poetry.lock

@@ -777,30 +777,6 @@ files = [
 [package.extras]
 all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
 
-[[package]]
-name = "imbalanced-learn"
-version = "0.12.4"
-description = "Toolbox for imbalanced dataset in machine learning."
-optional = false
-python-versions = "*"
-files = [
-    {file = "imbalanced-learn-0.12.4.tar.gz", hash = "sha256:8153ba385d296b07d97e0901a2624a86c06b48c94c2f92da3a5354827697b7a3"},
-    {file = "imbalanced_learn-0.12.4-py3-none-any.whl", hash = "sha256:d47fc599160d3ea882e712a3a6b02bdd353c1a6436d8d68d41b1922e6ee4a703"},
-]
-
-[package.dependencies]
-joblib = ">=1.1.1"
-numpy = ">=1.17.3"
-scikit-learn = ">=1.0.2"
-scipy = ">=1.5.0"
-threadpoolctl = ">=2.0.0"
-
-[package.extras]
-docs = ["keras (>=2.4.3)", "matplotlib (>=3.1.2)", "memory-profiler (>=0.57.0)", "numpydoc (>=1.5.0)", "pandas (>=1.0.5)", "pydata-sphinx-theme (>=0.13.3)", "seaborn (>=0.9.0)", "sphinx (>=6.0.0)", "sphinx-copybutton (>=0.5.2)", "sphinx-design (>=0.5.0)", "sphinx-gallery (>=0.13.0)", "sphinxcontrib-bibtex (>=2.4.1)", "tensorflow (>=2.4.3)"]
-examples = ["keras (>=2.4.3)", "matplotlib (>=3.1.2)", "pandas (>=1.0.5)", "seaborn (>=0.9.0)", "tensorflow (>=2.4.3)"]
-optional = ["keras (>=2.4.3)", "pandas (>=1.0.5)", "tensorflow (>=2.4.3)"]
-tests = ["black (>=23.3.0)", "flake8 (>=3.8.2)", "keras (>=2.4.3)", "mypy (>=1.3.0)", "pandas (>=1.0.5)", "pytest (>=5.0.1)", "pytest-cov (>=2.9.0)", "tensorflow (>=2.4.3)"]
-
 [[package]]
 name = "ipykernel"
 version = "6.29.5"
@@ -1899,23 +1875,6 @@ files = [
 qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
 testing = ["docopt", "pytest"]
 
-[[package]]
-name = "patsy"
-version = "1.0.1"
-description = "A Python package for describing statistical models and for building design matrices."
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "patsy-1.0.1-py2.py3-none-any.whl", hash = "sha256:751fb38f9e97e62312e921a1954b81e1bb2bcda4f5eeabaf94db251ee791509c"},
-    {file = "patsy-1.0.1.tar.gz", hash = "sha256:e786a9391eec818c054e359b737bbce692f051aee4c661f4141cc88fb459c0c4"},
-]
-
-[package.dependencies]
-numpy = ">=1.4"
-
-[package.extras]
-test = ["pytest", "pytest-cov", "scipy"]
-
 [[package]]
 name = "pexpect"
 version = "4.9.0"
@@ -2700,27 +2659,6 @@ dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodest
 doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.13.1)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<=7.3.7)", "sphinx-design (>=0.4.0)"]
 test = ["Cython", "array-api-strict (>=2.0)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
-[[package]]
-name = "seaborn"
-version = "0.13.2"
-description = "Statistical data visualization"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "seaborn-0.13.2-py3-none-any.whl", hash = "sha256:636f8336facf092165e27924f223d3c62ca560b1f2bb5dff7ab7fad265361987"},
-    {file = "seaborn-0.13.2.tar.gz", hash = "sha256:93e60a40988f4d65e9f4885df477e2fdaff6b73a9ded434c1ab356dd57eefff7"},
-]
-
-[package.dependencies]
-matplotlib = ">=3.4,<3.6.1 || >3.6.1"
-numpy = ">=1.20,<1.24.0 || >1.24.0"
-pandas = ">=1.2"
-
-[package.extras]
-dev = ["flake8", "flit", "mypy", "pandas-stubs", "pre-commit", "pytest", "pytest-cov", "pytest-xdist"]
-docs = ["ipykernel", "nbconvert", "numpydoc", "pydata_sphinx_theme (==0.10.0rc2)", "pyyaml", "sphinx (<6.0.0)", "sphinx-copybutton", "sphinx-design", "sphinx-issues"]
-stats = ["scipy (>=1.7)", "statsmodels (>=0.12)"]
-
 [[package]]
 name = "send2trash"
 version = "1.8.3"
@@ -2809,57 +2747,6 @@ pure-eval = "*"
 [package.extras]
 tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
-[[package]]
-name = "statsmodels"
-version = "0.14.4"
-description = "Statistical computations and models for Python"
-optional = false
-python-versions = ">=3.9"
-files = [
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-    {file = "statsmodels-0.14.4-cp310-cp310-win_amd64.whl", hash = "sha256:9729642884147ee9db67b5a06a355890663d21f76ed608a56ac2ad98b94d201a"},
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-    {file = "statsmodels-0.14.4.tar.gz", hash = "sha256:5d69e0f39060dc72c067f9bb6e8033b6dccdb0bae101d76a7ef0bcc94e898b67"},
-]
-
-[package.dependencies]
-numpy = ">=1.22.3,<3"
-packaging = ">=21.3"
-pandas = ">=1.4,<2.1.0 || >2.1.0"
-patsy = ">=0.5.6"
-scipy = ">=1.8,<1.9.2 || >1.9.2"
-
-[package.extras]
-build = ["cython (>=3.0.10)"]
-develop = ["colorama", "cython (>=3.0.10)", "cython (>=3.0.10,<4)", "flake8", "isort", "joblib", "matplotlib (>=3)", "pytest (>=7.3.0,<8)", "pytest-cov", "pytest-randomly", "pytest-xdist", "pywinpty", "setuptools-scm[toml] (>=8.0,<9.0)"]
-docs = ["ipykernel", "jupyter-client", "matplotlib", "nbconvert", "nbformat", "numpydoc", "pandas-datareader", "sphinx"]
-
 [[package]]
 name = "terminado"
 version = "0.18.1"
@@ -3065,4 +2952,4 @@ files = [
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.12"
-content-hash = "f75f65af68bf9b9ae845851b540a182e59667b51060659ab9f4a28bb09ff9880"
+content-hash = "ee2a6fb525a389426dad97d7f57b9ed7a8187d5892d01cc39a54830695f83a95"

pyproject.toml

@@ -15,9 +15,6 @@ matplotlib = "^3.9.2"
 scikit-learn = "^1.5.2"
 scikit-fuzzy = "^0.5.0"
 networkx = "^3.4.2"
-imbalanced-learn = "^0.12.3"
-seaborn = "^0.13.2"
-statsmodels = "^0.14.4"
 
 [build-system]
 requires = ["poetry-core"]

src/utils.py

@@ -1,137 +0,0 @@
-import math
-from typing import Dict, Tuple
-
-import numpy as np
-from pandas import DataFrame
-from sklearn import metrics
-from sklearn.model_selection import train_test_split
-from sklearn.pipeline import Pipeline
-
-
-def split_stratified_into_train_val_test(
-    df_input,
-    stratify_colname="y",
-    frac_train=0.6,
-    frac_val=0.15,
-    frac_test=0.25,
-    random_state=None,
-) -> Tuple:
-    """
-    Splits a Pandas dataframe into three subsets (train, val, and test)
-    following fractional ratios provided by the user, where each subset is
-    stratified by the values in a specific column (that is, each subset has
-    the same relative frequency of the values in the column). It performs this
-    splitting by running train_test_split() twice.
-
-    Parameters
-    ----------
-    df_input : Pandas dataframe
-        Input dataframe to be split.
-    stratify_colname : str
-        The name of the column that will be used for stratification. Usually
-        this column would be for the label.
-    frac_train : float
-    frac_val   : float
-    frac_test  : float
-        The ratios with which the dataframe will be split into train, val, and
-        test data. The values should be expressed as float fractions and should
-        sum to 1.0.
-    random_state : int, None, or RandomStateInstance
-        Value to be passed to train_test_split().
-
-    Returns
-    -------
-    df_train, df_val, df_test :
-        Dataframes containing the three splits.
-    """
-
-    if frac_train + frac_val + frac_test != 1.0:
-        raise ValueError(
-            "fractions %f, %f, %f do not add up to 1.0"
-            % (frac_train, frac_val, frac_test)
-        )
-
-    if stratify_colname not in df_input.columns:
-        raise ValueError("%s is not a column in the dataframe" % (stratify_colname))
-
-    # Contains all columns.
-    X = df_input.drop([stratify_colname], axis=1)
-    # Dataframe of just the column on which to stratify.
-    y = df_input[[stratify_colname]]
-
-    # Split original dataframe into train and temp dataframes.
-    df_train, df_temp, y_train, y_temp = train_test_split(
-        X, y, test_size=(1.0 - frac_train), random_state=random_state
-    )
-
-    if frac_val <= 0:
-        assert len(df_input) == len(df_train) + len(df_temp)
-        return df_train, df_temp, y_train, y_temp
-
-    # Split the temp dataframe into val and test dataframes.
-    relative_frac_test = frac_test / (frac_val + frac_test)
-    df_val, df_test, y_val, y_test = train_test_split(
-        df_temp,
-        y_temp,
-        stratify=y_temp,
-        test_size=relative_frac_test,
-        random_state=random_state,
-    )
-
-    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)
-    return df_train, df_val, df_test, y_train, y_val, y_test
-
-
-def run_classification(
-    model: Pipeline,
-    X_train: DataFrame,
-    X_test: DataFrame,
-    y_train: DataFrame,
-    y_test: DataFrame,
-) -> Dict:
-    result = {}
-    y_train_predict = model.predict(X_train)
-    y_test_probs = model.predict_proba(X_test)[:, 0]
-    y_test_predict = np.where(y_test_probs > 0.5, 1, 0)
-
-    result["pipeline"] = model
-    result["probs"] = y_test_probs
-    result["preds"] = y_test_predict
-
-    result["Precision_train"] = metrics.precision_score(y_train, y_train_predict)
-    result["Precision_test"] = metrics.precision_score(y_test, y_test_predict)
-    result["Recall_train"] = metrics.recall_score(y_train, y_train_predict)
-    result["Recall_test"] = metrics.recall_score(y_test, y_test_predict)
-    result["Accuracy_train"] = metrics.accuracy_score(y_train, y_train_predict)
-    result["Accuracy_test"] = metrics.accuracy_score(y_test, y_test_predict)
-    result["ROC_AUC_test"] = metrics.roc_auc_score(y_test, y_test_probs)
-    result["F1_train"] = metrics.f1_score(y_train, y_train_predict)
-    result["F1_test"] = metrics.f1_score(y_test, y_test_predict)
-    result["MCC_test"] = metrics.matthews_corrcoef(y_test, y_test_predict)
-    result["Cohen_kappa_test"] = metrics.cohen_kappa_score(y_test, y_test_predict)
-    result["Confusion_matrix"] = metrics.confusion_matrix(y_test, y_test_predict)
-
-    return result
-
-
-def run_regression(
-    model: Pipeline,
-    X_train: DataFrame,
-    X_test: DataFrame,
-    y_train: DataFrame,
-    y_test: DataFrame,
-) -> Dict:
-    result = {}
-    y_train_pred = model.predict(X_train.values)
-    y_test_pred = model.predict(X_test.values)
-
-    result["fitted"] = model
-    result["train_preds"] = y_train_pred
-    result["preds"] = y_test_pred
-
-    result["RMSE_train"] = math.sqrt(metrics.mean_squared_error(y_train, y_train_pred))
-    result["RMSE_test"] = math.sqrt(metrics.mean_squared_error(y_test, y_test_pred))
-    result["RMAE_test"] = math.sqrt(metrics.mean_absolute_error(y_test, y_test_pred))
-    result["R2_test"] = metrics.r2_score(y_test, y_test_pred)
-
-    return result