bump for 1.0.0

franneck94 · Oct 29, 2023 · 1694744 · 1694744
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diff --git a/README.md b/README.md
@@ -1,6 +1,6 @@
 # TensorCross
 
-![Python](https://img.shields.io/badge/python-%203.7+-blue)
+![Python](https://img.shields.io/badge/python-%203.9+-blue)
 [![License](https://camo.githubusercontent.com/890acbdcb87868b382af9a4b1fac507b9659d9bf/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d626c75652e737667)](https://github.com/franneck94/TensorCross/blob/main/LICENSE)
 [![Build](https://github.com/franneck94/TensorCross/workflows/Test/badge.svg)](https://github.com/franneck94/TensorCross/actions?query=workflow%3A%22Test+and+Coverage%22)
 [![codecov](https://codecov.io/gh/franneck94/TensorCross/branch/main/graph/badge.svg)](https://codecov.io/gh/franneck94/TensorCross)
@@ -10,16 +10,16 @@
 pip install tensorcross
 ```
 
-Cross-Validation, Grid Search, and Random Search for tf.data.Datasets in TensorFlow 2.0+ and Python 3.7+.
+Grid Search and Random Search with optionally CrossValidation for tf.data.Datasets in TensorFlow (Keras) 2.8+ and Python 3.9+.
 
 ## Motivation
 
-Currently, there is the tf.keras.wrapper.KerasClassifier/KerasRegressor class,
-which can be used to transform your tf.keras model into a sklearn estimator.
+There was the tf.keras.wrapper.KerasClassifier/KerasRegressor class,
+which can be used to transform your tf.keras model into a sklearn estimator.  
 However, this approach is only applicable if your dataset is a numpy.ndarray
-for your x and y data.
+for your x and y data and it was also removed from newer versions.  
 If you want to use the new tf.data.Dataset class, you cannot use the sklearn
-wrappers.
+wrappers.  
 This python package aims to help with this use-case.
 
 ## API

diff --git a/examples/example_grid_search.py b/examples/example_grid_search.py
@@ -11,10 +11,6 @@
 from tensorcross.utils import dataset_split
 
 
-np.random.seed(0)
-tf.random.set_seed(0)
-
-
 def build_model(
     optimizer: Optimizer,
     learning_rate: float,

diff --git a/examples/example_grid_search_cv.py b/examples/example_grid_search_cv.py
@@ -10,10 +10,6 @@
 from tensorcross.model_selection import GridSearchCV
 
 
-np.random.seed(0)
-tf.random.set_seed(0)
-
-
 def build_model(
     optimizer: Optimizer,
     learning_rate: float,

diff --git a/examples/example_random_search.py b/examples/example_random_search.py
@@ -12,10 +12,6 @@
 from tensorcross.utils import dataset_split
 
 
-np.random.seed(0)
-tf.random.set_seed(0)
-
-
 def build_model(
     optimizer: Optimizer,
     learning_rate: float,

diff --git a/examples/example_random_search_cv.py b/examples/example_random_search_cv.py
@@ -11,10 +11,6 @@
 from tensorcross.model_selection import RandomSearchCV
 
 
-np.random.seed(0)
-tf.random.set_seed(0)
-
-
 def build_model(
     optimizer: Optimizer,
     learning_rate: float,

diff --git a/pyproject.toml b/pyproject.toml
@@ -1,5 +1,5 @@
 [tool.black]
-target-version = ['py310']
+target-version = ['py39']
 line-length = 80
 skip-string-normalization = false
 skip-magic-trailing-comma = false
@@ -11,7 +11,7 @@ extend-exclude = '''
 '''
 
 [tool.isort]
-py_version = 310
+py_version = 39
 sections = [
     "FUTURE",
     "STDLIB",
@@ -39,12 +39,11 @@ known_third_party = [
     "tqdm",
     "cv2",
     "skimage",
-    "tensorcross",
     "tensorflow_datasets",
     "scikeras"
 ]
 known_first_party = []
-known_local_folder = []
+known_local_folder = ["tensorcross"]
 # style: black
 multi_line_output = 3
 include_trailing_comma = true
@@ -62,7 +61,7 @@ skip_glob = [
 
 [tool.mypy]
 # Platform configuration
-python_version = "3.10"
+python_version = "3.8"
 # imports related
 ignore_missing_imports = true
 follow_imports = "silent"
@@ -102,10 +101,10 @@ show_error_codes = true
 exclude = ["examples"]
 
 [tool.ruff]
-target-version = "py310"
+target-version = "py39"
 select = ["F", "E"]
 extend-select = ["W", "C90", "I", "N", "UP", "YTT", "ANN", "ASYNC", "B", "A", "C4", "DTZ", "FA", "ISC", "ICN", "PIE", "PYI", "PT", "RET", "SLOT", "SIM", "ARG", "PTH", "PD", "PLC", "PLE", "PLR", "PLW", "FLY", "NPY", "PERF", "RUF"]
-ignore = ["I001", "ANN401", "SIM300", "PERF203", "ANN101", "B905"]
+ignore = ["I001", "ANN401", "SIM300", "PERF203", "ANN101", "B905", "UP007", "UP006", "PTH", "UP035"]
 fixable = ["F", "E", "W", "C90", "I", "N", "UP", "YTT", "ANN", "ASYNC", "B", "A", "C4", "DTZ", "FA", "ISC", "ICN", "PIE", "PYI", "PT", "RET", "SLOT", "SIM", "ARG", "PTH", "PD", "PLC", "PLE", "PLR", "PLW", "FLY", "NPY", "PERF", "RUF"]
 unfixable = []
 line-length = 80
@@ -131,7 +130,7 @@ skip-magic-trailing-comma = false
 line-ending = "auto"
 
 [tool.pyright]
-pythonVersion = "3.10"
+pythonVersion = "3.9"
 typeCheckingMode = "basic"
 # enable subset of "strict"
 reportDuplicateImport = true

diff --git a/setup.py b/setup.py
@@ -6,11 +6,10 @@
 
 CLASSIFIERS = """\
 License :: OSI Approved :: MIT License
-Programming Language :: Python :: 3.7
-Programming Language :: Python :: 3.8
 Programming Language :: Python :: 3.9
 Programming Language :: Python :: 3.10
 Programming Language :: Python :: 3.11
+Programming Language :: Python :: 3.12
 Topic :: Software Development
 Operating System :: Microsoft :: Windows
 Operating System :: POSIX
@@ -34,13 +33,13 @@
 VERSION = __version__
 ISRELEASED = False
 
-MIN_PYTHON_VERSION = "3.7"
-INSTALL_REQUIRES = ["keras", "numpy", "scipy", "scikit-learn"]
+MIN_PYTHON_VERSION = "3.9"
+INSTALL_REQUIRES = ["keras>=2.8", "numpy", "scipy", "scikit-learn>=1.0"]
 
 
 PACKAGES = find_packages(include=["tensorcross", "tensorcross.*"])
 
-metadata = dict(
+metadata = dict(  # noqa: C408
     name=DISTNAME,
     version=VERSION,
     long_description=README,

diff --git a/tensorcross/_types.py b/tensorcross/_types.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from typing import Any
 from typing import Dict
 from typing import List

diff --git a/tensorcross/model_selection/search.py b/tensorcross/model_selection/search.py
@@ -1,12 +1,14 @@
+from __future__ import annotations
+
 import logging
 import os
 from abc import ABCMeta
 from abc import abstractmethod
+from collections.abc import Iterable
+from collections.abc import Mapping
 from typing import Any
 from typing import Callable
 from typing import Dict
-from typing import Iterable
-from typing import Mapping
 from typing import Optional
 from typing import Union
 
@@ -48,7 +50,7 @@ def __init__(
             "params": [],
         }
 
-    def _run_search(
+    def _run_search(  # noqa: PLR0912
         self,
         train_dataset: tf.data.Dataset,
         val_dataset: tf.data.Dataset,
@@ -136,7 +138,9 @@ def summary(self) -> str:
             f"Best score: {self.results_['best_score']} "
             f"using params: {self.results_['best_params']}"
         )
-        dashed_line = "".join(map(lambda x: "-", best_params_str))
+        dashed_line = "".join(
+            map(lambda x: "-", best_params_str)  # noqa: C417, ARG005
+        )
 
         current_line = f"\n{dashed_line}\n{best_params_str}\n{dashed_line}"
         results_str = current_line

diff --git a/tensorcross/model_selection/search_cv.py b/tensorcross/model_selection/search_cv.py
@@ -1,12 +1,14 @@
+from __future__ import annotations
+
 import logging
 import os
 from abc import ABCMeta
 from abc import abstractmethod
+from collections.abc import Iterable
+from collections.abc import Mapping
 from typing import Any
 from typing import Callable
 from typing import Dict
-from typing import Iterable
-from typing import Mapping
 from typing import Optional
 from typing import Union
 
@@ -56,7 +58,7 @@ def __init__(
             "params": [],
         }
 
-    def _run_search(
+    def _run_search(  # noqa: PLR0912
         self,
         dataset: tf.data.Dataset,
         parameter_obj: Union[ParameterGrid, ParameterSampler],
@@ -166,7 +168,9 @@ def summary(self) -> str:
             f"Best score: {self.results_['best_score']} "
             f"using params: {self.results_['best_params']}"
         )
-        dashed_line = "".join(map(lambda x: "-", best_params_str))
+        dashed_line = "".join(
+            map(lambda x: "-", best_params_str)  # noqa: C417, ARG005
+        )
 
         current_line = f"\n{dashed_line}\n{best_params_str}\n{dashed_line}"
         results_str = current_line
@@ -253,7 +257,7 @@ def fit(self, dataset: tf.data.Dataset, **kwargs: Any) -> None:
 
 
 class RandomSearchCV(BaseSearchCV):
-    def __init__(
+    def __init__(  # noqa: PLR0913
         self,
         model_fn: Callable[..., Model],
         param_distributions: Dict[str, Callable],

diff --git a/tensorcross/utils/dataset.py b/tensorcross/utils/dataset.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from typing import Tuple
 
 import tensorflow as tf
@@ -30,5 +32,4 @@ def dataset_split(
 def dataset_join(
     dataset_left: tf.data.Dataset, dataset_right: tf.data.Dataset
 ) -> tf.data.Dataset:
-    dataset_joined = dataset_left.concatenate(dataset_right)
-    return dataset_joined
+    return dataset_left.concatenate(dataset_right)
diff --git a/tensorcross/version.py b/tensorcross/version.py
@@ -1 +1 @@
-__version__ = "0.4.4"
+__version__ = "1.0.0"