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Imbalance_Coefficient/imb_coef_demo.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Imbalance Coefficient Demo\n",
+    "\n",
+    "This notebook demonstrates how to use the `imb_coef` function from the `imbalance_coefficient.py` module."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import the metric (assumes the imbalance_metric.py is in the same directory)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from imbalance_coefficient import imb_coef\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Create an imbalanced dataset (continuous target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "np.random.seed(42)\n",
+    "y = np.concatenate([\n",
+    "    np.random.normal(0, 1, 800),  # frequent values\n",
+    "    np.random.normal(5, 1, 200)   # rare values\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute and visualize imbalance metric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Imbalance ratio: 38.18%\n"
+     ]
+    }
+   ],
+   "source": [
+    "imb_ratio = imb_coef(y, disttype='cont', plot=True)\n",
+    "print(f\"Imbalance ratio: {imb_ratio:.2f}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Imbalance_Coefficient/imbalance_coefficient.R

@@ -0,0 +1,97 @@
+library("ggplot2")
+library("extraDistr")
+library("pracma")
+rep = "Your repository for saving plots"
+
+imb_coef = function(y, probfunc = 'density',bdw='SJ', n_map = 100000, distfunc = 'pdf', disttype = 'cont',plot=F, p=1,k=1, w = NULL,scale=T, quad=F, save=F){
+  if (disttype == 'cont'){
+    if (scale==T) {y=(y-min(y))/(max(y)-min(y))}
+    min_y=min(y)
+    max_y = max(y)
+    map = seq(min_y, max_y,length.out=n_map)
+    if (is.null(w)){
+      w = rep(1,n_map)
+    }
+    else{
+      w = approx(y,w,xout=map, yleft = min(w[y==min_y]), yright = min(w[y==max_y]))$y
+    }
+    if (distfunc == 'pdf'){
+      kde_y = density(y, from=min_y, to=max_y, bw=bdw,n=n_map)
+      y_map = kde_y$x
+      kde_map = kde_y$y
+      kde_map[is.na(kde_map)]=0
+      
+      d_best = dunif(map,min_y,max_y)
+      kde_x = function(x){
+        return(approx(kde_y$x,kde_map,xout=x)$y)
+      }
+      weight = function(x){
+        return(approx(map,w,xout=x)$y)
+      }
+      if (is.null(w)){
+        imb_ratio = round(quad(function(x) pmax(0,1-kde_x(x)), min_y, max_y,tol=1e-5),4)*100
+      } 
+      else{
+        imb_ratio = round(quad(function(x) pmax(0,1-kde_x(x))*weight(x), min_y, max_y,tol=1e-5)/quad(function(x) weight(x), min_y, max_y,tol=1e-5),4)*100
+      }
+      if (plot == TRUE) {
+        df_dens <- data.frame(map = map, kde_map = kde_map, d_best = d_best)
+        df_hist <- data.frame(y = y)
+        print(
+          ggplot() +
+            geom_histogram(data = df_hist, aes(x = y, y = ..density..),
+                           bins = 100, fill = "gray", alpha = 0.8, col = "darkblue") +
+            geom_line(data = df_dens, aes(x = map, y = kde_map), color = "darkred", linewidth = 1) +
+            geom_line(data = df_dens, aes(x = map, y = d_best), color = "darkgreen", linewidth = 1) +
+            ggtitle(paste(imb_ratio, "%")) +
+            labs(x = "map", y = "Value") +
+            theme(plot.title = element_text(color = "darkred", size = 20, face = "bold"))
+        )
+        if (exists("save") && save == TRUE) {
+          ggsave(paste0(rep, "imbMtric_dens_", k, ".png"), width = 7.29, height = 4.5)
+        }
+      }
+      return(imb_ratio)
+    }
+  }
+  else if (disttype == 'dis'){
+    min_y=min(y)
+    max_y = max(y)
+    map = seq(min_y,max_y,1)
+    if (is.null(w)){
+      w = rep(1,max_y-min_y+1)
+    }
+    else{
+      w=data.frame(map=y,w1=w)
+      w = aggregate(w1~map,w,mean)
+      temp = data.frame(map=seq(min_y,max_y),w0=rep(0,max_y-min_y+1))
+      temp = merge(temp,w,by="map",all.x = T)
+      temp[is.na(temp$w1),'w1']=0
+      temp['w']=temp['w0']+temp['w1']
+      w = aggregate(w~map,temp, mean)$w
+    }
+    if (distfunc == 'pdf'){
+      kde_map = data.frame(prop.table(table(y)))
+      prop0 = data.frame(y=map, freq0=rep(0,length(map)))
+      kde_map = merge(prop0, kde_map,all.x=T)
+      kde_map[is.na(kde_map$Freq),'Freq']=0
+      kde_map$Freq = kde_map$Freq + kde_map$freq0 
+      kde_map$freq0=NULL
+      d_best = ddunif(map,min_y,max_y)
+      if (plot==T){
+        df <- data.frame(map = map, freq = c(data.frame(kde_map)$Freq, d_best), dist=rep(c("Emp", "Uni"), each = length(map)))
+        error = abs(kde_map$Freq - d_best)^p * (kde_map$Freq < d_best) * w 
+        imb_ratio <- round(sum(error[w>0])/sum(d_best*w),4)*100
+        if (is.na(imb_ratio)){imb_ratio=100}
+        print(ggplot(df, aes(x = factor(map), y = freq, fill = dist)) +
+                geom_bar(stat = "identity", position = "identity", alpha = 0.5) +
+                scale_fill_manual(values = c("darkred", "darkgreen"))+ 
+                ggtitle(paste(imb_ratio, "%")) +   
+                labs(x = "y", y = "Fréquence", fill = "Type") + 
+                theme(plot.title = element_text(color="darkred", size=20, face = "bold")))
+        if (save==T) {ggsave(paste0(rep,"imbMtric_mass_",k,".png"),width=7.29, height=4.5)}
+      }
+      return(imb_ratio)
+    }
+  }
+}

Imbalance_Coefficient/imbalance_coefficient.py

@@ -0,0 +1,158 @@
+"""
+imbalance_coefficient.py
+
+Provides a function `imb_coef` to quantify imbalance in regression targets
+for both continuous and discrete settings. It estimates the deviation of the
+empirical distribution from the uniform distribution using KDE or frequency analysis.
+
+Author: Samuel Stocksieker
+License: MIT or CC-BY-4.0
+Date: 2025-08-06
+"""
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from scipy.stats import gaussian_kde, uniform
+from scipy.integrate import quad
+
+
+def imb_coef(
+    y,
+    bdw='scott',
+    n_map=100_000,
+    distfunc='pdf',
+    disttype='cont',  # 'cont' for continuous, 'dis' for discrete
+    plot=False,
+    p=1,
+    k=1,
+    w=None,
+    scale=True,
+    save=False,
+    rep='',
+):
+    """
+    Computes an imbalance coefficient for a target variable in regression tasks.
+
+    Parameters:
+    ----------
+    y : array-like
+        Target variable (continuous or discrete).
+    bdw : str or float
+        Bandwidth for KDE ('scott' or float).
+    n_map : int
+        Number of points for KDE evaluation.
+    distfunc : str
+        Not used currently (placeholder).
+    disttype : str
+        'cont' for continuous, 'dis' for discrete targets.
+    plot : bool
+        Whether to plot distribution comparison.
+    p : int
+        Exponent for penalizing deviations in discrete mode.
+    k : int
+        Index for saving figures (used in filenames).
+    w : array-like or None
+        Optional weights per observation.
+    scale : bool
+        Whether to scale `y` to [0, 1] in continuous mode.
+    save : bool
+        Whether to save plot as PNG.
+    rep : str
+        Folder path prefix for saving plots.
+
+    Returns:
+    -------
+    imb_ratio : float
+        imbalance coefficient in percentage.
+    """
+
+    y = np.array(y)
+
+    if disttype == 'cont':
+        # Continuous target
+        if scale:
+            y = (y - y.min()) / (y.max() - y.min())
+
+        min_y, max_y = y.min(), y.max()
+        map_vals = np.linspace(min_y, max_y, n_map)
+
+        # Weight setup
+        weights = (
+            np.ones(n_map) if w is None else np.interp(map_vals, y, w,
+                                                       left=min(w[y == min_y]),
+                                                       right=min(w[y == max_y]))
+        )
+
+        kde = gaussian_kde(y, bw_method=bdw)
+        kde_vals = kde(map_vals)
+        d_best = uniform.pdf(map_vals, loc=min_y, scale=max_y - min_y)
+
+        kde_func = lambda x: np.interp(x, map_vals, kde_vals)
+        weight_func = lambda x: np.interp(x, map_vals, weights)
+
+        if w is None:
+            integrand = lambda x: max(0, 1 - kde_func(x))
+            imb_ratio = round(quad(integrand, min_y, max_y, epsabs=1e-5)[0], 4) * 100
+        else:
+            num = quad(lambda x: max(0, 1 - kde_func(x)) * weight_func(x), min_y, max_y, epsabs=1e-5)[0]
+            den = quad(lambda x: weight_func(x), min_y, max_y, epsabs=1e-5)[0]
+            imb_ratio = round(num / den, 4) * 100
+
+        if plot:
+            plt.figure(figsize=(10, 5))
+            plt.hist(y, bins=100, density=True, color='gray', alpha=0.6, label='Histogram')
+            plt.plot(map_vals, kde_vals, label='KDE', color='darkred')
+            plt.plot(map_vals, d_best, label='Uniform', color='darkgreen')
+            plt.title(f"{imb_ratio:.2f}%", fontsize=16, color='darkred')
+            plt.xlabel("Target values")
+            plt.ylabel("Density")
+            plt.legend()
+            if save:
+                plt.savefig(f"{rep}imbMetric_dens_{k}.png", bbox_inches='tight')
+            plt.show()
+
+        return imb_ratio
+
+    elif disttype == 'dis':
+        # Discrete target
+        y = y.astype(int)
+        map_vals = np.arange(y.min(), y.max() + 1)
+
+        if w is None:
+            weights = np.ones_like(map_vals)
+        else:
+            df_w = pd.DataFrame({'map': y, 'w1': w})
+            w_agg = df_w.groupby('map')['w1'].mean().reset_index()
+            w_all = pd.DataFrame({'map': map_vals})
+            w_all = w_all.merge(w_agg, on='map', how='left').fillna(0)
+            weights = w_all['w1'].values
+
+        freqs = pd.Series(y).value_counts(normalize=True).reindex(map_vals, fill_value=0).values
+        d_best = np.ones_like(map_vals) / len(map_vals)
+
+        error = np.abs(freqs - d_best) ** p * (freqs < d_best) * weights
+        imb_ratio = round(np.sum(error[weights > 0]) / np.sum(d_best * weights), 4) * 100
+        if np.isnan(imb_ratio):
+            imb_ratio = 100
+
+        if plot:
+            df_plot = pd.DataFrame({
+                'map': list(map_vals) * 2,
+                'freq': list(freqs) + list(d_best),
+                'dist': ['Emp'] * len(map_vals) + ['Uni'] * len(map_vals)
+            })
+            plt.figure(figsize=(10, 5))
+            for label, group in df_plot.groupby('dist'):
+                plt.bar(group['map'], group['freq'], alpha=0.5, label=label)
+            plt.title(f"{imb_ratio:.2f}%", fontsize=16, color='darkred')
+            plt.xlabel("Target values")
+            plt.ylabel("Frequency")
+            plt.legend()
+            if save:
+                plt.savefig(f"{rep}imbMetric_mass_{k}.png", bbox_inches='tight')
+            plt.show()
+
+        return imb_ratio
+
+    return None