Dev

fgclustering/_version.py

@@ -0,0 +1,16 @@
+# file generated by setuptools_scm
+# don't change, don't track in version control
+TYPE_CHECKING = False
+if TYPE_CHECKING:
+    from typing import Tuple, Union
+    VERSION_TUPLE = Tuple[Union[int, str], ...]
+else:
+    VERSION_TUPLE = object
+
+version: str
+__version__: str
+__version_tuple__: VERSION_TUPLE
+version_tuple: VERSION_TUPLE
+
+__version__ = version = '1.0.4.dev21+gbe0cbb8.d20240902'
+__version_tuple__ = version_tuple = (1, 0, 4, 'dev21', 'gbe0cbb8.d20240902')

fgclustering/forest_guided_clustering.py

@@ -2,12 +2,16 @@
 # imports
 ############################################
 
+import numpy as np
 import pandas as pd
 import kmedoids
 import fgclustering.utils as utils
 import fgclustering.optimizer as optimizer
 import fgclustering.plotting as plotting
 import fgclustering.statistics as stats
+from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
+
+from typing import Union
 
 import warnings
 
@@ -40,7 +44,13 @@ class FgClustering:
                         or `sklearn.ensemble.RandomForestClassifier`.
     """
 
-    def __init__(self, model, data, target_column, random_state=42):
+    def __init__(
+        self,
+        model: Union[RandomForestClassifier, RandomForestRegressor],
+        data: pd.DataFrame,
+        target_column: Union[str, np.ndarray, pd.Series],
+        random_state: int = 42,
+    ):
         self.random_state = random_state
 
         # check if random forest is regressor or classifier
@@ -72,16 +82,16 @@ def __init__(self, model, data, target_column, random_state=42):
 
     def run(
         self,
-        number_of_clusters=None,
-        max_K=8,
-        method_clustering="pam",
-        init_clustering="random",
-        max_iter_clustering=100,
-        discart_value_JI=0.6,
-        bootstraps_JI=100,
-        bootstraps_p_value=100,
-        n_jobs=1,
-        verbose=1,
+        number_of_clusters: int = None,
+        max_K: int = 8,
+        method_clustering: str = "pam",
+        init_clustering: str = "random",
+        max_iter_clustering: int = 100,
+        discart_value_JI: float = 0.6,
+        bootstraps_JI: int = 100,
+        bootstraps_p_value: int = 100,
+        n_jobs: int = 1,
+        verbose: int = 1,
     ):
         """Runs the forest-guided clustering model. The optimal number of clusters for a k-medoids clustering is computed,
         based on the distance matrix computed from the Random Forest proximity matrix.
@@ -228,63 +238,56 @@ def calculate_statistics(self, data, target_column, bootstraps_p_value=100):
             data_clustering_ranked=self.data_clustering_ranked, bootstraps_p_value=bootstraps_p_value
         )
 
-    def plot_global_feature_importance(self, thr_pvalue=1, top_n=None, save=None):
-        """
-        Plots global feature importance based on p-values. The p-values are computed using ANOVA (for continuous variables)
-        or Chi-Square (for categorical variables) tests. Feature importance is defined as 1 minus the p-value.
-
-        :param thr_pvalue: Threshold p-value for filtering features. Features with p-values below this threshold are considered
-                        significant for plotting. Defaults to 1 (no filtering).
-        :type thr_pvalue: float, optional
-        :param top_n: Number of top features to display in the plot. If None, all significant features are displayed. Defaults
-                    to None.
-        :type top_n: int, optional
-        :param save: Filename to save the plot. If None, the plot is not saved. Defaults to None.
-        :type save: str, optional
+    def plot_feature_importance(
+        self, thr_pvalue: float = 1, top_n: int = None, num_cols: int = 4, save: str = None
+    ):
         """
-        # drop insignificant features
-        selected_features = self.p_value_of_features_ranked.loc["p_value"] < thr_pvalue
-        selected_features = self.p_value_of_features_ranked.columns[selected_features].tolist()
-
-        # select top n features for plotting
-        if top_n:
-            selected_features = selected_features[:top_n]
+        Plot feature importance based on p-values for global and local feature importance.
+        For the global feature importance, p-values are computed using ANOVA (for continuous variables)
+        or Chi-Square (for categorical variables) tests. The local feature importance, p-values are measured by the
+        variance and impurity of the feature within the cluster, i.e. a smaller p-value indicates lower variance/impurity.
+        Feature importance is defined as log transformation of the p-value with a small offset.
 
-        plotting._plot_global_feature_importance(
-            self.p_value_of_features_ranked[selected_features], top_n, save
-        )
+        $transformed_value=-log10(p-value + \epsilon) / -log10(\epsilon)$
 
-    def plot_local_feature_importance(self, thr_pvalue=1, top_n=None, num_cols=4, save=None):
-        """
-        Plot local feature importance to display the importance of each feature within each cluster.
-        Importance is measured by the variance and impurity of the feature within the cluster;
-        a higher feature importance indicates lower variance/impurity.
+        where $\epsilon$ is a small positive constant (1e-50) that avoids issues with log10(0), but also significant distortion
+        because $\epsilon$ is very small.
+        Displays both global and local importance for top n selected features.
 
-        :param thr_pvalue: P-value threshold for filtering features. Only features with p-values below this threshold
-                        are considered significant and plotted. Defaults to 1 (no filtering).
+        :param thr_pvalue: P-value threshold for display. Only features with p-values below this threshold
+                        are considered significant. Defaults to 1 (no filtering).
         :type thr_pvalue: float, optional
-        :param top_n: Number of top features to display in the plot. If None, all significant features are included.
+        :param top_n: Number of top features to display in the plot. If None, all features are included.
                     Defaults to None.
         :type top_n: int, optional
         :param num_cols: Number of plots per row in the output figure. Defaults to 4.
         :type num_cols: int, optional
         :param save: Filename to save the plot. If None, the plot will not be saved. Defaults to None.
         :type save: str, optional
         """
-        # drop insignificant features
-        selected_features = self.p_value_of_features_ranked.loc["p_value"] < thr_pvalue
-        selected_features = self.p_value_of_features_ranked.columns[selected_features].tolist()
 
         # select top n features for plotting
+        selected_features = self.p_value_of_features_ranked.columns.tolist()
         if top_n:
             selected_features = selected_features[:top_n]
 
-        plotting._plot_local_feature_importance(
-            self.p_value_of_features_per_cluster.loc[selected_features], thr_pvalue, top_n, num_cols, save
+        plotting._plot_feature_importance(
+            self.p_value_of_features_ranked[selected_features],
+            self.p_value_of_features_per_cluster.loc[selected_features],
+            thr_pvalue,
+            top_n,
+            num_cols,
+            save,
         )
 
     def plot_decision_paths(
-        self, distributions=True, heatmap=True, thr_pvalue=1, top_n=None, num_cols=6, save=None
+        self,
+        distributions: bool = True,
+        heatmap: bool = True,
+        thr_pvalue: float = 1,
+        top_n: int = None,
+        num_cols: int = 6,
+        save: str = None,
     ):
         """
         Plot decision paths of the Random Forest model. This function generates visualizations
@@ -322,12 +325,12 @@ def plot_decision_paths(
 
         selected_features = ["cluster", "target"] + selected_features
 
-        if heatmap:
-            plotting._plot_heatmap(
-                self.data_clustering_ranked[selected_features], thr_pvalue, top_n, self.model_type, save
-            )
-
         if distributions:
             plotting._plot_distributions(
                 self.data_clustering_ranked[selected_features], thr_pvalue, top_n, num_cols, save
             )
+
+        if heatmap:
+            plotting._plot_heatmap(
+                self.data_clustering_ranked[selected_features], thr_pvalue, top_n, self.model_type, save
+            )

fgclustering/plotting.py

@@ -17,87 +17,108 @@
 ############################################
 
 
-def _plot_global_feature_importance(p_value_of_features_ranked, top_n, save):
-    """Plot global feature importance based on p-values given as input.
-
-    :param p_value_of_features: dictionary where keys are names of features and values are p-values of these features
-    :type p_value_of_features: dict
-    :param save: Filename to save plot.
-    :type save: str
+def log_transform(p_values: list, epsilon: float = 1e-50):
     """
-    # Transform the DataFrame and calculate feature importance
-    importance = pd.melt(p_value_of_features_ranked, var_name="Feature", value_name="p_value")
-    importance["Importance"] = 1 - importance["p_value"]
-    importance.sort_values(by="Importance", ascending=False, inplace=True)
-
-    # Determine figure size dynamically based on the number of features
-    n_features = len(importance)
-    figure_height = max(6.5, int(np.ceil(5 * n_features / 25)))
-
-    # Plotting
-    plt.figure(figsize=(6.5, figure_height))
-    sns.set_theme(style="whitegrid")
-    sns.barplot(data=importance, x="Importance", y="Feature", color="#3470a3")
-
-    plt.title(f"Global Feature Importance for {'top ' + str(top_n) if top_n else 'all'} features")
-    plt.tight_layout()
-
-    # Save plot if a filename is provided
-    if save:
-        plt.savefig(f"{save}_global_feature_importance.png", bbox_inches="tight", dpi=300)
-
-    plt.show()
-
-
-def _plot_local_feature_importance(p_value_of_features_per_cluster, thr_pvalue, top_n, num_cols, save):
-    """Plot local feature importance to show the importance of each feature for each cluster.
+    Apply a log transformation to p-values to enhance numerical stability and highlight differences.
+    Adds a small constant `epsilon` to avoid taking the log of zero and normalizes by dividing by
+    the log of `epsilon`.
+
+    :param p_values: List of p-values to be transformed.
+    :type p_values: list
+    :param epsilon: Small constant added to p-values to avoid log of zero. Defaults to 1e-50.
+    :type epsilon: float, optional
+    :return: Transformed p-values after log transformation.
+    :rtype: numpy.ndarray
+    """
+    # add a small constant epsilon
+    p_values = np.clip(p_values, epsilon, 1)
+    return -np.log(p_values) / -np.log(epsilon)
+
+
+def _plot_feature_importance(
+    p_value_of_features_ranked: pd.DataFrame,
+    p_value_of_features_per_cluster: pd.DataFrame,
+    thr_pvalue: float,
+    top_n: int,
+    num_cols: int,
+    save: str,
+):
+    """
+    Generate and display a plot showing the importance of features based on p-values.
+    The plot includes both global feature importance and local feature importance for each cluster.
+    Global importance is based on all clusters combined, while local importance is specific to each cluster.
 
-    :param p_value_of_features_per_cluster: p-value matrix of all features per cluster.
+    :param p_value_of_features_ranked: DataFrame containing p-values of features, ranked by p-value.
+    :type p_value_of_features_ranked: pandas.DataFrame
+    :param p_value_of_features_per_cluster: DataFrame containing p-values of features for each cluster.
     :type p_value_of_features_per_cluster: pandas.DataFrame
-    :param thr_pvalue: P-value threshold used for feature filtering
+    :param thr_pvalue: P-value threshold for display. Only features with p-values below this threshold
+                    are considered significant. Defaults to 1 (no filtering).
     :type thr_pvalue: float, optional
-    :param num_cols: Number of plots in one row.
-    :type num_cols: int
-    :param save: Filename to save plot.
-    :type save: str
+    :param top_n: Number of top features to display in the plot. If None, all features are included.
+                Defaults to None.
+    :type top_n: int, optional
+    :param num_cols: Number of plots per row in the output figure. Defaults to 4.
+    :type num_cols: int, optional
+    :param save: Filename to save the plot. If None, the plot will not be saved. Defaults to None.
+    :type save: str, optional
     """
 
-    importance = 1 - p_value_of_features_per_cluster
-
-    # Reshape and sort the data
-    X_barplot = (
-        importance.melt(ignore_index=False, var_name="Cluster", value_name="Importance")
-        .rename_axis("Feature")
-        .reset_index(level=0, inplace=False)
-        .sort_values("Importance", ascending=False)
-    )
+    # Determine figure size dynamically based on the number of features
+    num_features = len(p_value_of_features_ranked.columns)
+    figsize_width = 6.5
+    figsize_height = max(figsize_width, int(np.ceil(5 * num_features / 25)))
 
-    # Determine figure size based on the number of features and clusters
-    n_features = len(X_barplot["Feature"].unique())
-    figure_height = max(6.5, int(np.ceil(5 * n_features / 25)))
-    num_cols = min(num_cols, len(importance.columns))
+    num_subplots = 1 + p_value_of_features_per_cluster.shape[1]
+    num_cols = min(num_cols, num_subplots)
+    num_rows = int(np.ceil(num_subplots / num_cols))
 
-    # Set up the Seaborn theme and create the FacetGrid
-    sns.set_theme(style="whitegrid")
-    g = sns.FacetGrid(X_barplot, col="Cluster", sharey=False, col_wrap=num_cols, height=figure_height)
-    g.map(sns.barplot, "Importance", "Feature", order=None, color="#3470a3")
-
-    # Label the axes and set the plot titles
-    g.set_titles(col_template="Cluster {col_name}")
+    plt.figure(figsize=(num_cols * figsize_width, num_rows * figsize_height))
+    plt.subplots_adjust(top=0.95, hspace=0.8, wspace=0.8)
     plt.suptitle(
-        f"Local Feature Importance - Showing {'top ' + str(top_n) if top_n else 'all'} features with p-value < {thr_pvalue}",
+        f"Feature Importance - Showing {'top ' + str(top_n) if top_n else 'all'} features",
         fontsize=14,
     )
-    plt.tight_layout(rect=[0, 0, 1, 0.95])
+    sns.set_theme(style="whitegrid")
 
-    # Save the plot if a save path is provided
-    if save:
-        plt.savefig(f"{save}_local_feature_importance.png", bbox_inches="tight", dpi=300)
+    # Plot global feature importance
+    importance_global = pd.DataFrame(
+        {
+            "Feature": p_value_of_features_ranked.columns,
+            "Importance": log_transform(p_value_of_features_ranked.loc["p_value"].to_list()),
+        }
+    ).sort_values(by="Importance", ascending=False)
+
+    ax = plt.subplot(num_rows, num_cols, 1)
+    sns.barplot(data=importance_global, x="Importance", y="Feature", color="#3470a3")
+    ax.axvline(x=log_transform(thr_pvalue), color="red", linestyle="--", label=f"thr p-value = {thr_pvalue}")
+    ax.set_title(f"Cluster all")
+    ax.legend(bbox_to_anchor=(1, 1), loc=2)
+
+    # Plot local feature importance
+    for n, cluster in enumerate(p_value_of_features_per_cluster.columns):
+        importance_local = pd.DataFrame(
+            {
+                "Feature": p_value_of_features_per_cluster.index,
+                "Importance": log_transform(p_value_of_features_per_cluster[cluster].to_list()),
+            }
+        ).sort_values(by="Importance", ascending=False)
+        ax = plt.subplot(num_rows, num_cols, n + 2)
+        sns.barplot(data=importance_local, x="Importance", y="Feature", color="#3470a3")
+        ax.axvline(x=log_transform(thr_pvalue), color="red", linestyle="--")
+        ax.set_title(f"Cluster {cluster}")
+        # ax.legend(bbox_to_anchor=(1, 1), loc=2)
 
+    plt.tight_layout(rect=[0, 0, 1, 0.95])
+
+    if save is not None:
+        plt.savefig(f"{save}_feature_importance.png", bbox_inches="tight", dpi=300)
     plt.show()
 
 
-def _plot_heatmap(data_clustering_ranked, thr_pvalue, top_n, model_type, save):
+def _plot_heatmap(
+    data_clustering_ranked: pd.DataFrame, thr_pvalue: float, top_n: int, model_type: str, save: str
+):
     """Plot feature heatmap sorted by clusters, where features are filtered and ranked
     with statistical tests (ANOVA for continuous featres, chi square for categorical features).
 
@@ -209,7 +230,9 @@ def _plot_heatmap(data_clustering_ranked, thr_pvalue, top_n, model_type, save):
     plt.show()
 
 
-def _plot_distributions(data_clustering_ranked, thr_pvalue, top_n, num_cols, save):
+def _plot_distributions(
+    data_clustering_ranked: pd.DataFrame, thr_pvalue: float, top_n: int, num_cols: int, save: str
+):
     """Plot feature boxplots (for continuous features) or barplots (for categorical features) divided by clusters,
     where features are filtered and ranked by p-value of a statistical test (ANOVA for continuous features,
     chi square for categorical features).

fgclustering/statistics.py

@@ -229,7 +229,6 @@ def calculate_global_feature_importance(X, y, cluster_labels, model_type):
                     f"Feature {feature} has dytpye {data_feature.dtype} but has to be of type category or numeric!"
                 )
 
-    print(p_value_of_features)
     # Convert p-value dictionary to a DataFrame and sort by p-value
     p_value_of_features_ranked = (
         pd.DataFrame.from_dict(p_value_of_features, orient="index", columns=["p_value"])