Updates v1

.gitignore

@@ -42,4 +42,5 @@ __pycache__/
 build/
 dist/
 sklearn_json.egg-info/
-
+.ignore/*
+test.*

.pre-commit-config.yaml

@@ -0,0 +1,16 @@
+exclude: "models/"
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v2.3.0
+    hooks:
+    -   id: check-yaml
+    -   id: end-of-file-fixer
+    -   id: trailing-whitespace
+-   repo: https://github.com/psf/black
+    rev: 22.10.0
+    hooks:
+    -   id: black
+-   repo: https://github.com/charliermarsh/ruff-pre-commit
+    rev: 'v0.0.265'
+    hooks:
+    -   id: ruff

README.md

@@ -35,7 +35,7 @@ deserialized_model.predict(X)
 ```
 
 # Features
-The list of supported models is rapidly growing. If you have a request for a model or feature, please reach out to support@mlrequest.com.
+The list of supported models is rapidly growing. If you have a request for a model or feature, please reach out to aheldrich@yahoo.com.
 
 sklearn-json requires scikit-learn >= 0.21.3.
 
@@ -65,3 +65,7 @@ sklearn-json requires scikit-learn >= 0.21.3.
     * `sklearn.ensemble.RandomForestRegressor`
     * `sklearn.ensemble.GradientBoostingRegressor`
     * `sklearn.neural_network.MLPRegressor`
+
+## Upcoming
+
+* preproccessing

pyproject.toml

@@ -0,0 +1,46 @@
+[tool.ruff]
+# Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default. Add isort
+select = ["E", "F", "I", "D"]
+ignore = ["D100", "D101", "D102", "D103", "D104"]
+
+# Allow autofix for all enabled rules (when `--fix`) is provided.
+fixable = ["A", "B", "C", "D", "E", "F", "G", "I", "N", "Q", "S", "T", "W", "ANN", "ARG", "BLE", "COM", "DJ", "DTZ", "EM", "ERA", "EXE", "FBT", "ICN", "INP", "ISC", "NPY", "PD", "PGH", "PIE", "PL", "PT", "PTH", "PYI", "RET", "RSE", "RUF", "SIM", "SLF", "TCH", "TID", "TRY", "UP", "YTT"]
+unfixable = []
+
+# Exclude a variety of commonly ignored directories.
+exclude = [
+    ".bzr",
+    ".direnv",
+    ".eggs",
+    ".git",
+    ".git-rewrite",
+    ".hg",
+    ".mypy_cache",
+    ".nox",
+    ".pants.d",
+    ".pytype",
+    ".ruff_cache",
+    ".svn",
+    ".tox",
+    ".venv",
+    "__pypackages__",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "node_modules",
+    "venv",
+]
+
+# Same as Black.
+line-length = 88
+
+# Allow unused variables when underscore-prefixed.
+dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
+
+# Assume Python 3.10.
+target-version = "py310"
+
+[tool.ruff.mccabe]
+# Unlike Flake8, default to a complexity level of 10.
+max-complexity = 10

setup.py

@@ -1,24 +1,25 @@
 import setuptools
+
 with open("README.md", "r") as fh:
     long_description = fh.read()
 
 setuptools.setup(
-     name='sklearn-json',
-     version='0.1.0',
-     author="Mathieu Rodrigue",
-     author_email="support@mlrequest.com",
-     description="A safe, transparent way to share and deploy scikit-learn models.",
-     long_description=long_description,
-     long_description_content_type="text/markdown",
-     url="https://github.com/mlrequest/sklearn-json",
-     packages=setuptools.find_packages(),
-     install_requires=[
-          'scikit-learn>=0.21.3',
-      ],
-     classifiers=[
-         "Programming Language :: Python :: 3",
-         "License :: OSI Approved :: MIT License",
-         "Operating System :: OS Independent",
-     ],
-     python_requires='>=3.5',
- )
+    name="sklearn-json",
+    version="0.1.0",
+    author="Mathieu Rodrigue",
+    author_email="support@mlrequest.com",
+    description="A safe, transparent way to share and deploy scikit-learn models.",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/mlrequest/sklearn-json",
+    packages=setuptools.find_packages(),
+    install_requires=[
+        "scikit-learn>=0.21.3",
+    ],
+    classifiers=[
+        "Programming Language :: Python :: 3",
+        "License :: OSI Approved :: MIT License",
+        "Operating System :: OS Independent",
+    ],
+    python_requires=">=3.5",
+)

sklearn_json/__init__.py

@@ -1,16 +1,28 @@
-from sklearn_json import classification as clf
-from sklearn_json import regression as reg
-from sklearn import svm, discriminant_analysis, dummy
-from sklearn.linear_model import LogisticRegression, Perceptron
-from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
-from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, RandomForestRegressor, GradientBoostingRegressor, _gb_losses
-from sklearn.naive_bayes import BernoulliNB, GaussianNB, MultinomialNB, ComplementNB
-from sklearn.linear_model import LinearRegression, Lasso, Ridge
+import json
+
+from sklearn import discriminant_analysis, svm
+from sklearn.ensemble import (
+    GradientBoostingClassifier,
+    GradientBoostingRegressor,
+    RandomForestClassifier,
+    RandomForestRegressor,
+)
+from sklearn.linear_model import (
+    Lasso,
+    LinearRegression,
+    LogisticRegression,
+    Perceptron,
+    Ridge,
+)
+from sklearn.naive_bayes import BernoulliNB, ComplementNB, GaussianNB, MultinomialNB
 from sklearn.neural_network import MLPClassifier, MLPRegressor
 from sklearn.svm import SVR
-import json
+from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
 
-__version__ = '0.1.0'
+from sklearn_json import classification as clf
+from sklearn_json import regression as reg
+
+__version__ = "0.1.0"
 
 
 def serialize_model(model):
@@ -58,55 +70,55 @@ def serialize_model(model):
     elif isinstance(model, MLPRegressor):
         return reg.serialize_mlp_regressor(model)
     else:
-        raise ModellNotSupported('This model type is not currently supported. Email support@mlrequest.com to request a feature or report a bug.')
+        raise ModellNotSupported("This model type is not currently supported.")
 
 
 def deserialize_model(model_dict):
-    if model_dict['meta'] == 'lr':
+    if model_dict["meta"] == "lr":
         return clf.deserialize_logistic_regression(model_dict)
-    elif model_dict['meta'] == 'bernoulli-nb':
+    elif model_dict["meta"] == "bernoulli-nb":
         return clf.deserialize_bernoulli_nb(model_dict)
-    elif model_dict['meta'] == 'gaussian-nb':
+    elif model_dict["meta"] == "gaussian-nb":
         return clf.deserialize_gaussian_nb(model_dict)
-    elif model_dict['meta'] == 'multinomial-nb':
+    elif model_dict["meta"] == "multinomial-nb":
         return clf.deserialize_multinomial_nb(model_dict)
-    elif model_dict['meta'] == 'complement-nb':
+    elif model_dict["meta"] == "complement-nb":
         return clf.deserialize_complement_nb(model_dict)
-    elif model_dict['meta'] == 'lda':
+    elif model_dict["meta"] == "lda":
         return clf.deserialize_lda(model_dict)
-    elif model_dict['meta'] == 'qda':
+    elif model_dict["meta"] == "qda":
         return clf.deserialize_qda(model_dict)
-    elif model_dict['meta'] == 'svm':
+    elif model_dict["meta"] == "svm":
         return clf.deserialize_svm(model_dict)
-    elif model_dict['meta'] == 'perceptron':
+    elif model_dict["meta"] == "perceptron":
         return clf.deserialize_perceptron(model_dict)
-    elif model_dict['meta'] == 'decision-tree':
+    elif model_dict["meta"] == "decision-tree":
         return clf.deserialize_decision_tree(model_dict)
-    elif model_dict['meta'] == 'gb':
+    elif model_dict["meta"] == "gb":
         return clf.deserialize_gradient_boosting(model_dict)
-    elif model_dict['meta'] == 'rf':
+    elif model_dict["meta"] == "rf":
         return clf.deserialize_random_forest(model_dict)
-    elif model_dict['meta'] == 'mlp':
+    elif model_dict["meta"] == "mlp":
         return clf.deserialize_mlp(model_dict)
 
-    elif model_dict['meta'] == 'linear-regression':
+    elif model_dict["meta"] == "linear-regression":
         return reg.deserialize_linear_regressor(model_dict)
-    elif model_dict['meta'] == 'lasso-regression':
+    elif model_dict["meta"] == "lasso-regression":
         return reg.deserialize_lasso_regressor(model_dict)
-    elif model_dict['meta'] == 'ridge-regression':
+    elif model_dict["meta"] == "ridge-regression":
         return reg.deserialize_ridge_regressor(model_dict)
-    elif model_dict['meta'] == 'svr':
+    elif model_dict["meta"] == "svr":
         return reg.deserialize_svr(model_dict)
-    elif model_dict['meta'] == 'decision-tree-regression':
+    elif model_dict["meta"] == "decision-tree-regression":
         return reg.deserialize_decision_tree_regressor(model_dict)
-    elif model_dict['meta'] == 'gb-regression':
+    elif model_dict["meta"] == "gb-regression":
         return reg.deserialize_gradient_boosting_regressor(model_dict)
-    elif model_dict['meta'] == 'rf-regression':
+    elif model_dict["meta"] == "rf-regression":
         return reg.deserialize_random_forest_regressor(model_dict)
-    elif model_dict['meta'] == 'mlp-regression':
+    elif model_dict["meta"] == "mlp-regression":
         return reg.deserialize_mlp_regressor(model_dict)
     else:
-        raise ModellNotSupported('Model type not supported or corrupt JSON file. Email support@mlrequest.com to request a feature or report a bug.')
+        raise ModellNotSupported("Model type not supported or corrupt JSON file.")
 
 
 def to_dict(model):
@@ -118,14 +130,15 @@ def from_dict(model_dict):
 
 
 def to_json(model, model_name):
-    with open(model_name, 'w') as model_json:
+    with open(model_name, "w") as model_json:
         json.dump(serialize_model(model), model_json)
 
 
 def from_json(model_name):
-    with open(model_name, 'r') as model_json:
+    with open(model_name, "r") as model_json:
         model_dict = json.load(model_json)
         return deserialize_model(model_dict)
 
+
 class ModellNotSupported(Exception):
-    pass
+    pass