From 63cf77a7b894f1ca9ce0ad58f5b49fcba42b9773 Mon Sep 17 00:00:00 2001
From: Nicholas Corona <ncorona3@gatech.edu>
Date: Sat, 30 Nov 2024 15:45:55 -0500
Subject: [PATCH 01/10] Update scipy version constraints in pyproject.toml

---
 poetry.lock    | 142 +++++++++++--------------------------------------
 pyproject.toml |   5 +-
 2 files changed, 34 insertions(+), 113 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 5f8f9338..a37b3bec 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
 
 [[package]]
 name = "alabaster"
@@ -1106,73 +1106,6 @@ files = [
     {file = "packaging-23.2.tar.gz", hash = "sha256:048fb0e9405036518eaaf48a55953c750c11e1a1b68e0dd1a9d62ed0c092cfc5"},
 ]
 
-[[package]]
-name = "pandas"
-version = "2.0.3"
-description = "Powerful data structures for data analysis, time series, and statistics"
-optional = false
-python-versions = ">=3.8"
-files = [
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-]
-
-[package.dependencies]
-numpy = [
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-    {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
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-python-dateutil = ">=2.8.2"
-pytz = ">=2020.1"
-tzdata = ">=2022.1"
-
-[package.extras]
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-aws = ["s3fs (>=2021.08.0)"]
-clipboard = ["PyQt5 (>=5.15.1)", "qtpy (>=2.2.0)"]
-compression = ["brotlipy (>=0.7.0)", "python-snappy (>=0.6.0)", "zstandard (>=0.15.2)"]
-computation = ["scipy (>=1.7.1)", "xarray (>=0.21.0)"]
-excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.0.7)", "pyxlsb (>=1.0.8)", "xlrd (>=2.0.1)", "xlsxwriter (>=1.4.3)"]
-feather = ["pyarrow (>=7.0.0)"]
-fss = ["fsspec (>=2021.07.0)"]
-gcp = ["gcsfs (>=2021.07.0)", "pandas-gbq (>=0.15.0)"]
-hdf5 = ["tables (>=3.6.1)"]
-html = ["beautifulsoup4 (>=4.9.3)", "html5lib (>=1.1)", "lxml (>=4.6.3)"]
-mysql = ["SQLAlchemy (>=1.4.16)", "pymysql (>=1.0.2)"]
-output-formatting = ["jinja2 (>=3.0.0)", "tabulate (>=0.8.9)"]
-parquet = ["pyarrow (>=7.0.0)"]
-performance = ["bottleneck (>=1.3.2)", "numba (>=0.53.1)", "numexpr (>=2.7.1)"]
-plot = ["matplotlib (>=3.6.1)"]
-postgresql = ["SQLAlchemy (>=1.4.16)", "psycopg2 (>=2.8.6)"]
-spss = ["pyreadstat (>=1.1.2)"]
-sql-other = ["SQLAlchemy (>=1.4.16)"]
-test = ["hypothesis (>=6.34.2)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)"]
-xml = ["lxml (>=4.6.3)"]
-
 [[package]]
 name = "pandocfilters"
 version = "1.5.1"
@@ -1791,45 +1724,45 @@ test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeo
 
 [[package]]
 name = "scipy"
-version = "1.11.4"
+version = "1.13.1"
 description = "Fundamental algorithms for scientific computing in Python"
 optional = false
 python-versions = ">=3.9"
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+    {file = "scipy-1.13.1.tar.gz", hash = "sha256:095a87a0312b08dfd6a6155cbbd310a8c51800fc931b8c0b84003014b874ed3c"},
 ]
 
 [package.dependencies]
-numpy = ">=1.21.6,<1.28.0"
+numpy = ">=1.22.4,<2.3"
 
 [package.extras]
-dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
-doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
-test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
+doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.12.0)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0)", "sphinx-design (>=0.4.0)"]
+test = ["array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
 [[package]]
 name = "six"
@@ -2130,17 +2063,6 @@ files = [
     {file = "typing_extensions-4.9.0.tar.gz", hash = "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783"},
 ]
 
-[[package]]
-name = "tzdata"
-version = "2024.1"
-description = "Provider of IANA time zone data"
-optional = false
-python-versions = ">=2"
-files = [
-    {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
-    {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
-]
-
 [[package]]
 name = "urllib3"
 version = "2.2.0"
@@ -2198,4 +2120,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1, <3.13"
-content-hash = "f908f8c1e11f3e2d1ce347673013bc3dd77eef943341e17c3106dd8fc31d6633"
+content-hash = "16086795ef82b4a6043e37364fac505084b653bd51b657d11afe1ac76237b49a"
diff --git a/pyproject.toml b/pyproject.toml
index 2fe8e8a0..e76846af 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,12 +14,11 @@ numpy = [
 ]
 progressbar2 = "^4.2.0"
 scipy = [
-  { version = ">=1.10.1,<1.11", python = "<3.9,>=3.8" },
-  { version = ">=1.11.1,<1.12", python = "<3.13,>=3.9" }
+  { version = ">=1.10.1,<1.13", python = "<3.9,>=3.8" },
+  { version = ">=1.13.0,<1.14", python = "<3.13,>=3.9" }
 ]
 
 [tool.poetry.group.dev.dependencies]
-pandas = ">=1.6"
 pytest = "^7.2.2"
 flake8 = "^6.0.0"
 codecov = "^2.1.12"

From 050feeb8319fbaa4d530432b65e8a266064a8a5e Mon Sep 17 00:00:00 2001
From: Nicholas Corona <ncorona3@gatech.edu>
Date: Sat, 30 Nov 2024 17:44:24 -0500
Subject: [PATCH 02/10] Update Python and SciPy version constraints, add
 .vscode settings to .gitignore, and implement tests for GAMRegressor and
 GAMClassifier

---
 .gitignore                |   1 +
 poetry.lock               | 181 ++++++++++++--------------------
 pygam/sklearn_api.py      | 216 ++++++++++++++++++++++++++++++++++++++
 pyproject.toml            |  10 +-
 tests/test_sklearn_api.py | 125 ++++++++++++++++++++++
 5 files changed, 412 insertions(+), 121 deletions(-)
 create mode 100644 pygam/sklearn_api.py
 create mode 100644 tests/test_sklearn_api.py

diff --git a/.gitignore b/.gitignore
index a30b5eb4..c4ff995e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -45,3 +45,4 @@ _build/
 # PyCharm
 #########
 .idea/
+.vscode/settings.json
diff --git a/poetry.lock b/poetry.lock
index a37b3bec..a724bec8 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -70,9 +70,6 @@ files = [
     {file = "Babel-2.14.0.tar.gz", hash = "sha256:6919867db036398ba21eb5c7a0f6b28ab8cbc3ae7a73a44ebe34ae74a4e7d363"},
 ]
 
-[package.dependencies]
-pytz = {version = ">=2015.7", markers = "python_version < \"3.9\""}
-
 [package.extras]
 dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
 
@@ -585,24 +582,6 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.link
 perf = ["ipython"]
 testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
 
-[[package]]
-name = "importlib-resources"
-version = "6.1.1"
-description = "Read resources from Python packages"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "importlib_resources-6.1.1-py3-none-any.whl", hash = "sha256:e8bf90d8213b486f428c9c39714b920041cb02c184686a3dee24905aaa8105d6"},
-    {file = "importlib_resources-6.1.1.tar.gz", hash = "sha256:3893a00122eafde6894c59914446a512f728a0c1a45f9bb9b63721b6bacf0b4a"},
-]
-
-[package.dependencies]
-zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-ruff", "zipp (>=3.17)"]
-
 [[package]]
 name = "iniconfig"
 version = "2.0.0"
@@ -689,6 +668,17 @@ MarkupSafe = ">=2.0"
 [package.extras]
 i18n = ["Babel (>=2.7)"]
 
+[[package]]
+name = "joblib"
+version = "1.4.2"
+description = "Lightweight pipelining with Python functions"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"},
+    {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"},
+]
+
 [[package]]
 name = "jsonschema"
 version = "4.21.1"
@@ -702,9 +692,7 @@ files = [
 
 [package.dependencies]
 attrs = ">=22.2.0"
-importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
 jsonschema-specifications = ">=2023.03.6"
-pkgutil-resolve-name = {version = ">=1.3.10", markers = "python_version < \"3.9\""}
 referencing = ">=0.28.4"
 rpds-py = ">=0.7.1"
 
@@ -724,7 +712,6 @@ files = [
 ]
 
 [package.dependencies]
-importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
 referencing = ">=0.31.0"
 
 [[package]]
@@ -1013,43 +1000,6 @@ nbformat = "*"
 sphinx = ">=1.8"
 traitlets = ">=5"
 
-[[package]]
-name = "numpy"
-version = "1.24.4"
-description = "Fundamental package for array computing in Python"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "numpy-1.24.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c0bfb52d2169d58c1cdb8cc1f16989101639b34c7d3ce60ed70b19c63eba0b64"},
-    {file = "numpy-1.24.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ed094d4f0c177b1b8e7aa9cba7d6ceed51c0e569a5318ac0ca9a090680a6a1b1"},
-    {file = "numpy-1.24.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:79fc682a374c4a8ed08b331bef9c5f582585d1048fa6d80bc6c35bc384eee9b4"},
-    {file = "numpy-1.24.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7ffe43c74893dbf38c2b0a1f5428760a1a9c98285553c89e12d70a96a7f3a4d6"},
-    {file = "numpy-1.24.4-cp310-cp310-win32.whl", hash = "sha256:4c21decb6ea94057331e111a5bed9a79d335658c27ce2adb580fb4d54f2ad9bc"},
-    {file = "numpy-1.24.4-cp310-cp310-win_amd64.whl", hash = "sha256:b4bea75e47d9586d31e892a7401f76e909712a0fd510f58f5337bea9572c571e"},
-    {file = "numpy-1.24.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:f136bab9c2cfd8da131132c2cf6cc27331dd6fae65f95f69dcd4ae3c3639c810"},
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-    {file = "numpy-1.24.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7215847ce88a85ce39baf9e89070cb860c98fdddacbaa6c0da3ffb31b3350bd5"},
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-    {file = "numpy-1.24.4-cp39-cp39-win32.whl", hash = "sha256:6620c0acd41dbcb368610bb2f4d83145674040025e5536954782467100aa8835"},
-    {file = "numpy-1.24.4-cp39-cp39-win_amd64.whl", hash = "sha256:befe2bf740fd8373cf56149a5c23a0f601e82869598d41f8e188a0e9869926f8"},
-    {file = "numpy-1.24.4-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:31f13e25b4e304632a4619d0e0777662c2ffea99fcae2029556b17d8ff958aef"},
-    {file = "numpy-1.24.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:95f7ac6540e95bc440ad77f56e520da5bf877f87dca58bd095288dce8940532a"},
-    {file = "numpy-1.24.4-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:e98f220aa76ca2a977fe435f5b04d7b3470c0a2e6312907b37ba6068f26787f2"},
-    {file = "numpy-1.24.4.tar.gz", hash = "sha256:80f5e3a4e498641401868df4208b74581206afbee7cf7b8329daae82676d9463"},
-]
-
 [[package]]
 name = "numpy"
 version = "1.26.4"
@@ -1168,17 +1118,6 @@ files = [
     {file = "pickleshare-0.7.5.tar.gz", hash = "sha256:87683d47965c1da65cdacaf31c8441d12b8044cdec9aca500cd78fc2c683afca"},
 ]
 
-[[package]]
-name = "pkgutil-resolve-name"
-version = "1.3.10"
-description = "Resolve a name to an object."
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "pkgutil_resolve_name-1.3.10-py3-none-any.whl", hash = "sha256:ca27cc078d25c5ad71a9de0a7a330146c4e014c2462d9af19c6b828280649c5e"},
-    {file = "pkgutil_resolve_name-1.3.10.tar.gz", hash = "sha256:357d6c9e6a755653cfd78893817c0853af365dd51ec97f3d358a819373bbd174"},
-]
-
 [[package]]
 name = "platformdirs"
 version = "4.2.0"
@@ -1401,17 +1340,6 @@ docs = ["mock", "python-utils", "sphinx"]
 loguru = ["loguru"]
 tests = ["flake8", "loguru", "pytest", "pytest-asyncio", "pytest-cov", "pytest-mypy", "sphinx", "types-setuptools"]
 
-[[package]]
-name = "pytz"
-version = "2024.1"
-description = "World timezone definitions, modern and historical"
-optional = false
-python-versions = "*"
-files = [
-    {file = "pytz-2024.1-py2.py3-none-any.whl", hash = "sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319"},
-    {file = "pytz-2024.1.tar.gz", hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"},
-]
-
 [[package]]
 name = "pywin32"
 version = "306"
@@ -1685,42 +1613,54 @@ files = [
 ]
 
 [[package]]
-name = "scipy"
-version = "1.10.1"
-description = "Fundamental algorithms for scientific computing in Python"
+name = "scikit-learn"
+version = "1.5.2"
+description = "A set of python modules for machine learning and data mining"
 optional = false
-python-versions = "<3.12,>=3.8"
-files = [
-    {file = "scipy-1.10.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e7354fd7527a4b0377ce55f286805b34e8c54b91be865bac273f527e1b839019"},
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+python-versions = ">=3.9"
+files = [
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+    {file = "scikit_learn-1.5.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:e9a702e2de732bbb20d3bad29ebd77fc05a6b427dc49964300340e4c9328b3f5"},
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+    {file = "scikit_learn-1.5.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:757c7d514ddb00ae249832fe87100d9c73c6ea91423802872d9e74970a0e40b9"},
+    {file = "scikit_learn-1.5.2-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:52788f48b5d8bca5c0736c175fa6bdaab2ef00a8f536cda698db61bd89c551c1"},
+    {file = "scikit_learn-1.5.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:643964678f4b5fbdc95cbf8aec638acc7aa70f5f79ee2cdad1eec3df4ba6ead8"},
+    {file = "scikit_learn-1.5.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ca64b3089a6d9b9363cd3546f8978229dcbb737aceb2c12144ee3f70f95684b7"},
+    {file = "scikit_learn-1.5.2-cp39-cp39-win_amd64.whl", hash = "sha256:3bed4909ba187aca80580fe2ef370d9180dcf18e621a27c4cf2ef10d279a7efe"},
+    {file = "scikit_learn-1.5.2.tar.gz", hash = "sha256:b4237ed7b3fdd0a4882792e68ef2545d5baa50aca3bb45aa7df468138ad8f94d"},
 ]
 
 [package.dependencies]
-numpy = ">=1.19.5,<1.27.0"
+joblib = ">=1.2.0"
+numpy = ">=1.19.5"
+scipy = ">=1.6.0"
+threadpoolctl = ">=3.1.0"
 
 [package.extras]
-dev = ["click", "doit (>=0.36.0)", "flake8", "mypy", "pycodestyle", "pydevtool", "rich-click", "typing_extensions"]
-doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
-test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+benchmark = ["matplotlib (>=3.3.4)", "memory_profiler (>=0.57.0)", "pandas (>=1.1.5)"]
+build = ["cython (>=3.0.10)", "meson-python (>=0.16.0)", "numpy (>=1.19.5)", "scipy (>=1.6.0)"]
+docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.3.4)", "memory_profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "polars (>=0.20.30)", "pooch (>=1.6.0)", "pydata-sphinx-theme (>=0.15.3)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)", "sphinx (>=7.3.7)", "sphinx-copybutton (>=0.5.2)", "sphinx-design (>=0.5.0)", "sphinx-design (>=0.6.0)", "sphinx-gallery (>=0.16.0)", "sphinx-prompt (>=1.4.0)", "sphinx-remove-toctrees (>=1.0.0.post1)", "sphinxcontrib-sass (>=0.3.4)", "sphinxext-opengraph (>=0.9.1)"]
+examples = ["matplotlib (>=3.3.4)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)"]
+install = ["joblib (>=1.2.0)", "numpy (>=1.19.5)", "scipy (>=1.6.0)", "threadpoolctl (>=3.1.0)"]
+maintenance = ["conda-lock (==2.5.6)"]
+tests = ["black (>=24.3.0)", "matplotlib (>=3.3.4)", "mypy (>=1.9)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "polars (>=0.20.30)", "pooch (>=1.6.0)", "pyamg (>=4.0.0)", "pyarrow (>=12.0.0)", "pytest (>=7.1.2)", "pytest-cov (>=2.9.0)", "ruff (>=0.2.1)", "scikit-image (>=0.17.2)"]
 
 [[package]]
 name = "scipy"
@@ -1988,6 +1928,17 @@ pure-eval = "*"
 [package.extras]
 tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
+[[package]]
+name = "threadpoolctl"
+version = "3.5.0"
+description = "threadpoolctl"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "threadpoolctl-3.5.0-py3-none-any.whl", hash = "sha256:56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467"},
+    {file = "threadpoolctl-3.5.0.tar.gz", hash = "sha256:082433502dd922bf738de0d8bcc4fdcbf0979ff44c42bd40f5af8a282f6fa107"},
+]
+
 [[package]]
 name = "tinycss2"
 version = "1.2.1"
@@ -2119,5 +2070,5 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 
 [metadata]
 lock-version = "2.0"
-python-versions = ">=3.8.1, <3.13"
-content-hash = "16086795ef82b4a6043e37364fac505084b653bd51b657d11afe1ac76237b49a"
+python-versions = ">=3.9, <=3.13"
+content-hash = "84ba875e24ed4d7ac984dabe842dbf2e10056a16d645ae7131bf449b92a14d3f"
diff --git a/pygam/sklearn_api.py b/pygam/sklearn_api.py
new file mode 100644
index 00000000..b29ce38c
--- /dev/null
+++ b/pygam/sklearn_api.py
@@ -0,0 +1,216 @@
+"""
+sklearn_api.py
+
+This module provides scikit-learn compatible classes for Generalized Additive Models (GAM) regressors and classifiers.
+It integrates pygam's GAM capabilities with scikit-learn's estimator interface, enabling seamless use in machine learning pipelines.
+"""
+
+from sklearn.base import BaseEstimator, RegressorMixin, ClassifierMixin
+from pygam import GAM
+from pygam.terms import te, TermList  # Import te for interactions
+import numpy as np
+
+class GAMRegressor(BaseEstimator, RegressorMixin):
+    """
+    GAMRegressor
+
+    A scikit-learn compatible regressor using Generalized Additive Models (GAM).
+
+    Parameters
+    ----------
+    distribution : str, default='normal'
+        The distribution of the response variable.
+    link : str, default='identity'
+        The link function.
+    terms : 'auto' or TermList, default='auto'
+        The terms to include in the model.
+    interactions : list of tuples, optional
+        Interaction terms to include in the model.
+    callbacks : list, default=['deviance', 'diffs']
+        List of callbacks to monitor during training.
+    fit_intercept : bool, default=True
+        Whether to fit an intercept.
+    max_iter : int, default=100
+        Maximum number of iterations.
+    tol : float, default=1e-4
+        Tolerance for stopping criteria.
+    verbose : bool, default=False
+        Verbosity mode.
+    **gam_params :
+        Additional parameters for the GAM model.
+    
+    Attributes
+    ----------
+    model_ : GAM
+        The underlying pygam GAM model fitted to the data.
+    """
+
+    def __init__(
+        self,
+        distribution='normal',
+        link='identity',
+        terms='auto',
+        interactions=None,  # Added interactions parameter
+        callbacks=['deviance', 'diffs'],
+        fit_intercept=True,
+        max_iter=100,
+        tol=1e-4,
+        verbose=False,
+        **gam_params
+    ):
+        self.distribution = distribution
+        self.link = link
+        self.terms = TermList() if terms == 'auto' else terms
+        self.interactions = interactions  # Store interactions
+        self.callbacks = callbacks
+        self.fit_intercept = fit_intercept
+        self.max_iter = max_iter
+        self.tol = tol
+        self.verbose = verbose
+        self.gam_params = gam_params
+        
+        # ...existing code...
+        if self.interactions:
+            # Ensure terms is a list before appending
+            if isinstance(self.terms, str):
+                self.terms = [self.terms]
+            elif not isinstance(self.terms, list):
+                self.terms = list(self.terms)
+            # Convert interaction tuples to te instances
+            for interaction in self.interactions:
+                self.terms.append(te(*interaction))
+        
+        if not isinstance(self.terms, TermList):
+            self.terms = TermList(*self.terms)
+    
+        self.model_ = GAM(
+            distribution=self.distribution,
+            link=self.link,
+            terms=self.terms,
+            callbacks=self.callbacks,
+            fit_intercept=self.fit_intercept,
+            max_iter=self.max_iter,
+            tol=self.tol,
+            verbose=self.verbose,
+            **self.gam_params
+        )
+
+    def fit(self, X, y):
+        self.model_.fit(X, y)
+        return self
+
+    def predict(self, X):
+        return self.model_.predict(X)
+
+    def score(self, X, y):
+        return float(self.model_.statistics_.get('pseudo R-squared', 0))
+
+class GAMClassifier(BaseEstimator, ClassifierMixin):
+    """
+    GAMClassifier
+
+    A scikit-learn compatible classifier using Generalized Additive Models (GAM).
+
+    Parameters
+    ----------
+    distribution : str, default='binomial'
+        The distribution of the response variable.
+    link : str, default='logit'
+        The link function.
+    terms : 'auto' or TermList, default='auto'
+        The terms to include in the model.
+    interactions : list of tuples, optional
+        Interaction terms to include in the model.
+    callbacks : list, default=['deviance', 'diffs', 'accuracy']
+        List of callbacks to monitor during training.
+    fit_intercept : bool, default=True
+        Whether to fit an intercept.
+    max_iter : int, default=100
+        Maximum number of iterations.
+    tol : float, default=1e-4
+        Tolerance for stopping criteria.
+    verbose : bool, default=False
+        Verbosity mode.
+    **gam_params :
+        Additional parameters for the GAM model.
+    
+    Attributes
+    ----------
+    model_ : GAM
+        The underlying pygam GAM model fitted to the data.
+    classes_ : array-like
+        Unique class labels.
+    """
+
+    def __init__(
+        self,
+        distribution='binomial',
+        link='logit',
+        terms='auto',
+        interactions=None,  # Added interactions parameter
+        callbacks=['deviance', 'diffs', 'accuracy'],
+        fit_intercept=True,
+        max_iter=100,
+        tol=1e-4,
+        verbose=False,
+        **gam_params
+    ):
+        self.distribution = distribution
+        self.link = link
+        self.terms = TermList() if terms == 'auto' else terms
+        self.interactions = interactions  # Store interactions
+        self.callbacks = callbacks
+        self.fit_intercept = fit_intercept
+        self.max_iter = max_iter
+        self.tol = tol
+        self.verbose = verbose
+        self.gam_params = gam_params
+        
+        # ...existing code...
+        if self.interactions:
+            # Ensure terms is a list before appending
+            if isinstance(self.terms, str):
+                self.terms = [self.terms]
+            elif not isinstance(self.terms, list):
+                self.terms = list(self.terms)
+            # Convert interaction tuples to te instances
+            for interaction in self.interactions:
+                self.terms.append(te(*interaction))
+        
+        if not isinstance(self.terms, TermList):
+            self.terms = TermList(*self.terms)
+    
+        self.model_ = GAM(
+            distribution=self.distribution,
+            link=self.link,
+            terms=self.terms,
+            callbacks=self.callbacks,
+            fit_intercept=self.fit_intercept,
+            max_iter=self.max_iter,
+            tol=self.tol,
+            verbose=self.verbose,
+            **self.gam_params
+        )
+
+    def fit(self, X, y):
+        self.model_.fit(X, y)
+        self.classes_ = np.unique(y)
+        return self
+
+    def predict(self, X):
+        proba = self.model_.predict(X)
+        if len(self.classes_) == 2:
+            return (proba >= 0.5).astype(int)
+        else:
+            return self.classes_[np.argmax(proba, axis=1)]
+
+    def predict_proba(self, X):
+        proba = self.model_.predict(X)
+        if len(self.classes_) == 2:
+            return np.vstack([1 - proba, proba]).T
+        else:
+            return proba  # Assume GAM model returns probabilities for each class
+
+    def score(self, X, y):
+        from sklearn.metrics import accuracy_score
+        return accuracy_score(y, self.predict(X))
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index e76846af..f55672ae 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,16 +7,14 @@ license = "Apache-2.0"
 readme = "README.md"
 
 [tool.poetry.dependencies]
-python = ">=3.8.1, <3.13"
+python = ">=3.9, <=3.13"
 numpy = [
   { version = ">=1.24.2,<1.25", python = "<3.9,>=3.8" },
   { version = ">=1.25", python = "<3.13,>=3.9" },
 ]
 progressbar2 = "^4.2.0"
-scipy = [
-  { version = ">=1.10.1,<1.13", python = "<3.9,>=3.8" },
-  { version = ">=1.13.0,<1.14", python = "<3.13,>=3.9" }
-]
+scipy = ">=1.11.4"
+scikit-learn = "^1.5.2"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.2.2"
@@ -57,4 +55,4 @@ build-backend = "poetry_dynamic_versioning.backend"
 [tool.poetry-dynamic-versioning]
 enable = true
 vcs = "git"
-style = "semver"
+style = "semver"
\ No newline at end of file
diff --git a/tests/test_sklearn_api.py b/tests/test_sklearn_api.py
new file mode 100644
index 00000000..7c03fc16
--- /dev/null
+++ b/tests/test_sklearn_api.py
@@ -0,0 +1,125 @@
+import pytest
+import numpy as np
+from sklearn.datasets import make_regression, make_classification
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import r2_score, accuracy_score
+from pygam.sklearn_api import GAMRegressor, GAMClassifier
+
+@pytest.fixture
+def regression_data():
+    X, y = make_regression(n_samples=100, n_features=5, noise=0.1, random_state=42)
+    return train_test_split(X, y, test_size=0.2, random_state=42)
+
+@pytest.fixture
+def classification_data():
+    X, y = make_classification(n_samples=100, n_features=5, n_classes=2, random_state=42)
+    return train_test_split(X, y, test_size=0.2, random_state=42)
+
+def test_gam_regressor_fit_predict(regression_data):
+    X_train, X_test, y_train, y_test = regression_data
+    reg = GAMRegressor()
+    reg.fit(X_train, y_train)
+    predictions = reg.predict(X_test)
+    assert predictions.shape == y_test.shape
+    assert r2_score(y_test, predictions) >= 0  # Basic sanity check
+
+def test_gam_regressor_score(regression_data):
+    X_train, X_test, y_train, y_test = regression_data
+    reg = GAMRegressor()
+    reg.fit(X_train, y_train)
+    score = reg.score(X_test, y_test)
+    assert isinstance(score, float)
+    assert score >= 0  # R-squared should be non-negative
+
+def test_gam_classifier_fit_predict(classification_data):
+    X_train, X_test, y_train, y_test = classification_data
+    clf = GAMClassifier()
+    clf.fit(X_train, y_train)
+    predictions = clf.predict(X_test)
+    assert predictions.shape == y_test.shape
+    assert set(predictions).issubset({0, 1})  # Binary classification
+
+def test_gam_classifier_predict_proba(classification_data):
+    X_train, X_test, y_train, y_test = classification_data
+    clf = GAMClassifier()
+    clf.fit(X_train, y_train)
+    proba = clf.predict_proba(X_test)
+    assert proba.shape == (X_test.shape[0], 2)
+    assert np.allclose(proba.sum(axis=1), 1)
+
+def test_gam_classifier_score(classification_data):
+    X_train, X_test, y_train, y_test = classification_data
+    clf = GAMClassifier()
+    clf.fit(X_train, y_train)
+    score = clf.score(X_test, y_test)
+    assert isinstance(score, float)
+    assert 0 <= score <= 1  # Accuracy between 0 and 1
+
+def test_gam_regressor_with_custom_params(regression_data):
+    X_train, X_test, y_train, y_test = regression_data
+    reg = GAMRegressor(distribution='normal', link='identity', max_iter=200, tol=1e-5)
+    reg.fit(X_train, y_train)
+    predictions = reg.predict(X_test)
+    assert r2_score(y_test, predictions) >= 0
+
+def test_gam_classifier_with_custom_params(classification_data):
+    X_train, X_test, y_train, y_test = classification_data
+    clf = GAMClassifier(distribution='binomial', link='logit', max_iter=200, tol=1e-5)
+    clf.fit(X_train, y_train)
+    predictions = clf.predict(X_test)
+    proba = clf.predict_proba(X_test)
+    assert accuracy_score(y_test, predictions) >= 0
+    assert proba.shape == (X_test.shape[0], 2)
+
+def test_gam_regressor_with_callbacks(regression_data):
+    X_train, X_test, y_train, y_test = regression_data
+    reg = GAMRegressor(callbacks=['deviance', 'diffs'])
+    reg.fit(X_train, y_train)
+    assert 'deviance' in reg.model_.logs_
+    assert 'diffs' in reg.model_.logs_
+
+def test_gam_classifier_with_callbacks(classification_data):
+    X_train, X_test, y_train, y_test = classification_data
+    clf = GAMClassifier(callbacks=['deviance', 'diffs', 'accuracy'])
+    clf.fit(X_train, y_train)
+    assert 'deviance' in clf.model_.logs_
+    assert 'diffs' in clf.model_.logs_
+    assert 'accuracy' in clf.model_.logs_
+
+def test_gam_regressor_gamma():
+    X = np.random.rand(100, 2)
+    y = np.random.gamma(shape=2.0, scale=1.0, size=100)
+    model = GAMRegressor(distribution='gamma')
+    model.fit(X, y)
+    predictions = model.predict(X)
+    assert predictions.shape == y.shape
+
+def test_gam_regressor_poisson():
+    X = np.random.rand(100, 2)
+    y = np.random.poisson(lam=3.0, size=100)
+    model = GAMRegressor(distribution='poisson')
+    model.fit(X, y)
+    predictions = model.predict(X)
+    assert predictions.shape == y.shape
+
+def test_gam_regressor_with_interactions(regression_data):
+    X_train, X_test, y_train, y_test = regression_data
+    interactions = [(0, 1), (2, 3)]  # Specify feature indices for interactions
+    reg = GAMRegressor(interactions=interactions)
+    reg.fit(X_train, y_train)
+    predictions = reg.predict(X_test)
+    assert predictions.shape == y_test.shape
+    assert r2_score(y_test, predictions) >= 0  # Basic sanity check
+
+def test_gam_classifier_with_interactions(classification_data):
+    X_train, X_test, y_train, y_test = classification_data
+    interactions = [(0, 1), (2, 3)]  # Specify feature indices for interactions
+    clf = GAMClassifier(interactions=interactions)
+    clf.fit(X_train, y_train)
+    predictions = clf.predict(X_test)
+    proba = clf.predict_proba(X_test)
+    assert predictions.shape == y_test.shape
+    assert set(predictions).issubset({0, 1})  # Binary classification
+    assert proba.shape == (X_test.shape[0], 2)
+    assert np.allclose(proba.sum(axis=1), 1)
+    assert accuracy_score(y_test, predictions) >= 0

From 9873e8a521b5109f1ad957fd0b2d2805f50ef8a8 Mon Sep 17 00:00:00 2001
From: Nicholas Corona <ncorona3@gatech.edu>
Date: Sun, 1 Dec 2024 09:08:23 -0500
Subject: [PATCH 03/10] better than before

---
 pygam/sklearn_api.py | 72 ++++++++++++++++++++++++++++----------------
 1 file changed, 46 insertions(+), 26 deletions(-)

diff --git a/pygam/sklearn_api.py b/pygam/sklearn_api.py
index b29ce38c..f9bc6075 100644
--- a/pygam/sklearn_api.py
+++ b/pygam/sklearn_api.py
@@ -7,8 +7,10 @@
 
 from sklearn.base import BaseEstimator, RegressorMixin, ClassifierMixin
 from pygam import GAM
-from pygam.terms import te, TermList  # Import te for interactions
+from pygam.terms import te, TermList, Term # Import te for interactions
 import numpy as np
+from sklearn.datasets import make_regression
+from sklearn.model_selection import train_test_split
 
 class GAMRegressor(BaseEstimator, RegressorMixin):
     """
@@ -49,8 +51,8 @@ def __init__(
         self,
         distribution='normal',
         link='identity',
-        terms='auto',
-        interactions=None,  # Added interactions parameter
+        terms='auto',  # Will be handled by GAM class
+        interactions=None,
         callbacks=['deviance', 'diffs'],
         fit_intercept=True,
         max_iter=100,
@@ -60,8 +62,8 @@ def __init__(
     ):
         self.distribution = distribution
         self.link = link
-        self.terms = TermList() if terms == 'auto' else terms
-        self.interactions = interactions  # Store interactions
+        self.terms = terms  # Simply pass through to GAM
+        self.interactions = interactions
         self.callbacks = callbacks
         self.fit_intercept = fit_intercept
         self.max_iter = max_iter
@@ -69,24 +71,24 @@ def __init__(
         self.verbose = verbose
         self.gam_params = gam_params
         
-        # ...existing code...
+        # Handle interactions if specified
         if self.interactions:
-            # Ensure terms is a list before appending
-            if isinstance(self.terms, str):
+            if isinstance(self.terms, str) and self.terms == 'auto':
+                self.terms = []  # Convert 'auto' to empty list to append to
+            elif isinstance(self.terms, str):
                 self.terms = [self.terms]
             elif not isinstance(self.terms, list):
                 self.terms = list(self.terms)
-            # Convert interaction tuples to te instances
+                
+            # Add interaction terms
             for interaction in self.interactions:
                 self.terms.append(te(*interaction))
         
-        if not isinstance(self.terms, TermList):
-            self.terms = TermList(*self.terms)
-    
+        # Initialize the GAM model
         self.model_ = GAM(
             distribution=self.distribution,
             link=self.link,
-            terms=self.terms,
+            terms=self.terms,  # Pass terms directly, let GAM handle 'auto'
             callbacks=self.callbacks,
             fit_intercept=self.fit_intercept,
             max_iter=self.max_iter,
@@ -146,8 +148,8 @@ def __init__(
         self,
         distribution='binomial',
         link='logit',
-        terms='auto',
-        interactions=None,  # Added interactions parameter
+        terms='auto',  # Will be handled by GAM class
+        interactions=None,
         callbacks=['deviance', 'diffs', 'accuracy'],
         fit_intercept=True,
         max_iter=100,
@@ -157,8 +159,8 @@ def __init__(
     ):
         self.distribution = distribution
         self.link = link
-        self.terms = TermList() if terms == 'auto' else terms
-        self.interactions = interactions  # Store interactions
+        self.terms = terms  # Simply pass through to GAM
+        self.interactions = interactions
         self.callbacks = callbacks
         self.fit_intercept = fit_intercept
         self.max_iter = max_iter
@@ -166,24 +168,24 @@ def __init__(
         self.verbose = verbose
         self.gam_params = gam_params
         
-        # ...existing code...
+        # Handle interactions if specified
         if self.interactions:
-            # Ensure terms is a list before appending
-            if isinstance(self.terms, str):
+            if isinstance(self.terms, str) and self.terms == 'auto':
+                self.terms = []  # Convert 'auto' to empty list to append to
+            elif isinstance(self.terms, str):
                 self.terms = [self.terms]
             elif not isinstance(self.terms, list):
                 self.terms = list(self.terms)
-            # Convert interaction tuples to te instances
+                
+            # Add interaction terms
             for interaction in self.interactions:
                 self.terms.append(te(*interaction))
         
-        if not isinstance(self.terms, TermList):
-            self.terms = TermList(*self.terms)
-    
+        # Initialize the GAM model
         self.model_ = GAM(
             distribution=self.distribution,
             link=self.link,
-            terms=self.terms,
+            terms=self.terms,  # Pass terms directly, let GAM handle 'auto'
             callbacks=self.callbacks,
             fit_intercept=self.fit_intercept,
             max_iter=self.max_iter,
@@ -213,4 +215,22 @@ def predict_proba(self, X):
 
     def score(self, X, y):
         from sklearn.metrics import accuracy_score
-        return accuracy_score(y, self.predict(X))
\ No newline at end of file
+        return accuracy_score(y, self.predict(X))
+    
+
+if __name__ == '__main__':
+
+    # Generate synthetic data
+    X, y = make_regression(n_samples=100, n_features=3, noise=0.1)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+    # Initialize GAMRegressor with 'auto' terms
+    model = GAMRegressor(terms='auto', verbose=True)
+    model.fit(X_train, y_train)
+
+    # Inspect the generated terms
+    print(model.model_.terms)
+
+    # Predict and evaluate
+    y_pred = model.predict(X_test)
+    print(f"Test RMSE: {model.rmse(X_test, y_test):.4f}")
\ No newline at end of file

From 31352c232734580370fb1f4e9893f5c378b62712 Mon Sep 17 00:00:00 2001
From: Nicholas Corona <ncorona3@gatech.edu>
Date: Sun, 1 Dec 2024 09:12:21 -0500
Subject: [PATCH 04/10] Fix term list bug in test cases

---
 pygam/sklearn_api.py | 41 ++++++++++++++++++++++++++---------------
 1 file changed, 26 insertions(+), 15 deletions(-)

diff --git a/pygam/sklearn_api.py b/pygam/sklearn_api.py
index f9bc6075..5aa1a2aa 100644
--- a/pygam/sklearn_api.py
+++ b/pygam/sklearn_api.py
@@ -7,11 +7,12 @@
 
 from sklearn.base import BaseEstimator, RegressorMixin, ClassifierMixin
 from pygam import GAM
-from pygam.terms import te, TermList, Term # Import te for interactions
+from pygam.terms import te, TermList, Term  # Import te for interactions
 import numpy as np
 from sklearn.datasets import make_regression
 from sklearn.model_selection import train_test_split
 
+
 class GAMRegressor(BaseEstimator, RegressorMixin):
     """
     GAMRegressor
@@ -40,7 +41,7 @@ class GAMRegressor(BaseEstimator, RegressorMixin):
         Verbosity mode.
     **gam_params :
         Additional parameters for the GAM model.
-    
+
     Attributes
     ----------
     model_ : GAM
@@ -58,7 +59,7 @@ def __init__(
         max_iter=100,
         tol=1e-4,
         verbose=False,
-        **gam_params
+        **gam_params,
     ):
         self.distribution = distribution
         self.link = link
@@ -70,7 +71,7 @@ def __init__(
         self.tol = tol
         self.verbose = verbose
         self.gam_params = gam_params
-        
+
         # Handle interactions if specified
         if self.interactions:
             if isinstance(self.terms, str) and self.terms == 'auto':
@@ -79,11 +80,15 @@ def __init__(
                 self.terms = [self.terms]
             elif not isinstance(self.terms, list):
                 self.terms = list(self.terms)
-                
+
             # Add interaction terms
             for interaction in self.interactions:
                 self.terms.append(te(*interaction))
-        
+
+        # Convert terms to TermList if necessary
+        if isinstance(self.terms, list):
+            self.terms = TermList(*self.terms)
+
         # Initialize the GAM model
         self.model_ = GAM(
             distribution=self.distribution,
@@ -94,7 +99,7 @@ def __init__(
             max_iter=self.max_iter,
             tol=self.tol,
             verbose=self.verbose,
-            **self.gam_params
+            **self.gam_params,
         )
 
     def fit(self, X, y):
@@ -107,6 +112,7 @@ def predict(self, X):
     def score(self, X, y):
         return float(self.model_.statistics_.get('pseudo R-squared', 0))
 
+
 class GAMClassifier(BaseEstimator, ClassifierMixin):
     """
     GAMClassifier
@@ -135,7 +141,7 @@ class GAMClassifier(BaseEstimator, ClassifierMixin):
         Verbosity mode.
     **gam_params :
         Additional parameters for the GAM model.
-    
+
     Attributes
     ----------
     model_ : GAM
@@ -155,7 +161,7 @@ def __init__(
         max_iter=100,
         tol=1e-4,
         verbose=False,
-        **gam_params
+        **gam_params,
     ):
         self.distribution = distribution
         self.link = link
@@ -167,7 +173,7 @@ def __init__(
         self.tol = tol
         self.verbose = verbose
         self.gam_params = gam_params
-        
+
         # Handle interactions if specified
         if self.interactions:
             if isinstance(self.terms, str) and self.terms == 'auto':
@@ -176,11 +182,15 @@ def __init__(
                 self.terms = [self.terms]
             elif not isinstance(self.terms, list):
                 self.terms = list(self.terms)
-                
+
             # Add interaction terms
             for interaction in self.interactions:
                 self.terms.append(te(*interaction))
-        
+
+        # Convert terms to TermList if necessary
+        if isinstance(self.terms, list):
+            self.terms = TermList(*self.terms)
+
         # Initialize the GAM model
         self.model_ = GAM(
             distribution=self.distribution,
@@ -191,7 +201,7 @@ def __init__(
             max_iter=self.max_iter,
             tol=self.tol,
             verbose=self.verbose,
-            **self.gam_params
+            **self.gam_params,
         )
 
     def fit(self, X, y):
@@ -215,8 +225,9 @@ def predict_proba(self, X):
 
     def score(self, X, y):
         from sklearn.metrics import accuracy_score
+
         return accuracy_score(y, self.predict(X))
-    
+
 
 if __name__ == '__main__':
 
@@ -233,4 +244,4 @@ def score(self, X, y):
 
     # Predict and evaluate
     y_pred = model.predict(X_test)
-    print(f"Test RMSE: {model.rmse(X_test, y_test):.4f}")
\ No newline at end of file
+    print(f"Test RMSE: {model.rmse(X_test, y_test):.4f}")

From a856b6a5279b5644d4d5c906d71cc7f037a99da8 Mon Sep 17 00:00:00 2001
From: Nicholas Corona <ncorona3@gatech.edu>
Date: Sun, 1 Dec 2024 09:14:52 -0500
Subject: [PATCH 05/10] Refactor tests for GAMRegressor and GAMClassifier:
 simplify assertions and restore test_sklearn_api.py

---
 pygam/tests/test_penalties.py              | 14 +++++++-------
 {tests => pygam/tests}/test_sklearn_api.py |  0
 2 files changed, 7 insertions(+), 7 deletions(-)
 rename {tests => pygam/tests}/test_sklearn_api.py (100%)

diff --git a/pygam/tests/test_penalties.py b/pygam/tests/test_penalties.py
index bf6dd68a..43b3ee24 100644
--- a/pygam/tests/test_penalties.py
+++ b/pygam/tests/test_penalties.py
@@ -23,13 +23,13 @@ def test_single_spline_penalty():
     monotonic_ and convexity_ should be 0.
     """
     coef = np.array(1.0)
-    assert np.alltrue(derivative(1, coef).A == 0.0)
-    assert np.alltrue(l2(1, coef).A == 1.0)
-    assert np.alltrue(monotonic_inc(1, coef).A == 0.0)
-    assert np.alltrue(monotonic_dec(1, coef).A == 0.0)
-    assert np.alltrue(convex(1, coef).A == 0.0)
-    assert np.alltrue(concave(1, coef).A == 0.0)
-    assert np.alltrue(none(1, coef).A == 0.0)
+    assert np.all(derivative(1, coef).A == 0.0)
+    assert np.all(l2(1, coef).A == 1.0)
+    assert np.all(monotonic_inc(1, coef).A == 0.0)
+    assert np.all(monotonic_dec(1, coef).A == 0.0)
+    assert np.all(convex(1, coef).A == 0.0)
+    assert np.all(concave(1, coef).A == 0.0)
+    assert np.all(none(1, coef).A == 0.0)
 
 
 def test_wrap_penalty():
diff --git a/tests/test_sklearn_api.py b/pygam/tests/test_sklearn_api.py
similarity index 100%
rename from tests/test_sklearn_api.py
rename to pygam/tests/test_sklearn_api.py

From f772acc5aad671ce999d9f2a86141c49143ff871 Mon Sep 17 00:00:00 2001
From: Nicholas Corona <ncorona3@gatech.edu>
Date: Sun, 1 Dec 2024 09:22:39 -0500
Subject: [PATCH 06/10] Reorganize imports in sklearn_api.py for clarity and
 consistency

---
 pygam/sklearn_api.py | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/pygam/sklearn_api.py b/pygam/sklearn_api.py
index 5aa1a2aa..4b041861 100644
--- a/pygam/sklearn_api.py
+++ b/pygam/sklearn_api.py
@@ -5,12 +5,18 @@
 It integrates pygam's GAM capabilities with scikit-learn's estimator interface, enabling seamless use in machine learning pipelines.
 """
 
-from sklearn.base import BaseEstimator, RegressorMixin, ClassifierMixin
-from pygam import GAM
-from pygam.terms import te, TermList, Term  # Import te for interactions
+# Standard library imports
 import numpy as np
+
+# Third-party imports
+from sklearn.base import BaseEstimator, RegressorMixin, ClassifierMixin
 from sklearn.datasets import make_regression
 from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+# Local application imports
+from pygam import GAM
+from pygam.terms import te, TermList, Term  # Import te for interactions
 
 
 class GAMRegressor(BaseEstimator, RegressorMixin):
@@ -224,8 +230,6 @@ def predict_proba(self, X):
             return proba  # Assume GAM model returns probabilities for each class
 
     def score(self, X, y):
-        from sklearn.metrics import accuracy_score
-
         return accuracy_score(y, self.predict(X))
 
 

From c9a672bbbc1b32ed29c28f05c814fc324a955d8d Mon Sep 17 00:00:00 2001
From: Nicholas Corona <ncorona3@gatech.edu>
Date: Sun, 1 Dec 2024 09:33:08 -0500
Subject: [PATCH 07/10] Add pandas and pytz dependencies to poetry.lock and
 update pyproject.toml

---
 poetry.lock    | 61 +++++++++++++++++++++++++++++++++++++++++++++++++-
 pyproject.toml |  1 +
 2 files changed, 61 insertions(+), 1 deletion(-)

diff --git a/poetry.lock b/poetry.lock
index a724bec8..07eadd60 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1056,6 +1056,54 @@ files = [
     {file = "packaging-23.2.tar.gz", hash = "sha256:048fb0e9405036518eaaf48a55953c750c11e1a1b68e0dd1a9d62ed0c092cfc5"},
 ]
 
+[[package]]
+name = "pandas"
+version = "1.5.3"
+description = "Powerful data structures for data analysis, time series, and statistics"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pandas-1.5.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:3749077d86e3a2f0ed51367f30bf5b82e131cc0f14260c4d3e499186fccc4406"},
+    {file = "pandas-1.5.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:972d8a45395f2a2d26733eb8d0f629b2f90bebe8e8eddbb8829b180c09639572"},
+    {file = "pandas-1.5.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:50869a35cbb0f2e0cd5ec04b191e7b12ed688874bd05dd777c19b28cbea90996"},
+    {file = "pandas-1.5.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c3ac844a0fe00bfaeb2c9b51ab1424e5c8744f89860b138434a363b1f620f354"},
+    {file = "pandas-1.5.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7a0a56cef15fd1586726dace5616db75ebcfec9179a3a55e78f72c5639fa2a23"},
+    {file = "pandas-1.5.3-cp310-cp310-win_amd64.whl", hash = "sha256:478ff646ca42b20376e4ed3fa2e8d7341e8a63105586efe54fa2508ee087f328"},
+    {file = "pandas-1.5.3-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:6973549c01ca91ec96199e940495219c887ea815b2083722821f1d7abfa2b4dc"},
+    {file = "pandas-1.5.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:c39a8da13cede5adcd3be1182883aea1c925476f4e84b2807a46e2775306305d"},
+    {file = "pandas-1.5.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:f76d097d12c82a535fda9dfe5e8dd4127952b45fea9b0276cb30cca5ea313fbc"},
+    {file = "pandas-1.5.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e474390e60ed609cec869b0da796ad94f420bb057d86784191eefc62b65819ae"},
+    {file = "pandas-1.5.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5f2b952406a1588ad4cad5b3f55f520e82e902388a6d5a4a91baa8d38d23c7f6"},
+    {file = "pandas-1.5.3-cp311-cp311-win_amd64.whl", hash = "sha256:bc4c368f42b551bf72fac35c5128963a171b40dce866fb066540eeaf46faa003"},
+    {file = "pandas-1.5.3-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:14e45300521902689a81f3f41386dc86f19b8ba8dd5ac5a3c7010ef8d2932813"},
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+    {file = "pandas-1.5.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:26d9c71772c7afb9d5046e6e9cf42d83dd147b5cf5bcb9d97252077118543792"},
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+    {file = "pandas-1.5.3-cp38-cp38-win32.whl", hash = "sha256:87bd9c03da1ac870a6d2c8902a0e1fd4267ca00f13bc494c9e5a9020920e1d51"},
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+    {file = "pandas-1.5.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9f69c4029613de47816b1bb30ff5ac778686688751a5e9c99ad8c7031f6508e5"},
+    {file = "pandas-1.5.3-cp39-cp39-win32.whl", hash = "sha256:7cec0bee9f294e5de5bbfc14d0573f65526071029d036b753ee6507d2a21480a"},
+    {file = "pandas-1.5.3-cp39-cp39-win_amd64.whl", hash = "sha256:dfd681c5dc216037e0b0a2c821f5ed99ba9f03ebcf119c7dac0e9a7b960b9ec9"},
+    {file = "pandas-1.5.3.tar.gz", hash = "sha256:74a3fd7e5a7ec052f183273dc7b0acd3a863edf7520f5d3a1765c04ffdb3b0b1"},
+]
+
+[package.dependencies]
+numpy = [
+    {version = ">=1.20.3", markers = "python_version < \"3.10\""},
+    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
+    {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
+]
+python-dateutil = ">=2.8.1"
+pytz = ">=2020.1"
+
+[package.extras]
+test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"]
+
 [[package]]
 name = "pandocfilters"
 version = "1.5.1"
@@ -1340,6 +1388,17 @@ docs = ["mock", "python-utils", "sphinx"]
 loguru = ["loguru"]
 tests = ["flake8", "loguru", "pytest", "pytest-asyncio", "pytest-cov", "pytest-mypy", "sphinx", "types-setuptools"]
 
+[[package]]
+name = "pytz"
+version = "2024.2"
+description = "World timezone definitions, modern and historical"
+optional = false
+python-versions = "*"
+files = [
+    {file = "pytz-2024.2-py2.py3-none-any.whl", hash = "sha256:31c7c1817eb7fae7ca4b8c7ee50c72f93aa2dd863de768e1ef4245d426aa0725"},
+    {file = "pytz-2024.2.tar.gz", hash = "sha256:2aa355083c50a0f93fa581709deac0c9ad65cca8a9e9beac660adcbd493c798a"},
+]
+
 [[package]]
 name = "pywin32"
 version = "306"
@@ -2071,4 +2130,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9, <=3.13"
-content-hash = "84ba875e24ed4d7ac984dabe842dbf2e10056a16d645ae7131bf449b92a14d3f"
+content-hash = "324627eba8faf274ca5bee70303e1854f31029b86a8fc61aaa9b27df4b87a2ed"
diff --git a/pyproject.toml b/pyproject.toml
index f55672ae..4170a419 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,6 +15,7 @@ numpy = [
 progressbar2 = "^4.2.0"
 scipy = ">=1.11.4"
 scikit-learn = "^1.5.2"
+pandas = "^1.3.3"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.2.2"

From b2623eda041087333ea79484a16a0dc863100a1a Mon Sep 17 00:00:00 2001
From: Nicholas Corona <ncorona3@gatech.edu>
Date: Sun, 1 Dec 2024 11:03:45 -0500
Subject: [PATCH 08/10] Add .env to .gitignore to prevent environment variable
 files from being tracked

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index c4ff995e..ab63179a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -46,3 +46,4 @@ _build/
 #########
 .idea/
 .vscode/settings.json
+.env

From 0afb8f024625c0884a793a99c11820371846d108 Mon Sep 17 00:00:00 2001
From: Nicholas Corona <ncorona3@gatech.edu>
Date: Sun, 1 Dec 2024 18:13:32 -0500
Subject: [PATCH 09/10] Update .gitignore and pyproject.toml; enhance
 GAMRegressor and GAMClassifier to support categorical features

---
 .gitignore           |   1 +
 poetry.lock          | 119 ++++++++++++++++++++++++++++++-------------
 pygam/sklearn_api.py | 117 +++++++++++++++++++++++++-----------------
 pyproject.toml       |   2 +-
 4 files changed, 157 insertions(+), 82 deletions(-)

diff --git a/.gitignore b/.gitignore
index ab63179a..63479341 100644
--- a/.gitignore
+++ b/.gitignore
@@ -47,3 +47,4 @@ _build/
 .idea/
 .vscode/settings.json
 .env
+.vscode/launch.json
diff --git a/poetry.lock b/poetry.lock
index 07eadd60..e177aab3 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1058,51 +1058,89 @@ files = [
 
 [[package]]
 name = "pandas"
-version = "1.5.3"
+version = "2.2.3"
 description = "Powerful data structures for data analysis, time series, and statistics"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
 numpy = [
-    {version = ">=1.20.3", markers = "python_version < \"3.10\""},
-    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
-    {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
+    {version = ">=1.22.4", markers = "python_version < \"3.11\""},
+    {version = ">=1.23.2", markers = "python_version == \"3.11\""},
+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
-python-dateutil = ">=2.8.1"
+python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
+tzdata = ">=2022.7"
 
 [package.extras]
-test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"]
+all = ["PyQt5 (>=5.15.9)", "SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)", "beautifulsoup4 (>=4.11.2)", "bottleneck (>=1.3.6)", "dataframe-api-compat (>=0.1.7)", "fastparquet (>=2022.12.0)", "fsspec (>=2022.11.0)", "gcsfs (>=2022.11.0)", "html5lib (>=1.1)", "hypothesis (>=6.46.1)", "jinja2 (>=3.1.2)", "lxml (>=4.9.2)", "matplotlib (>=3.6.3)", "numba (>=0.56.4)", "numexpr (>=2.8.4)", "odfpy (>=1.4.1)", "openpyxl (>=3.1.0)", "pandas-gbq (>=0.19.0)", "psycopg2 (>=2.9.6)", "pyarrow (>=10.0.1)", "pymysql (>=1.0.2)", "pyreadstat (>=1.2.0)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)", "python-calamine (>=0.1.7)", "pyxlsb (>=1.0.10)", "qtpy (>=2.3.0)", "s3fs (>=2022.11.0)", "scipy (>=1.10.0)", "tables (>=3.8.0)", "tabulate (>=0.9.0)", "xarray (>=2022.12.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.5)", "zstandard (>=0.19.0)"]
+aws = ["s3fs (>=2022.11.0)"]
+clipboard = ["PyQt5 (>=5.15.9)", "qtpy (>=2.3.0)"]
+compression = ["zstandard (>=0.19.0)"]
+computation = ["scipy (>=1.10.0)", "xarray (>=2022.12.0)"]
+consortium-standard = ["dataframe-api-compat (>=0.1.7)"]
+excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.1.0)", "python-calamine (>=0.1.7)", "pyxlsb (>=1.0.10)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.5)"]
+feather = ["pyarrow (>=10.0.1)"]
+fss = ["fsspec (>=2022.11.0)"]
+gcp = ["gcsfs (>=2022.11.0)", "pandas-gbq (>=0.19.0)"]
+hdf5 = ["tables (>=3.8.0)"]
+html = ["beautifulsoup4 (>=4.11.2)", "html5lib (>=1.1)", "lxml (>=4.9.2)"]
+mysql = ["SQLAlchemy (>=2.0.0)", "pymysql (>=1.0.2)"]
+output-formatting = ["jinja2 (>=3.1.2)", "tabulate (>=0.9.0)"]
+parquet = ["pyarrow (>=10.0.1)"]
+performance = ["bottleneck (>=1.3.6)", "numba (>=0.56.4)", "numexpr (>=2.8.4)"]
+plot = ["matplotlib (>=3.6.3)"]
+postgresql = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "psycopg2 (>=2.9.6)"]
+pyarrow = ["pyarrow (>=10.0.1)"]
+spss = ["pyreadstat (>=1.2.0)"]
+sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)"]
+test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
+xml = ["lxml (>=4.9.2)"]
 
 [[package]]
 name = "pandocfilters"
@@ -2073,6 +2111,17 @@ files = [
     {file = "typing_extensions-4.9.0.tar.gz", hash = "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783"},
 ]
 
+[[package]]
+name = "tzdata"
+version = "2024.2"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
+files = [
+    {file = "tzdata-2024.2-py2.py3-none-any.whl", hash = "sha256:a48093786cdcde33cad18c2555e8532f34422074448fbc874186f0abd79565cd"},
+    {file = "tzdata-2024.2.tar.gz", hash = "sha256:7d85cc416e9382e69095b7bdf4afd9e3880418a2413feec7069d533d6b4e31cc"},
+]
+
 [[package]]
 name = "urllib3"
 version = "2.2.0"
@@ -2130,4 +2179,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9, <=3.13"
-content-hash = "324627eba8faf274ca5bee70303e1854f31029b86a8fc61aaa9b27df4b87a2ed"
+content-hash = "6374b0bba22da8a0e240ae5f655897a449c79134d4e4ef416ec5a1f270385d2f"
diff --git a/pygam/sklearn_api.py b/pygam/sklearn_api.py
index 4b041861..fda4882b 100644
--- a/pygam/sklearn_api.py
+++ b/pygam/sklearn_api.py
@@ -17,6 +17,22 @@
 # Local application imports
 from pygam import GAM
 from pygam.terms import te, TermList, Term  # Import te for interactions
+from pygam.terms import s, f, l, intercept  # Import s, f, l for splines
+
+def create_default_terms(X, categorical_features=None):
+    """Generate default terms for each feature in X, handling categoricals."""
+    n_features = X.shape[1]
+    terms = []
+    if categorical_features is None:
+        categorical_features = []
+    for i in range(n_features):
+        if i in categorical_features:
+            terms.append(f(i))
+        elif not np.issubdtype(X[:, i].dtype, np.number):
+            terms.append(f(i))
+        else:
+            terms.append(s(i))
+    return terms
 
 
 class GAMRegressor(BaseEstimator, RegressorMixin):
@@ -31,9 +47,10 @@ class GAMRegressor(BaseEstimator, RegressorMixin):
         The distribution of the response variable.
     link : str, default='identity'
         The link function.
-    terms : 'auto' or TermList, default='auto'
-        The terms to include in the model.
-    interactions : list of tuples, optional
+    terms : 'auto', None, or list of Term objects, default='auto'
+        The terms to include in the model. If 'auto', terms are automatically inferred based on X.
+        If None, no terms are used. If a list of Term objects, they are used as specified.
+    interactions : None or list of Term objects, optional
         Interaction terms to include in the model.
     callbacks : list, default=['deviance', 'diffs']
         List of callbacks to monitor during training.
@@ -45,6 +62,8 @@ class GAMRegressor(BaseEstimator, RegressorMixin):
         Tolerance for stopping criteria.
     verbose : bool, default=False
         Verbosity mode.
+    categorical_features : list, optional
+        List of indices of categorical features.
     **gam_params :
         Additional parameters for the GAM model.
 
@@ -58,48 +77,50 @@ def __init__(
         self,
         distribution='normal',
         link='identity',
-        terms='auto',  # Will be handled by GAM class
+        terms='auto',
         interactions=None,
         callbacks=['deviance', 'diffs'],
         fit_intercept=True,
         max_iter=100,
         tol=1e-4,
         verbose=False,
+        categorical_features=None,
         **gam_params,
     ):
         self.distribution = distribution
         self.link = link
-        self.terms = terms  # Simply pass through to GAM
+        self.terms = terms
         self.interactions = interactions
         self.callbacks = callbacks
         self.fit_intercept = fit_intercept
         self.max_iter = max_iter
         self.tol = tol
         self.verbose = verbose
+        self.categorical_features = categorical_features
         self.gam_params = gam_params
 
-        # Handle interactions if specified
-        if self.interactions:
-            if isinstance(self.terms, str) and self.terms == 'auto':
-                self.terms = []  # Convert 'auto' to empty list to append to
-            elif isinstance(self.terms, str):
-                self.terms = [self.terms]
-            elif not isinstance(self.terms, list):
-                self.terms = list(self.terms)
+    def fit(self, X, y):
+        if self.terms == 'auto':
+            self.terms_ = create_default_terms(X, self.categorical_features)
+        elif self.terms is None:
+            self.terms_ = []
+        else:
+            self.terms_ = self.terms
 
-            # Add interaction terms
-            for interaction in self.interactions:
-                self.terms.append(te(*interaction))
+        if self.interactions is not None:
+            self.interactions_ = [te(*interaction) if isinstance(interaction, tuple) else interaction for interaction in self.interactions]
+        else:
+            self.interactions_ = []
 
-        # Convert terms to TermList if necessary
-        if isinstance(self.terms, list):
-            self.terms = TermList(*self.terms)
+        # Combine terms and interactions
+        terms = self.terms_ + self.interactions_
+        terms = TermList(*terms)
 
-        # Initialize the GAM model
+        # Create the GAM model with the specified terms
         self.model_ = GAM(
             distribution=self.distribution,
             link=self.link,
-            terms=self.terms,  # Pass terms directly, let GAM handle 'auto'
+            terms=terms,
             callbacks=self.callbacks,
             fit_intercept=self.fit_intercept,
             max_iter=self.max_iter,
@@ -107,8 +128,6 @@ def __init__(
             verbose=self.verbose,
             **self.gam_params,
         )
-
-    def fit(self, X, y):
         self.model_.fit(X, y)
         return self
 
@@ -131,9 +150,10 @@ class GAMClassifier(BaseEstimator, ClassifierMixin):
         The distribution of the response variable.
     link : str, default='logit'
         The link function.
-    terms : 'auto' or TermList, default='auto'
-        The terms to include in the model.
-    interactions : list of tuples, optional
+    terms : 'auto', None, or list of Term objects, default='auto'
+        The terms to include in the model. If 'auto', terms are automatically inferred based on X.
+        If None, no terms are used. If a list of Term objects, they are used as specified.
+    interactions : None or list of Term objects, optional
         Interaction terms to include in the model.
     callbacks : list, default=['deviance', 'diffs', 'accuracy']
         List of callbacks to monitor during training.
@@ -145,6 +165,8 @@ class GAMClassifier(BaseEstimator, ClassifierMixin):
         Tolerance for stopping criteria.
     verbose : bool, default=False
         Verbosity mode.
+    categorical_features : list, optional
+        List of indices of categorical features.
     **gam_params :
         Additional parameters for the GAM model.
 
@@ -160,48 +182,53 @@ def __init__(
         self,
         distribution='binomial',
         link='logit',
-        terms='auto',  # Will be handled by GAM class
+        terms='auto',
         interactions=None,
         callbacks=['deviance', 'diffs', 'accuracy'],
         fit_intercept=True,
         max_iter=100,
         tol=1e-4,
         verbose=False,
+        categorical_features=None,
         **gam_params,
     ):
         self.distribution = distribution
         self.link = link
-        self.terms = terms  # Simply pass through to GAM
+        self.terms = terms
         self.interactions = interactions
         self.callbacks = callbacks
         self.fit_intercept = fit_intercept
         self.max_iter = max_iter
         self.tol = tol
         self.verbose = verbose
+        self.categorical_features = categorical_features
         self.gam_params = gam_params
 
-        # Handle interactions if specified
-        if self.interactions:
-            if isinstance(self.terms, str) and self.terms == 'auto':
-                self.terms = []  # Convert 'auto' to empty list to append to
-            elif isinstance(self.terms, str):
-                self.terms = [self.terms]
-            elif not isinstance(self.terms, list):
-                self.terms = list(self.terms)
+    def fit(self, X, y):
+        if self.terms == 'auto':
+            self.terms_ = create_default_terms(X, self.categorical_features)
+        elif self.terms is None:
+            self.terms_ = []
+        else:
+            self.terms_ = self.terms
 
-            # Add interaction terms
-            for interaction in self.interactions:
-                self.terms.append(te(*interaction))
+        if self.interactions is not None:
+            self.interactions_ = [
+                te(*interaction) if isinstance(interaction, tuple) else interaction
+                for interaction in self.interactions
+            ]
+        else:
+            self.interactions_ = []
 
-        # Convert terms to TermList if necessary
-        if isinstance(self.terms, list):
-            self.terms = TermList(*self.terms)
+        # Combine terms and interactions
+        terms = self.terms_ + self.interactions_
+        terms = TermList(*terms)
 
-        # Initialize the GAM model
+        # Create the GAM model with the specified terms
         self.model_ = GAM(
             distribution=self.distribution,
             link=self.link,
-            terms=self.terms,  # Pass terms directly, let GAM handle 'auto'
+            terms=terms,
             callbacks=self.callbacks,
             fit_intercept=self.fit_intercept,
             max_iter=self.max_iter,
@@ -209,8 +236,6 @@ def __init__(
             verbose=self.verbose,
             **self.gam_params,
         )
-
-    def fit(self, X, y):
         self.model_.fit(X, y)
         self.classes_ = np.unique(y)
         return self
diff --git a/pyproject.toml b/pyproject.toml
index 4170a419..2cf5ff35 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,7 +15,7 @@ numpy = [
 progressbar2 = "^4.2.0"
 scipy = ">=1.11.4"
 scikit-learn = "^1.5.2"
-pandas = "^1.3.3"
+pandas = ">=1.4.0"  # Updated line
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.2.2"

From 7069f5a4b1a8307f9a2b1e077cdba121b1bd050b Mon Sep 17 00:00:00 2001
From: Nicholas Corona <ncorona3@gatech.edu>
Date: Sun, 1 Dec 2024 18:17:52 -0500
Subject: [PATCH 10/10] Refactor GAMRegressor and GAMClassifier: improve code
 readability and remove main execution block

---
 pygam/sklearn_api.py | 24 +++++-------------------
 1 file changed, 5 insertions(+), 19 deletions(-)

diff --git a/pygam/sklearn_api.py b/pygam/sklearn_api.py
index fda4882b..f218ff11 100644
--- a/pygam/sklearn_api.py
+++ b/pygam/sklearn_api.py
@@ -19,6 +19,7 @@
 from pygam.terms import te, TermList, Term  # Import te for interactions
 from pygam.terms import s, f, l, intercept  # Import s, f, l for splines
 
+
 def create_default_terms(X, categorical_features=None):
     """Generate default terms for each feature in X, handling categoricals."""
     n_features = X.shape[1]
@@ -108,7 +109,10 @@ def fit(self, X, y):
             self.terms_ = self.terms
 
         if self.interactions is not None:
-            self.interactions_ = [te(*interaction) if isinstance(interaction, tuple) else interaction for interaction in self.interactions]
+            self.interactions_ = [
+                te(*interaction) if isinstance(interaction, tuple) else interaction
+                for interaction in self.interactions
+            ]
         else:
             self.interactions_ = []
 
@@ -256,21 +260,3 @@ def predict_proba(self, X):
 
     def score(self, X, y):
         return accuracy_score(y, self.predict(X))
-
-
-if __name__ == '__main__':
-
-    # Generate synthetic data
-    X, y = make_regression(n_samples=100, n_features=3, noise=0.1)
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
-
-    # Initialize GAMRegressor with 'auto' terms
-    model = GAMRegressor(terms='auto', verbose=True)
-    model.fit(X_train, y_train)
-
-    # Inspect the generated terms
-    print(model.model_.terms)
-
-    # Predict and evaluate
-    y_pred = model.predict(X_test)
-    print(f"Test RMSE: {model.rmse(X_test, y_test):.4f}")