CoreText. + Always force Chaining GPOS8 for now (#2540). +- [glifLib] Added ``outputImpliedClosingLine=False`` parameter to ``Glyph.draw()``, + to control behaviour of ``PointToSegmentPen`` (6b4e2e7). +- [varLib.interpolatable] Check for wrong contour starting point (#2571). +- [cffLib] Remove leftover ``GlobalState`` class and fix calls to ``TopDictIndex()`` + (#2569, #2570). +- [instancer] Clear ``AxisValueArray`` if it is empty after instantiating (#2563). + +4.31.2 (released 2022-03-22) +---------------------------- + +- [varLib] fix instantiation of GPOS SinglePos values (#2555). + +4.31.1 (released 2022-03-18) +---------------------------- + +- [subset] fix subsetting OT-SVG when glyph id attribute is on the root ```` + element (#2553). + +4.31.0 (released 2022-03-18) +---------------------------- + +- [ttCollection] Fixed 'ResourceWarning: unclosed file' warning (#2549). +- [varLib.merger] Handle merging SinglePos with valueformat=0 (#2550). +- [ttFont] Update glyf's glyphOrder when calling TTFont.setGlyphOrder() (#2544). +- [ttFont] Added ``ensureDecompiled`` method to load all tables irrespective + of the ``lazy`` attribute (#2551). +- [otBase] Added ``iterSubTable`` method to iterate over BaseTable's children of + type BaseTable; useful for traversing a tree of otTables (#2551). + +4.30.0 (released 2022-03-10) +---------------------------- + +- [varLib] Added debug logger showing the glyph name for which ``gvar`` is built (#2542). +- [varLib.errors] Fixed undefined names in ``FoundANone`` and ``UnsupportedFormat`` + exceptions (ac4d5611). +- [otlLib.builder] Added ``windowsNames`` and ``macNames`` (bool) parameters to the + ``buildStatTabe`` function, so that one can select whether to only add one or both + of the two sets (#2528). +- [t1Lib] Added the ability to recreate PostScript stream (#2504). +- [name] Added ``getFirstDebugName``, ``getBest{Family,SubFamily,Full}Name`` methods (#2526). + +4.29.1 (released 2022-02-01) +---------------------------- + +- [colorLib] Fixed rounding issue with radial gradient's start/end circles inside + one another (#2521). +- [freetypePen] Handle rotate/skew transform when auto-computing width/height of the + buffer; raise PenError wen missing moveTo (#2517) + +4.29.0 (released 2022-01-24) +---------------------------- + +- [ufoLib] Fixed illegal characters and expanded reserved filenames (#2506). +- [COLRv1] Don't emit useless PaintColrLayers of lenght=1 in LayerListBuilder (#2513). +- [ttx] Removed legacy ``waitForKeyPress`` method on Windows (#2509). +- [pens] Added FreeTypePen that uses ``freetype-py`` and the pen protocol for + rasterizating outline paths (#2494). +- [unicodedata] Updated the script direction list to Unicode 14.0 (#2484). + Bumped unicodedata2 dependency to 14.0 (#2499). +- [psLib] Fixed type of ``fontName`` in ``suckfont`` (#2496). + +4.28.5 (released 2021-12-19) +---------------------------- + +- [svgPathPen] Continuation of #2471: make sure all occurrences of ``str()`` are now + replaced with user-defined ``ntos`` callable. +- [merge] Refactored code into submodules, plus several bugfixes and improvements: + fixed duplicate-glyph-resolution GSUB-lookup generation code; use tolerance in glyph + comparison for empty glyph's width; ignore space of default ignorable glyphs; + downgrade duplicates-resolution missing-GSUB from assert to warn; added --drop-tables + option (#2473, #2475, #2476). + +4.28.4 (released 2021-12-15) +---------------------------- + +- [merge] Merge GDEF marksets in Lookups properly (#2474). +- [feaLib] Have ``fontTools feaLib`` script exit with error code when build fails (#2459) +- [svgPathPen] Added ``ntos`` option to customize number formatting (e.g. rounding) (#2471). +- [subset] Speed up subsetting of large CFF fonts (#2467). +- [otTables] Speculatively promote lookups to extension to speed up compilation. If the + offset to lookup N is too big to fit in a ushort, the offset to lookup N+1 is going to + be too big as well, so we promote to extension all lookups from lookup N onwards (#2465). + +4.28.3 (released 2021-12-03) +---------------------------- + +- [subset] Fixed bug while subsetting ``COLR`` table, whereby incomplete layer records + pointing to missing glyphs were being retained leading to ``struct.error`` upon + compiling. Make it so that ``glyf`` glyph closure, which follows the ``COLR`` glyph + closure, does not influence the ``COLR`` table subsetting (#2461, #2462). +- [docs] Fully document the ``cmap`` and ``glyf`` tables (#2454, #2457). +- [colorLib.unbuilder] Fixed CLI by deleting no longer existing parameter (180bb1867). + +4.28.2 (released 2021-11-22) +---------------------------- + +- [otlLib] Remove duplicates when building coverage (#2433). +- [docs] Add interrogate configuration (#2443). +- [docs] Remove comment about missing “start” optional argument to ``calcChecksum`` (#2448). +- [cu2qu/cli] Adapt to the latest ufoLib2. +- [subset] Support subsetting SVG table and remove it from the list of drop by default tables (#534). +- [subset] add ``--pretty-svg`` option to pretty print SVG table contents (#2452). +- [merge] Support merging ``CFF`` tables (CID-keyed ``CFF`` is still not supported) (#2447). +- [merge] Support ``--output-file`` (#2447). +- [docs] Split table docs into individual pages (#2444). +- [feaLib] Forbid empty classes (#2446). +- [docs] Improve documentation for ``fontTools.ttLib.ttFont`` (#2442). + +4.28.1 (released 2021-11-08) +---------------------------- + +- [subset] Fixed AttributeError while traversing a color glyph's Paint graph when there is no + LayerList, which is optional (#2441). + +4.28.0 (released 2021-11-05) +---------------------------- + +- Dropped support for EOL Python 3.6, require Python 3.7 (#2417). +- [ufoLib/glifLib] Make filename-clash checks faster by using a set instead of a list (#2422). +- [subset] Don't crash if optional ClipList and LayerList are ``None`` (empty) (#2424, 2439). +- [OT-SVG] Removed support for old deprecated version 1 and embedded color palettes, + which were never officially part of the OpenType SVG spec. Upon compile, reuse offsets + to SVG documents that are identical (#2430). +- [feaLib] Added support for Variable Feature File syntax. This is experimental and subject + to change until it is finalized in the Adobe FEA spec (#2432). +- [unicodedata] Update Scripts/ScriptExtensions/Blocks to UnicodeData 14.0 (#2437). + +4.27.1 (released 2021-09-23) +---------------------------- + +- [otlLib] Fixed error when chained contextual lookup builder overflows (#2404, #2411). +- [bezierTools] Fixed two floating-point bugs: one when computing `t` for a point + lying on an almost horizontal/vertical line; another when computing the intersection + point between a curve and a line (#2413). + +4.27.0 (released 2021-09-14) +---------------------------- + +- [ttLib/otTables] Cleaned up virtual GID handling: allow virtual GIDs in ``Coverage`` + and ``ClassDef`` readers; removed unused ``allowVID`` argument from ``TTFont`` + constructor, and ``requireReal`` argument in ``TTFont.getGlyphID`` method. + Make ``TTFont.setGlyphOrder`` clear reverse glyphOrder map, and assume ``glyphOrder`` + internal attribute is never modified outside setGlyphOrder; added ``TTFont.getGlyphNameMany`` + and ``getGlyphIDMany`` (#1536, #1654, #2334, #2398). +- [py23] Dropped internal use of ``fontTools.py23`` module to fix deprecation warnings + in client code that imports from fontTools (#2234, #2399, #2400). +- [subset] Fix subsetting COLRv1 clip boxes when font is loaded lazily (#2408). + +4.26.2 (released 2021-08-09) +---------------------------- + +- [otTables] Added missing ``CompositeMode.PLUS`` operator (#2390). + +4.26.1 (released 2021-08-03) +---------------------------- + +- [transform] Added ``transformVector`` and ``transformVectors`` methods to the + ``Transform`` class. Similar to ``transformPoint`` but ignore the translation + part (#2386). + +4.26.0 (released 2021-08-03) +---------------------------- + +- [xmlWriter] Default to ``"\n"`` for ``newlinestr`` instead of platform-specific + ``os.linesep`` (#2384). +- [otData] Define COLRv1 ClipList and ClipBox (#2379). +- [removeOverlaps/instancer] Added --ignore-overlap-errors option to work around + Skia PathOps.Simplify bug (#2382, #2363, google/fonts#3365). +- NOTE: This will be the last version to support Python 3.6. FontTools will require + Python 3.7 or above from the next release (#2350) + +4.25.2 (released 2021-07-26) +---------------------------- + +- [COLRv1] Various changes to sync with the latest CORLv1 draft spec. In particular: + define COLR.VarIndexMap, remove/inline ColorIndex struct, add VarIndexBase to ``PaintVar*`` tables (#2372); + add reduced-precicion specialized transform Paints; + define Angle as fraction of half circle encoded as F2Dot14; + use FWORD (int16) for all Paint center coordinates; + change PaintTransform to have an offset to Affine2x3; +- [ttLib] when importing XML, only set sfntVersion if the font has no reader and is empty (#2376) + +4.25.1 (released 2021-07-16) +---------------------------- + +- [ttGlyphPen] Fixed bug in ``TTGlyphPointPen``, whereby open contours (i.e. starting + with segmentType "move") would throw ``NotImplementedError``. They are now treated + as if they are closed, like with the ``TTGlyphPen`` (#2364, #2366). + +4.25.0 (released 2021-07-05) +---------------------------- + +- [tfmLib] Added new library for parsing TeX Font Metric (TFM) files (#2354). +- [TupleVariation] Make shared tuples order deterministic on python < 3.7 where + Counter (subclass of dict) doesn't remember insertion order (#2351, #2353). +- [otData] Renamed COLRv1 structs to remove 'v1' suffix and match the updated draft + spec: 'LayerV1List' -> 'LayerList', 'BaseGlyphV1List' -> 'BaseGlyphList', + 'BaseGlyphV1Record' -> 'BaseGlyphPaintRecord' (#2346). + Added 8 new ``PaintScale*`` tables: with/without centers, uniform vs non-uniform. + Added ``*AroundCenter`` variants to ``PaintRotate`` and ``PaintSkew``: the default + versions no longer have centerX/Y, but default to origin. + ``PaintRotate``, ``PaintSkew`` and ``PaintComposite`` formats were re-numbered. + NOTE: these are breaking changes; clients using the experimental COLRv1 API will + have to be updated (#2348). +- [pointPens] Allow ``GuessSmoothPointPen`` to accept a tolerance. Fixed call to + ``math.atan2`` with x/y parameters inverted. Sync the code with fontPens (#2344). +- [post] Fixed parsing ``post`` table format 2.0 when it contains extra garbage + at the end of the stringData array (#2314). +- [subset] drop empty features unless 'size' with FeatureParams table (#2324). +- [otlLib] Added ``otlLib.optimize`` module; added GPOS compaction algorithm. + The compaction can be run on existing fonts with ``fonttools otlLib.optimize`` + or using the snippet ``compact_gpos.py``. There's experimental support for + compacting fonts at compilation time using an environment variable, but that + might be removed later (#2326). + +4.24.4 (released 2021-05-25) +---------------------------- + +- [subset/instancer] Fixed ``AttributeError`` when instantiating a VF that + contains GPOS ValueRecords with ``Device`` tables but without the respective + non-Device values (e.g. ``XAdvDevice`` without ``XAdvance``). When not + explicitly set, the latter are assumed to be 0 (#2323). + +4.24.3 (released 2021-05-20) +---------------------------- + +- [otTables] Fixed ``AttributeError`` in methods that split LigatureSubst, + MultipleSubst and AlternateSubst subtables when an offset overflow occurs. + The ``Format`` attribute was removed in v4.22.0 (#2319). + +4.24.2 (released 2021-05-20) +---------------------------- + +- [ttGlyphPen] Fixed typing annotation of TTGlyphPen glyphSet parameter (#2315). +- Fixed two instances of DeprecationWarning: invalid escape sequence (#2311). + +4.24.1 (released 2021-05-20) +---------------------------- + +- [subset] Fixed AttributeError when SinglePos subtable has None Value (ValueFormat 0) + (#2312, #2313). + +4.24.0 (released 2021-05-17) +---------------------------- + +- [pens] Add ``ttGlyphPen.TTGlyphPointPen`` similar to ``TTGlyphPen`` (#2205). + +4.23.1 (released 2021-05-14) +---------------------------- + +- [subset] Fix ``KeyError`` after subsetting ``COLR`` table that initially contains + both v0 and v1 color glyphs when the subset only requested v1 glyphs; we were + not pruning the v0 portion of the table (#2308). +- [colorLib] Set ``LayerV1List`` attribute to ``None`` when empty, it's optional + in CORLv1 (#2308). + +4.23.0 (released 2021-05-13) +---------------------------- + +- [designspaceLib] Allow to use ``\\UNC`` absolute paths on Windows (#2299, #2306). +- [varLib.merger] Fixed bug where ``VarLibMergeError`` was raised with incorrect + parameters (#2300). +- [feaLib] Allow substituting a glyph class with ``NULL`` to delete multiple glyphs + (#2303). +- [glyf] Fixed ``NameError`` exception in ``getPhantomPoints`` (#2295, #2305). +- [removeOverlaps] Retry pathops.simplify after rounding path coordinates to integers + if it fails the first time using floats, to work around a rare and hard to debug + Skia bug (#2288). +- [varLib] Added support for building, reading, writing and optimizing 32-bit + ``ItemVariationStore`` as used in COLRv1 table (#2285). +- [otBase/otConverters] Add array readers/writers for int types (#2285). +- [feaLib] Allow more than one lookahead glyph/class in contextual positioning with + "value at end" (#2293, #2294). +- [COLRv1] Default varIdx should be 0xFFFFFFFF (#2297, #2298). +- [pens] Make RecordingPointPen actually pass on identifiers; replace asserts with + explicit ``PenError`` exception (#2284). +- [mutator] Round lsb for CF2 fonts as well (#2286). + +4.22.1 (released 2021-04-26) +---------------------------- + +- [feaLib] Skip references to named lookups if the lookup block definition + is empty, similarly to makeotf. This also fixes an ``AttributeError`` while + generating ``aalt`` feature (#2276, #2277). +- [subset] Fixed bug with ``--no-hinting`` implementation for Device tables (#2272, + #2275). The previous code was alwyas dropping Device tables if no-hinting was + requested, but some Device tables (DeltaFormat=0x8000) are also used to encode + variation indices and need to be retained. +- [otBase] Fixed bug in getting the ValueRecordSize when decompiling ``MVAR`` + table with ``lazy=True`` (#2273, #2274). +- [varLib/glyf/gvar] Optimized and simplified ``GlyphCoordinates`` and + ``TupleVariation`` classes, use ``bytearray`` where possible, refactored + phantom-points calculations. We measured about 30% speedup in total time + of loading master ttfs, building gvar, and saving (#2261, #2266). +- [subset] Fixed ``AssertionError`` while pruning unused CPAL palettes when + ``0xFFFF`` is present (#2257, #2259). + +4.22.0 (released 2021-04-01) +---------------------------- + +- [ttLib] Remove .Format from Coverage, ClassDef, SingleSubst, LigatureSubst, + AlternateSubst, MultipleSubst (#2238). + ATTENTION: This will change your TTX dumps! +- [misc.arrayTools] move Vector to its own submodule, and rewrite as a tuple + subclass (#2201). +- [docs] Added a terminology section for varLib (#2209). +- [varLib] Move rounding to VariationModel, to avoid error accumulation from + multiple deltas (#2214) +- [varLib] Explain merge errors in more human-friendly terms (#2223, #2226) +- [otlLib] Correct some documentation (#2225) +- [varLib/otlLib] Allow merging into VariationFont without first saving GPOS + PairPos2 (#2229) +- [subset] Improve PairPosFormat2 subsetting (#2221) +- [ttLib] TTFont.save: create file on disk as late as possible (#2253) +- [cffLib] Add missing CFF2 dict operators LanguageGroup and ExpansionFactor + (#2249) + ATTENTION: This will change your TTX dumps! + +4.21.1 (released 2021-02-26) +---------------------------- + +- [pens] Reverted breaking change that turned ``AbstractPen`` and ``AbstractPointPen`` + into abstract base classes (#2164, #2198). + +4.21.0 (released 2021-02-26) +---------------------------- + +- [feaLib] Indent anchor statements in ``asFea()`` to make them more legible and + diff-able (#2193). +- [pens] Turn ``AbstractPen`` and ``AbstractPointPen`` into abstract base classes + (#2164). +- [feaLib] Added support for parsing and building ``STAT`` table from AFDKO feature + files (#2039). +- [instancer] Added option to update name table of generated instance using ``STAT`` + table's axis values (#2189). +- [bezierTools] Added functions to compute bezier point-at-time, as well as line-line, + curve-line and curve-curve intersections (#2192). + +4.20.0 (released 2021-02-15) +---------------------------- + +- [COLRv1] Added ``unbuildColrV1`` to deconstruct COLRv1 otTables to raw json-able + data structure; it does the reverse of ``buildColrV1`` (#2171). +- [feaLib] Allow ``sub X by NULL`` sequence to delete a glyph (#2170). +- [arrayTools] Fixed ``Vector`` division (#2173). +- [COLRv1] Define new ``PaintSweepGradient`` (#2172). +- [otTables] Moved ``Paint.Format`` enum class outside of ``Paint`` class definition, + now named ``PaintFormat``. It was clashing with paint instance ``Format`` attribute + and thus was breaking lazy load of COLR table which relies on magic ``__getattr__`` + (#2175). +- [COLRv1] Replace hand-coded builder functions with otData-driven dynamic + implementation (#2181). +- [COLRv1] Define additional static (non-variable) Paint formats (#2181). +- [subset] Added support for subsetting COLR v1 and CPAL tables (#2174, #2177). +- [fontBuilder] Allow ``setupFvar`` to optionally take ``designspaceLib.AxisDescriptor`` + objects. Added new ``setupAvar`` method. Support localised names for axes and + named instances (#2185). + +4.19.1 (released 2021-01-28) +---------------------------- + +- [woff2] An initial off-curve point with an overlap flag now stays an off-curve + point after compression. + +4.19.0 (released 2021-01-25) +---------------------------- + +- [codecs] Handle ``errors`` parameter different from 'strict' for the custom + extended mac encodings (#2137, #2132). +- [featureVars] Raise better error message when a script is missing the required + default language system (#2154). +- [COLRv1] Avoid abrupt change caused by rounding ``PaintRadialGradient.c0`` when + the start circle almost touches the end circle's perimeter (#2148). +- [COLRv1] Support building unlimited lists of paints as 255-ary trees of + ``PaintColrLayers`` tables (#2153). +- [subset] Prune redundant format-12 cmap subtables when all non-BMP characters + are dropped (#2146). +- [basePen] Raise ``MissingComponentError`` instead of bare ``KeyError`` when a + referenced component is missing (#2145). + +4.18.2 (released 2020-12-16) +---------------------------- + +- [COLRv1] Implemented ``PaintTranslate`` paint format (#2129). +- [varLib.cff] Fixed unbound local variable error (#1787). +- [otlLib] Don't crash when creating OpenType class definitions if some glyphs + occur more than once (#2125). + +4.18.1 (released 2020-12-09) +---------------------------- + +- [colorLib] Speed optimization for ``LayerV1ListBuilder`` (#2119). +- [mutator] Fixed missing tab in ``interpolate_cff2_metrics`` (0957dc7a). + +4.18.0 (released 2020-12-04) +---------------------------- + +- [COLRv1] Update to latest draft: added ``PaintRotate`` and ``PaintSkew`` (#2118). +- [woff2] Support new ``brotlicffi`` bindings for PyPy (#2117). +- [glifLib] Added ``expectContentsFile`` parameter to ``GlyphSet``, for use when + reading existing UFOs, to comply with the specification stating that a + ``contents.plist`` file must exist in a glyph set (#2114). +- [subset] Allow ``LangSys`` tags in ``--layout-scripts`` option (#2112). For example: + ``--layout-scripts=arab.dflt,arab.URD,latn``; this will keep ``DefaultLangSys`` + and ``URD`` language for ``arab`` script, and all languages for ``latn`` script. +- [varLib.interpolatable] Allow UFOs to be checked; report open paths, non existant + glyphs; add a ``--json`` option to produce a machine-readable list of + incompatibilities +- [pens] Added ``QuartzPen`` to create ``CGPath`` from glyph outlines on macOS. + Requires pyobjc (#2107). +- [feaLib] You can export ``FONTTOOLS_LOOKUP_DEBUGGING=1`` to enable feature file + debugging info stored in ``Debg`` table (#2106). +- [otlLib] Build more efficient format 1 and format 2 contextual lookups whenever + possible (#2101). + +4.17.1 (released 2020-11-16) +---------------------------- + +- [colorLib] Fixed regression in 4.17.0 when building COLR v0 table; when color + layers are stored in UFO lib plist, we can't distinguish tuples from lists so + we need to accept either types (e5439eb9, googlefonts/ufo2ft/issues#426). + +4.17.0 (released 2020-11-12) +---------------------------- + +- [colorLib/otData] Updated to latest draft ``COLR`` v1 spec (#2092). +- [svgLib] Fixed parsing error when arc commands' boolean flags are not separated + by space or comma (#2094). +- [varLib] Interpret empty non-default glyphs as 'missing', if the default glyph is + not empty (#2082). +- [feaLib.builder] Only stash lookup location for ``Debg`` if ``Builder.buildLookups_`` + has cooperated (#2065, #2067). +- [varLib] Fixed bug in VarStore optimizer (#2073, #2083). +- [varLib] Add designspace lib key for custom feavar feature tag (#2080). +- Add HashPointPen adapted from psautohint. With this pen, a hash value of a glyph + can be computed, which can later be used to detect glyph changes (#2005). + +4.16.1 (released 2020-10-05) +---------------------------- + +- [varLib.instancer] Fixed ``TypeError`` exception when instantiating a VF with + a GSUB table 1.1 in which ``FeatureVariations`` attribute is present but set to + ``None`` -- indicating that optional ``FeatureVariations`` is missing (#2077). +- [glifLib] Make ``x`` and ``y`` attributes of the ``point`` element required + even when validation is turned off, and raise a meaningful ``GlifLibError`` + message when that happens (#2075). + +4.16.0 (released 2020-09-30) +---------------------------- + +- [removeOverlaps] Added new module and ``removeOverlaps`` function that merges + overlapping contours and components in TrueType glyphs. It requires the + `skia-pathops `__ module. + Note that removing overlaps invalidates the TrueType hinting (#2068). +- [varLib.instancer] Added ``--remove-overlaps`` command-line option. + The ``overlap`` option in ``instantiateVariableFont`` now takes an ``OverlapMode`` + enum: 0: KEEP_AND_DONT_SET_FLAGS, 1: KEEP_AND_SET_FLAGS (default), and 2: REMOVE. + The latter is equivalent to calling ``removeOverlaps`` on the generated static + instance. The option continues to accept ``bool`` value for backward compatibility. + + +4.15.0 (released 2020-09-21) +---------------------------- + +- [plistlib] Added typing annotations to plistlib module. Set up mypy static + typechecker to run automatically on CI (#2061). +- [ttLib] Implement private ``Debg`` table, a reverse-DNS namespaced JSON dict. +- [feaLib] Optionally add an entry into the ``Debg`` table with the original + lookup name (if any), feature name / script / language combination (if any), + and original source filename and line location. Annotate the ttx output for + a lookup with the information from the Debg table (#2052). +- [sfnt] Disabled checksum checking by default in ``SFNTReader`` (#2058). +- [Docs] Document ``mtiLib`` module (#2027). +- [varLib.interpolatable] Added checks for contour node count and operation type + of each node (#2054). +- [ttLib] Added API to register custom table packer/unpacker classes (#2055). + +4.14.0 (released 2020-08-19) +---------------------------- + +- [feaLib] Allow anonymous classes in LookupFlags definitions (#2037). +- [Docs] Better document DesignSpace rules processing order (#2041). +- [ttLib] Fixed 21-year old bug in ``maxp.maxComponentDepth`` calculation (#2044, + #2045). +- [varLib.models] Fixed misspelled argument name in CLI entry point (81d0042a). +- [subset] When subsetting GSUB v1.1, fixed TypeError by checking whether the + optional FeatureVariations table is present (e63ecc5b). +- [Snippets] Added snippet to show how to decompose glyphs in a TTF (#2030). +- [otlLib] Generate GSUB type 5 and GPOS type 7 contextual lookups where appropriate + (#2016). + +4.13.0 (released 2020-07-10) +---------------------------- + +- [feaLib/otlLib] Moved lookup subtable builders from feaLib to otlLib; refactored + some common code (#2004, #2007). +- [docs] Document otlLib module (#2009). +- [glifLib] Fixed bug with some UFO .glif filenames clashing on case-insensitive + filesystems (#2001, #2002). +- [colorLib] Updated COLRv1 implementation following changes in the draft spec: + (#2008, googlefonts/colr-gradients-spec#24). + +4.12.1 (released 2020-06-16) +---------------------------- + +- [_n_a_m_e] Fixed error in ``addMultilingualName`` with one-character names. + Only attempt to recovered malformed UTF-16 data from a ``bytes`` string, + not from unicode ``str`` (#1997, #1998). + +4.12.0 (released 2020-06-09) +---------------------------- + +- [otlLib/varLib] Ensure that the ``AxisNameID`` in the ``STAT`` and ``fvar`` + tables is grater than 255 as per OpenType spec (#1985, #1986). +- [docs] Document more modules in ``fontTools.misc`` package: ``filenames``, + ``fixedTools``, ``intTools``, ``loggingTools``, ``macCreatorType``, ``macRes``, + ``plistlib`` (#1981). +- [OS/2] Don't calculate whole sets of unicode codepoints, use faster and more memory + efficient ranges and bisect lookups (#1984). +- [voltLib] Support writing back abstract syntax tree as VOLT data (#1983). +- [voltLib] Accept DO_NOT_TOUCH_CMAP keyword (#1987). +- [subset/merge] Fixed a namespace clash involving a private helper class (#1955). + +4.11.0 (released 2020-05-28) +---------------------------- + +- [feaLib] Introduced ``includeDir`` parameter on Parser and IncludingLexer to + explicitly specify the directory to search when ``include()`` statements are + encountered (#1973). +- [ufoLib] Silently delete duplicate glyphs within the same kerning group when reading + groups (#1970). +- [ttLib] Set version of COLR table when decompiling COLRv1 (commit 9d8a7e2). + +4.10.2 (released 2020-05-20) +---------------------------- + +- [sfnt] Fixed ``NameError: SimpleNamespace`` while reading TTC header. The regression + was introduced with 4.10.1 after removing ``py23`` star import. + +4.10.1 (released 2020-05-19) +---------------------------- + +- [sfnt] Make ``SFNTReader`` pickleable even when TTFont is loaded with lazy=True + option and thus keeps a reference to an external file (#1962, #1967). +- [feaLib.ast] Restore backward compatibility (broken in 4.10 with #1905) for + ``ChainContextPosStatement`` and ``ChainContextSubstStatement`` classes. + Make them accept either list of lookups or list of lists of lookups (#1961). +- [docs] Document some modules in ``fontTools.misc`` package: ``arrayTools``, + ``bezierTools`` ``cliTools`` and ``eexec`` (#1956). +- [ttLib._n_a_m_e] Fixed ``findMultilingualName()`` when name record's ``string`` is + encoded as bytes sequence (#1963). + +4.10.0 (released 2020-05-15) +---------------------------- + +- [varLib] Allow feature variations to be active across the entire space (#1957). +- [ufoLib] Added support for ``formatVersionMinor`` in UFO's ``fontinfo.plist`` and for + ``formatMinor`` attribute in GLIF file as discussed in unified-font-object/ufo-spec#78. + No changes in reading or writing UFOs until an upcoming (non-0) minor update of the + UFO specification is published (#1786). +- [merge] Fixed merging fonts with different versions of ``OS/2`` table (#1865, #1952). +- [subset] Fixed ``AttributeError`` while subsetting ``ContextSubst`` and ``ContextPos`` + Format 3 subtable (#1879, #1944). +- [ttLib.table._m_e_t_a] if data happens to be ascii, emit comment in TTX (#1938). +- [feaLib] Support multiple lookups per glyph position (#1905). +- [psCharStrings] Use inheritance to avoid repeated code in initializer (#1932). +- [Doc] Improved documentation for the following modules: ``afmLib`` (#1933), ``agl`` + (#1934), ``cffLib`` (#1935), ``cu2qu`` (#1937), ``encodings`` (#1940), ``feaLib`` + (#1941), ``merge`` (#1949). +- [Doc] Split off developer-centric info to new page, making front page of docs more + user-focused. List all utilities and sub-modules with brief descriptions. + Make README more concise and focused (#1914). +- [otlLib] Add function to build STAT table from high-level description (#1926). +- [ttLib._n_a_m_e] Add ``findMultilingualName()`` method (#1921). +- [unicodedata] Update ``RTL_SCRIPTS`` for Unicode 13.0 (#1925). +- [gvar] Sort ``gvar`` XML output by glyph name, not glyph order (#1907, #1908). +- [Doc] Added help options to ``fonttools`` command line tool (#1913, #1920). + Ensure all fonttools CLI tools have help documentation (#1948). +- [ufoLib] Only write fontinfo.plist when there actually is content (#1911). + +4.9.0 (released 2020-04-29) +--------------------------- + +- [subset] Fixed subsetting of FeatureVariations table. The subsetter no longer drops + FeatureVariationRecords that have empty substitutions as that will keep the search + going and thus change the logic. It will only drop empty records that occur at the + end of the FeatureVariationRecords array (#1881). +- [subset] Remove FeatureVariations table and downgrade GSUB/GPOS to version 0x10000 + when FeatureVariations contain no FeatureVariationRecords after subsetting (#1903). +- [agl] Add support for legacy Adobe Glyph List of glyph names in ``fontTools.agl`` + (#1895). +- [feaLib] Ignore superfluous script statements (#1883). +- [feaLib] Hide traceback by default on ``fonttools feaLib`` command line. + Use ``--traceback`` option to show (#1898). +- [feaLib] Check lookup index in chaining sub/pos lookups and print better error + message (#1896, #1897). +- [feaLib] Fix building chained alt substitutions (#1902). +- [Doc] Included all fontTools modules in the sphinx-generated documentation, and + published it to ReadTheDocs for continuous documentation of the fontTools project + (#1333). Check it out at https://fonttools.readthedocs.io/. Thanks to Chris Simpkins! +- [transform] The ``Transform`` class is now subclass of ``typing.NamedTuple``. No + change in functionality (#1904). + + +4.8.1 (released 2020-04-17) +--------------------------- + +- [feaLib] Fixed ``AttributeError: 'NoneType' has no attribute 'getAlternateGlyphs'`` + when ``aalt`` feature references a chain contextual substitution lookup + (googlefonts/fontmake#648, #1878). + +4.8.0 (released 2020-04-16) +--------------------------- + +- [feaLib] If Parser is initialized without a ``glyphNames`` parameter, it cannot + distinguish between a glyph name containing an hyphen, or a range of glyph names; + instead of raising an error, it now interprets them as literal glyph names, while + also outputting a logging warning to alert user about the ambiguity (#1768, #1870). +- [feaLib] When serializing AST to string, emit spaces around hyphens that denote + ranges. Also, fixed an issue with CID ranges when round-tripping AST->string->AST + (#1872). +- [Snippets/otf2ttf] In otf2ttf.py script update LSB in hmtx to match xMin (#1873). +- [colorLib] Added experimental support for building ``COLR`` v1 tables as per + the `colr-gradients-spec `__ + draft proposal. **NOTE**: both the API and the XML dump of ``COLR`` v1 are + susceptible to change while the proposal is being discussed and formalized (#1822). + +4.7.0 (released 2020-04-03) +--------------------------- + +- [cu2qu] Added ``fontTools.cu2qu`` package, imported from the original + `cu2qu `__ project. The ``cu2qu.pens`` module + was moved to ``fontTools.pens.cu2quPen``. The optional cu2qu extension module + can be compiled by installing `Cython `__ before installing + fonttools from source (i.e. git repo or sdist tarball). The wheel package that + is published on PyPI (i.e. the one ``pip`` downloads, unless ``--no-binary`` + option is used), will continue to be pure-Python for now (#1868). + +4.6.0 (released 2020-03-24) +--------------------------- + +- [varLib] Added support for building variable ``BASE`` table version 1.1 (#1858). +- [CPAL] Added ``fromRGBA`` method to ``Color`` class (#1861). + + +4.5.0 (released 2020-03-20) +--------------------------- + +- [designspaceLib] Added ``add{Axis,Source,Instance,Rule}Descriptor`` methods to + ``DesignSpaceDocument`` class, to initialize new descriptor objects using keyword + arguments, and at the same time append them to the current document (#1860). +- [unicodedata] Update to Unicode 13.0 (#1859). + +4.4.3 (released 2020-03-13) +--------------------------- + +- [varLib] Always build ``gvar`` table for TrueType-flavored Variable Fonts, + even if it contains no variation data. The table is required according to + the OpenType spec (#1855, #1857). + +4.4.2 (released 2020-03-12) +--------------------------- + +- [ttx] Annotate ``LookupFlag`` in XML dump with comment explaining what bits + are set and what they mean (#1850). +- [feaLib] Added more descriptive message to ``IncludedFeaNotFound`` error (#1842). + +4.4.1 (released 2020-02-26) +--------------------------- + +- [woff2] Skip normalizing ``glyf`` and ``loca`` tables if these are missing from + a font (e.g. in NotoColorEmoji using ``CBDT/CBLC`` tables). +- [timeTools] Use non-localized date parsing in ``timestampFromString``, to fix + error when non-English ``LC_TIME`` locale is set (#1838, #1839). +- [fontBuilder] Make sure the CFF table generated by fontBuilder can be used by varLib + without having to compile and decompile the table first. This was breaking in + converting the CFF table to CFF2 due to some unset attributes (#1836). + +4.4.0 (released 2020-02-18) +--------------------------- + +- [colorLib] Added ``fontTools.colorLib.builder`` module, initially with ``buildCOLR`` + and ``buildCPAL`` public functions. More color font formats will follow (#1827). +- [fontBuilder] Added ``setupCOLR`` and ``setupCPAL`` methods (#1826). +- [ttGlyphPen] Quantize ``GlyphComponent.transform`` floats to ``F2Dot14`` to fix + round-trip issue when computing bounding boxes of transformed components (#1830). +- [glyf] If a component uses reference points (``firstPt`` and ``secondPt``) for + alignment (instead of X and Y offsets), compute the effective translation offset + *after* having applied any transform (#1831). +- [glyf] When all glyphs have zero contours, compile ``glyf`` table data as a single + null byte in order to pass validation by OTS and Windows (#1829). +- [feaLib] Parsing feature code now ensures that referenced glyph names are part of + the known glyph set, unless a glyph set was not provided. +- [varLib] When filling in the default axis value for a missing location of a source or + instance, correctly map the value forward. +- [varLib] The avar table can now contain mapping output values that are greater than + OR EQUAL to the preceeding value, as the avar specification allows this. +- [varLib] The errors of the module are now ordered hierarchically below VarLibError. + See #1821. + +4.3.0 (released 2020-02-03) +--------------------------- + +- [EBLC/CBLC] Fixed incorrect padding length calculation for Format 3 IndexSubTable + (#1817, #1818). +- [varLib] Fixed error when merging OTL tables and TTFonts were loaded as ``lazy=True`` + (#1808, #1809). +- [varLib] Allow to use master fonts containing ``CFF2`` table when building VF (#1816). +- [ttLib] Make ``recalcBBoxes`` option work also with ``CFF2`` table (#1816). +- [feaLib] Don't reset ``lookupflag`` in lookups defined inside feature blocks. + They will now inherit the current ``lookupflag`` of the feature. This is what + Adobe ``makeotf`` also does in this case (#1815). +- [feaLib] Fixed bug with mixed single/multiple substitutions. If a single substitution + involved a glyph class, we were incorrectly using only the first glyph in the class + (#1814). + +4.2.5 (released 2020-01-29) +--------------------------- + +- [feaLib] Do not fail on duplicate multiple substitutions, only warn (#1811). +- [subset] Optimize SinglePos subtables to Format 1 if all ValueRecords are the same + (#1802). + +4.2.4 (released 2020-01-09) +--------------------------- + +- [unicodedata] Update RTL_SCRIPTS for Unicode 11 and 12. + +4.2.3 (released 2020-01-07) +--------------------------- + +- [otTables] Fixed bug when splitting `MarkBasePos` subtables as offsets overflow. + The mark class values in the split subtable were not being updated, leading to + invalid mark-base attachments (#1797, googlefonts/noto-source#145). +- [feaLib] Only log a warning instead of error when features contain duplicate + substitutions (#1767). +- [glifLib] Strip XML comments when parsing with lxml (#1784, #1785). + +4.2.2 (released 2019-12-12) +--------------------------- + +- [subset] Fixed issue with subsetting FeatureVariations table when the index + of features changes as features get dropped. The feature index need to be + remapped to point to index of the remaining features (#1777, #1782). +- [fontBuilder] Added `addFeatureVariations` method to `FontBuilder` class. This + is a shorthand for calling `featureVars.addFeatureVariations` on the builder's + TTFont object (#1781). +- [glyf] Fixed the flags bug in glyph.drawPoints() like we did for glyph.draw() + (#1771, #1774). + +4.2.1 (released 2019-12-06) +--------------------------- + +- [glyf] Use the ``flagOnCurve`` bit mask in ``glyph.draw()``, so that we ignore + the ``overlap`` flag that may be set when instantiating variable fonts (#1771). + +4.2.0 (released 2019-11-28) +--------------------------- + +- [pens] Added the following pens: + + * ``roundingPen.RoundingPen``: filter pen that rounds coordinates and components' + offsets to integer; + * ``roundingPen.RoundingPointPen``: like the above, but using PointPen protocol. + * ``filterPen.FilterPointPen``: base class for filter point pens; + * ``transformPen.TransformPointPen``: filter point pen to apply affine transform; + * ``recordingPen.RecordingPointPen``: records and replays point-pen commands. + +- [ttGlyphPen] Always round float coordinates and component offsets to integers + (#1763). +- [ufoLib] When converting kerning groups from UFO2 to UFO3, avoid confusing + groups with the same name as one of the glyphs (#1761, #1762, + unified-font-object/ufo-spec#98). + +4.1.0 (released 2019-11-18) +--------------------------- + +- [instancer] Implemented restricting axis ranges (level 3 partial instancing). + You can now pass ``{axis_tag: (min, max)}`` tuples as input to the + ``instantiateVariableFont`` function. Note that changing the default axis + position is not supported yet. The command-line script also accepts axis ranges + in the form of colon-separated float values, e.g. ``wght=400:700`` (#1753, #1537). +- [instancer] Never drop STAT ``DesignAxis`` records, but only prune out-of-range + ``AxisValue`` records. +- [otBase/otTables] Enforce that VarStore.RegionAxisCount == fvar.axisCount, even + when regions list is empty to appease OTS < v8.0 (#1752). +- [designspaceLib] Defined new ``processing`` attribute for ```` element, + with values "first" or "last", plus other editorial changes to DesignSpace + specification. Bumped format version to 4.1 (#1750). +- [varLib] Improved error message when masters' glyph orders do not match (#1758, + #1759). +- [featureVars] Allow to specify custom feature tag in ``addFeatureVariations``; + allow said feature to already exist, in which case we append new lookup indices + to existing features. Implemented ```` attribute ``processing`` according to + DesignSpace specification update in #1750. Depending on this flag, we generate + either an 'rvrn' (always processed first) or a 'rclt' feature (follows lookup order, + therefore last) (#1747, #1625, #1371). +- [ttCollection] Added support for context manager auto-closing via ``with`` statement + like with ``TTFont`` (#1751). +- [unicodedata] Require unicodedata2 >= 12.1.0. +- [py2.py3] Removed yet more PY2 vestiges (#1743). +- [_n_a_m_e] Fixed issue when comparing NameRecords with different string types (#1742). +- [fixedTools] Changed ``fixedToFloat`` to not do any rounding but simply return + ``value / (1 << precisionBits)``. Added ``floatToFixedToStr`` and + ``strToFixedToFloat`` functions to be used when loading from or dumping to XML. + Fixed values (e.g. fvar axes and instance coordinates, avar mappings, etc.) are + are now stored as un-rounded decimal floats upon decompiling (#1740, #737). +- [feaLib] Fixed handling of multiple ``LigatureCaret`` statements for the same glyph. + Only the first rule per glyph is used, additional ones are ignored (#1733). + +4.0.2 (released 2019-09-26) +--------------------------- + +- [voltLib] Added support for ``ALL`` and ``NONE`` in ``PROCESS_MARKS`` (#1732). +- [Silf] Fixed issue in ``Silf`` table compilation and decompilation regarding str vs + bytes in python3 (#1728). +- [merge] Handle duplicate glyph names better: instead of appending font index to + all glyph names, use similar code like we use in ``post`` and ``CFF`` tables (#1729). + +4.0.1 (released 2019-09-11) +--------------------------- + +- [otTables] Support fixing offset overflows in ``MultipleSubst`` lookup subtables + (#1706). +- [subset] Prune empty strikes in ``EBDT`` and ``CBDT`` table data (#1698, #1633). +- [pens] Fixed issue in ``PointToSegmentPen`` when last point of closed contour has + same coordinates as the starting point and was incorrectly dropped (#1720). +- [Graphite] Fixed ``Sill`` table output to pass OTS (#1705). +- [name] Added ``removeNames`` method to ``table__n_a_m_e`` class (#1719). +- [ttLib] Added aliases for renamed entries ``ascender`` and ``descender`` in + ``hhea`` table (#1715). + +4.0.0 (released 2019-08-22) +--------------------------- + +- NOTE: The v4.x version series only supports Python 3.6 or greater. You can keep + using fonttools 3.x if you need support for Python 2. +- [py23] Removed all the python2-only code since it is no longer reachable, thus + unused; only the Python3 symbols were kept, but these are no-op. The module is now + DEPRECATED and will removed in the future. +- [ttLib] Fixed UnboundLocalError for empty loca/glyph tables (#1680). Also, allow + the glyf table to be incomplete when dumping to XML (#1681). +- [varLib.models] Fixed KeyError while sorting masters and there are no on-axis for + a given axis (38a8eb0e). +- [cffLib] Make sure glyph names are unique (#1699). +- [feaLib] Fix feature parser to correctly handle octal numbers (#1700). + +3.44.0 (released 2019-08-02) +---------------------------- + +- NOTE: This is the last scheduled release to support Python 2.7. The upcoming fonttools + v4.x series is going to require Python 3.6 or greater. +- [varLib] Added new ``varLib.instancer`` module for partially instantiating variable + fonts. This extends (and will eventually replace) ``varLib.mutator`` module, as + it allows to create not just full static instances from a variable font, but also + "partial" or "less variable" fonts where some of the axes are dropped or + instantiated at a particular value. + Also available from the command-line as `fonttools varLib.instancer --help` + (#1537, #1628). +- [cffLib] Added support for ``FDSelect`` format 4 (#1677). +- [subset] Added support for subsetting ``sbix`` (Apple bitmap color font) table. +- [t1Lib] Fixed issue parsing ``eexec`` section in Type1 fonts when whitespace + characters are interspersed among the trailing zeros (#1676). +- [cffLib.specializer] Fixed bug in ``programToCommands`` with CFF2 charstrings (#1669). + +3.43.2 (released 2019-07-10) +---------------------------- + +- [featureVars] Fixed region-merging code on python3 (#1659). +- [varLib.cff] Fixed merging of sparse PrivateDict items (#1653). + +3.43.1 (released 2019-06-19) +---------------------------- + +- [subset] Fixed regression when passing ``--flavor=woff2`` option with an input font + that was already compressed as WOFF 1.0 (#1650). + +3.43.0 (released 2019-06-18) +---------------------------- + +- [woff2] Added support for compressing/decompressing WOFF2 fonts with non-transformed + ``glyf`` and ``loca`` tables, as well as with transformed ``hmtx`` table. + Removed ``Snippets/woff2_compress.py`` and ``Snippets/woff2_decompress.py`` scripts, + and replaced them with a new console entry point ``fonttools ttLib.woff2`` + that provides two sub-commands ``compress`` and ``decompress``. +- [varLib.cff] Fixed bug when merging CFF2 ``PrivateDicts``. The ``PrivateDict`` + data from the first region font was incorrecty used for all subsequent fonts. + The bug would only affect variable CFF2 fonts with hinting (#1643, #1644). + Also, fixed a merging bug when VF masters have no blends or marking glyphs (#1632, + #1642). +- [loggingTools] Removed unused backport of ``LastResortLogger`` class. +- [subset] Gracefully handle partial MATH table (#1635). +- [featureVars] Avoid duplicate references to ``rvrn`` feature record in + ``DefaultLangSys`` tables when calling ``addFeatureVariations`` on a font that + does not already have a ``GSUB`` table (aa8a5bc6). +- [varLib] Fixed merging of class-based kerning. Before, the process could introduce + rogue kerning values and variations for random classes against class zero (everything + not otherwise classed). +- [varLib] Fixed merging GPOS tables from master fonts with different number of + ``SinglePos`` subtables (#1621, #1641). +- [unicodedata] Updated Blocks, Scripts and ScriptExtensions to Unicode 12.1. + +3.42.0 (released 2019-05-28) +---------------------------- + +- [OS/2] Fixed sign of ``fsType``: it should be ``uint16``, not ``int16`` (#1619). +- [subset] Skip out-of-range class values in mark attachment (#1478). +- [fontBuilder] Add an empty ``DSIG`` table with ``setupDummyDSIG`` method (#1621). +- [varLib.merger] Fixed bug whereby ``GDEF.GlyphClassDef`` were being dropped + when generating instance via ``varLib.mutator`` (#1614). +- [varLib] Added command-line options ``-v`` and ``-q`` to configure logging (#1613). +- [subset] Update font extents in head table (#1612). +- [subset] Make --retain-gids truncate empty glyphs after the last non-empty glyph + (#1611). +- [requirements] Updated ``unicodedata2`` backport for Unicode 12.0. + +3.41.2 (released 2019-05-13) +---------------------------- + +- [cffLib] Fixed issue when importing a ``CFF2`` variable font from XML, whereby + the VarStore state was not propagated to PrivateDict (#1598). +- [varLib] Don't drop ``post`` glyph names when building CFF2 variable font (#1609). + + +3.41.1 (released 2019-05-13) +---------------------------- + +- [designspaceLib] Added ``loadSourceFonts`` method to load source fonts using + custom opener function (#1606). +- [head] Round font bounding box coordinates to integers to fix compile error + if CFF font has float coordinates (#1604, #1605). +- [feaLib] Don't write ``None`` in ``ast.ValueRecord.asFea()`` (#1599). +- [subset] Fixed issue ``AssertionError`` when using ``--desubroutinize`` option + (#1590, #1594). +- [graphite] Fixed bug in ``Silf`` table's ``decompile`` method unmasked by + previous typo fix (#1597). Decode languange code as UTF-8 in ``Sill`` table's + ``decompile`` method (#1600). + +3.41.0 (released 2019-04-29) +---------------------------- + +- [varLib/cffLib] Added support for building ``CFF2`` variable font from sparse + masters, or masters with more than one model (multiple ``VarStore.VarData``). + In ``cffLib.specializer``, added support for ``CFF2`` CharStrings with + ``blend`` operators (#1547, #1591). +- [subset] Fixed subsetting ``HVAR`` and ``VVAR`` with ``--retain-gids`` option, + and when advances mapping is null while sidebearings mappings are non-null + (#1587, #1588). +- Added ``otlLib.maxContextCalc`` module to compute ``OS/2.usMaxContext`` value. + Calculate it automatically when compiling features with feaLib. Added option + ``--recalc-max-context`` to ``subset`` module (#1582). +- [otBase/otTables] Fixed ``AttributeError`` on missing OT table fields after + importing font from TTX (#1584). +- [graphite] Fixed typo ``Silf`` table's ``decompile`` method (#1586). +- [otlLib] Better compress ``GPOS`` SinglePos (LookupType 1) subtables (#1539). + +3.40.0 (released 2019-04-08) +---------------------------- + +- [subset] Fixed error while subsetting ``VVAR`` with ``--retain-gids`` + option (#1552). +- [designspaceLib] Use up-to-date default location in ``findDefault`` method + (#1554). +- [voltLib] Allow passing file-like object to Parser. +- [arrayTools/glyf] ``calcIntBounds`` (used to compute bounding boxes of glyf + table's glyphs) now uses ``otRound`` instead of ``round3`` (#1566). +- [svgLib] Added support for converting more SVG shapes to path ``d`` strings + (ellipse, line, polyline), as well as support for ``transform`` attributes. + Only ``matrix`` transformations are currently supported (#1564, #1564). +- [varLib] Added support for building ``VVAR`` table from ``vmtx`` and ``VORG`` + tables (#1551). +- [fontBuilder] Enable making CFF2 fonts with ``post`` table format 2 (#1557). +- Fixed ``DeprecationWarning`` on invalid escape sequences (#1562). + +3.39.0 (released 2019-03-19) +---------------------------- + +- [ttLib/glyf] Raise more specific error when encountering recursive + component references (#1545, #1546). +- [Doc/designspaceLib] Defined new ``public.skipExportGlyphs`` lib key (#1534, + unified-font-object/ufo-spec#84). +- [varLib] Use ``vmtx`` to compute vertical phantom points; or ``hhea.ascent`` + and ``head.unitsPerEM`` if ``vmtx`` is missing (#1528). +- [gvar/cvar] Sort XML element's min/value/max attributes in TupleVariation + toXML to improve readability of TTX dump (#1527). +- [varLib.plot] Added support for 2D plots with only 1 variation axis (#1522). +- [designspaceLib] Use axes maps when normalizing locations in + DesignSpaceDocument (#1226, #1521), and when finding default source (#1535). +- [mutator] Set ``OVERLAP_SIMPLE`` and ``OVERLAP_COMPOUND`` glyf flags by + default in ``instantiateVariableFont``. Added ``--no-overlap`` cli option + to disable this (#1518). +- [subset] Fixed subsetting ``VVAR`` table (#1516, #1517). + Fixed subsetting an ``HVAR`` table that has an ``AdvanceWidthMap`` when the + option ``--retain-gids`` is used. +- [feaLib] Added ``forceChained`` in MultipleSubstStatement (#1511). + Fixed double indentation of ``subtable`` statement (#1512). + Added support for ``subtable`` statement in more places than just PairPos + lookups (#1520). + Handle lookupflag 0 and lookupflag without a value (#1540). +- [varLib] In ``load_designspace``, provide a default English name for the + ``ital`` axis tag. +- Remove pyftinspect because it is unmaintained and bitrotted. + +3.38.0 (released 2019-02-18) +---------------------------- + +- [cffLib] Fixed RecursionError when unpickling or deepcopying TTFont with + CFF table (#1488, 649dc49). +- [subset] Fixed AttributeError when using --desubroutinize option (#1490). + Also, fixed desubroutinizing bug when subrs contain hints (#1499). +- [CPAL] Make Color a subclass of namedtuple (173a0f5). +- [feaLib] Allow hyphen in glyph class names. +- [feaLib] Added 'tables' option to __main__.py (#1497). +- [feaLib] Add support for special-case contextual positioning formatting + (#1501). +- [svgLib] Support converting SVG basic shapes (rect, circle, etc.) into + equivalent SVG paths (#1500, #1508). +- [Snippets] Added name-viewer.ipynb Jupyter notebook. + + +3.37.3 (released 2019-02-05) +---------------------------- + +- The previous release accidentally changed several files from Unix to DOS + line-endings. Fix that. + +3.37.2 (released 2019-02-05) +---------------------------- + +- [varLib] Temporarily revert the fix to ``load_masters()``, which caused a + crash in ``interpolate_layout()`` when ``deepcopy``-ing OTFs. + +3.37.1 (released 2019-02-05) +---------------------------- + +- [varLib] ``load_masters()`` now actually assigns the fonts it loads to the + source.font attributes. +- [varLib] Fixed an MVAR table generation crash when sparse masters were + involved. +- [voltLib] ``parse_coverage_()`` returns a tuple instead of an ast.Enum. +- [feaLib] A MarkClassDefinition inside a block is no longer doubly indented + compared to the rest of the block. + +3.37.0 (released 2019-01-28) +---------------------------- + +- [svgLib] Added support for converting elliptical arcs to cubic bezier curves + (#1464). +- [py23] Added backport for ``math.isfinite``. +- [varLib] Apply HIDDEN flag to fvar axis if designspace axis has attribute + ``hidden=1``. +- Fixed "DeprecationWarning: invalid escape sequence" in Python 3.7. +- [voltLib] Fixed parsing glyph groups. Distinguish different PROCESS_MARKS. + Accept COMPONENT glyph type. +- [feaLib] Distinguish missing value and explicit ```` for PairPos2 + format A (#1459). Round-trip ``useExtension`` keyword. Implemented + ``ValueRecord.asFea`` method. +- [subset] Insert empty widths into hdmx when retaining gids (#1458). + +3.36.0 (released 2019-01-17) +---------------------------- + +- [ttx] Added ``--no-recalc-timestamp`` option to keep the original font's + ``head.modified`` timestamp (#1455, #46). +- [ttx/psCharStrings] Fixed issues while dumping and round-tripping CFF2 table + with ttx (#1451, #1452, #1456). +- [voltLib] Fixed check for duplicate anchors (#1450). Don't try to read past + the ``END`` operator in .vtp file (#1453). +- [varLib] Use sentinel value -0x8000 (-32768) to ignore post.underlineThickness + and post.underlinePosition when generating MVAR deltas (#1449, + googlei18n/ufo2ft#308). +- [subset] Added ``--retain-gids`` option to subset font without modifying the + current glyph indices (#1443, #1447). +- [ufoLib] Replace deprecated calls to ``getbytes`` and ``setbytes`` with new + equivalent ``readbytes`` and ``writebytes`` calls. ``fs`` >= 2.2 no required. +- [varLib] Allow loading masters from TTX files as well (#1441). + +3.35.2 (released 2019-01-14) +---------------------------- + +- [hmtx/vmtx]: Allow to compile/decompile ``hmtx`` and ``vmtx`` tables even + without the corresponding (required) metrics header tables, ``hhea`` and + ``vhea`` (#1439). +- [varLib] Added support for localized axes' ``labelname`` and named instances' + ``stylename`` (#1438). + +3.35.1 (released 2019-01-09) +---------------------------- + +- [_m_a_x_p] Include ``maxComponentElements`` in ``maxp`` table's recalculation. + +3.35.0 (released 2019-01-07) +---------------------------- + +- [psCharStrings] In ``encodeFloat`` function, use float's "general format" with + 8 digits of precision (i.e. ``%8g``) instead of ``str()``. This works around + a macOS rendering issue when real numbers in CFF table are too long, and + also makes sure that floats are encoded with the same precision in python 2.7 + and 3.x (#1430, googlei18n/ufo2ft#306). +- [_n_a_m_e/fontBuilder] Make ``_n_a_m_e_table.addMultilingualName`` also add + Macintosh (platformID=1) names by default. Added options to ``FontBuilder`` + ``setupNameTable`` method to optionally disable Macintosh or Windows names. + (#1359, #1431). +- [varLib] Make ``build`` optionally accept a ``DesignSpaceDocument`` object, + instead of a designspace file path. The caller can now set the ``font`` + attribute of designspace's sources to a TTFont object, thus allowing to + skip filenames manipulation altogether (#1416, #1425). +- [sfnt] Allow SFNTReader objects to be deep-copied. +- Require typing>=3.6.4 on py27 to fix issue with singledispatch (#1423). +- [designspaceLib/t1Lib/macRes] Fixed some cases where pathlib.Path objects were + not accepted (#1421). +- [varLib] Fixed merging of multiple PairPosFormat2 subtables (#1411). +- [varLib] The default STAT table version is now set to 1.1, to improve + compatibility with legacy applications (#1413). + +3.34.2 (released 2018-12-17) +---------------------------- + +- [merge] Fixed AssertionError when none of the script tables in GPOS/GSUB have + a DefaultLangSys record (#1408, 135a4a1). + +3.34.1 (released 2018-12-17) +---------------------------- + +- [varLib] Work around macOS rendering issue for composites without gvar entry (#1381). + +3.34.0 (released 2018-12-14) +---------------------------- + +- [varLib] Support generation of CFF2 variable fonts. ``model.reorderMasters()`` + now supports arbitrary mapping. Fix handling of overlapping ranges for feature + variations (#1400). +- [cffLib, subset] Code clean-up and fixing related to CFF2 support. +- [ttLib.tables.ttProgram] Use raw strings for regex patterns (#1389). +- [fontbuilder] Initial support for building CFF2 fonts. Set CFF's + ``FontMatrix`` automatically from unitsPerEm. +- [plistLib] Accept the more general ``collections.Mapping`` instead of the + specific ``dict`` class to support custom data classes that should serialize + to dictionaries. + +3.33.0 (released 2018-11-30) +---------------------------- +- [subset] subsetter bug fix with variable fonts. +- [varLib.featureVar] Improve FeatureVariations generation with many rules. +- [varLib] Enable sparse masters when building variable fonts: + https://github.com/fonttools/fonttools/pull/1368#issuecomment-437257368 +- [varLib.mutator] Add IDEF for GETVARIATION opcode, for handling hints in an + instance. +- [ttLib] Ignore the length of kern table subtable format 0 + +3.32.0 (released 2018-11-01) +---------------------------- + +- [ufoLib] Make ``UFOWriter`` a subclass of ``UFOReader``, and use mixins + for shared methods (#1344). +- [featureVars] Fixed normalization error when a condition's minimum/maximum + attributes are missing in designspace ```` (#1366). +- [setup.py] Added ``[plot]`` to extras, to optionally install ``matplotlib``, + needed to use the ``fonTools.varLib.plot`` module. +- [varLib] Take total bounding box into account when resolving model (7ee81c8). + If multiple axes have the same range ratio, cut across both (62003f4). +- [subset] Don't error if ``STAT`` has no ``AxisValue`` tables. +- [fontBuilder] Added a new submodule which contains a ``FontBuilder`` wrapper + class around ``TTFont`` that makes it easier to create a working TTF or OTF + font from scratch with code. NOTE: the API is still experimental and may + change in future versions. + +3.31.0 (released 2018-10-21) +---------------------------- + +- [ufoLib] Merged the `ufoLib `__ + master branch into a new ``fontTools.ufoLib`` package (#1335, #1095). + Moved ``ufoLib.pointPen`` module to ``fontTools.pens.pointPen``. + Moved ``ufoLib.etree`` module to ``fontTools.misc.etree``. + Moved ``ufoLib.plistlib`` module to ``fontTools.misc.plistlib``. + To use the new ``fontTools.ufoLib`` module you need to install fonttools + with the ``[ufo]`` extra, or you can manually install the required additional + dependencies (cf. README.rst). +- [morx] Support AAT action type to insert glyphs and clean up compilation + of AAT action tables (4a1871f, 2011ccf). +- [subset] The ``--no-hinting`` on a CFF font now also drops the optional + hinting keys in Private dict: ``ForceBold``, ``LanguageGroup``, and + ``ExpansionFactor`` (#1322). +- [subset] Include nameIDs referenced by STAT table (#1327). +- [loggingTools] Added ``msg=None`` argument to + ``CapturingLogHandler.assertRegex`` (0245f2c). +- [varLib.mutator] Implemented ``FeatureVariations`` instantiation (#1244). +- [g_l_y_f] Added PointPen support to ``_TTGlyph`` objects (#1334). + +3.30.0 (released 2018-09-18) +---------------------------- + +- [feaLib] Skip building noop class PairPos subtables when Coverage is NULL + (#1318). +- [ttx] Expose the previously reserved bit flag ``OVERLAP_SIMPLE`` of + glyf table's contour points in the TTX dump. This is used in some + implementations to specify a non-zero fill with overlapping contours (#1316). +- [ttLib] Added support for decompiling/compiling ``TS1C`` tables containing + VTT sources for ``cvar`` variation table (#1310). +- [varLib] Use ``fontTools.designspaceLib`` to read DesignSpaceDocument. The + ``fontTools.varLib.designspace`` module is now deprecated and will be removed + in future versions. The presence of an explicit ``axes`` element is now + required in order to build a variable font (#1224, #1313). +- [varLib] Implemented building GSUB FeatureVariations table from the ``rules`` + element of DesignSpace document (#1240, #713, #1314). +- [subset] Added ``--no-layout-closure`` option to not expand the subset with + the glyphs produced by OpenType layout features. Instead, OpenType features + will be subset to only rules that are relevant to the otherwise-specified + glyph set (#43, #1121). + +3.29.1 (released 2018-09-10) +---------------------------- + +- [feaLib] Fixed issue whereby lookups from DFLT/dflt were not included in the + DFLT/non-dflt language systems (#1307). +- [graphite] Fixed issue on big-endian architectures (e.g. ppc64) (#1311). +- [subset] Added ``--layout-scripts`` option to add/exclude set of OpenType + layout scripts that will be preserved. By default all scripts are retained + (``'*'``) (#1303). + +3.29.0 (released 2018-07-26) +---------------------------- + +- [feaLib] In the OTL table builder, when the ``name`` table is excluded + from the list of tables to be build, skip compiling ``featureNames`` blocks, + as the records referenced in ``FeatureParams`` table don't exist (68951b7). +- [otBase] Try ``ExtensionLookup`` if other offset-overflow methods fail + (05f95f0). +- [feaLib] Added support for explicit ``subtable;`` break statements in + PairPos lookups; previously these were ignored (#1279, #1300, #1302). +- [cffLib.specializer] Make sure the stack depth does not exceed maxstack - 1, + so that a subroutinizer can insert subroutine calls (#1301, + https://github.com/googlei18n/ufo2ft/issues/266). +- [otTables] Added support for fixing offset overflow errors occurring inside + ``MarkBasePos`` subtables (#1297). +- [subset] Write the default output file extension based on ``--flavor`` option, + or the value of ``TTFont.sfntVersion`` (d7ac0ad). +- [unicodedata] Updated Blocks, Scripts and ScriptExtensions for Unicode 11 + (452c85e). +- [xmlWriter] Added context manager to XMLWriter class to autoclose file + descriptor on exit (#1290). +- [psCharStrings] Optimize the charstring's bytecode by encoding as integers + all float values that have no decimal portion (8d7774a). +- [ttFont] Fixed missing import of ``TTLibError`` exception (#1285). +- [feaLib] Allow any languages other than ``dflt`` under ``DFLT`` script + (#1278, #1292). + +3.28.0 (released 2018-06-19) +---------------------------- + +- [featureVars] Added experimental module to build ``FeatureVariations`` + tables. Still needs to be hooked up to ``varLib.build`` (#1240). +- [fixedTools] Added ``otRound`` to round floats to nearest integer towards + positive Infinity. This is now used where we deal with visual data like X/Y + coordinates, advance widths/heights, variation deltas, and similar (#1274, + #1248). +- [subset] Improved GSUB closure memoize algorithm. +- [varLib.models] Fixed regression in model resolution (180124, #1269). +- [feaLib.ast] Fixed error when converting ``SubtableStatement`` to string + (#1275). +- [varLib.mutator] Set ``OS/2.usWeightClass`` and ``usWidthClass``, and + ``post.italicAngle`` based on the 'wght', 'wdth' and 'slnt' axis values + (#1276, #1264). +- [py23/loggingTools] Don't automatically set ``logging.lastResort`` handler + on py27. Moved ``LastResortLogger`` to the ``loggingTools`` module (#1277). + +3.27.1 (released 2018-06-11) +---------------------------- + +- [ttGlyphPen] Issue a warning and skip building non-existing components + (https://github.com/googlei18n/fontmake/issues/411). +- [tests] Fixed issue running ttx_test.py from a tagged commit. + +3.27.0 (released 2018-06-11) +---------------------------- + +- [designspaceLib] Added new ``conditionSet`` element to ``rule`` element in + designspace document. Bumped ``format`` attribute to ``4.0`` (previously, + it was formatted as an integer). Removed ``checkDefault``, ``checkAxes`` + methods, and any kind of guessing about the axes when the ```` element + is missing. The default master is expected at the intersection of all default + values for each axis (#1254, #1255, #1267). +- [cffLib] Fixed issues when compiling CFF2 or converting from CFF when the + font has an FDArray (#1211, #1271). +- [varLib] Avoid attempting to build ``cvar`` table when ``glyf`` table is not + present, as is the case for CFF2 fonts. +- [subset] Handle None coverages in MarkGlyphSets; revert commit 02616ab that + sets empty Coverage tables in MarkGlyphSets to None, to make OTS happy. +- [ttFont] Allow to build glyph order from ``maxp.numGlyphs`` when ``post`` or + ``cmap`` are missing. +- [ttFont] Added ``__len__`` method to ``_TTGlyphSet``. +- [glyf] Ensure ``GlyphCoordinates`` never overflow signed shorts (#1230). +- [py23] Added alias for ``itertools.izip`` shadowing the built-in ``zip``. +- [loggingTools] Memoize ``log`` property of ``LogMixin`` class (fbab12). +- [ttx] Impoved test coverage (#1261). +- [Snippets] Addded script to append a suffix to all family names in a font. +- [varLib.plot] Make it work with matplotlib >= 2.1 (b38e2b). + +3.26.0 (released 2018-05-03) +---------------------------- + +- [designspace] Added a new optional ``layer`` attribute to the source element, + and a corresponding ``layerName`` attribute to the ``SourceDescriptor`` + object (#1253). + Added ``conditionset`` element to the ``rule`` element to the spec, but not + implemented in designspace reader/writer yet (#1254). +- [varLib.models] Refine modeling one last time (0ecf5c5). +- [otBase] Fixed sharing of tables referred to by different offset sizes + (795f2f9). +- [subset] Don't drop a GDEF that only has VarStore (fc819d6). Set to None + empty Coverage tables in MarkGlyphSets (02616ab). +- [varLib]: Added ``--master-finder`` command-line option (#1249). +- [varLib.mutator] Prune fvar nameIDs from instance's name table (#1245). +- [otTables] Allow decompiling bad ClassDef tables with invalid format, with + warning (#1236). +- [varLib] Make STAT v1.2 and reuse nameIDs from fvar table (#1242). +- [varLib.plot] Show master locations. Set axis limits to -1, +1. +- [subset] Handle HVAR direct mapping. Passthrough 'cvar'. + Added ``--font-number`` command-line option for collections. +- [t1Lib] Allow a text encoding to be specified when parsing a Type 1 font + (#1234). Added ``kind`` argument to T1Font constructor (c5c161c). +- [ttLib] Added context manager API to ``TTFont`` class, so it can be used in + ``with`` statements to auto-close the file when exiting the context (#1232). + +3.25.0 (released 2018-04-03) +---------------------------- + +- [varLib] Improved support-resolution algorithm. Previously, the on-axis + masters would always cut the space. They don't anymore. That's more + consistent, and fixes the main issue Erik showed at TYPO Labs 2017. + Any varfont built that had an unusual master configuration will change + when rebuilt (42bef17, a523a697, + https://github.com/googlei18n/fontmake/issues/264). +- [varLib.models] Added a ``main()`` entry point, that takes positions and + prints model results. +- [varLib.plot] Added new module to plot a designspace's + VariationModel. Requires ``matplotlib``. +- [varLib.mutator] Added -o option to specify output file path (2ef60fa). +- [otTables] Fixed IndexError while pruning of HVAR pre-write (6b6c34a). +- [varLib.models] Convert delta array to floats if values overflows signed + short integer (0055f94). + +3.24.2 (released 2018-03-26) +---------------------------- + +- [otBase] Don't fail during ``ValueRecord`` copy if src has more items. + We drop hinting in the subsetter by simply changing ValueFormat, without + cleaning up the actual ValueRecords. This was causing assertion error if + a variable font was subsetted without hinting and then passed directly to + the mutator for instantiation without first it saving to disk. + +3.24.1 (released 2018-03-06) +---------------------------- + +- [varLib] Don't remap the same ``DeviceTable`` twice in VarStore optimizer + (#1206). +- [varLib] Add ``--disable-iup`` option to ``fonttools varLib`` script, + and a ``optimize=True`` keyword argument to ``varLib.build`` function, + to optionally disable IUP optimization while building varfonts. +- [ttCollection] Fixed issue while decompiling ttc with python3 (#1207). + +3.24.0 (released 2018-03-01) +---------------------------- + +- [ttGlyphPen] Decompose composite glyphs if any components' transform is too + large to fit a ``F2Dot14`` value, or clamp transform values that are + (almost) equal to +2.0 to make them fit and avoid decomposing (#1200, + #1204, #1205). +- [ttx] Added new ``-g`` option to dump glyphs from the ``glyf`` table + splitted as individual ttx files (#153, #1035, #1132, #1202). +- Copied ``ufoLib.filenames`` module to ``fontTools.misc.filenames``, used + for the ttx split-glyphs option (#1202). +- [feaLib] Added support for ``cvParameters`` blocks in Character Variant + feautures ``cv01-cv99`` (#860, #1169). +- [Snippets] Added ``checksum.py`` script to generate/check SHA1 hash of + ttx files (#1197). +- [varLib.mutator] Fixed issue while instantiating some variable fonts + whereby the horizontal advance width computed from ``gvar`` phantom points + could turn up to be negative (#1198). +- [varLib/subset] Fixed issue with subsetting GPOS variation data not + picking up ``ValueRecord`` ``Device`` objects (54fd71f). +- [feaLib/voltLib] In all AST elements, the ``location`` is no longer a + required positional argument, but an optional kewyord argument (defaults + to ``None``). This will make it easier to construct feature AST from + code (#1201). + + +3.23.0 (released 2018-02-26) +---------------------------- + +- [designspaceLib] Added an optional ``lib`` element to the designspace as a + whole, as well as to the instance elements, to store arbitrary data in a + property list dictionary, similar to the UFO's ``lib``. Added an optional + ``font`` attribute to the ``SourceDescriptor``, to allow operating on + in-memory font objects (#1175). +- [cffLib] Fixed issue with lazy-loading of attributes when attempting to + set the CFF TopDict.Encoding (#1177, #1187). +- [ttx] Fixed regression introduced in 3.22.0 that affected the split tables + ``-s`` option (#1188). +- [feaLib] Added ``IncludedFeaNotFound`` custom exception subclass, raised + when an included feature file cannot be found (#1186). +- [otTables] Changed ``VarIdxMap`` to use glyph names internally instead of + glyph indexes. The old ttx dumps of HVAR/VVAR tables that contain indexes + can still be imported (21cbab8, 38a0ffb). +- [varLib] Implemented VarStore optimizer (#1184). +- [subset] Implemented pruning of GDEF VarStore, HVAR and MVAR (#1179). +- [sfnt] Restore backward compatiblity with ``numFonts`` attribute of + ``SFNTReader`` object (#1181). +- [merge] Initial support for merging ``LangSysRecords`` (#1180). +- [ttCollection] don't seek(0) when writing to possibly unseekable strems. +- [subset] Keep all ``--name-IDs`` from 0 to 6 by default (#1170, #605, #114). +- [cffLib] Added ``width`` module to calculate optimal CFF default and + nominal glyph widths. +- [varLib] Don’t fail if STAT already in the master fonts (#1166). + +3.22.0 (released 2018-02-04) +---------------------------- + +- [subset] Support subsetting ``endchar`` acting as ``seac``-like components + in ``CFF`` (fixes #1162). +- [feaLib] Allow to build from pre-parsed ``ast.FeatureFile`` object. + Added ``tables`` argument to only build some tables instead of all (#1159, + #1163). +- [textTools] Replaced ``safeEval`` with ``ast.literal_eval`` (#1139). +- [feaLib] Added option to the parser to not resolve ``include`` statements + (#1154). +- [ttLib] Added new ``ttCollection`` module to read/write TrueType and + OpenType Collections. Exports a ``TTCollection`` class with a ``fonts`` + attribute containing a list of ``TTFont`` instances, the methods ``save`` + and ``saveXML``, plus some list-like methods. The ``importXML`` method is + not implemented yet (#17). +- [unicodeadata] Added ``ot_tag_to_script`` function that converts from + OpenType script tag to Unicode script code. +- Added new ``designspaceLib`` subpackage, originally from Erik Van Blokland's + ``designSpaceDocument``: https://github.com/LettError/designSpaceDocument + NOTE: this is not yet used internally by varLib, and the API may be subject + to changes (#911, #1110, LettError/designSpaceDocument#28). +- Added new FontTools icon images (8ee7c32). +- [unicodedata] Added ``script_horizontal_direction`` function that returns + either "LTR" or "RTL" given a unicode script code. +- [otConverters] Don't write descriptive name string as XML comment if the + NameID value is 0 (== NULL) (#1151, #1152). +- [unicodedata] Add ``ot_tags_from_script`` function to get the list of + OpenType script tags associated with unicode script code (#1150). +- [feaLib] Don't error when "enumerated" kern pairs conflict with preceding + single pairs; emit warning and chose the first value (#1147, #1148). +- [loggingTools] In ``CapturingLogHandler.assertRegex`` method, match the + fully formatted log message. +- [sbix] Fixed TypeError when concatenating str and bytes (#1154). +- [bezierTools] Implemented cusp support and removed ``approximate_fallback`` + arg in ``calcQuadraticArcLength``. Added ``calcCubicArcLength`` (#1142). + +3.21.2 (released 2018-01-08) +---------------------------- + +- [varLib] Fixed merging PairPos Format1/2 with missing subtables (#1125). + +3.21.1 (released 2018-01-03) +---------------------------- + +- [feaLib] Allow mixed single/multiple substitutions (#612) +- Added missing ``*.afm`` test assets to MAINFEST.in (#1137). +- Fixed dumping ``SVG`` tables containing color palettes (#1124). + +3.21.0 (released 2017-12-18) +---------------------------- + +- [cmap] when compiling format6 subtable, don't assume gid0 is always called + '.notdef' (1e42224). +- [ot] Allow decompiling fonts with bad Coverage format number (1aafae8). +- Change FontTools licence to MIT (#1127). +- [post] Prune extra names already in standard Mac set (df1e8c7). +- [subset] Delete empty SubrsIndex after subsetting (#994, #1118). +- [varLib] Don't share points in cvar by default, as it currently fails on + some browsers (#1113). +- [afmLib] Make poor old afmLib work on python3. + +3.20.1 (released 2017-11-22) +---------------------------- + +- [unicodedata] Fixed issue with ``script`` and ``script_extension`` functions + returning inconsistent short vs long names. They both return the short four- + letter script codes now. Added ``script_name`` and ``script_code`` functions + to look up the long human-readable script name from the script code, and + viceversa (#1109, #1111). + +3.20.0 (released 2017-11-21) +---------------------------- + +- [unicodedata] Addded new module ``fontTools.unicodedata`` which exports the + same interface as the built-in ``unicodedata`` module, with the addition of + a few functions that are missing from the latter, such as ``script``, + ``script_extension`` and ``block``. Added a ``MetaTools/buildUCD.py`` script + to download and parse data files from the Unicode Character Database and + generate python modules containing lists of ranges and property values. +- [feaLib] Added ``__str__`` method to all ``ast`` elements (delegates to the + ``asFea`` method). +- [feaLib] ``Parser`` constructor now accepts a ``glyphNames`` iterable + instead of ``glyphMap`` dict. The latter still works but with a pending + deprecation warning (#1104). +- [bezierTools] Added arc length calculation functions originally from + ``pens.perimeterPen`` module (#1101). +- [varLib] Started generating STAT table (8af4309). Right now it just reflects + the axes, and even that with certain limitations: + * AxisOrdering is set to the order axes are defined, + * Name-table entries are not shared with fvar. +- [py23] Added backports for ``redirect_stdout`` and ``redirect_stderr`` + context managers (#1097). +- [Graphite] Fixed some round-trip bugs (#1093). + +3.19.0 (released 2017-11-06) +---------------------------- + +- [varLib] Try set of used points instead of all points when testing whether to + share points between tuples (#1090). +- [CFF2] Fixed issue with reading/writing PrivateDict BlueValues to TTX file. + Read the commit message 8b02b5a and issue #1030 for more details. + NOTE: this change invalidates all the TTX files containing CFF2 tables + that where dumped with previous verisons of fonttools. + CFF2 Subr items can have values on the stack after the last operator, thus + a ``CFF2Subr`` class was added to accommodate this (#1091). +- [_k_e_r_n] Fixed compilation of AAT kern version=1.0 tables (#1089, #1094) +- [ttLib] Added getBestCmap() convenience method to TTFont class and cmap table + class that returns a preferred Unicode cmap subtable given a list of options + (#1092). +- [morx] Emit more meaningful subtable flags. Implement InsertionMorphAction + +3.18.0 (released 2017-10-30) +---------------------------- + +- [feaLib] Fixed writing back nested glyph classes (#1086). +- [TupleVariation] Reactivated shared points logic, bugfixes (#1009). +- [AAT] Implemented ``morx`` ligature subtables (#1082). +- [reverseContourPen] Keep duplicate lineTo following a moveTo (#1080, + https://github.com/googlei18n/cu2qu/issues/51). +- [varLib.mutator] Suport instantiation of GPOS, GDEF and MVAR (#1079). +- [sstruct] Fixed issue with ``unicode_literals`` and ``struct`` module in + old versions of python 2.7 (#993). + +3.17.0 (released 2017-10-16) +---------------------------- + +- [svgPathPen] Added an ``SVGPathPen`` that translates segment pen commands + into SVG path descriptions. Copied from Tal Leming's ``ufo2svg.svgPathPen`` + https://github.com/typesupply/ufo2svg/blob/d69f992/Lib/ufo2svg/svgPathPen.py +- [reverseContourPen] Added ``ReverseContourPen``, a filter pen that draws + contours with the winding direction reversed, while keeping the starting + point (#1071). +- [filterPen] Added ``ContourFilterPen`` to manipulate contours as a whole + rather than segment by segment. +- [arrayTools] Added ``Vector`` class to apply math operations on an array + of numbers, and ``pairwise`` function to loop over pairs of items in an + iterable. +- [varLib] Added support for building and interpolation of ``cvar`` table + (f874cf6, a25a401). + +3.16.0 (released 2017-10-03) +---------------------------- + +- [head] Try using ``SOURCE_DATE_EPOCH`` environment variable when setting + the ``head`` modified timestamp to ensure reproducible builds (#1063). + See https://reproducible-builds.org/specs/source-date-epoch/ +- [VTT] Decode VTT's ``TSI*`` tables text as UTF-8 (#1060). +- Added support for Graphite font tables: Feat, Glat, Gloc, Silf and Sill. + Thanks @mhosken! (#1054). +- [varLib] Default to using axis "name" attribute if "labelname" element + is missing (588f524). +- [merge] Added support for merging Script records. Remove unused features + and lookups after merge (d802580, 556508b). +- Added ``fontTools.svgLib`` package. Includes a parser for SVG Paths that + supports the Pen protocol (#1051). Also, added a snippet to convert SVG + outlines to UFO GLIF (#1053). +- [AAT] Added support for ``ankr``, ``bsln``, ``mort``, ``morx``, ``gcid``, + and ``cidg``. +- [subset] Implemented subsetting of ``prop``, ``opbd``, ``bsln``, ``lcar``. + +3.15.1 (released 2017-08-18) +---------------------------- + +- [otConverters] Implemented ``__add__`` and ``__radd__`` methods on + ``otConverters._LazyList`` that decompile a lazy list before adding + it to another list or ``_LazyList`` instance. Fixes an ``AttributeError`` + in the ``subset`` module when attempting to sum ``_LazyList`` objects + (6ef48bd2, 1aef1683). +- [AAT] Support the `opbd` table with optical bounds (a47f6588). +- [AAT] Support `prop` table with glyph properties (d05617b4). + + +3.15.0 (released 2017-08-17) +---------------------------- + +- [AAT] Added support for AAT lookups. The ``lcar`` table can be decompiled + and recompiled; futher work needed to handle ``morx`` table (#1025). +- [subset] Keep (empty) DefaultLangSys for Script 'DFLT' (6eb807b5). +- [subset] Support GSUB/GPOS.FeatureVariations (fe01d87b). +- [varLib] In ``models.supportScalars``, ignore an axis when its peak value + is 0 (fixes #1020). +- [varLib] Add default mappings to all axes in avar to fix rendering issue + in some rasterizers (19c4b377, 04eacf13). +- [varLib] Flatten multiple tail PairPosFormat2 subtables before merging + (c55ef525). +- [ttLib] Added support for recalculating font bounding box in ``CFF`` and + ``head`` tables, and min/max values in ``hhea`` and ``vhea`` tables (#970). + +3.14.0 (released 2017-07-31) +---------------------------- + +- [varLib.merger] Remove Extensions subtables before merging (f7c20cf8). +- [varLib] Initialize the avar segment map with required default entries + (#1014). +- [varLib] Implemented optimal IUP optmiziation (#1019). +- [otData] Add ``AxisValueFormat4`` for STAT table v1.2 from OT v1.8.2 + (#1015). +- [name] Fixed BCP46 language tag for Mac langID=9: 'si' -> 'sl'. +- [subset] Return value from ``_DehintingT2Decompiler.op_hintmask`` + (c0d672ba). +- [cffLib] Allow to get TopDict by index as well as by name (dca96c9c). +- [cffLib] Removed global ``isCFF2`` state; use one set of classes for + both CFF and CFF2, maintaining backward compatibility existing code (#1007). +- [cffLib] Deprecated maxstack operator, per OpenType spec update 1.8.1. +- [cffLib] Added missing default (-100) for UnderlinePosition (#983). +- [feaLib] Enable setting nameIDs greater than 255 (#1003). +- [varLib] Recalculate ValueFormat when merging SinglePos (#996). +- [varLib] Do not emit MVAR if there are no entries in the variation store + (#987). +- [ttx] For ``-x`` option, pad with space if table tag length is < 4. + +3.13.1 (released 2017-05-30) +---------------------------- + +- [feaLib.builder] Removed duplicate lookups optimization. The original + lookup order and semantics of the feature file are preserved (#976). + +3.13.0 (released 2017-05-24) +---------------------------- + +- [varLib.mutator] Implement IUP optimization (#969). +- [_g_l_y_f.GlyphCoordinates] Changed ``__bool__()`` semantics to match those + of other iterables (e46f949). Removed ``__abs__()`` (3db5be2). +- [varLib.interpolate_layout] Added ``mapped`` keyword argument to + ``interpolate_layout`` to allow disabling avar mapping: if False (default), + the location is mapped using the map element of the axes in designspace file; + if True, it is assumed that location is in designspace's internal space and + no mapping is performed (#950, #975). +- [varLib.interpolate_layout] Import designspace-loading logic from varLib. +- [varLib] Fixed bug with recombining PairPosClass2 subtables (81498e5, #914). +- [cffLib.specializer] When copying iterables, cast to list (462b7f86). + +3.12.1 (released 2017-05-18) +---------------------------- + +- [pens.t2CharStringPen] Fixed AttributeError when calling addComponent in + T2CharStringPen (#965). + +3.12.0 (released 2017-05-17) +---------------------------- + +- [cffLib.specializer] Added new ``specializer`` module to optimize CFF + charstrings, used by the T2CharStringPen (#948). +- [varLib.mutator] Sort glyphs by component depth before calculating composite + glyphs' bounding boxes to ensure deltas are correctly caclulated (#945). +- [_g_l_y_f] Fixed loss of precision in GlyphCoordinates by using 'd' (double) + instead of 'f' (float) as ``array.array`` typecode (#963, #964). + +3.11.0 (released 2017-05-03) +---------------------------- + +- [t2CharStringPen] Initial support for specialized Type2 path operators: + vmoveto, hmoveto, vlineto, hlineto, vvcurveto, hhcurveto, vhcurveto and + hvcurveto. This should produce more compact charstrings (#940, #403). +- [Doc] Added Sphinx sources for the documentation. Thanks @gferreira (#935). +- [fvar] Expose flags in XML (#932) +- [name] Add helper function for building multi-lingual names (#921) +- [varLib] Fixed kern merging when a PairPosFormat2 has ClassDef1 with glyphs + that are NOT present in the Coverage (1b5e1c4, #939). +- [varLib] Fixed non-deterministic ClassDef order with PY3 (f056c12, #927). +- [feLib] Throw an error when the same glyph is defined in multiple mark + classes within the same lookup (3e3ff00, #453). + +3.10.0 (released 2017-04-14) +---------------------------- + +- [varLib] Added support for building ``avar`` table, using the designspace + ```` elements. +- [varLib] Removed unused ``build(..., axisMap)`` argument. Axis map should + be specified in designspace file now. We do not accept nonstandard axes + if ```` element is not present. +- [varLib] Removed "custom" axis from the ``standard_axis_map``. This was + added before when glyphsLib was always exporting the (unused) custom axis. +- [varLib] Added partial support for building ``MVAR`` table; does not + implement ``gasp`` table variations yet. +- [pens] Added FilterPen base class, for pens that control another pen; + factored out ``addComponent`` method from BasePen into a separate abstract + DecomposingPen class; added DecomposingRecordingPen, which records + components decomposed as regular contours. +- [TSI1] Fixed computation of the textLength of VTT private tables (#913). +- [loggingTools] Added ``LogMixin`` class providing a ``log`` property to + subclasses, which returns a ``logging.Logger`` named after the latter. +- [loggingTools] Added ``assertRegex`` method to ``CapturingLogHandler``. +- [py23] Added backport for python 3's ``types.SimpleNamespace`` class. +- [EBLC] Fixed issue with python 3 ``zip`` iterator. + +3.9.2 (released 2017-04-08) +--------------------------- + +- [pens] Added pen to draw glyphs using WxPython ``GraphicsPath`` class: + https://wxpython.org/docs/api/wx.GraphicsPath-class.html +- [varLib.merger] Fixed issue with recombining multiple PairPosFormat2 + subtables (#888) +- [varLib] Do not encode gvar deltas that are all zeroes, or if all values + are smaller than tolerance. +- [ttLib] _TTGlyphSet glyphs now also have ``height`` and ``tsb`` (top + side bearing) attributes from the ``vmtx`` table, if present. +- [glyf] In ``GlyphCoordintes`` class, added ``__bool__`` / ``__nonzero__`` + methods, and ``array`` property to get raw array. +- [ttx] Support reading TTX files with BOM (#896) +- [CFF2] Fixed the reporting of the number of regions in the font. + +3.9.1 (released 2017-03-20) +--------------------------- + +- [varLib.merger] Fixed issue while recombining multiple PairPosFormat2 + subtables if they were split because of offset overflows (9798c30). +- [varLib.merger] Only merge multiple PairPosFormat1 subtables if there is + at least one of the fonts with a non-empty Format1 subtable (0f5a46b). +- [varLib.merger] Fixed IndexError with empty ClassDef1 in PairPosFormat2 + (aad0d46). +- [varLib.merger] Avoid reusing Class2Record (mutable) objects (e6125b3). +- [varLib.merger] Calculate ClassDef1 and ClassDef2's Format when merging + PairPosFormat2 (23511fd). +- [macUtils] Added missing ttLib import (b05f203). + +3.9.0 (released 2017-03-13) +--------------------------- + +- [feaLib] Added (partial) support for parsing feature file comments ``# ...`` + appearing in between statements (#879). +- [feaLib] Cleaned up syntax tree for FeatureNames. +- [ttLib] Added support for reading/writing ``CFF2`` table (thanks to + @readroberts at Adobe), and ``TTFA`` (ttfautohint) table. +- [varLib] Fixed regression introduced with 3.8.0 in the calculation of + ``NumShorts``, i.e. the number of deltas in ItemVariationData's delta sets + that use a 16-bit representation (b2825ff). + +3.8.0 (released 2017-03-05) +--------------------------- + +- New pens: MomentsPen, StatisticsPen, RecordingPen, and TeePen. +- [misc] Added new ``fontTools.misc.symfont`` module, for symbolic font + statistical analysis; requires ``sympy`` (http://www.sympy.org/en/index.html) +- [varLib] Added experimental ``fontTools.varLib.interpolatable`` module for + finding wrong contour order between different masters +- [varLib] designspace.load() now returns a dictionary, instead of a tuple, + and supports element (#864); the 'masters' item was renamed 'sources', + like the element in the designspace document +- [ttLib] Fixed issue with recalculating ``head`` modified timestamp when + saving CFF fonts +- [ttLib] In TupleVariation, round deltas before compiling (#861, fixed #592) +- [feaLib] Ignore duplicate glyphs in classes used as MarkFilteringSet and + MarkAttachmentType (#863) +- [merge] Changed the ``gasp`` table merge logic so that only the one from + the first font is retained, similar to other hinting tables (#862) +- [Tests] Added tests for the ``varLib`` package, as well as test fonts + from the "Annotated OpenType Specification" (AOTS) to exercise ``ttLib``'s + table readers/writers () + +3.7.2 (released 2017-02-17) +--------------------------- + +- [subset] Keep advance widths when stripping ".notdef" glyph outline in + CID-keyed CFF fonts (#845) +- [feaLib] Zero values now produce the same results as makeotf (#633, #848) +- [feaLib] More compact encoding for “Contextual positioning with in-line + single positioning rules” (#514) + +3.7.1 (released 2017-02-15) +--------------------------- + +- [subset] Fixed issue with ``--no-hinting`` option whereby advance widths in + Type 2 charstrings were also being stripped (#709, #343) +- [feaLib] include statements now resolve relative paths like makeotf (#838) +- [feaLib] table ``name`` now handles Unicode codepoints beyond the Basic + Multilingual Plane, also supports old-style MacOS platform encodings (#842) +- [feaLib] correctly escape string literals when emitting feature syntax (#780) + +3.7.0 (released 2017-02-11) +--------------------------- + +- [ttx, mtiLib] Preserve ordering of glyph alternates in GSUB type 3 (#833). +- [feaLib] Glyph names can have dashes, as per new AFDKO syntax v1.20 (#559). +- [feaLib] feaLib.Parser now needs the font's glyph map for parsing. +- [varLib] Fix regression where GPOS values were stored as 0. +- [varLib] Allow merging of class-based kerning when ClassDefs are different + +3.6.3 (released 2017-02-06) +--------------------------- + +- [varLib] Fix building variation of PairPosFormat2 (b5c34ce). +- Populate defaults even for otTables that have postRead (e45297b). +- Fix compiling of MultipleSubstFormat1 with zero 'out' glyphs (b887860). + +3.6.2 (released 2017-01-30) +--------------------------- + +- [varLib.merger] Fixed "TypeError: reduce() of empty sequence with no + initial value" (3717dc6). + +3.6.1 (released 2017-01-28) +--------------------------- + +- [py23] Fixed unhandled exception occurring at interpreter shutdown in + the "last resort" logging handler (972b3e6). +- [agl] Ensure all glyph names are of native 'str' type; avoid mixing + 'str' and 'unicode' in TTFont.glyphOrder (d8c4058). +- Fixed inconsistent title levels in README.rst that caused PyPI to + incorrectly render the reStructuredText page. + +3.6.0 (released 2017-01-26) +--------------------------- + +- [varLib] Refactored and improved the variation-font-building process. +- Assembly code in the fpgm, prep, and glyf tables is now indented in + XML output for improved readability. The ``instruction`` element is + written as a simple tag if empty (#819). +- [ttx] Fixed 'I/O operation on closed file' error when dumping + multiple TTXs to standard output with the '-o -' option. +- The unit test modules (``*_test.py``) have been moved outside of the + fontTools package to the Tests folder, thus they are no longer + installed (#811). + +3.5.0 (released 2017-01-14) +--------------------------- + +- Font tables read from XML can now be written back to XML with no + loss. +- GSUB/GPOS LookupType is written out in XML as an element, not + comment. (#792) +- When parsing cmap table, do not store items mapped to glyph id 0. + (#790) +- [otlLib] Make ClassDef sorting deterministic. Fixes #766 (7d1ddb2) +- [mtiLib] Added unit tests (#787) +- [cvar] Implemented cvar table +- [gvar] Renamed GlyphVariation to TupleVariation to match OpenType + terminology. +- [otTables] Handle gracefully empty VarData.Item array when compiling + XML. (#797) +- [varLib] Re-enabled generation of ``HVAR`` table for fonts with + TrueType outlines; removed ``--build-HVAR`` command-line option. +- [feaLib] The parser can now be extended to support non-standard + statements in FEA code by using a customized Abstract Syntax Tree. + See, for example, ``feaLib.builder_test.test_extensions`` and + baseClass.feax (#794, fixes #773). +- [feaLib] Added ``feaLib`` command to the 'fonttools' command-line + tool; applies a feature file to a font. ``fonttools feaLib -h`` for + help. +- [pens] The ``T2CharStringPen`` now takes an optional + ``roundTolerance`` argument to control the rounding of coordinates + (#804, fixes #769). +- [ci] Measure test coverage on all supported python versions and OSes, + combine coverage data and upload to + https://codecov.io/gh/fonttools/fonttools (#786) +- [ci] Configured Travis and Appveyor for running tests on Python 3.6 + (#785, 55c03bc) +- The manual pages installation directory can be customized through + ``FONTTOOLS_MANPATH`` environment variable (#799, fixes #84). +- [Snippets] Added otf2ttf.py, for converting fonts from CFF to + TrueType using the googlei18n/cu2qu module (#802) + +3.4.0 (released 2016-12-21) +--------------------------- + +- [feaLib] Added support for generating FEA text from abstract syntax + tree (AST) objects (#776). Thanks @mhosken +- Added ``agl.toUnicode`` function to convert AGL-compliant glyph names + to Unicode strings (#774) +- Implemented MVAR table (b4d5381) + +3.3.1 (released 2016-12-15) +--------------------------- + +- [setup] We no longer use versioneer.py to compute fonttools version + from git metadata, as this has caused issues for some users (#767). + Now we bump the version strings manually with a custom ``release`` + command of setup.py script. + +3.3.0 (released 2016-12-06) +--------------------------- + +- [ttLib] Implemented STAT table from OpenType 1.8 (#758) +- [cffLib] Fixed decompilation of CFF fonts containing non-standard + key/value pairs in FontDict (issue #740; PR #744) +- [py23] minor: in ``round3`` function, allow the second argument to be + ``None`` (#757) +- The standalone ``sstruct`` and ``xmlWriter`` modules, deprecated + since vesion 3.2.0, have been removed. They can be imported from the + ``fontTools.misc`` package. + +3.2.3 (released 2016-12-02) +--------------------------- + +- [py23] optimized performance of round3 function; added backport for + py35 math.isclose() (9d8dacb) +- [subset] fixed issue with 'narrow' (UCS-2) Python 2 builds and + ``--text``/``--text-file`` options containing non-BMP chararcters + (16d0e5e) +- [varLib] fixed issuewhen normalizing location values (8fa2ee1, #749) +- [inspect] Made it compatible with both python2 and python3 (167ee60, + #748). Thanks @pnemade + +3.2.2 (released 2016-11-24) +--------------------------- + +- [varLib] Do not emit null axes in fvar (1bebcec). Thanks @robmck-ms +- [varLib] Handle fonts without GPOS (7915a45) +- [merge] Ignore LangSys if None (a11bc56) +- [subset] Fix subsetting MathVariants (78d3cbe) +- [OS/2] Fix "Private Use (plane 15)" range (08a0d55). Thanks @mashabow + +3.2.1 (released 2016-11-03) +--------------------------- + +- [OS/2] fix checking ``fsSelection`` bits matching ``head.macStyle`` + bits +- [varLib] added ``--build-HVAR`` option to generate ``HVAR`` table for + fonts with TrueType outlines. For ``CFF2``, it is enabled by default. + +3.2.0 (released 2016-11-02) +--------------------------- + +- [varLib] Improve support for OpenType 1.8 Variable Fonts: +- Implement GDEF's VariationStore +- Implement HVAR/VVAR tables +- Partial support for loading MutatorMath .designspace files with + varLib.designspace module +- Add varLib.models with Variation fonts interpolation models +- Implement GSUB/GPOS FeatureVariations +- Initial support for interpolating and merging OpenType Layout tables + (see ``varLib.interpolate_layout`` and ``varLib.merger`` modules) +- [API change] Change version to be an integer instead of a float in + XML output for GSUB, GPOS, GDEF, MATH, BASE, JSTF, HVAR, VVAR, feat, + hhea and vhea tables. Scripts that set the Version for those to 1.0 + or other float values also need fixing. A warning is emitted when + code or XML needs fix. +- several bug fixes to the cffLib module, contributed by Adobe's + @readroberts +- The XML output for CFF table now has a 'major' and 'minor' elements + for specifying whether it's version 1.0 or 2.0 (support for CFF2 is + coming soon) +- [setup.py] remove undocumented/deprecated ``extra_path`` Distutils + argument. This means that we no longer create a "FontTools" subfolder + in site-packages containing the actual fontTools package, as well as + the standalone xmlWriter and sstruct modules. The latter modules are + also deprecated, and scheduled for removal in upcoming releases. + Please change your import statements to point to from fontTools.misc + import xmlWriter and from fontTools.misc import sstruct. +- [scripts] Add a 'fonttools' command-line tool that simply runs + ``fontTools.*`` sub-modules: e.g. ``fonttools ttx``, + ``fonttools subset``, etc. +- [hmtx/vmts] Read advance width/heights as unsigned short (uint16); + automatically round float values to integers. +- [ttLib/xmlWriter] add 'newlinestr=None' keyword argument to + ``TTFont.saveXML`` for overriding os-specific line endings (passed on + to ``XMLWriter`` instances). +- [versioning] Use versioneer instead of ``setuptools_scm`` to + dynamically load version info from a git checkout at import time. +- [feaLib] Support backslash-prefixed glyph names. + +3.1.2 (released 2016-09-27) +--------------------------- + +- restore Makefile as an alternative way to build/check/install +- README.md: update instructions for installing package from source, + and for running test suite +- NEWS: Change log was out of sync with tagged release + +3.1.1 (released 2016-09-27) +--------------------------- + +- Fix ``ttLibVersion`` attribute in TTX files still showing '3.0' + instead of '3.1'. +- Use ``setuptools_scm`` to manage package versions. + +3.1.0 (released 2016-09-26) +--------------------------- + +- [feaLib] New library to parse and compile Adobe FDK OpenType Feature + files. +- [mtiLib] New library to parse and compile Monotype 'FontDame' + OpenType Layout Tables files. +- [voltLib] New library to parse Microsoft VOLT project files. +- [otlLib] New library to work with OpenType Layout tables. +- [varLib] New library to work with OpenType Font Variations. +- [pens] Add ttGlyphPen to draw to TrueType glyphs, and t2CharStringPen + to draw to Type 2 Charstrings (CFF); add areaPen and perimeterPen. +- [ttLib.tables] Implement 'meta' and 'trak' tables. +- [ttx] Add --flavor option for compiling to 'woff' or 'woff2'; add + ``--with-zopfli`` option to use Zopfli to compress WOFF 1.0 fonts. +- [subset] Support subsetting 'COLR'/'CPAL' and 'CBDT'/'CBLC' color + fonts tables, and 'gvar' table for variation fonts. +- [Snippets] Add ``symfont.py``, for symbolic font statistics analysis; + interpolatable.py, a preliminary script for detecting interpolation + errors; ``{merge,dump}_woff_metadata.py``. +- [classifyTools] Helpers to classify things into classes. +- [CI] Run tests on Windows, Linux and macOS using Appveyor and Travis + CI; check unit test coverage with Coverage.py/Coveralls; automatic + deployment to PyPI on tags. +- [loggingTools] Use Python built-in logging module to print messages. +- [py23] Make round() behave like Python 3 built-in round(); define + round2() and round3(). + +3.0 (released 2015-09-01) +------------------------- + +- Add Snippet scripts for cmap subtable format conversion, printing + GSUB/GPOS features, building a GX font from two masters +- TTX WOFF2 support and a ``-f`` option to overwrite output file(s) +- Support GX tables: ``avar``, ``gvar``, ``fvar``, ``meta`` +- Support ``feat`` and gzip-compressed SVG tables +- Upgrade Mac East Asian encodings to native implementation if + available +- Add Roman Croatian and Romanian encodings, codecs for mac-extended + East Asian encodings +- Implement optimal GLYF glyph outline packing; disabled by default + +2.5 (released 2014-09-24) +------------------------- + +- Add a Qt pen +- Add VDMX table converter +- Load all OpenType sub-structures lazily +- Add support for cmap format 13. +- Add pyftmerge tool +- Update to Unicode 6.3.0d3 +- Add pyftinspect tool +- Add support for Google CBLC/CBDT color bitmaps, standard EBLC/EBDT + embedded bitmaps, and ``SVG`` table (thanks to Read Roberts at Adobe) +- Add support for loading, saving and ttx'ing WOFF file format +- Add support for Microsoft COLR/CPAL layered color glyphs +- Support PyPy +- Support Jython, by replacing numpy with array/lists modules and + removed it, pure-Python StringIO, not cStringIO +- Add pyftsubset and Subsetter object, supporting CFF and TTF +- Add to ttx args for -q for quiet mode, -z to choose a bitmap dump + format + +2.4 (released 2013-06-22) +------------------------- + +- Option to write to arbitrary files +- Better dump format for DSIG +- Better detection of OTF XML +- Fix issue with Apple's kern table format +- Fix mangling of TT glyph programs +- Fix issues related to mona.ttf +- Fix Windows Installer instructions +- Fix some modern MacOS issues +- Fix minor issues and typos + +2.3 (released 2009-11-08) +------------------------- + +- TrueType Collection (TTC) support +- Python 2.6 support +- Update Unicode data to 5.2.0 +- Couple of bug fixes + +2.2 (released 2008-05-18) +------------------------- + +- ClearType support +- cmap format 1 support +- PFA font support +- Switched from Numeric to numpy +- Update Unicode data to 5.1.0 +- Update AGLFN data to 1.6 +- Many bug fixes + +2.1 (released 2008-01-28) +------------------------- + +- Many years worth of fixes and features + +2.0b2 (released 2002-??-??) +--------------------------- + +- Be "forgiving" when interpreting the maxp table version field: + interpret any value as 1.0 if it's not 0.5. Fixes dumping of these + GPL fonts: http://www.freebsd.org/cgi/pds.cgi?ports/chinese/wangttf +- Fixed ttx -l: it turned out this part of the code didn't work with + Python 2.2.1 and earlier. My bad to do most of my testing with a + different version than I shipped TTX with :-( +- Fixed bug in ClassDef format 1 subtable (Andreas Seidel bumped into + this one). + +2.0b1 (released 2002-09-10) +--------------------------- + +- Fixed embarrassing bug: the master checksum in the head table is now + calculated correctly even on little-endian platforms (such as Intel). +- Made the cmap format 4 compiler smarter: the binary data it creates + is now more or less as compact as possible. TTX now makes more + compact data than in any shipping font I've tested it with. +- Dump glyph names as a separate "GlyphOrder" pseudo table as opposed + to as part of the glyf table (obviously needed for CFF-OTF's). +- Added proper support for the CFF table. +- Don't barf on empty tables (questionable, but "there are font out + there...") +- When writing TT glyf data, align glyphs on 4-byte boundaries. This + seems to be the current recommendation by MS. Also: don't barf on + fonts which are already 4-byte aligned. +- Windows installer contributed bu Adam Twardoch! Yay! +- Changed the command line interface again, now by creating one new + tool replacing the old ones: ttx It dumps and compiles, depending on + input file types. The options have changed somewhat. +- The -d option is back (output dir) +- ttcompile's -i options is now called -m (as in "merge"), to avoid + clash with dump's -i. +- The -s option ("split tables") no longer creates a directory, but + instead outputs a small .ttx file containing references to the + individual table files. This is not a true link, it's a simple file + name, and the referenced file should be in the same directory so + ttcompile can find them. +- compile no longer accepts a directory as input argument. Instead it + can parse the new "mini-ttx" format as output by "ttx -s". +- all arguments are input files +- Renamed the command line programs and moved them to the Tools + subdirectory. They are now installed by the setup.py install script. +- Added OpenType support. BASE, GDEF, GPOS, GSUB and JSTF are (almost) + fully supported. The XML output is not yet final, as I'm still + considering to output certain subtables in a more human-friendly + manner. +- Fixed 'kern' table to correctly accept subtables it doesn't know + about, as well as interpreting Apple's definition of the 'kern' table + headers correctly. +- Fixed bug where glyphnames were not calculated from 'cmap' if it was + (one of the) first tables to be decompiled. More specifically: it + cmap was the first to ask for a glyphID -> glyphName mapping. +- Switched XML parsers: use expat instead of xmlproc. Should be faster. +- Removed my UnicodeString object: I now require Python 2.0 or up, + which has unicode support built in. +- Removed assert in glyf table: redundant data at the end of the table + is now ignored instead of raising an error. Should become a warning. +- Fixed bug in hmtx/vmtx code that only occured if all advances were + equal. +- Fixed subtle bug in TT instruction disassembler. +- Couple of fixes to the 'post' table. +- Updated OS/2 table to latest spec. + +1.0b1 (released 2001-08-10) +--------------------------- + +- Reorganized the command line interface for ttDump.py and + ttCompile.py, they now behave more like "normal" command line tool, + in that they accept multiple input files for batch processing. +- ttDump.py and ttCompile.py don't silently override files anymore, but + ask before doing so. Can be overridden by -f. +- Added -d option to both ttDump.py and ttCompile.py. +- Installation is now done with distutils. (Needs work for environments + without compilers.) +- Updated installation instructions. +- Added some workarounds so as to handle certain buggy fonts more + gracefully. +- Updated Unicode table to Unicode 3.0 (Thanks Antoine!) +- Included a Python script by Adam Twardoch that adds some useful stuff + to the Windows registry. +- Moved the project to SourceForge. + +1.0a6 (released 2000-03-15) +--------------------------- + +- Big reorganization: made ttLib a subpackage of the new fontTools + package, changed several module names. Called the entire suite + "FontTools" +- Added several submodules to fontTools, some new, some older. +- Added experimental CFF/GPOS/GSUB support to ttLib, read-only (but XML + dumping of GPOS/GSUB is for now disabled) +- Fixed hdmx endian bug +- Added -b option to ttCompile.py, it disables recalculation of + bounding boxes, as requested by Werner Lemberg. +- Renamed tt2xml.pt to ttDump.py and xml2tt.py to ttCompile.py +- Use ".ttx" as file extension instead of ".xml". +- TTX is now the name of the XML-based *format* for TT fonts, and not + just an application. + +1.0a5 +----- + +Never released + +- More tables supported: hdmx, vhea, vmtx + +1.0a3 & 1.0a4 +------------- + +Never released + +- fixed most portability issues +- retracted the "Euro_or_currency" change from 1.0a2: it was + nonsense! + +1.0a2 (released 1999-05-02) +--------------------------- + +- binary release for MacOS +- genenates full FOND resources: including width table, PS font name + info and kern table if applicable. +- added cmap format 4 support. Extra: dumps Unicode char names as XML + comments! +- added cmap format 6 support +- now accepts true type files starting with "true" (instead of just + 0x00010000 and "OTTO") +- 'glyf' table support is now complete: I added support for composite + scale, xy-scale and two-by-two for the 'glyf' table. For now, + component offset scale behaviour defaults to Apple-style. This only + affects the (re)calculation of the glyph bounding box. +- changed "Euro" to "Euro_or_currency" in the Standard Apple Glyph + order list, since we cannot tell from the 'post' table which is + meant. I should probably doublecheck with a Unicode encoding if + available. (This does not affect the output!) + +Fixed bugs: - 'hhea' table is now recalculated correctly - fixed wrong +assumption about sfnt resource names + +1.0a1 (released 1999-04-27) +--------------------------- + +- initial binary release for MacOS diff --git a/videollama2/lib/python3.10/site-packages/fonttools-4.53.1.dist-info/RECORD b/videollama2/lib/python3.10/site-packages/fonttools-4.53.1.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..28ca309238820cf90622053dc27a9731f855657c --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/fonttools-4.53.1.dist-info/RECORD @@ -0,0 +1,643 @@ +../../../bin/fonttools,sha256=Qr0KANCAM6Kc29A8YTJW8zxLnx2a37fOxcTjfopkEZA,234 +../../../bin/pyftmerge,sha256=p4GXmXJ96UFT0VomiMe9dwfI2hOcAXThl7Lo3NrLJZA,231 +../../../bin/pyftsubset,sha256=wwPg5Pge_fVyHKIw5FKCyDEG9FTb2Rft9bRtpND7NLw,232 +../../../bin/ttx,sha256=QCaFJ4V_iauAfNWpUTV7NFAgeZ_YS3L160YQbS9PDEM,229 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+pyftmerge = fontTools.merge:main +pyftsubset = fontTools.subset:main +ttx = fontTools.ttx:main diff --git a/videollama2/lib/python3.10/site-packages/fonttools-4.53.1.dist-info/top_level.txt b/videollama2/lib/python3.10/site-packages/fonttools-4.53.1.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..9af65ba39d292309497df4accdc44bd6f8143d10 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/fonttools-4.53.1.dist-info/top_level.txt @@ -0,0 +1 @@ +fontTools diff --git a/videollama2/lib/python3.10/site-packages/orjson-3.10.7.dist-info/INSTALLER b/videollama2/lib/python3.10/site-packages/orjson-3.10.7.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/orjson-3.10.7.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/videollama2/lib/python3.10/site-packages/orjson-3.10.7.dist-info/METADATA b/videollama2/lib/python3.10/site-packages/orjson-3.10.7.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..0cee418537cb0f9d5e42515a8141e9d16d0942ba --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/orjson-3.10.7.dist-info/METADATA @@ -0,0 +1,1264 @@ +Metadata-Version: 2.3 +Name: orjson +Version: 3.10.7 +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Developers +Classifier: License :: OSI Approved :: Apache Software License +Classifier: License :: OSI Approved :: MIT License +Classifier: Operating System :: MacOS +Classifier: Operating System :: Microsoft :: Windows +Classifier: Operating System :: POSIX :: Linux +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Programming Language :: Python +Classifier: Programming Language :: Rust +Classifier: Typing :: Typed +License-File: LICENSE-APACHE +License-File: LICENSE-MIT +Summary: Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy +Keywords: fast,json,dataclass,dataclasses,datetime,rfc,8259,3339 +Home-Page: https://github.com/ijl/orjson +Author: ijl +Author-email: ijl +License: Apache-2.0 OR MIT +Requires-Python: >=3.8 +Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM +Project-URL: Changelog, https://github.com/ijl/orjson/blob/master/CHANGELOG.md + +# orjson + +orjson is a fast, correct JSON library for Python. It +[benchmarks](https://github.com/ijl/orjson?tab=readme-ov-file#performance) as the fastest Python +library for JSON and is more correct than the standard json library or other +third-party libraries. It serializes +[dataclass](https://github.com/ijl/orjson?tab=readme-ov-file#dataclass), +[datetime](https://github.com/ijl/orjson?tab=readme-ov-file#datetime), +[numpy](https://github.com/ijl/orjson?tab=readme-ov-file#numpy), and +[UUID](https://github.com/ijl/orjson?tab=readme-ov-file#uuid) instances natively. + +Its features and drawbacks compared to other Python JSON libraries: + +* serializes `dataclass` instances 40-50x as fast as other libraries +* serializes `datetime`, `date`, and `time` instances to RFC 3339 format, +e.g., "1970-01-01T00:00:00+00:00" +* serializes `numpy.ndarray` instances 4-12x as fast with 0.3x the memory +usage of other libraries +* pretty prints 10x to 20x as fast as the standard library +* serializes to `bytes` rather than `str`, i.e., is not a drop-in replacement +* serializes `str` without escaping unicode to ASCII, e.g., "好" rather than +"\\\u597d" +* serializes `float` 10x as fast and deserializes twice as fast as other +libraries +* serializes subclasses of `str`, `int`, `list`, and `dict` natively, +requiring `default` to specify how to serialize others +* serializes arbitrary types using a `default` hook +* has strict UTF-8 conformance, more correct than the standard library +* has strict JSON conformance in not supporting Nan/Infinity/-Infinity +* has an option for strict JSON conformance on 53-bit integers with default +support for 64-bit +* does not provide `load()` or `dump()` functions for reading from/writing to +file-like objects + +orjson supports CPython 3.8, 3.9, 3.10, 3.11, 3.12, and 3.13. It distributes +amd64/x86_64, aarch64/armv8, arm7, POWER/ppc64le, and s390x wheels for Linux, +amd64 and aarch64 wheels for macOS, and amd64 and i686/x86 wheels for Windows. +orjson does not and will not support PyPy. orjson does not and will not +support PEP 554 subinterpreters. Releases follow semantic versioning and +serializing a new object type without an opt-in flag is considered a +breaking change. + +orjson is licensed under both the Apache 2.0 and MIT licenses. The +repository and issue tracker is +[github.com/ijl/orjson](https://github.com/ijl/orjson), and patches may be +submitted there. There is a +[CHANGELOG](https://github.com/ijl/orjson/blob/master/CHANGELOG.md) +available in the repository. + +1. [Usage](https://github.com/ijl/orjson?tab=readme-ov-file#usage) + 1. [Install](https://github.com/ijl/orjson?tab=readme-ov-file#install) + 2. [Quickstart](https://github.com/ijl/orjson?tab=readme-ov-file#quickstart) + 3. [Migrating](https://github.com/ijl/orjson?tab=readme-ov-file#migrating) + 4. [Serialize](https://github.com/ijl/orjson?tab=readme-ov-file#serialize) + 1. [default](https://github.com/ijl/orjson?tab=readme-ov-file#default) + 2. [option](https://github.com/ijl/orjson?tab=readme-ov-file#option) + 3. [Fragment](https://github.com/ijl/orjson?tab=readme-ov-file#fragment) + 5. [Deserialize](https://github.com/ijl/orjson?tab=readme-ov-file#deserialize) +2. [Types](https://github.com/ijl/orjson?tab=readme-ov-file#types) + 1. [dataclass](https://github.com/ijl/orjson?tab=readme-ov-file#dataclass) + 2. [datetime](https://github.com/ijl/orjson?tab=readme-ov-file#datetime) + 3. [enum](https://github.com/ijl/orjson?tab=readme-ov-file#enum) + 4. [float](https://github.com/ijl/orjson?tab=readme-ov-file#float) + 5. [int](https://github.com/ijl/orjson?tab=readme-ov-file#int) + 6. [numpy](https://github.com/ijl/orjson?tab=readme-ov-file#numpy) + 7. [str](https://github.com/ijl/orjson?tab=readme-ov-file#str) + 8. [uuid](https://github.com/ijl/orjson?tab=readme-ov-file#uuid) +3. [Testing](https://github.com/ijl/orjson?tab=readme-ov-file#testing) +4. [Performance](https://github.com/ijl/orjson?tab=readme-ov-file#performance) + 1. [Latency](https://github.com/ijl/orjson?tab=readme-ov-file#latency) + 2. [Memory](https://github.com/ijl/orjson?tab=readme-ov-file#memory) + 3. [Reproducing](https://github.com/ijl/orjson?tab=readme-ov-file#reproducing) +5. [Questions](https://github.com/ijl/orjson?tab=readme-ov-file#questions) +6. [Packaging](https://github.com/ijl/orjson?tab=readme-ov-file#packaging) +7. [License](https://github.com/ijl/orjson?tab=readme-ov-file#license) + +## Usage + +### Install + +To install a wheel from PyPI: + +```sh +pip install --upgrade "pip>=20.3" # manylinux_x_y, universal2 wheel support +pip install --upgrade orjson +``` + +To build a wheel, see [packaging](https://github.com/ijl/orjson?tab=readme-ov-file#packaging). + +### Quickstart + +This is an example of serializing, with options specified, and deserializing: + +```python +>>> import orjson, datetime, numpy +>>> data = { + "type": "job", + "created_at": datetime.datetime(1970, 1, 1), + "status": "🆗", + "payload": numpy.array([[1, 2], [3, 4]]), +} +>>> orjson.dumps(data, option=orjson.OPT_NAIVE_UTC | orjson.OPT_SERIALIZE_NUMPY) +b'{"type":"job","created_at":"1970-01-01T00:00:00+00:00","status":"\xf0\x9f\x86\x97","payload":[[1,2],[3,4]]}' +>>> orjson.loads(_) +{'type': 'job', 'created_at': '1970-01-01T00:00:00+00:00', 'status': '🆗', 'payload': [[1, 2], [3, 4]]} +``` + +### Migrating + +orjson version 3 serializes more types than version 2. Subclasses of `str`, +`int`, `dict`, and `list` are now serialized. This is faster and more similar +to the standard library. It can be disabled with +`orjson.OPT_PASSTHROUGH_SUBCLASS`.`dataclasses.dataclass` instances +are now serialized by default and cannot be customized in a +`default` function unless `option=orjson.OPT_PASSTHROUGH_DATACLASS` is +specified. `uuid.UUID` instances are serialized by default. +For any type that is now serialized, +implementations in a `default` function and options enabling them can be +removed but do not need to be. There was no change in deserialization. + +To migrate from the standard library, the largest difference is that +`orjson.dumps` returns `bytes` and `json.dumps` returns a `str`. Users with +`dict` objects using non-`str` keys should specify +`option=orjson.OPT_NON_STR_KEYS`. `sort_keys` is replaced by +`option=orjson.OPT_SORT_KEYS`. `indent` is replaced by +`option=orjson.OPT_INDENT_2` and other levels of indentation are not +supported. + +### Serialize + +```python +def dumps( + __obj: Any, + default: Optional[Callable[[Any], Any]] = ..., + option: Optional[int] = ..., +) -> bytes: ... +``` + +`dumps()` serializes Python objects to JSON. + +It natively serializes +`str`, `dict`, `list`, `tuple`, `int`, `float`, `bool`, `None`, +`dataclasses.dataclass`, `typing.TypedDict`, `datetime.datetime`, +`datetime.date`, `datetime.time`, `uuid.UUID`, `numpy.ndarray`, and +`orjson.Fragment` instances. It supports arbitrary types through `default`. It +serializes subclasses of `str`, `int`, `dict`, `list`, +`dataclasses.dataclass`, and `enum.Enum`. It does not serialize subclasses +of `tuple` to avoid serializing `namedtuple` objects as arrays. To avoid +serializing subclasses, specify the option `orjson.OPT_PASSTHROUGH_SUBCLASS`. + +The output is a `bytes` object containing UTF-8. + +The global interpreter lock (GIL) is held for the duration of the call. + +It raises `JSONEncodeError` on an unsupported type. This exception message +describes the invalid object with the error message +`Type is not JSON serializable: ...`. To fix this, specify +[default](https://github.com/ijl/orjson?tab=readme-ov-file#default). + +It raises `JSONEncodeError` on a `str` that contains invalid UTF-8. + +It raises `JSONEncodeError` on an integer that exceeds 64 bits by default or, +with `OPT_STRICT_INTEGER`, 53 bits. + +It raises `JSONEncodeError` if a `dict` has a key of a type other than `str`, +unless `OPT_NON_STR_KEYS` is specified. + +It raises `JSONEncodeError` if the output of `default` recurses to handling by +`default` more than 254 levels deep. + +It raises `JSONEncodeError` on circular references. + +It raises `JSONEncodeError` if a `tzinfo` on a datetime object is +unsupported. + +`JSONEncodeError` is a subclass of `TypeError`. This is for compatibility +with the standard library. + +If the failure was caused by an exception in `default` then +`JSONEncodeError` chains the original exception as `__cause__`. + +#### default + +To serialize a subclass or arbitrary types, specify `default` as a +callable that returns a supported type. `default` may be a function, +lambda, or callable class instance. To specify that a type was not +handled by `default`, raise an exception such as `TypeError`. + +```python +>>> import orjson, decimal +>>> +def default(obj): + if isinstance(obj, decimal.Decimal): + return str(obj) + raise TypeError + +>>> orjson.dumps(decimal.Decimal("0.0842389659712649442845")) +JSONEncodeError: Type is not JSON serializable: decimal.Decimal +>>> orjson.dumps(decimal.Decimal("0.0842389659712649442845"), default=default) +b'"0.0842389659712649442845"' +>>> orjson.dumps({1, 2}, default=default) +orjson.JSONEncodeError: Type is not JSON serializable: set +``` + +The `default` callable may return an object that itself +must be handled by `default` up to 254 times before an exception +is raised. + +It is important that `default` raise an exception if a type cannot be handled. +Python otherwise implicitly returns `None`, which appears to the caller +like a legitimate value and is serialized: + +```python +>>> import orjson, json, rapidjson +>>> +def default(obj): + if isinstance(obj, decimal.Decimal): + return str(obj) + +>>> orjson.dumps({"set":{1, 2}}, default=default) +b'{"set":null}' +>>> json.dumps({"set":{1, 2}}, default=default) +'{"set":null}' +>>> rapidjson.dumps({"set":{1, 2}}, default=default) +'{"set":null}' +``` + +#### option + +To modify how data is serialized, specify `option`. Each `option` is an integer +constant in `orjson`. To specify multiple options, mask them together, e.g., +`option=orjson.OPT_STRICT_INTEGER | orjson.OPT_NAIVE_UTC`. + +##### OPT_APPEND_NEWLINE + +Append `\n` to the output. This is a convenience and optimization for the +pattern of `dumps(...) + "\n"`. `bytes` objects are immutable and this +pattern copies the original contents. + +```python +>>> import orjson +>>> orjson.dumps([]) +b"[]" +>>> orjson.dumps([], option=orjson.OPT_APPEND_NEWLINE) +b"[]\n" +``` + +##### OPT_INDENT_2 + +Pretty-print output with an indent of two spaces. This is equivalent to +`indent=2` in the standard library. Pretty printing is slower and the output +larger. orjson is the fastest compared library at pretty printing and has +much less of a slowdown to pretty print than the standard library does. This +option is compatible with all other options. + +```python +>>> import orjson +>>> orjson.dumps({"a": "b", "c": {"d": True}, "e": [1, 2]}) +b'{"a":"b","c":{"d":true},"e":[1,2]}' +>>> orjson.dumps( + {"a": "b", "c": {"d": True}, "e": [1, 2]}, + option=orjson.OPT_INDENT_2 +) +b'{\n "a": "b",\n "c": {\n "d": true\n },\n "e": [\n 1,\n 2\n ]\n}' +``` + +If displayed, the indentation and linebreaks appear like this: + +```json +{ + "a": "b", + "c": { + "d": true + }, + "e": [ + 1, + 2 + ] +} +``` + +This measures serializing the github.json fixture as compact (52KiB) or +pretty (64KiB): + +| Library | compact (ms) | pretty (ms) | vs. orjson | +|------------|----------------|---------------|--------------| +| orjson | 0.03 | 0.04 | 1 | +| ujson | 0.18 | 0.19 | 4.6 | +| rapidjson | 0.1 | 0.12 | 2.9 | +| simplejson | 0.25 | 0.89 | 21.4 | +| json | 0.18 | 0.71 | 17 | + +This measures serializing the citm_catalog.json fixture, more of a worst +case due to the amount of nesting and newlines, as compact (489KiB) or +pretty (1.1MiB): + +| Library | compact (ms) | pretty (ms) | vs. orjson | +|------------|----------------|---------------|--------------| +| orjson | 0.59 | 0.71 | 1 | +| ujson | 2.9 | 3.59 | 5 | +| rapidjson | 1.81 | 2.8 | 3.9 | +| simplejson | 10.43 | 42.13 | 59.1 | +| json | 4.16 | 33.42 | 46.9 | + +This can be reproduced using the `pyindent` script. + +##### OPT_NAIVE_UTC + +Serialize `datetime.datetime` objects without a `tzinfo` as UTC. This +has no effect on `datetime.datetime` objects that have `tzinfo` set. + +```python +>>> import orjson, datetime +>>> orjson.dumps( + datetime.datetime(1970, 1, 1, 0, 0, 0), + ) +b'"1970-01-01T00:00:00"' +>>> orjson.dumps( + datetime.datetime(1970, 1, 1, 0, 0, 0), + option=orjson.OPT_NAIVE_UTC, + ) +b'"1970-01-01T00:00:00+00:00"' +``` + +##### OPT_NON_STR_KEYS + +Serialize `dict` keys of type other than `str`. This allows `dict` keys +to be one of `str`, `int`, `float`, `bool`, `None`, `datetime.datetime`, +`datetime.date`, `datetime.time`, `enum.Enum`, and `uuid.UUID`. For comparison, +the standard library serializes `str`, `int`, `float`, `bool` or `None` by +default. orjson benchmarks as being faster at serializing non-`str` keys +than other libraries. This option is slower for `str` keys than the default. + +```python +>>> import orjson, datetime, uuid +>>> orjson.dumps( + {uuid.UUID("7202d115-7ff3-4c81-a7c1-2a1f067b1ece"): [1, 2, 3]}, + option=orjson.OPT_NON_STR_KEYS, + ) +b'{"7202d115-7ff3-4c81-a7c1-2a1f067b1ece":[1,2,3]}' +>>> orjson.dumps( + {datetime.datetime(1970, 1, 1, 0, 0, 0): [1, 2, 3]}, + option=orjson.OPT_NON_STR_KEYS | orjson.OPT_NAIVE_UTC, + ) +b'{"1970-01-01T00:00:00+00:00":[1,2,3]}' +``` + +These types are generally serialized how they would be as +values, e.g., `datetime.datetime` is still an RFC 3339 string and respects +options affecting it. The exception is that `int` serialization does not +respect `OPT_STRICT_INTEGER`. + +This option has the risk of creating duplicate keys. This is because non-`str` +objects may serialize to the same `str` as an existing key, e.g., +`{"1": true, 1: false}`. The last key to be inserted to the `dict` will be +serialized last and a JSON deserializer will presumably take the last +occurrence of a key (in the above, `false`). The first value will be lost. + +This option is compatible with `orjson.OPT_SORT_KEYS`. If sorting is used, +note the sort is unstable and will be unpredictable for duplicate keys. + +```python +>>> import orjson, datetime +>>> orjson.dumps( + {"other": 1, datetime.date(1970, 1, 5): 2, datetime.date(1970, 1, 3): 3}, + option=orjson.OPT_NON_STR_KEYS | orjson.OPT_SORT_KEYS +) +b'{"1970-01-03":3,"1970-01-05":2,"other":1}' +``` + +This measures serializing 589KiB of JSON comprising a `list` of 100 `dict` +in which each `dict` has both 365 randomly-sorted `int` keys representing epoch +timestamps as well as one `str` key and the value for each key is a +single integer. In "str keys", the keys were converted to `str` before +serialization, and orjson still specifes `option=orjson.OPT_NON_STR_KEYS` +(which is always somewhat slower). + +| Library | str keys (ms) | int keys (ms) | int keys sorted (ms) | +|------------|-----------------|-----------------|------------------------| +| orjson | 1.53 | 2.16 | 4.29 | +| ujson | 3.07 | 5.65 | | +| rapidjson | 4.29 | | | +| simplejson | 11.24 | 14.50 | 21.86 | +| json | 7.17 | 8.49 | | + +ujson is blank for sorting because it segfaults. json is blank because it +raises `TypeError` on attempting to sort before converting all keys to `str`. +rapidjson is blank because it does not support non-`str` keys. This can +be reproduced using the `pynonstr` script. + +##### OPT_OMIT_MICROSECONDS + +Do not serialize the `microsecond` field on `datetime.datetime` and +`datetime.time` instances. + +```python +>>> import orjson, datetime +>>> orjson.dumps( + datetime.datetime(1970, 1, 1, 0, 0, 0, 1), + ) +b'"1970-01-01T00:00:00.000001"' +>>> orjson.dumps( + datetime.datetime(1970, 1, 1, 0, 0, 0, 1), + option=orjson.OPT_OMIT_MICROSECONDS, + ) +b'"1970-01-01T00:00:00"' +``` + +##### OPT_PASSTHROUGH_DATACLASS + +Passthrough `dataclasses.dataclass` instances to `default`. This allows +customizing their output but is much slower. + + +```python +>>> import orjson, dataclasses +>>> +@dataclasses.dataclass +class User: + id: str + name: str + password: str + +def default(obj): + if isinstance(obj, User): + return {"id": obj.id, "name": obj.name} + raise TypeError + +>>> orjson.dumps(User("3b1", "asd", "zxc")) +b'{"id":"3b1","name":"asd","password":"zxc"}' +>>> orjson.dumps(User("3b1", "asd", "zxc"), option=orjson.OPT_PASSTHROUGH_DATACLASS) +TypeError: Type is not JSON serializable: User +>>> orjson.dumps( + User("3b1", "asd", "zxc"), + option=orjson.OPT_PASSTHROUGH_DATACLASS, + default=default, + ) +b'{"id":"3b1","name":"asd"}' +``` + +##### OPT_PASSTHROUGH_DATETIME + +Passthrough `datetime.datetime`, `datetime.date`, and `datetime.time` instances +to `default`. This allows serializing datetimes to a custom format, e.g., +HTTP dates: + +```python +>>> import orjson, datetime +>>> +def default(obj): + if isinstance(obj, datetime.datetime): + return obj.strftime("%a, %d %b %Y %H:%M:%S GMT") + raise TypeError + +>>> orjson.dumps({"created_at": datetime.datetime(1970, 1, 1)}) +b'{"created_at":"1970-01-01T00:00:00"}' +>>> orjson.dumps({"created_at": datetime.datetime(1970, 1, 1)}, option=orjson.OPT_PASSTHROUGH_DATETIME) +TypeError: Type is not JSON serializable: datetime.datetime +>>> orjson.dumps( + {"created_at": datetime.datetime(1970, 1, 1)}, + option=orjson.OPT_PASSTHROUGH_DATETIME, + default=default, + ) +b'{"created_at":"Thu, 01 Jan 1970 00:00:00 GMT"}' +``` + +This does not affect datetimes in `dict` keys if using OPT_NON_STR_KEYS. + +##### OPT_PASSTHROUGH_SUBCLASS + +Passthrough subclasses of builtin types to `default`. + +```python +>>> import orjson +>>> +class Secret(str): + pass + +def default(obj): + if isinstance(obj, Secret): + return "******" + raise TypeError + +>>> orjson.dumps(Secret("zxc")) +b'"zxc"' +>>> orjson.dumps(Secret("zxc"), option=orjson.OPT_PASSTHROUGH_SUBCLASS) +TypeError: Type is not JSON serializable: Secret +>>> orjson.dumps(Secret("zxc"), option=orjson.OPT_PASSTHROUGH_SUBCLASS, default=default) +b'"******"' +``` + +This does not affect serializing subclasses as `dict` keys if using +OPT_NON_STR_KEYS. + +##### OPT_SERIALIZE_DATACLASS + +This is deprecated and has no effect in version 3. In version 2 this was +required to serialize `dataclasses.dataclass` instances. For more, see +[dataclass](https://github.com/ijl/orjson?tab=readme-ov-file#dataclass). + +##### OPT_SERIALIZE_NUMPY + +Serialize `numpy.ndarray` instances. For more, see +[numpy](https://github.com/ijl/orjson?tab=readme-ov-file#numpy). + +##### OPT_SERIALIZE_UUID + +This is deprecated and has no effect in version 3. In version 2 this was +required to serialize `uuid.UUID` instances. For more, see +[UUID](https://github.com/ijl/orjson?tab=readme-ov-file#UUID). + +##### OPT_SORT_KEYS + +Serialize `dict` keys in sorted order. The default is to serialize in an +unspecified order. This is equivalent to `sort_keys=True` in the standard +library. + +This can be used to ensure the order is deterministic for hashing or tests. +It has a substantial performance penalty and is not recommended in general. + +```python +>>> import orjson +>>> orjson.dumps({"b": 1, "c": 2, "a": 3}) +b'{"b":1,"c":2,"a":3}' +>>> orjson.dumps({"b": 1, "c": 2, "a": 3}, option=orjson.OPT_SORT_KEYS) +b'{"a":3,"b":1,"c":2}' +``` + +This measures serializing the twitter.json fixture unsorted and sorted: + +| Library | unsorted (ms) | sorted (ms) | vs. orjson | +|------------|-----------------|---------------|--------------| +| orjson | 0.32 | 0.54 | 1 | +| ujson | 1.6 | 2.07 | 3.8 | +| rapidjson | 1.12 | 1.65 | 3.1 | +| simplejson | 2.25 | 3.13 | 5.8 | +| json | 1.78 | 2.32 | 4.3 | + +The benchmark can be reproduced using the `pysort` script. + +The sorting is not collation/locale-aware: + +```python +>>> import orjson +>>> orjson.dumps({"a": 1, "ä": 2, "A": 3}, option=orjson.OPT_SORT_KEYS) +b'{"A":3,"a":1,"\xc3\xa4":2}' +``` + +This is the same sorting behavior as the standard library, rapidjson, +simplejson, and ujson. + +`dataclass` also serialize as maps but this has no effect on them. + +##### OPT_STRICT_INTEGER + +Enforce 53-bit limit on integers. The limit is otherwise 64 bits, the same as +the Python standard library. For more, see [int](https://github.com/ijl/orjson?tab=readme-ov-file#int). + +##### OPT_UTC_Z + +Serialize a UTC timezone on `datetime.datetime` instances as `Z` instead +of `+00:00`. + +```python +>>> import orjson, datetime, zoneinfo +>>> orjson.dumps( + datetime.datetime(1970, 1, 1, 0, 0, 0, tzinfo=zoneinfo.ZoneInfo("UTC")), + ) +b'"1970-01-01T00:00:00+00:00"' +>>> orjson.dumps( + datetime.datetime(1970, 1, 1, 0, 0, 0, tzinfo=zoneinfo.ZoneInfo("UTC")), + option=orjson.OPT_UTC_Z + ) +b'"1970-01-01T00:00:00Z"' +``` + +#### Fragment + +`orjson.Fragment` includes already-serialized JSON in a document. This is an +efficient way to include JSON blobs from a cache, JSONB field, or separately +serialized object without first deserializing to Python objects via `loads()`. + +```python +>>> import orjson +>>> orjson.dumps({"key": "zxc", "data": orjson.Fragment(b'{"a": "b", "c": 1}')}) +b'{"key":"zxc","data":{"a": "b", "c": 1}}' +``` + +It does no reformatting: `orjson.OPT_INDENT_2` will not affect a +compact blob nor will a pretty-printed JSON blob be rewritten as compact. + +The input must be `bytes` or `str` and given as a positional argument. + +This raises `orjson.JSONEncodeError` if a `str` is given and the input is +not valid UTF-8. It otherwise does no validation and it is possible to +write invalid JSON. This does not escape characters. The implementation is +tested to not crash if given invalid strings or invalid JSON. + +This is similar to `RawJSON` in rapidjson. + +### Deserialize + +```python +def loads(__obj: Union[bytes, bytearray, memoryview, str]) -> Any: ... +``` + +`loads()` deserializes JSON to Python objects. It deserializes to `dict`, +`list`, `int`, `float`, `str`, `bool`, and `None` objects. + +`bytes`, `bytearray`, `memoryview`, and `str` input are accepted. If the input +exists as a `memoryview`, `bytearray`, or `bytes` object, it is recommended to +pass these directly rather than creating an unnecessary `str` object. That is, +`orjson.loads(b"{}")` instead of `orjson.loads(b"{}".decode("utf-8"))`. This +has lower memory usage and lower latency. + +The input must be valid UTF-8. + +orjson maintains a cache of map keys for the duration of the process. This +causes a net reduction in memory usage by avoiding duplicate strings. The +keys must be at most 64 bytes to be cached and 2048 entries are stored. + +The global interpreter lock (GIL) is held for the duration of the call. + +It raises `JSONDecodeError` if given an invalid type or invalid +JSON. This includes if the input contains `NaN`, `Infinity`, or `-Infinity`, +which the standard library allows, but is not valid JSON. + +It raises `JSONDecodeError` if a combination of array or object recurses +1024 levels deep. + +`JSONDecodeError` is a subclass of `json.JSONDecodeError` and `ValueError`. +This is for compatibility with the standard library. + +## Types + +### dataclass + +orjson serializes instances of `dataclasses.dataclass` natively. It serializes +instances 40-50x as fast as other libraries and avoids a severe slowdown seen +in other libraries compared to serializing `dict`. + +It is supported to pass all variants of dataclasses, including dataclasses +using `__slots__`, frozen dataclasses, those with optional or default +attributes, and subclasses. There is a performance benefit to not +using `__slots__`. + +| Library | dict (ms) | dataclass (ms) | vs. orjson | +|------------|-------------|------------------|--------------| +| orjson | 1.40 | 1.60 | 1 | +| ujson | | | | +| rapidjson | 3.64 | 68.48 | 42 | +| simplejson | 14.21 | 92.18 | 57 | +| json | 13.28 | 94.90 | 59 | + +This measures serializing 555KiB of JSON, orjson natively and other libraries +using `default` to serialize the output of `dataclasses.asdict()`. This can be +reproduced using the `pydataclass` script. + +Dataclasses are serialized as maps, with every attribute serialized and in +the order given on class definition: + +```python +>>> import dataclasses, orjson, typing + +@dataclasses.dataclass +class Member: + id: int + active: bool = dataclasses.field(default=False) + +@dataclasses.dataclass +class Object: + id: int + name: str + members: typing.List[Member] + +>>> orjson.dumps(Object(1, "a", [Member(1, True), Member(2)])) +b'{"id":1,"name":"a","members":[{"id":1,"active":true},{"id":2,"active":false}]}' +``` + +### datetime + +orjson serializes `datetime.datetime` objects to +[RFC 3339](https://tools.ietf.org/html/rfc3339) format, +e.g., "1970-01-01T00:00:00+00:00". This is a subset of ISO 8601 and is +compatible with `isoformat()` in the standard library. + +```python +>>> import orjson, datetime, zoneinfo +>>> orjson.dumps( + datetime.datetime(2018, 12, 1, 2, 3, 4, 9, tzinfo=zoneinfo.ZoneInfo("Australia/Adelaide")) +) +b'"2018-12-01T02:03:04.000009+10:30"' +>>> orjson.dumps( + datetime.datetime(2100, 9, 1, 21, 55, 2).replace(tzinfo=zoneinfo.ZoneInfo("UTC")) +) +b'"2100-09-01T21:55:02+00:00"' +>>> orjson.dumps( + datetime.datetime(2100, 9, 1, 21, 55, 2) +) +b'"2100-09-01T21:55:02"' +``` + +`datetime.datetime` supports instances with a `tzinfo` that is `None`, +`datetime.timezone.utc`, a timezone instance from the python3.9+ `zoneinfo` +module, or a timezone instance from the third-party `pendulum`, `pytz`, or +`dateutil`/`arrow` libraries. + +It is fastest to use the standard library's `zoneinfo.ZoneInfo` for timezones. + +`datetime.time` objects must not have a `tzinfo`. + +```python +>>> import orjson, datetime +>>> orjson.dumps(datetime.time(12, 0, 15, 290)) +b'"12:00:15.000290"' +``` + +`datetime.date` objects will always serialize. + +```python +>>> import orjson, datetime +>>> orjson.dumps(datetime.date(1900, 1, 2)) +b'"1900-01-02"' +``` + +Errors with `tzinfo` result in `JSONEncodeError` being raised. + +To disable serialization of `datetime` objects specify the option +`orjson.OPT_PASSTHROUGH_DATETIME`. + +To use "Z" suffix instead of "+00:00" to indicate UTC ("Zulu") time, use the option +`orjson.OPT_UTC_Z`. + +To assume datetimes without timezone are UTC, use the option `orjson.OPT_NAIVE_UTC`. + +### enum + +orjson serializes enums natively. Options apply to their values. + +```python +>>> import enum, datetime, orjson +>>> +class DatetimeEnum(enum.Enum): + EPOCH = datetime.datetime(1970, 1, 1, 0, 0, 0) +>>> orjson.dumps(DatetimeEnum.EPOCH) +b'"1970-01-01T00:00:00"' +>>> orjson.dumps(DatetimeEnum.EPOCH, option=orjson.OPT_NAIVE_UTC) +b'"1970-01-01T00:00:00+00:00"' +``` + +Enums with members that are not supported types can be serialized using +`default`: + +```python +>>> import enum, orjson +>>> +class Custom: + def __init__(self, val): + self.val = val + +def default(obj): + if isinstance(obj, Custom): + return obj.val + raise TypeError + +class CustomEnum(enum.Enum): + ONE = Custom(1) + +>>> orjson.dumps(CustomEnum.ONE, default=default) +b'1' +``` + +### float + +orjson serializes and deserializes double precision floats with no loss of +precision and consistent rounding. + +`orjson.dumps()` serializes Nan, Infinity, and -Infinity, which are not +compliant JSON, as `null`: + +```python +>>> import orjson, ujson, rapidjson, json +>>> orjson.dumps([float("NaN"), float("Infinity"), float("-Infinity")]) +b'[null,null,null]' +>>> ujson.dumps([float("NaN"), float("Infinity"), float("-Infinity")]) +OverflowError: Invalid Inf value when encoding double +>>> rapidjson.dumps([float("NaN"), float("Infinity"), float("-Infinity")]) +'[NaN,Infinity,-Infinity]' +>>> json.dumps([float("NaN"), float("Infinity"), float("-Infinity")]) +'[NaN, Infinity, -Infinity]' +``` + +### int + +orjson serializes and deserializes 64-bit integers by default. The range +supported is a signed 64-bit integer's minimum (-9223372036854775807) to +an unsigned 64-bit integer's maximum (18446744073709551615). This +is widely compatible, but there are implementations +that only support 53-bits for integers, e.g., +web browsers. For those implementations, `dumps()` can be configured to +raise a `JSONEncodeError` on values exceeding the 53-bit range. + +```python +>>> import orjson +>>> orjson.dumps(9007199254740992) +b'9007199254740992' +>>> orjson.dumps(9007199254740992, option=orjson.OPT_STRICT_INTEGER) +JSONEncodeError: Integer exceeds 53-bit range +>>> orjson.dumps(-9007199254740992, option=orjson.OPT_STRICT_INTEGER) +JSONEncodeError: Integer exceeds 53-bit range +``` + +### numpy + +orjson natively serializes `numpy.ndarray` and individual +`numpy.float64`, `numpy.float32`, `numpy.float16` (`numpy.half`), +`numpy.int64`, `numpy.int32`, `numpy.int16`, `numpy.int8`, +`numpy.uint64`, `numpy.uint32`, `numpy.uint16`, `numpy.uint8`, +`numpy.uintp`, `numpy.intp`, `numpy.datetime64`, and `numpy.bool` +instances. + +orjson is compatible with both numpy v1 and v2. + +orjson is faster than all compared libraries at serializing +numpy instances. Serializing numpy data requires specifying +`option=orjson.OPT_SERIALIZE_NUMPY`. + +```python +>>> import orjson, numpy +>>> orjson.dumps( + numpy.array([[1, 2, 3], [4, 5, 6]]), + option=orjson.OPT_SERIALIZE_NUMPY, +) +b'[[1,2,3],[4,5,6]]' +``` + +The array must be a contiguous C array (`C_CONTIGUOUS`) and one of the +supported datatypes. + +Note a difference between serializing `numpy.float32` using `ndarray.tolist()` +or `orjson.dumps(..., option=orjson.OPT_SERIALIZE_NUMPY)`: `tolist()` converts +to a `double` before serializing and orjson's native path does not. This +can result in different rounding. + +`numpy.datetime64` instances are serialized as RFC 3339 strings and +datetime options affect them. + +```python +>>> import orjson, numpy +>>> orjson.dumps( + numpy.datetime64("2021-01-01T00:00:00.172"), + option=orjson.OPT_SERIALIZE_NUMPY, +) +b'"2021-01-01T00:00:00.172000"' +>>> orjson.dumps( + numpy.datetime64("2021-01-01T00:00:00.172"), + option=( + orjson.OPT_SERIALIZE_NUMPY | + orjson.OPT_NAIVE_UTC | + orjson.OPT_OMIT_MICROSECONDS + ), +) +b'"2021-01-01T00:00:00+00:00"' +``` + +If an array is not a contiguous C array, contains an unsupported datatype, +or contains a `numpy.datetime64` using an unsupported representation +(e.g., picoseconds), orjson falls through to `default`. In `default`, +`obj.tolist()` can be specified. + +If an array is not in the native endianness, e.g., an array of big-endian values +on a little-endian system, `orjson.JSONEncodeError` is raised. + +If an array is malformed, `orjson.JSONEncodeError` is raised. + +This measures serializing 92MiB of JSON from an `numpy.ndarray` with +dimensions of `(50000, 100)` and `numpy.float64` values: + +| Library | Latency (ms) | RSS diff (MiB) | vs. orjson | +|------------|----------------|------------------|--------------| +| orjson | 194 | 99 | 1.0 | +| ujson | | | | +| rapidjson | 3,048 | 309 | 15.7 | +| simplejson | 3,023 | 297 | 15.6 | +| json | 3,133 | 297 | 16.1 | + +This measures serializing 100MiB of JSON from an `numpy.ndarray` with +dimensions of `(100000, 100)` and `numpy.int32` values: + +| Library | Latency (ms) | RSS diff (MiB) | vs. orjson | +|------------|----------------|------------------|--------------| +| orjson | 178 | 115 | 1.0 | +| ujson | | | | +| rapidjson | 1,512 | 551 | 8.5 | +| simplejson | 1,606 | 504 | 9.0 | +| json | 1,506 | 503 | 8.4 | + +This measures serializing 105MiB of JSON from an `numpy.ndarray` with +dimensions of `(100000, 200)` and `numpy.bool` values: + +| Library | Latency (ms) | RSS diff (MiB) | vs. orjson | +|------------|----------------|------------------|--------------| +| orjson | 157 | 120 | 1.0 | +| ujson | | | | +| rapidjson | 710 | 327 | 4.5 | +| simplejson | 931 | 398 | 5.9 | +| json | 996 | 400 | 6.3 | + +In these benchmarks, orjson serializes natively, ujson is blank because it +does not support a `default` parameter, and the other libraries serialize +`ndarray.tolist()` via `default`. The RSS column measures peak memory +usage during serialization. This can be reproduced using the `pynumpy` script. + +orjson does not have an installation or compilation dependency on numpy. The +implementation is independent, reading `numpy.ndarray` using +`PyArrayInterface`. + +### str + +orjson is strict about UTF-8 conformance. This is stricter than the standard +library's json module, which will serialize and deserialize UTF-16 surrogates, +e.g., "\ud800", that are invalid UTF-8. + +If `orjson.dumps()` is given a `str` that does not contain valid UTF-8, +`orjson.JSONEncodeError` is raised. If `loads()` receives invalid UTF-8, +`orjson.JSONDecodeError` is raised. + +orjson and rapidjson are the only compared JSON libraries to consistently +error on bad input. + +```python +>>> import orjson, ujson, rapidjson, json +>>> orjson.dumps('\ud800') +JSONEncodeError: str is not valid UTF-8: surrogates not allowed +>>> ujson.dumps('\ud800') +UnicodeEncodeError: 'utf-8' codec ... +>>> rapidjson.dumps('\ud800') +UnicodeEncodeError: 'utf-8' codec ... +>>> json.dumps('\ud800') +'"\\ud800"' +>>> orjson.loads('"\\ud800"') +JSONDecodeError: unexpected end of hex escape at line 1 column 8: line 1 column 1 (char 0) +>>> ujson.loads('"\\ud800"') +'' +>>> rapidjson.loads('"\\ud800"') +ValueError: Parse error at offset 1: The surrogate pair in string is invalid. +>>> json.loads('"\\ud800"') +'\ud800' +``` + +To make a best effort at deserializing bad input, first decode `bytes` using +the `replace` or `lossy` argument for `errors`: + +```python +>>> import orjson +>>> orjson.loads(b'"\xed\xa0\x80"') +JSONDecodeError: str is not valid UTF-8: surrogates not allowed +>>> orjson.loads(b'"\xed\xa0\x80"'.decode("utf-8", "replace")) +'���' +``` + +### uuid + +orjson serializes `uuid.UUID` instances to +[RFC 4122](https://tools.ietf.org/html/rfc4122) format, e.g., +"f81d4fae-7dec-11d0-a765-00a0c91e6bf6". + +``` python +>>> import orjson, uuid +>>> orjson.dumps(uuid.UUID('f81d4fae-7dec-11d0-a765-00a0c91e6bf6')) +b'"f81d4fae-7dec-11d0-a765-00a0c91e6bf6"' +>>> orjson.dumps(uuid.uuid5(uuid.NAMESPACE_DNS, "python.org")) +b'"886313e1-3b8a-5372-9b90-0c9aee199e5d"' +``` + +## Testing + +The library has comprehensive tests. There are tests against fixtures in the +[JSONTestSuite](https://github.com/nst/JSONTestSuite) and +[nativejson-benchmark](https://github.com/miloyip/nativejson-benchmark) +repositories. It is tested to not crash against the +[Big List of Naughty Strings](https://github.com/minimaxir/big-list-of-naughty-strings). +It is tested to not leak memory. It is tested to not crash +against and not accept invalid UTF-8. There are integration tests +exercising the library's use in web servers (gunicorn using multiprocess/forked +workers) and when +multithreaded. It also uses some tests from the ultrajson library. + +orjson is the most correct of the compared libraries. This graph shows how each +library handles a combined 342 JSON fixtures from the +[JSONTestSuite](https://github.com/nst/JSONTestSuite) and +[nativejson-benchmark](https://github.com/miloyip/nativejson-benchmark) tests: + +| Library | Invalid JSON documents not rejected | Valid JSON documents not deserialized | +|------------|---------------------------------------|-----------------------------------------| +| orjson | 0 | 0 | +| ujson | 31 | 0 | +| rapidjson | 6 | 0 | +| simplejson | 10 | 0 | +| json | 17 | 0 | + +This shows that all libraries deserialize valid JSON but only orjson +correctly rejects the given invalid JSON fixtures. Errors are largely due to +accepting invalid strings and numbers. + +The graph above can be reproduced using the `pycorrectness` script. + +## Performance + +Serialization and deserialization performance of orjson is better than +ultrajson, rapidjson, simplejson, or json. The benchmarks are done on +fixtures of real data: + +* twitter.json, 631.5KiB, results of a search on Twitter for "一", containing +CJK strings, dictionaries of strings and arrays of dictionaries, indented. + +* github.json, 55.8KiB, a GitHub activity feed, containing dictionaries of +strings and arrays of dictionaries, not indented. + +* citm_catalog.json, 1.7MiB, concert data, containing nested dictionaries of +strings and arrays of integers, indented. + +* canada.json, 2.2MiB, coordinates of the Canadian border in GeoJSON +format, containing floats and arrays, indented. + +### Latency + +![Serialization](doc/serialization.png) + +![Deserialization](doc/deserialization.png) + +#### twitter.json serialization + +| Library | Median latency (milliseconds) | Operations per second | Relative (latency) | +|------------|---------------------------------|-------------------------|----------------------| +| orjson | 0.1 | 8377 | 1 | +| ujson | 0.9 | 1088 | 7.3 | +| rapidjson | 0.8 | 1228 | 6.8 | +| simplejson | 1.9 | 531 | 15.6 | +| json | 1.4 | 744 | 11.3 | + +#### twitter.json deserialization + +| Library | Median latency (milliseconds) | Operations per second | Relative (latency) | +|------------|---------------------------------|-------------------------|----------------------| +| orjson | 0.6 | 1811 | 1 | +| ujson | 1.2 | 814 | 2.1 | +| rapidjson | 2.1 | 476 | 3.8 | +| simplejson | 1.6 | 626 | 3 | +| json | 1.8 | 557 | 3.3 | + +#### github.json serialization + +| Library | Median latency (milliseconds) | Operations per second | Relative (latency) | +|------------|---------------------------------|-------------------------|----------------------| +| orjson | 0.01 | 104424 | 1 | +| ujson | 0.09 | 10594 | 9.8 | +| rapidjson | 0.07 | 13667 | 7.6 | +| simplejson | 0.2 | 5051 | 20.6 | +| json | 0.14 | 7133 | 14.6 | + +#### github.json deserialization + +| Library | Median latency (milliseconds) | Operations per second | Relative (latency) | +|------------|---------------------------------|-------------------------|----------------------| +| orjson | 0.05 | 20069 | 1 | +| ujson | 0.11 | 8913 | 2.3 | +| rapidjson | 0.13 | 8077 | 2.6 | +| simplejson | 0.11 | 9342 | 2.1 | +| json | 0.11 | 9291 | 2.2 | + +#### citm_catalog.json serialization + +| Library | Median latency (milliseconds) | Operations per second | Relative (latency) | +|------------|---------------------------------|-------------------------|----------------------| +| orjson | 0.3 | 3757 | 1 | +| ujson | 1.7 | 598 | 6.3 | +| rapidjson | 1.3 | 768 | 4.9 | +| simplejson | 8.3 | 120 | 31.1 | +| json | 3 | 331 | 11.3 | + +#### citm_catalog.json deserialization + +| Library | Median latency (milliseconds) | Operations per second | Relative (latency) | +|------------|---------------------------------|-------------------------|----------------------| +| orjson | 1.4 | 730 | 1 | +| ujson | 2.6 | 384 | 1.9 | +| rapidjson | 4 | 246 | 3 | +| simplejson | 3.7 | 271 | 2.7 | +| json | 3.7 | 267 | 2.7 | + +#### canada.json serialization + +| Library | Median latency (milliseconds) | Operations per second | Relative (latency) | +|------------|---------------------------------|-------------------------|----------------------| +| orjson | 2.4 | 410 | 1 | +| ujson | 9.6 | 104 | 3.9 | +| rapidjson | 28.7 | 34 | 11.8 | +| simplejson | 49.3 | 20 | 20.3 | +| json | 30.6 | 32 | 12.6 | + +#### canada.json deserialization + +| Library | Median latency (milliseconds) | Operations per second | Relative (latency) | +|------------|---------------------------------|-------------------------|----------------------| +| orjson | 3 | 336 | 1 | +| ujson | 7.1 | 141 | 2.4 | +| rapidjson | 20.1 | 49 | 6.7 | +| simplejson | 16.8 | 59 | 5.6 | +| json | 18.2 | 55 | 6.1 | + +### Memory + +orjson as of 3.7.0 has higher baseline memory usage than other libraries +due to a persistent buffer used for parsing. Incremental memory usage when +deserializing is similar to the standard library and other third-party +libraries. + +This measures, in the first column, RSS after importing a library and reading +the fixture, and in the second column, increases in RSS after repeatedly +calling `loads()` on the fixture. + +#### twitter.json + +| Library | import, read() RSS (MiB) | loads() increase in RSS (MiB) | +|------------|----------------------------|---------------------------------| +| orjson | 15.7 | 3.4 | +| ujson | 16.4 | 3.4 | +| rapidjson | 16.6 | 4.4 | +| simplejson | 14.5 | 1.8 | +| json | 13.9 | 1.8 | + +#### github.json + +| Library | import, read() RSS (MiB) | loads() increase in RSS (MiB) | +|------------|----------------------------|---------------------------------| +| orjson | 15.2 | 0.4 | +| ujson | 15.4 | 0.4 | +| rapidjson | 15.7 | 0.5 | +| simplejson | 13.7 | 0.2 | +| json | 13.3 | 0.1 | + +#### citm_catalog.json + +| Library | import, read() RSS (MiB) | loads() increase in RSS (MiB) | +|------------|----------------------------|---------------------------------| +| orjson | 16.8 | 10.1 | +| ujson | 17.3 | 10.2 | +| rapidjson | 17.6 | 28.7 | +| simplejson | 15.8 | 30.1 | +| json | 14.8 | 20.5 | + +#### canada.json + +| Library | import, read() RSS (MiB) | loads() increase in RSS (MiB) | +|------------|----------------------------|---------------------------------| +| orjson | 17.2 | 22.1 | +| ujson | 17.4 | 18.3 | +| rapidjson | 18 | 23.5 | +| simplejson | 15.7 | 21.4 | +| json | 15.4 | 20.4 | + +### Reproducing + +The above was measured using Python 3.11.9 on Linux (amd64) with +orjson 3.10.6, ujson 5.10.0, python-rapidson 1.18, and simplejson 3.19.2. + +The latency results can be reproduced using the `pybench` and `graph` +scripts. The memory results can be reproduced using the `pymem` script. + +## Questions + +### Why can't I install it from PyPI? + +Probably `pip` needs to be upgraded to version 20.3 or later to support +the latest manylinux_x_y or universal2 wheel formats. + +### "Cargo, the Rust package manager, is not installed or is not on PATH." + +This happens when there are no binary wheels (like manylinux) for your +platform on PyPI. You can install [Rust](https://www.rust-lang.org/) through +`rustup` or a package manager and then it will compile. + +### Will it deserialize to dataclasses, UUIDs, decimals, etc or support object_hook? + +No. This requires a schema specifying what types are expected and how to +handle errors etc. This is addressed by data validation libraries a +level above this. + +### Will it serialize to `str`? + +No. `bytes` is the correct type for a serialized blob. + +## Packaging + +To package orjson requires at least [Rust](https://www.rust-lang.org/) 1.72 +and the [maturin](https://github.com/PyO3/maturin) build tool. The recommended +build command is: + +```sh +maturin build --release --strip +``` + +It benefits from also having a C build environment to compile a faster +deserialization backend. See this project's `manylinux_2_28` builds for an +example using clang and LTO. + +The project's own CI tests against `nightly-2024-08-05` and stable 1.72. It +is prudent to pin the nightly version because that channel can introduce +breaking changes. + +orjson is tested for amd64, aarch64, arm7, ppc64le, and s390x on Linux. It +is tested for either aarch64 or amd64 on macOS and cross-compiles for the other, +depending on version. For Windows it is tested on amd64 and i686. + +There are no runtime dependencies other than libc. + +The source distribution on PyPI contains all dependencies' source and can be +built without network access. The file can be downloaded from +`https://files.pythonhosted.org/packages/source/o/orjson/orjson-${version}.tar.gz`. + +orjson's tests are included in the source distribution on PyPI. The +requirements to run the tests are specified in `test/requirements.txt`. The +tests should be run as part of the build. It can be run with +`pytest -q test`. + +## License + +orjson was written by ijl <>, copyright 2018 - 2024, available +to you under either the Apache 2 license or MIT license at your choice. + diff --git a/videollama2/lib/python3.10/site-packages/orjson-3.10.7.dist-info/RECORD b/videollama2/lib/python3.10/site-packages/orjson-3.10.7.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..10730ebdf825a046c14eebd9c72a02ff8d70d41b --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/orjson-3.10.7.dist-info/RECORD @@ -0,0 +1,12 @@ +orjson-3.10.7.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +orjson-3.10.7.dist-info/METADATA,sha256=LkYArj2ULP-_u_k_ZbkyO8nOw1z2Umzz9PdKNX6k4jc,50426 +orjson-3.10.7.dist-info/RECORD,, +orjson-3.10.7.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +orjson-3.10.7.dist-info/WHEEL,sha256=UIc1ECP_dnCUiW619xk9MYbNbBBlRzDjPoQHu0ycM0I,129 +orjson-3.10.7.dist-info/license_files/LICENSE-APACHE,sha256=pg7qgXUUUxZo1-AHZXMUSf4U0FnTJJ4LyTs23kX3WfI,10847 +orjson-3.10.7.dist-info/license_files/LICENSE-MIT,sha256=I_GOA9xJ35FiL-KnYXZJdATkbO2KcV2dK2enRGVxzKM,1023 +orjson/__init__.py,sha256=1SdrKCoU_OHDXXY54LqX9Q-12MjiMWic3r52Cp_CYXA,589 +orjson/__init__.pyi,sha256=z1Mm1FjuLWrnyY028gsq3KY_bhSu3tZT75F0JZqN2-o,761 +orjson/__pycache__/__init__.cpython-310.pyc,, +orjson/orjson.cpython-310-x86_64-linux-gnu.so,sha256=HPejAFftqQPW2jVO4ntBX1eTLT809CtN-TDTZ6_wIqE,265168 +orjson/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 diff --git a/videollama2/lib/python3.10/site-packages/orjson-3.10.7.dist-info/REQUESTED b/videollama2/lib/python3.10/site-packages/orjson-3.10.7.dist-info/REQUESTED new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/videollama2/lib/python3.10/site-packages/orjson-3.10.7.dist-info/WHEEL b/videollama2/lib/python3.10/site-packages/orjson-3.10.7.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..9bac20f3555eb0e104f4a33a4536113bfc5a8de7 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/orjson-3.10.7.dist-info/WHEEL @@ -0,0 +1,4 @@ +Wheel-Version: 1.0 +Generator: maturin (1.7.0) +Root-Is-Purelib: false +Tag: cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64 diff --git a/videollama2/lib/python3.10/site-packages/orjson-3.10.7.dist-info/license_files/LICENSE-APACHE b/videollama2/lib/python3.10/site-packages/orjson-3.10.7.dist-info/license_files/LICENSE-APACHE new file mode 100644 index 0000000000000000000000000000000000000000..16fe87b06e802f094b3fbb0894b137bca2b16ef1 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/orjson-3.10.7.dist-info/license_files/LICENSE-APACHE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + +TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + +1. 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If not, see . + + Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short + notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + + The hypothetical commands `show w' and `show c' should show the appropriate + parts of the General Public License. Of course, your program's commands + might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, + if any, to sign a "copyright disclaimer" for the program, if necessary. + For more information on this, and how to apply and follow the GNU GPL, see + . + + The GNU General Public License does not permit incorporating your program + into proprietary programs. If your program is a subroutine library, you + may consider it more useful to permit linking proprietary applications with + the library. If this is what you want to do, use the GNU Lesser General + Public License instead of this License. But first, please read + . + + + Name: libquadmath + Files: scipy.libs/libquadmath*.so + Description: dynamically linked to files compiled with gcc + Availability: https://gcc.gnu.org/git/?p=gcc.git;a=tree;f=libquadmath + License: LGPL-2.1-or-later + + GCC Quad-Precision Math Library + Copyright (C) 2010-2019 Free Software Foundation, Inc. + Written by Francois-Xavier Coudert + + This file is part of the libquadmath library. + Libquadmath is free software; you can redistribute it and/or + modify it under the terms of the GNU Library General Public + License as published by the Free Software Foundation; either + version 2.1 of the License, or (at your option) any later version. + + Libquadmath is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU + Lesser General Public License for more details. + https://www.gnu.org/licenses/old-licenses/lgpl-2.1.html +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Science/Research +Classifier: Intended Audience :: Developers +Classifier: License :: OSI Approved :: BSD License +Classifier: Programming Language :: C +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Topic :: Software Development :: Libraries +Classifier: Topic :: Scientific/Engineering +Classifier: Operating System :: Microsoft :: Windows +Classifier: Operating System :: POSIX :: Linux +Classifier: Operating System :: POSIX +Classifier: Operating System :: Unix +Classifier: Operating System :: MacOS +Project-URL: Homepage, https://scipy.org/ +Project-URL: Documentation, https://docs.scipy.org/doc/scipy/ +Project-URL: Source, https://github.com/scipy/scipy +Project-URL: Download, https://github.com/scipy/scipy/releases +Project-URL: Tracker, https://github.com/scipy/scipy/issues +Requires-Python: >=3.10 +Requires-Dist: numpy<2.3,>=1.23.5 +Requires-Dist: pytest; extra == "test" +Requires-Dist: pytest-cov; extra == "test" +Requires-Dist: pytest-timeout; extra == "test" +Requires-Dist: pytest-xdist; extra == "test" +Requires-Dist: asv; extra == "test" +Requires-Dist: mpmath; extra == "test" +Requires-Dist: gmpy2; extra == "test" +Requires-Dist: threadpoolctl; extra == "test" +Requires-Dist: scikit-umfpack; extra == "test" +Requires-Dist: pooch; extra == "test" +Requires-Dist: hypothesis>=6.30; extra == "test" +Requires-Dist: array-api-strict>=2.0; extra == "test" +Requires-Dist: Cython; extra == "test" +Requires-Dist: meson; extra == "test" +Requires-Dist: ninja; sys_platform != "emscripten" and extra == "test" +Requires-Dist: sphinx<=7.3.7,>=5.0.0; extra == "doc" +Requires-Dist: pydata-sphinx-theme>=0.15.2; extra == "doc" +Requires-Dist: sphinx-design>=0.4.0; extra == "doc" +Requires-Dist: matplotlib>=3.5; extra == "doc" +Requires-Dist: numpydoc; extra == "doc" +Requires-Dist: jupytext; extra == "doc" +Requires-Dist: myst-nb; extra == "doc" +Requires-Dist: pooch; extra == "doc" +Requires-Dist: jupyterlite-sphinx>=0.13.1; extra == "doc" +Requires-Dist: jupyterlite-pyodide-kernel; extra == "doc" +Requires-Dist: mypy==1.10.0; extra == "dev" +Requires-Dist: typing_extensions; extra == "dev" +Requires-Dist: types-psutil; extra == "dev" +Requires-Dist: pycodestyle; extra == "dev" +Requires-Dist: ruff>=0.0.292; extra == "dev" +Requires-Dist: cython-lint>=0.12.2; extra == "dev" +Requires-Dist: rich-click; extra == "dev" +Requires-Dist: doit>=0.36.0; extra == "dev" +Requires-Dist: pydevtool; extra == "dev" +Provides-Extra: test +Provides-Extra: doc +Provides-Extra: dev +Description-Content-Type: text/x-rst + +.. image:: https://raw.githubusercontent.com/scipy/scipy/main/doc/source/_static/logo.svg + :target: https://scipy.org + :width: 110 + :height: 110 + :align: left + +.. image:: https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A + :target: https://numfocus.org + +.. image:: https://img.shields.io/pypi/dm/scipy.svg?label=Pypi%20downloads + :target: https://pypi.org/project/scipy/ + +.. image:: https://img.shields.io/conda/dn/conda-forge/scipy.svg?label=Conda%20downloads + :target: https://anaconda.org/conda-forge/scipy + +.. image:: https://img.shields.io/badge/stackoverflow-Ask%20questions-blue.svg + :target: https://stackoverflow.com/questions/tagged/scipy + +.. image:: https://img.shields.io/badge/DOI-10.1038%2Fs41592--019--0686--2-blue.svg + :target: https://www.nature.com/articles/s41592-019-0686-2 + +SciPy (pronounced "Sigh Pie") is an open-source software for mathematics, +science, and engineering. It includes modules for statistics, optimization, +integration, linear algebra, Fourier transforms, signal and image processing, +ODE solvers, and more. + +- **Website:** https://scipy.org +- **Documentation:** https://docs.scipy.org/doc/scipy/ +- **Development version of the documentation:** https://scipy.github.io/devdocs +- **SciPy development forum:** https://discuss.scientific-python.org/c/contributor/scipy +- **Source code:** https://github.com/scipy/scipy +- **Contributing:** https://scipy.github.io/devdocs/dev/index.html +- **Bug reports:** https://github.com/scipy/scipy/issues +- **Code of Conduct:** https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html +- **Report a security vulnerability:** https://tidelift.com/docs/security +- **Citing in your work:** https://www.scipy.org/citing-scipy/ + +SciPy is built to work with +NumPy arrays, and provides many user-friendly and efficient numerical routines, +such as routines for numerical integration and optimization. Together, they +run on all popular operating systems, are quick to install, and are free of +charge. NumPy and SciPy are easy to use, but powerful enough to be depended +upon by some of the world's leading scientists and engineers. If you need to +manipulate numbers on a computer and display or publish the results, give +SciPy a try! + +For the installation instructions, see `our install +guide `__. + + +Call for Contributions +---------------------- + +We appreciate and welcome contributions. Small improvements or fixes are always appreciated; issues labeled as "good +first issue" may be a good starting point. Have a look at `our contributing +guide `__. + +Writing code isn’t the only way to contribute to SciPy. You can also: + +- review pull requests +- triage issues +- develop tutorials, presentations, and other educational materials +- maintain and improve `our website `__ +- develop graphic design for our brand assets and promotional materials +- help with outreach and onboard new contributors +- write grant proposals and help with other fundraising efforts + +If you’re unsure where to start or how your skills fit in, reach out! You can +ask on the `forum `__ +or here, on GitHub, by leaving a comment on a relevant issue that is already +open. + +If you are new to contributing to open source, `this +guide `__ helps explain why, what, +and how to get involved. diff --git a/videollama2/lib/python3.10/site-packages/scipy-1.14.1.dist-info/RECORD b/videollama2/lib/python3.10/site-packages/scipy-1.14.1.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..cececa060eae2693a6f55fe6cb285dac0a12ab33 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/scipy-1.14.1.dist-info/RECORD @@ -0,0 +1,2265 @@ +scipy-1.14.1.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +scipy-1.14.1.dist-info/LICENSE.txt,sha256=GBKL4U2eo7yUQAdaiYqUjjMn6WiG0BZ47o4FJRBXFYA,46805 +scipy-1.14.1.dist-info/METADATA,sha256=Ax7fD86V7ecktyoe3PjCb0uL9gMFFlgdE9wRuqVLepI,60767 +scipy-1.14.1.dist-info/RECORD,, +scipy-1.14.1.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 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a/videollama2/lib/python3.10/site-packages/scipy-1.14.1.dist-info/WHEEL b/videollama2/lib/python3.10/site-packages/scipy-1.14.1.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..4e4c38ae320920b8f083b87f408214cdecd350d2 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/scipy-1.14.1.dist-info/WHEEL @@ -0,0 +1,6 @@ +Wheel-Version: 1.0 +Generator: meson +Root-Is-Purelib: false +Tag: cp310-cp310-manylinux_2_17_x86_64 +Tag: cp310-cp310-manylinux2014_x86_64 + diff --git a/videollama2/lib/python3.10/site-packages/sklearn/compose/__init__.py b/videollama2/lib/python3.10/site-packages/sklearn/compose/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8be8d17040e820f47e2027b59c92a7e5538ca41c --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/sklearn/compose/__init__.py @@ -0,0 +1,21 @@ +"""Meta-estimators for building composite models with transformers + +In addition to its current contents, this module will eventually be home to +refurbished versions of Pipeline and FeatureUnion. + +""" + +from ._column_transformer import ( + ColumnTransformer, + make_column_transformer, + make_column_selector, +) +from ._target import TransformedTargetRegressor + + +__all__ = [ + "ColumnTransformer", + "make_column_transformer", + "TransformedTargetRegressor", + "make_column_selector", +] diff --git a/videollama2/lib/python3.10/site-packages/sklearn/compose/__pycache__/__init__.cpython-310.pyc b/videollama2/lib/python3.10/site-packages/sklearn/compose/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5b8b9a67b55a60ab3869a52d02ec588eae18688d Binary files /dev/null and b/videollama2/lib/python3.10/site-packages/sklearn/compose/__pycache__/__init__.cpython-310.pyc differ diff --git a/videollama2/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py b/videollama2/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..e1c253dcb8cca309c9150d8dcb926fb8ecec83c1 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py @@ -0,0 +1,1124 @@ +""" +The :mod:`sklearn.compose._column_transformer` module implements utilities +to work with heterogeneous data and to apply different transformers to +different columns. +""" +# Author: Andreas Mueller +# Joris Van den Bossche +# License: BSD +from itertools import chain +from collections import Counter + +import numpy as np +from scipy import sparse + +from ..base import clone, TransformerMixin +from ..utils._estimator_html_repr import _VisualBlock +from ..pipeline import _fit_transform_one, _transform_one, _name_estimators +from ..preprocessing import FunctionTransformer +from ..utils import Bunch +from ..utils import _safe_indexing +from ..utils import _get_column_indices +from ..utils._set_output import _get_output_config, _safe_set_output +from ..utils import check_pandas_support +from ..utils.metaestimators import _BaseComposition +from ..utils.validation import check_array, check_is_fitted, _check_feature_names_in +from ..utils.parallel import delayed, Parallel + + +__all__ = ["ColumnTransformer", "make_column_transformer", "make_column_selector"] + + +_ERR_MSG_1DCOLUMN = ( + "1D data passed to a transformer that expects 2D data. " + "Try to specify the column selection as a list of one " + "item instead of a scalar." +) + + +class ColumnTransformer(TransformerMixin, _BaseComposition): + """Applies transformers to columns of an array or pandas DataFrame. + + This estimator allows different columns or column subsets of the input + to be transformed separately and the features generated by each transformer + will be concatenated to form a single feature space. + This is useful for heterogeneous or columnar data, to combine several + feature extraction mechanisms or transformations into a single transformer. + + Read more in the :ref:`User Guide `. + + .. versionadded:: 0.20 + + Parameters + ---------- + transformers : list of tuples + List of (name, transformer, columns) tuples specifying the + transformer objects to be applied to subsets of the data. + + name : str + Like in Pipeline and FeatureUnion, this allows the transformer and + its parameters to be set using ``set_params`` and searched in grid + search. + transformer : {'drop', 'passthrough'} or estimator + Estimator must support :term:`fit` and :term:`transform`. + Special-cased strings 'drop' and 'passthrough' are accepted as + well, to indicate to drop the columns or to pass them through + untransformed, respectively. + columns : str, array-like of str, int, array-like of int, \ + array-like of bool, slice or callable + Indexes the data on its second axis. Integers are interpreted as + positional columns, while strings can reference DataFrame columns + by name. A scalar string or int should be used where + ``transformer`` expects X to be a 1d array-like (vector), + otherwise a 2d array will be passed to the transformer. + A callable is passed the input data `X` and can return any of the + above. To select multiple columns by name or dtype, you can use + :obj:`make_column_selector`. + + remainder : {'drop', 'passthrough'} or estimator, default='drop' + By default, only the specified columns in `transformers` are + transformed and combined in the output, and the non-specified + columns are dropped. (default of ``'drop'``). + By specifying ``remainder='passthrough'``, all remaining columns that + were not specified in `transformers`, but present in the data passed + to `fit` will be automatically passed through. This subset of columns + is concatenated with the output of the transformers. For dataframes, + extra columns not seen during `fit` will be excluded from the output + of `transform`. + By setting ``remainder`` to be an estimator, the remaining + non-specified columns will use the ``remainder`` estimator. The + estimator must support :term:`fit` and :term:`transform`. + Note that using this feature requires that the DataFrame columns + input at :term:`fit` and :term:`transform` have identical order. + + sparse_threshold : float, default=0.3 + If the output of the different transformers contains sparse matrices, + these will be stacked as a sparse matrix if the overall density is + lower than this value. Use ``sparse_threshold=0`` to always return + dense. When the transformed output consists of all dense data, the + stacked result will be dense, and this keyword will be ignored. + + n_jobs : int, default=None + Number of jobs to run in parallel. + ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. + ``-1`` means using all processors. See :term:`Glossary ` + for more details. + + transformer_weights : dict, default=None + Multiplicative weights for features per transformer. The output of the + transformer is multiplied by these weights. Keys are transformer names, + values the weights. + + verbose : bool, default=False + If True, the time elapsed while fitting each transformer will be + printed as it is completed. + + verbose_feature_names_out : bool, default=True + If True, :meth:`get_feature_names_out` will prefix all feature names + with the name of the transformer that generated that feature. + If False, :meth:`get_feature_names_out` will not prefix any feature + names and will error if feature names are not unique. + + .. versionadded:: 1.0 + + Attributes + ---------- + transformers_ : list + The collection of fitted transformers as tuples of + (name, fitted_transformer, column). `fitted_transformer` can be an + estimator, 'drop', or 'passthrough'. In case there were no columns + selected, this will be the unfitted transformer. + If there are remaining columns, the final element is a tuple of the + form: + ('remainder', transformer, remaining_columns) corresponding to the + ``remainder`` parameter. If there are remaining columns, then + ``len(transformers_)==len(transformers)+1``, otherwise + ``len(transformers_)==len(transformers)``. + + named_transformers_ : :class:`~sklearn.utils.Bunch` + Read-only attribute to access any transformer by given name. + Keys are transformer names and values are the fitted transformer + objects. + + sparse_output_ : bool + Boolean flag indicating whether the output of ``transform`` is a + sparse matrix or a dense numpy array, which depends on the output + of the individual transformers and the `sparse_threshold` keyword. + + output_indices_ : dict + A dictionary from each transformer name to a slice, where the slice + corresponds to indices in the transformed output. This is useful to + inspect which transformer is responsible for which transformed + feature(s). + + .. versionadded:: 1.0 + + n_features_in_ : int + Number of features seen during :term:`fit`. Only defined if the + underlying transformers expose such an attribute when fit. + + .. versionadded:: 0.24 + + See Also + -------- + make_column_transformer : Convenience function for + combining the outputs of multiple transformer objects applied to + column subsets of the original feature space. + make_column_selector : Convenience function for selecting + columns based on datatype or the columns name with a regex pattern. + + Notes + ----- + The order of the columns in the transformed feature matrix follows the + order of how the columns are specified in the `transformers` list. + Columns of the original feature matrix that are not specified are + dropped from the resulting transformed feature matrix, unless specified + in the `passthrough` keyword. Those columns specified with `passthrough` + are added at the right to the output of the transformers. + + Examples + -------- + >>> import numpy as np + >>> from sklearn.compose import ColumnTransformer + >>> from sklearn.preprocessing import Normalizer + >>> ct = ColumnTransformer( + ... [("norm1", Normalizer(norm='l1'), [0, 1]), + ... ("norm2", Normalizer(norm='l1'), slice(2, 4))]) + >>> X = np.array([[0., 1., 2., 2.], + ... [1., 1., 0., 1.]]) + >>> # Normalizer scales each row of X to unit norm. A separate scaling + >>> # is applied for the two first and two last elements of each + >>> # row independently. + >>> ct.fit_transform(X) + array([[0. , 1. , 0.5, 0.5], + [0.5, 0.5, 0. , 1. ]]) + + :class:`ColumnTransformer` can be configured with a transformer that requires + a 1d array by setting the column to a string: + + >>> from sklearn.feature_extraction import FeatureHasher + >>> from sklearn.preprocessing import MinMaxScaler + >>> import pandas as pd # doctest: +SKIP + >>> X = pd.DataFrame({ + ... "documents": ["First item", "second one here", "Is this the last?"], + ... "width": [3, 4, 5], + ... }) # doctest: +SKIP + >>> # "documents" is a string which configures ColumnTransformer to + >>> # pass the documents column as a 1d array to the FeatureHasher + >>> ct = ColumnTransformer( + ... [("text_preprocess", FeatureHasher(input_type="string"), "documents"), + ... ("num_preprocess", MinMaxScaler(), ["width"])]) + >>> X_trans = ct.fit_transform(X) # doctest: +SKIP + """ + + _required_parameters = ["transformers"] + + def __init__( + self, + transformers, + *, + remainder="drop", + sparse_threshold=0.3, + n_jobs=None, + transformer_weights=None, + verbose=False, + verbose_feature_names_out=True, + ): + self.transformers = transformers + self.remainder = remainder + self.sparse_threshold = sparse_threshold + self.n_jobs = n_jobs + self.transformer_weights = transformer_weights + self.verbose = verbose + self.verbose_feature_names_out = verbose_feature_names_out + + @property + def _transformers(self): + """ + Internal list of transformer only containing the name and + transformers, dropping the columns. This is for the implementation + of get_params via BaseComposition._get_params which expects lists + of tuples of len 2. + """ + try: + return [(name, trans) for name, trans, _ in self.transformers] + except (TypeError, ValueError): + return self.transformers + + @_transformers.setter + def _transformers(self, value): + try: + self.transformers = [ + (name, trans, col) + for ((name, trans), (_, _, col)) in zip(value, self.transformers) + ] + except (TypeError, ValueError): + self.transformers = value + + def set_output(self, *, transform=None): + """Set the output container when `"transform"` and `"fit_transform"` are called. + + Calling `set_output` will set the output of all estimators in `transformers` + and `transformers_`. + + Parameters + ---------- + transform : {"default", "pandas"}, default=None + Configure output of `transform` and `fit_transform`. + + - `"default"`: Default output format of a transformer + - `"pandas"`: DataFrame output + - `None`: Transform configuration is unchanged + + Returns + ------- + self : estimator instance + Estimator instance. + """ + super().set_output(transform=transform) + transformers = ( + trans + for _, trans, _ in chain( + self.transformers, getattr(self, "transformers_", []) + ) + if trans not in {"passthrough", "drop"} + ) + for trans in transformers: + _safe_set_output(trans, transform=transform) + + return self + + def get_params(self, deep=True): + """Get parameters for this estimator. + + Returns the parameters given in the constructor as well as the + estimators contained within the `transformers` of the + `ColumnTransformer`. + + Parameters + ---------- + deep : bool, default=True + If True, will return the parameters for this estimator and + contained subobjects that are estimators. + + Returns + ------- + params : dict + Parameter names mapped to their values. + """ + return self._get_params("_transformers", deep=deep) + + def set_params(self, **kwargs): + """Set the parameters of this estimator. + + Valid parameter keys can be listed with ``get_params()``. Note that you + can directly set the parameters of the estimators contained in + `transformers` of `ColumnTransformer`. + + Parameters + ---------- + **kwargs : dict + Estimator parameters. + + Returns + ------- + self : ColumnTransformer + This estimator. + """ + self._set_params("_transformers", **kwargs) + return self + + def _iter(self, fitted=False, replace_strings=False, column_as_strings=False): + """ + Generate (name, trans, column, weight) tuples. + + If fitted=True, use the fitted transformers, else use the + user specified transformers updated with converted column names + and potentially appended with transformer for remainder. + + """ + if fitted: + if replace_strings: + # Replace "passthrough" with the fitted version in + # _name_to_fitted_passthrough + def replace_passthrough(name, trans, columns): + if name not in self._name_to_fitted_passthrough: + return name, trans, columns + return name, self._name_to_fitted_passthrough[name], columns + + transformers = [ + replace_passthrough(*trans) for trans in self.transformers_ + ] + else: + transformers = self.transformers_ + else: + # interleave the validated column specifiers + transformers = [ + (name, trans, column) + for (name, trans, _), column in zip(self.transformers, self._columns) + ] + # add transformer tuple for remainder + if self._remainder[2]: + transformers = chain(transformers, [self._remainder]) + get_weight = (self.transformer_weights or {}).get + + output_config = _get_output_config("transform", self) + for name, trans, columns in transformers: + if replace_strings: + # replace 'passthrough' with identity transformer and + # skip in case of 'drop' + if trans == "passthrough": + trans = FunctionTransformer( + accept_sparse=True, + check_inverse=False, + feature_names_out="one-to-one", + ).set_output(transform=output_config["dense"]) + elif trans == "drop": + continue + elif _is_empty_column_selection(columns): + continue + + if column_as_strings: + # Convert all columns to using their string labels + columns_is_scalar = np.isscalar(columns) + + indices = self._transformer_to_input_indices[name] + columns = self.feature_names_in_[indices] + + if columns_is_scalar: + # selection is done with one dimension + columns = columns[0] + + yield (name, trans, columns, get_weight(name)) + + def _validate_transformers(self): + if not self.transformers: + return + + names, transformers, _ = zip(*self.transformers) + + # validate names + self._validate_names(names) + + # validate estimators + for t in transformers: + if t in ("drop", "passthrough"): + continue + if not (hasattr(t, "fit") or hasattr(t, "fit_transform")) or not hasattr( + t, "transform" + ): + raise TypeError( + "All estimators should implement fit and " + "transform, or can be 'drop' or 'passthrough' " + "specifiers. '%s' (type %s) doesn't." % (t, type(t)) + ) + + def _validate_column_callables(self, X): + """ + Converts callable column specifications. + """ + all_columns = [] + transformer_to_input_indices = {} + for name, _, columns in self.transformers: + if callable(columns): + columns = columns(X) + all_columns.append(columns) + transformer_to_input_indices[name] = _get_column_indices(X, columns) + + self._columns = all_columns + self._transformer_to_input_indices = transformer_to_input_indices + + def _validate_remainder(self, X): + """ + Validates ``remainder`` and defines ``_remainder`` targeting + the remaining columns. + """ + is_transformer = ( + hasattr(self.remainder, "fit") or hasattr(self.remainder, "fit_transform") + ) and hasattr(self.remainder, "transform") + if self.remainder not in ("drop", "passthrough") and not is_transformer: + raise ValueError( + "The remainder keyword needs to be one of 'drop', " + "'passthrough', or estimator. '%s' was passed instead" + % self.remainder + ) + + self._n_features = X.shape[1] + cols = set(chain(*self._transformer_to_input_indices.values())) + remaining = sorted(set(range(self._n_features)) - cols) + self._remainder = ("remainder", self.remainder, remaining) + self._transformer_to_input_indices["remainder"] = remaining + + @property + def named_transformers_(self): + """Access the fitted transformer by name. + + Read-only attribute to access any transformer by given name. + Keys are transformer names and values are the fitted transformer + objects. + """ + # Use Bunch object to improve autocomplete + return Bunch(**{name: trans for name, trans, _ in self.transformers_}) + + def _get_feature_name_out_for_transformer( + self, name, trans, column, feature_names_in + ): + """Gets feature names of transformer. + + Used in conjunction with self._iter(fitted=True) in get_feature_names_out. + """ + column_indices = self._transformer_to_input_indices[name] + names = feature_names_in[column_indices] + if trans == "drop" or _is_empty_column_selection(column): + return + elif trans == "passthrough": + return names + + # An actual transformer + if not hasattr(trans, "get_feature_names_out"): + raise AttributeError( + f"Transformer {name} (type {type(trans).__name__}) does " + "not provide get_feature_names_out." + ) + return trans.get_feature_names_out(names) + + def get_feature_names_out(self, input_features=None): + """Get output feature names for transformation. + + Parameters + ---------- + input_features : array-like of str or None, default=None + Input features. + + - If `input_features` is `None`, then `feature_names_in_` is + used as feature names in. If `feature_names_in_` is not defined, + then the following input feature names are generated: + `["x0", "x1", ..., "x(n_features_in_ - 1)"]`. + - If `input_features` is an array-like, then `input_features` must + match `feature_names_in_` if `feature_names_in_` is defined. + + Returns + ------- + feature_names_out : ndarray of str objects + Transformed feature names. + """ + check_is_fitted(self) + input_features = _check_feature_names_in(self, input_features) + + # List of tuples (name, feature_names_out) + transformer_with_feature_names_out = [] + for name, trans, column, _ in self._iter(fitted=True): + feature_names_out = self._get_feature_name_out_for_transformer( + name, trans, column, input_features + ) + if feature_names_out is None: + continue + transformer_with_feature_names_out.append((name, feature_names_out)) + + if not transformer_with_feature_names_out: + # No feature names + return np.array([], dtype=object) + + return self._add_prefix_for_feature_names_out( + transformer_with_feature_names_out + ) + + def _add_prefix_for_feature_names_out(self, transformer_with_feature_names_out): + """Add prefix for feature names out that includes the transformer names. + + Parameters + ---------- + transformer_with_feature_names_out : list of tuples of (str, array-like of str) + The tuple consistent of the transformer's name and its feature names out. + + Returns + ------- + feature_names_out : ndarray of shape (n_features,), dtype=str + Transformed feature names. + """ + if self.verbose_feature_names_out: + # Prefix the feature names out with the transformers name + names = list( + chain.from_iterable( + (f"{name}__{i}" for i in feature_names_out) + for name, feature_names_out in transformer_with_feature_names_out + ) + ) + return np.asarray(names, dtype=object) + + # verbose_feature_names_out is False + # Check that names are all unique without a prefix + feature_names_count = Counter( + chain.from_iterable(s for _, s in transformer_with_feature_names_out) + ) + top_6_overlap = [ + name for name, count in feature_names_count.most_common(6) if count > 1 + ] + top_6_overlap.sort() + if top_6_overlap: + if len(top_6_overlap) == 6: + # There are more than 5 overlapping names, we only show the 5 + # of the feature names + names_repr = str(top_6_overlap[:5])[:-1] + ", ...]" + else: + names_repr = str(top_6_overlap) + raise ValueError( + f"Output feature names: {names_repr} are not unique. Please set " + "verbose_feature_names_out=True to add prefixes to feature names" + ) + + return np.concatenate( + [name for _, name in transformer_with_feature_names_out], + ) + + def _update_fitted_transformers(self, transformers): + # transformers are fitted; excludes 'drop' cases + fitted_transformers = iter(transformers) + transformers_ = [] + self._name_to_fitted_passthrough = {} + + for name, old, column, _ in self._iter(): + if old == "drop": + trans = "drop" + elif old == "passthrough": + # FunctionTransformer is present in list of transformers, + # so get next transformer, but save original string + func_transformer = next(fitted_transformers) + trans = "passthrough" + + # The fitted FunctionTransformer is saved in another attribute, + # so it can be used during transform for set_output. + self._name_to_fitted_passthrough[name] = func_transformer + elif _is_empty_column_selection(column): + trans = old + else: + trans = next(fitted_transformers) + transformers_.append((name, trans, column)) + + # sanity check that transformers is exhausted + assert not list(fitted_transformers) + self.transformers_ = transformers_ + + def _validate_output(self, result): + """ + Ensure that the output of each transformer is 2D. Otherwise + hstack can raise an error or produce incorrect results. + """ + names = [ + name for name, _, _, _ in self._iter(fitted=True, replace_strings=True) + ] + for Xs, name in zip(result, names): + if not getattr(Xs, "ndim", 0) == 2: + raise ValueError( + "The output of the '{0}' transformer should be 2D (scipy " + "matrix, array, or pandas DataFrame).".format(name) + ) + + def _record_output_indices(self, Xs): + """ + Record which transformer produced which column. + """ + idx = 0 + self.output_indices_ = {} + + for transformer_idx, (name, _, _, _) in enumerate( + self._iter(fitted=True, replace_strings=True) + ): + n_columns = Xs[transformer_idx].shape[1] + self.output_indices_[name] = slice(idx, idx + n_columns) + idx += n_columns + + # `_iter` only generates transformers that have a non empty + # selection. Here we set empty slices for transformers that + # generate no output, which are safe for indexing + all_names = [t[0] for t in self.transformers] + ["remainder"] + for name in all_names: + if name not in self.output_indices_: + self.output_indices_[name] = slice(0, 0) + + def _log_message(self, name, idx, total): + if not self.verbose: + return None + return "(%d of %d) Processing %s" % (idx, total, name) + + def _fit_transform(self, X, y, func, fitted=False, column_as_strings=False): + """ + Private function to fit and/or transform on demand. + + Return value (transformers and/or transformed X data) depends + on the passed function. + ``fitted=True`` ensures the fitted transformers are used. + """ + transformers = list( + self._iter( + fitted=fitted, replace_strings=True, column_as_strings=column_as_strings + ) + ) + try: + return Parallel(n_jobs=self.n_jobs)( + delayed(func)( + transformer=clone(trans) if not fitted else trans, + X=_safe_indexing(X, column, axis=1), + y=y, + weight=weight, + message_clsname="ColumnTransformer", + message=self._log_message(name, idx, len(transformers)), + ) + for idx, (name, trans, column, weight) in enumerate(transformers, 1) + ) + except ValueError as e: + if "Expected 2D array, got 1D array instead" in str(e): + raise ValueError(_ERR_MSG_1DCOLUMN) from e + else: + raise + + def fit(self, X, y=None): + """Fit all transformers using X. + + Parameters + ---------- + X : {array-like, dataframe} of shape (n_samples, n_features) + Input data, of which specified subsets are used to fit the + transformers. + + y : array-like of shape (n_samples,...), default=None + Targets for supervised learning. + + Returns + ------- + self : ColumnTransformer + This estimator. + """ + # we use fit_transform to make sure to set sparse_output_ (for which we + # need the transformed data) to have consistent output type in predict + self.fit_transform(X, y=y) + return self + + def fit_transform(self, X, y=None): + """Fit all transformers, transform the data and concatenate results. + + Parameters + ---------- + X : {array-like, dataframe} of shape (n_samples, n_features) + Input data, of which specified subsets are used to fit the + transformers. + + y : array-like of shape (n_samples,), default=None + Targets for supervised learning. + + Returns + ------- + X_t : {array-like, sparse matrix} of \ + shape (n_samples, sum_n_components) + Horizontally stacked results of transformers. sum_n_components is the + sum of n_components (output dimension) over transformers. If + any result is a sparse matrix, everything will be converted to + sparse matrices. + """ + self._check_feature_names(X, reset=True) + + X = _check_X(X) + # set n_features_in_ attribute + self._check_n_features(X, reset=True) + self._validate_transformers() + self._validate_column_callables(X) + self._validate_remainder(X) + + result = self._fit_transform(X, y, _fit_transform_one) + + if not result: + self._update_fitted_transformers([]) + # All transformers are None + return np.zeros((X.shape[0], 0)) + + Xs, transformers = zip(*result) + + # determine if concatenated output will be sparse or not + if any(sparse.issparse(X) for X in Xs): + nnz = sum(X.nnz if sparse.issparse(X) else X.size for X in Xs) + total = sum( + X.shape[0] * X.shape[1] if sparse.issparse(X) else X.size for X in Xs + ) + density = nnz / total + self.sparse_output_ = density < self.sparse_threshold + else: + self.sparse_output_ = False + + self._update_fitted_transformers(transformers) + self._validate_output(Xs) + self._record_output_indices(Xs) + + return self._hstack(list(Xs)) + + def transform(self, X): + """Transform X separately by each transformer, concatenate results. + + Parameters + ---------- + X : {array-like, dataframe} of shape (n_samples, n_features) + The data to be transformed by subset. + + Returns + ------- + X_t : {array-like, sparse matrix} of \ + shape (n_samples, sum_n_components) + Horizontally stacked results of transformers. sum_n_components is the + sum of n_components (output dimension) over transformers. If + any result is a sparse matrix, everything will be converted to + sparse matrices. + """ + check_is_fitted(self) + X = _check_X(X) + + fit_dataframe_and_transform_dataframe = hasattr( + self, "feature_names_in_" + ) and hasattr(X, "columns") + + if fit_dataframe_and_transform_dataframe: + named_transformers = self.named_transformers_ + # check that all names seen in fit are in transform, unless + # they were dropped + non_dropped_indices = [ + ind + for name, ind in self._transformer_to_input_indices.items() + if name in named_transformers + and isinstance(named_transformers[name], str) + and named_transformers[name] != "drop" + ] + + all_indices = set(chain(*non_dropped_indices)) + all_names = set(self.feature_names_in_[ind] for ind in all_indices) + + diff = all_names - set(X.columns) + if diff: + raise ValueError(f"columns are missing: {diff}") + else: + # ndarray was used for fitting or transforming, thus we only + # check that n_features_in_ is consistent + self._check_n_features(X, reset=False) + + Xs = self._fit_transform( + X, + None, + _transform_one, + fitted=True, + column_as_strings=fit_dataframe_and_transform_dataframe, + ) + self._validate_output(Xs) + + if not Xs: + # All transformers are None + return np.zeros((X.shape[0], 0)) + + return self._hstack(list(Xs)) + + def _hstack(self, Xs): + """Stacks Xs horizontally. + + This allows subclasses to control the stacking behavior, while reusing + everything else from ColumnTransformer. + + Parameters + ---------- + Xs : list of {array-like, sparse matrix, dataframe} + """ + if self.sparse_output_: + try: + # since all columns should be numeric before stacking them + # in a sparse matrix, `check_array` is used for the + # dtype conversion if necessary. + converted_Xs = [ + check_array(X, accept_sparse=True, force_all_finite=False) + for X in Xs + ] + except ValueError as e: + raise ValueError( + "For a sparse output, all columns should " + "be a numeric or convertible to a numeric." + ) from e + + return sparse.hstack(converted_Xs).tocsr() + else: + Xs = [f.toarray() if sparse.issparse(f) else f for f in Xs] + config = _get_output_config("transform", self) + if config["dense"] == "pandas" and all(hasattr(X, "iloc") for X in Xs): + pd = check_pandas_support("transform") + output = pd.concat(Xs, axis=1) + + # If all transformers define `get_feature_names_out`, then transform + # will adjust the column names to be consistent with + # verbose_feature_names_out. Here we prefix the feature names if + # verbose_feature_names_out=True. + + if not self.verbose_feature_names_out: + return output + + transformer_names = [ + t[0] for t in self._iter(fitted=True, replace_strings=True) + ] + # Selection of columns might be empty. + # Hence feature names are filtered for non-emptiness. + feature_names_outs = [X.columns for X in Xs if X.shape[1] != 0] + names_out = self._add_prefix_for_feature_names_out( + list(zip(transformer_names, feature_names_outs)) + ) + output.columns = names_out + return output + + return np.hstack(Xs) + + def _sk_visual_block_(self): + if isinstance(self.remainder, str) and self.remainder == "drop": + transformers = self.transformers + elif hasattr(self, "_remainder"): + remainder_columns = self._remainder[2] + if ( + hasattr(self, "feature_names_in_") + and remainder_columns + and not all(isinstance(col, str) for col in remainder_columns) + ): + remainder_columns = self.feature_names_in_[remainder_columns].tolist() + transformers = chain( + self.transformers, [("remainder", self.remainder, remainder_columns)] + ) + else: + transformers = chain(self.transformers, [("remainder", self.remainder, "")]) + + names, transformers, name_details = zip(*transformers) + return _VisualBlock( + "parallel", transformers, names=names, name_details=name_details + ) + + +def _check_X(X): + """Use check_array only on lists and other non-array-likes / sparse""" + if hasattr(X, "__array__") or sparse.issparse(X): + return X + return check_array(X, force_all_finite="allow-nan", dtype=object) + + +def _is_empty_column_selection(column): + """ + Return True if the column selection is empty (empty list or all-False + boolean array). + + """ + if hasattr(column, "dtype") and np.issubdtype(column.dtype, np.bool_): + return not column.any() + elif hasattr(column, "__len__"): + return ( + len(column) == 0 + or all(isinstance(col, bool) for col in column) + and not any(column) + ) + else: + return False + + +def _get_transformer_list(estimators): + """ + Construct (name, trans, column) tuples from list + + """ + transformers, columns = zip(*estimators) + names, _ = zip(*_name_estimators(transformers)) + + transformer_list = list(zip(names, transformers, columns)) + return transformer_list + + +def make_column_transformer( + *transformers, + remainder="drop", + sparse_threshold=0.3, + n_jobs=None, + verbose=False, + verbose_feature_names_out=True, +): + """Construct a ColumnTransformer from the given transformers. + + This is a shorthand for the ColumnTransformer constructor; it does not + require, and does not permit, naming the transformers. Instead, they will + be given names automatically based on their types. It also does not allow + weighting with ``transformer_weights``. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + *transformers : tuples + Tuples of the form (transformer, columns) specifying the + transformer objects to be applied to subsets of the data. + + transformer : {'drop', 'passthrough'} or estimator + Estimator must support :term:`fit` and :term:`transform`. + Special-cased strings 'drop' and 'passthrough' are accepted as + well, to indicate to drop the columns or to pass them through + untransformed, respectively. + columns : str, array-like of str, int, array-like of int, slice, \ + array-like of bool or callable + Indexes the data on its second axis. Integers are interpreted as + positional columns, while strings can reference DataFrame columns + by name. A scalar string or int should be used where + ``transformer`` expects X to be a 1d array-like (vector), + otherwise a 2d array will be passed to the transformer. + A callable is passed the input data `X` and can return any of the + above. To select multiple columns by name or dtype, you can use + :obj:`make_column_selector`. + + remainder : {'drop', 'passthrough'} or estimator, default='drop' + By default, only the specified columns in `transformers` are + transformed and combined in the output, and the non-specified + columns are dropped. (default of ``'drop'``). + By specifying ``remainder='passthrough'``, all remaining columns that + were not specified in `transformers` will be automatically passed + through. This subset of columns is concatenated with the output of + the transformers. + By setting ``remainder`` to be an estimator, the remaining + non-specified columns will use the ``remainder`` estimator. The + estimator must support :term:`fit` and :term:`transform`. + + sparse_threshold : float, default=0.3 + If the transformed output consists of a mix of sparse and dense data, + it will be stacked as a sparse matrix if the density is lower than this + value. Use ``sparse_threshold=0`` to always return dense. + When the transformed output consists of all sparse or all dense data, + the stacked result will be sparse or dense, respectively, and this + keyword will be ignored. + + n_jobs : int, default=None + Number of jobs to run in parallel. + ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. + ``-1`` means using all processors. See :term:`Glossary ` + for more details. + + verbose : bool, default=False + If True, the time elapsed while fitting each transformer will be + printed as it is completed. + + verbose_feature_names_out : bool, default=True + If True, :meth:`get_feature_names_out` will prefix all feature names + with the name of the transformer that generated that feature. + If False, :meth:`get_feature_names_out` will not prefix any feature + names and will error if feature names are not unique. + + .. versionadded:: 1.0 + + Returns + ------- + ct : ColumnTransformer + Returns a :class:`ColumnTransformer` object. + + See Also + -------- + ColumnTransformer : Class that allows combining the + outputs of multiple transformer objects used on column subsets + of the data into a single feature space. + + Examples + -------- + >>> from sklearn.preprocessing import StandardScaler, OneHotEncoder + >>> from sklearn.compose import make_column_transformer + >>> make_column_transformer( + ... (StandardScaler(), ['numerical_column']), + ... (OneHotEncoder(), ['categorical_column'])) + ColumnTransformer(transformers=[('standardscaler', StandardScaler(...), + ['numerical_column']), + ('onehotencoder', OneHotEncoder(...), + ['categorical_column'])]) + """ + # transformer_weights keyword is not passed through because the user + # would need to know the automatically generated names of the transformers + transformer_list = _get_transformer_list(transformers) + return ColumnTransformer( + transformer_list, + n_jobs=n_jobs, + remainder=remainder, + sparse_threshold=sparse_threshold, + verbose=verbose, + verbose_feature_names_out=verbose_feature_names_out, + ) + + +class make_column_selector: + """Create a callable to select columns to be used with + :class:`ColumnTransformer`. + + :func:`make_column_selector` can select columns based on datatype or the + columns name with a regex. When using multiple selection criteria, **all** + criteria must match for a column to be selected. + + Parameters + ---------- + pattern : str, default=None + Name of columns containing this regex pattern will be included. If + None, column selection will not be selected based on pattern. + + dtype_include : column dtype or list of column dtypes, default=None + A selection of dtypes to include. For more details, see + :meth:`pandas.DataFrame.select_dtypes`. + + dtype_exclude : column dtype or list of column dtypes, default=None + A selection of dtypes to exclude. For more details, see + :meth:`pandas.DataFrame.select_dtypes`. + + Returns + ------- + selector : callable + Callable for column selection to be used by a + :class:`ColumnTransformer`. + + See Also + -------- + ColumnTransformer : Class that allows combining the + outputs of multiple transformer objects used on column subsets + of the data into a single feature space. + + Examples + -------- + >>> from sklearn.preprocessing import StandardScaler, OneHotEncoder + >>> from sklearn.compose import make_column_transformer + >>> from sklearn.compose import make_column_selector + >>> import numpy as np + >>> import pandas as pd # doctest: +SKIP + >>> X = pd.DataFrame({'city': ['London', 'London', 'Paris', 'Sallisaw'], + ... 'rating': [5, 3, 4, 5]}) # doctest: +SKIP + >>> ct = make_column_transformer( + ... (StandardScaler(), + ... make_column_selector(dtype_include=np.number)), # rating + ... (OneHotEncoder(), + ... make_column_selector(dtype_include=object))) # city + >>> ct.fit_transform(X) # doctest: +SKIP + array([[ 0.90453403, 1. , 0. , 0. ], + [-1.50755672, 1. , 0. , 0. ], + [-0.30151134, 0. , 1. , 0. ], + [ 0.90453403, 0. , 0. , 1. ]]) + """ + + def __init__(self, pattern=None, *, dtype_include=None, dtype_exclude=None): + self.pattern = pattern + self.dtype_include = dtype_include + self.dtype_exclude = dtype_exclude + + def __call__(self, df): + """Callable for column selection to be used by a + :class:`ColumnTransformer`. + + Parameters + ---------- + df : dataframe of shape (n_features, n_samples) + DataFrame to select columns from. + """ + if not hasattr(df, "iloc"): + raise ValueError( + "make_column_selector can only be applied to pandas dataframes" + ) + df_row = df.iloc[:1] + if self.dtype_include is not None or self.dtype_exclude is not None: + df_row = df_row.select_dtypes( + include=self.dtype_include, exclude=self.dtype_exclude + ) + cols = 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:mod:`sklearn.preprocessing` module includes scaling, centering, +normalization, binarization methods. +""" + +from ._function_transformer import FunctionTransformer + +from ._data import Binarizer +from ._data import KernelCenterer +from ._data import MinMaxScaler +from ._data import MaxAbsScaler +from ._data import Normalizer +from ._data import RobustScaler +from ._data import StandardScaler +from ._data import QuantileTransformer +from ._data import add_dummy_feature +from ._data import binarize +from ._data import normalize +from ._data import scale +from ._data import robust_scale +from ._data import maxabs_scale +from ._data import minmax_scale +from ._data import quantile_transform +from ._data import power_transform +from ._data import PowerTransformer + +from ._encoders import OneHotEncoder +from ._encoders import OrdinalEncoder + +from ._label import label_binarize +from ._label import LabelBinarizer +from ._label import LabelEncoder +from ._label import MultiLabelBinarizer + +from ._discretization import KBinsDiscretizer + +from ._polynomial import PolynomialFeatures +from ._polynomial import SplineTransformer + + +__all__ = [ + "Binarizer", + "FunctionTransformer", + "KBinsDiscretizer", + "KernelCenterer", + "LabelBinarizer", + "LabelEncoder", + "MultiLabelBinarizer", + "MinMaxScaler", + "MaxAbsScaler", + "QuantileTransformer", + "Normalizer", + "OneHotEncoder", + "OrdinalEncoder", + "PowerTransformer", + "RobustScaler", + "SplineTransformer", + "StandardScaler", + "add_dummy_feature", + "PolynomialFeatures", + "binarize", + "normalize", + "scale", + "robust_scale", + "maxabs_scale", + "minmax_scale", + "label_binarize", + "quantile_transform", + "power_transform", +] diff --git a/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/__pycache__/__init__.cpython-310.pyc b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 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0000000000000000000000000000000000000000..0287dd2e863c81d244e86f7bb11e4f2a6a547025 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/_csr_polynomial_expansion.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0402d2472dbb97c3ac1001bdebc332d2a6f02febb8da2a093d8a13e5210cedf3 +size 278233 diff --git a/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/_data.py b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/_data.py new file mode 100644 index 0000000000000000000000000000000000000000..a363db0bbc8c2a693f4e92c47f980b2778441d20 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/_data.py @@ -0,0 +1,3456 @@ +# Authors: Alexandre Gramfort +# Mathieu Blondel +# Olivier Grisel +# Andreas Mueller +# Eric Martin +# Giorgio Patrini +# Eric Chang +# License: BSD 3 clause + + +import warnings +from numbers import Integral, Real + +import numpy as np +from scipy import sparse +from scipy import stats +from scipy import optimize +from scipy.special import boxcox + +from ..base import ( + BaseEstimator, + TransformerMixin, + OneToOneFeatureMixin, + ClassNamePrefixFeaturesOutMixin, +) +from ..utils import check_array +from ..utils._param_validation import Interval, StrOptions +from ..utils.extmath import _incremental_mean_and_var, row_norms +from ..utils.sparsefuncs_fast import ( + inplace_csr_row_normalize_l1, + inplace_csr_row_normalize_l2, +) +from ..utils.sparsefuncs import ( + inplace_column_scale, + mean_variance_axis, + incr_mean_variance_axis, + min_max_axis, +) +from ..utils.validation import ( + check_is_fitted, + check_random_state, + _check_sample_weight, + FLOAT_DTYPES, +) + +from ._encoders import OneHotEncoder + + +BOUNDS_THRESHOLD = 1e-7 + +__all__ = [ + "Binarizer", + "KernelCenterer", + "MinMaxScaler", + "MaxAbsScaler", + "Normalizer", + "OneHotEncoder", + "RobustScaler", + "StandardScaler", + "QuantileTransformer", + "PowerTransformer", + "add_dummy_feature", + "binarize", + "normalize", + "scale", + "robust_scale", + "maxabs_scale", + "minmax_scale", + "quantile_transform", + "power_transform", +] + + +def _is_constant_feature(var, mean, n_samples): + """Detect if a feature is indistinguishable from a constant feature. + + The detection is based on its computed variance and on the theoretical + error bounds of the '2 pass algorithm' for variance computation. + + See "Algorithms for computing the sample variance: analysis and + recommendations", by Chan, Golub, and LeVeque. + """ + # In scikit-learn, variance is always computed using float64 accumulators. + eps = np.finfo(np.float64).eps + + upper_bound = n_samples * eps * var + (n_samples * mean * eps) ** 2 + return var <= upper_bound + + +def _handle_zeros_in_scale(scale, copy=True, constant_mask=None): + """Set scales of near constant features to 1. + + The goal is to avoid division by very small or zero values. + + Near constant features are detected automatically by identifying + scales close to machine precision unless they are precomputed by + the caller and passed with the `constant_mask` kwarg. + + Typically for standard scaling, the scales are the standard + deviation while near constant features are better detected on the + computed variances which are closer to machine precision by + construction. + """ + # if we are fitting on 1D arrays, scale might be a scalar + if np.isscalar(scale): + if scale == 0.0: + scale = 1.0 + return scale + elif isinstance(scale, np.ndarray): + if constant_mask is None: + # Detect near constant values to avoid dividing by a very small + # value that could lead to surprising results and numerical + # stability issues. + constant_mask = scale < 10 * np.finfo(scale.dtype).eps + + if copy: + # New array to avoid side-effects + scale = scale.copy() + scale[constant_mask] = 1.0 + return scale + + +def scale(X, *, axis=0, with_mean=True, with_std=True, copy=True): + """Standardize a dataset along any axis. + + Center to the mean and component wise scale to unit variance. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data to center and scale. + + axis : int, default=0 + Axis used to compute the means and standard deviations along. If 0, + independently standardize each feature, otherwise (if 1) standardize + each sample. + + with_mean : bool, default=True + If True, center the data before scaling. + + with_std : bool, default=True + If True, scale the data to unit variance (or equivalently, + unit standard deviation). + + copy : bool, default=True + Set to False to perform inplace row normalization and avoid a + copy (if the input is already a numpy array or a scipy.sparse + CSC matrix and if axis is 1). + + Returns + ------- + X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) + The transformed data. + + See Also + -------- + StandardScaler : Performs scaling to unit variance using the Transformer + API (e.g. as part of a preprocessing + :class:`~sklearn.pipeline.Pipeline`). + + Notes + ----- + This implementation will refuse to center scipy.sparse matrices + since it would make them non-sparse and would potentially crash the + program with memory exhaustion problems. + + Instead the caller is expected to either set explicitly + `with_mean=False` (in that case, only variance scaling will be + performed on the features of the CSC matrix) or to call `X.toarray()` + if he/she expects the materialized dense array to fit in memory. + + To avoid memory copy the caller should pass a CSC matrix. + + NaNs are treated as missing values: disregarded to compute the statistics, + and maintained during the data transformation. + + We use a biased estimator for the standard deviation, equivalent to + `numpy.std(x, ddof=0)`. Note that the choice of `ddof` is unlikely to + affect model performance. + + For a comparison of the different scalers, transformers, and normalizers, + see :ref:`examples/preprocessing/plot_all_scaling.py + `. + + .. warning:: Risk of data leak + + Do not use :func:`~sklearn.preprocessing.scale` unless you know + what you are doing. A common mistake is to apply it to the entire data + *before* splitting into training and test sets. This will bias the + model evaluation because information would have leaked from the test + set to the training set. + In general, we recommend using + :class:`~sklearn.preprocessing.StandardScaler` within a + :ref:`Pipeline ` in order to prevent most risks of data + leaking: `pipe = make_pipeline(StandardScaler(), LogisticRegression())`. + """ # noqa + X = check_array( + X, + accept_sparse="csc", + copy=copy, + ensure_2d=False, + estimator="the scale function", + dtype=FLOAT_DTYPES, + force_all_finite="allow-nan", + ) + if sparse.issparse(X): + if with_mean: + raise ValueError( + "Cannot center sparse matrices: pass `with_mean=False` instead" + " See docstring for motivation and alternatives." + ) + if axis != 0: + raise ValueError( + "Can only scale sparse matrix on axis=0, got axis=%d" % axis + ) + if with_std: + _, var = mean_variance_axis(X, axis=0) + var = _handle_zeros_in_scale(var, copy=False) + inplace_column_scale(X, 1 / np.sqrt(var)) + else: + X = np.asarray(X) + if with_mean: + mean_ = np.nanmean(X, axis) + if with_std: + scale_ = np.nanstd(X, axis) + # Xr is a view on the original array that enables easy use of + # broadcasting on the axis in which we are interested in + Xr = np.rollaxis(X, axis) + if with_mean: + Xr -= mean_ + mean_1 = np.nanmean(Xr, axis=0) + # Verify that mean_1 is 'close to zero'. If X contains very + # large values, mean_1 can also be very large, due to a lack of + # precision of mean_. In this case, a pre-scaling of the + # concerned feature is efficient, for instance by its mean or + # maximum. + if not np.allclose(mean_1, 0): + warnings.warn( + "Numerical issues were encountered " + "when centering the data " + "and might not be solved. Dataset may " + "contain too large values. You may need " + "to prescale your features." + ) + Xr -= mean_1 + if with_std: + scale_ = _handle_zeros_in_scale(scale_, copy=False) + Xr /= scale_ + if with_mean: + mean_2 = np.nanmean(Xr, axis=0) + # If mean_2 is not 'close to zero', it comes from the fact that + # scale_ is very small so that mean_2 = mean_1/scale_ > 0, even + # if mean_1 was close to zero. The problem is thus essentially + # due to the lack of precision of mean_. A solution is then to + # subtract the mean again: + if not np.allclose(mean_2, 0): + warnings.warn( + "Numerical issues were encountered " + "when scaling the data " + "and might not be solved. The standard " + "deviation of the data is probably " + "very close to 0. " + ) + Xr -= mean_2 + return X + + +class MinMaxScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): + """Transform features by scaling each feature to a given range. + + This estimator scales and translates each feature individually such + that it is in the given range on the training set, e.g. between + zero and one. + + The transformation is given by:: + + X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0)) + X_scaled = X_std * (max - min) + min + + where min, max = feature_range. + + This transformation is often used as an alternative to zero mean, + unit variance scaling. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + feature_range : tuple (min, max), default=(0, 1) + Desired range of transformed data. + + copy : bool, default=True + Set to False to perform inplace row normalization and avoid a + copy (if the input is already a numpy array). + + clip : bool, default=False + Set to True to clip transformed values of held-out data to + provided `feature range`. + + .. versionadded:: 0.24 + + Attributes + ---------- + min_ : ndarray of shape (n_features,) + Per feature adjustment for minimum. Equivalent to + ``min - X.min(axis=0) * self.scale_`` + + scale_ : ndarray of shape (n_features,) + Per feature relative scaling of the data. Equivalent to + ``(max - min) / (X.max(axis=0) - X.min(axis=0))`` + + .. versionadded:: 0.17 + *scale_* attribute. + + data_min_ : ndarray of shape (n_features,) + Per feature minimum seen in the data + + .. versionadded:: 0.17 + *data_min_* + + data_max_ : ndarray of shape (n_features,) + Per feature maximum seen in the data + + .. versionadded:: 0.17 + *data_max_* + + data_range_ : ndarray of shape (n_features,) + Per feature range ``(data_max_ - data_min_)`` seen in the data + + .. versionadded:: 0.17 + *data_range_* + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + n_samples_seen_ : int + The number of samples processed by the estimator. + It will be reset on new calls to fit, but increments across + ``partial_fit`` calls. + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + See Also + -------- + minmax_scale : Equivalent function without the estimator API. + + Notes + ----- + NaNs are treated as missing values: disregarded in fit, and maintained in + transform. + + For a comparison of the different scalers, transformers, and normalizers, + see :ref:`examples/preprocessing/plot_all_scaling.py + `. + + Examples + -------- + >>> from sklearn.preprocessing import MinMaxScaler + >>> data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]] + >>> scaler = MinMaxScaler() + >>> print(scaler.fit(data)) + MinMaxScaler() + >>> print(scaler.data_max_) + [ 1. 18.] + >>> print(scaler.transform(data)) + [[0. 0. ] + [0.25 0.25] + [0.5 0.5 ] + [1. 1. ]] + >>> print(scaler.transform([[2, 2]])) + [[1.5 0. ]] + """ + + _parameter_constraints: dict = { + "feature_range": [tuple], + "copy": ["boolean"], + "clip": ["boolean"], + } + + def __init__(self, feature_range=(0, 1), *, copy=True, clip=False): + self.feature_range = feature_range + self.copy = copy + self.clip = clip + + def _reset(self): + """Reset internal data-dependent state of the scaler, if necessary. + + __init__ parameters are not touched. + """ + # Checking one attribute is enough, because they are all set together + # in partial_fit + if hasattr(self, "scale_"): + del self.scale_ + del self.min_ + del self.n_samples_seen_ + del self.data_min_ + del self.data_max_ + del self.data_range_ + + def fit(self, X, y=None): + """Compute the minimum and maximum to be used for later scaling. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data used to compute the per-feature minimum and maximum + used for later scaling along the features axis. + + y : None + Ignored. + + Returns + ------- + self : object + Fitted scaler. + """ + # Reset internal state before fitting + self._reset() + return self.partial_fit(X, y) + + def partial_fit(self, X, y=None): + """Online computation of min and max on X for later scaling. + + All of X is processed as a single batch. This is intended for cases + when :meth:`fit` is not feasible due to very large number of + `n_samples` or because X is read from a continuous stream. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data used to compute the mean and standard deviation + used for later scaling along the features axis. + + y : None + Ignored. + + Returns + ------- + self : object + Fitted scaler. + """ + self._validate_params() + + feature_range = self.feature_range + if feature_range[0] >= feature_range[1]: + raise ValueError( + "Minimum of desired feature range must be smaller than maximum. Got %s." + % str(feature_range) + ) + + if sparse.issparse(X): + raise TypeError( + "MinMaxScaler does not support sparse input. " + "Consider using MaxAbsScaler instead." + ) + + first_pass = not hasattr(self, "n_samples_seen_") + X = self._validate_data( + X, + reset=first_pass, + dtype=FLOAT_DTYPES, + force_all_finite="allow-nan", + ) + + data_min = np.nanmin(X, axis=0) + data_max = np.nanmax(X, axis=0) + + if first_pass: + self.n_samples_seen_ = X.shape[0] + else: + data_min = np.minimum(self.data_min_, data_min) + data_max = np.maximum(self.data_max_, data_max) + self.n_samples_seen_ += X.shape[0] + + data_range = data_max - data_min + self.scale_ = (feature_range[1] - feature_range[0]) / _handle_zeros_in_scale( + data_range, copy=True + ) + self.min_ = feature_range[0] - data_min * self.scale_ + self.data_min_ = data_min + self.data_max_ = data_max + self.data_range_ = data_range + return self + + def transform(self, X): + """Scale features of X according to feature_range. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Input data that will be transformed. + + Returns + ------- + Xt : ndarray of shape (n_samples, n_features) + Transformed data. + """ + check_is_fitted(self) + + X = self._validate_data( + X, + copy=self.copy, + dtype=FLOAT_DTYPES, + force_all_finite="allow-nan", + reset=False, + ) + + X *= self.scale_ + X += self.min_ + if self.clip: + np.clip(X, self.feature_range[0], self.feature_range[1], out=X) + return X + + def inverse_transform(self, X): + """Undo the scaling of X according to feature_range. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Input data that will be transformed. It cannot be sparse. + + Returns + ------- + Xt : ndarray of shape (n_samples, n_features) + Transformed data. + """ + check_is_fitted(self) + + X = check_array( + X, copy=self.copy, dtype=FLOAT_DTYPES, force_all_finite="allow-nan" + ) + + X -= self.min_ + X /= self.scale_ + return X + + def _more_tags(self): + return {"allow_nan": True} + + +def minmax_scale(X, feature_range=(0, 1), *, axis=0, copy=True): + """Transform features by scaling each feature to a given range. + + This estimator scales and translates each feature individually such + that it is in the given range on the training set, i.e. between + zero and one. + + The transformation is given by (when ``axis=0``):: + + X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0)) + X_scaled = X_std * (max - min) + min + + where min, max = feature_range. + + The transformation is calculated as (when ``axis=0``):: + + X_scaled = scale * X + min - X.min(axis=0) * scale + where scale = (max - min) / (X.max(axis=0) - X.min(axis=0)) + + This transformation is often used as an alternative to zero mean, + unit variance scaling. + + Read more in the :ref:`User Guide `. + + .. versionadded:: 0.17 + *minmax_scale* function interface + to :class:`~sklearn.preprocessing.MinMaxScaler`. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data. + + feature_range : tuple (min, max), default=(0, 1) + Desired range of transformed data. + + axis : int, default=0 + Axis used to scale along. If 0, independently scale each feature, + otherwise (if 1) scale each sample. + + copy : bool, default=True + Set to False to perform inplace scaling and avoid a copy (if the input + is already a numpy array). + + Returns + ------- + X_tr : ndarray of shape (n_samples, n_features) + The transformed data. + + .. warning:: Risk of data leak + + Do not use :func:`~sklearn.preprocessing.minmax_scale` unless you know + what you are doing. A common mistake is to apply it to the entire data + *before* splitting into training and test sets. This will bias the + model evaluation because information would have leaked from the test + set to the training set. + In general, we recommend using + :class:`~sklearn.preprocessing.MinMaxScaler` within a + :ref:`Pipeline ` in order to prevent most risks of data + leaking: `pipe = make_pipeline(MinMaxScaler(), LogisticRegression())`. + + See Also + -------- + MinMaxScaler : Performs scaling to a given range using the Transformer + API (e.g. as part of a preprocessing + :class:`~sklearn.pipeline.Pipeline`). + + Notes + ----- + For a comparison of the different scalers, transformers, and normalizers, + see :ref:`examples/preprocessing/plot_all_scaling.py + `. + """ + # Unlike the scaler object, this function allows 1d input. + # If copy is required, it will be done inside the scaler object. + X = check_array( + X, copy=False, ensure_2d=False, dtype=FLOAT_DTYPES, force_all_finite="allow-nan" + ) + original_ndim = X.ndim + + if original_ndim == 1: + X = X.reshape(X.shape[0], 1) + + s = MinMaxScaler(feature_range=feature_range, copy=copy) + if axis == 0: + X = s.fit_transform(X) + else: + X = s.fit_transform(X.T).T + + if original_ndim == 1: + X = X.ravel() + + return X + + +class StandardScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): + """Standardize features by removing the mean and scaling to unit variance. + + The standard score of a sample `x` is calculated as: + + z = (x - u) / s + + where `u` is the mean of the training samples or zero if `with_mean=False`, + and `s` is the standard deviation of the training samples or one if + `with_std=False`. + + Centering and scaling happen independently on each feature by computing + the relevant statistics on the samples in the training set. Mean and + standard deviation are then stored to be used on later data using + :meth:`transform`. + + Standardization of a dataset is a common requirement for many + machine learning estimators: they might behave badly if the + individual features do not more or less look like standard normally + distributed data (e.g. Gaussian with 0 mean and unit variance). + + For instance many elements used in the objective function of + a learning algorithm (such as the RBF kernel of Support Vector + Machines or the L1 and L2 regularizers of linear models) assume that + all features are centered around 0 and have variance in the same + order. If a feature has a variance that is orders of magnitude larger + than others, it might dominate the objective function and make the + estimator unable to learn from other features correctly as expected. + + This scaler can also be applied to sparse CSR or CSC matrices by passing + `with_mean=False` to avoid breaking the sparsity structure of the data. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + copy : bool, default=True + If False, try to avoid a copy and do inplace scaling instead. + This is not guaranteed to always work inplace; e.g. if the data is + not a NumPy array or scipy.sparse CSR matrix, a copy may still be + returned. + + with_mean : bool, default=True + If True, center the data before scaling. + This does not work (and will raise an exception) when attempted on + sparse matrices, because centering them entails building a dense + matrix which in common use cases is likely to be too large to fit in + memory. + + with_std : bool, default=True + If True, scale the data to unit variance (or equivalently, + unit standard deviation). + + Attributes + ---------- + scale_ : ndarray of shape (n_features,) or None + Per feature relative scaling of the data to achieve zero mean and unit + variance. Generally this is calculated using `np.sqrt(var_)`. If a + variance is zero, we can't achieve unit variance, and the data is left + as-is, giving a scaling factor of 1. `scale_` is equal to `None` + when `with_std=False`. + + .. versionadded:: 0.17 + *scale_* + + mean_ : ndarray of shape (n_features,) or None + The mean value for each feature in the training set. + Equal to ``None`` when ``with_mean=False``. + + var_ : ndarray of shape (n_features,) or None + The variance for each feature in the training set. Used to compute + `scale_`. Equal to ``None`` when ``with_std=False``. + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + n_samples_seen_ : int or ndarray of shape (n_features,) + The number of samples processed by the estimator for each feature. + If there are no missing samples, the ``n_samples_seen`` will be an + integer, otherwise it will be an array of dtype int. If + `sample_weights` are used it will be a float (if no missing data) + or an array of dtype float that sums the weights seen so far. + Will be reset on new calls to fit, but increments across + ``partial_fit`` calls. + + See Also + -------- + scale : Equivalent function without the estimator API. + + :class:`~sklearn.decomposition.PCA` : Further removes the linear + correlation across features with 'whiten=True'. + + Notes + ----- + NaNs are treated as missing values: disregarded in fit, and maintained in + transform. + + We use a biased estimator for the standard deviation, equivalent to + `numpy.std(x, ddof=0)`. Note that the choice of `ddof` is unlikely to + affect model performance. + + For a comparison of the different scalers, transformers, and normalizers, + see :ref:`examples/preprocessing/plot_all_scaling.py + `. + + Examples + -------- + >>> from sklearn.preprocessing import StandardScaler + >>> data = [[0, 0], [0, 0], [1, 1], [1, 1]] + >>> scaler = StandardScaler() + >>> print(scaler.fit(data)) + StandardScaler() + >>> print(scaler.mean_) + [0.5 0.5] + >>> print(scaler.transform(data)) + [[-1. -1.] + [-1. -1.] + [ 1. 1.] + [ 1. 1.]] + >>> print(scaler.transform([[2, 2]])) + [[3. 3.]] + """ + + _parameter_constraints: dict = { + "copy": ["boolean"], + "with_mean": ["boolean"], + "with_std": ["boolean"], + } + + def __init__(self, *, copy=True, with_mean=True, with_std=True): + self.with_mean = with_mean + self.with_std = with_std + self.copy = copy + + def _reset(self): + """Reset internal data-dependent state of the scaler, if necessary. + + __init__ parameters are not touched. + """ + # Checking one attribute is enough, because they are all set together + # in partial_fit + if hasattr(self, "scale_"): + del self.scale_ + del self.n_samples_seen_ + del self.mean_ + del self.var_ + + def fit(self, X, y=None, sample_weight=None): + """Compute the mean and std to be used for later scaling. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data used to compute the mean and standard deviation + used for later scaling along the features axis. + + y : None + Ignored. + + sample_weight : array-like of shape (n_samples,), default=None + Individual weights for each sample. + + .. versionadded:: 0.24 + parameter *sample_weight* support to StandardScaler. + + Returns + ------- + self : object + Fitted scaler. + """ + # Reset internal state before fitting + self._reset() + return self.partial_fit(X, y, sample_weight) + + def partial_fit(self, X, y=None, sample_weight=None): + """Online computation of mean and std on X for later scaling. + + All of X is processed as a single batch. This is intended for cases + when :meth:`fit` is not feasible due to very large number of + `n_samples` or because X is read from a continuous stream. + + The algorithm for incremental mean and std is given in Equation 1.5a,b + in Chan, Tony F., Gene H. Golub, and Randall J. LeVeque. "Algorithms + for computing the sample variance: Analysis and recommendations." + The American Statistician 37.3 (1983): 242-247: + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data used to compute the mean and standard deviation + used for later scaling along the features axis. + + y : None + Ignored. + + sample_weight : array-like of shape (n_samples,), default=None + Individual weights for each sample. + + .. versionadded:: 0.24 + parameter *sample_weight* support to StandardScaler. + + Returns + ------- + self : object + Fitted scaler. + """ + self._validate_params() + + first_call = not hasattr(self, "n_samples_seen_") + X = self._validate_data( + X, + accept_sparse=("csr", "csc"), + dtype=FLOAT_DTYPES, + force_all_finite="allow-nan", + reset=first_call, + ) + n_features = X.shape[1] + + if sample_weight is not None: + sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype) + + # Even in the case of `with_mean=False`, we update the mean anyway + # This is needed for the incremental computation of the var + # See incr_mean_variance_axis and _incremental_mean_variance_axis + + # if n_samples_seen_ is an integer (i.e. no missing values), we need to + # transform it to a NumPy array of shape (n_features,) required by + # incr_mean_variance_axis and _incremental_variance_axis + dtype = np.int64 if sample_weight is None else X.dtype + if not hasattr(self, "n_samples_seen_"): + self.n_samples_seen_ = np.zeros(n_features, dtype=dtype) + elif np.size(self.n_samples_seen_) == 1: + self.n_samples_seen_ = np.repeat(self.n_samples_seen_, X.shape[1]) + self.n_samples_seen_ = self.n_samples_seen_.astype(dtype, copy=False) + + if sparse.issparse(X): + if self.with_mean: + raise ValueError( + "Cannot center sparse matrices: pass `with_mean=False` " + "instead. See docstring for motivation and alternatives." + ) + sparse_constructor = ( + sparse.csr_matrix if X.format == "csr" else sparse.csc_matrix + ) + + if self.with_std: + # First pass + if not hasattr(self, "scale_"): + self.mean_, self.var_, self.n_samples_seen_ = mean_variance_axis( + X, axis=0, weights=sample_weight, return_sum_weights=True + ) + # Next passes + else: + ( + self.mean_, + self.var_, + self.n_samples_seen_, + ) = incr_mean_variance_axis( + X, + axis=0, + last_mean=self.mean_, + last_var=self.var_, + last_n=self.n_samples_seen_, + weights=sample_weight, + ) + # We force the mean and variance to float64 for large arrays + # See https://github.com/scikit-learn/scikit-learn/pull/12338 + self.mean_ = self.mean_.astype(np.float64, copy=False) + self.var_ = self.var_.astype(np.float64, copy=False) + else: + self.mean_ = None # as with_mean must be False for sparse + self.var_ = None + weights = _check_sample_weight(sample_weight, X) + sum_weights_nan = weights @ sparse_constructor( + (np.isnan(X.data), X.indices, X.indptr), shape=X.shape + ) + self.n_samples_seen_ += (np.sum(weights) - sum_weights_nan).astype( + dtype + ) + else: + # First pass + if not hasattr(self, "scale_"): + self.mean_ = 0.0 + if self.with_std: + self.var_ = 0.0 + else: + self.var_ = None + + if not self.with_mean and not self.with_std: + self.mean_ = None + self.var_ = None + self.n_samples_seen_ += X.shape[0] - np.isnan(X).sum(axis=0) + + else: + self.mean_, self.var_, self.n_samples_seen_ = _incremental_mean_and_var( + X, + self.mean_, + self.var_, + self.n_samples_seen_, + sample_weight=sample_weight, + ) + + # for backward-compatibility, reduce n_samples_seen_ to an integer + # if the number of samples is the same for each feature (i.e. no + # missing values) + if np.ptp(self.n_samples_seen_) == 0: + self.n_samples_seen_ = self.n_samples_seen_[0] + + if self.with_std: + # Extract the list of near constant features on the raw variances, + # before taking the square root. + constant_mask = _is_constant_feature( + self.var_, self.mean_, self.n_samples_seen_ + ) + self.scale_ = _handle_zeros_in_scale( + np.sqrt(self.var_), copy=False, constant_mask=constant_mask + ) + else: + self.scale_ = None + + return self + + def transform(self, X, copy=None): + """Perform standardization by centering and scaling. + + Parameters + ---------- + X : {array-like, sparse matrix of shape (n_samples, n_features) + The data used to scale along the features axis. + copy : bool, default=None + Copy the input X or not. + + Returns + ------- + X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) + Transformed array. + """ + check_is_fitted(self) + + copy = copy if copy is not None else self.copy + X = self._validate_data( + X, + reset=False, + accept_sparse="csr", + copy=copy, + dtype=FLOAT_DTYPES, + force_all_finite="allow-nan", + ) + + if sparse.issparse(X): + if self.with_mean: + raise ValueError( + "Cannot center sparse matrices: pass `with_mean=False` " + "instead. See docstring for motivation and alternatives." + ) + if self.scale_ is not None: + inplace_column_scale(X, 1 / self.scale_) + else: + if self.with_mean: + X -= self.mean_ + if self.with_std: + X /= self.scale_ + return X + + def inverse_transform(self, X, copy=None): + """Scale back the data to the original representation. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data used to scale along the features axis. + copy : bool, default=None + Copy the input X or not. + + Returns + ------- + X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) + Transformed array. + """ + check_is_fitted(self) + + copy = copy if copy is not None else self.copy + X = check_array( + X, + accept_sparse="csr", + copy=copy, + dtype=FLOAT_DTYPES, + force_all_finite="allow-nan", + ) + + if sparse.issparse(X): + if self.with_mean: + raise ValueError( + "Cannot uncenter sparse matrices: pass `with_mean=False` " + "instead See docstring for motivation and alternatives." + ) + if self.scale_ is not None: + inplace_column_scale(X, self.scale_) + else: + if self.with_std: + X *= self.scale_ + if self.with_mean: + X += self.mean_ + return X + + def _more_tags(self): + return {"allow_nan": True, "preserves_dtype": [np.float64, np.float32]} + + +class MaxAbsScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): + """Scale each feature by its maximum absolute value. + + This estimator scales and translates each feature individually such + that the maximal absolute value of each feature in the + training set will be 1.0. It does not shift/center the data, and + thus does not destroy any sparsity. + + This scaler can also be applied to sparse CSR or CSC matrices. + + .. versionadded:: 0.17 + + Parameters + ---------- + copy : bool, default=True + Set to False to perform inplace scaling and avoid a copy (if the input + is already a numpy array). + + Attributes + ---------- + scale_ : ndarray of shape (n_features,) + Per feature relative scaling of the data. + + .. versionadded:: 0.17 + *scale_* attribute. + + max_abs_ : ndarray of shape (n_features,) + Per feature maximum absolute value. + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + n_samples_seen_ : int + The number of samples processed by the estimator. Will be reset on + new calls to fit, but increments across ``partial_fit`` calls. + + See Also + -------- + maxabs_scale : Equivalent function without the estimator API. + + Notes + ----- + NaNs are treated as missing values: disregarded in fit, and maintained in + transform. + + For a comparison of the different scalers, transformers, and normalizers, + see :ref:`examples/preprocessing/plot_all_scaling.py + `. + + Examples + -------- + >>> from sklearn.preprocessing import MaxAbsScaler + >>> X = [[ 1., -1., 2.], + ... [ 2., 0., 0.], + ... [ 0., 1., -1.]] + >>> transformer = MaxAbsScaler().fit(X) + >>> transformer + MaxAbsScaler() + >>> transformer.transform(X) + array([[ 0.5, -1. , 1. ], + [ 1. , 0. , 0. ], + [ 0. , 1. , -0.5]]) + """ + + _parameter_constraints: dict = {"copy": ["boolean"]} + + def __init__(self, *, copy=True): + self.copy = copy + + def _reset(self): + """Reset internal data-dependent state of the scaler, if necessary. + + __init__ parameters are not touched. + """ + # Checking one attribute is enough, because they are all set together + # in partial_fit + if hasattr(self, "scale_"): + del self.scale_ + del self.n_samples_seen_ + del self.max_abs_ + + def fit(self, X, y=None): + """Compute the maximum absolute value to be used for later scaling. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data used to compute the per-feature minimum and maximum + used for later scaling along the features axis. + + y : None + Ignored. + + Returns + ------- + self : object + Fitted scaler. + """ + # Reset internal state before fitting + self._reset() + return self.partial_fit(X, y) + + def partial_fit(self, X, y=None): + """Online computation of max absolute value of X for later scaling. + + All of X is processed as a single batch. This is intended for cases + when :meth:`fit` is not feasible due to very large number of + `n_samples` or because X is read from a continuous stream. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data used to compute the mean and standard deviation + used for later scaling along the features axis. + + y : None + Ignored. + + Returns + ------- + self : object + Fitted scaler. + """ + self._validate_params() + + first_pass = not hasattr(self, "n_samples_seen_") + X = self._validate_data( + X, + reset=first_pass, + accept_sparse=("csr", "csc"), + dtype=FLOAT_DTYPES, + force_all_finite="allow-nan", + ) + + if sparse.issparse(X): + mins, maxs = min_max_axis(X, axis=0, ignore_nan=True) + max_abs = np.maximum(np.abs(mins), np.abs(maxs)) + else: + max_abs = np.nanmax(np.abs(X), axis=0) + + if first_pass: + self.n_samples_seen_ = X.shape[0] + else: + max_abs = np.maximum(self.max_abs_, max_abs) + self.n_samples_seen_ += X.shape[0] + + self.max_abs_ = max_abs + self.scale_ = _handle_zeros_in_scale(max_abs, copy=True) + return self + + def transform(self, X): + """Scale the data. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data that should be scaled. + + Returns + ------- + X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) + Transformed array. + """ + check_is_fitted(self) + X = self._validate_data( + X, + accept_sparse=("csr", "csc"), + copy=self.copy, + reset=False, + dtype=FLOAT_DTYPES, + force_all_finite="allow-nan", + ) + + if sparse.issparse(X): + inplace_column_scale(X, 1.0 / self.scale_) + else: + X /= self.scale_ + return X + + def inverse_transform(self, X): + """Scale back the data to the original representation. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data that should be transformed back. + + Returns + ------- + X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) + Transformed array. + """ + check_is_fitted(self) + X = check_array( + X, + accept_sparse=("csr", "csc"), + copy=self.copy, + dtype=FLOAT_DTYPES, + force_all_finite="allow-nan", + ) + + if sparse.issparse(X): + inplace_column_scale(X, self.scale_) + else: + X *= self.scale_ + return X + + def _more_tags(self): + return {"allow_nan": True} + + +def maxabs_scale(X, *, axis=0, copy=True): + """Scale each feature to the [-1, 1] range without breaking the sparsity. + + This estimator scales each feature individually such + that the maximal absolute value of each feature in the + training set will be 1.0. + + This scaler can also be applied to sparse CSR or CSC matrices. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data. + + axis : int, default=0 + Axis used to scale along. If 0, independently scale each feature, + otherwise (if 1) scale each sample. + + copy : bool, default=True + Set to False to perform inplace scaling and avoid a copy (if the input + is already a numpy array). + + Returns + ------- + X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) + The transformed data. + + .. warning:: Risk of data leak + + Do not use :func:`~sklearn.preprocessing.maxabs_scale` unless you know + what you are doing. A common mistake is to apply it to the entire data + *before* splitting into training and test sets. This will bias the + model evaluation because information would have leaked from the test + set to the training set. + In general, we recommend using + :class:`~sklearn.preprocessing.MaxAbsScaler` within a + :ref:`Pipeline ` in order to prevent most risks of data + leaking: `pipe = make_pipeline(MaxAbsScaler(), LogisticRegression())`. + + See Also + -------- + MaxAbsScaler : Performs scaling to the [-1, 1] range using + the Transformer API (e.g. as part of a preprocessing + :class:`~sklearn.pipeline.Pipeline`). + + Notes + ----- + NaNs are treated as missing values: disregarded to compute the statistics, + and maintained during the data transformation. + + For a comparison of the different scalers, transformers, and normalizers, + see :ref:`examples/preprocessing/plot_all_scaling.py + `. + """ + # Unlike the scaler object, this function allows 1d input. + + # If copy is required, it will be done inside the scaler object. + X = check_array( + X, + accept_sparse=("csr", "csc"), + copy=False, + ensure_2d=False, + dtype=FLOAT_DTYPES, + force_all_finite="allow-nan", + ) + original_ndim = X.ndim + + if original_ndim == 1: + X = X.reshape(X.shape[0], 1) + + s = MaxAbsScaler(copy=copy) + if axis == 0: + X = s.fit_transform(X) + else: + X = s.fit_transform(X.T).T + + if original_ndim == 1: + X = X.ravel() + + return X + + +class RobustScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): + """Scale features using statistics that are robust to outliers. + + This Scaler removes the median and scales the data according to + the quantile range (defaults to IQR: Interquartile Range). + The IQR is the range between the 1st quartile (25th quantile) + and the 3rd quartile (75th quantile). + + Centering and scaling happen independently on each feature by + computing the relevant statistics on the samples in the training + set. Median and interquartile range are then stored to be used on + later data using the :meth:`transform` method. + + Standardization of a dataset is a common requirement for many + machine learning estimators. Typically this is done by removing the mean + and scaling to unit variance. However, outliers can often influence the + sample mean / variance in a negative way. In such cases, the median and + the interquartile range often give better results. + + .. versionadded:: 0.17 + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + with_centering : bool, default=True + If `True`, center the data before scaling. + This will cause :meth:`transform` to raise an exception when attempted + on sparse matrices, because centering them entails building a dense + matrix which in common use cases is likely to be too large to fit in + memory. + + with_scaling : bool, default=True + If `True`, scale the data to interquartile range. + + quantile_range : tuple (q_min, q_max), 0.0 < q_min < q_max < 100.0, \ + default=(25.0, 75.0) + Quantile range used to calculate `scale_`. By default this is equal to + the IQR, i.e., `q_min` is the first quantile and `q_max` is the third + quantile. + + .. versionadded:: 0.18 + + copy : bool, default=True + If `False`, try to avoid a copy and do inplace scaling instead. + This is not guaranteed to always work inplace; e.g. if the data is + not a NumPy array or scipy.sparse CSR matrix, a copy may still be + returned. + + unit_variance : bool, default=False + If `True`, scale data so that normally distributed features have a + variance of 1. In general, if the difference between the x-values of + `q_max` and `q_min` for a standard normal distribution is greater + than 1, the dataset will be scaled down. If less than 1, the dataset + will be scaled up. + + .. versionadded:: 0.24 + + Attributes + ---------- + center_ : array of floats + The median value for each feature in the training set. + + scale_ : array of floats + The (scaled) interquartile range for each feature in the training set. + + .. versionadded:: 0.17 + *scale_* attribute. + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + See Also + -------- + robust_scale : Equivalent function without the estimator API. + sklearn.decomposition.PCA : Further removes the linear correlation across + features with 'whiten=True'. + + Notes + ----- + For a comparison of the different scalers, transformers, and normalizers, + see :ref:`examples/preprocessing/plot_all_scaling.py + `. + + https://en.wikipedia.org/wiki/Median + https://en.wikipedia.org/wiki/Interquartile_range + + Examples + -------- + >>> from sklearn.preprocessing import RobustScaler + >>> X = [[ 1., -2., 2.], + ... [ -2., 1., 3.], + ... [ 4., 1., -2.]] + >>> transformer = RobustScaler().fit(X) + >>> transformer + RobustScaler() + >>> transformer.transform(X) + array([[ 0. , -2. , 0. ], + [-1. , 0. , 0.4], + [ 1. , 0. , -1.6]]) + """ + + _parameter_constraints: dict = { + "with_centering": ["boolean"], + "with_scaling": ["boolean"], + "quantile_range": [tuple], + "copy": ["boolean"], + "unit_variance": ["boolean"], + } + + def __init__( + self, + *, + with_centering=True, + with_scaling=True, + quantile_range=(25.0, 75.0), + copy=True, + unit_variance=False, + ): + self.with_centering = with_centering + self.with_scaling = with_scaling + self.quantile_range = quantile_range + self.unit_variance = unit_variance + self.copy = copy + + def fit(self, X, y=None): + """Compute the median and quantiles to be used for scaling. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data used to compute the median and quantiles + used for later scaling along the features axis. + + y : Ignored + Not used, present here for API consistency by convention. + + Returns + ------- + self : object + Fitted scaler. + """ + self._validate_params() + + # at fit, convert sparse matrices to csc for optimized computation of + # the quantiles + X = self._validate_data( + X, + accept_sparse="csc", + dtype=FLOAT_DTYPES, + force_all_finite="allow-nan", + ) + + q_min, q_max = self.quantile_range + if not 0 <= q_min <= q_max <= 100: + raise ValueError("Invalid quantile range: %s" % str(self.quantile_range)) + + if self.with_centering: + if sparse.issparse(X): + raise ValueError( + "Cannot center sparse matrices: use `with_centering=False`" + " instead. See docstring for motivation and alternatives." + ) + self.center_ = np.nanmedian(X, axis=0) + else: + self.center_ = None + + if self.with_scaling: + quantiles = [] + for feature_idx in range(X.shape[1]): + if sparse.issparse(X): + column_nnz_data = X.data[ + X.indptr[feature_idx] : X.indptr[feature_idx + 1] + ] + column_data = np.zeros(shape=X.shape[0], dtype=X.dtype) + column_data[: len(column_nnz_data)] = column_nnz_data + else: + column_data = X[:, feature_idx] + + quantiles.append(np.nanpercentile(column_data, self.quantile_range)) + + quantiles = np.transpose(quantiles) + + self.scale_ = quantiles[1] - quantiles[0] + self.scale_ = _handle_zeros_in_scale(self.scale_, copy=False) + if self.unit_variance: + adjust = stats.norm.ppf(q_max / 100.0) - stats.norm.ppf(q_min / 100.0) + self.scale_ = self.scale_ / adjust + else: + self.scale_ = None + + return self + + def transform(self, X): + """Center and scale the data. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data used to scale along the specified axis. + + Returns + ------- + X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) + Transformed array. + """ + check_is_fitted(self) + X = self._validate_data( + X, + accept_sparse=("csr", "csc"), + copy=self.copy, + dtype=FLOAT_DTYPES, + reset=False, + force_all_finite="allow-nan", + ) + + if sparse.issparse(X): + if self.with_scaling: + inplace_column_scale(X, 1.0 / self.scale_) + else: + if self.with_centering: + X -= self.center_ + if self.with_scaling: + X /= self.scale_ + return X + + def inverse_transform(self, X): + """Scale back the data to the original representation. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The rescaled data to be transformed back. + + Returns + ------- + X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) + Transformed array. + """ + check_is_fitted(self) + X = check_array( + X, + accept_sparse=("csr", "csc"), + copy=self.copy, + dtype=FLOAT_DTYPES, + force_all_finite="allow-nan", + ) + + if sparse.issparse(X): + if self.with_scaling: + inplace_column_scale(X, self.scale_) + else: + if self.with_scaling: + X *= self.scale_ + if self.with_centering: + X += self.center_ + return X + + def _more_tags(self): + return {"allow_nan": True} + + +def robust_scale( + X, + *, + axis=0, + with_centering=True, + with_scaling=True, + quantile_range=(25.0, 75.0), + copy=True, + unit_variance=False, +): + """Standardize a dataset along any axis. + + Center to the median and component wise scale + according to the interquartile range. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_sample, n_features) + The data to center and scale. + + axis : int, default=0 + Axis used to compute the medians and IQR along. If 0, + independently scale each feature, otherwise (if 1) scale + each sample. + + with_centering : bool, default=True + If `True`, center the data before scaling. + + with_scaling : bool, default=True + If `True`, scale the data to unit variance (or equivalently, + unit standard deviation). + + quantile_range : tuple (q_min, q_max), 0.0 < q_min < q_max < 100.0,\ + default=(25.0, 75.0) + Quantile range used to calculate `scale_`. By default this is equal to + the IQR, i.e., `q_min` is the first quantile and `q_max` is the third + quantile. + + .. versionadded:: 0.18 + + copy : bool, default=True + Set to `False` to perform inplace row normalization and avoid a + copy (if the input is already a numpy array or a scipy.sparse + CSR matrix and if axis is 1). + + unit_variance : bool, default=False + If `True`, scale data so that normally distributed features have a + variance of 1. In general, if the difference between the x-values of + `q_max` and `q_min` for a standard normal distribution is greater + than 1, the dataset will be scaled down. If less than 1, the dataset + will be scaled up. + + .. versionadded:: 0.24 + + Returns + ------- + X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) + The transformed data. + + See Also + -------- + RobustScaler : Performs centering and scaling using the Transformer API + (e.g. as part of a preprocessing :class:`~sklearn.pipeline.Pipeline`). + + Notes + ----- + This implementation will refuse to center scipy.sparse matrices + since it would make them non-sparse and would potentially crash the + program with memory exhaustion problems. + + Instead the caller is expected to either set explicitly + `with_centering=False` (in that case, only variance scaling will be + performed on the features of the CSR matrix) or to call `X.toarray()` + if he/she expects the materialized dense array to fit in memory. + + To avoid memory copy the caller should pass a CSR matrix. + + For a comparison of the different scalers, transformers, and normalizers, + see :ref:`examples/preprocessing/plot_all_scaling.py + `. + + .. warning:: Risk of data leak + + Do not use :func:`~sklearn.preprocessing.robust_scale` unless you know + what you are doing. A common mistake is to apply it to the entire data + *before* splitting into training and test sets. This will bias the + model evaluation because information would have leaked from the test + set to the training set. + In general, we recommend using + :class:`~sklearn.preprocessing.RobustScaler` within a + :ref:`Pipeline ` in order to prevent most risks of data + leaking: `pipe = make_pipeline(RobustScaler(), LogisticRegression())`. + """ + X = check_array( + X, + accept_sparse=("csr", "csc"), + copy=False, + ensure_2d=False, + dtype=FLOAT_DTYPES, + force_all_finite="allow-nan", + ) + original_ndim = X.ndim + + if original_ndim == 1: + X = X.reshape(X.shape[0], 1) + + s = RobustScaler( + with_centering=with_centering, + with_scaling=with_scaling, + quantile_range=quantile_range, + unit_variance=unit_variance, + copy=copy, + ) + if axis == 0: + X = s.fit_transform(X) + else: + X = s.fit_transform(X.T).T + + if original_ndim == 1: + X = X.ravel() + + return X + + +def normalize(X, norm="l2", *, axis=1, copy=True, return_norm=False): + """Scale input vectors individually to unit norm (vector length). + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data to normalize, element by element. + scipy.sparse matrices should be in CSR format to avoid an + un-necessary copy. + + norm : {'l1', 'l2', 'max'}, default='l2' + The norm to use to normalize each non zero sample (or each non-zero + feature if axis is 0). + + axis : {0, 1}, default=1 + Define axis used to normalize the data along. If 1, independently + normalize each sample, otherwise (if 0) normalize each feature. + + copy : bool, default=True + Set to False to perform inplace row normalization and avoid a + copy (if the input is already a numpy array or a scipy.sparse + CSR matrix and if axis is 1). + + return_norm : bool, default=False + Whether to return the computed norms. + + Returns + ------- + X : {ndarray, sparse matrix} of shape (n_samples, n_features) + Normalized input X. + + norms : ndarray of shape (n_samples, ) if axis=1 else (n_features, ) + An array of norms along given axis for X. + When X is sparse, a NotImplementedError will be raised + for norm 'l1' or 'l2'. + + See Also + -------- + Normalizer : Performs normalization using the Transformer API + (e.g. as part of a preprocessing :class:`~sklearn.pipeline.Pipeline`). + + Notes + ----- + For a comparison of the different scalers, transformers, and normalizers, + see :ref:`examples/preprocessing/plot_all_scaling.py + `. + """ + if norm not in ("l1", "l2", "max"): + raise ValueError("'%s' is not a supported norm" % norm) + + if axis == 0: + sparse_format = "csc" + elif axis == 1: + sparse_format = "csr" + else: + raise ValueError("'%d' is not a supported axis" % axis) + + X = check_array( + X, + accept_sparse=sparse_format, + copy=copy, + estimator="the normalize function", + dtype=FLOAT_DTYPES, + ) + if axis == 0: + X = X.T + + if sparse.issparse(X): + if return_norm and norm in ("l1", "l2"): + raise NotImplementedError( + "return_norm=True is not implemented " + "for sparse matrices with norm 'l1' " + "or norm 'l2'" + ) + if norm == "l1": + inplace_csr_row_normalize_l1(X) + elif norm == "l2": + inplace_csr_row_normalize_l2(X) + elif norm == "max": + mins, maxes = min_max_axis(X, 1) + norms = np.maximum(abs(mins), maxes) + norms_elementwise = norms.repeat(np.diff(X.indptr)) + mask = norms_elementwise != 0 + X.data[mask] /= norms_elementwise[mask] + else: + if norm == "l1": + norms = np.abs(X).sum(axis=1) + elif norm == "l2": + norms = row_norms(X) + elif norm == "max": + norms = np.max(abs(X), axis=1) + norms = _handle_zeros_in_scale(norms, copy=False) + X /= norms[:, np.newaxis] + + if axis == 0: + X = X.T + + if return_norm: + return X, norms + else: + return X + + +class Normalizer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): + """Normalize samples individually to unit norm. + + Each sample (i.e. each row of the data matrix) with at least one + non zero component is rescaled independently of other samples so + that its norm (l1, l2 or inf) equals one. + + This transformer is able to work both with dense numpy arrays and + scipy.sparse matrix (use CSR format if you want to avoid the burden of + a copy / conversion). + + Scaling inputs to unit norms is a common operation for text + classification or clustering for instance. For instance the dot + product of two l2-normalized TF-IDF vectors is the cosine similarity + of the vectors and is the base similarity metric for the Vector + Space Model commonly used by the Information Retrieval community. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + norm : {'l1', 'l2', 'max'}, default='l2' + The norm to use to normalize each non zero sample. If norm='max' + is used, values will be rescaled by the maximum of the absolute + values. + + copy : bool, default=True + Set to False to perform inplace row normalization and avoid a + copy (if the input is already a numpy array or a scipy.sparse + CSR matrix). + + Attributes + ---------- + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + See Also + -------- + normalize : Equivalent function without the estimator API. + + Notes + ----- + This estimator is :term:`stateless` and does not need to be fitted. + However, we recommend to call :meth:`fit_transform` instead of + :meth:`transform`, as parameter validation is only performed in + :meth:`fit`. + + For a comparison of the different scalers, transformers, and normalizers, + see :ref:`examples/preprocessing/plot_all_scaling.py + `. + + Examples + -------- + >>> from sklearn.preprocessing import Normalizer + >>> X = [[4, 1, 2, 2], + ... [1, 3, 9, 3], + ... [5, 7, 5, 1]] + >>> transformer = Normalizer().fit(X) # fit does nothing. + >>> transformer + Normalizer() + >>> transformer.transform(X) + array([[0.8, 0.2, 0.4, 0.4], + [0.1, 0.3, 0.9, 0.3], + [0.5, 0.7, 0.5, 0.1]]) + """ + + _parameter_constraints: dict = { + "norm": [StrOptions({"l1", "l2", "max"})], + "copy": ["boolean"], + } + + def __init__(self, norm="l2", *, copy=True): + self.norm = norm + self.copy = copy + + def fit(self, X, y=None): + """Only validates estimator's parameters. + + This method allows to: (i) validate the estimator's parameters and + (ii) be consistent with the scikit-learn transformer API. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data to estimate the normalization parameters. + + y : Ignored + Not used, present here for API consistency by convention. + + Returns + ------- + self : object + Fitted transformer. + """ + self._validate_params() + self._validate_data(X, accept_sparse="csr") + return self + + def transform(self, X, copy=None): + """Scale each non zero row of X to unit norm. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data to normalize, row by row. scipy.sparse matrices should be + in CSR format to avoid an un-necessary copy. + + copy : bool, default=None + Copy the input X or not. + + Returns + ------- + X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) + Transformed array. + """ + copy = copy if copy is not None else self.copy + X = self._validate_data(X, accept_sparse="csr", reset=False) + return normalize(X, norm=self.norm, axis=1, copy=copy) + + def _more_tags(self): + return {"stateless": True} + + +def binarize(X, *, threshold=0.0, copy=True): + """Boolean thresholding of array-like or scipy.sparse matrix. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data to binarize, element by element. + scipy.sparse matrices should be in CSR or CSC format to avoid an + un-necessary copy. + + threshold : float, default=0.0 + Feature values below or equal to this are replaced by 0, above it by 1. + Threshold may not be less than 0 for operations on sparse matrices. + + copy : bool, default=True + Set to False to perform inplace binarization and avoid a copy + (if the input is already a numpy array or a scipy.sparse CSR / CSC + matrix and if axis is 1). + + Returns + ------- + X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) + The transformed data. + + See Also + -------- + Binarizer : Performs binarization using the Transformer API + (e.g. as part of a preprocessing :class:`~sklearn.pipeline.Pipeline`). + """ + X = check_array(X, accept_sparse=["csr", "csc"], copy=copy) + if sparse.issparse(X): + if threshold < 0: + raise ValueError("Cannot binarize a sparse matrix with threshold < 0") + cond = X.data > threshold + not_cond = np.logical_not(cond) + X.data[cond] = 1 + X.data[not_cond] = 0 + X.eliminate_zeros() + else: + cond = X > threshold + not_cond = np.logical_not(cond) + X[cond] = 1 + X[not_cond] = 0 + return X + + +class Binarizer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): + """Binarize data (set feature values to 0 or 1) according to a threshold. + + Values greater than the threshold map to 1, while values less than + or equal to the threshold map to 0. With the default threshold of 0, + only positive values map to 1. + + Binarization is a common operation on text count data where the + analyst can decide to only consider the presence or absence of a + feature rather than a quantified number of occurrences for instance. + + It can also be used as a pre-processing step for estimators that + consider boolean random variables (e.g. modelled using the Bernoulli + distribution in a Bayesian setting). + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + threshold : float, default=0.0 + Feature values below or equal to this are replaced by 0, above it by 1. + Threshold may not be less than 0 for operations on sparse matrices. + + copy : bool, default=True + Set to False to perform inplace binarization and avoid a copy (if + the input is already a numpy array or a scipy.sparse CSR matrix). + + Attributes + ---------- + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + See Also + -------- + binarize : Equivalent function without the estimator API. + KBinsDiscretizer : Bin continuous data into intervals. + OneHotEncoder : Encode categorical features as a one-hot numeric array. + + Notes + ----- + If the input is a sparse matrix, only the non-zero values are subject + to update by the :class:`Binarizer` class. + + This estimator is :term:`stateless` and does not need to be fitted. + However, we recommend to call :meth:`fit_transform` instead of + :meth:`transform`, as parameter validation is only performed in + :meth:`fit`. + + Examples + -------- + >>> from sklearn.preprocessing import Binarizer + >>> X = [[ 1., -1., 2.], + ... [ 2., 0., 0.], + ... [ 0., 1., -1.]] + >>> transformer = Binarizer().fit(X) # fit does nothing. + >>> transformer + Binarizer() + >>> transformer.transform(X) + array([[1., 0., 1.], + [1., 0., 0.], + [0., 1., 0.]]) + """ + + _parameter_constraints: dict = { + "threshold": [Real], + "copy": ["boolean"], + } + + def __init__(self, *, threshold=0.0, copy=True): + self.threshold = threshold + self.copy = copy + + def fit(self, X, y=None): + """Only validates estimator's parameters. + + This method allows to: (i) validate the estimator's parameters and + (ii) be consistent with the scikit-learn transformer API. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data. + + y : None + Ignored. + + Returns + ------- + self : object + Fitted transformer. + """ + self._validate_params() + self._validate_data(X, accept_sparse="csr") + return self + + def transform(self, X, copy=None): + """Binarize each element of X. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data to binarize, element by element. + scipy.sparse matrices should be in CSR format to avoid an + un-necessary copy. + + copy : bool + Copy the input X or not. + + Returns + ------- + X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) + Transformed array. + """ + copy = copy if copy is not None else self.copy + # TODO: This should be refactored because binarize also calls + # check_array + X = self._validate_data(X, accept_sparse=["csr", "csc"], copy=copy, reset=False) + return binarize(X, threshold=self.threshold, copy=False) + + def _more_tags(self): + return {"stateless": True} + + +class KernelCenterer(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator): + r"""Center an arbitrary kernel matrix :math:`K`. + + Let define a kernel :math:`K` such that: + + .. math:: + K(X, Y) = \phi(X) . \phi(Y)^{T} + + :math:`\phi(X)` is a function mapping of rows of :math:`X` to a + Hilbert space and :math:`K` is of shape `(n_samples, n_samples)`. + + This class allows to compute :math:`\tilde{K}(X, Y)` such that: + + .. math:: + \tilde{K(X, Y)} = \tilde{\phi}(X) . \tilde{\phi}(Y)^{T} + + :math:`\tilde{\phi}(X)` is the centered mapped data in the Hilbert + space. + + `KernelCenterer` centers the features without explicitly computing the + mapping :math:`\phi(\cdot)`. Working with centered kernels is sometime + expected when dealing with algebra computation such as eigendecomposition + for :class:`~sklearn.decomposition.KernelPCA` for instance. + + Read more in the :ref:`User Guide `. + + Attributes + ---------- + K_fit_rows_ : ndarray of shape (n_samples,) + Average of each column of kernel matrix. + + K_fit_all_ : float + Average of kernel matrix. + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + See Also + -------- + sklearn.kernel_approximation.Nystroem : Approximate a kernel map + using a subset of the training data. + + References + ---------- + .. [1] `Schölkopf, Bernhard, Alexander Smola, and Klaus-Robert Müller. + "Nonlinear component analysis as a kernel eigenvalue problem." + Neural computation 10.5 (1998): 1299-1319. + `_ + + Examples + -------- + >>> from sklearn.preprocessing import KernelCenterer + >>> from sklearn.metrics.pairwise import pairwise_kernels + >>> X = [[ 1., -2., 2.], + ... [ -2., 1., 3.], + ... [ 4., 1., -2.]] + >>> K = pairwise_kernels(X, metric='linear') + >>> K + array([[ 9., 2., -2.], + [ 2., 14., -13.], + [ -2., -13., 21.]]) + >>> transformer = KernelCenterer().fit(K) + >>> transformer + KernelCenterer() + >>> transformer.transform(K) + array([[ 5., 0., -5.], + [ 0., 14., -14.], + [ -5., -14., 19.]]) + """ + + def __init__(self): + # Needed for backported inspect.signature compatibility with PyPy + pass + + def fit(self, K, y=None): + """Fit KernelCenterer. + + Parameters + ---------- + K : ndarray of shape (n_samples, n_samples) + Kernel matrix. + + y : None + Ignored. + + Returns + ------- + self : object + Returns the instance itself. + """ + K = self._validate_data(K, dtype=FLOAT_DTYPES) + + if K.shape[0] != K.shape[1]: + raise ValueError( + "Kernel matrix must be a square matrix." + " Input is a {}x{} matrix.".format(K.shape[0], K.shape[1]) + ) + + n_samples = K.shape[0] + self.K_fit_rows_ = np.sum(K, axis=0) / n_samples + self.K_fit_all_ = self.K_fit_rows_.sum() / n_samples + return self + + def transform(self, K, copy=True): + """Center kernel matrix. + + Parameters + ---------- + K : ndarray of shape (n_samples1, n_samples2) + Kernel matrix. + + copy : bool, default=True + Set to False to perform inplace computation. + + Returns + ------- + K_new : ndarray of shape (n_samples1, n_samples2) + Returns the instance itself. + """ + check_is_fitted(self) + + K = self._validate_data(K, copy=copy, dtype=FLOAT_DTYPES, reset=False) + + K_pred_cols = (np.sum(K, axis=1) / self.K_fit_rows_.shape[0])[:, np.newaxis] + + K -= self.K_fit_rows_ + K -= K_pred_cols + K += self.K_fit_all_ + + return K + + @property + def _n_features_out(self): + """Number of transformed output features.""" + # Used by ClassNamePrefixFeaturesOutMixin. This model preserves the + # number of input features but this is not a one-to-one mapping in the + # usual sense. Hence the choice not to use OneToOneFeatureMixin to + # implement get_feature_names_out for this class. + return self.n_features_in_ + + def _more_tags(self): + return {"pairwise": True} + + +def add_dummy_feature(X, value=1.0): + """Augment dataset with an additional dummy feature. + + This is useful for fitting an intercept term with implementations which + cannot otherwise fit it directly. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + Data. + + value : float + Value to use for the dummy feature. + + Returns + ------- + X : {ndarray, sparse matrix} of shape (n_samples, n_features + 1) + Same data with dummy feature added as first column. + + Examples + -------- + >>> from sklearn.preprocessing import add_dummy_feature + >>> add_dummy_feature([[0, 1], [1, 0]]) + array([[1., 0., 1.], + [1., 1., 0.]]) + """ + X = check_array(X, accept_sparse=["csc", "csr", "coo"], dtype=FLOAT_DTYPES) + n_samples, n_features = X.shape + shape = (n_samples, n_features + 1) + if sparse.issparse(X): + if sparse.isspmatrix_coo(X): + # Shift columns to the right. + col = X.col + 1 + # Column indices of dummy feature are 0 everywhere. + col = np.concatenate((np.zeros(n_samples), col)) + # Row indices of dummy feature are 0, ..., n_samples-1. + row = np.concatenate((np.arange(n_samples), X.row)) + # Prepend the dummy feature n_samples times. + data = np.concatenate((np.full(n_samples, value), X.data)) + return sparse.coo_matrix((data, (row, col)), shape) + elif sparse.isspmatrix_csc(X): + # Shift index pointers since we need to add n_samples elements. + indptr = X.indptr + n_samples + # indptr[0] must be 0. + indptr = np.concatenate((np.array([0]), indptr)) + # Row indices of dummy feature are 0, ..., n_samples-1. + indices = np.concatenate((np.arange(n_samples), X.indices)) + # Prepend the dummy feature n_samples times. + data = np.concatenate((np.full(n_samples, value), X.data)) + return sparse.csc_matrix((data, indices, indptr), shape) + else: + klass = X.__class__ + return klass(add_dummy_feature(X.tocoo(), value)) + else: + return np.hstack((np.full((n_samples, 1), value), X)) + + +class QuantileTransformer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): + """Transform features using quantiles information. + + This method transforms the features to follow a uniform or a normal + distribution. Therefore, for a given feature, this transformation tends + to spread out the most frequent values. It also reduces the impact of + (marginal) outliers: this is therefore a robust preprocessing scheme. + + The transformation is applied on each feature independently. First an + estimate of the cumulative distribution function of a feature is + used to map the original values to a uniform distribution. The obtained + values are then mapped to the desired output distribution using the + associated quantile function. Features values of new/unseen data that fall + below or above the fitted range will be mapped to the bounds of the output + distribution. Note that this transform is non-linear. It may distort linear + correlations between variables measured at the same scale but renders + variables measured at different scales more directly comparable. + + Read more in the :ref:`User Guide `. + + .. versionadded:: 0.19 + + Parameters + ---------- + n_quantiles : int, default=1000 or n_samples + Number of quantiles to be computed. It corresponds to the number + of landmarks used to discretize the cumulative distribution function. + If n_quantiles is larger than the number of samples, n_quantiles is set + to the number of samples as a larger number of quantiles does not give + a better approximation of the cumulative distribution function + estimator. + + output_distribution : {'uniform', 'normal'}, default='uniform' + Marginal distribution for the transformed data. The choices are + 'uniform' (default) or 'normal'. + + ignore_implicit_zeros : bool, default=False + Only applies to sparse matrices. If True, the sparse entries of the + matrix are discarded to compute the quantile statistics. If False, + these entries are treated as zeros. + + subsample : int, default=10_000 + Maximum number of samples used to estimate the quantiles for + computational efficiency. Note that the subsampling procedure may + differ for value-identical sparse and dense matrices. + + random_state : int, RandomState instance or None, default=None + Determines random number generation for subsampling and smoothing + noise. + Please see ``subsample`` for more details. + Pass an int for reproducible results across multiple function calls. + See :term:`Glossary `. + + copy : bool, default=True + Set to False to perform inplace transformation and avoid a copy (if the + input is already a numpy array). + + Attributes + ---------- + n_quantiles_ : int + The actual number of quantiles used to discretize the cumulative + distribution function. + + quantiles_ : ndarray of shape (n_quantiles, n_features) + The values corresponding the quantiles of reference. + + references_ : ndarray of shape (n_quantiles, ) + Quantiles of references. + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + See Also + -------- + quantile_transform : Equivalent function without the estimator API. + PowerTransformer : Perform mapping to a normal distribution using a power + transform. + StandardScaler : Perform standardization that is faster, but less robust + to outliers. + RobustScaler : Perform robust standardization that removes the influence + of outliers but does not put outliers and inliers on the same scale. + + Notes + ----- + NaNs are treated as missing values: disregarded in fit, and maintained in + transform. + + For a comparison of the different scalers, transformers, and normalizers, + see :ref:`examples/preprocessing/plot_all_scaling.py + `. + + Examples + -------- + >>> import numpy as np + >>> from sklearn.preprocessing import QuantileTransformer + >>> rng = np.random.RandomState(0) + >>> X = np.sort(rng.normal(loc=0.5, scale=0.25, size=(25, 1)), axis=0) + >>> qt = QuantileTransformer(n_quantiles=10, random_state=0) + >>> qt.fit_transform(X) + array([...]) + """ + + _parameter_constraints: dict = { + "n_quantiles": [Interval(Integral, 1, None, closed="left")], + "output_distribution": [StrOptions({"uniform", "normal"})], + "ignore_implicit_zeros": ["boolean"], + "subsample": [Interval(Integral, 1, None, closed="left")], + "random_state": ["random_state"], + "copy": ["boolean"], + } + + def __init__( + self, + *, + n_quantiles=1000, + output_distribution="uniform", + ignore_implicit_zeros=False, + subsample=10_000, + random_state=None, + copy=True, + ): + self.n_quantiles = n_quantiles + self.output_distribution = output_distribution + self.ignore_implicit_zeros = ignore_implicit_zeros + self.subsample = subsample + self.random_state = random_state + self.copy = copy + + def _dense_fit(self, X, random_state): + """Compute percentiles for dense matrices. + + Parameters + ---------- + X : ndarray of shape (n_samples, n_features) + The data used to scale along the features axis. + """ + if self.ignore_implicit_zeros: + warnings.warn( + "'ignore_implicit_zeros' takes effect only with" + " sparse matrix. This parameter has no effect." + ) + + n_samples, n_features = X.shape + references = self.references_ * 100 + + self.quantiles_ = [] + for col in X.T: + if self.subsample < n_samples: + subsample_idx = random_state.choice( + n_samples, size=self.subsample, replace=False + ) + col = col.take(subsample_idx, mode="clip") + self.quantiles_.append(np.nanpercentile(col, references)) + self.quantiles_ = np.transpose(self.quantiles_) + # Due to floating-point precision error in `np.nanpercentile`, + # make sure that quantiles are monotonically increasing. + # Upstream issue in numpy: + # https://github.com/numpy/numpy/issues/14685 + self.quantiles_ = np.maximum.accumulate(self.quantiles_) + + def _sparse_fit(self, X, random_state): + """Compute percentiles for sparse matrices. + + Parameters + ---------- + X : sparse matrix of shape (n_samples, n_features) + The data used to scale along the features axis. The sparse matrix + needs to be nonnegative. If a sparse matrix is provided, + it will be converted into a sparse ``csc_matrix``. + """ + n_samples, n_features = X.shape + references = self.references_ * 100 + + self.quantiles_ = [] + for feature_idx in range(n_features): + column_nnz_data = X.data[X.indptr[feature_idx] : X.indptr[feature_idx + 1]] + if len(column_nnz_data) > self.subsample: + column_subsample = self.subsample * len(column_nnz_data) // n_samples + if self.ignore_implicit_zeros: + column_data = np.zeros(shape=column_subsample, dtype=X.dtype) + else: + column_data = np.zeros(shape=self.subsample, dtype=X.dtype) + column_data[:column_subsample] = random_state.choice( + column_nnz_data, size=column_subsample, replace=False + ) + else: + if self.ignore_implicit_zeros: + column_data = np.zeros(shape=len(column_nnz_data), dtype=X.dtype) + else: + column_data = np.zeros(shape=n_samples, dtype=X.dtype) + column_data[: len(column_nnz_data)] = column_nnz_data + + if not column_data.size: + # if no nnz, an error will be raised for computing the + # quantiles. Force the quantiles to be zeros. + self.quantiles_.append([0] * len(references)) + else: + self.quantiles_.append(np.nanpercentile(column_data, references)) + self.quantiles_ = np.transpose(self.quantiles_) + # due to floating-point precision error in `np.nanpercentile`, + # make sure the quantiles are monotonically increasing + # Upstream issue in numpy: + # https://github.com/numpy/numpy/issues/14685 + self.quantiles_ = np.maximum.accumulate(self.quantiles_) + + def fit(self, X, y=None): + """Compute the quantiles used for transforming. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data used to scale along the features axis. If a sparse + matrix is provided, it will be converted into a sparse + ``csc_matrix``. Additionally, the sparse matrix needs to be + nonnegative if `ignore_implicit_zeros` is False. + + y : None + Ignored. + + Returns + ------- + self : object + Fitted transformer. + """ + self._validate_params() + + if self.n_quantiles > self.subsample: + raise ValueError( + "The number of quantiles cannot be greater than" + " the number of samples used. Got {} quantiles" + " and {} samples.".format(self.n_quantiles, self.subsample) + ) + + X = self._check_inputs(X, in_fit=True, copy=False) + n_samples = X.shape[0] + + if self.n_quantiles > n_samples: + warnings.warn( + "n_quantiles (%s) is greater than the total number " + "of samples (%s). n_quantiles is set to " + "n_samples." % (self.n_quantiles, n_samples) + ) + self.n_quantiles_ = max(1, min(self.n_quantiles, n_samples)) + + rng = check_random_state(self.random_state) + + # Create the quantiles of reference + self.references_ = np.linspace(0, 1, self.n_quantiles_, endpoint=True) + if sparse.issparse(X): + self._sparse_fit(X, rng) + else: + self._dense_fit(X, rng) + + return self + + def _transform_col(self, X_col, quantiles, inverse): + """Private function to transform a single feature.""" + + output_distribution = self.output_distribution + + if not inverse: + lower_bound_x = quantiles[0] + upper_bound_x = quantiles[-1] + lower_bound_y = 0 + upper_bound_y = 1 + else: + lower_bound_x = 0 + upper_bound_x = 1 + lower_bound_y = quantiles[0] + upper_bound_y = quantiles[-1] + # for inverse transform, match a uniform distribution + with np.errstate(invalid="ignore"): # hide NaN comparison warnings + if output_distribution == "normal": + X_col = stats.norm.cdf(X_col) + # else output distribution is already a uniform distribution + + # find index for lower and higher bounds + with np.errstate(invalid="ignore"): # hide NaN comparison warnings + if output_distribution == "normal": + lower_bounds_idx = X_col - BOUNDS_THRESHOLD < lower_bound_x + upper_bounds_idx = X_col + BOUNDS_THRESHOLD > upper_bound_x + if output_distribution == "uniform": + lower_bounds_idx = X_col == lower_bound_x + upper_bounds_idx = X_col == upper_bound_x + + isfinite_mask = ~np.isnan(X_col) + X_col_finite = X_col[isfinite_mask] + if not inverse: + # Interpolate in one direction and in the other and take the + # mean. This is in case of repeated values in the features + # and hence repeated quantiles + # + # If we don't do this, only one extreme of the duplicated is + # used (the upper when we do ascending, and the + # lower for descending). We take the mean of these two + X_col[isfinite_mask] = 0.5 * ( + np.interp(X_col_finite, quantiles, self.references_) + - np.interp(-X_col_finite, -quantiles[::-1], -self.references_[::-1]) + ) + else: + X_col[isfinite_mask] = np.interp(X_col_finite, self.references_, quantiles) + + X_col[upper_bounds_idx] = upper_bound_y + X_col[lower_bounds_idx] = lower_bound_y + # for forward transform, match the output distribution + if not inverse: + with np.errstate(invalid="ignore"): # hide NaN comparison warnings + if output_distribution == "normal": + X_col = stats.norm.ppf(X_col) + # find the value to clip the data to avoid mapping to + # infinity. Clip such that the inverse transform will be + # consistent + clip_min = stats.norm.ppf(BOUNDS_THRESHOLD - np.spacing(1)) + clip_max = stats.norm.ppf(1 - (BOUNDS_THRESHOLD - np.spacing(1))) + X_col = np.clip(X_col, clip_min, clip_max) + # else output distribution is uniform and the ppf is the + # identity function so we let X_col unchanged + + return X_col + + def _check_inputs(self, X, in_fit, accept_sparse_negative=False, copy=False): + """Check inputs before fit and transform.""" + X = self._validate_data( + X, + reset=in_fit, + accept_sparse="csc", + copy=copy, + dtype=FLOAT_DTYPES, + force_all_finite="allow-nan", + ) + # we only accept positive sparse matrix when ignore_implicit_zeros is + # false and that we call fit or transform. + with np.errstate(invalid="ignore"): # hide NaN comparison warnings + if ( + not accept_sparse_negative + and not self.ignore_implicit_zeros + and (sparse.issparse(X) and np.any(X.data < 0)) + ): + raise ValueError( + "QuantileTransformer only accepts non-negative sparse matrices." + ) + + return X + + def _transform(self, X, inverse=False): + """Forward and inverse transform. + + Parameters + ---------- + X : ndarray of shape (n_samples, n_features) + The data used to scale along the features axis. + + inverse : bool, default=False + If False, apply forward transform. If True, apply + inverse transform. + + Returns + ------- + X : ndarray of shape (n_samples, n_features) + Projected data. + """ + if sparse.issparse(X): + for feature_idx in range(X.shape[1]): + column_slice = slice(X.indptr[feature_idx], X.indptr[feature_idx + 1]) + X.data[column_slice] = self._transform_col( + X.data[column_slice], self.quantiles_[:, feature_idx], inverse + ) + else: + for feature_idx in range(X.shape[1]): + X[:, feature_idx] = self._transform_col( + X[:, feature_idx], self.quantiles_[:, feature_idx], inverse + ) + + return X + + def transform(self, X): + """Feature-wise transformation of the data. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data used to scale along the features axis. If a sparse + matrix is provided, it will be converted into a sparse + ``csc_matrix``. Additionally, the sparse matrix needs to be + nonnegative if `ignore_implicit_zeros` is False. + + Returns + ------- + Xt : {ndarray, sparse matrix} of shape (n_samples, n_features) + The projected data. + """ + check_is_fitted(self) + X = self._check_inputs(X, in_fit=False, copy=self.copy) + + return self._transform(X, inverse=False) + + def inverse_transform(self, X): + """Back-projection to the original space. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data used to scale along the features axis. If a sparse + matrix is provided, it will be converted into a sparse + ``csc_matrix``. Additionally, the sparse matrix needs to be + nonnegative if `ignore_implicit_zeros` is False. + + Returns + ------- + Xt : {ndarray, sparse matrix} of (n_samples, n_features) + The projected data. + """ + check_is_fitted(self) + X = self._check_inputs( + X, in_fit=False, accept_sparse_negative=True, copy=self.copy + ) + + return self._transform(X, inverse=True) + + def _more_tags(self): + return {"allow_nan": True} + + +def quantile_transform( + X, + *, + axis=0, + n_quantiles=1000, + output_distribution="uniform", + ignore_implicit_zeros=False, + subsample=int(1e5), + random_state=None, + copy=True, +): + """Transform features using quantiles information. + + This method transforms the features to follow a uniform or a normal + distribution. Therefore, for a given feature, this transformation tends + to spread out the most frequent values. It also reduces the impact of + (marginal) outliers: this is therefore a robust preprocessing scheme. + + The transformation is applied on each feature independently. First an + estimate of the cumulative distribution function of a feature is + used to map the original values to a uniform distribution. The obtained + values are then mapped to the desired output distribution using the + associated quantile function. Features values of new/unseen data that fall + below or above the fitted range will be mapped to the bounds of the output + distribution. Note that this transform is non-linear. It may distort linear + correlations between variables measured at the same scale but renders + variables measured at different scales more directly comparable. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data to transform. + + axis : int, default=0 + Axis used to compute the means and standard deviations along. If 0, + transform each feature, otherwise (if 1) transform each sample. + + n_quantiles : int, default=1000 or n_samples + Number of quantiles to be computed. It corresponds to the number + of landmarks used to discretize the cumulative distribution function. + If n_quantiles is larger than the number of samples, n_quantiles is set + to the number of samples as a larger number of quantiles does not give + a better approximation of the cumulative distribution function + estimator. + + output_distribution : {'uniform', 'normal'}, default='uniform' + Marginal distribution for the transformed data. The choices are + 'uniform' (default) or 'normal'. + + ignore_implicit_zeros : bool, default=False + Only applies to sparse matrices. If True, the sparse entries of the + matrix are discarded to compute the quantile statistics. If False, + these entries are treated as zeros. + + subsample : int, default=1e5 + Maximum number of samples used to estimate the quantiles for + computational efficiency. Note that the subsampling procedure may + differ for value-identical sparse and dense matrices. + + random_state : int, RandomState instance or None, default=None + Determines random number generation for subsampling and smoothing + noise. + Please see ``subsample`` for more details. + Pass an int for reproducible results across multiple function calls. + See :term:`Glossary `. + + copy : bool, default=True + Set to False to perform inplace transformation and avoid a copy (if the + input is already a numpy array). If True, a copy of `X` is transformed, + leaving the original `X` unchanged. + + .. versionchanged:: 0.23 + The default value of `copy` changed from False to True in 0.23. + + Returns + ------- + Xt : {ndarray, sparse matrix} of shape (n_samples, n_features) + The transformed data. + + See Also + -------- + QuantileTransformer : Performs quantile-based scaling using the + Transformer API (e.g. as part of a preprocessing + :class:`~sklearn.pipeline.Pipeline`). + power_transform : Maps data to a normal distribution using a + power transformation. + scale : Performs standardization that is faster, but less robust + to outliers. + robust_scale : Performs robust standardization that removes the influence + of outliers but does not put outliers and inliers on the same scale. + + Notes + ----- + NaNs are treated as missing values: disregarded in fit, and maintained in + transform. + + .. warning:: Risk of data leak + + Do not use :func:`~sklearn.preprocessing.quantile_transform` unless + you know what you are doing. A common mistake is to apply it + to the entire data *before* splitting into training and + test sets. This will bias the model evaluation because + information would have leaked from the test set to the + training set. + In general, we recommend using + :class:`~sklearn.preprocessing.QuantileTransformer` within a + :ref:`Pipeline ` in order to prevent most risks of data + leaking:`pipe = make_pipeline(QuantileTransformer(), + LogisticRegression())`. + + For a comparison of the different scalers, transformers, and normalizers, + see :ref:`examples/preprocessing/plot_all_scaling.py + `. + + Examples + -------- + >>> import numpy as np + >>> from sklearn.preprocessing import quantile_transform + >>> rng = np.random.RandomState(0) + >>> X = np.sort(rng.normal(loc=0.5, scale=0.25, size=(25, 1)), axis=0) + >>> quantile_transform(X, n_quantiles=10, random_state=0, copy=True) + array([...]) + """ + n = QuantileTransformer( + n_quantiles=n_quantiles, + output_distribution=output_distribution, + subsample=subsample, + ignore_implicit_zeros=ignore_implicit_zeros, + random_state=random_state, + copy=copy, + ) + if axis == 0: + return n.fit_transform(X) + elif axis == 1: + return n.fit_transform(X.T).T + else: + raise ValueError( + "axis should be either equal to 0 or 1. Got axis={}".format(axis) + ) + + +class PowerTransformer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): + """Apply a power transform featurewise to make data more Gaussian-like. + + Power transforms are a family of parametric, monotonic transformations + that are applied to make data more Gaussian-like. This is useful for + modeling issues related to heteroscedasticity (non-constant variance), + or other situations where normality is desired. + + Currently, PowerTransformer supports the Box-Cox transform and the + Yeo-Johnson transform. The optimal parameter for stabilizing variance and + minimizing skewness is estimated through maximum likelihood. + + Box-Cox requires input data to be strictly positive, while Yeo-Johnson + supports both positive or negative data. + + By default, zero-mean, unit-variance normalization is applied to the + transformed data. + + Read more in the :ref:`User Guide `. + + .. versionadded:: 0.20 + + Parameters + ---------- + method : {'yeo-johnson', 'box-cox'}, default='yeo-johnson' + The power transform method. Available methods are: + + - 'yeo-johnson' [1]_, works with positive and negative values + - 'box-cox' [2]_, only works with strictly positive values + + standardize : bool, default=True + Set to True to apply zero-mean, unit-variance normalization to the + transformed output. + + copy : bool, default=True + Set to False to perform inplace computation during transformation. + + Attributes + ---------- + lambdas_ : ndarray of float of shape (n_features,) + The parameters of the power transformation for the selected features. + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + See Also + -------- + power_transform : Equivalent function without the estimator API. + + QuantileTransformer : Maps data to a standard normal distribution with + the parameter `output_distribution='normal'`. + + Notes + ----- + NaNs are treated as missing values: disregarded in ``fit``, and maintained + in ``transform``. + + For a comparison of the different scalers, transformers, and normalizers, + see :ref:`examples/preprocessing/plot_all_scaling.py + `. + + References + ---------- + + .. [1] I.K. Yeo and R.A. Johnson, "A new family of power transformations to + improve normality or symmetry." Biometrika, 87(4), pp.954-959, + (2000). + + .. [2] G.E.P. Box and D.R. Cox, "An Analysis of Transformations", Journal + of the Royal Statistical Society B, 26, 211-252 (1964). + + Examples + -------- + >>> import numpy as np + >>> from sklearn.preprocessing import PowerTransformer + >>> pt = PowerTransformer() + >>> data = [[1, 2], [3, 2], [4, 5]] + >>> print(pt.fit(data)) + PowerTransformer() + >>> print(pt.lambdas_) + [ 1.386... -3.100...] + >>> print(pt.transform(data)) + [[-1.316... -0.707...] + [ 0.209... -0.707...] + [ 1.106... 1.414...]] + """ + + _parameter_constraints: dict = { + "method": [StrOptions({"yeo-johnson", "box-cox"})], + "standardize": ["boolean"], + "copy": ["boolean"], + } + + def __init__(self, method="yeo-johnson", *, standardize=True, copy=True): + self.method = method + self.standardize = standardize + self.copy = copy + + def fit(self, X, y=None): + """Estimate the optimal parameter lambda for each feature. + + The optimal lambda parameter for minimizing skewness is estimated on + each feature independently using maximum likelihood. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data used to estimate the optimal transformation parameters. + + y : None + Ignored. + + Returns + ------- + self : object + Fitted transformer. + """ + self._validate_params() + self._fit(X, y=y, force_transform=False) + return self + + def fit_transform(self, X, y=None): + """Fit `PowerTransformer` to `X`, then transform `X`. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data used to estimate the optimal transformation parameters + and to be transformed using a power transformation. + + y : Ignored + Not used, present for API consistency by convention. + + Returns + ------- + X_new : ndarray of shape (n_samples, n_features) + Transformed data. + """ + self._validate_params() + return self._fit(X, y, force_transform=True) + + def _fit(self, X, y=None, force_transform=False): + X = self._check_input(X, in_fit=True, check_positive=True) + + if not self.copy and not force_transform: # if call from fit() + X = X.copy() # force copy so that fit does not change X inplace + + optim_function = { + "box-cox": self._box_cox_optimize, + "yeo-johnson": self._yeo_johnson_optimize, + }[self.method] + with np.errstate(invalid="ignore"): # hide NaN warnings + self.lambdas_ = np.array([optim_function(col) for col in X.T]) + + if self.standardize or force_transform: + transform_function = { + "box-cox": boxcox, + "yeo-johnson": self._yeo_johnson_transform, + }[self.method] + for i, lmbda in enumerate(self.lambdas_): + with np.errstate(invalid="ignore"): # hide NaN warnings + X[:, i] = transform_function(X[:, i], lmbda) + + if self.standardize: + self._scaler = StandardScaler(copy=False) + if force_transform: + X = self._scaler.fit_transform(X) + else: + self._scaler.fit(X) + + return X + + def transform(self, X): + """Apply the power transform to each feature using the fitted lambdas. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to be transformed using a power transformation. + + Returns + ------- + X_trans : ndarray of shape (n_samples, n_features) + The transformed data. + """ + check_is_fitted(self) + X = self._check_input(X, in_fit=False, check_positive=True, check_shape=True) + + transform_function = { + "box-cox": boxcox, + "yeo-johnson": self._yeo_johnson_transform, + }[self.method] + for i, lmbda in enumerate(self.lambdas_): + with np.errstate(invalid="ignore"): # hide NaN warnings + X[:, i] = transform_function(X[:, i], lmbda) + + if self.standardize: + X = self._scaler.transform(X) + + return X + + def inverse_transform(self, X): + """Apply the inverse power transformation using the fitted lambdas. + + The inverse of the Box-Cox transformation is given by:: + + if lambda_ == 0: + X = exp(X_trans) + else: + X = (X_trans * lambda_ + 1) ** (1 / lambda_) + + The inverse of the Yeo-Johnson transformation is given by:: + + if X >= 0 and lambda_ == 0: + X = exp(X_trans) - 1 + elif X >= 0 and lambda_ != 0: + X = (X_trans * lambda_ + 1) ** (1 / lambda_) - 1 + elif X < 0 and lambda_ != 2: + X = 1 - (-(2 - lambda_) * X_trans + 1) ** (1 / (2 - lambda_)) + elif X < 0 and lambda_ == 2: + X = 1 - exp(-X_trans) + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The transformed data. + + Returns + ------- + X : ndarray of shape (n_samples, n_features) + The original data. + """ + check_is_fitted(self) + X = self._check_input(X, in_fit=False, check_shape=True) + + if self.standardize: + X = self._scaler.inverse_transform(X) + + inv_fun = { + "box-cox": self._box_cox_inverse_tranform, + "yeo-johnson": self._yeo_johnson_inverse_transform, + }[self.method] + for i, lmbda in enumerate(self.lambdas_): + with np.errstate(invalid="ignore"): # hide NaN warnings + X[:, i] = inv_fun(X[:, i], lmbda) + + return X + + def _box_cox_inverse_tranform(self, x, lmbda): + """Return inverse-transformed input x following Box-Cox inverse + transform with parameter lambda. + """ + if lmbda == 0: + x_inv = np.exp(x) + else: + x_inv = (x * lmbda + 1) ** (1 / lmbda) + + return x_inv + + def _yeo_johnson_inverse_transform(self, x, lmbda): + """Return inverse-transformed input x following Yeo-Johnson inverse + transform with parameter lambda. + """ + x_inv = np.zeros_like(x) + pos = x >= 0 + + # when x >= 0 + if abs(lmbda) < np.spacing(1.0): + x_inv[pos] = np.exp(x[pos]) - 1 + else: # lmbda != 0 + x_inv[pos] = np.power(x[pos] * lmbda + 1, 1 / lmbda) - 1 + + # when x < 0 + if abs(lmbda - 2) > np.spacing(1.0): + x_inv[~pos] = 1 - np.power(-(2 - lmbda) * x[~pos] + 1, 1 / (2 - lmbda)) + else: # lmbda == 2 + x_inv[~pos] = 1 - np.exp(-x[~pos]) + + return x_inv + + def _yeo_johnson_transform(self, x, lmbda): + """Return transformed input x following Yeo-Johnson transform with + parameter lambda. + """ + + out = np.zeros_like(x) + pos = x >= 0 # binary mask + + # when x >= 0 + if abs(lmbda) < np.spacing(1.0): + out[pos] = np.log1p(x[pos]) + else: # lmbda != 0 + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + + # when x < 0 + if abs(lmbda - 2) > np.spacing(1.0): + out[~pos] = -(np.power(-x[~pos] + 1, 2 - lmbda) - 1) / (2 - lmbda) + else: # lmbda == 2 + out[~pos] = -np.log1p(-x[~pos]) + + return out + + def _box_cox_optimize(self, x): + """Find and return optimal lambda parameter of the Box-Cox transform by + MLE, for observed data x. + + We here use scipy builtins which uses the brent optimizer. + """ + # the computation of lambda is influenced by NaNs so we need to + # get rid of them + _, lmbda = stats.boxcox(x[~np.isnan(x)], lmbda=None) + + return lmbda + + def _yeo_johnson_optimize(self, x): + """Find and return optimal lambda parameter of the Yeo-Johnson + transform by MLE, for observed data x. + + Like for Box-Cox, MLE is done via the brent optimizer. + """ + x_tiny = np.finfo(np.float64).tiny + + def _neg_log_likelihood(lmbda): + """Return the negative log likelihood of the observed data x as a + function of lambda.""" + x_trans = self._yeo_johnson_transform(x, lmbda) + n_samples = x.shape[0] + x_trans_var = x_trans.var() + + # Reject transformed data that would raise a RuntimeWarning in np.log + if x_trans_var < x_tiny: + return np.inf + + log_var = np.log(x_trans_var) + loglike = -n_samples / 2 * log_var + loglike += (lmbda - 1) * (np.sign(x) * np.log1p(np.abs(x))).sum() + + return -loglike + + # the computation of lambda is influenced by NaNs so we need to + # get rid of them + x = x[~np.isnan(x)] + # choosing bracket -2, 2 like for boxcox + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + + def _check_input(self, X, in_fit, check_positive=False, check_shape=False): + """Validate the input before fit and transform. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + + in_fit : bool + Whether or not `_check_input` is called from `fit` or other + methods, e.g. `predict`, `transform`, etc. + + check_positive : bool, default=False + If True, check that all data is positive and non-zero (only if + ``self.method=='box-cox'``). + + check_shape : bool, default=False + If True, check that n_features matches the length of self.lambdas_ + """ + X = self._validate_data( + X, + ensure_2d=True, + dtype=FLOAT_DTYPES, + copy=self.copy, + force_all_finite="allow-nan", + reset=in_fit, + ) + + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", r"All-NaN (slice|axis) encountered") + if check_positive and self.method == "box-cox" and np.nanmin(X) <= 0: + raise ValueError( + "The Box-Cox transformation can only be " + "applied to strictly positive data" + ) + + if check_shape and not X.shape[1] == len(self.lambdas_): + raise ValueError( + "Input data has a different number of features " + "than fitting data. Should have {n}, data has {m}".format( + n=len(self.lambdas_), m=X.shape[1] + ) + ) + + return X + + def _more_tags(self): + return {"allow_nan": True} + + +def power_transform(X, method="yeo-johnson", *, standardize=True, copy=True): + """Parametric, monotonic transformation to make data more Gaussian-like. + + Power transforms are a family of parametric, monotonic transformations + that are applied to make data more Gaussian-like. This is useful for + modeling issues related to heteroscedasticity (non-constant variance), + or other situations where normality is desired. + + Currently, power_transform supports the Box-Cox transform and the + Yeo-Johnson transform. The optimal parameter for stabilizing variance and + minimizing skewness is estimated through maximum likelihood. + + Box-Cox requires input data to be strictly positive, while Yeo-Johnson + supports both positive or negative data. + + By default, zero-mean, unit-variance normalization is applied to the + transformed data. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to be transformed using a power transformation. + + method : {'yeo-johnson', 'box-cox'}, default='yeo-johnson' + The power transform method. Available methods are: + + - 'yeo-johnson' [1]_, works with positive and negative values + - 'box-cox' [2]_, only works with strictly positive values + + .. versionchanged:: 0.23 + The default value of the `method` parameter changed from + 'box-cox' to 'yeo-johnson' in 0.23. + + standardize : bool, default=True + Set to True to apply zero-mean, unit-variance normalization to the + transformed output. + + copy : bool, default=True + Set to False to perform inplace computation during transformation. + + Returns + ------- + X_trans : ndarray of shape (n_samples, n_features) + The transformed data. + + See Also + -------- + PowerTransformer : Equivalent transformation with the + Transformer API (e.g. as part of a preprocessing + :class:`~sklearn.pipeline.Pipeline`). + + quantile_transform : Maps data to a standard normal distribution with + the parameter `output_distribution='normal'`. + + Notes + ----- + NaNs are treated as missing values: disregarded in ``fit``, and maintained + in ``transform``. + + For a comparison of the different scalers, transformers, and normalizers, + see :ref:`examples/preprocessing/plot_all_scaling.py + `. + + References + ---------- + + .. [1] I.K. Yeo and R.A. Johnson, "A new family of power transformations to + improve normality or symmetry." Biometrika, 87(4), pp.954-959, + (2000). + + .. [2] G.E.P. Box and D.R. Cox, "An Analysis of Transformations", Journal + of the Royal Statistical Society B, 26, 211-252 (1964). + + Examples + -------- + >>> import numpy as np + >>> from sklearn.preprocessing import power_transform + >>> data = [[1, 2], [3, 2], [4, 5]] + >>> print(power_transform(data, method='box-cox')) + [[-1.332... -0.707...] + [ 0.256... -0.707...] + [ 1.076... 1.414...]] + + .. warning:: Risk of data leak. + Do not use :func:`~sklearn.preprocessing.power_transform` unless you + know what you are doing. A common mistake is to apply it to the entire + data *before* splitting into training and test sets. This will bias the + model evaluation because information would have leaked from the test + set to the training set. + In general, we recommend using + :class:`~sklearn.preprocessing.PowerTransformer` within a + :ref:`Pipeline ` in order to prevent most risks of data + leaking, e.g.: `pipe = make_pipeline(PowerTransformer(), + LogisticRegression())`. + """ + pt = PowerTransformer(method=method, standardize=standardize, copy=copy) + return pt.fit_transform(X) diff --git a/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/_discretization.py b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/_discretization.py new file mode 100644 index 0000000000000000000000000000000000000000..2c5dcff3e23bf66752a70bd7a976e5b89ba6db8e --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/_discretization.py @@ -0,0 +1,428 @@ +# Author: Henry Lin +# Tom Dupré la Tour + +# License: BSD + + +from numbers import Integral +import numpy as np +import warnings + +from . import OneHotEncoder + +from ..base import BaseEstimator, TransformerMixin +from ..utils._param_validation import Hidden, Interval, StrOptions, Options +from ..utils.validation import check_array +from ..utils.validation import check_is_fitted +from ..utils.validation import check_random_state +from ..utils.validation import _check_feature_names_in +from ..utils import _safe_indexing + + +class KBinsDiscretizer(TransformerMixin, BaseEstimator): + """ + Bin continuous data into intervals. + + Read more in the :ref:`User Guide `. + + .. versionadded:: 0.20 + + Parameters + ---------- + n_bins : int or array-like of shape (n_features,), default=5 + The number of bins to produce. Raises ValueError if ``n_bins < 2``. + + encode : {'onehot', 'onehot-dense', 'ordinal'}, default='onehot' + Method used to encode the transformed result. + + - 'onehot': Encode the transformed result with one-hot encoding + and return a sparse matrix. Ignored features are always + stacked to the right. + - 'onehot-dense': Encode the transformed result with one-hot encoding + and return a dense array. Ignored features are always + stacked to the right. + - 'ordinal': Return the bin identifier encoded as an integer value. + + strategy : {'uniform', 'quantile', 'kmeans'}, default='quantile' + Strategy used to define the widths of the bins. + + - 'uniform': All bins in each feature have identical widths. + - 'quantile': All bins in each feature have the same number of points. + - 'kmeans': Values in each bin have the same nearest center of a 1D + k-means cluster. + + dtype : {np.float32, np.float64}, default=None + The desired data-type for the output. If None, output dtype is + consistent with input dtype. Only np.float32 and np.float64 are + supported. + + .. versionadded:: 0.24 + + subsample : int or None (default='warn') + Maximum number of samples, used to fit the model, for computational + efficiency. Used when `strategy="quantile"`. + `subsample=None` means that all the training samples are used when + computing the quantiles that determine the binning thresholds. + Since quantile computation relies on sorting each column of `X` and + that sorting has an `n log(n)` time complexity, + it is recommended to use subsampling on datasets with a + very large number of samples. + + .. deprecated:: 1.1 + In version 1.3 and onwards, `subsample=2e5` will be the default. + + random_state : int, RandomState instance or None, default=None + Determines random number generation for subsampling. + Pass an int for reproducible results across multiple function calls. + See the `subsample` parameter for more details. + See :term:`Glossary `. + + .. versionadded:: 1.1 + + Attributes + ---------- + bin_edges_ : ndarray of ndarray of shape (n_features,) + The edges of each bin. Contain arrays of varying shapes ``(n_bins_, )`` + Ignored features will have empty arrays. + + n_bins_ : ndarray of shape (n_features,), dtype=np.int_ + Number of bins per feature. Bins whose width are too small + (i.e., <= 1e-8) are removed with a warning. + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + See Also + -------- + Binarizer : Class used to bin values as ``0`` or + ``1`` based on a parameter ``threshold``. + + Notes + ----- + In bin edges for feature ``i``, the first and last values are used only for + ``inverse_transform``. During transform, bin edges are extended to:: + + np.concatenate([-np.inf, bin_edges_[i][1:-1], np.inf]) + + You can combine ``KBinsDiscretizer`` with + :class:`~sklearn.compose.ColumnTransformer` if you only want to preprocess + part of the features. + + ``KBinsDiscretizer`` might produce constant features (e.g., when + ``encode = 'onehot'`` and certain bins do not contain any data). + These features can be removed with feature selection algorithms + (e.g., :class:`~sklearn.feature_selection.VarianceThreshold`). + + Examples + -------- + >>> from sklearn.preprocessing import KBinsDiscretizer + >>> X = [[-2, 1, -4, -1], + ... [-1, 2, -3, -0.5], + ... [ 0, 3, -2, 0.5], + ... [ 1, 4, -1, 2]] + >>> est = KBinsDiscretizer(n_bins=3, encode='ordinal', strategy='uniform') + >>> est.fit(X) + KBinsDiscretizer(...) + >>> Xt = est.transform(X) + >>> Xt # doctest: +SKIP + array([[ 0., 0., 0., 0.], + [ 1., 1., 1., 0.], + [ 2., 2., 2., 1.], + [ 2., 2., 2., 2.]]) + + Sometimes it may be useful to convert the data back into the original + feature space. The ``inverse_transform`` function converts the binned + data into the original feature space. Each value will be equal to the mean + of the two bin edges. + + >>> est.bin_edges_[0] + array([-2., -1., 0., 1.]) + >>> est.inverse_transform(Xt) + array([[-1.5, 1.5, -3.5, -0.5], + [-0.5, 2.5, -2.5, -0.5], + [ 0.5, 3.5, -1.5, 0.5], + [ 0.5, 3.5, -1.5, 1.5]]) + """ + + _parameter_constraints: dict = { + "n_bins": [Interval(Integral, 2, None, closed="left"), "array-like"], + "encode": [StrOptions({"onehot", "onehot-dense", "ordinal"})], + "strategy": [StrOptions({"uniform", "quantile", "kmeans"})], + "dtype": [Options(type, {np.float64, np.float32}), None], + "subsample": [ + Interval(Integral, 1, None, closed="left"), + None, + Hidden(StrOptions({"warn"})), + ], + "random_state": ["random_state"], + } + + def __init__( + self, + n_bins=5, + *, + encode="onehot", + strategy="quantile", + dtype=None, + subsample="warn", + random_state=None, + ): + self.n_bins = n_bins + self.encode = encode + self.strategy = strategy + self.dtype = dtype + self.subsample = subsample + self.random_state = random_state + + def fit(self, X, y=None): + """ + Fit the estimator. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Data to be discretized. + + y : None + Ignored. This parameter exists only for compatibility with + :class:`~sklearn.pipeline.Pipeline`. + + Returns + ------- + self : object + Returns the instance itself. + """ + self._validate_params() + X = self._validate_data(X, dtype="numeric") + + if self.dtype in (np.float64, np.float32): + output_dtype = self.dtype + else: # self.dtype is None + output_dtype = X.dtype + + n_samples, n_features = X.shape + + if self.strategy == "quantile" and self.subsample is not None: + if self.subsample == "warn": + if n_samples > 2e5: + warnings.warn( + "In version 1.3 onwards, subsample=2e5 " + "will be used by default. Set subsample explicitly to " + "silence this warning in the mean time. Set " + "subsample=None to disable subsampling explicitly.", + FutureWarning, + ) + else: + rng = check_random_state(self.random_state) + if n_samples > self.subsample: + subsample_idx = rng.choice( + n_samples, size=self.subsample, replace=False + ) + X = _safe_indexing(X, subsample_idx) + elif self.strategy != "quantile" and isinstance(self.subsample, Integral): + raise ValueError( + f"Invalid parameter for `strategy`: {self.strategy}. " + '`subsample` must be used with `strategy="quantile"`.' + ) + + n_features = X.shape[1] + n_bins = self._validate_n_bins(n_features) + + bin_edges = np.zeros(n_features, dtype=object) + for jj in range(n_features): + column = X[:, jj] + col_min, col_max = column.min(), column.max() + + if col_min == col_max: + warnings.warn( + "Feature %d is constant and will be replaced with 0." % jj + ) + n_bins[jj] = 1 + bin_edges[jj] = np.array([-np.inf, np.inf]) + continue + + if self.strategy == "uniform": + bin_edges[jj] = np.linspace(col_min, col_max, n_bins[jj] + 1) + + elif self.strategy == "quantile": + quantiles = np.linspace(0, 100, n_bins[jj] + 1) + bin_edges[jj] = np.asarray(np.percentile(column, quantiles)) + + elif self.strategy == "kmeans": + from ..cluster import KMeans # fixes import loops + + # Deterministic initialization with uniform spacing + uniform_edges = np.linspace(col_min, col_max, n_bins[jj] + 1) + init = (uniform_edges[1:] + uniform_edges[:-1])[:, None] * 0.5 + + # 1D k-means procedure + km = KMeans(n_clusters=n_bins[jj], init=init, n_init=1) + centers = km.fit(column[:, None]).cluster_centers_[:, 0] + # Must sort, centers may be unsorted even with sorted init + centers.sort() + bin_edges[jj] = (centers[1:] + centers[:-1]) * 0.5 + bin_edges[jj] = np.r_[col_min, bin_edges[jj], col_max] + + # Remove bins whose width are too small (i.e., <= 1e-8) + if self.strategy in ("quantile", "kmeans"): + mask = np.ediff1d(bin_edges[jj], to_begin=np.inf) > 1e-8 + bin_edges[jj] = bin_edges[jj][mask] + if len(bin_edges[jj]) - 1 != n_bins[jj]: + warnings.warn( + "Bins whose width are too small (i.e., <= " + "1e-8) in feature %d are removed. Consider " + "decreasing the number of bins." % jj + ) + n_bins[jj] = len(bin_edges[jj]) - 1 + + self.bin_edges_ = bin_edges + self.n_bins_ = n_bins + + if "onehot" in self.encode: + self._encoder = OneHotEncoder( + categories=[np.arange(i) for i in self.n_bins_], + sparse_output=self.encode == "onehot", + dtype=output_dtype, + ) + # Fit the OneHotEncoder with toy datasets + # so that it's ready for use after the KBinsDiscretizer is fitted + self._encoder.fit(np.zeros((1, len(self.n_bins_)))) + + return self + + def _validate_n_bins(self, n_features): + """Returns n_bins_, the number of bins per feature.""" + orig_bins = self.n_bins + if isinstance(orig_bins, Integral): + return np.full(n_features, orig_bins, dtype=int) + + n_bins = check_array(orig_bins, dtype=int, copy=True, ensure_2d=False) + + if n_bins.ndim > 1 or n_bins.shape[0] != n_features: + raise ValueError("n_bins must be a scalar or array of shape (n_features,).") + + bad_nbins_value = (n_bins < 2) | (n_bins != orig_bins) + + violating_indices = np.where(bad_nbins_value)[0] + if violating_indices.shape[0] > 0: + indices = ", ".join(str(i) for i in violating_indices) + raise ValueError( + "{} received an invalid number " + "of bins at indices {}. Number of bins " + "must be at least 2, and must be an int.".format( + KBinsDiscretizer.__name__, indices + ) + ) + return n_bins + + def transform(self, X): + """ + Discretize the data. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Data to be discretized. + + Returns + ------- + Xt : {ndarray, sparse matrix}, dtype={np.float32, np.float64} + Data in the binned space. Will be a sparse matrix if + `self.encode='onehot'` and ndarray otherwise. + """ + check_is_fitted(self) + + # check input and attribute dtypes + dtype = (np.float64, np.float32) if self.dtype is None else self.dtype + Xt = self._validate_data(X, copy=True, dtype=dtype, reset=False) + + bin_edges = self.bin_edges_ + for jj in range(Xt.shape[1]): + Xt[:, jj] = np.searchsorted(bin_edges[jj][1:-1], Xt[:, jj], side="right") + + if self.encode == "ordinal": + return Xt + + dtype_init = None + if "onehot" in self.encode: + dtype_init = self._encoder.dtype + self._encoder.dtype = Xt.dtype + try: + Xt_enc = self._encoder.transform(Xt) + finally: + # revert the initial dtype to avoid modifying self. + self._encoder.dtype = dtype_init + return Xt_enc + + def inverse_transform(self, Xt): + """ + Transform discretized data back to original feature space. + + Note that this function does not regenerate the original data + due to discretization rounding. + + Parameters + ---------- + Xt : array-like of shape (n_samples, n_features) + Transformed data in the binned space. + + Returns + ------- + Xinv : ndarray, dtype={np.float32, np.float64} + Data in the original feature space. + """ + check_is_fitted(self) + + if "onehot" in self.encode: + Xt = self._encoder.inverse_transform(Xt) + + Xinv = check_array(Xt, copy=True, dtype=(np.float64, np.float32)) + n_features = self.n_bins_.shape[0] + if Xinv.shape[1] != n_features: + raise ValueError( + "Incorrect number of features. Expecting {}, received {}.".format( + n_features, Xinv.shape[1] + ) + ) + + for jj in range(n_features): + bin_edges = self.bin_edges_[jj] + bin_centers = (bin_edges[1:] + bin_edges[:-1]) * 0.5 + Xinv[:, jj] = bin_centers[np.int_(Xinv[:, jj])] + + return Xinv + + def get_feature_names_out(self, input_features=None): + """Get output feature names. + + Parameters + ---------- + input_features : array-like of str or None, default=None + Input features. + + - If `input_features` is `None`, then `feature_names_in_` is + used as feature names in. If `feature_names_in_` is not defined, + then the following input feature names are generated: + `["x0", "x1", ..., "x(n_features_in_ - 1)"]`. + - If `input_features` is an array-like, then `input_features` must + match `feature_names_in_` if `feature_names_in_` is defined. + + Returns + ------- + feature_names_out : ndarray of str objects + Transformed feature names. + """ + input_features = _check_feature_names_in(self, input_features) + if hasattr(self, "_encoder"): + return self._encoder.get_feature_names_out(input_features) + + # ordinal encoding + return input_features diff --git a/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py new file mode 100644 index 0000000000000000000000000000000000000000..ed8fc410b7ae1929833e5748d1bb9cf5ecc76c71 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py @@ -0,0 +1,1451 @@ +# Authors: Andreas Mueller +# Joris Van den Bossche +# License: BSD 3 clause + +import numbers +from numbers import Integral, Real +import warnings + +import numpy as np +from scipy import sparse + +from ..base import BaseEstimator, TransformerMixin, OneToOneFeatureMixin +from ..utils import check_array, is_scalar_nan, _safe_indexing +from ..utils.validation import check_is_fitted +from ..utils.validation import _check_feature_names_in +from ..utils._param_validation import Interval, StrOptions, Hidden +from ..utils._mask import _get_mask + +from ..utils._encode import _encode, _check_unknown, _unique, _get_counts + + +__all__ = ["OneHotEncoder", "OrdinalEncoder"] + + +class _BaseEncoder(TransformerMixin, BaseEstimator): + """ + Base class for encoders that includes the code to categorize and + transform the input features. + + """ + + def _check_X(self, X, force_all_finite=True): + """ + Perform custom check_array: + - convert list of strings to object dtype + - check for missing values for object dtype data (check_array does + not do that) + - return list of features (arrays): this list of features is + constructed feature by feature to preserve the data types + of pandas DataFrame columns, as otherwise information is lost + and cannot be used, e.g. for the `categories_` attribute. + + """ + if not (hasattr(X, "iloc") and getattr(X, "ndim", 0) == 2): + # if not a dataframe, do normal check_array validation + X_temp = check_array(X, dtype=None, force_all_finite=force_all_finite) + if not hasattr(X, "dtype") and np.issubdtype(X_temp.dtype, np.str_): + X = check_array(X, dtype=object, force_all_finite=force_all_finite) + else: + X = X_temp + needs_validation = False + else: + # pandas dataframe, do validation later column by column, in order + # to keep the dtype information to be used in the encoder. + needs_validation = force_all_finite + + n_samples, n_features = X.shape + X_columns = [] + + for i in range(n_features): + Xi = _safe_indexing(X, indices=i, axis=1) + Xi = check_array( + Xi, ensure_2d=False, dtype=None, force_all_finite=needs_validation + ) + X_columns.append(Xi) + + return X_columns, n_samples, n_features + + def _fit( + self, X, handle_unknown="error", force_all_finite=True, return_counts=False + ): + self._check_n_features(X, reset=True) + self._check_feature_names(X, reset=True) + X_list, n_samples, n_features = self._check_X( + X, force_all_finite=force_all_finite + ) + self.n_features_in_ = n_features + + if self.categories != "auto": + if len(self.categories) != n_features: + raise ValueError( + "Shape mismatch: if categories is an array," + " it has to be of shape (n_features,)." + ) + + self.categories_ = [] + category_counts = [] + + for i in range(n_features): + Xi = X_list[i] + + if self.categories == "auto": + result = _unique(Xi, return_counts=return_counts) + if return_counts: + cats, counts = result + category_counts.append(counts) + else: + cats = result + else: + if np.issubdtype(Xi.dtype, np.str_): + # Always convert string categories to objects to avoid + # unexpected string truncation for longer category labels + # passed in the constructor. + Xi_dtype = object + else: + Xi_dtype = Xi.dtype + + cats = np.array(self.categories[i], dtype=Xi_dtype) + if ( + cats.dtype == object + and isinstance(cats[0], bytes) + and Xi.dtype.kind != "S" + ): + msg = ( + f"In column {i}, the predefined categories have type 'bytes'" + " which is incompatible with values of type" + f" '{type(Xi[0]).__name__}'." + ) + raise ValueError(msg) + + if Xi.dtype.kind not in "OUS": + sorted_cats = np.sort(cats) + error_msg = ( + "Unsorted categories are not supported for numerical categories" + ) + # if there are nans, nan should be the last element + stop_idx = -1 if np.isnan(sorted_cats[-1]) else None + if np.any(sorted_cats[:stop_idx] != cats[:stop_idx]) or ( + np.isnan(sorted_cats[-1]) and not np.isnan(sorted_cats[-1]) + ): + raise ValueError(error_msg) + + if handle_unknown == "error": + diff = _check_unknown(Xi, cats) + if diff: + msg = ( + "Found unknown categories {0} in column {1}" + " during fit".format(diff, i) + ) + raise ValueError(msg) + if return_counts: + category_counts.append(_get_counts(Xi, cats)) + + self.categories_.append(cats) + + output = {"n_samples": n_samples} + if return_counts: + output["category_counts"] = category_counts + return output + + def _transform( + self, X, handle_unknown="error", force_all_finite=True, warn_on_unknown=False + ): + self._check_feature_names(X, reset=False) + self._check_n_features(X, reset=False) + X_list, n_samples, n_features = self._check_X( + X, force_all_finite=force_all_finite + ) + + X_int = np.zeros((n_samples, n_features), dtype=int) + X_mask = np.ones((n_samples, n_features), dtype=bool) + + columns_with_unknown = [] + for i in range(n_features): + Xi = X_list[i] + diff, valid_mask = _check_unknown(Xi, self.categories_[i], return_mask=True) + + if not np.all(valid_mask): + if handle_unknown == "error": + msg = ( + "Found unknown categories {0} in column {1}" + " during transform".format(diff, i) + ) + raise ValueError(msg) + else: + if warn_on_unknown: + columns_with_unknown.append(i) + # Set the problematic rows to an acceptable value and + # continue `The rows are marked `X_mask` and will be + # removed later. + X_mask[:, i] = valid_mask + # cast Xi into the largest string type necessary + # to handle different lengths of numpy strings + if ( + self.categories_[i].dtype.kind in ("U", "S") + and self.categories_[i].itemsize > Xi.itemsize + ): + Xi = Xi.astype(self.categories_[i].dtype) + elif self.categories_[i].dtype.kind == "O" and Xi.dtype.kind == "U": + # categories are objects and Xi are numpy strings. + # Cast Xi to an object dtype to prevent truncation + # when setting invalid values. + Xi = Xi.astype("O") + else: + Xi = Xi.copy() + + Xi[~valid_mask] = self.categories_[i][0] + # We use check_unknown=False, since _check_unknown was + # already called above. + X_int[:, i] = _encode(Xi, uniques=self.categories_[i], check_unknown=False) + if columns_with_unknown: + warnings.warn( + "Found unknown categories in columns " + f"{columns_with_unknown} during transform. These " + "unknown categories will be encoded as all zeros", + UserWarning, + ) + + return X_int, X_mask + + def _more_tags(self): + return {"X_types": ["categorical"]} + + +class OneHotEncoder(_BaseEncoder): + """ + Encode categorical features as a one-hot numeric array. + + The input to this transformer should be an array-like of integers or + strings, denoting the values taken on by categorical (discrete) features. + The features are encoded using a one-hot (aka 'one-of-K' or 'dummy') + encoding scheme. This creates a binary column for each category and + returns a sparse matrix or dense array (depending on the ``sparse_output`` + parameter) + + By default, the encoder derives the categories based on the unique values + in each feature. Alternatively, you can also specify the `categories` + manually. + + This encoding is needed for feeding categorical data to many scikit-learn + estimators, notably linear models and SVMs with the standard kernels. + + Note: a one-hot encoding of y labels should use a LabelBinarizer + instead. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + categories : 'auto' or a list of array-like, default='auto' + Categories (unique values) per feature: + + - 'auto' : Determine categories automatically from the training data. + - list : ``categories[i]`` holds the categories expected in the ith + column. The passed categories should not mix strings and numeric + values within a single feature, and should be sorted in case of + numeric values. + + The used categories can be found in the ``categories_`` attribute. + + .. versionadded:: 0.20 + + drop : {'first', 'if_binary'} or an array-like of shape (n_features,), \ + default=None + Specifies a methodology to use to drop one of the categories per + feature. This is useful in situations where perfectly collinear + features cause problems, such as when feeding the resulting data + into an unregularized linear regression model. + + However, dropping one category breaks the symmetry of the original + representation and can therefore induce a bias in downstream models, + for instance for penalized linear classification or regression models. + + - None : retain all features (the default). + - 'first' : drop the first category in each feature. If only one + category is present, the feature will be dropped entirely. + - 'if_binary' : drop the first category in each feature with two + categories. Features with 1 or more than 2 categories are + left intact. + - array : ``drop[i]`` is the category in feature ``X[:, i]`` that + should be dropped. + + When `max_categories` or `min_frequency` is configured to group + infrequent categories, the dropping behavior is handled after the + grouping. + + .. versionadded:: 0.21 + The parameter `drop` was added in 0.21. + + .. versionchanged:: 0.23 + The option `drop='if_binary'` was added in 0.23. + + .. versionchanged:: 1.1 + Support for dropping infrequent categories. + + sparse : bool, default=True + Will return sparse matrix if set True else will return an array. + + .. deprecated:: 1.2 + `sparse` is deprecated in 1.2 and will be removed in 1.4. Use + `sparse_output` instead. + + sparse_output : bool, default=True + Will return sparse matrix if set True else will return an array. + + .. versionadded:: 1.2 + `sparse` was renamed to `sparse_output` + + dtype : number type, default=float + Desired dtype of output. + + handle_unknown : {'error', 'ignore', 'infrequent_if_exist'}, \ + default='error' + Specifies the way unknown categories are handled during :meth:`transform`. + + - 'error' : Raise an error if an unknown category is present during transform. + - 'ignore' : When an unknown category is encountered during + transform, the resulting one-hot encoded columns for this feature + will be all zeros. In the inverse transform, an unknown category + will be denoted as None. + - 'infrequent_if_exist' : When an unknown category is encountered + during transform, the resulting one-hot encoded columns for this + feature will map to the infrequent category if it exists. The + infrequent category will be mapped to the last position in the + encoding. During inverse transform, an unknown category will be + mapped to the category denoted `'infrequent'` if it exists. If the + `'infrequent'` category does not exist, then :meth:`transform` and + :meth:`inverse_transform` will handle an unknown category as with + `handle_unknown='ignore'`. Infrequent categories exist based on + `min_frequency` and `max_categories`. Read more in the + :ref:`User Guide `. + + .. versionchanged:: 1.1 + `'infrequent_if_exist'` was added to automatically handle unknown + categories and infrequent categories. + + min_frequency : int or float, default=None + Specifies the minimum frequency below which a category will be + considered infrequent. + + - If `int`, categories with a smaller cardinality will be considered + infrequent. + + - If `float`, categories with a smaller cardinality than + `min_frequency * n_samples` will be considered infrequent. + + .. versionadded:: 1.1 + Read more in the :ref:`User Guide `. + + max_categories : int, default=None + Specifies an upper limit to the number of output features for each input + feature when considering infrequent categories. If there are infrequent + categories, `max_categories` includes the category representing the + infrequent categories along with the frequent categories. If `None`, + there is no limit to the number of output features. + + .. versionadded:: 1.1 + Read more in the :ref:`User Guide `. + + Attributes + ---------- + categories_ : list of arrays + The categories of each feature determined during fitting + (in order of the features in X and corresponding with the output + of ``transform``). This includes the category specified in ``drop`` + (if any). + + drop_idx_ : array of shape (n_features,) + - ``drop_idx_[i]`` is the index in ``categories_[i]`` of the category + to be dropped for each feature. + - ``drop_idx_[i] = None`` if no category is to be dropped from the + feature with index ``i``, e.g. when `drop='if_binary'` and the + feature isn't binary. + - ``drop_idx_ = None`` if all the transformed features will be + retained. + + If infrequent categories are enabled by setting `min_frequency` or + `max_categories` to a non-default value and `drop_idx[i]` corresponds + to a infrequent category, then the entire infrequent category is + dropped. + + .. versionchanged:: 0.23 + Added the possibility to contain `None` values. + + infrequent_categories_ : list of ndarray + Defined only if infrequent categories are enabled by setting + `min_frequency` or `max_categories` to a non-default value. + `infrequent_categories_[i]` are the infrequent categories for feature + `i`. If the feature `i` has no infrequent categories + `infrequent_categories_[i]` is None. + + .. versionadded:: 1.1 + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 1.0 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + See Also + -------- + OrdinalEncoder : Performs an ordinal (integer) + encoding of the categorical features. + sklearn.feature_extraction.DictVectorizer : Performs a one-hot encoding of + dictionary items (also handles string-valued features). + sklearn.feature_extraction.FeatureHasher : Performs an approximate one-hot + encoding of dictionary items or strings. + LabelBinarizer : Binarizes labels in a one-vs-all + fashion. + MultiLabelBinarizer : Transforms between iterable of + iterables and a multilabel format, e.g. a (samples x classes) binary + matrix indicating the presence of a class label. + + Examples + -------- + Given a dataset with two features, we let the encoder find the unique + values per feature and transform the data to a binary one-hot encoding. + + >>> from sklearn.preprocessing import OneHotEncoder + + One can discard categories not seen during `fit`: + + >>> enc = OneHotEncoder(handle_unknown='ignore') + >>> X = [['Male', 1], ['Female', 3], ['Female', 2]] + >>> enc.fit(X) + OneHotEncoder(handle_unknown='ignore') + >>> enc.categories_ + [array(['Female', 'Male'], dtype=object), array([1, 2, 3], dtype=object)] + >>> enc.transform([['Female', 1], ['Male', 4]]).toarray() + array([[1., 0., 1., 0., 0.], + [0., 1., 0., 0., 0.]]) + >>> enc.inverse_transform([[0, 1, 1, 0, 0], [0, 0, 0, 1, 0]]) + array([['Male', 1], + [None, 2]], dtype=object) + >>> enc.get_feature_names_out(['gender', 'group']) + array(['gender_Female', 'gender_Male', 'group_1', 'group_2', 'group_3'], ...) + + One can always drop the first column for each feature: + + >>> drop_enc = OneHotEncoder(drop='first').fit(X) + >>> drop_enc.categories_ + [array(['Female', 'Male'], dtype=object), array([1, 2, 3], dtype=object)] + >>> drop_enc.transform([['Female', 1], ['Male', 2]]).toarray() + array([[0., 0., 0.], + [1., 1., 0.]]) + + Or drop a column for feature only having 2 categories: + + >>> drop_binary_enc = OneHotEncoder(drop='if_binary').fit(X) + >>> drop_binary_enc.transform([['Female', 1], ['Male', 2]]).toarray() + array([[0., 1., 0., 0.], + [1., 0., 1., 0.]]) + + Infrequent categories are enabled by setting `max_categories` or `min_frequency`. + + >>> import numpy as np + >>> X = np.array([["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3], dtype=object).T + >>> ohe = OneHotEncoder(max_categories=3, sparse_output=False).fit(X) + >>> ohe.infrequent_categories_ + [array(['a', 'd'], dtype=object)] + >>> ohe.transform([["a"], ["b"]]) + array([[0., 0., 1.], + [1., 0., 0.]]) + """ + + _parameter_constraints: dict = { + "categories": [StrOptions({"auto"}), list], + "drop": [StrOptions({"first", "if_binary"}), "array-like", None], + "dtype": "no_validation", # validation delegated to numpy + "handle_unknown": [StrOptions({"error", "ignore", "infrequent_if_exist"})], + "max_categories": [Interval(Integral, 1, None, closed="left"), None], + "min_frequency": [ + Interval(Integral, 1, None, closed="left"), + Interval(Real, 0, 1, closed="neither"), + None, + ], + "sparse": [Hidden(StrOptions({"deprecated"})), "boolean"], # deprecated + "sparse_output": ["boolean"], + } + + def __init__( + self, + *, + categories="auto", + drop=None, + sparse="deprecated", + sparse_output=True, + dtype=np.float64, + handle_unknown="error", + min_frequency=None, + max_categories=None, + ): + self.categories = categories + # TODO(1.4): Remove self.sparse + self.sparse = sparse + self.sparse_output = sparse_output + self.dtype = dtype + self.handle_unknown = handle_unknown + self.drop = drop + self.min_frequency = min_frequency + self.max_categories = max_categories + + @property + def infrequent_categories_(self): + """Infrequent categories for each feature.""" + # raises an AttributeError if `_infrequent_indices` is not defined + infrequent_indices = self._infrequent_indices + return [ + None if indices is None else category[indices] + for category, indices in zip(self.categories_, infrequent_indices) + ] + + def _check_infrequent_enabled(self): + """ + This functions checks whether _infrequent_enabled is True or False. + This has to be called after parameter validation in the fit function. + """ + self._infrequent_enabled = ( + self.max_categories is not None and self.max_categories >= 1 + ) or self.min_frequency is not None + + def _map_drop_idx_to_infrequent(self, feature_idx, drop_idx): + """Convert `drop_idx` into the index for infrequent categories. + + If there are no infrequent categories, then `drop_idx` is + returned. This method is called in `_set_drop_idx` when the `drop` + parameter is an array-like. + """ + if not self._infrequent_enabled: + return drop_idx + + default_to_infrequent = self._default_to_infrequent_mappings[feature_idx] + if default_to_infrequent is None: + return drop_idx + + # Raise error when explicitly dropping a category that is infrequent + infrequent_indices = self._infrequent_indices[feature_idx] + if infrequent_indices is not None and drop_idx in infrequent_indices: + categories = self.categories_[feature_idx] + raise ValueError( + f"Unable to drop category {categories[drop_idx]!r} from feature" + f" {feature_idx} because it is infrequent" + ) + return default_to_infrequent[drop_idx] + + def _set_drop_idx(self): + """Compute the drop indices associated with `self.categories_`. + + If `self.drop` is: + - `None`, No categories have been dropped. + - `'first'`, All zeros to drop the first category. + - `'if_binary'`, All zeros if the category is binary and `None` + otherwise. + - array-like, The indices of the categories that match the + categories in `self.drop`. If the dropped category is an infrequent + category, then the index for the infrequent category is used. This + means that the entire infrequent category is dropped. + + This methods defines a public `drop_idx_` and a private + `_drop_idx_after_grouping`. + + - `drop_idx_`: Public facing API that references the drop category in + `self.categories_`. + - `_drop_idx_after_grouping`: Used internally to drop categories *after* the + infrequent categories are grouped together. + + If there are no infrequent categories or drop is `None`, then + `drop_idx_=_drop_idx_after_grouping`. + """ + if self.drop is None: + drop_idx_after_grouping = None + elif isinstance(self.drop, str): + if self.drop == "first": + drop_idx_after_grouping = np.zeros(len(self.categories_), dtype=object) + elif self.drop == "if_binary": + n_features_out_no_drop = [len(cat) for cat in self.categories_] + if self._infrequent_enabled: + for i, infreq_idx in enumerate(self._infrequent_indices): + if infreq_idx is None: + continue + n_features_out_no_drop[i] -= infreq_idx.size - 1 + + drop_idx_after_grouping = np.array( + [ + 0 if n_features_out == 2 else None + for n_features_out in n_features_out_no_drop + ], + dtype=object, + ) + + else: + drop_array = np.asarray(self.drop, dtype=object) + droplen = len(drop_array) + + if droplen != len(self.categories_): + msg = ( + "`drop` should have length equal to the number " + "of features ({}), got {}" + ) + raise ValueError(msg.format(len(self.categories_), droplen)) + missing_drops = [] + drop_indices = [] + for feature_idx, (drop_val, cat_list) in enumerate( + zip(drop_array, self.categories_) + ): + if not is_scalar_nan(drop_val): + drop_idx = np.where(cat_list == drop_val)[0] + if drop_idx.size: # found drop idx + drop_indices.append( + self._map_drop_idx_to_infrequent(feature_idx, drop_idx[0]) + ) + else: + missing_drops.append((feature_idx, drop_val)) + continue + + # drop_val is nan, find nan in categories manually + for cat_idx, cat in enumerate(cat_list): + if is_scalar_nan(cat): + drop_indices.append( + self._map_drop_idx_to_infrequent(feature_idx, cat_idx) + ) + break + else: # loop did not break thus drop is missing + missing_drops.append((feature_idx, drop_val)) + + if any(missing_drops): + msg = ( + "The following categories were supposed to be " + "dropped, but were not found in the training " + "data.\n{}".format( + "\n".join( + [ + "Category: {}, Feature: {}".format(c, v) + for c, v in missing_drops + ] + ) + ) + ) + raise ValueError(msg) + drop_idx_after_grouping = np.array(drop_indices, dtype=object) + + # `_drop_idx_after_grouping` are the categories to drop *after* the infrequent + # categories are grouped together. If needed, we remap `drop_idx` back + # to the categories seen in `self.categories_`. + self._drop_idx_after_grouping = drop_idx_after_grouping + + if not self._infrequent_enabled or drop_idx_after_grouping is None: + self.drop_idx_ = self._drop_idx_after_grouping + else: + drop_idx_ = [] + for feature_idx, drop_idx in enumerate(drop_idx_after_grouping): + default_to_infrequent = self._default_to_infrequent_mappings[ + feature_idx + ] + if drop_idx is None or default_to_infrequent is None: + orig_drop_idx = drop_idx + else: + orig_drop_idx = np.flatnonzero(default_to_infrequent == drop_idx)[0] + + drop_idx_.append(orig_drop_idx) + + self.drop_idx_ = np.asarray(drop_idx_, dtype=object) + + def _identify_infrequent(self, category_count, n_samples, col_idx): + """Compute the infrequent indices. + + Parameters + ---------- + category_count : ndarray of shape (n_cardinality,) + Category counts. + + n_samples : int + Number of samples. + + col_idx : int + Index of the current category. Only used for the error message. + + Returns + ------- + output : ndarray of shape (n_infrequent_categories,) or None + If there are infrequent categories, indices of infrequent + categories. Otherwise None. + """ + if isinstance(self.min_frequency, numbers.Integral): + infrequent_mask = category_count < self.min_frequency + elif isinstance(self.min_frequency, numbers.Real): + min_frequency_abs = n_samples * self.min_frequency + infrequent_mask = category_count < min_frequency_abs + else: + infrequent_mask = np.zeros(category_count.shape[0], dtype=bool) + + n_current_features = category_count.size - infrequent_mask.sum() + 1 + if self.max_categories is not None and self.max_categories < n_current_features: + # stable sort to preserve original count order + smallest_levels = np.argsort(category_count, kind="mergesort")[ + : -self.max_categories + 1 + ] + infrequent_mask[smallest_levels] = True + + output = np.flatnonzero(infrequent_mask) + return output if output.size > 0 else None + + def _fit_infrequent_category_mapping(self, n_samples, category_counts): + """Fit infrequent categories. + + Defines the private attribute: `_default_to_infrequent_mappings`. For + feature `i`, `_default_to_infrequent_mappings[i]` defines the mapping + from the integer encoding returned by `super().transform()` into + infrequent categories. If `_default_to_infrequent_mappings[i]` is None, + there were no infrequent categories in the training set. + + For example if categories 0, 2 and 4 were frequent, while categories + 1, 3, 5 were infrequent for feature 7, then these categories are mapped + to a single output: + `_default_to_infrequent_mappings[7] = array([0, 3, 1, 3, 2, 3])` + + Defines private attribute: `_infrequent_indices`. `_infrequent_indices[i]` + is an array of indices such that + `categories_[i][_infrequent_indices[i]]` are all the infrequent category + labels. If the feature `i` has no infrequent categories + `_infrequent_indices[i]` is None. + + .. versionadded:: 1.1 + + Parameters + ---------- + n_samples : int + Number of samples in training set. + category_counts: list of ndarray + `category_counts[i]` is the category counts corresponding to + `self.categories_[i]`. + """ + self._infrequent_indices = [ + self._identify_infrequent(category_count, n_samples, col_idx) + for col_idx, category_count in enumerate(category_counts) + ] + + # compute mapping from default mapping to infrequent mapping + self._default_to_infrequent_mappings = [] + + for cats, infreq_idx in zip(self.categories_, self._infrequent_indices): + # no infrequent categories + if infreq_idx is None: + self._default_to_infrequent_mappings.append(None) + continue + + n_cats = len(cats) + # infrequent indices exist + mapping = np.empty(n_cats, dtype=np.int64) + n_infrequent_cats = infreq_idx.size + + # infrequent categories are mapped to the last element. + n_frequent_cats = n_cats - n_infrequent_cats + mapping[infreq_idx] = n_frequent_cats + + frequent_indices = np.setdiff1d(np.arange(n_cats), infreq_idx) + mapping[frequent_indices] = np.arange(n_frequent_cats) + + self._default_to_infrequent_mappings.append(mapping) + + def _map_infrequent_categories(self, X_int, X_mask): + """Map infrequent categories to integer representing the infrequent category. + + This modifies X_int in-place. Values that were invalid based on `X_mask` + are mapped to the infrequent category if there was an infrequent + category for that feature. + + Parameters + ---------- + X_int: ndarray of shape (n_samples, n_features) + Integer encoded categories. + + X_mask: ndarray of shape (n_samples, n_features) + Bool mask for valid values in `X_int`. + """ + if not self._infrequent_enabled: + return + + for col_idx in range(X_int.shape[1]): + infrequent_idx = self._infrequent_indices[col_idx] + if infrequent_idx is None: + continue + + X_int[~X_mask[:, col_idx], col_idx] = infrequent_idx[0] + if self.handle_unknown == "infrequent_if_exist": + # All the unknown values are now mapped to the + # infrequent_idx[0], which makes the unknown values valid + # This is needed in `transform` when the encoding is formed + # using `X_mask`. + X_mask[:, col_idx] = True + + # Remaps encoding in `X_int` where the infrequent categories are + # grouped together. + for i, mapping in enumerate(self._default_to_infrequent_mappings): + if mapping is None: + continue + X_int[:, i] = np.take(mapping, X_int[:, i]) + + def _compute_transformed_categories(self, i, remove_dropped=True): + """Compute the transformed categories used for column `i`. + + 1. If there are infrequent categories, the category is named + 'infrequent_sklearn'. + 2. Dropped columns are removed when remove_dropped=True. + """ + cats = self.categories_[i] + + if self._infrequent_enabled: + infreq_map = self._default_to_infrequent_mappings[i] + if infreq_map is not None: + frequent_mask = infreq_map < infreq_map.max() + infrequent_cat = "infrequent_sklearn" + # infrequent category is always at the end + cats = np.concatenate( + (cats[frequent_mask], np.array([infrequent_cat], dtype=object)) + ) + + if remove_dropped: + cats = self._remove_dropped_categories(cats, i) + return cats + + def _remove_dropped_categories(self, categories, i): + """Remove dropped categories.""" + if ( + self._drop_idx_after_grouping is not None + and self._drop_idx_after_grouping[i] is not None + ): + return np.delete(categories, self._drop_idx_after_grouping[i]) + return categories + + def _compute_n_features_outs(self): + """Compute the n_features_out for each input feature.""" + output = [len(cats) for cats in self.categories_] + + if self._drop_idx_after_grouping is not None: + for i, drop_idx in enumerate(self._drop_idx_after_grouping): + if drop_idx is not None: + output[i] -= 1 + + if not self._infrequent_enabled: + return output + + # infrequent is enabled, the number of features out are reduced + # because the infrequent categories are grouped together + for i, infreq_idx in enumerate(self._infrequent_indices): + if infreq_idx is None: + continue + output[i] -= infreq_idx.size - 1 + + return output + + def fit(self, X, y=None): + """ + Fit OneHotEncoder to X. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to determine the categories of each feature. + + y : None + Ignored. This parameter exists only for compatibility with + :class:`~sklearn.pipeline.Pipeline`. + + Returns + ------- + self + Fitted encoder. + """ + self._validate_params() + + if self.sparse != "deprecated": + warnings.warn( + "`sparse` was renamed to `sparse_output` in version 1.2 and " + "will be removed in 1.4. `sparse_output` is ignored unless you " + "leave `sparse` to its default value.", + FutureWarning, + ) + self.sparse_output = self.sparse + + self._check_infrequent_enabled() + + fit_results = self._fit( + X, + handle_unknown=self.handle_unknown, + force_all_finite="allow-nan", + return_counts=self._infrequent_enabled, + ) + if self._infrequent_enabled: + self._fit_infrequent_category_mapping( + fit_results["n_samples"], fit_results["category_counts"] + ) + self._set_drop_idx() + self._n_features_outs = self._compute_n_features_outs() + return self + + def transform(self, X): + """ + Transform X using one-hot encoding. + + If there are infrequent categories for a feature, the infrequent + categories will be grouped into a single category. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to encode. + + Returns + ------- + X_out : {ndarray, sparse matrix} of shape \ + (n_samples, n_encoded_features) + Transformed input. If `sparse_output=True`, a sparse matrix will be + returned. + """ + check_is_fitted(self) + # validation of X happens in _check_X called by _transform + warn_on_unknown = self.drop is not None and self.handle_unknown in { + "ignore", + "infrequent_if_exist", + } + X_int, X_mask = self._transform( + X, + handle_unknown=self.handle_unknown, + force_all_finite="allow-nan", + warn_on_unknown=warn_on_unknown, + ) + self._map_infrequent_categories(X_int, X_mask) + + n_samples, n_features = X_int.shape + + if self._drop_idx_after_grouping is not None: + to_drop = self._drop_idx_after_grouping.copy() + # We remove all the dropped categories from mask, and decrement all + # categories that occur after them to avoid an empty column. + keep_cells = X_int != to_drop + for i, cats in enumerate(self.categories_): + # drop='if_binary' but feature isn't binary + if to_drop[i] is None: + # set to cardinality to not drop from X_int + to_drop[i] = len(cats) + + to_drop = to_drop.reshape(1, -1) + X_int[X_int > to_drop] -= 1 + X_mask &= keep_cells + + mask = X_mask.ravel() + feature_indices = np.cumsum([0] + self._n_features_outs) + indices = (X_int + feature_indices[:-1]).ravel()[mask] + + indptr = np.empty(n_samples + 1, dtype=int) + indptr[0] = 0 + np.sum(X_mask, axis=1, out=indptr[1:], dtype=indptr.dtype) + np.cumsum(indptr[1:], out=indptr[1:]) + data = np.ones(indptr[-1]) + + out = sparse.csr_matrix( + (data, indices, indptr), + shape=(n_samples, feature_indices[-1]), + dtype=self.dtype, + ) + if not self.sparse_output: + return out.toarray() + else: + return out + + def inverse_transform(self, X): + """ + Convert the data back to the original representation. + + When unknown categories are encountered (all zeros in the + one-hot encoding), ``None`` is used to represent this category. If the + feature with the unknown category has a dropped category, the dropped + category will be its inverse. + + For a given input feature, if there is an infrequent category, + 'infrequent_sklearn' will be used to represent the infrequent category. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape \ + (n_samples, n_encoded_features) + The transformed data. + + Returns + ------- + X_tr : ndarray of shape (n_samples, n_features) + Inverse transformed array. + """ + check_is_fitted(self) + X = check_array(X, accept_sparse="csr") + + n_samples, _ = X.shape + n_features = len(self.categories_) + + n_features_out = np.sum(self._n_features_outs) + + # validate shape of passed X + msg = ( + "Shape of the passed X data is not correct. Expected {0} columns, got {1}." + ) + if X.shape[1] != n_features_out: + raise ValueError(msg.format(n_features_out, X.shape[1])) + + transformed_features = [ + self._compute_transformed_categories(i, remove_dropped=False) + for i, _ in enumerate(self.categories_) + ] + + # create resulting array of appropriate dtype + dt = np.result_type(*[cat.dtype for cat in transformed_features]) + X_tr = np.empty((n_samples, n_features), dtype=dt) + + j = 0 + found_unknown = {} + + if self._infrequent_enabled: + infrequent_indices = self._infrequent_indices + else: + infrequent_indices = [None] * n_features + + for i in range(n_features): + cats_wo_dropped = self._remove_dropped_categories( + transformed_features[i], i + ) + n_categories = cats_wo_dropped.shape[0] + + # Only happens if there was a column with a unique + # category. In this case we just fill the column with this + # unique category value. + if n_categories == 0: + X_tr[:, i] = self.categories_[i][self._drop_idx_after_grouping[i]] + j += n_categories + continue + sub = X[:, j : j + n_categories] + # for sparse X argmax returns 2D matrix, ensure 1D array + labels = np.asarray(sub.argmax(axis=1)).flatten() + X_tr[:, i] = cats_wo_dropped[labels] + + if self.handle_unknown == "ignore" or ( + self.handle_unknown == "infrequent_if_exist" + and infrequent_indices[i] is None + ): + unknown = np.asarray(sub.sum(axis=1) == 0).flatten() + # ignored unknown categories: we have a row of all zero + if unknown.any(): + # if categories were dropped then unknown categories will + # be mapped to the dropped category + if ( + self._drop_idx_after_grouping is None + or self._drop_idx_after_grouping[i] is None + ): + found_unknown[i] = unknown + else: + X_tr[unknown, i] = self.categories_[i][ + self._drop_idx_after_grouping[i] + ] + else: + dropped = np.asarray(sub.sum(axis=1) == 0).flatten() + if dropped.any(): + if self._drop_idx_after_grouping is None: + all_zero_samples = np.flatnonzero(dropped) + raise ValueError( + f"Samples {all_zero_samples} can not be inverted " + "when drop=None and handle_unknown='error' " + "because they contain all zeros" + ) + # we can safely assume that all of the nulls in each column + # are the dropped value + drop_idx = self._drop_idx_after_grouping[i] + X_tr[dropped, i] = transformed_features[i][drop_idx] + + j += n_categories + + # if ignored are found: potentially need to upcast result to + # insert None values + if found_unknown: + if X_tr.dtype != object: + X_tr = X_tr.astype(object) + + for idx, mask in found_unknown.items(): + X_tr[mask, idx] = None + + return X_tr + + def get_feature_names_out(self, input_features=None): + """Get output feature names for transformation. + + Parameters + ---------- + input_features : array-like of str or None, default=None + Input features. + + - If `input_features` is `None`, then `feature_names_in_` is + used as feature names in. If `feature_names_in_` is not defined, + then the following input feature names are generated: + `["x0", "x1", ..., "x(n_features_in_ - 1)"]`. + - If `input_features` is an array-like, then `input_features` must + match `feature_names_in_` if `feature_names_in_` is defined. + + Returns + ------- + feature_names_out : ndarray of str objects + Transformed feature names. + """ + check_is_fitted(self) + input_features = _check_feature_names_in(self, input_features) + cats = [ + self._compute_transformed_categories(i) + for i, _ in enumerate(self.categories_) + ] + + feature_names = [] + for i in range(len(cats)): + names = [input_features[i] + "_" + str(t) for t in cats[i]] + feature_names.extend(names) + + return np.array(feature_names, dtype=object) + + +class OrdinalEncoder(OneToOneFeatureMixin, _BaseEncoder): + """ + Encode categorical features as an integer array. + + The input to this transformer should be an array-like of integers or + strings, denoting the values taken on by categorical (discrete) features. + The features are converted to ordinal integers. This results in + a single column of integers (0 to n_categories - 1) per feature. + + Read more in the :ref:`User Guide `. + + .. versionadded:: 0.20 + + Parameters + ---------- + categories : 'auto' or a list of array-like, default='auto' + Categories (unique values) per feature: + + - 'auto' : Determine categories automatically from the training data. + - list : ``categories[i]`` holds the categories expected in the ith + column. The passed categories should not mix strings and numeric + values, and should be sorted in case of numeric values. + + The used categories can be found in the ``categories_`` attribute. + + dtype : number type, default np.float64 + Desired dtype of output. + + handle_unknown : {'error', 'use_encoded_value'}, default='error' + When set to 'error' an error will be raised in case an unknown + categorical feature is present during transform. When set to + 'use_encoded_value', the encoded value of unknown categories will be + set to the value given for the parameter `unknown_value`. In + :meth:`inverse_transform`, an unknown category will be denoted as None. + + .. versionadded:: 0.24 + + unknown_value : int or np.nan, default=None + When the parameter handle_unknown is set to 'use_encoded_value', this + parameter is required and will set the encoded value of unknown + categories. It has to be distinct from the values used to encode any of + the categories in `fit`. If set to np.nan, the `dtype` parameter must + be a float dtype. + + .. versionadded:: 0.24 + + encoded_missing_value : int or np.nan, default=np.nan + Encoded value of missing categories. If set to `np.nan`, then the `dtype` + parameter must be a float dtype. + + .. versionadded:: 1.1 + + Attributes + ---------- + categories_ : list of arrays + The categories of each feature determined during ``fit`` (in order of + the features in X and corresponding with the output of ``transform``). + This does not include categories that weren't seen during ``fit``. + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 1.0 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + See Also + -------- + OneHotEncoder : Performs a one-hot encoding of categorical features. + LabelEncoder : Encodes target labels with values between 0 and + ``n_classes-1``. + + Notes + ----- + With a high proportion of `nan` values, inferring categories becomes slow with + Python versions before 3.10. The handling of `nan` values was improved + from Python 3.10 onwards, (c.f. + `bpo-43475 `_). + + Examples + -------- + Given a dataset with two features, we let the encoder find the unique + values per feature and transform the data to an ordinal encoding. + + >>> from sklearn.preprocessing import OrdinalEncoder + >>> enc = OrdinalEncoder() + >>> X = [['Male', 1], ['Female', 3], ['Female', 2]] + >>> enc.fit(X) + OrdinalEncoder() + >>> enc.categories_ + [array(['Female', 'Male'], dtype=object), array([1, 2, 3], dtype=object)] + >>> enc.transform([['Female', 3], ['Male', 1]]) + array([[0., 2.], + [1., 0.]]) + + >>> enc.inverse_transform([[1, 0], [0, 1]]) + array([['Male', 1], + ['Female', 2]], dtype=object) + + By default, :class:`OrdinalEncoder` is lenient towards missing values by + propagating them. + + >>> import numpy as np + >>> X = [['Male', 1], ['Female', 3], ['Female', np.nan]] + >>> enc.fit_transform(X) + array([[ 1., 0.], + [ 0., 1.], + [ 0., nan]]) + + You can use the parameter `encoded_missing_value` to encode missing values. + + >>> enc.set_params(encoded_missing_value=-1).fit_transform(X) + array([[ 1., 0.], + [ 0., 1.], + [ 0., -1.]]) + """ + + _parameter_constraints: dict = { + "categories": [StrOptions({"auto"}), list], + "dtype": "no_validation", # validation delegated to numpy + "encoded_missing_value": [Integral, type(np.nan)], + "handle_unknown": [StrOptions({"error", "use_encoded_value"})], + "unknown_value": [Integral, type(np.nan), None], + } + + def __init__( + self, + *, + categories="auto", + dtype=np.float64, + handle_unknown="error", + unknown_value=None, + encoded_missing_value=np.nan, + ): + self.categories = categories + self.dtype = dtype + self.handle_unknown = handle_unknown + self.unknown_value = unknown_value + self.encoded_missing_value = encoded_missing_value + + def fit(self, X, y=None): + """ + Fit the OrdinalEncoder to X. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to determine the categories of each feature. + + y : None + Ignored. This parameter exists only for compatibility with + :class:`~sklearn.pipeline.Pipeline`. + + Returns + ------- + self : object + Fitted encoder. + """ + self._validate_params() + + if self.handle_unknown == "use_encoded_value": + if is_scalar_nan(self.unknown_value): + if np.dtype(self.dtype).kind != "f": + raise ValueError( + "When unknown_value is np.nan, the dtype " + "parameter should be " + f"a float dtype. Got {self.dtype}." + ) + elif not isinstance(self.unknown_value, numbers.Integral): + raise TypeError( + "unknown_value should be an integer or " + "np.nan when " + "handle_unknown is 'use_encoded_value', " + f"got {self.unknown_value}." + ) + elif self.unknown_value is not None: + raise TypeError( + "unknown_value should only be set when " + "handle_unknown is 'use_encoded_value', " + f"got {self.unknown_value}." + ) + + # `_fit` will only raise an error when `self.handle_unknown="error"` + self._fit(X, handle_unknown=self.handle_unknown, force_all_finite="allow-nan") + + cardinalities = [len(categories) for categories in self.categories_] + + # stores the missing indices per category + self._missing_indices = {} + for cat_idx, categories_for_idx in enumerate(self.categories_): + for i, cat in enumerate(categories_for_idx): + if is_scalar_nan(cat): + self._missing_indices[cat_idx] = i + + # missing values are not considered part of the cardinality + # when considering unknown categories or encoded_missing_value + cardinalities[cat_idx] -= 1 + continue + + if self.handle_unknown == "use_encoded_value": + for cardinality in cardinalities: + if 0 <= self.unknown_value < cardinality: + raise ValueError( + "The used value for unknown_value " + f"{self.unknown_value} is one of the " + "values already used for encoding the " + "seen categories." + ) + + if self._missing_indices: + if np.dtype(self.dtype).kind != "f" and is_scalar_nan( + self.encoded_missing_value + ): + raise ValueError( + "There are missing values in features " + f"{list(self._missing_indices)}. For OrdinalEncoder to " + f"encode missing values with dtype: {self.dtype}, set " + "encoded_missing_value to a non-nan value, or " + "set dtype to a float" + ) + + if not is_scalar_nan(self.encoded_missing_value): + # Features are invalid when they contain a missing category + # and encoded_missing_value was already used to encode a + # known category + invalid_features = [ + cat_idx + for cat_idx, cardinality in enumerate(cardinalities) + if cat_idx in self._missing_indices + and 0 <= self.encoded_missing_value < cardinality + ] + + if invalid_features: + # Use feature names if they are avaliable + if hasattr(self, "feature_names_in_"): + invalid_features = self.feature_names_in_[invalid_features] + raise ValueError( + f"encoded_missing_value ({self.encoded_missing_value}) " + "is already used to encode a known category in features: " + f"{invalid_features}" + ) + + return self + + def transform(self, X): + """ + Transform X to ordinal codes. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to encode. + + Returns + ------- + X_out : ndarray of shape (n_samples, n_features) + Transformed input. + """ + X_int, X_mask = self._transform( + X, handle_unknown=self.handle_unknown, force_all_finite="allow-nan" + ) + X_trans = X_int.astype(self.dtype, copy=False) + + for cat_idx, missing_idx in self._missing_indices.items(): + X_missing_mask = X_int[:, cat_idx] == missing_idx + X_trans[X_missing_mask, cat_idx] = self.encoded_missing_value + + # create separate category for unknown values + if self.handle_unknown == "use_encoded_value": + X_trans[~X_mask] = self.unknown_value + return X_trans + + def inverse_transform(self, X): + """ + Convert the data back to the original representation. + + Parameters + ---------- + X : array-like of shape (n_samples, n_encoded_features) + The transformed data. + + Returns + ------- + X_tr : ndarray of shape (n_samples, n_features) + Inverse transformed array. + """ + check_is_fitted(self) + X = check_array(X, force_all_finite="allow-nan") + + n_samples, _ = X.shape + n_features = len(self.categories_) + + # validate shape of passed X + msg = ( + "Shape of the passed X data is not correct. Expected {0} columns, got {1}." + ) + if X.shape[1] != n_features: + raise ValueError(msg.format(n_features, X.shape[1])) + + # create resulting array of appropriate dtype + dt = np.result_type(*[cat.dtype for cat in self.categories_]) + X_tr = np.empty((n_samples, n_features), dtype=dt) + + found_unknown = {} + + for i in range(n_features): + labels = X[:, i] + + # replace values of X[:, i] that were nan with actual indices + if i in self._missing_indices: + X_i_mask = _get_mask(labels, self.encoded_missing_value) + labels[X_i_mask] = self._missing_indices[i] + + if self.handle_unknown == "use_encoded_value": + unknown_labels = _get_mask(labels, self.unknown_value) + + known_labels = ~unknown_labels + X_tr[known_labels, i] = self.categories_[i][ + labels[known_labels].astype("int64", copy=False) + ] + found_unknown[i] = unknown_labels + else: + X_tr[:, i] = self.categories_[i][labels.astype("int64", copy=False)] + + # insert None values for unknown values + if found_unknown: + X_tr = X_tr.astype(object, copy=False) + + for idx, mask in found_unknown.items(): + X_tr[mask, idx] = None + + return X_tr diff --git a/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/_label.py b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/_label.py new file mode 100644 index 0000000000000000000000000000000000000000..b9da2254ad60fc8494010521423772dc78c03ca5 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/_label.py @@ -0,0 +1,946 @@ +# Authors: Alexandre Gramfort +# Mathieu Blondel +# Olivier Grisel +# Andreas Mueller +# Joel Nothman +# Hamzeh Alsalhi +# License: BSD 3 clause + +from collections import defaultdict +from numbers import Integral +import itertools +import array +import warnings + +import numpy as np +import scipy.sparse as sp + +from ..base import BaseEstimator, TransformerMixin + +from ..utils.sparsefuncs import min_max_axis +from ..utils import column_or_1d +from ..utils.validation import _num_samples, check_array, check_is_fitted +from ..utils.multiclass import unique_labels +from ..utils.multiclass import type_of_target +from ..utils._encode import _encode, _unique + + +__all__ = [ + "label_binarize", + "LabelBinarizer", + "LabelEncoder", + "MultiLabelBinarizer", +] + + +class LabelEncoder(TransformerMixin, BaseEstimator): + """Encode target labels with value between 0 and n_classes-1. + + This transformer should be used to encode target values, *i.e.* `y`, and + not the input `X`. + + Read more in the :ref:`User Guide `. + + .. versionadded:: 0.12 + + Attributes + ---------- + classes_ : ndarray of shape (n_classes,) + Holds the label for each class. + + See Also + -------- + OrdinalEncoder : Encode categorical features using an ordinal encoding + scheme. + OneHotEncoder : Encode categorical features as a one-hot numeric array. + + Examples + -------- + `LabelEncoder` can be used to normalize labels. + + >>> from sklearn import preprocessing + >>> le = preprocessing.LabelEncoder() + >>> le.fit([1, 2, 2, 6]) + LabelEncoder() + >>> le.classes_ + array([1, 2, 6]) + >>> le.transform([1, 1, 2, 6]) + array([0, 0, 1, 2]...) + >>> le.inverse_transform([0, 0, 1, 2]) + array([1, 1, 2, 6]) + + It can also be used to transform non-numerical labels (as long as they are + hashable and comparable) to numerical labels. + + >>> le = preprocessing.LabelEncoder() + >>> le.fit(["paris", "paris", "tokyo", "amsterdam"]) + LabelEncoder() + >>> list(le.classes_) + ['amsterdam', 'paris', 'tokyo'] + >>> le.transform(["tokyo", "tokyo", "paris"]) + array([2, 2, 1]...) + >>> list(le.inverse_transform([2, 2, 1])) + ['tokyo', 'tokyo', 'paris'] + """ + + def fit(self, y): + """Fit label encoder. + + Parameters + ---------- + y : array-like of shape (n_samples,) + Target values. + + Returns + ------- + self : returns an instance of self. + Fitted label encoder. + """ + y = column_or_1d(y, warn=True) + self.classes_ = _unique(y) + return self + + def fit_transform(self, y): + """Fit label encoder and return encoded labels. + + Parameters + ---------- + y : array-like of shape (n_samples,) + Target values. + + Returns + ------- + y : array-like of shape (n_samples,) + Encoded labels. + """ + y = column_or_1d(y, warn=True) + self.classes_, y = _unique(y, return_inverse=True) + return y + + def transform(self, y): + """Transform labels to normalized encoding. + + Parameters + ---------- + y : array-like of shape (n_samples,) + Target values. + + Returns + ------- + y : array-like of shape (n_samples,) + Labels as normalized encodings. + """ + check_is_fitted(self) + y = column_or_1d(y, dtype=self.classes_.dtype, warn=True) + # transform of empty array is empty array + if _num_samples(y) == 0: + return np.array([]) + + return _encode(y, uniques=self.classes_) + + def inverse_transform(self, y): + """Transform labels back to original encoding. + + Parameters + ---------- + y : ndarray of shape (n_samples,) + Target values. + + Returns + ------- + y : ndarray of shape (n_samples,) + Original encoding. + """ + check_is_fitted(self) + y = column_or_1d(y, warn=True) + # inverse transform of empty array is empty array + if _num_samples(y) == 0: + return np.array([]) + + diff = np.setdiff1d(y, np.arange(len(self.classes_))) + if len(diff): + raise ValueError("y contains previously unseen labels: %s" % str(diff)) + y = np.asarray(y) + return self.classes_[y] + + def _more_tags(self): + return {"X_types": ["1dlabels"]} + + +class LabelBinarizer(TransformerMixin, BaseEstimator): + """Binarize labels in a one-vs-all fashion. + + Several regression and binary classification algorithms are + available in scikit-learn. A simple way to extend these algorithms + to the multi-class classification case is to use the so-called + one-vs-all scheme. + + At learning time, this simply consists in learning one regressor + or binary classifier per class. In doing so, one needs to convert + multi-class labels to binary labels (belong or does not belong + to the class). LabelBinarizer makes this process easy with the + transform method. + + At prediction time, one assigns the class for which the corresponding + model gave the greatest confidence. LabelBinarizer makes this easy + with the inverse_transform method. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + neg_label : int, default=0 + Value with which negative labels must be encoded. + + pos_label : int, default=1 + Value with which positive labels must be encoded. + + sparse_output : bool, default=False + True if the returned array from transform is desired to be in sparse + CSR format. + + Attributes + ---------- + classes_ : ndarray of shape (n_classes,) + Holds the label for each class. + + y_type_ : str + Represents the type of the target data as evaluated by + utils.multiclass.type_of_target. Possible type are 'continuous', + 'continuous-multioutput', 'binary', 'multiclass', + 'multiclass-multioutput', 'multilabel-indicator', and 'unknown'. + + sparse_input_ : bool + True if the input data to transform is given as a sparse matrix, False + otherwise. + + See Also + -------- + label_binarize : Function to perform the transform operation of + LabelBinarizer with fixed classes. + OneHotEncoder : Encode categorical features using a one-hot aka one-of-K + scheme. + + Examples + -------- + >>> from sklearn import preprocessing + >>> lb = preprocessing.LabelBinarizer() + >>> lb.fit([1, 2, 6, 4, 2]) + LabelBinarizer() + >>> lb.classes_ + array([1, 2, 4, 6]) + >>> lb.transform([1, 6]) + array([[1, 0, 0, 0], + [0, 0, 0, 1]]) + + Binary targets transform to a column vector + + >>> lb = preprocessing.LabelBinarizer() + >>> lb.fit_transform(['yes', 'no', 'no', 'yes']) + array([[1], + [0], + [0], + [1]]) + + Passing a 2D matrix for multilabel classification + + >>> import numpy as np + >>> lb.fit(np.array([[0, 1, 1], [1, 0, 0]])) + LabelBinarizer() + >>> lb.classes_ + array([0, 1, 2]) + >>> lb.transform([0, 1, 2, 1]) + array([[1, 0, 0], + [0, 1, 0], + [0, 0, 1], + [0, 1, 0]]) + """ + + _parameter_constraints: dict = { + "neg_label": [Integral], + "pos_label": [Integral], + "sparse_output": ["boolean"], + } + + def __init__(self, *, neg_label=0, pos_label=1, sparse_output=False): + + self.neg_label = neg_label + self.pos_label = pos_label + self.sparse_output = sparse_output + + def fit(self, y): + """Fit label binarizer. + + Parameters + ---------- + y : ndarray of shape (n_samples,) or (n_samples, n_classes) + Target values. The 2-d matrix should only contain 0 and 1, + represents multilabel classification. + + Returns + ------- + self : object + Returns the instance itself. + """ + + self._validate_params() + + if self.neg_label >= self.pos_label: + raise ValueError( + f"neg_label={self.neg_label} must be strictly less than " + f"pos_label={self.pos_label}." + ) + + if self.sparse_output and (self.pos_label == 0 or self.neg_label != 0): + raise ValueError( + "Sparse binarization is only supported with non " + "zero pos_label and zero neg_label, got " + f"pos_label={self.pos_label} and neg_label={self.neg_label}" + ) + + self.y_type_ = type_of_target(y, input_name="y") + + if "multioutput" in self.y_type_: + raise ValueError( + "Multioutput target data is not supported with label binarization" + ) + if _num_samples(y) == 0: + raise ValueError("y has 0 samples: %r" % y) + + self.sparse_input_ = sp.issparse(y) + self.classes_ = unique_labels(y) + return self + + def fit_transform(self, y): + """Fit label binarizer/transform multi-class labels to binary labels. + + The output of transform is sometimes referred to as + the 1-of-K coding scheme. + + Parameters + ---------- + y : {ndarray, sparse matrix} of shape (n_samples,) or \ + (n_samples, n_classes) + Target values. The 2-d matrix should only contain 0 and 1, + represents multilabel classification. Sparse matrix can be + CSR, CSC, COO, DOK, or LIL. + + Returns + ------- + Y : {ndarray, sparse matrix} of shape (n_samples, n_classes) + Shape will be (n_samples, 1) for binary problems. Sparse matrix + will be of CSR format. + """ + return self.fit(y).transform(y) + + def transform(self, y): + """Transform multi-class labels to binary labels. + + The output of transform is sometimes referred to by some authors as + the 1-of-K coding scheme. + + Parameters + ---------- + y : {array, sparse matrix} of shape (n_samples,) or \ + (n_samples, n_classes) + Target values. The 2-d matrix should only contain 0 and 1, + represents multilabel classification. Sparse matrix can be + CSR, CSC, COO, DOK, or LIL. + + Returns + ------- + Y : {ndarray, sparse matrix} of shape (n_samples, n_classes) + Shape will be (n_samples, 1) for binary problems. Sparse matrix + will be of CSR format. + """ + check_is_fitted(self) + + y_is_multilabel = type_of_target(y).startswith("multilabel") + if y_is_multilabel and not self.y_type_.startswith("multilabel"): + raise ValueError("The object was not fitted with multilabel input.") + + return label_binarize( + y, + classes=self.classes_, + pos_label=self.pos_label, + neg_label=self.neg_label, + sparse_output=self.sparse_output, + ) + + def inverse_transform(self, Y, threshold=None): + """Transform binary labels back to multi-class labels. + + Parameters + ---------- + Y : {ndarray, sparse matrix} of shape (n_samples, n_classes) + Target values. All sparse matrices are converted to CSR before + inverse transformation. + + threshold : float, default=None + Threshold used in the binary and multi-label cases. + + Use 0 when ``Y`` contains the output of decision_function + (classifier). + Use 0.5 when ``Y`` contains the output of predict_proba. + + If None, the threshold is assumed to be half way between + neg_label and pos_label. + + Returns + ------- + y : {ndarray, sparse matrix} of shape (n_samples,) + Target values. Sparse matrix will be of CSR format. + + Notes + ----- + In the case when the binary labels are fractional + (probabilistic), inverse_transform chooses the class with the + greatest value. Typically, this allows to use the output of a + linear model's decision_function method directly as the input + of inverse_transform. + """ + check_is_fitted(self) + + if threshold is None: + threshold = (self.pos_label + self.neg_label) / 2.0 + + if self.y_type_ == "multiclass": + y_inv = _inverse_binarize_multiclass(Y, self.classes_) + else: + y_inv = _inverse_binarize_thresholding( + Y, self.y_type_, self.classes_, threshold + ) + + if self.sparse_input_: + y_inv = sp.csr_matrix(y_inv) + elif sp.issparse(y_inv): + y_inv = y_inv.toarray() + + return y_inv + + def _more_tags(self): + return {"X_types": ["1dlabels"]} + + +def label_binarize(y, *, classes, neg_label=0, pos_label=1, sparse_output=False): + """Binarize labels in a one-vs-all fashion. + + Several regression and binary classification algorithms are + available in scikit-learn. A simple way to extend these algorithms + to the multi-class classification case is to use the so-called + one-vs-all scheme. + + This function makes it possible to compute this transformation for a + fixed set of class labels known ahead of time. + + Parameters + ---------- + y : array-like + Sequence of integer labels or multilabel data to encode. + + classes : array-like of shape (n_classes,) + Uniquely holds the label for each class. + + neg_label : int, default=0 + Value with which negative labels must be encoded. + + pos_label : int, default=1 + Value with which positive labels must be encoded. + + sparse_output : bool, default=False, + Set to true if output binary array is desired in CSR sparse format. + + Returns + ------- + Y : {ndarray, sparse matrix} of shape (n_samples, n_classes) + Shape will be (n_samples, 1) for binary problems. Sparse matrix will + be of CSR format. + + See Also + -------- + LabelBinarizer : Class used to wrap the functionality of label_binarize and + allow for fitting to classes independently of the transform operation. + + Examples + -------- + >>> from sklearn.preprocessing import label_binarize + >>> label_binarize([1, 6], classes=[1, 2, 4, 6]) + array([[1, 0, 0, 0], + [0, 0, 0, 1]]) + + The class ordering is preserved: + + >>> label_binarize([1, 6], classes=[1, 6, 4, 2]) + array([[1, 0, 0, 0], + [0, 1, 0, 0]]) + + Binary targets transform to a column vector + + >>> label_binarize(['yes', 'no', 'no', 'yes'], classes=['no', 'yes']) + array([[1], + [0], + [0], + [1]]) + """ + if not isinstance(y, list): + # XXX Workaround that will be removed when list of list format is + # dropped + y = check_array( + y, input_name="y", accept_sparse="csr", ensure_2d=False, dtype=None + ) + else: + if _num_samples(y) == 0: + raise ValueError("y has 0 samples: %r" % y) + if neg_label >= pos_label: + raise ValueError( + "neg_label={0} must be strictly less than pos_label={1}.".format( + neg_label, pos_label + ) + ) + + if sparse_output and (pos_label == 0 or neg_label != 0): + raise ValueError( + "Sparse binarization is only supported with non " + "zero pos_label and zero neg_label, got " + "pos_label={0} and neg_label={1}" + "".format(pos_label, neg_label) + ) + + # To account for pos_label == 0 in the dense case + pos_switch = pos_label == 0 + if pos_switch: + pos_label = -neg_label + + y_type = type_of_target(y) + if "multioutput" in y_type: + raise ValueError( + "Multioutput target data is not supported with label binarization" + ) + if y_type == "unknown": + raise ValueError("The type of target data is not known") + + n_samples = y.shape[0] if sp.issparse(y) else len(y) + n_classes = len(classes) + classes = np.asarray(classes) + + if y_type == "binary": + if n_classes == 1: + if sparse_output: + return sp.csr_matrix((n_samples, 1), dtype=int) + else: + Y = np.zeros((len(y), 1), dtype=int) + Y += neg_label + return Y + elif len(classes) >= 3: + y_type = "multiclass" + + sorted_class = np.sort(classes) + if y_type == "multilabel-indicator": + y_n_classes = y.shape[1] if hasattr(y, "shape") else len(y[0]) + if classes.size != y_n_classes: + raise ValueError( + "classes {0} mismatch with the labels {1} found in the data".format( + classes, unique_labels(y) + ) + ) + + if y_type in ("binary", "multiclass"): + y = column_or_1d(y) + + # pick out the known labels from y + y_in_classes = np.in1d(y, classes) + y_seen = y[y_in_classes] + indices = np.searchsorted(sorted_class, y_seen) + indptr = np.hstack((0, np.cumsum(y_in_classes))) + + data = np.empty_like(indices) + data.fill(pos_label) + Y = sp.csr_matrix((data, indices, indptr), shape=(n_samples, n_classes)) + elif y_type == "multilabel-indicator": + Y = sp.csr_matrix(y) + if pos_label != 1: + data = np.empty_like(Y.data) + data.fill(pos_label) + Y.data = data + else: + raise ValueError( + "%s target data is not supported with label binarization" % y_type + ) + + if not sparse_output: + Y = Y.toarray() + Y = Y.astype(int, copy=False) + + if neg_label != 0: + Y[Y == 0] = neg_label + + if pos_switch: + Y[Y == pos_label] = 0 + else: + Y.data = Y.data.astype(int, copy=False) + + # preserve label ordering + if np.any(classes != sorted_class): + indices = np.searchsorted(sorted_class, classes) + Y = Y[:, indices] + + if y_type == "binary": + if sparse_output: + Y = Y.getcol(-1) + else: + Y = Y[:, -1].reshape((-1, 1)) + + return Y + + +def _inverse_binarize_multiclass(y, classes): + """Inverse label binarization transformation for multiclass. + + Multiclass uses the maximal score instead of a threshold. + """ + classes = np.asarray(classes) + + if sp.issparse(y): + # Find the argmax for each row in y where y is a CSR matrix + + y = y.tocsr() + n_samples, n_outputs = y.shape + outputs = np.arange(n_outputs) + row_max = min_max_axis(y, 1)[1] + row_nnz = np.diff(y.indptr) + + y_data_repeated_max = np.repeat(row_max, row_nnz) + # picks out all indices obtaining the maximum per row + y_i_all_argmax = np.flatnonzero(y_data_repeated_max == y.data) + + # For corner case where last row has a max of 0 + if row_max[-1] == 0: + y_i_all_argmax = np.append(y_i_all_argmax, [len(y.data)]) + + # Gets the index of the first argmax in each row from y_i_all_argmax + index_first_argmax = np.searchsorted(y_i_all_argmax, y.indptr[:-1]) + # first argmax of each row + y_ind_ext = np.append(y.indices, [0]) + y_i_argmax = y_ind_ext[y_i_all_argmax[index_first_argmax]] + # Handle rows of all 0 + y_i_argmax[np.where(row_nnz == 0)[0]] = 0 + + # Handles rows with max of 0 that contain negative numbers + samples = np.arange(n_samples)[(row_nnz > 0) & (row_max.ravel() == 0)] + for i in samples: + ind = y.indices[y.indptr[i] : y.indptr[i + 1]] + y_i_argmax[i] = classes[np.setdiff1d(outputs, ind)][0] + + return classes[y_i_argmax] + else: + return classes.take(y.argmax(axis=1), mode="clip") + + +def _inverse_binarize_thresholding(y, output_type, classes, threshold): + """Inverse label binarization transformation using thresholding.""" + + if output_type == "binary" and y.ndim == 2 and y.shape[1] > 2: + raise ValueError("output_type='binary', but y.shape = {0}".format(y.shape)) + + if output_type != "binary" and y.shape[1] != len(classes): + raise ValueError( + "The number of class is not equal to the number of dimension of y." + ) + + classes = np.asarray(classes) + + # Perform thresholding + if sp.issparse(y): + if threshold > 0: + if y.format not in ("csr", "csc"): + y = y.tocsr() + y.data = np.array(y.data > threshold, dtype=int) + y.eliminate_zeros() + else: + y = np.array(y.toarray() > threshold, dtype=int) + else: + y = np.array(y > threshold, dtype=int) + + # Inverse transform data + if output_type == "binary": + if sp.issparse(y): + y = y.toarray() + if y.ndim == 2 and y.shape[1] == 2: + return classes[y[:, 1]] + else: + if len(classes) == 1: + return np.repeat(classes[0], len(y)) + else: + return classes[y.ravel()] + + elif output_type == "multilabel-indicator": + return y + + else: + raise ValueError("{0} format is not supported".format(output_type)) + + +class MultiLabelBinarizer(TransformerMixin, BaseEstimator): + """Transform between iterable of iterables and a multilabel format. + + Although a list of sets or tuples is a very intuitive format for multilabel + data, it is unwieldy to process. This transformer converts between this + intuitive format and the supported multilabel format: a (samples x classes) + binary matrix indicating the presence of a class label. + + Parameters + ---------- + classes : array-like of shape (n_classes,), default=None + Indicates an ordering for the class labels. + All entries should be unique (cannot contain duplicate classes). + + sparse_output : bool, default=False + Set to True if output binary array is desired in CSR sparse format. + + Attributes + ---------- + classes_ : ndarray of shape (n_classes,) + A copy of the `classes` parameter when provided. + Otherwise it corresponds to the sorted set of classes found + when fitting. + + See Also + -------- + OneHotEncoder : Encode categorical features using a one-hot aka one-of-K + scheme. + + Examples + -------- + >>> from sklearn.preprocessing import MultiLabelBinarizer + >>> mlb = MultiLabelBinarizer() + >>> mlb.fit_transform([(1, 2), (3,)]) + array([[1, 1, 0], + [0, 0, 1]]) + >>> mlb.classes_ + array([1, 2, 3]) + + >>> mlb.fit_transform([{'sci-fi', 'thriller'}, {'comedy'}]) + array([[0, 1, 1], + [1, 0, 0]]) + >>> list(mlb.classes_) + ['comedy', 'sci-fi', 'thriller'] + + A common mistake is to pass in a list, which leads to the following issue: + + >>> mlb = MultiLabelBinarizer() + >>> mlb.fit(['sci-fi', 'thriller', 'comedy']) + MultiLabelBinarizer() + >>> mlb.classes_ + array(['-', 'c', 'd', 'e', 'f', 'h', 'i', 'l', 'm', 'o', 'r', 's', 't', + 'y'], dtype=object) + + To correct this, the list of labels should be passed in as: + + >>> mlb = MultiLabelBinarizer() + >>> mlb.fit([['sci-fi', 'thriller', 'comedy']]) + MultiLabelBinarizer() + >>> mlb.classes_ + array(['comedy', 'sci-fi', 'thriller'], dtype=object) + """ + + _parameter_constraints: dict = { + "classes": ["array-like", None], + "sparse_output": ["boolean"], + } + + def __init__(self, *, classes=None, sparse_output=False): + self.classes = classes + self.sparse_output = sparse_output + + def fit(self, y): + """Fit the label sets binarizer, storing :term:`classes_`. + + Parameters + ---------- + y : iterable of iterables + A set of labels (any orderable and hashable object) for each + sample. If the `classes` parameter is set, `y` will not be + iterated. + + Returns + ------- + self : object + Fitted estimator. + """ + self._validate_params() + self._cached_dict = None + + if self.classes is None: + classes = sorted(set(itertools.chain.from_iterable(y))) + elif len(set(self.classes)) < len(self.classes): + raise ValueError( + "The classes argument contains duplicate " + "classes. Remove these duplicates before passing " + "them to MultiLabelBinarizer." + ) + else: + classes = self.classes + dtype = int if all(isinstance(c, int) for c in classes) else object + self.classes_ = np.empty(len(classes), dtype=dtype) + self.classes_[:] = classes + return self + + def fit_transform(self, y): + """Fit the label sets binarizer and transform the given label sets. + + Parameters + ---------- + y : iterable of iterables + A set of labels (any orderable and hashable object) for each + sample. If the `classes` parameter is set, `y` will not be + iterated. + + Returns + ------- + y_indicator : {ndarray, sparse matrix} of shape (n_samples, n_classes) + A matrix such that `y_indicator[i, j] = 1` iff `classes_[j]` + is in `y[i]`, and 0 otherwise. Sparse matrix will be of CSR + format. + """ + if self.classes is not None: + return self.fit(y).transform(y) + + self._validate_params() + self._cached_dict = None + + # Automatically increment on new class + class_mapping = defaultdict(int) + class_mapping.default_factory = class_mapping.__len__ + yt = self._transform(y, class_mapping) + + # sort classes and reorder columns + tmp = sorted(class_mapping, key=class_mapping.get) + + # (make safe for tuples) + dtype = int if all(isinstance(c, int) for c in tmp) else object + class_mapping = np.empty(len(tmp), dtype=dtype) + class_mapping[:] = tmp + self.classes_, inverse = np.unique(class_mapping, return_inverse=True) + # ensure yt.indices keeps its current dtype + yt.indices = np.array(inverse[yt.indices], dtype=yt.indices.dtype, copy=False) + + if not self.sparse_output: + yt = yt.toarray() + + return yt + + def transform(self, y): + """Transform the given label sets. + + Parameters + ---------- + y : iterable of iterables + A set of labels (any orderable and hashable object) for each + sample. If the `classes` parameter is set, `y` will not be + iterated. + + Returns + ------- + y_indicator : array or CSR matrix, shape (n_samples, n_classes) + A matrix such that `y_indicator[i, j] = 1` iff `classes_[j]` is in + `y[i]`, and 0 otherwise. + """ + check_is_fitted(self) + + class_to_index = self._build_cache() + yt = self._transform(y, class_to_index) + + if not self.sparse_output: + yt = yt.toarray() + + return yt + + def _build_cache(self): + if self._cached_dict is None: + self._cached_dict = dict(zip(self.classes_, range(len(self.classes_)))) + + return self._cached_dict + + def _transform(self, y, class_mapping): + """Transforms the label sets with a given mapping. + + Parameters + ---------- + y : iterable of iterables + A set of labels (any orderable and hashable object) for each + sample. If the `classes` parameter is set, `y` will not be + iterated. + + class_mapping : Mapping + Maps from label to column index in label indicator matrix. + + Returns + ------- + y_indicator : sparse matrix of shape (n_samples, n_classes) + Label indicator matrix. Will be of CSR format. + """ + indices = array.array("i") + indptr = array.array("i", [0]) + unknown = set() + for labels in y: + index = set() + for label in labels: + try: + index.add(class_mapping[label]) + except KeyError: + unknown.add(label) + indices.extend(index) + indptr.append(len(indices)) + if unknown: + warnings.warn( + "unknown class(es) {0} will be ignored".format(sorted(unknown, key=str)) + ) + data = np.ones(len(indices), dtype=int) + + return sp.csr_matrix( + (data, indices, indptr), shape=(len(indptr) - 1, len(class_mapping)) + ) + + def inverse_transform(self, yt): + """Transform the given indicator matrix into label sets. + + Parameters + ---------- + yt : {ndarray, sparse matrix} of shape (n_samples, n_classes) + A matrix containing only 1s ands 0s. + + Returns + ------- + y : list of tuples + The set of labels for each sample such that `y[i]` consists of + `classes_[j]` for each `yt[i, j] == 1`. + """ + check_is_fitted(self) + + if yt.shape[1] != len(self.classes_): + raise ValueError( + "Expected indicator for {0} classes, but got {1}".format( + len(self.classes_), yt.shape[1] + ) + ) + + if sp.issparse(yt): + yt = yt.tocsr() + if len(yt.data) != 0 and len(np.setdiff1d(yt.data, [0, 1])) > 0: + raise ValueError("Expected only 0s and 1s in label indicator.") + return [ + tuple(self.classes_.take(yt.indices[start:end])) + for start, end in zip(yt.indptr[:-1], yt.indptr[1:]) + ] + else: + unexpected = np.setdiff1d(yt, [0, 1]) + if len(unexpected) > 0: + raise ValueError( + "Expected only 0s and 1s in label indicator. Also got {0}".format( + unexpected + ) + ) + return [tuple(self.classes_.compress(indicators)) for indicators in yt] + + def _more_tags(self): + return {"X_types": ["2dlabels"]} diff --git a/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/_polynomial.py b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/_polynomial.py new file mode 100644 index 0000000000000000000000000000000000000000..3f09a8dddfd0fce9204ef7d1b284082680455c9c --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/_polynomial.py @@ -0,0 +1,937 @@ +""" +This file contains preprocessing tools based on polynomials. +""" +import collections +from numbers import Integral +from itertools import chain, combinations +from itertools import combinations_with_replacement as combinations_w_r + +import numpy as np +from scipy import sparse +from scipy.interpolate import BSpline +from scipy.special import comb + +from ..base import BaseEstimator, TransformerMixin +from ..utils import check_array +from ..utils.validation import check_is_fitted, FLOAT_DTYPES, _check_sample_weight +from ..utils.validation import _check_feature_names_in +from ..utils._param_validation import Interval, StrOptions +from ..utils.stats import _weighted_percentile + +from ._csr_polynomial_expansion import _csr_polynomial_expansion + + +__all__ = [ + "PolynomialFeatures", + "SplineTransformer", +] + + +class PolynomialFeatures(TransformerMixin, BaseEstimator): + """Generate polynomial and interaction features. + + Generate a new feature matrix consisting of all polynomial combinations + of the features with degree less than or equal to the specified degree. + For example, if an input sample is two dimensional and of the form + [a, b], the degree-2 polynomial features are [1, a, b, a^2, ab, b^2]. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + degree : int or tuple (min_degree, max_degree), default=2 + If a single int is given, it specifies the maximal degree of the + polynomial features. If a tuple `(min_degree, max_degree)` is passed, + then `min_degree` is the minimum and `max_degree` is the maximum + polynomial degree of the generated features. Note that `min_degree=0` + and `min_degree=1` are equivalent as outputting the degree zero term is + determined by `include_bias`. + + interaction_only : bool, default=False + If `True`, only interaction features are produced: features that are + products of at most `degree` *distinct* input features, i.e. terms with + power of 2 or higher of the same input feature are excluded: + + - included: `x[0]`, `x[1]`, `x[0] * x[1]`, etc. + - excluded: `x[0] ** 2`, `x[0] ** 2 * x[1]`, etc. + + include_bias : bool, default=True + If `True` (default), then include a bias column, the feature in which + all polynomial powers are zero (i.e. a column of ones - acts as an + intercept term in a linear model). + + order : {'C', 'F'}, default='C' + Order of output array in the dense case. `'F'` order is faster to + compute, but may slow down subsequent estimators. + + .. versionadded:: 0.21 + + Attributes + ---------- + powers_ : ndarray of shape (`n_output_features_`, `n_features_in_`) + `powers_[i, j]` is the exponent of the jth input in the ith output. + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + n_output_features_ : int + The total number of polynomial output features. The number of output + features is computed by iterating over all suitably sized combinations + of input features. + + See Also + -------- + SplineTransformer : Transformer that generates univariate B-spline bases + for features. + + Notes + ----- + Be aware that the number of features in the output array scales + polynomially in the number of features of the input array, and + exponentially in the degree. High degrees can cause overfitting. + + See :ref:`examples/linear_model/plot_polynomial_interpolation.py + ` + + Examples + -------- + >>> import numpy as np + >>> from sklearn.preprocessing import PolynomialFeatures + >>> X = np.arange(6).reshape(3, 2) + >>> X + array([[0, 1], + [2, 3], + [4, 5]]) + >>> poly = PolynomialFeatures(2) + >>> poly.fit_transform(X) + array([[ 1., 0., 1., 0., 0., 1.], + [ 1., 2., 3., 4., 6., 9.], + [ 1., 4., 5., 16., 20., 25.]]) + >>> poly = PolynomialFeatures(interaction_only=True) + >>> poly.fit_transform(X) + array([[ 1., 0., 1., 0.], + [ 1., 2., 3., 6.], + [ 1., 4., 5., 20.]]) + """ + + _parameter_constraints: dict = { + "degree": [Interval(Integral, 0, None, closed="left"), "array-like"], + "interaction_only": ["boolean"], + "include_bias": ["boolean"], + "order": [StrOptions({"C", "F"})], + } + + def __init__( + self, degree=2, *, interaction_only=False, include_bias=True, order="C" + ): + self.degree = degree + self.interaction_only = interaction_only + self.include_bias = include_bias + self.order = order + + @staticmethod + def _combinations( + n_features, min_degree, max_degree, interaction_only, include_bias + ): + comb = combinations if interaction_only else combinations_w_r + start = max(1, min_degree) + iter = chain.from_iterable( + comb(range(n_features), i) for i in range(start, max_degree + 1) + ) + if include_bias: + iter = chain(comb(range(n_features), 0), iter) + return iter + + @staticmethod + def _num_combinations( + n_features, min_degree, max_degree, interaction_only, include_bias + ): + """Calculate number of terms in polynomial expansion + + This should be equivalent to counting the number of terms returned by + _combinations(...) but much faster. + """ + + if interaction_only: + combinations = sum( + [ + comb(n_features, i, exact=True) + for i in range(max(1, min_degree), min(max_degree, n_features) + 1) + ] + ) + else: + combinations = comb(n_features + max_degree, max_degree, exact=True) - 1 + if min_degree > 0: + d = min_degree - 1 + combinations -= comb(n_features + d, d, exact=True) - 1 + + if include_bias: + combinations += 1 + + return combinations + + @property + def powers_(self): + """Exponent for each of the inputs in the output.""" + check_is_fitted(self) + + combinations = self._combinations( + n_features=self.n_features_in_, + min_degree=self._min_degree, + max_degree=self._max_degree, + interaction_only=self.interaction_only, + include_bias=self.include_bias, + ) + return np.vstack( + [np.bincount(c, minlength=self.n_features_in_) for c in combinations] + ) + + def get_feature_names_out(self, input_features=None): + """Get output feature names for transformation. + + Parameters + ---------- + input_features : array-like of str or None, default=None + Input features. + + - If `input_features is None`, then `feature_names_in_` is + used as feature names in. If `feature_names_in_` is not defined, + then the following input feature names are generated: + `["x0", "x1", ..., "x(n_features_in_ - 1)"]`. + - If `input_features` is an array-like, then `input_features` must + match `feature_names_in_` if `feature_names_in_` is defined. + + Returns + ------- + feature_names_out : ndarray of str objects + Transformed feature names. + """ + powers = self.powers_ + input_features = _check_feature_names_in(self, input_features) + feature_names = [] + for row in powers: + inds = np.where(row)[0] + if len(inds): + name = " ".join( + "%s^%d" % (input_features[ind], exp) + if exp != 1 + else input_features[ind] + for ind, exp in zip(inds, row[inds]) + ) + else: + name = "1" + feature_names.append(name) + return np.asarray(feature_names, dtype=object) + + def fit(self, X, y=None): + """ + Compute number of output features. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data. + + y : Ignored + Not used, present here for API consistency by convention. + + Returns + ------- + self : object + Fitted transformer. + """ + self._validate_params() + _, n_features = self._validate_data(X, accept_sparse=True).shape + + if isinstance(self.degree, Integral): + if self.degree == 0 and not self.include_bias: + raise ValueError( + "Setting degree to zero and include_bias to False would result in" + " an empty output array." + ) + + self._min_degree = 0 + self._max_degree = self.degree + elif ( + isinstance(self.degree, collections.abc.Iterable) and len(self.degree) == 2 + ): + self._min_degree, self._max_degree = self.degree + if not ( + isinstance(self._min_degree, Integral) + and isinstance(self._max_degree, Integral) + and self._min_degree >= 0 + and self._min_degree <= self._max_degree + ): + raise ValueError( + "degree=(min_degree, max_degree) must " + "be non-negative integers that fulfil " + "min_degree <= max_degree, got " + f"{self.degree}." + ) + elif self._max_degree == 0 and not self.include_bias: + raise ValueError( + "Setting both min_degree and max_degree to zero and include_bias to" + " False would result in an empty output array." + ) + else: + raise ValueError( + "degree must be a non-negative int or tuple " + "(min_degree, max_degree), got " + f"{self.degree}." + ) + + self.n_output_features_ = self._num_combinations( + n_features=n_features, + min_degree=self._min_degree, + max_degree=self._max_degree, + interaction_only=self.interaction_only, + include_bias=self.include_bias, + ) + # We also record the number of output features for + # _max_degree = 0 + self._n_out_full = self._num_combinations( + n_features=n_features, + min_degree=0, + max_degree=self._max_degree, + interaction_only=self.interaction_only, + include_bias=self.include_bias, + ) + + return self + + def transform(self, X): + """Transform data to polynomial features. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The data to transform, row by row. + + Prefer CSR over CSC for sparse input (for speed), but CSC is + required if the degree is 4 or higher. If the degree is less than + 4 and the input format is CSC, it will be converted to CSR, have + its polynomial features generated, then converted back to CSC. + + If the degree is 2 or 3, the method described in "Leveraging + Sparsity to Speed Up Polynomial Feature Expansions of CSR Matrices + Using K-Simplex Numbers" by Andrew Nystrom and John Hughes is + used, which is much faster than the method used on CSC input. For + this reason, a CSC input will be converted to CSR, and the output + will be converted back to CSC prior to being returned, hence the + preference of CSR. + + Returns + ------- + XP : {ndarray, sparse matrix} of shape (n_samples, NP) + The matrix of features, where `NP` is the number of polynomial + features generated from the combination of inputs. If a sparse + matrix is provided, it will be converted into a sparse + `csr_matrix`. + """ + check_is_fitted(self) + + X = self._validate_data( + X, order="F", dtype=FLOAT_DTYPES, reset=False, accept_sparse=("csr", "csc") + ) + + n_samples, n_features = X.shape + + if sparse.isspmatrix_csr(X): + if self._max_degree > 3: + return self.transform(X.tocsc()).tocsr() + to_stack = [] + if self.include_bias: + to_stack.append( + sparse.csc_matrix(np.ones(shape=(n_samples, 1), dtype=X.dtype)) + ) + if self._min_degree <= 1 and self._max_degree > 0: + to_stack.append(X) + for deg in range(max(2, self._min_degree), self._max_degree + 1): + Xp_next = _csr_polynomial_expansion( + X.data, X.indices, X.indptr, X.shape[1], self.interaction_only, deg + ) + if Xp_next is None: + break + to_stack.append(Xp_next) + if len(to_stack) == 0: + # edge case: deal with empty matrix + XP = sparse.csr_matrix((n_samples, 0), dtype=X.dtype) + else: + XP = sparse.hstack(to_stack, format="csr") + elif sparse.isspmatrix_csc(X) and self._max_degree < 4: + return self.transform(X.tocsr()).tocsc() + elif sparse.isspmatrix(X): + combinations = self._combinations( + n_features=n_features, + min_degree=self._min_degree, + max_degree=self._max_degree, + interaction_only=self.interaction_only, + include_bias=self.include_bias, + ) + columns = [] + for combi in combinations: + if combi: + out_col = 1 + for col_idx in combi: + out_col = X[:, col_idx].multiply(out_col) + columns.append(out_col) + else: + bias = sparse.csc_matrix(np.ones((X.shape[0], 1))) + columns.append(bias) + XP = sparse.hstack(columns, dtype=X.dtype).tocsc() + else: + # Do as if _min_degree = 0 and cut down array after the + # computation, i.e. use _n_out_full instead of n_output_features_. + XP = np.empty( + shape=(n_samples, self._n_out_full), dtype=X.dtype, order=self.order + ) + + # What follows is a faster implementation of: + # for i, comb in enumerate(combinations): + # XP[:, i] = X[:, comb].prod(1) + # This implementation uses two optimisations. + # First one is broadcasting, + # multiply ([X1, ..., Xn], X1) -> [X1 X1, ..., Xn X1] + # multiply ([X2, ..., Xn], X2) -> [X2 X2, ..., Xn X2] + # ... + # multiply ([X[:, start:end], X[:, start]) -> ... + # Second optimisation happens for degrees >= 3. + # Xi^3 is computed reusing previous computation: + # Xi^3 = Xi^2 * Xi. + + # degree 0 term + if self.include_bias: + XP[:, 0] = 1 + current_col = 1 + else: + current_col = 0 + + if self._max_degree == 0: + return XP + + # degree 1 term + XP[:, current_col : current_col + n_features] = X + index = list(range(current_col, current_col + n_features)) + current_col += n_features + index.append(current_col) + + # loop over degree >= 2 terms + for _ in range(2, self._max_degree + 1): + new_index = [] + end = index[-1] + for feature_idx in range(n_features): + start = index[feature_idx] + new_index.append(current_col) + if self.interaction_only: + start += index[feature_idx + 1] - index[feature_idx] + next_col = current_col + end - start + if next_col <= current_col: + break + # XP[:, start:end] are terms of degree d - 1 + # that exclude feature #feature_idx. + np.multiply( + XP[:, start:end], + X[:, feature_idx : feature_idx + 1], + out=XP[:, current_col:next_col], + casting="no", + ) + current_col = next_col + + new_index.append(current_col) + index = new_index + + if self._min_degree > 1: + n_XP, n_Xout = self._n_out_full, self.n_output_features_ + if self.include_bias: + Xout = np.empty( + shape=(n_samples, n_Xout), dtype=XP.dtype, order=self.order + ) + Xout[:, 0] = 1 + Xout[:, 1:] = XP[:, n_XP - n_Xout + 1 :] + else: + Xout = XP[:, n_XP - n_Xout :].copy() + XP = Xout + return XP + + +# TODO: +# - sparse support (either scipy or own cython solution)? +class SplineTransformer(TransformerMixin, BaseEstimator): + """Generate univariate B-spline bases for features. + + Generate a new feature matrix consisting of + `n_splines=n_knots + degree - 1` (`n_knots - 1` for + `extrapolation="periodic"`) spline basis functions + (B-splines) of polynomial order=`degree` for each feature. + + Read more in the :ref:`User Guide `. + + .. versionadded:: 1.0 + + Parameters + ---------- + n_knots : int, default=5 + Number of knots of the splines if `knots` equals one of + {'uniform', 'quantile'}. Must be larger or equal 2. Ignored if `knots` + is array-like. + + degree : int, default=3 + The polynomial degree of the spline basis. Must be a non-negative + integer. + + knots : {'uniform', 'quantile'} or array-like of shape \ + (n_knots, n_features), default='uniform' + Set knot positions such that first knot <= features <= last knot. + + - If 'uniform', `n_knots` number of knots are distributed uniformly + from min to max values of the features. + - If 'quantile', they are distributed uniformly along the quantiles of + the features. + - If an array-like is given, it directly specifies the sorted knot + positions including the boundary knots. Note that, internally, + `degree` number of knots are added before the first knot, the same + after the last knot. + + extrapolation : {'error', 'constant', 'linear', 'continue', 'periodic'}, \ + default='constant' + If 'error', values outside the min and max values of the training + features raises a `ValueError`. If 'constant', the value of the + splines at minimum and maximum value of the features is used as + constant extrapolation. If 'linear', a linear extrapolation is used. + If 'continue', the splines are extrapolated as is, i.e. option + `extrapolate=True` in :class:`scipy.interpolate.BSpline`. If + 'periodic', periodic splines with a periodicity equal to the distance + between the first and last knot are used. Periodic splines enforce + equal function values and derivatives at the first and last knot. + For example, this makes it possible to avoid introducing an arbitrary + jump between Dec 31st and Jan 1st in spline features derived from a + naturally periodic "day-of-year" input feature. In this case it is + recommended to manually set the knot values to control the period. + + include_bias : bool, default=True + If True (default), then the last spline element inside the data range + of a feature is dropped. As B-splines sum to one over the spline basis + functions for each data point, they implicitly include a bias term, + i.e. a column of ones. It acts as an intercept term in a linear models. + + order : {'C', 'F'}, default='C' + Order of output array. 'F' order is faster to compute, but may slow + down subsequent estimators. + + Attributes + ---------- + bsplines_ : list of shape (n_features,) + List of BSplines objects, one for each feature. + + n_features_in_ : int + The total number of input features. + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + n_features_out_ : int + The total number of output features, which is computed as + `n_features * n_splines`, where `n_splines` is + the number of bases elements of the B-splines, + `n_knots + degree - 1` for non-periodic splines and + `n_knots - 1` for periodic ones. + If `include_bias=False`, then it is only + `n_features * (n_splines - 1)`. + + See Also + -------- + KBinsDiscretizer : Transformer that bins continuous data into intervals. + + PolynomialFeatures : Transformer that generates polynomial and interaction + features. + + Notes + ----- + High degrees and a high number of knots can cause overfitting. + + See :ref:`examples/linear_model/plot_polynomial_interpolation.py + `. + + Examples + -------- + >>> import numpy as np + >>> from sklearn.preprocessing import SplineTransformer + >>> X = np.arange(6).reshape(6, 1) + >>> spline = SplineTransformer(degree=2, n_knots=3) + >>> spline.fit_transform(X) + array([[0.5 , 0.5 , 0. , 0. ], + [0.18, 0.74, 0.08, 0. ], + [0.02, 0.66, 0.32, 0. ], + [0. , 0.32, 0.66, 0.02], + [0. , 0.08, 0.74, 0.18], + [0. , 0. , 0.5 , 0.5 ]]) + """ + + _parameter_constraints: dict = { + "n_knots": [Interval(Integral, 2, None, closed="left")], + "degree": [Interval(Integral, 0, None, closed="left")], + "knots": [StrOptions({"uniform", "quantile"}), "array-like"], + "extrapolation": [ + StrOptions({"error", "constant", "linear", "continue", "periodic"}) + ], + "include_bias": ["boolean"], + "order": [StrOptions({"C", "F"})], + } + + def __init__( + self, + n_knots=5, + degree=3, + *, + knots="uniform", + extrapolation="constant", + include_bias=True, + order="C", + ): + self.n_knots = n_knots + self.degree = degree + self.knots = knots + self.extrapolation = extrapolation + self.include_bias = include_bias + self.order = order + + @staticmethod + def _get_base_knot_positions(X, n_knots=10, knots="uniform", sample_weight=None): + """Calculate base knot positions. + + Base knots such that first knot <= feature <= last knot. For the + B-spline construction with scipy.interpolate.BSpline, 2*degree knots + beyond the base interval are added. + + Returns + ------- + knots : ndarray of shape (n_knots, n_features), dtype=np.float64 + Knot positions (points) of base interval. + """ + if knots == "quantile": + percentiles = 100 * np.linspace( + start=0, stop=1, num=n_knots, dtype=np.float64 + ) + + if sample_weight is None: + knots = np.percentile(X, percentiles, axis=0) + else: + knots = np.array( + [ + _weighted_percentile(X, sample_weight, percentile) + for percentile in percentiles + ] + ) + + else: + # knots == 'uniform': + # Note that the variable `knots` has already been validated and + # `else` is therefore safe. + # Disregard observations with zero weight. + mask = slice(None, None, 1) if sample_weight is None else sample_weight > 0 + x_min = np.amin(X[mask], axis=0) + x_max = np.amax(X[mask], axis=0) + + knots = np.linspace( + start=x_min, + stop=x_max, + num=n_knots, + endpoint=True, + dtype=np.float64, + ) + + return knots + + def get_feature_names_out(self, input_features=None): + """Get output feature names for transformation. + + Parameters + ---------- + input_features : array-like of str or None, default=None + Input features. + + - If `input_features` is `None`, then `feature_names_in_` is + used as feature names in. If `feature_names_in_` is not defined, + then the following input feature names are generated: + `["x0", "x1", ..., "x(n_features_in_ - 1)"]`. + - If `input_features` is an array-like, then `input_features` must + match `feature_names_in_` if `feature_names_in_` is defined. + + Returns + ------- + feature_names_out : ndarray of str objects + Transformed feature names. + """ + n_splines = self.bsplines_[0].c.shape[1] + + input_features = _check_feature_names_in(self, input_features) + feature_names = [] + for i in range(self.n_features_in_): + for j in range(n_splines - 1 + self.include_bias): + feature_names.append(f"{input_features[i]}_sp_{j}") + return np.asarray(feature_names, dtype=object) + + def fit(self, X, y=None, sample_weight=None): + """Compute knot positions of splines. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data. + + y : None + Ignored. + + sample_weight : array-like of shape (n_samples,), default = None + Individual weights for each sample. Used to calculate quantiles if + `knots="quantile"`. For `knots="uniform"`, zero weighted + observations are ignored for finding the min and max of `X`. + + Returns + ------- + self : object + Fitted transformer. + """ + self._validate_params() + + X = self._validate_data( + X, + reset=True, + accept_sparse=False, + ensure_min_samples=2, + ensure_2d=True, + ) + if sample_weight is not None: + sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype) + + _, n_features = X.shape + + if isinstance(self.knots, str): + base_knots = self._get_base_knot_positions( + X, n_knots=self.n_knots, knots=self.knots, sample_weight=sample_weight + ) + else: + base_knots = check_array(self.knots, dtype=np.float64) + if base_knots.shape[0] < 2: + raise ValueError("Number of knots, knots.shape[0], must be >= 2.") + elif base_knots.shape[1] != n_features: + raise ValueError("knots.shape[1] == n_features is violated.") + elif not np.all(np.diff(base_knots, axis=0) > 0): + raise ValueError("knots must be sorted without duplicates.") + + # number of knots for base interval + n_knots = base_knots.shape[0] + + if self.extrapolation == "periodic" and n_knots <= self.degree: + raise ValueError( + "Periodic splines require degree < n_knots. Got n_knots=" + f"{n_knots} and degree={self.degree}." + ) + + # number of splines basis functions + if self.extrapolation != "periodic": + n_splines = n_knots + self.degree - 1 + else: + # periodic splines have self.degree less degrees of freedom + n_splines = n_knots - 1 + + degree = self.degree + n_out = n_features * n_splines + # We have to add degree number of knots below, and degree number knots + # above the base knots in order to make the spline basis complete. + if self.extrapolation == "periodic": + # For periodic splines the spacing of the first / last degree knots + # needs to be a continuation of the spacing of the last / first + # base knots. + period = base_knots[-1] - base_knots[0] + knots = np.r_[ + base_knots[-(degree + 1) : -1] - period, + base_knots, + base_knots[1 : (degree + 1)] + period, + ] + + else: + # Eilers & Marx in "Flexible smoothing with B-splines and + # penalties" https://doi.org/10.1214/ss/1038425655 advice + # against repeating first and last knot several times, which + # would have inferior behaviour at boundaries if combined with + # a penalty (hence P-Spline). We follow this advice even if our + # splines are unpenalized. Meaning we do not: + # knots = np.r_[ + # np.tile(base_knots.min(axis=0), reps=[degree, 1]), + # base_knots, + # np.tile(base_knots.max(axis=0), reps=[degree, 1]) + # ] + # Instead, we reuse the distance of the 2 fist/last knots. + dist_min = base_knots[1] - base_knots[0] + dist_max = base_knots[-1] - base_knots[-2] + + knots = np.r_[ + np.linspace( + base_knots[0] - degree * dist_min, + base_knots[0] - dist_min, + num=degree, + ), + base_knots, + np.linspace( + base_knots[-1] + dist_max, + base_knots[-1] + degree * dist_max, + num=degree, + ), + ] + + # With a diagonal coefficient matrix, we get back the spline basis + # elements, i.e. the design matrix of the spline. + # Note, BSpline appreciates C-contiguous float64 arrays as c=coef. + coef = np.eye(n_splines, dtype=np.float64) + if self.extrapolation == "periodic": + coef = np.concatenate((coef, coef[:degree, :])) + + extrapolate = self.extrapolation in ["periodic", "continue"] + + bsplines = [ + BSpline.construct_fast( + knots[:, i], coef, self.degree, extrapolate=extrapolate + ) + for i in range(n_features) + ] + self.bsplines_ = bsplines + + self.n_features_out_ = n_out - n_features * (1 - self.include_bias) + return self + + def transform(self, X): + """Transform each feature data to B-splines. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to transform. + + Returns + ------- + XBS : ndarray of shape (n_samples, n_features * n_splines) + The matrix of features, where n_splines is the number of bases + elements of the B-splines, n_knots + degree - 1. + """ + check_is_fitted(self) + + X = self._validate_data(X, reset=False, accept_sparse=False, ensure_2d=True) + + n_samples, n_features = X.shape + n_splines = self.bsplines_[0].c.shape[1] + degree = self.degree + + # Note that scipy BSpline returns float64 arrays and converts input + # x=X[:, i] to c-contiguous float64. + n_out = self.n_features_out_ + n_features * (1 - self.include_bias) + if X.dtype in FLOAT_DTYPES: + dtype = X.dtype + else: + dtype = np.float64 + XBS = np.zeros((n_samples, n_out), dtype=dtype, order=self.order) + + for i in range(n_features): + spl = self.bsplines_[i] + + if self.extrapolation in ("continue", "error", "periodic"): + if self.extrapolation == "periodic": + # With periodic extrapolation we map x to the segment + # [spl.t[k], spl.t[n]]. + # This is equivalent to BSpline(.., extrapolate="periodic") + # for scipy>=1.0.0. + n = spl.t.size - spl.k - 1 + # Assign to new array to avoid inplace operation + x = spl.t[spl.k] + (X[:, i] - spl.t[spl.k]) % ( + spl.t[n] - spl.t[spl.k] + ) + else: + x = X[:, i] + + XBS[:, (i * n_splines) : ((i + 1) * n_splines)] = spl(x) + + else: + xmin = spl.t[degree] + xmax = spl.t[-degree - 1] + mask = (xmin <= X[:, i]) & (X[:, i] <= xmax) + XBS[mask, (i * n_splines) : ((i + 1) * n_splines)] = spl(X[mask, i]) + + # Note for extrapolation: + # 'continue' is already returned as is by scipy BSplines + if self.extrapolation == "error": + # BSpline with extrapolate=False does not raise an error, but + # output np.nan. + if np.any(np.isnan(XBS[:, (i * n_splines) : ((i + 1) * n_splines)])): + raise ValueError( + "X contains values beyond the limits of the knots." + ) + elif self.extrapolation == "constant": + # Set all values beyond xmin and xmax to the value of the + # spline basis functions at those two positions. + # Only the first degree and last degree number of splines + # have non-zero values at the boundaries. + + # spline values at boundaries + f_min = spl(xmin) + f_max = spl(xmax) + mask = X[:, i] < xmin + if np.any(mask): + XBS[mask, (i * n_splines) : (i * n_splines + degree)] = f_min[ + :degree + ] + + mask = X[:, i] > xmax + if np.any(mask): + XBS[ + mask, + ((i + 1) * n_splines - degree) : ((i + 1) * n_splines), + ] = f_max[-degree:] + + elif self.extrapolation == "linear": + # Continue the degree first and degree last spline bases + # linearly beyond the boundaries, with slope = derivative at + # the boundary. + # Note that all others have derivative = value = 0 at the + # boundaries. + + # spline values at boundaries + f_min, f_max = spl(xmin), spl(xmax) + # spline derivatives = slopes at boundaries + fp_min, fp_max = spl(xmin, nu=1), spl(xmax, nu=1) + # Compute the linear continuation. + if degree <= 1: + # For degree=1, the derivative of 2nd spline is not zero at + # boundary. For degree=0 it is the same as 'constant'. + degree += 1 + for j in range(degree): + mask = X[:, i] < xmin + if np.any(mask): + XBS[mask, i * n_splines + j] = ( + f_min[j] + (X[mask, i] - xmin) * fp_min[j] + ) + + mask = X[:, i] > xmax + if np.any(mask): + k = n_splines - 1 - j + XBS[mask, i * n_splines + k] = ( + f_max[k] + (X[mask, i] - xmax) * fp_max[k] + ) + + if self.include_bias: + return XBS + else: + # We throw away one spline basis per feature. + # We chose the last one. + indices = [j for j in range(XBS.shape[1]) if (j + 1) % n_splines != 0] + return XBS[:, indices] diff --git a/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/tests/__pycache__/__init__.cpython-310.pyc b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/tests/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9e2f94c33f06b108de41279ed2835d350683e03b Binary files /dev/null and b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/tests/__pycache__/__init__.cpython-310.pyc differ diff --git a/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/tests/__pycache__/test_common.cpython-310.pyc b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/tests/__pycache__/test_common.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..28a6af43e9cd90143e3f38db8f83637daabcc24f Binary files /dev/null and b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/tests/__pycache__/test_common.cpython-310.pyc differ diff --git a/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/tests/__pycache__/test_discretization.cpython-310.pyc b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/tests/__pycache__/test_discretization.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8619a359f750f9f3f42d6e9a781231241db2e0de Binary files /dev/null and b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/tests/__pycache__/test_discretization.cpython-310.pyc differ diff --git a/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/tests/__pycache__/test_encoders.cpython-310.pyc b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/tests/__pycache__/test_encoders.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ce3a2eb2edd3cd7eacba40f95646f42943f2fb72 Binary files /dev/null and b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/tests/__pycache__/test_encoders.cpython-310.pyc differ diff --git a/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/tests/test_common.py b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/tests/test_common.py new file mode 100644 index 0000000000000000000000000000000000000000..98b8dcdfe0e2a78ae8f6b2728125e188fbef95fe --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/tests/test_common.py @@ -0,0 +1,183 @@ +import warnings + +import pytest +import numpy as np + +from scipy import sparse + +from sklearn.datasets import load_iris +from sklearn.model_selection import train_test_split + +from sklearn.base import clone + +from sklearn.preprocessing import maxabs_scale +from sklearn.preprocessing import minmax_scale +from sklearn.preprocessing import scale +from sklearn.preprocessing import power_transform +from sklearn.preprocessing import quantile_transform +from sklearn.preprocessing import robust_scale + +from sklearn.preprocessing import MaxAbsScaler +from sklearn.preprocessing import MinMaxScaler +from sklearn.preprocessing import StandardScaler +from sklearn.preprocessing import PowerTransformer +from sklearn.preprocessing import QuantileTransformer +from sklearn.preprocessing import RobustScaler + +from sklearn.utils._testing import assert_array_equal +from sklearn.utils._testing import assert_allclose + +iris = load_iris() + + +def _get_valid_samples_by_column(X, col): + """Get non NaN samples in column of X""" + return X[:, [col]][~np.isnan(X[:, col])] + + +@pytest.mark.parametrize( + "est, func, support_sparse, strictly_positive, omit_kwargs", + [ + (MaxAbsScaler(), maxabs_scale, True, False, []), + (MinMaxScaler(), minmax_scale, False, False, ["clip"]), + (StandardScaler(), scale, False, False, []), + (StandardScaler(with_mean=False), scale, True, False, []), + (PowerTransformer("yeo-johnson"), power_transform, False, False, []), + (PowerTransformer("box-cox"), power_transform, False, True, []), + (QuantileTransformer(n_quantiles=10), quantile_transform, True, False, []), + (RobustScaler(), robust_scale, False, False, []), + (RobustScaler(with_centering=False), robust_scale, True, False, []), + ], +) +def test_missing_value_handling( + est, func, support_sparse, strictly_positive, omit_kwargs +): + # check that the preprocessing method let pass nan + rng = np.random.RandomState(42) + X = iris.data.copy() + n_missing = 50 + X[ + rng.randint(X.shape[0], size=n_missing), rng.randint(X.shape[1], size=n_missing) + ] = np.nan + if strictly_positive: + X += np.nanmin(X) + 0.1 + X_train, X_test = train_test_split(X, random_state=1) + # sanity check + assert not np.all(np.isnan(X_train), axis=0).any() + assert np.any(np.isnan(X_train), axis=0).all() + assert np.any(np.isnan(X_test), axis=0).all() + X_test[:, 0] = np.nan # make sure this boundary case is tested + + with warnings.catch_warnings(): + warnings.simplefilter("error", RuntimeWarning) + Xt = est.fit(X_train).transform(X_test) + # ensure no warnings are raised + # missing values should still be missing, and only them + assert_array_equal(np.isnan(Xt), np.isnan(X_test)) + + # check that the function leads to the same results as the class + with warnings.catch_warnings(): + warnings.simplefilter("error", RuntimeWarning) + Xt_class = est.transform(X_train) + kwargs = est.get_params() + # remove the parameters which should be omitted because they + # are not defined in the counterpart function of the preprocessing class + for kwarg in omit_kwargs: + _ = kwargs.pop(kwarg) + Xt_func = func(X_train, **kwargs) + assert_array_equal(np.isnan(Xt_func), np.isnan(Xt_class)) + assert_allclose(Xt_func[~np.isnan(Xt_func)], Xt_class[~np.isnan(Xt_class)]) + + # check that the inverse transform keep NaN + Xt_inv = est.inverse_transform(Xt) + assert_array_equal(np.isnan(Xt_inv), np.isnan(X_test)) + # FIXME: we can introduce equal_nan=True in recent version of numpy. + # For the moment which just check that non-NaN values are almost equal. + assert_allclose(Xt_inv[~np.isnan(Xt_inv)], X_test[~np.isnan(X_test)]) + + for i in range(X.shape[1]): + # train only on non-NaN + est.fit(_get_valid_samples_by_column(X_train, i)) + # check transforming with NaN works even when training without NaN + with warnings.catch_warnings(): + warnings.simplefilter("error", RuntimeWarning) + Xt_col = est.transform(X_test[:, [i]]) + assert_allclose(Xt_col, Xt[:, [i]]) + # check non-NaN is handled as before - the 1st column is all nan + if not np.isnan(X_test[:, i]).all(): + Xt_col_nonan = est.transform(_get_valid_samples_by_column(X_test, i)) + assert_array_equal(Xt_col_nonan, Xt_col[~np.isnan(Xt_col.squeeze())]) + + if support_sparse: + est_dense = clone(est) + est_sparse = clone(est) + + with warnings.catch_warnings(): + warnings.simplefilter("error", RuntimeWarning) + Xt_dense = est_dense.fit(X_train).transform(X_test) + Xt_inv_dense = est_dense.inverse_transform(Xt_dense) + + for sparse_constructor in ( + sparse.csr_matrix, + sparse.csc_matrix, + sparse.bsr_matrix, + sparse.coo_matrix, + sparse.dia_matrix, + sparse.dok_matrix, + sparse.lil_matrix, + ): + # check that the dense and sparse inputs lead to the same results + # precompute the matrix to avoid catching side warnings + X_train_sp = sparse_constructor(X_train) + X_test_sp = sparse_constructor(X_test) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", PendingDeprecationWarning) + warnings.simplefilter("error", RuntimeWarning) + Xt_sp = est_sparse.fit(X_train_sp).transform(X_test_sp) + + assert_allclose(Xt_sp.A, Xt_dense) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", PendingDeprecationWarning) + warnings.simplefilter("error", RuntimeWarning) + Xt_inv_sp = est_sparse.inverse_transform(Xt_sp) + + assert_allclose(Xt_inv_sp.A, Xt_inv_dense) + + +@pytest.mark.parametrize( + "est, func", + [ + (MaxAbsScaler(), maxabs_scale), + (MinMaxScaler(), minmax_scale), + (StandardScaler(), scale), + (StandardScaler(with_mean=False), scale), + (PowerTransformer("yeo-johnson"), power_transform), + ( + PowerTransformer("box-cox"), + power_transform, + ), + (QuantileTransformer(n_quantiles=3), quantile_transform), + (RobustScaler(), robust_scale), + (RobustScaler(with_centering=False), robust_scale), + ], +) +def test_missing_value_pandas_na_support(est, func): + # Test pandas IntegerArray with pd.NA + pd = pytest.importorskip("pandas") + + X = np.array( + [ + [1, 2, 3, np.nan, np.nan, 4, 5, 1], + [np.nan, np.nan, 8, 4, 6, np.nan, np.nan, 8], + [1, 2, 3, 4, 5, 6, 7, 8], + ] + ).T + + # Creates dataframe with IntegerArrays with pd.NA + X_df = pd.DataFrame(X, dtype="Int16", columns=["a", "b", "c"]) + X_df["c"] = X_df["c"].astype("int") + + X_trans = est.fit_transform(X) + X_df_trans = est.fit_transform(X_df) + + assert_allclose(X_trans, X_df_trans) diff --git a/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/tests/test_discretization.py b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/tests/test_discretization.py new file mode 100644 index 0000000000000000000000000000000000000000..e2be36e6414887e56ba44758d79e1d21b93de28f --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/sklearn/preprocessing/tests/test_discretization.py @@ -0,0 +1,410 @@ +import pytest +import numpy as np +import scipy.sparse as sp +import warnings + +from sklearn import clone +from sklearn.preprocessing import KBinsDiscretizer +from sklearn.preprocessing import OneHotEncoder +from sklearn.utils._testing import ( + assert_array_almost_equal, + assert_array_equal, + assert_allclose_dense_sparse, +) + +X = [[-2, 1.5, -4, -1], [-1, 2.5, -3, -0.5], [0, 3.5, -2, 0.5], [1, 4.5, -1, 2]] + + +@pytest.mark.parametrize( + "strategy, expected", + [ + ("uniform", [[0, 0, 0, 0], [1, 1, 1, 0], [2, 2, 2, 1], [2, 2, 2, 2]]), + ("kmeans", [[0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 1, 1], [2, 2, 2, 2]]), + ("quantile", [[0, 0, 0, 0], [1, 1, 1, 1], [2, 2, 2, 2], [2, 2, 2, 2]]), + ], +) +def test_fit_transform(strategy, expected): + est = KBinsDiscretizer(n_bins=3, encode="ordinal", strategy=strategy) + est.fit(X) + assert_array_equal(expected, est.transform(X)) + + +def test_valid_n_bins(): + KBinsDiscretizer(n_bins=2).fit_transform(X) + KBinsDiscretizer(n_bins=np.array([2])[0]).fit_transform(X) + assert KBinsDiscretizer(n_bins=2).fit(X).n_bins_.dtype == np.dtype(int) + + +def test_invalid_n_bins_array(): + # Bad shape + n_bins = np.full((2, 4), 2.0) + est = KBinsDiscretizer(n_bins=n_bins) + err_msg = r"n_bins must be a scalar or array of shape \(n_features,\)." + with pytest.raises(ValueError, match=err_msg): + est.fit_transform(X) + + # Incorrect number of features + n_bins = [1, 2, 2] + est = KBinsDiscretizer(n_bins=n_bins) + err_msg = r"n_bins must be a scalar or array of shape \(n_features,\)." + with pytest.raises(ValueError, match=err_msg): + est.fit_transform(X) + + # Bad bin values + n_bins = [1, 2, 2, 1] + est = KBinsDiscretizer(n_bins=n_bins) + err_msg = ( + "KBinsDiscretizer received an invalid number of bins " + "at indices 0, 3. Number of bins must be at least 2, " + "and must be an int." + ) + with pytest.raises(ValueError, match=err_msg): + est.fit_transform(X) + + # Float bin values + n_bins = [2.1, 2, 2.1, 2] + est = KBinsDiscretizer(n_bins=n_bins) + err_msg = ( + "KBinsDiscretizer received an invalid number of bins " + "at indices 0, 2. Number of bins must be at least 2, " + "and must be an int." + ) + with pytest.raises(ValueError, match=err_msg): + est.fit_transform(X) + + +@pytest.mark.parametrize( + "strategy, expected", + [ + ("uniform", [[0, 0, 0, 0], [0, 1, 1, 0], [1, 2, 2, 1], [1, 2, 2, 2]]), + ("kmeans", [[0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 1, 1], [1, 2, 2, 2]]), + ("quantile", [[0, 0, 0, 0], [0, 1, 1, 1], [1, 2, 2, 2], [1, 2, 2, 2]]), + ], +) +def test_fit_transform_n_bins_array(strategy, expected): + est = KBinsDiscretizer( + n_bins=[2, 3, 3, 3], encode="ordinal", strategy=strategy + ).fit(X) + assert_array_equal(expected, est.transform(X)) + + # test the shape of bin_edges_ + n_features = np.array(X).shape[1] + assert est.bin_edges_.shape == (n_features,) + for bin_edges, n_bins in zip(est.bin_edges_, est.n_bins_): + assert bin_edges.shape == (n_bins + 1,) + + +@pytest.mark.parametrize("strategy", ["uniform", "kmeans", "quantile"]) +def test_same_min_max(strategy): + warnings.simplefilter("always") + X = np.array([[1, -2], [1, -1], [1, 0], [1, 1]]) + est = KBinsDiscretizer(strategy=strategy, n_bins=3, encode="ordinal") + warning_message = "Feature 0 is constant and will be replaced with 0." + with pytest.warns(UserWarning, match=warning_message): + est.fit(X) + assert est.n_bins_[0] == 1 + # replace the feature with zeros + Xt = est.transform(X) + assert_array_equal(Xt[:, 0], np.zeros(X.shape[0])) + + +def test_transform_1d_behavior(): + X = np.arange(4) + est = KBinsDiscretizer(n_bins=2) + with pytest.raises(ValueError): + est.fit(X) + + est = KBinsDiscretizer(n_bins=2) + est.fit(X.reshape(-1, 1)) + with pytest.raises(ValueError): + est.transform(X) + + +@pytest.mark.parametrize("i", range(1, 9)) +def test_numeric_stability(i): + X_init = np.array([2.0, 4.0, 6.0, 8.0, 10.0]).reshape(-1, 1) + Xt_expected = np.array([0, 0, 1, 1, 1]).reshape(-1, 1) + + # Test up to discretizing nano units + X = X_init / 10**i + Xt = KBinsDiscretizer(n_bins=2, encode="ordinal").fit_transform(X) + assert_array_equal(Xt_expected, Xt) + + +def test_encode_options(): + est = KBinsDiscretizer(n_bins=[2, 3, 3, 3], encode="ordinal").fit(X) + Xt_1 = est.transform(X) + est = KBinsDiscretizer(n_bins=[2, 3, 3, 3], encode="onehot-dense").fit(X) + Xt_2 = est.transform(X) + assert not sp.issparse(Xt_2) + assert_array_equal( + OneHotEncoder( + categories=[np.arange(i) for i in [2, 3, 3, 3]], sparse_output=False + ).fit_transform(Xt_1), + Xt_2, + ) + est = KBinsDiscretizer(n_bins=[2, 3, 3, 3], encode="onehot").fit(X) + Xt_3 = est.transform(X) + assert sp.issparse(Xt_3) + assert_array_equal( + OneHotEncoder( + categories=[np.arange(i) for i in [2, 3, 3, 3]], sparse_output=True + ) + .fit_transform(Xt_1) + .toarray(), + Xt_3.toarray(), + ) + + +@pytest.mark.parametrize( + "strategy, expected_2bins, expected_3bins, expected_5bins", + [ + ("uniform", [0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 2, 2], [0, 0, 1, 1, 4, 4]), + ("kmeans", [0, 0, 0, 0, 1, 1], [0, 0, 1, 1, 2, 2], [0, 0, 1, 2, 3, 4]), + ("quantile", [0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 2, 2], [0, 1, 2, 3, 4, 4]), + ], +) +def test_nonuniform_strategies( + strategy, expected_2bins, expected_3bins, expected_5bins +): + X = np.array([0, 0.5, 2, 3, 9, 10]).reshape(-1, 1) + + # with 2 bins + est = KBinsDiscretizer(n_bins=2, strategy=strategy, encode="ordinal") + Xt = est.fit_transform(X) + assert_array_equal(expected_2bins, Xt.ravel()) + + # with 3 bins + est = KBinsDiscretizer(n_bins=3, strategy=strategy, encode="ordinal") + Xt = est.fit_transform(X) + assert_array_equal(expected_3bins, Xt.ravel()) + + # with 5 bins + est = KBinsDiscretizer(n_bins=5, strategy=strategy, encode="ordinal") + Xt = est.fit_transform(X) + assert_array_equal(expected_5bins, Xt.ravel()) + + +@pytest.mark.parametrize( + "strategy, expected_inv", + [ + ( + "uniform", + [ + [-1.5, 2.0, -3.5, -0.5], + [-0.5, 3.0, -2.5, -0.5], + [0.5, 4.0, -1.5, 0.5], + [0.5, 4.0, -1.5, 1.5], + ], + ), + ( + "kmeans", + [ + [-1.375, 2.125, -3.375, -0.5625], + [-1.375, 2.125, -3.375, -0.5625], + [-0.125, 3.375, -2.125, 0.5625], + [0.75, 4.25, -1.25, 1.625], + ], + ), + ( + "quantile", + [ + [-1.5, 2.0, -3.5, -0.75], + [-0.5, 3.0, -2.5, 0.0], + [0.5, 4.0, -1.5, 1.25], + [0.5, 4.0, -1.5, 1.25], + ], + ), + ], +) +@pytest.mark.parametrize("encode", ["ordinal", "onehot", "onehot-dense"]) +def test_inverse_transform(strategy, encode, expected_inv): + kbd = KBinsDiscretizer(n_bins=3, strategy=strategy, encode=encode) + Xt = kbd.fit_transform(X) + Xinv = kbd.inverse_transform(Xt) + assert_array_almost_equal(expected_inv, Xinv) + + +@pytest.mark.parametrize("strategy", ["uniform", "kmeans", "quantile"]) +def test_transform_outside_fit_range(strategy): + X = np.array([0, 1, 2, 3])[:, None] + kbd = KBinsDiscretizer(n_bins=4, strategy=strategy, encode="ordinal") + kbd.fit(X) + + X2 = np.array([-2, 5])[:, None] + X2t = kbd.transform(X2) + assert_array_equal(X2t.max(axis=0) + 1, kbd.n_bins_) + assert_array_equal(X2t.min(axis=0), [0]) + + +def test_overwrite(): + X = np.array([0, 1, 2, 3])[:, None] + X_before = X.copy() + + est = KBinsDiscretizer(n_bins=3, encode="ordinal") + Xt = est.fit_transform(X) + assert_array_equal(X, X_before) + + Xt_before = Xt.copy() + Xinv = est.inverse_transform(Xt) + assert_array_equal(Xt, Xt_before) + assert_array_equal(Xinv, np.array([[0.5], [1.5], [2.5], [2.5]])) + + +@pytest.mark.parametrize( + "strategy, expected_bin_edges", [("quantile", [0, 1, 3]), ("kmeans", [0, 1.5, 3])] +) +def test_redundant_bins(strategy, expected_bin_edges): + X = [[0], [0], [0], [0], [3], [3]] + kbd = KBinsDiscretizer(n_bins=3, strategy=strategy) + warning_message = "Consider decreasing the number of bins." + with pytest.warns(UserWarning, match=warning_message): + kbd.fit(X) + assert_array_almost_equal(kbd.bin_edges_[0], expected_bin_edges) + + +def test_percentile_numeric_stability(): + X = np.array([0.05, 0.05, 0.95]).reshape(-1, 1) + bin_edges = np.array([0.05, 0.23, 0.41, 0.59, 0.77, 0.95]) + Xt = np.array([0, 0, 4]).reshape(-1, 1) + kbd = KBinsDiscretizer(n_bins=10, encode="ordinal", strategy="quantile") + warning_message = "Consider decreasing the number of bins." + with pytest.warns(UserWarning, match=warning_message): + kbd.fit(X) + + assert_array_almost_equal(kbd.bin_edges_[0], bin_edges) + assert_array_almost_equal(kbd.transform(X), Xt) + + +@pytest.mark.parametrize("in_dtype", [np.float16, np.float32, np.float64]) +@pytest.mark.parametrize("out_dtype", [None, np.float32, np.float64]) +@pytest.mark.parametrize("encode", ["ordinal", "onehot", "onehot-dense"]) +def test_consistent_dtype(in_dtype, out_dtype, encode): + X_input = np.array(X, dtype=in_dtype) + kbd = KBinsDiscretizer(n_bins=3, encode=encode, dtype=out_dtype) + kbd.fit(X_input) + + # test output dtype + if out_dtype is not None: + expected_dtype = out_dtype + elif out_dtype is None and X_input.dtype == np.float16: + # wrong numeric input dtype are cast in np.float64 + expected_dtype = np.float64 + else: + expected_dtype = X_input.dtype + Xt = kbd.transform(X_input) + assert Xt.dtype == expected_dtype + + +@pytest.mark.parametrize("input_dtype", [np.float16, np.float32, np.float64]) +@pytest.mark.parametrize("encode", ["ordinal", "onehot", "onehot-dense"]) +def test_32_equal_64(input_dtype, encode): + # TODO this check is redundant with common checks and can be removed + # once #16290 is merged + X_input = np.array(X, dtype=input_dtype) + + # 32 bit output + kbd_32 = KBinsDiscretizer(n_bins=3, encode=encode, dtype=np.float32) + kbd_32.fit(X_input) + Xt_32 = kbd_32.transform(X_input) + + # 64 bit output + kbd_64 = KBinsDiscretizer(n_bins=3, encode=encode, dtype=np.float64) + kbd_64.fit(X_input) + Xt_64 = kbd_64.transform(X_input) + + assert_allclose_dense_sparse(Xt_32, Xt_64) + + +# FIXME: remove the `filterwarnings` in 1.3 +@pytest.mark.filterwarnings("ignore:In version 1.3 onwards, subsample=2e5") +@pytest.mark.parametrize("subsample", [None, "warn"]) +def test_kbinsdiscretizer_subsample_default(subsample): + # Since the size of X is small (< 2e5), subsampling will not take place. + X = np.array([-2, 1.5, -4, -1]).reshape(-1, 1) + kbd_default = KBinsDiscretizer(n_bins=10, encode="ordinal", strategy="quantile") + kbd_default.fit(X) + + kbd_with_subsampling = clone(kbd_default) + kbd_with_subsampling.set_params(subsample=subsample) + kbd_with_subsampling.fit(X) + + for bin_kbd_default, bin_kbd_with_subsampling in zip( + kbd_default.bin_edges_[0], kbd_with_subsampling.bin_edges_[0] + ): + np.testing.assert_allclose(bin_kbd_default, bin_kbd_with_subsampling) + assert kbd_default.bin_edges_.shape == kbd_with_subsampling.bin_edges_.shape + + +def test_kbinsdiscretizer_subsample_invalid_strategy(): + X = np.array([-2, 1.5, -4, -1]).reshape(-1, 1) + kbd = KBinsDiscretizer(n_bins=10, encode="ordinal", strategy="uniform", subsample=3) + + err_msg = '`subsample` must be used with `strategy="quantile"`.' + with pytest.raises(ValueError, match=err_msg): + kbd.fit(X) + + +# TODO: Remove in 1.3 +def test_kbinsdiscretizer_subsample_warn(): + X = np.random.rand(200001, 1).reshape(-1, 1) + kbd = KBinsDiscretizer(n_bins=100, encode="ordinal", strategy="quantile") + + msg = "In version 1.3 onwards, subsample=2e5 will be used by default." + with pytest.warns(FutureWarning, match=msg): + kbd.fit(X) + + +# TODO(1.3) remove +def test_kbinsdiscretizer_subsample_values(): + X = np.random.rand(220000, 1).reshape(-1, 1) + kbd_default = KBinsDiscretizer(n_bins=10, encode="ordinal", strategy="quantile") + + kbd_with_subsampling = clone(kbd_default) + kbd_with_subsampling.set_params(subsample=int(2e5)) + + msg = "In version 1.3 onwards, subsample=2e5 will be used by default." + with pytest.warns(FutureWarning, match=msg): + kbd_default.fit(X) + + kbd_with_subsampling.fit(X) + assert not np.all(kbd_default.bin_edges_[0] == kbd_with_subsampling.bin_edges_[0]) + assert kbd_default.bin_edges_.shape == kbd_with_subsampling.bin_edges_.shape + + +@pytest.mark.parametrize( + "encode, expected_names", + [ + ( + "onehot", + [ + f"feat{col_id}_{float(bin_id)}" + for col_id in range(3) + for bin_id in range(4) + ], + ), + ( + "onehot-dense", + [ + f"feat{col_id}_{float(bin_id)}" + for col_id in range(3) + for bin_id in range(4) + ], + ), + ("ordinal", [f"feat{col_id}" for col_id in range(3)]), + ], +) +def test_kbinsdiscrtizer_get_feature_names_out(encode, expected_names): + """Check get_feature_names_out for different settings. + Non-regression test for #22731 + """ + X = [[-2, 1, -4], [-1, 2, -3], [0, 3, -2], [1, 4, -1]] + + kbd = KBinsDiscretizer(n_bins=4, encode=encode).fit(X) + Xt = kbd.transform(X) + + input_features = [f"feat{i}" for i in range(3)] + output_names = kbd.get_feature_names_out(input_features) + assert Xt.shape[1] == output_names.shape[0] + + assert_array_equal(output_names, expected_names) diff --git a/videollama2/lib/python3.10/site-packages/yarl/__init__.py b/videollama2/lib/python3.10/site-packages/yarl/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..98c096453623a13cad0f7f89d2c50f338c1f1cc0 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/yarl/__init__.py @@ -0,0 +1,21 @@ +from ._url import ( + URL, + Query, + QueryVariable, + SimpleQuery, + cache_clear, + cache_configure, + cache_info, +) + +__version__ = "1.11.0" + +__all__ = ( + "URL", + "SimpleQuery", + "QueryVariable", + "Query", + "cache_clear", + "cache_configure", + "cache_info", +) diff --git a/videollama2/lib/python3.10/site-packages/yarl/__pycache__/__init__.cpython-310.pyc b/videollama2/lib/python3.10/site-packages/yarl/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2f7f0739ed4206eac4b72f925ea94e86ad43d392 Binary files /dev/null and b/videollama2/lib/python3.10/site-packages/yarl/__pycache__/__init__.cpython-310.pyc differ diff --git a/videollama2/lib/python3.10/site-packages/yarl/__pycache__/_helpers.cpython-310.pyc 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b/videollama2/lib/python3.10/site-packages/yarl/__pycache__/_url.cpython-310.pyc differ diff --git a/videollama2/lib/python3.10/site-packages/yarl/_helpers.py b/videollama2/lib/python3.10/site-packages/yarl/_helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..ac01158c7f5bf11bdb719e8ab8ed9fb5f80fbed8 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/yarl/_helpers.py @@ -0,0 +1,31 @@ +import os +import sys +from typing import TYPE_CHECKING + +__all__ = ("cached_property",) + + +NO_EXTENSIONS = bool(os.environ.get("YARL_NO_EXTENSIONS")) # type: bool +if sys.implementation.name != "cpython": + NO_EXTENSIONS = True + + +# isort: off +if TYPE_CHECKING: + from ._helpers_py import cached_property as cached_property_py + + cached_property = cached_property_py +elif not NO_EXTENSIONS: # pragma: no branch + try: + from ._helpers_c import cached_property as cached_property_c # type: ignore[attr-defined, unused-ignore] # noqa: E501 + + cached_property = cached_property_c + except ImportError: # pragma: no cover + from ._helpers_py import cached_property as cached_property_py + + cached_property = cached_property_py # type: ignore[assignment, misc] +else: + from ._helpers_py import cached_property as cached_property_py + + cached_property = cached_property_py # type: ignore[assignment, misc] +# isort: on diff --git a/videollama2/lib/python3.10/site-packages/yarl/_helpers_c.pyi b/videollama2/lib/python3.10/site-packages/yarl/_helpers_c.pyi new file mode 100644 index 0000000000000000000000000000000000000000..690349212193ac3480574e7264b679d50820b61c --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/yarl/_helpers_c.pyi @@ -0,0 +1,6 @@ +from typing import Any + +class cached_property: + def __init__(self, wrapped: Any) -> None: ... + def __get__(self, inst: Any, owner: Any) -> Any: ... + def __set__(self, inst: Any, value: Any) -> None: ... diff --git a/videollama2/lib/python3.10/site-packages/yarl/_helpers_c.pyx b/videollama2/lib/python3.10/site-packages/yarl/_helpers_c.pyx new file mode 100644 index 0000000000000000000000000000000000000000..e6eec375435ea293d26309c4715ce8053bfc21a6 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/yarl/_helpers_c.pyx @@ -0,0 +1,36 @@ +# cython: language_level=3 + +cdef _sentinel = object() + +cdef class cached_property: + """Use as a class method decorator. It operates almost exactly like + the Python `@property` decorator, but it puts the result of the + method it decorates into the instance dict after the first call, + effectively replacing the function it decorates with an instance + variable. It is, in Python parlance, a data descriptor. + + """ + + cdef object wrapped + cdef object name + + def __init__(self, wrapped): + self.wrapped = wrapped + self.name = wrapped.__name__ + + @property + def __doc__(self): + return self.wrapped.__doc__ + + def __get__(self, inst, owner): + if inst is None: + return self + cdef dict cache = inst._cache + val = cache.get(self.name, _sentinel) + if val is _sentinel: + val = self.wrapped(inst) + cache[self.name] = val + return val + + def __set__(self, inst, value): + raise AttributeError("cached property is read-only") diff --git a/videollama2/lib/python3.10/site-packages/yarl/_helpers_py.py b/videollama2/lib/python3.10/site-packages/yarl/_helpers_py.py new file mode 100644 index 0000000000000000000000000000000000000000..5a18afb5c39ba33057f4ff35f24d9438fc5ef9b4 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/yarl/_helpers_py.py @@ -0,0 +1,41 @@ +"""Various helper functions.""" + +from typing import Any, Callable, Dict, Generic, Optional, Protocol, Type, TypeVar + +_T = TypeVar("_T") + + +class _TSelf(Protocol, Generic[_T]): + _cache: Dict[str, _T] + + +class cached_property(Generic[_T]): + """Use as a class method decorator. + + It operates almost exactly like + the Python `@property` decorator, but it puts the result of the + method it decorates into the instance dict after the first call, + effectively replacing the function it decorates with an instance + variable. It is, in Python parlance, a data descriptor. + """ + + def __init__(self, wrapped: Callable[..., _T]) -> None: + self.wrapped = wrapped + self.__doc__ = wrapped.__doc__ + self.name = wrapped.__name__ + + def __get__(self, inst: _TSelf[_T], owner: Optional[Type[Any]] = None) -> _T: + try: + try: + return inst._cache[self.name] + except KeyError: + val = self.wrapped(inst) + inst._cache[self.name] = val + return val + except AttributeError: + if inst is None: + return self + raise + + def __set__(self, inst: _TSelf[_T], value: _T) -> None: + raise AttributeError("cached property is read-only") diff --git a/videollama2/lib/python3.10/site-packages/yarl/_quoting.py b/videollama2/lib/python3.10/site-packages/yarl/_quoting.py new file mode 100644 index 0000000000000000000000000000000000000000..95e86095d1dec243a96559a39460ba99bdfa6826 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/yarl/_quoting.py @@ -0,0 +1,18 @@ +import os +import sys + +__all__ = ("_Quoter", "_Unquoter") + + +NO_EXTENSIONS = bool(os.environ.get("YARL_NO_EXTENSIONS")) # type: bool +if sys.implementation.name != "cpython": + NO_EXTENSIONS = True + + +if not NO_EXTENSIONS: # pragma: no branch + try: + from ._quoting_c import _Quoter, _Unquoter + except ImportError: # pragma: no cover + from ._quoting_py import _Quoter, _Unquoter # type: ignore[assignment] +else: + from ._quoting_py import _Quoter, _Unquoter # type: ignore[assignment] diff --git a/videollama2/lib/python3.10/site-packages/yarl/_quoting_c.pyi b/videollama2/lib/python3.10/site-packages/yarl/_quoting_c.pyi new file mode 100644 index 0000000000000000000000000000000000000000..1dc1a5b3c2dfae2e45c6f661797966bab4ea3fd3 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/yarl/_quoting_c.pyi @@ -0,0 +1,18 @@ +from typing import Optional + +class _Quoter: + def __init__( + self, + *, + safe: str = ..., + protected: str = ..., + qs: bool = ..., + requote: bool = ... + ) -> None: ... + def __call__(self, val: str = ...) -> str: ... + +class _Unquoter: + def __init__( + self, *, ignore: str = ..., unsafe: str = ..., qs: bool = ... + ) -> None: ... + def __call__(self, val: str = ...) -> str: ... diff --git a/videollama2/lib/python3.10/site-packages/yarl/_quoting_c.pyx b/videollama2/lib/python3.10/site-packages/yarl/_quoting_c.pyx new file mode 100644 index 0000000000000000000000000000000000000000..6ac44fdf3a9b42922c9135305b30da6350162209 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/yarl/_quoting_c.pyx @@ -0,0 +1,373 @@ +# cython: language_level=3 + +from cpython.exc cimport PyErr_NoMemory +from cpython.mem cimport PyMem_Free, PyMem_Malloc, PyMem_Realloc +from cpython.unicode cimport PyUnicode_DecodeASCII, PyUnicode_DecodeUTF8Stateful +from libc.stdint cimport uint8_t, uint64_t +from libc.string cimport memcpy, memset + +from string import ascii_letters, digits + + +cdef str GEN_DELIMS = ":/?#[]@" +cdef str SUB_DELIMS_WITHOUT_QS = "!$'()*," +cdef str SUB_DELIMS = SUB_DELIMS_WITHOUT_QS + '+?=;' +cdef str RESERVED = GEN_DELIMS + SUB_DELIMS +cdef str UNRESERVED = ascii_letters + digits + '-._~' +cdef str ALLOWED = UNRESERVED + SUB_DELIMS_WITHOUT_QS +cdef str QS = '+&=;' + +DEF BUF_SIZE = 8 * 1024 # 8KiB +cdef char BUFFER[BUF_SIZE] + +cdef inline Py_UCS4 _to_hex(uint8_t v): + if v < 10: + return (v+0x30) # ord('0') == 0x30 + else: + return (v+0x41-10) # ord('A') == 0x41 + + +cdef inline int _from_hex(Py_UCS4 v): + if '0' <= v <= '9': + return (v) - 0x30 # ord('0') == 0x30 + elif 'A' <= v <= 'F': + return (v) - 0x41 + 10 # ord('A') == 0x41 + elif 'a' <= v <= 'f': + return (v) - 0x61 + 10 # ord('a') == 0x61 + else: + return -1 + + +cdef inline int _is_lower_hex(Py_UCS4 v): + return 'a' <= v <= 'f' + + +cdef inline Py_UCS4 _restore_ch(Py_UCS4 d1, Py_UCS4 d2): + cdef int digit1 = _from_hex(d1) + if digit1 < 0: + return -1 + cdef int digit2 = _from_hex(d2) + if digit2 < 0: + return -1 + return (digit1 << 4 | digit2) + + +cdef uint8_t ALLOWED_TABLE[16] +cdef uint8_t ALLOWED_NOTQS_TABLE[16] + + +cdef inline bint bit_at(uint8_t array[], uint64_t ch): + return array[ch >> 3] & (1 << (ch & 7)) + + +cdef inline void set_bit(uint8_t array[], uint64_t ch): + array[ch >> 3] |= (1 << (ch & 7)) + + +memset(ALLOWED_TABLE, 0, sizeof(ALLOWED_TABLE)) +memset(ALLOWED_NOTQS_TABLE, 0, sizeof(ALLOWED_NOTQS_TABLE)) + +for i in range(128): + if chr(i) in ALLOWED: + set_bit(ALLOWED_TABLE, i) + set_bit(ALLOWED_NOTQS_TABLE, i) + if chr(i) in QS: + set_bit(ALLOWED_NOTQS_TABLE, i) + +# ----------------- writer --------------------------- + +cdef struct Writer: + char *buf + Py_ssize_t size + Py_ssize_t pos + bint changed + + +cdef inline void _init_writer(Writer* writer): + writer.buf = &BUFFER[0] + writer.size = BUF_SIZE + writer.pos = 0 + writer.changed = 0 + + +cdef inline void _release_writer(Writer* writer): + if writer.buf != BUFFER: + PyMem_Free(writer.buf) + + +cdef inline int _write_char(Writer* writer, Py_UCS4 ch, bint changed): + cdef char * buf + cdef Py_ssize_t size + + if writer.pos == writer.size: + # reallocate + size = writer.size + BUF_SIZE + if writer.buf == BUFFER: + buf = PyMem_Malloc(size) + if buf == NULL: + PyErr_NoMemory() + return -1 + memcpy(buf, writer.buf, writer.size) + else: + buf = PyMem_Realloc(writer.buf, size) + if buf == NULL: + PyErr_NoMemory() + return -1 + writer.buf = buf + writer.size = size + writer.buf[writer.pos] = ch + writer.pos += 1 + writer.changed |= changed + return 0 + + +cdef inline int _write_pct(Writer* writer, uint8_t ch, bint changed): + if _write_char(writer, '%', changed) < 0: + return -1 + if _write_char(writer, _to_hex(ch >> 4), changed) < 0: + return -1 + return _write_char(writer, _to_hex(ch & 0x0f), changed) + + +cdef inline int _write_utf8(Writer* writer, Py_UCS4 symbol): + cdef uint64_t utf = symbol + + if utf < 0x80: + return _write_pct(writer, utf, True) + elif utf < 0x800: + if _write_pct(writer, (0xc0 | (utf >> 6)), True) < 0: + return -1 + return _write_pct(writer, (0x80 | (utf & 0x3f)), True) + elif 0xD800 <= utf <= 0xDFFF: + # surogate pair, ignored + return 0 + elif utf < 0x10000: + if _write_pct(writer, (0xe0 | (utf >> 12)), True) < 0: + return -1 + if _write_pct(writer, (0x80 | ((utf >> 6) & 0x3f)), + True) < 0: + return -1 + return _write_pct(writer, (0x80 | (utf & 0x3f)), True) + elif utf > 0x10FFFF: + # symbol is too large + return 0 + else: + if _write_pct(writer, (0xf0 | (utf >> 18)), True) < 0: + return -1 + if _write_pct(writer, (0x80 | ((utf >> 12) & 0x3f)), + True) < 0: + return -1 + if _write_pct(writer, (0x80 | ((utf >> 6) & 0x3f)), + True) < 0: + return -1 + return _write_pct(writer, (0x80 | (utf & 0x3f)), True) + + +# --------------------- end writer -------------------------- + + +cdef class _Quoter: + cdef bint _qs + cdef bint _requote + + cdef uint8_t _safe_table[16] + cdef uint8_t _protected_table[16] + + def __init__( + self, *, str safe='', str protected='', bint qs=False, bint requote=True, + ): + cdef Py_UCS4 ch + + self._qs = qs + self._requote = requote + + if not self._qs: + memcpy(self._safe_table, + ALLOWED_NOTQS_TABLE, + sizeof(self._safe_table)) + else: + memcpy(self._safe_table, + ALLOWED_TABLE, + sizeof(self._safe_table)) + for ch in safe: + if ord(ch) > 127: + raise ValueError("Only safe symbols with ORD < 128 are allowed") + set_bit(self._safe_table, ch) + + memset(self._protected_table, 0, sizeof(self._protected_table)) + for ch in protected: + if ord(ch) > 127: + raise ValueError("Only safe symbols with ORD < 128 are allowed") + set_bit(self._safe_table, ch) + set_bit(self._protected_table, ch) + + def __call__(self, val): + cdef Writer writer + if val is None: + return None + if type(val) is not str: + if isinstance(val, str): + # derived from str + val = str(val) + else: + raise TypeError("Argument should be str") + _init_writer(&writer) + try: + return self._do_quote(val, &writer) + finally: + _release_writer(&writer) + + cdef str _do_quote(self, str val, Writer *writer): + cdef Py_UCS4 ch + cdef int changed + cdef int idx = 0 + cdef int length = len(val) + + while idx < length: + ch = val[idx] + idx += 1 + if ch == '%' and self._requote and idx <= length - 2: + ch = _restore_ch(val[idx], val[idx + 1]) + if ch != -1: + idx += 2 + if ch < 128: + if bit_at(self._protected_table, ch): + if _write_pct(writer, ch, True) < 0: + raise + continue + + if bit_at(self._safe_table, ch): + if _write_char(writer, ch, True) < 0: + raise + continue + + changed = (_is_lower_hex(val[idx - 2]) or + _is_lower_hex(val[idx - 1])) + if _write_pct(writer, ch, changed) < 0: + raise + continue + else: + ch = '%' + + if self._write(writer, ch) < 0: + raise + + if not writer.changed: + return val + else: + return PyUnicode_DecodeASCII(writer.buf, writer.pos, "strict") + + cdef inline int _write(self, Writer *writer, Py_UCS4 ch): + if self._qs: + if ch == ' ': + return _write_char(writer, '+', True) + + if ch < 128 and bit_at(self._safe_table, ch): + return _write_char(writer, ch, False) + + return _write_utf8(writer, ch) + + +cdef class _Unquoter: + cdef str _ignore + cdef str _unsafe + cdef bint _qs + cdef _Quoter _quoter + cdef _Quoter _qs_quoter + + def __init__(self, *, ignore="", unsafe="", qs=False): + self._ignore = ignore + self._unsafe = unsafe + self._qs = qs + self._quoter = _Quoter() + self._qs_quoter = _Quoter(qs=True) + + def __call__(self, val): + if val is None: + return None + if type(val) is not str: + if isinstance(val, str): + # derived from str + val = str(val) + else: + raise TypeError("Argument should be str") + return self._do_unquote(val) + + cdef str _do_unquote(self, str val): + if len(val) == 0: + return val + cdef list ret = [] + cdef char buffer[4] + cdef Py_ssize_t buflen = 0 + cdef Py_ssize_t consumed + cdef str unquoted + cdef Py_UCS4 ch = 0 + cdef Py_ssize_t idx = 0 + cdef Py_ssize_t length = len(val) + cdef Py_ssize_t start_pct + + while idx < length: + ch = val[idx] + idx += 1 + if ch == '%' and idx <= length - 2: + ch = _restore_ch(val[idx], val[idx + 1]) + if ch != -1: + idx += 2 + assert buflen < 4 + buffer[buflen] = ch + buflen += 1 + try: + unquoted = PyUnicode_DecodeUTF8Stateful(buffer, buflen, + NULL, &consumed) + except UnicodeDecodeError: + start_pct = idx - buflen * 3 + buffer[0] = ch + buflen = 1 + ret.append(val[start_pct : idx - 3]) + try: + unquoted = PyUnicode_DecodeUTF8Stateful(buffer, buflen, + NULL, &consumed) + except UnicodeDecodeError: + buflen = 0 + ret.append(val[idx - 3 : idx]) + continue + if not unquoted: + assert consumed == 0 + continue + assert consumed == buflen + buflen = 0 + if self._qs and unquoted in '+=&;': + ret.append(self._qs_quoter(unquoted)) + elif unquoted in self._unsafe or unquoted in self._ignore: + ret.append(self._quoter(unquoted)) + else: + ret.append(unquoted) + continue + else: + ch = '%' + + if buflen: + start_pct = idx - 1 - buflen * 3 + ret.append(val[start_pct : idx - 1]) + buflen = 0 + + if ch == '+': + if not self._qs or ch in self._unsafe: + ret.append('+') + else: + ret.append(' ') + continue + + if ch in self._unsafe: + ret.append('%') + h = hex(ord(ch)).upper()[2:] + for ch in h: + ret.append(ch) + continue + + ret.append(ch) + + if buflen: + ret.append(val[length - buflen * 3 : length]) + + return ''.join(ret) diff --git a/videollama2/lib/python3.10/site-packages/yarl/_quoting_py.py b/videollama2/lib/python3.10/site-packages/yarl/_quoting_py.py new file mode 100644 index 0000000000000000000000000000000000000000..e5b1d3a3bd169df13e9d8661357ae4686bf5ba14 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/yarl/_quoting_py.py @@ -0,0 +1,198 @@ +import codecs +import re +from string import ascii_letters, ascii_lowercase, digits +from typing import cast + +BASCII_LOWERCASE = ascii_lowercase.encode("ascii") +BPCT_ALLOWED = {f"%{i:02X}".encode("ascii") for i in range(256)} +GEN_DELIMS = ":/?#[]@" +SUB_DELIMS_WITHOUT_QS = "!$'()*," +SUB_DELIMS = SUB_DELIMS_WITHOUT_QS + "+&=;" +RESERVED = GEN_DELIMS + SUB_DELIMS +UNRESERVED = ascii_letters + digits + "-._~" +ALLOWED = UNRESERVED + SUB_DELIMS_WITHOUT_QS + + +_IS_HEX = re.compile(b"[A-Z0-9][A-Z0-9]") +_IS_HEX_STR = re.compile("[A-Fa-f0-9][A-Fa-f0-9]") + +utf8_decoder = codecs.getincrementaldecoder("utf-8") + + +class _Quoter: + def __init__( + self, + *, + safe: str = "", + protected: str = "", + qs: bool = False, + requote: bool = True, + ) -> None: + self._safe = safe + self._protected = protected + self._qs = qs + self._requote = requote + + def __call__(self, val: str) -> str: + if val is None: + return None + if not isinstance(val, str): + raise TypeError("Argument should be str") + if not val: + return "" + bval = val.encode("utf8", errors="ignore") + ret = bytearray() + pct = bytearray() + safe = self._safe + safe += ALLOWED + if not self._qs: + safe += "+&=;" + safe += self._protected + bsafe = safe.encode("ascii") + idx = 0 + while idx < len(bval): + ch = bval[idx] + idx += 1 + + if pct: + if ch in BASCII_LOWERCASE: + ch = ch - 32 # convert to uppercase + pct.append(ch) + if len(pct) == 3: # pragma: no branch # peephole optimizer + buf = pct[1:] + if not _IS_HEX.match(buf): + ret.extend(b"%25") + pct.clear() + idx -= 2 + continue + try: + unquoted = chr(int(pct[1:].decode("ascii"), base=16)) + except ValueError: + ret.extend(b"%25") + pct.clear() + idx -= 2 + continue + + if unquoted in self._protected: + ret.extend(pct) + elif unquoted in safe: + ret.append(ord(unquoted)) + else: + ret.extend(pct) + pct.clear() + + # special case, if we have only one char after "%" + elif len(pct) == 2 and idx == len(bval): + ret.extend(b"%25") + pct.clear() + idx -= 1 + + continue + + elif ch == ord("%") and self._requote: + pct.clear() + pct.append(ch) + + # special case if "%" is last char + if idx == len(bval): + ret.extend(b"%25") + + continue + + if self._qs: + if ch == ord(" "): + ret.append(ord("+")) + continue + if ch in bsafe: + ret.append(ch) + continue + + ret.extend((f"%{ch:02X}").encode("ascii")) + + ret2 = ret.decode("ascii") + if ret2 == val: + return val + return ret2 + + +class _Unquoter: + def __init__(self, *, ignore: str = "", unsafe: str = "", qs: bool = False) -> None: + self._ignore = ignore + self._unsafe = unsafe + self._qs = qs + self._quoter = _Quoter() + self._qs_quoter = _Quoter(qs=True) + + def __call__(self, val: str) -> str: + if val is None: + return None + if not isinstance(val, str): + raise TypeError("Argument should be str") + if not val: + return "" + decoder = cast(codecs.BufferedIncrementalDecoder, utf8_decoder()) + ret = [] + idx = 0 + while idx < len(val): + ch = val[idx] + idx += 1 + if ch == "%" and idx <= len(val) - 2: + pct = val[idx : idx + 2] + if _IS_HEX_STR.fullmatch(pct): + b = bytes([int(pct, base=16)]) + idx += 2 + try: + unquoted = decoder.decode(b) + except UnicodeDecodeError: + start_pct = idx - 3 - len(decoder.buffer) * 3 + ret.append(val[start_pct : idx - 3]) + decoder.reset() + try: + unquoted = decoder.decode(b) + except UnicodeDecodeError: + ret.append(val[idx - 3 : idx]) + continue + if not unquoted: + continue + if self._qs and unquoted in "+=&;": + to_add = self._qs_quoter(unquoted) + if to_add is None: # pragma: no cover + raise RuntimeError("Cannot quote None") + ret.append(to_add) + elif unquoted in self._unsafe or unquoted in self._ignore: + to_add = self._quoter(unquoted) + if to_add is None: # pragma: no cover + raise RuntimeError("Cannot quote None") + ret.append(to_add) + else: + ret.append(unquoted) + continue + + if decoder.buffer: + start_pct = idx - 1 - len(decoder.buffer) * 3 + ret.append(val[start_pct : idx - 1]) + decoder.reset() + + if ch == "+": + if not self._qs or ch in self._unsafe: + ret.append("+") + else: + ret.append(" ") + continue + + if ch in self._unsafe: + ret.append("%") + h = hex(ord(ch)).upper()[2:] + for ch in h: + ret.append(ch) + continue + + ret.append(ch) + + if decoder.buffer: + ret.append(val[-len(decoder.buffer) * 3 :]) + + ret2 = "".join(ret) + if ret2 == val: + return val + return ret2 diff --git a/videollama2/lib/python3.10/site-packages/yarl/_url.py b/videollama2/lib/python3.10/site-packages/yarl/_url.py new file mode 100644 index 0000000000000000000000000000000000000000..b73562259306ebc7b6028c93e8ed65532ce90313 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/yarl/_url.py @@ -0,0 +1,1543 @@ +import math +import sys +import warnings +from collections.abc import Mapping, Sequence +from contextlib import suppress +from functools import _CacheInfo, lru_cache +from ipaddress import ip_address +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Iterable, + Iterator, + List, + Tuple, + TypedDict, + TypeVar, + Union, + overload, +) +from urllib.parse import ( + SplitResult, + parse_qsl, + quote, + urlsplit, + urlunsplit, + uses_netloc, + uses_relative, +) + +import idna +from multidict import MultiDict, MultiDictProxy, istr + +from ._helpers import cached_property +from ._quoting import _Quoter, _Unquoter + +DEFAULT_PORTS = {"http": 80, "https": 443, "ws": 80, "wss": 443} +USES_AUTHORITY = frozenset(uses_netloc) +USES_RELATIVE = frozenset(uses_relative) + +sentinel = object() + +SimpleQuery = Union[str, int, float] +QueryVariable = Union[SimpleQuery, "Sequence[SimpleQuery]"] +Query = Union[ + None, str, "Mapping[str, QueryVariable]", "Sequence[Tuple[str, QueryVariable]]" +] +_T = TypeVar("_T") + +if sys.version_info >= (3, 11): + from typing import Self +else: + Self = Any + + +class CacheInfo(TypedDict): + """Host encoding cache.""" + + idna_encode: _CacheInfo + idna_decode: _CacheInfo + ip_address: _CacheInfo + + +class _SplitResultDict(TypedDict, total=False): + + scheme: str + netloc: str + path: str + query: str + fragment: str + + +class _InternalURLCache(TypedDict, total=False): + + absolute: bool + scheme: str + raw_authority: str + _default_port: Union[int, None] + _port_not_default: Union[int, None] + authority: str + raw_user: Union[str, None] + user: Union[str, None] + raw_password: Union[str, None] + password: Union[str, None] + raw_host: Union[str, None] + host: Union[str, None] + port: Union[int, None] + explicit_port: Union[int, None] + raw_path: str + path: str + _parsed_query: List[Tuple[str, str]] + query: "MultiDictProxy[str]" + raw_query_string: str + query_string: str + path_qs: str + raw_path_qs: str + raw_fragment: str + fragment: str + raw_parts: Tuple[str, ...] + parts: Tuple[str, ...] + parent: "URL" + raw_name: str + name: str + raw_suffix: str + suffix: str + raw_suffixes: Tuple[str, ...] + suffixes: Tuple[str, ...] + + +def rewrite_module(obj: _T) -> _T: + obj.__module__ = "yarl" + return obj + + +def _normalize_path_segments(segments: "Sequence[str]") -> List[str]: + """Drop '.' and '..' from a sequence of str segments""" + + resolved_path: List[str] = [] + + for seg in segments: + if seg == "..": + # ignore any .. segments that would otherwise cause an + # IndexError when popped from resolved_path if + # resolving for rfc3986 + with suppress(IndexError): + resolved_path.pop() + elif seg != ".": + resolved_path.append(seg) + + if segments and segments[-1] in (".", ".."): + # do some post-processing here. + # if the last segment was a relative dir, + # then we need to append the trailing '/' + resolved_path.append("") + + return resolved_path + + +@rewrite_module +class URL: + # Don't derive from str + # follow pathlib.Path design + # probably URL will not suffer from pathlib problems: + # it's intended for libraries like aiohttp, + # not to be passed into standard library functions like os.open etc. + + # URL grammar (RFC 3986) + # pct-encoded = "%" HEXDIG HEXDIG + # reserved = gen-delims / sub-delims + # gen-delims = ":" / "/" / "?" / "#" / "[" / "]" / "@" + # sub-delims = "!" / "$" / "&" / "'" / "(" / ")" + # / "*" / "+" / "," / ";" / "=" + # unreserved = ALPHA / DIGIT / "-" / "." / "_" / "~" + # URI = scheme ":" hier-part [ "?" query ] [ "#" fragment ] + # hier-part = "//" authority path-abempty + # / path-absolute + # / path-rootless + # / path-empty + # scheme = ALPHA *( ALPHA / DIGIT / "+" / "-" / "." ) + # authority = [ userinfo "@" ] host [ ":" port ] + # userinfo = *( unreserved / pct-encoded / sub-delims / ":" ) + # host = IP-literal / IPv4address / reg-name + # IP-literal = "[" ( IPv6address / IPvFuture ) "]" + # IPvFuture = "v" 1*HEXDIG "." 1*( unreserved / sub-delims / ":" ) + # IPv6address = 6( h16 ":" ) ls32 + # / "::" 5( h16 ":" ) ls32 + # / [ h16 ] "::" 4( h16 ":" ) ls32 + # / [ *1( h16 ":" ) h16 ] "::" 3( h16 ":" ) ls32 + # / [ *2( h16 ":" ) h16 ] "::" 2( h16 ":" ) ls32 + # / [ *3( h16 ":" ) h16 ] "::" h16 ":" ls32 + # / [ *4( h16 ":" ) h16 ] "::" ls32 + # / [ *5( h16 ":" ) h16 ] "::" h16 + # / [ *6( h16 ":" ) h16 ] "::" + # ls32 = ( h16 ":" h16 ) / IPv4address + # ; least-significant 32 bits of address + # h16 = 1*4HEXDIG + # ; 16 bits of address represented in hexadecimal + # IPv4address = dec-octet "." dec-octet "." dec-octet "." dec-octet + # dec-octet = DIGIT ; 0-9 + # / %x31-39 DIGIT ; 10-99 + # / "1" 2DIGIT ; 100-199 + # / "2" %x30-34 DIGIT ; 200-249 + # / "25" %x30-35 ; 250-255 + # reg-name = *( unreserved / pct-encoded / sub-delims ) + # port = *DIGIT + # path = path-abempty ; begins with "/" or is empty + # / path-absolute ; begins with "/" but not "//" + # / path-noscheme ; begins with a non-colon segment + # / path-rootless ; begins with a segment + # / path-empty ; zero characters + # path-abempty = *( "/" segment ) + # path-absolute = "/" [ segment-nz *( "/" segment ) ] + # path-noscheme = segment-nz-nc *( "/" segment ) + # path-rootless = segment-nz *( "/" segment ) + # path-empty = 0 + # segment = *pchar + # segment-nz = 1*pchar + # segment-nz-nc = 1*( unreserved / pct-encoded / sub-delims / "@" ) + # ; non-zero-length segment without any colon ":" + # pchar = unreserved / pct-encoded / sub-delims / ":" / "@" + # query = *( pchar / "/" / "?" ) + # fragment = *( pchar / "/" / "?" ) + # URI-reference = URI / relative-ref + # relative-ref = relative-part [ "?" query ] [ "#" fragment ] + # relative-part = "//" authority path-abempty + # / path-absolute + # / path-noscheme + # / path-empty + # absolute-URI = scheme ":" hier-part [ "?" query ] + __slots__ = ("_cache", "_val") + + _QUOTER = _Quoter(requote=False) + _REQUOTER = _Quoter() + _PATH_QUOTER = _Quoter(safe="@:", protected="/+", requote=False) + _PATH_REQUOTER = _Quoter(safe="@:", protected="/+") + _QUERY_QUOTER = _Quoter(safe="?/:@", protected="=+&;", qs=True, requote=False) + _QUERY_REQUOTER = _Quoter(safe="?/:@", protected="=+&;", qs=True) + _QUERY_PART_QUOTER = _Quoter(safe="?/:@", qs=True, requote=False) + _FRAGMENT_QUOTER = _Quoter(safe="?/:@", requote=False) + _FRAGMENT_REQUOTER = _Quoter(safe="?/:@") + + _UNQUOTER = _Unquoter() + _PATH_UNQUOTER = _Unquoter(ignore="/", unsafe="+") + _QS_UNQUOTER = _Unquoter(qs=True) + + _val: SplitResult + + def __new__( + cls, + val: Union[str, SplitResult, "URL"] = "", + *, + encoded: bool = False, + strict: Union[bool, None] = None, + ) -> Self: + if strict is not None: # pragma: no cover + warnings.warn("strict parameter is ignored") + if type(val) is cls: + return val + if type(val) is str: + val = urlsplit(val) + elif type(val) is SplitResult: + if not encoded: + raise ValueError("Cannot apply decoding to SplitResult") + elif isinstance(val, str): + val = urlsplit(str(val)) + else: + raise TypeError("Constructor parameter should be str") + + cache: _InternalURLCache = {} + if not encoded: + host: Union[str, None] + scheme, netloc, path, query, fragment = val + if not netloc: # netloc + host = "" + else: + username, password, host, port = cls._split_netloc(val[1]) + if host is None: + raise ValueError("Invalid URL: host is required for absolute urls") + host = cls._encode_host(host) + raw_user = None if username is None else cls._REQUOTER(username) + raw_password = None if password is None else cls._REQUOTER(password) + netloc = cls._make_netloc( + raw_user, raw_password, host, port, encode_host=False + ) + if "[" in host: + # Our host encoder adds back brackets for IPv6 addresses + # so we need to remove them here to get the raw host + _, _, bracketed = host.partition("[") + raw_host, _, _ = bracketed.partition("]") + else: + raw_host = host + cache["raw_host"] = raw_host + cache["raw_user"] = raw_user + cache["raw_password"] = raw_password + cache["explicit_port"] = port + + if path: + path = cls._PATH_REQUOTER(path) + if netloc: + path = cls._normalize_path(path) + + cls._validate_authority_uri_abs_path(host=host, path=path) + query = cls._QUERY_REQUOTER(query) if query else query + fragment = cls._FRAGMENT_REQUOTER(fragment) if fragment else fragment + cache["scheme"] = scheme + cache["raw_query_string"] = query + cache["raw_fragment"] = fragment + val = SplitResult(scheme, netloc, path, query, fragment) + + self = object.__new__(cls) + self._val = val + self._cache = cache + return self + + @classmethod + def build( + cls, + *, + scheme: str = "", + authority: str = "", + user: Union[str, None] = None, + password: Union[str, None] = None, + host: str = "", + port: Union[int, None] = None, + path: str = "", + query: Union[Query, None] = None, + query_string: str = "", + fragment: str = "", + encoded: bool = False, + ) -> "URL": + """Creates and returns a new URL""" + + if authority and (user or password or host or port): + raise ValueError( + 'Can\'t mix "authority" with "user", "password", "host" or "port".' + ) + if not isinstance(port, (int, type(None))): + raise TypeError("The port is required to be int.") + if port and not host: + raise ValueError('Can\'t build URL with "port" but without "host".') + if query and query_string: + raise ValueError('Only one of "query" or "query_string" should be passed') + if ( + scheme is None + or authority is None + or host is None + or path is None + or query_string is None + or fragment is None + ): + raise TypeError( + 'NoneType is illegal for "scheme", "authority", "host", "path", ' + '"query_string", and "fragment" args, use empty string instead.' + ) + + if authority: + if encoded: + netloc = authority + else: + tmp = SplitResult("", authority, "", "", "") + port = None if tmp.port == DEFAULT_PORTS.get(scheme) else tmp.port + netloc = cls._make_netloc( + tmp.username, tmp.password, tmp.hostname, port, encode=True + ) + elif not user and not password and not host and not port: + netloc = "" + else: + port = None if port == DEFAULT_PORTS.get(scheme) else port + netloc = cls._make_netloc( + user, password, host, port, encode=not encoded, encode_host=not encoded + ) + if not encoded: + path = cls._PATH_QUOTER(path) if path else path + if path and netloc: + path = cls._normalize_path(path) + + cls._validate_authority_uri_abs_path(host=host, path=path) + query_string = ( + cls._QUERY_QUOTER(query_string) if query_string else query_string + ) + fragment = cls._FRAGMENT_QUOTER(fragment) if fragment else fragment + + url = cls( + SplitResult(scheme, netloc, path, query_string, fragment), encoded=True + ) + + if query: + return url.with_query(query) + return url + + def __init_subclass__(cls): + raise TypeError(f"Inheriting a class {cls!r} from URL is forbidden") + + def __str__(self) -> str: + val = self._val + if not val.path and self.absolute and (val.query or val.fragment): + val = val._replace(path="/") + if (port := self._port_not_default) is None: + # port normalization - using None for default ports to remove from rendering + # https://datatracker.ietf.org/doc/html/rfc3986.html#section-6.2.3 + val = val._replace( + netloc=self._make_netloc( + self.raw_user, + self.raw_password, + self.raw_host, + port, + encode_host=False, + ) + ) + return urlunsplit(val) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}('{str(self)}')" + + def __bytes__(self) -> bytes: + return str(self).encode("ascii") + + def __eq__(self, other: object) -> bool: + if type(other) is not URL: + return NotImplemented + + val1 = self._val + if not val1.path and self.absolute: + val1 = val1._replace(path="/") + + val2 = other._val + if not val2.path and other.absolute: + val2 = val2._replace(path="/") + + return val1 == val2 + + def __hash__(self) -> int: + ret = self._cache.get("hash") + if ret is None: + val = self._val + if not val.path and self.absolute: + val = val._replace(path="/") + ret = self._cache["hash"] = hash(val) + return ret + + def __le__(self, other: object) -> bool: + if type(other) is not URL: + return NotImplemented + return self._val <= other._val + + def __lt__(self, other: object) -> bool: + if type(other) is not URL: + return NotImplemented + return self._val < other._val + + def __ge__(self, other: object) -> bool: + if type(other) is not URL: + return NotImplemented + return self._val >= other._val + + def __gt__(self, other: object) -> bool: + if type(other) is not URL: + return NotImplemented + return self._val > other._val + + def __truediv__(self, name: str) -> "URL": + if not isinstance(name, str): + return NotImplemented + return self._make_child((str(name),)) + + def __mod__(self, query: Query) -> "URL": + return self.update_query(query) + + def __bool__(self) -> bool: + return bool( + self._val.netloc or self._val.path or self._val.query or self._val.fragment + ) + + def __getstate__(self) -> Tuple[SplitResult]: + return (self._val,) + + def __setstate__(self, state): + if state[0] is None and isinstance(state[1], dict): + # default style pickle + self._val = state[1]["_val"] + else: + self._val, *unused = state + self._cache = {} + + def _cache_netloc(self) -> None: + """Cache the netloc parts of the URL.""" + cache = self._cache + ( + cache["raw_user"], + cache["raw_password"], + cache["raw_host"], + cache["explicit_port"], + ) = self._split_netloc(self._val.netloc) + + def is_absolute(self) -> bool: + """A check for absolute URLs. + + Return True for absolute ones (having scheme or starting + with //), False otherwise. + + Is is preferred to call the .absolute property instead + as it is cached. + """ + return self.absolute + + def is_default_port(self) -> bool: + """A check for default port. + + Return True if port is default for specified scheme, + e.g. 'http://python.org' or 'http://python.org:80', False + otherwise. + + Return False for relative URLs. + + """ + default = self._default_port + explicit = self.explicit_port + if explicit is None: + # A relative URL does not have an implicit port / default port + return default is not None + return explicit == default + + def origin(self) -> "URL": + """Return an URL with scheme, host and port parts only. + + user, password, path, query and fragment are removed. + + """ + # TODO: add a keyword-only option for keeping user/pass maybe? + if not self.absolute: + raise ValueError("URL should be absolute") + if not self._val.scheme: + raise ValueError("URL should have scheme") + v = self._val + netloc = self._make_netloc(None, None, v.hostname, v.port) + val = v._replace(netloc=netloc, path="", query="", fragment="") + return URL(val, encoded=True) + + def relative(self) -> "URL": + """Return a relative part of the URL. + + scheme, user, password, host and port are removed. + + """ + if not self.absolute: + raise ValueError("URL should be absolute") + val = self._val._replace(scheme="", netloc="") + return URL(val, encoded=True) + + @cached_property + def absolute(self) -> bool: + """A check for absolute URLs. + + Return True for absolute ones (having scheme or starting + with //), False otherwise. + + """ + # `netloc`` is an empty string for relative URLs + # Checking `netloc` is faster than checking `hostname` + # because `hostname` is a property that does some extra work + # to parse the host from the `netloc` + return self._val.netloc != "" + + @cached_property + def scheme(self) -> str: + """Scheme for absolute URLs. + + Empty string for relative URLs or URLs starting with // + + """ + return self._val.scheme + + @cached_property + def raw_authority(self) -> str: + """Encoded authority part of URL. + + Empty string for relative URLs. + + """ + return self._val.netloc + + @cached_property + def _default_port(self) -> Union[int, None]: + """Default port for the scheme or None if not known.""" + return DEFAULT_PORTS.get(self.scheme) + + @cached_property + def _port_not_default(self) -> Union[int, None]: + """The port part of URL normalized to None if its the default port.""" + port = self.port + if self._default_port == port: + return None + return port + + @cached_property + def authority(self) -> str: + """Decoded authority part of URL. + + Empty string for relative URLs. + + """ + return self._make_netloc( + self.user, self.password, self.host, self.port, encode_host=False + ) + + @cached_property + def raw_user(self) -> Union[str, None]: + """Encoded user part of URL. + + None if user is missing. + + """ + # not .username + self._cache_netloc() + return self._cache["raw_user"] + + @cached_property + def user(self) -> Union[str, None]: + """Decoded user part of URL. + + None if user is missing. + + """ + raw_user = self.raw_user + if raw_user is None: + return None + return self._UNQUOTER(raw_user) + + @cached_property + def raw_password(self) -> Union[str, None]: + """Encoded password part of URL. + + None if password is missing. + + """ + self._cache_netloc() + return self._cache["raw_password"] + + @cached_property + def password(self) -> Union[str, None]: + """Decoded password part of URL. + + None if password is missing. + + """ + raw_password = self.raw_password + if raw_password is None: + return None + return self._UNQUOTER(raw_password) + + @cached_property + def raw_host(self) -> Union[str, None]: + """Encoded host part of URL. + + None for relative URLs. + + """ + # Use host instead of hostname for sake of shortness + # May add .hostname prop later + self._cache_netloc() + return self._cache["raw_host"] + + @cached_property + def host(self) -> Union[str, None]: + """Decoded host part of URL. + + None for relative URLs. + + """ + raw = self.raw_host + if raw is None: + return None + if "%" in raw: + # Hack for scoped IPv6 addresses like + # fe80::2%Перевірка + # presence of '%' sign means only IPv6 address, so idna is useless. + return raw + return _idna_decode(raw) + + @cached_property + def port(self) -> Union[int, None]: + """Port part of URL, with scheme-based fallback. + + None for relative URLs or URLs without explicit port and + scheme without default port substitution. + + """ + return self.explicit_port or self._default_port + + @cached_property + def explicit_port(self) -> Union[int, None]: + """Port part of URL, without scheme-based fallback. + + None for relative URLs or URLs without explicit port. + + """ + self._cache_netloc() + return self._cache["explicit_port"] + + @cached_property + def raw_path(self) -> str: + """Encoded path of URL. + + / for absolute URLs without path part. + + """ + ret = self._val.path + if not ret and self.absolute: + ret = "/" + return ret + + @cached_property + def path(self) -> str: + """Decoded path of URL. + + / for absolute URLs without path part. + + """ + return self._PATH_UNQUOTER(self.raw_path) + + @cached_property + def _parsed_query(self) -> List[Tuple[str, str]]: + """Parse query part of URL.""" + return parse_qsl(self.raw_query_string, keep_blank_values=True) + + @cached_property + def query(self) -> "MultiDictProxy[str]": + """A MultiDictProxy representing parsed query parameters in decoded + representation. + + Empty value if URL has no query part. + + """ + return MultiDictProxy(MultiDict(self._parsed_query)) + + @cached_property + def raw_query_string(self) -> str: + """Encoded query part of URL. + + Empty string if query is missing. + + """ + return self._val.query + + @cached_property + def query_string(self) -> str: + """Decoded query part of URL. + + Empty string if query is missing. + + """ + return self._QS_UNQUOTER(self.raw_query_string) + + @cached_property + def path_qs(self) -> str: + """Decoded path of URL with query.""" + if not self.query_string: + return self.path + return f"{self.path}?{self.query_string}" + + @cached_property + def raw_path_qs(self) -> str: + """Encoded path of URL with query.""" + if not self.raw_query_string: + return self.raw_path + return f"{self.raw_path}?{self.raw_query_string}" + + @cached_property + def raw_fragment(self) -> str: + """Encoded fragment part of URL. + + Empty string if fragment is missing. + + """ + return self._val.fragment + + @cached_property + def fragment(self) -> str: + """Decoded fragment part of URL. + + Empty string if fragment is missing. + + """ + return self._UNQUOTER(self.raw_fragment) + + @cached_property + def raw_parts(self) -> Tuple[str, ...]: + """A tuple containing encoded *path* parts. + + ('/',) for absolute URLs if *path* is missing. + + """ + path = self._val.path + if self.absolute: + if not path: + parts = ["/"] + else: + parts = ["/"] + path[1:].split("/") + else: + if path.startswith("/"): + parts = ["/"] + path[1:].split("/") + else: + parts = path.split("/") + return tuple(parts) + + @cached_property + def parts(self) -> Tuple[str, ...]: + """A tuple containing decoded *path* parts. + + ('/',) for absolute URLs if *path* is missing. + + """ + return tuple(self._UNQUOTER(part) for part in self.raw_parts) + + @cached_property + def parent(self) -> "URL": + """A new URL with last part of path removed and cleaned up query and + fragment. + + """ + path = self.raw_path + if not path or path == "/": + if self.raw_fragment or self.raw_query_string: + return URL(self._val._replace(query="", fragment=""), encoded=True) + return self + parts = path.split("/") + val = self._val._replace(path="/".join(parts[:-1]), query="", fragment="") + return URL(val, encoded=True) + + @cached_property + def raw_name(self) -> str: + """The last part of raw_parts.""" + parts = self.raw_parts + if self.absolute: + parts = parts[1:] + if not parts: + return "" + else: + return parts[-1] + else: + return parts[-1] + + @cached_property + def name(self) -> str: + """The last part of parts.""" + return self._UNQUOTER(self.raw_name) + + @cached_property + def raw_suffix(self) -> str: + name = self.raw_name + i = name.rfind(".") + if 0 < i < len(name) - 1: + return name[i:] + else: + return "" + + @cached_property + def suffix(self) -> str: + return self._UNQUOTER(self.raw_suffix) + + @cached_property + def raw_suffixes(self) -> Tuple[str, ...]: + name = self.raw_name + if name.endswith("."): + return () + name = name.lstrip(".") + return tuple("." + suffix for suffix in name.split(".")[1:]) + + @cached_property + def suffixes(self) -> Tuple[str, ...]: + return tuple(self._UNQUOTER(suffix) for suffix in self.raw_suffixes) + + @staticmethod + def _validate_authority_uri_abs_path(host: str, path: str) -> None: + """Ensure that path in URL with authority starts with a leading slash. + + Raise ValueError if not. + """ + if host and path and not path.startswith("/"): + raise ValueError( + "Path in a URL with authority should start with a slash ('/') if set" + ) + + def _make_child(self, paths: "Sequence[str]", encoded: bool = False) -> "URL": + """ + add paths to self._val.path, accounting for absolute vs relative paths, + keep existing, but do not create new, empty segments + """ + parsed = [] + for idx, path in enumerate(reversed(paths)): + # empty segment of last is not removed + last = idx == 0 + if path and path[0] == "/": + raise ValueError( + f"Appending path {path!r} starting from slash is forbidden" + ) + path = path if encoded else self._PATH_QUOTER(path) + segments = list(reversed(path.split("/"))) + # remove trailing empty segment for all but the last path + segment_slice_start = int(not last and segments[0] == "") + parsed += segments[segment_slice_start:] + parsed.reverse() + + if self._val.path and (old_path_segments := self._val.path.split("/")): + old_path_cutoff = -1 if old_path_segments[-1] == "" else None + parsed = [*old_path_segments[:old_path_cutoff], *parsed] + + if self.absolute: + parsed = _normalize_path_segments(parsed) + if parsed and parsed[0] != "": + # inject a leading slash when adding a path to an absolute URL + # where there was none before + parsed = ["", *parsed] + new_path = "/".join(parsed) + return URL( + self._val._replace(path=new_path, query="", fragment=""), encoded=True + ) + + @classmethod + def _normalize_path(cls, path: str) -> str: + # Drop '.' and '..' from str path + + prefix = "" + if path.startswith("/"): + # preserve the "/" root element of absolute paths, copying it to the + # normalised output as per sections 5.2.4 and 6.2.2.3 of rfc3986. + prefix = "/" + path = path[1:] + + segments = path.split("/") + return prefix + "/".join(_normalize_path_segments(segments)) + + @classmethod + def _encode_host(cls, host: str, human: bool = False) -> str: + if "%" in host: + raw_ip, sep, zone = host.partition("%") + else: + raw_ip = host + sep = zone = "" + + if raw_ip and raw_ip[-1].isdigit() or ":" in raw_ip: + # Might be an IP address, check it + # + # IP Addresses can look like: + # https://datatracker.ietf.org/doc/html/rfc3986#section-3.2.2 + # - 127.0.0.1 (last character is a digit) + # - 2001:db8::ff00:42:8329 (contains a colon) + # - 2001:db8::ff00:42:8329%eth0 (contains a colon) + # - [2001:db8::ff00:42:8329] (contains a colon) + # Rare IP Address formats are not supported per: + # https://datatracker.ietf.org/doc/html/rfc3986#section-7.4 + # + # We try to avoid parsing IP addresses as much as possible + # since its orders of magnitude slower than almost any other operation + # this library does. + # + # IP parsing is slow, so its wrapped in an LRU + try: + ip_compressed_version = _ip_compressed_version(raw_ip) + except ValueError: + pass + else: + # These checks should not happen in the + # LRU to keep the cache size small + host, version = ip_compressed_version + if sep: + host += "%" + zone + if version == 6: + return f"[{host}]" + return host + + host = host.lower() + # IDNA encoding is slow, + # skip it for ASCII-only strings + # Don't move the check into _idna_encode() helper + # to reduce the cache size + if human or host.isascii(): + return host + return _idna_encode(host) + + @classmethod + def _make_netloc( + cls, + user: Union[str, None], + password: Union[str, None], + host: Union[str, None], + port: Union[int, None], + encode: bool = False, + encode_host: bool = True, + requote: bool = False, + ) -> str: + if host is None: + return "" + quoter = cls._REQUOTER if requote else cls._QUOTER + if encode_host: + ret = cls._encode_host(host) + else: + ret = host + if port is not None: + ret = f"{ret}:{port}" + if password is not None: + if not user: + user = "" + else: + if encode: + user = quoter(user) + if encode: + password = quoter(password) + user = user + ":" + password + elif user and encode: + user = quoter(user) + if user: + ret = user + "@" + ret + return ret + + @classmethod + @lru_cache # match the same size as urlsplit + def _split_netloc( + cls, + netloc: str, + ) -> Tuple[Union[str, None], Union[str, None], Union[str, None], Union[int, None]]: + """Split netloc into username, password, host and port.""" + if "@" not in netloc: + username: Union[str, None] = None + password: Union[str, None] = None + hostinfo = netloc + else: + userinfo, _, hostinfo = netloc.rpartition("@") + username, have_password, password = userinfo.partition(":") + if not have_password: + password = None + + if "[" in hostinfo: + _, _, bracketed = hostinfo.partition("[") + hostname, _, port_str = bracketed.partition("]") + _, _, port_str = port_str.partition(":") + else: + hostname, _, port_str = hostinfo.partition(":") + + if not port_str: + port: Union[int, None] = None + else: + try: + port = int(port_str) + except ValueError: + raise ValueError("Invalid URL: port can't be converted to integer") + if not (0 <= port <= 65535): + raise ValueError("Port out of range 0-65535") + + return username or None, password, hostname or None, port + + def with_scheme(self, scheme: str) -> "URL": + """Return a new URL with scheme replaced.""" + # N.B. doesn't cleanup query/fragment + if not isinstance(scheme, str): + raise TypeError("Invalid scheme type") + if not self.absolute: + raise ValueError("scheme replacement is not allowed for relative URLs") + return URL(self._val._replace(scheme=scheme.lower()), encoded=True) + + def with_user(self, user: Union[str, None]) -> "URL": + """Return a new URL with user replaced. + + Autoencode user if needed. + + Clear user/password if user is None. + + """ + # N.B. doesn't cleanup query/fragment + val = self._val + if user is None: + password = None + elif isinstance(user, str): + user = self._QUOTER(user) + password = val.password + else: + raise TypeError("Invalid user type") + if not self.absolute: + raise ValueError("user replacement is not allowed for relative URLs") + return URL( + self._val._replace( + netloc=self._make_netloc(user, password, val.hostname, val.port) + ), + encoded=True, + ) + + def with_password(self, password: Union[str, None]) -> "URL": + """Return a new URL with password replaced. + + Autoencode password if needed. + + Clear password if argument is None. + + """ + # N.B. doesn't cleanup query/fragment + if password is None: + pass + elif isinstance(password, str): + password = self._QUOTER(password) + else: + raise TypeError("Invalid password type") + if not self.absolute: + raise ValueError("password replacement is not allowed for relative URLs") + val = self._val + return URL( + self._val._replace( + netloc=self._make_netloc(val.username, password, val.hostname, val.port) + ), + encoded=True, + ) + + def with_host(self, host: str) -> "URL": + """Return a new URL with host replaced. + + Autoencode host if needed. + + Changing host for relative URLs is not allowed, use .join() + instead. + + """ + # N.B. doesn't cleanup query/fragment + if not isinstance(host, str): + raise TypeError("Invalid host type") + if not self.absolute: + raise ValueError("host replacement is not allowed for relative URLs") + if not host: + raise ValueError("host removing is not allowed") + val = self._val + return URL( + self._val._replace( + netloc=self._make_netloc(val.username, val.password, host, val.port) + ), + encoded=True, + ) + + def with_port(self, port: Union[int, None]) -> "URL": + """Return a new URL with port replaced. + + Clear port to default if None is passed. + + """ + # N.B. doesn't cleanup query/fragment + if port is not None: + if isinstance(port, bool) or not isinstance(port, int): + raise TypeError(f"port should be int or None, got {type(port)}") + if port < 0 or port > 65535: + raise ValueError(f"port must be between 0 and 65535, got {port}") + if not self.absolute: + raise ValueError("port replacement is not allowed for relative URLs") + val = self._val + return URL( + self._val._replace( + netloc=self._make_netloc(val.username, val.password, val.hostname, port) + ), + encoded=True, + ) + + def with_path(self, path: str, *, encoded: bool = False) -> "URL": + """Return a new URL with path replaced.""" + if not encoded: + path = self._PATH_QUOTER(path) + if self.absolute: + path = self._normalize_path(path) + if len(path) > 0 and path[0] != "/": + path = "/" + path + return URL(self._val._replace(path=path, query="", fragment=""), encoded=True) + + @classmethod + def _query_seq_pairs( + cls, quoter: Callable[[str], str], pairs: Iterable[Tuple[str, QueryVariable]] + ) -> Iterator[str]: + for key, val in pairs: + if isinstance(val, (list, tuple)): + for v in val: + yield quoter(key) + "=" + quoter(cls._query_var(v)) + else: + yield quoter(key) + "=" + quoter(cls._query_var(val)) + + @staticmethod + def _query_var(v: QueryVariable) -> str: + cls = type(v) + if cls is str or issubclass(cls, str): + if TYPE_CHECKING: + assert isinstance(v, str) + return v + if issubclass(cls, float): + if TYPE_CHECKING: + assert isinstance(v, float) + if math.isinf(v): + raise ValueError("float('inf') is not supported") + if math.isnan(v): + raise ValueError("float('nan') is not supported") + return str(float(v)) + if cls is not bool and issubclass(cls, int): + if TYPE_CHECKING: + assert isinstance(v, int) + return str(int(v)) + raise TypeError( + "Invalid variable type: value " + "should be str, int or float, got {!r} " + "of type {}".format(v, cls) + ) + + def _get_str_query_from_iterable( + self, items: Iterable[Tuple[Union[str, istr], str]] + ) -> str: + """Return a query string from an iterable.""" + quoter = self._QUERY_PART_QUOTER + # A listcomp is used since listcomps are inlined on CPython 3.12+ and + # they are a bit faster than a generator expression. + return "&".join([f"{quoter(k)}={quoter(self._query_var(v))}" for k, v in items]) + + def _get_str_query(self, *args: Any, **kwargs: Any) -> Union[str, None]: + query: Union[str, Mapping[str, QueryVariable], None] + if kwargs: + if len(args) > 0: + raise ValueError( + "Either kwargs or single query parameter must be present" + ) + query = kwargs + elif len(args) == 1: + query = args[0] + else: + raise ValueError("Either kwargs or single query parameter must be present") + + if query is None: + return None + if isinstance(query, Mapping): + quoter = self._QUERY_PART_QUOTER + return "&".join(self._query_seq_pairs(quoter, query.items())) + if isinstance(query, str): + return self._QUERY_QUOTER(query) + if isinstance(query, (bytes, bytearray, memoryview)): + raise TypeError( + "Invalid query type: bytes, bytearray and memoryview are forbidden" + ) + if isinstance(query, Sequence): + # We don't expect sequence values if we're given a list of pairs + # already; only mappings like builtin `dict` which can't have the + # same key pointing to multiple values are allowed to use + # `_query_seq_pairs`. + return self._get_str_query_from_iterable(query) + + raise TypeError( + "Invalid query type: only str, mapping or " + "sequence of (key, value) pairs is allowed" + ) + + @overload + def with_query(self, query: Query) -> "URL": ... + + @overload + def with_query(self, **kwargs: QueryVariable) -> "URL": ... + + def with_query(self, *args: Any, **kwargs: Any) -> "URL": + """Return a new URL with query part replaced. + + Accepts any Mapping (e.g. dict, multidict.MultiDict instances) + or str, autoencode the argument if needed. + + A sequence of (key, value) pairs is supported as well. + + It also can take an arbitrary number of keyword arguments. + + Clear query if None is passed. + + """ + # N.B. doesn't cleanup query/fragment + + new_query = self._get_str_query(*args, **kwargs) or "" + return URL(self._val._replace(query=new_query), encoded=True) + + @overload + def extend_query(self, query: Query) -> "URL": ... + + @overload + def extend_query(self, **kwargs: QueryVariable) -> "URL": ... + + def extend_query(self, *args: Any, **kwargs: Any) -> "URL": + """Return a new URL with query part combined with the existing. + + This method will not remove existing query parameters. + + Example: + >>> url = URL('http://example.com/?a=1&b=2') + >>> url.extend_query(a=3, c=4) + URL('http://example.com/?a=1&b=2&a=3&c=4') + """ + new_query_string = self._get_str_query(*args, **kwargs) + if not new_query_string: + return self + if current_query := self.raw_query_string: + # both strings are already encoded so we can use a simple + # string join + if current_query[-1] == "&": + combined_query = f"{current_query}{new_query_string}" + else: + combined_query = f"{current_query}&{new_query_string}" + else: + combined_query = new_query_string + return URL(self._val._replace(query=combined_query), encoded=True) + + @overload + def update_query(self, query: Query) -> "URL": ... + + @overload + def update_query(self, **kwargs: QueryVariable) -> "URL": ... + + def update_query(self, *args: Any, **kwargs: Any) -> "URL": + """Return a new URL with query part updated. + + This method will overwrite existing query parameters. + + Example: + >>> url = URL('http://example.com/?a=1&b=2') + >>> url.update_query(a=3, c=4) + URL('http://example.com/?a=3&b=2&c=4') + """ + s = self._get_str_query(*args, **kwargs) + if s is None: + return URL(self._val._replace(query=""), encoded=True) + + query = MultiDict(self._parsed_query) + query.update(parse_qsl(s, keep_blank_values=True)) + new_str = self._get_str_query_from_iterable(query.items()) + return URL(self._val._replace(query=new_str), encoded=True) + + def without_query_params(self, *query_params: str) -> "URL": + """Remove some keys from query part and return new URL.""" + params_to_remove = set(query_params) & self.query.keys() + if not params_to_remove: + return self + return self.with_query( + tuple( + (name, value) + for name, value in self.query.items() + if name not in params_to_remove + ) + ) + + def with_fragment(self, fragment: Union[str, None]) -> "URL": + """Return a new URL with fragment replaced. + + Autoencode fragment if needed. + + Clear fragment to default if None is passed. + + """ + # N.B. doesn't cleanup query/fragment + if fragment is None: + raw_fragment = "" + elif not isinstance(fragment, str): + raise TypeError("Invalid fragment type") + else: + raw_fragment = self._FRAGMENT_QUOTER(fragment) + if self.raw_fragment == raw_fragment: + return self + return URL(self._val._replace(fragment=raw_fragment), encoded=True) + + def with_name(self, name: str) -> "URL": + """Return a new URL with name (last part of path) replaced. + + Query and fragment parts are cleaned up. + + Name is encoded if needed. + + """ + # N.B. DOES cleanup query/fragment + if not isinstance(name, str): + raise TypeError("Invalid name type") + if "/" in name: + raise ValueError("Slash in name is not allowed") + name = self._PATH_QUOTER(name) + if name in (".", ".."): + raise ValueError(". and .. values are forbidden") + parts = list(self.raw_parts) + if self.absolute: + if len(parts) == 1: + parts.append(name) + else: + parts[-1] = name + parts[0] = "" # replace leading '/' + else: + parts[-1] = name + if parts[0] == "/": + parts[0] = "" # replace leading '/' + return URL( + self._val._replace(path="/".join(parts), query="", fragment=""), + encoded=True, + ) + + def with_suffix(self, suffix: str) -> "URL": + """Return a new URL with suffix (file extension of name) replaced. + + Query and fragment parts are cleaned up. + + suffix is encoded if needed. + """ + if not isinstance(suffix, str): + raise TypeError("Invalid suffix type") + if suffix and not suffix.startswith(".") or suffix == ".": + raise ValueError(f"Invalid suffix {suffix!r}") + name = self.raw_name + if not name: + raise ValueError(f"{self!r} has an empty name") + old_suffix = self.raw_suffix + if not old_suffix: + name = name + suffix + else: + name = name[: -len(old_suffix)] + suffix + return self.with_name(name) + + def join(self, url: "URL") -> "URL": + """Join URLs + + Construct a full (“absolute”) URL by combining a “base URL” + (self) with another URL (url). + + Informally, this uses components of the base URL, in + particular the addressing scheme, the network location and + (part of) the path, to provide missing components in the + relative URL. + + """ + if type(url) is not URL: + raise TypeError("url should be URL") + val = self._val + other_val = url._val + scheme = other_val.scheme or val.scheme + + if scheme != val.scheme or scheme not in USES_RELATIVE: + return url + + # scheme is in uses_authority as uses_authority is a superset of uses_relative + if other_val.netloc and scheme in USES_AUTHORITY: + return URL(other_val._replace(scheme=scheme), encoded=True) + + parts: _SplitResultDict = {"scheme": scheme} + if other_val.path or other_val.fragment: + parts["fragment"] = other_val.fragment + if other_val.path or other_val.query: + parts["query"] = other_val.query + + if not other_val.path: + return URL(val._replace(**parts), encoded=True) + + if other_val.path[0] == "/": + path = other_val.path + elif not val.path: + path = f"/{other_val.path}" + elif val.path[-1] == "/": + path = f"{val.path}{other_val.path}" + else: + # … + # and relativizing ".." + # parts[0] is / for absolute urls, this join will add a double slash there + path = "/".join([*self.parts[:-1], ""]) + path += other_val.path + # which has to be removed + if val.path[0] == "/": + path = path[1:] + + parts["path"] = self._normalize_path(path) + return URL(val._replace(**parts), encoded=True) + + def joinpath(self, *other: str, encoded: bool = False) -> "URL": + """Return a new URL with the elements in other appended to the path.""" + return self._make_child(other, encoded=encoded) + + def human_repr(self) -> str: + """Return decoded human readable string for URL representation.""" + user = _human_quote(self.user, "#/:?@[]") + password = _human_quote(self.password, "#/:?@[]") + host = self.host + if host: + host = self._encode_host(host, human=True) + path = _human_quote(self.path, "#?") + if TYPE_CHECKING: + assert path is not None + query_string = "&".join( + "{}={}".format(_human_quote(k, "#&+;="), _human_quote(v, "#&+;=")) + for k, v in self.query.items() + ) + fragment = _human_quote(self.fragment, "") + if TYPE_CHECKING: + assert fragment is not None + netloc = self._make_netloc( + user, password, host, self.explicit_port, encode_host=False + ) + val = SplitResult(self.scheme, netloc, path, query_string, fragment) + return urlunsplit(val) + + +def _human_quote(s: Union[str, None], unsafe: str) -> Union[str, None]: + if not s: + return s + for c in "%" + unsafe: + if c in s: + s = s.replace(c, f"%{ord(c):02X}") + if s.isprintable(): + return s + return "".join(c if c.isprintable() else quote(c) for c in s) + + +_MAXCACHE = 256 + + +@lru_cache(_MAXCACHE) +def _idna_decode(raw: str) -> str: + try: + return idna.decode(raw.encode("ascii")) + except UnicodeError: # e.g. '::1' + return raw.encode("ascii").decode("idna") + + +@lru_cache(_MAXCACHE) +def _idna_encode(host: str) -> str: + try: + return idna.encode(host, uts46=True).decode("ascii") + except UnicodeError: + return host.encode("idna").decode("ascii") + + +@lru_cache(_MAXCACHE) +def _ip_compressed_version(raw_ip: str) -> Tuple[str, int]: + """Return compressed version of IP address and its version.""" + ip = ip_address(raw_ip) + return ip.compressed, ip.version + + +@rewrite_module +def cache_clear() -> None: + """Clear all LRU caches.""" + _idna_decode.cache_clear() + _idna_encode.cache_clear() + _ip_compressed_version.cache_clear() + + +@rewrite_module +def cache_info() -> CacheInfo: + """Report cache statistics.""" + return { + "idna_encode": _idna_encode.cache_info(), + "idna_decode": _idna_decode.cache_info(), + "ip_address": _ip_compressed_version.cache_info(), + } + + +@rewrite_module +def cache_configure( + *, + idna_encode_size: Union[int, None] = _MAXCACHE, + idna_decode_size: Union[int, None] = _MAXCACHE, + ip_address_size: Union[int, None] = _MAXCACHE, +) -> None: + """Configure LRU cache sizes.""" + global _idna_decode, _idna_encode, _ip_compressed_version + + _idna_encode = lru_cache(idna_encode_size)(_idna_encode.__wrapped__) + _idna_decode = lru_cache(idna_decode_size)(_idna_decode.__wrapped__) + _ip_compressed_version = lru_cache(ip_address_size)( + _ip_compressed_version.__wrapped__ + ) diff --git a/videollama2/lib/python3.10/site-packages/yarl/py.typed b/videollama2/lib/python3.10/site-packages/yarl/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..dcf2c804da5e19d617a03a6c68aa128d1d1f89a0 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/yarl/py.typed @@ -0,0 +1 @@ +# Placeholder diff --git a/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/_adaptive_avg_pool3d_cuda_dispatch.h b/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/_adaptive_avg_pool3d_cuda_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..99f42c7793fd4276a94e513b5c2e183349cf1362 --- /dev/null +++ b/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/_adaptive_avg_pool3d_cuda_dispatch.h @@ -0,0 +1,24 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cuda { + +TORCH_API at::Tensor _adaptive_avg_pool3d(const at::Tensor & self, at::IntArrayRef output_size); +TORCH_API at::Tensor _adaptive_avg_pool3d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size); + +} // namespace cuda +} // namespace at diff --git a/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/_log_softmax_cuda_dispatch.h b/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/_log_softmax_cuda_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..c9ba77e6761996ee377812e1a5be1c00593f8e36 --- /dev/null +++ b/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/_log_softmax_cuda_dispatch.h @@ -0,0 +1,25 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cuda { + +TORCH_API at::Tensor _log_softmax(const at::Tensor & self, int64_t dim, bool half_to_float); +TORCH_API at::Tensor & _log_softmax_out(at::Tensor & out, const at::Tensor & self, int64_t dim, bool half_to_float); +TORCH_API at::Tensor & _log_softmax_outf(const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out); + +} // namespace cuda +} // namespace at diff --git a/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/_nested_get_values_native.h b/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/_nested_get_values_native.h new file mode 100644 index 0000000000000000000000000000000000000000..b9d2e5fd5be542077158871f57ae9c937e4f7895 --- /dev/null +++ b/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/_nested_get_values_native.h @@ -0,0 +1,20 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +} // namespace native +} // namespace at diff --git a/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/_slow_conv2d_backward_compositeexplicitautograd_dispatch.h b/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/_slow_conv2d_backward_compositeexplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..6844c06089055c7a9d828963dd67cf7c01cee983 --- /dev/null +++ b/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/_slow_conv2d_backward_compositeexplicitautograd_dispatch.h @@ -0,0 +1,26 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeexplicitautograd { + +TORCH_API ::std::tuple _slow_conv2d_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, ::std::array output_mask); +TORCH_API ::std::tuple _slow_conv2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); +TORCH_API ::std::tuple _slow_conv2d_backward_symint_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, ::std::array output_mask); +TORCH_API ::std::tuple _slow_conv2d_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); + +} // namespace compositeexplicitautograd +} // namespace at diff --git a/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/_sparse_bsr_tensor_unsafe_native.h b/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/_sparse_bsr_tensor_unsafe_native.h new file mode 100644 index 0000000000000000000000000000000000000000..5e377f7cbcaed596319e8c890ccf316733a5edae --- /dev/null +++ b/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/_sparse_bsr_tensor_unsafe_native.h @@ -0,0 +1,21 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API at::Tensor _sparse_bsr_tensor_unsafe(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional dtype={}, ::std::optional layout={}, ::std::optional device={}, ::std::optional pin_memory={}); +} // namespace native +} // namespace at diff --git a/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/avg_pool1d_native.h b/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/avg_pool1d_native.h new file mode 100644 index 0000000000000000000000000000000000000000..b1e6b9f5ae2f079906968018d9b2919e2adac398 --- /dev/null +++ b/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/avg_pool1d_native.h @@ -0,0 +1,21 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API at::Tensor avg_pool1d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true); +} // namespace native +} // namespace at diff --git a/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/native_layer_norm_compositeexplicitautograd_dispatch.h b/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/native_layer_norm_compositeexplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..db9fb8ea53326e8b899b5744eb2df33aecb2dd9b --- /dev/null +++ b/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/native_layer_norm_compositeexplicitautograd_dispatch.h @@ -0,0 +1,28 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeexplicitautograd { + +TORCH_API ::std::tuple native_layer_norm(const at::Tensor & input, at::IntArrayRef normalized_shape, const ::std::optional & weight, const ::std::optional & bias, double eps); +TORCH_API ::std::tuple native_layer_norm_symint(const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const ::std::optional & weight, const ::std::optional & bias, double eps); +TORCH_API ::std::tuple native_layer_norm_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, at::IntArrayRef normalized_shape, const ::std::optional & weight, const ::std::optional & bias, double eps); +TORCH_API ::std::tuple native_layer_norm_outf(const at::Tensor & input, at::IntArrayRef normalized_shape, const ::std::optional & weight, const ::std::optional & bias, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); +TORCH_API ::std::tuple native_layer_norm_symint_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const ::std::optional & weight, const ::std::optional & bias, double eps); +TORCH_API ::std::tuple native_layer_norm_symint_outf(const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const ::std::optional & weight, const ::std::optional & bias, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); + +} // namespace compositeexplicitautograd +} // namespace at diff --git a/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/resolve_neg.h b/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/resolve_neg.h new file mode 100644 index 0000000000000000000000000000000000000000..90f45b4260f7bab5a4ddd6903f306608eb68a479 --- /dev/null +++ b/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/resolve_neg.h @@ -0,0 +1,30 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + +// aten::resolve_neg(Tensor(a) self) -> Tensor(a) +inline at::Tensor resolve_neg(const at::Tensor & self) { + return at::_ops::resolve_neg::call(self); +} + +} diff --git a/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/slow_conv_dilated2d.h b/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/slow_conv_dilated2d.h new file mode 100644 index 0000000000000000000000000000000000000000..eaa151a25bd3320bacf9e05c6e4d70aa3c187227 --- /dev/null +++ b/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/slow_conv_dilated2d.h @@ -0,0 +1,91 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + +// aten::slow_conv_dilated2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1) -> Tensor +inline at::Tensor slow_conv_dilated2d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1) { + return at::_ops::slow_conv_dilated2d::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation)); +} +namespace symint { + template ::value>> + at::Tensor slow_conv_dilated2d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1) { + return at::_ops::slow_conv_dilated2d::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation)); + } +} + +// aten::slow_conv_dilated2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1) -> Tensor +inline at::Tensor slow_conv_dilated2d_symint(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) { + return at::_ops::slow_conv_dilated2d::call(self, weight, kernel_size, bias, stride, padding, dilation); +} +namespace symint { + template ::value>> + at::Tensor slow_conv_dilated2d(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) { + return at::_ops::slow_conv_dilated2d::call(self, weight, kernel_size, bias, stride, padding, dilation); + } +} + +// aten::slow_conv_dilated2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & slow_conv_dilated2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1) { + return at::_ops::slow_conv_dilated2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out); +} +namespace symint { + template ::value>> + at::Tensor & slow_conv_dilated2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1) { + return at::_ops::slow_conv_dilated2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out); + } +} + +// aten::slow_conv_dilated2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & slow_conv_dilated2d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, at::Tensor & out) { + return at::_ops::slow_conv_dilated2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out); +} +namespace symint { + template ::value>> + at::Tensor & slow_conv_dilated2d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, at::Tensor & out) { + return at::_ops::slow_conv_dilated2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out); + } +} + +// aten::slow_conv_dilated2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & slow_conv_dilated2d_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) { + return at::_ops::slow_conv_dilated2d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out); +} +namespace symint { + template ::value>> + at::Tensor & slow_conv_dilated2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) { + return at::_ops::slow_conv_dilated2d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out); + } +} + +// aten::slow_conv_dilated2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & slow_conv_dilated2d_symint_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, at::Tensor & out) { + return at::_ops::slow_conv_dilated2d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out); +} +namespace symint { + template ::value>> + at::Tensor & slow_conv_dilated2d_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, at::Tensor & out) { + return at::_ops::slow_conv_dilated2d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out); + } +} + +} diff --git a/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/slow_conv_dilated3d_cuda_dispatch.h b/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/slow_conv_dilated3d_cuda_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..c1eaa6905305a465c0631956e44fd72f8866d5cc --- /dev/null +++ b/vllm/lib/python3.10/site-packages/torch/include/ATen/ops/slow_conv_dilated3d_cuda_dispatch.h @@ -0,0 +1,24 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cuda { + +TORCH_API at::Tensor slow_conv_dilated3d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1); +TORCH_API at::Tensor slow_conv_dilated3d_symint(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)); + +} // namespace cuda +} // namespace at