diff --git a/docs/data_tools/class_balancing.md b/docs/data_tools/class_balancing.md new file mode 100644 index 00000000..46066943 --- /dev/null +++ b/docs/data_tools/class_balancing.md @@ -0,0 +1,3 @@ +# Class balancing + +::: eis_toolkit.data_tools.class_balancing diff --git a/eis_toolkit/data_tools/__init__.py b/eis_toolkit/data_tools/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/eis_toolkit/data_tools/class_balancing.py b/eis_toolkit/data_tools/class_balancing.py new file mode 100644 index 00000000..11550a36 --- /dev/null +++ b/eis_toolkit/data_tools/class_balancing.py @@ -0,0 +1,44 @@ +from typing import Optional, Union + +import numpy as np +import pandas as pd +from beartype import beartype +from imblearn.combine import SMOTETomek + +from eis_toolkit import exceptions + + +@beartype +def balance_SMOTETomek( + X: Union[pd.DataFrame, np.ndarray], + y: Union[pd.Series, np.ndarray], + sampling_strategy: Union[float, str, dict] = "auto", + random_state: Optional[int] = None, +) -> tuple[Union[pd.DataFrame, np.ndarray], Union[pd.Series, np.ndarray]]: + """Balances the classes of input dataset using SMOTETomek resampling method. + + Args: + X: The feature matrix (input data as a DataFrame). + y: The target labels corresponding to the feature matrix. + sampling_strategy: Parameter controlling how to perform the resampling. + If float, specifies the ratio of samples in minority class to samples of majority class, + if str, specifies classes to be resampled ("minority", "not minority", "not majority", "all", "auto"), + if dict, the keys should be targeted classes and values the desired number of samples for the class. + Defaults to "auto", which will resample all classes except the majority class. + random_state: Parameter controlling randomization of the algorithm. Can be given a seed (number). + Defaults to None, which randomizes the seed. + + Returns: + Resampled feature matrix and target labels. + + Raises: + NonMatchingParameterLengthsException: If X and y have different length. + """ + + if len(X) != len(y): + raise exceptions.NonMatchingParameterLengthsException( + "Feature matrix X and target labels y must have the same length." + ) + + X_res, y_res = SMOTETomek(sampling_strategy=sampling_strategy, random_state=random_state).fit_resample(X, y) + return X_res, y_res diff --git a/environment.yml b/environment.yml index e5fa606c..33a5f7e7 100755 --- a/environment.yml +++ b/environment.yml @@ -20,5 +20,6 @@ dependencies: - seaborn >=0.12.2 - pykrige >=1.7.0 - rtree >= 1.0.1 + - imbalanced-learn >= 0.11.0 # Dependencies for testing - pytest >=7.2.1 diff --git a/poetry.lock b/poetry.lock index dbbde980..2568b368 100755 --- a/poetry.lock +++ b/poetry.lock @@ -2,48 +2,76 @@ [[package]] name = "absl-py" -version = "1.2.0" +version = "2.0.0" 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["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.11" -content-hash = "99eb3423fe990b00ad5983b439ef86d605f6976c451d612c475470046de0481d" +content-hash = "13164b86aa49f4e518968e2382f76191bb6091498fbfd2988283b848bfcf9e2b" diff --git a/pyproject.toml b/pyproject.toml index 0fa18571..e3188b50 100755 --- a/pyproject.toml +++ b/pyproject.toml @@ -2,7 +2,7 @@ name = "eis_toolkit" version = "0.1.0" description = "EIS Toolkit is a comprehensive collection of tools suitable for mineral prospectivity mapping. This toolkit has been developed as part of the Exploration Information System project which has been funded by European Union." -authors = ["pavetsu14 "] +authors = [] maintainers = ["Gispo Ltd. "] license = "EUPL-1.2" readme = "README.md" @@ -33,6 +33,7 @@ beartype = "^0.13.1" seaborn = "^0.12.2" pykrige = "^1.7.0" rtree = "^1.0.1" +imbalanced-learn = "^0.11.0" [tool.poetry.dev-dependencies] jupyterlab = "^3.4.5" diff --git a/requirements.txt b/requirements.txt index 187cb73b..6f7a7833 100644 --- a/requirements.txt +++ b/requirements.txt @@ -24,9 +24,10 @@ google-pasta==0.2.0 ; python_version >= "3.8" and python_version < "3.11" grpcio==1.48.1 ; python_version >= "3.8" and python_version < "3.11" h5py==3.7.0 ; python_version >= "3.8" and python_version < "3.11" idna==3.3 ; python_version >= "3.8" and python_version < "3.11" +imbalanced-learn==0.11.0 ; python_version >= "3.8" and python_version < "3.11" importlib-metadata==4.12.0 ; python_version >= "3.8" and python_version < "3.11" jinja2==3.1.2 ; python_version >= "3.8" and python_version < "3.11" -joblib==1.1.0 ; python_version >= "3.8" and python_version < "3.11" +joblib==1.3.2 ; python_version >= "3.8" and python_version < "3.11" keras-preprocessing==1.1.2 ; python_version >= "3.8" and python_version < "3.11" keras==2.9.0 ; python_version >= "3.8" and python_version < "3.11" kiwisolver==1.4.4 ; python_version >= "3.8" and python_version < "3.11" diff --git a/tests/data_tools/class_balancing_test.py b/tests/data_tools/class_balancing_test.py new file mode 100644 index 00000000..343e0b06 --- /dev/null +++ b/tests/data_tools/class_balancing_test.py @@ -0,0 +1,42 @@ +import numpy as np +import pytest +from sklearn.datasets import make_classification + +from eis_toolkit import exceptions +from eis_toolkit.data_tools.class_balancing import balance_SMOTETomek + +# CREATE TEST DATA +X, y = make_classification( + n_classes=2, + class_sep=2, + weights=[0.1, 0.9], + n_informative=3, + n_redundant=1, + flip_y=0, + n_features=20, + n_clusters_per_class=1, + n_samples=1000, + random_state=10, +) + + +def test_SMOTETomek(): + """Test that balance_SMOTETomek function works as expected.""" + assert not np.array_equal(np.count_nonzero(y == 0), np.count_nonzero(y == 1)) # Class imbalance before balancing + + X_res, y_res = balance_SMOTETomek(X, y) + + np.testing.assert_equal(len(X_res), len(y_res)) + np.testing.assert_equal(np.count_nonzero(y_res == 0), np.count_nonzero(y_res == 1)) # Class balance after balancing + + +def test_invalid_label_length(): + """Test that different length for feature matrix and labels raises the correct exception.""" + with pytest.raises(exceptions.NonMatchingParameterLengthsException): + balance_SMOTETomek(X, np.append(y, "C")) + + +def test_invalid_sampling_strategy(): + """Test that invalid value for sampling strategy raises the correct exception (generated by imblearn).""" + with pytest.raises(ValueError): + balance_SMOTETomek(X, y, sampling_strategy="invalid_strategy")