mcfs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.sparse import *\n",
    "from sklearn.metrics.pairwise import pairwise_distances\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import adjusted_rand_score, confusion_matrix, classification_report, accuracy_score\n",
    "def feature_ranking(W):\n",
    "    \"\"\"\n",
    "    This function computes MCFS score and ranking features according to feature weights matrix W\n",
    "    \"\"\"\n",
    "    mcfs_score = np.abs(W).max(1)\n",
    "    idx = np.argsort(mcfs_score)\n",
    "    idx = idx[::-1]\n",
    "    return idx\n",
    "def construct_W2(X, **kwargs):\n",
    "    # default metric is 'cosine'\n",
    "    if \"metric\" not in kwargs.keys():\n",
    "        kwargs[\"metric\"] = \"cosine\"\n",
    "\n",
    "    # default neighbor mode is 'knn' and default neighbor size is 5\n",
    "    if \"neighbor_mode\" not in kwargs.keys():\n",
    "        kwargs[\"neighbor_mode\"] = \"knn\"\n",
    "    if kwargs[\"neighbor_mode\"] == \"knn\" and \"k\" not in kwargs.keys():\n",
    "        kwargs[\"k\"] = 5\n",
    "    if kwargs[\"neighbor_mode\"] == \"supervised\" and \"k\" not in kwargs.keys():\n",
    "        kwargs[\"k\"] = 5\n",
    "    if kwargs[\"neighbor_mode\"] == \"supervised\" and \"y\" not in kwargs.keys():\n",
    "        print(\"Warning: label is required in the supervised neighborMode!!!\")\n",
    "        exit(0)\n",
    "\n",
    "    # default weight mode is 'binary', default t in heat kernel mode is 1\n",
    "    if \"weight_mode\" not in kwargs.keys():\n",
    "        kwargs[\"weight_mode\"] = \"binary\"\n",
    "    if kwargs[\"weight_mode\"] == \"heat_kernel\":\n",
    "        if kwargs[\"metric\"] != \"euclidean\":\n",
    "            kwargs[\"metric\"] = \"euclidean\"\n",
    "        if \"t\" not in kwargs.keys():\n",
    "            kwargs[\"t\"] = 1\n",
    "    elif kwargs[\"weight_mode\"] == \"cosine\":\n",
    "        if kwargs[\"metric\"] != \"cosine\":\n",
    "            kwargs[\"metric\"] = \"cosine\"\n",
    "\n",
    "    # default fisher_score and reliefF mode are 'false'\n",
    "    if \"fisher_score\" not in kwargs.keys():\n",
    "        kwargs[\"fisher_score\"] = False\n",
    "    if \"reliefF\" not in kwargs.keys():\n",
    "        kwargs[\"reliefF\"] = False\n",
    "\n",
    "    n_samples, n_features = np.shape(X)\n",
    "\n",
    "    # choose 'knn' neighbor mode\n",
    "    if kwargs[\"neighbor_mode\"] == \"knn\":\n",
    "        k = kwargs[\"k\"]\n",
    "        if kwargs[\"weight_mode\"] == \"binary\":\n",
    "            if kwargs[\"metric\"] == \"euclidean\":\n",
    "                # compute pairwise euclidean distances\n",
    "                D = pairwise_distances(X)\n",
    "                D **= 2\n",
    "                # sort the distance matrix D in ascending order\n",
    "                dump = np.sort(D, axis=1)\n",
    "                idx = np.argsort(D, axis=1)\n",
    "                # choose the k-nearest neighbors for each instance\n",
    "                idx_new = idx[:, 0 : k + 1]\n",
    "                G = np.zeros((n_samples * (k + 1), 3))\n",
    "                G[:, 0] = np.tile(np.arange(n_samples), (k + 1, 1)).reshape(-1)\n",
    "                G[:, 1] = np.ravel(idx_new, order=\"F\")\n",
    "                G[:, 2] = 1\n",
    "                # build the sparse affinity matrix W\n",
    "                W = csc_matrix((G[:, 2], (G[:, 0], G[:, 1])), shape=(n_samples, n_samples))\n",
    "                bigger = np.transpose(W) > W\n",
    "                W = W - W.multiply(bigger) + np.transpose(W).multiply(bigger)\n",
    "                return W\n",
    "\n",
    "            elif kwargs[\"metric\"] == \"cosine\":\n",
    "                # normalize the data first\n",
    "                X_normalized = np.power(np.sum(X * X, axis=1), 0.5)\n",
    "                for i in range(n_samples):\n",
    "                    X[i, :] = X[i, :] / max(1e-12, X_normalized[i])\n",
    "                # compute pairwise cosine distances\n",
    "                D_cosine = np.dot(X, np.transpose(X))\n",
    "                # sort the distance matrix D in descending order\n",
    "                dump = np.sort(-D_cosine, axis=1)\n",
    "                idx = np.argsort(-D_cosine, axis=1)\n",
    "                idx_new = idx[:, 0 : k + 1]\n",
    "                G = np.zeros((n_samples * (k + 1), 3))\n",
    "                G[:, 0] = np.tile(np.arange(n_samples), (k + 1, 1)).reshape(-1)\n",
    "                G[:, 1] = np.ravel(idx_new, order=\"F\")\n",
    "                G[:, 2] = 1\n",
    "                # build the sparse affinity matrix W\n",
    "                W = csc_matrix((G[:, 2], (G[:, 0], G[:, 1])), shape=(n_samples, n_samples))\n",
    "                bigger = np.transpose(W) > W\n",
    "                W = W - W.multiply(bigger) + np.transpose(W).multiply(bigger)\n",
    "                return W\n",
    "\n",
    "        elif kwargs[\"weight_mode\"] == \"heat_kernel\":\n",
    "            t = kwargs[\"t\"]\n",
    "            # compute pairwise euclidean distances\n",
    "            D = pairwise_distances(X)\n",
    "            D **= 2\n",
    "            # sort the distance matrix D in ascending order\n",
    "            dump = np.sort(D, axis=1)\n",
    "            idx = np.argsort(D, axis=1)\n",
    "            idx_new = idx[:, 0 : k + 1]\n",
    "            dump_new = dump[:, 0 : k + 1]\n",
    "            # compute the pairwise heat kernel distances\n",
    "            dump_heat_kernel = np.exp(-dump_new / (2 * t * t))\n",
    "            G = np.zeros((n_samples * (k + 1), 3))\n",
    "            G[:, 0] = np.tile(np.arange(n_samples), (k + 1, 1)).reshape(-1)\n",
    "            G[:, 1] = np.ravel(idx_new, order=\"F\")\n",
    "            G[:, 2] = np.ravel(dump_heat_kernel, order=\"F\")\n",
    "            # build the sparse affinity matrix W\n",
    "            W = csc_matrix((G[:, 2], (G[:, 0], G[:, 1])), shape=(n_samples, n_samples))\n",
    "            bigger = np.transpose(W) > W\n",
    "            W = W - W.multiply(bigger) + np.transpose(W).multiply(bigger)\n",
    "            return W\n",
    "\n",
    "        elif kwargs[\"weight_mode\"] == \"cosine\":\n",
    "            # normalize the data first\n",
    "            X_normalized = np.power(np.sum(X * X, axis=1), 0.5)\n",
    "            for i in range(n_samples):\n",
    "                X[i, :] = X[i, :] / max(1e-12, X_normalized[i])\n",
    "            # compute pairwise cosine distances\n",
    "            D_cosine = np.dot(X, np.transpose(X))\n",
    "            # sort the distance matrix D in ascending order\n",
    "            dump = np.sort(-D_cosine, axis=1)\n",
    "            idx = np.argsort(-D_cosine, axis=1)\n",
    "            idx_new = idx[:, 0 : k + 1]\n",
    "            dump_new = -dump[:, 0 : k + 1]\n",
    "            print(\"cosine idx idx new\")\n",
    "            print(idx)\n",
    "            print(idx_new)\n",
    "            print(\"dump new\")\n",
    "            print(dump_new)\n",
    "            G = np.zeros((n_samples * (k + 1), 3))\n",
    "            G[:, 0] = np.tile(np.arange(n_samples), (k + 1, 1)).reshape(-1)\n",
    "            G[:, 1] = np.ravel(idx_new, order=\"F\")\n",
    "            G[:, 2] = np.ravel(dump_new, order=\"F\")\n",
    "            # build the sparse affinity matrix W\n",
    "            W = csc_matrix((G[:, 2], (G[:, 0], G[:, 1])), shape=(n_samples, n_samples))\n",
    "            bigger = np.transpose(W) > W\n",
    "            print(W)\n",
    "            W = W - W.multiply(bigger) + np.transpose(W).multiply(bigger)\n",
    "            print(W)\n",
    "            return W\n",
    "\n",
    "    # choose supervised neighborMode\n",
    "    elif kwargs[\"neighbor_mode\"] == \"supervised\":\n",
    "        k = kwargs[\"k\"]\n",
    "        # get true labels and the number of classes\n",
    "        y = kwargs[\"y\"]\n",
    "        label = np.unique(y)\n",
    "        n_classes = np.unique(y).size\n",
    "        # construct the weight matrix W in a fisherScore way, W_ij = 1/n_l if yi = yj = l, otherwise W_ij = 0\n",
    "        if kwargs[\"fisher_score\"] is True:\n",
    "            W = lil_matrix((n_samples, n_samples))\n",
    "            for i in range(n_classes):\n",
    "                class_idx = y == label[i]\n",
    "                class_idx_all = class_idx[:, np.newaxis] & class_idx[np.newaxis, :]\n",
    "                W[class_idx_all] = 1.0 / np.sum(np.sum(class_idx))\n",
    "            return W\n",
    "\n",
    "        # construct the weight matrix W in a reliefF way, NH(x) and NM(x,y) denotes a set of k nearest\n",
    "        # points to x with the same class as x, a different class (the class y), respectively. W_ij = 1 if i = j;\n",
    "        # W_ij = 1/k if x_j \\in NH(x_i); W_ij = -1/(c-1)k if x_j \\in NM(x_i, y)\n",
    "        if kwargs[\"reliefF\"] is True:\n",
    "            # when xj in NH(xi)\n",
    "            G = np.zeros((n_samples * (k + 1), 3))\n",
    "            id_now = 0\n",
    "            for i in range(n_classes):\n",
    "                class_idx = np.column_stack(np.where(y == label[i]))[:, 0]\n",
    "                D = pairwise_distances(X[class_idx, :])\n",
    "                D **= 2\n",
    "                idx = np.argsort(D, axis=1)\n",
    "                idx_new = idx[:, 0 : k + 1]\n",
    "                n_smp_class = (class_idx[idx_new[:]]).size\n",
    "                if len(class_idx) <= k:\n",
    "                    k = len(class_idx) - 1\n",
    "                G[id_now : n_smp_class + id_now, 0] = np.tile(class_idx, (k + 1, 1)).reshape(-1)\n",
    "                G[id_now : n_smp_class + id_now, 1] = np.ravel(class_idx[idx_new[:]], order=\"F\")\n",
    "                G[id_now : n_smp_class + id_now, 2] = 1.0 / k\n",
    "                id_now += n_smp_class\n",
    "            W1 = csc_matrix((G[:, 2], (G[:, 0], G[:, 1])), shape=(n_samples, n_samples))\n",
    "            # when i = j, W_ij = 1\n",
    "            for i in range(n_samples):\n",
    "                W1[i, i] = 1\n",
    "            # when x_j in NM(x_i, y)\n",
    "            G = np.zeros((n_samples * k * (n_classes - 1), 3))\n",
    "            id_now = 0\n",
    "            for i in range(n_classes):\n",
    "                class_idx1 = np.column_stack(np.where(y == label[i]))[:, 0]\n",
    "                X1 = X[class_idx1, :]\n",
    "                for j in range(n_classes):\n",
    "                    if label[j] != label[i]:\n",
    "                        class_idx2 = np.column_stack(np.where(y == label[j]))[:, 0]\n",
    "                        X2 = X[class_idx2, :]\n",
    "                        D = pairwise_distances(X1, X2)\n",
    "                        idx = np.argsort(D, axis=1)\n",
    "                        idx_new = idx[:, 0:k]\n",
    "                        n_smp_class = len(class_idx1) * k\n",
    "                        G[id_now : n_smp_class + id_now, 0] = np.tile(class_idx1, (k, 1)).reshape(-1)\n",
    "                        G[id_now : n_smp_class + id_now, 1] = np.ravel(class_idx2[idx_new[:]], order=\"F\")\n",
    "                        G[id_now : n_smp_class + id_now, 2] = -1.0 / ((n_classes - 1) * k)\n",
    "                        id_now += n_smp_class\n",
    "            W2 = csc_matrix((G[:, 2], (G[:, 0], G[:, 1])), shape=(n_samples, n_samples))\n",
    "            bigger = np.transpose(W2) > W2\n",
    "            W2 = W2 - W2.multiply(bigger) + np.transpose(W2).multiply(bigger)\n",
    "            W = W1 + W2\n",
    "            return W\n",
    "\n",
    "        if kwargs[\"weight_mode\"] == \"binary\":\n",
    "            if kwargs[\"metric\"] == \"euclidean\":\n",
    "                G = np.zeros((n_samples * (k + 1), 3))\n",
    "                id_now = 0\n",
    "                for i in range(n_classes):\n",
    "                    class_idx = np.column_stack(np.where(y == label[i]))[:, 0]\n",
    "                    # compute pairwise euclidean distances for instances in class i\n",
    "                    D = pairwise_distances(X[class_idx, :])\n",
    "                    D **= 2\n",
    "                    # sort the distance matrix D in ascending order for instances in class i\n",
    "                    idx = np.argsort(D, axis=1)\n",
    "                    idx_new = idx[:, 0 : k + 1]\n",
    "                    n_smp_class = len(class_idx) * (k + 1)\n",
    "                    G[id_now : n_smp_class + id_now, 0] = np.tile(class_idx, (k + 1, 1)).reshape(-1)\n",
    "                    G[id_now : n_smp_class + id_now, 1] = np.ravel(class_idx[idx_new[:]], order=\"F\")\n",
    "                    G[id_now : n_smp_class + id_now, 2] = 1\n",
    "                    id_now += n_smp_class\n",
    "                # build the sparse affinity matrix W\n",
    "                W = csc_matrix((G[:, 2], (G[:, 0], G[:, 1])), shape=(n_samples, n_samples))\n",
    "                bigger = np.transpose(W) > W\n",
    "                W = W - W.multiply(bigger) + np.transpose(W).multiply(bigger)\n",
    "                return W\n",
    "\n",
    "            if kwargs[\"metric\"] == \"cosine\":\n",
    "                # normalize the data first\n",
    "                X_normalized = np.power(np.sum(X * X, axis=1), 0.5)\n",
    "                for i in range(n_samples):\n",
    "                    X[i, :] = X[i, :] / max(1e-12, X_normalized[i])\n",
    "                G = np.zeros((n_samples * (k + 1), 3))\n",
    "                id_now = 0\n",
    "                for i in range(n_classes):\n",
    "                    class_idx = np.column_stack(np.where(y == label[i]))[:, 0]\n",
    "                    # compute pairwise cosine distances for instances in class i\n",
    "                    D_cosine = np.dot(X[class_idx, :], np.transpose(X[class_idx, :]))\n",
    "                    # sort the distance matrix D in descending order for instances in class i\n",
    "                    idx = np.argsort(-D_cosine, axis=1)\n",
    "                    idx_new = idx[:, 0 : k + 1]\n",
    "                    n_smp_class = len(class_idx) * (k + 1)\n",
    "                    G[id_now : n_smp_class + id_now, 0] = np.tile(class_idx, (k + 1, 1)).reshape(-1)\n",
    "                    G[id_now : n_smp_class + id_now, 1] = np.ravel(class_idx[idx_new[:]], order=\"F\")\n",
    "                    G[id_now : n_smp_class + id_now, 2] = 1\n",
    "                    id_now += n_smp_class\n",
    "                # build the sparse affinity matrix W\n",
    "                W = csc_matrix((G[:, 2], (G[:, 0], G[:, 1])), shape=(n_samples, n_samples))\n",
    "                bigger = np.transpose(W) > W\n",
    "                W = W - W.multiply(bigger) + np.transpose(W).multiply(bigger)\n",
    "                return W\n",
    "\n",
    "        elif kwargs[\"weight_mode\"] == \"heat_kernel\":\n",
    "            G = np.zeros((n_samples * (k + 1), 3))\n",
    "            id_now = 0\n",
    "            for i in range(n_classes):\n",
    "                class_idx = np.column_stack(np.where(y == label[i]))[:, 0]\n",
    "                # compute pairwise cosine distances for instances in class i\n",
    "                D = pairwise_distances(X[class_idx, :])\n",
    "                D **= 2\n",
    "                # sort the distance matrix D in ascending order for instances in class i\n",
    "                dump = np.sort(D, axis=1)\n",
    "                idx = np.argsort(D, axis=1)\n",
    "                idx_new = idx[:, 0 : k + 1]\n",
    "                dump_new = dump[:, 0 : k + 1]\n",
    "                t = kwargs[\"t\"]\n",
    "                # compute pairwise heat kernel distances for instances in class i\n",
    "                dump_heat_kernel = np.exp(-dump_new / (2 * t * t))\n",
    "                n_smp_class = len(class_idx) * (k + 1)\n",
    "                G[id_now : n_smp_class + id_now, 0] = np.tile(class_idx, (k + 1, 1)).reshape(-1)\n",
    "                G[id_now : n_smp_class + id_now, 1] = np.ravel(class_idx[idx_new[:]], order=\"F\")\n",
    "                G[id_now : n_smp_class + id_now, 2] = np.ravel(dump_heat_kernel, order=\"F\")\n",
    "                id_now += n_smp_class\n",
    "            # build the sparse affinity matrix W\n",
    "            W = csc_matrix((G[:, 2], (G[:, 0], G[:, 1])), shape=(n_samples, n_samples))\n",
    "            bigger = np.transpose(W) > W\n",
    "            W = W - W.multiply(bigger) + np.transpose(W).multiply(bigger)\n",
    "            return W\n",
    "\n",
    "        elif kwargs[\"weight_mode\"] == \"cosine\":\n",
    "            # normalize the data first\n",
    "            X_normalized = np.power(np.sum(X * X, axis=1), 0.5)\n",
    "            for i in range(n_samples):\n",
    "                X[i, :] = X[i, :] / max(1e-12, X_normalized[i])\n",
    "            G = np.zeros((n_samples * (k + 1), 3))\n",
    "            id_now = 0\n",
    "            for i in range(n_classes):\n",
    "                class_idx = np.column_stack(np.where(y == label[i]))[:, 0]\n",
    "                # compute pairwise cosine distances for instances in class i\n",
    "                D_cosine = np.dot(X[class_idx, :], np.transpose(X[class_idx, :]))\n",
    "                # sort the distance matrix D in descending order for instances in class i\n",
    "                dump = np.sort(-D_cosine, axis=1)\n",
    "                idx = np.argsort(-D_cosine, axis=1)\n",
    "                idx_new = idx[:, 0 : k + 1]\n",
    "                dump_new = -dump[:, 0 : k + 1]\n",
    "                n_smp_class = len(class_idx) * (k + 1)\n",
    "                G[id_now : n_smp_class + id_now, 0] = np.tile(class_idx, (k + 1, 1)).reshape(-1)\n",
    "                G[id_now : n_smp_class + id_now, 1] = np.ravel(class_idx[idx_new[:]], order=\"F\")\n",
    "                G[id_now : n_smp_class + id_now, 2] = np.ravel(dump_new, order=\"F\")\n",
    "                id_now += n_smp_class\n",
    "            # build the sparse affinity matrix W\n",
    "            W = csc_matrix((G[:, 2], (G[:, 0], G[:, 1])), shape=(n_samples, n_samples))\n",
    "            bigger = np.transpose(W) > W\n",
    "            W = W - W.multiply(bigger) + np.transpose(W).multiply(bigger)\n",
    "            return W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy\n",
    "from scipy.linalg import eigh\n",
    "from sklearn import linear_model\n",
    "\n",
    "# from skfeature.utility.construct_W import construct_W\n",
    "from skfeature.utility.util import reverse_argsort\n",
    "\n",
    "\n",
    "def mcfs2(X, y=None, n_selected_features=None, mode=\"rank\", **kwargs):\n",
    "    \"\"\"\n",
    "    This function implements unsupervised feature selection for multi-cluster data.\n",
    "\n",
    "    Input\n",
    "    -----\n",
    "    X: {numpy array}, shape (n_samples, n_features)\n",
    "        input data\n",
    "    n_selected_features: {int}\n",
    "        number of features to select\n",
    "    kwargs: {dictionary}\n",
    "        W: {sparse matrix}, shape (n_samples, n_samples)\n",
    "            affinity matrix\n",
    "        n_clusters: {int}\n",
    "            number of clusters (default is 5)\n",
    "\n",
    "    Output\n",
    "    ------\n",
    "    W: {numpy array}, shape(n_features, n_clusters)\n",
    "        feature weight matrix\n",
    "\n",
    "    Reference\n",
    "    ---------\n",
    "    Cai, Deng et al. \"Unsupervised Feature Selection for Multi-Cluster Data.\" KDD 2010.\n",
    "    \"\"\"\n",
    "\n",
    "    def feature_ranking(W):\n",
    "        \"\"\"\n",
    "        This function computes MCFS score and ranking features according to feature weights matrix W\n",
    "        \"\"\"\n",
    "        mcfs_score = np.abs(W).max(1)\n",
    "        idx = np.argsort(mcfs_score)\n",
    "        idx = idx[::-1]\n",
    "        return idx\n",
    "    \n",
    "\n",
    "    if n_selected_features is None:\n",
    "        n_selected_features = int(X.shape[1])\n",
    "\n",
    "    # use the default affinity matrix\n",
    "    if \"W\" not in kwargs:\n",
    "        W = construct_W2(X) #15:32\n",
    "    else:\n",
    "        W = kwargs[\"W\"]\n",
    "    # default number of clusters is 5\n",
    "    if \"n_clusters\" not in kwargs:\n",
    "        n_clusters = 5\n",
    "    else:\n",
    "        n_clusters = kwargs[\"n_clusters\"]\n",
    "\n",
    "    # solve the generalized eigen-decomposition problem and get the top K\n",
    "    # eigen-vectors with respect to the smallest eigenvalues\n",
    "    W = W.toarray()\n",
    "    # W = (W + W.T) / 2\n",
    "    D = np.diag(W.sum(axis=1))\n",
    "    L = D - W\n",
    "    eigenvalues, eigenvectors = eigh(L, D)\n",
    "    \n",
    "\n",
    "    # solve K L1-regularized regression problem using LARs algorithm with cardinality constraint being d\n",
    "    n_sample, n_feature = X.shape\n",
    "    W = np.zeros((n_feature, n_clusters))\n",
    "    for i in range(n_clusters):\n",
    "        clf = linear_model.Lars(n_nonzero_coefs=n_selected_features)\n",
    "        clf.fit(X, eigenvectors[:, i])\n",
    "        W[:, i] = clf.coef_\n",
    "\n",
    "    if mode == \"raw\":\n",
    "        return W\n",
    "    elif mode == \"index\":\n",
    "        return feature_ranking(W)\n",
    "    elif mode == \"rank\":\n",
    "        W_idx = feature_ranking(W)\n",
    "        return reverse_argsort(W_idx, X.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n",
      "First few rows of the dataset:\n",
      "         ID Diagnosis  Mean Radius  Mean Texture  Mean Perimeter  Mean Area  \\\n",
      "0    842302         M        17.99         10.38          122.80     1001.0   \n",
      "1    842517         M        20.57         17.77          132.90     1326.0   \n",
      "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
      "3  84348301         M        11.42         20.38           77.58      386.1   \n",
      "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
      "\n",
      "   Mean Smoothness  Mean Compactness  Mean Concavity  Mean Concave Points  \\\n",
      "0          0.11840           0.27760          0.3001              0.14710   \n",
      "1          0.08474           0.07864          0.0869              0.07017   \n",
      "2          0.10960           0.15990          0.1974              0.12790   \n",
      "3          0.14250           0.28390          0.2414              0.10520   \n",
      "4          0.10030           0.13280          0.1980              0.10430   \n",
      "\n",
      "   ...  Worst Radius  Worst Texture  Worst Perimeter  Worst Area  \\\n",
      "0  ...         25.38          17.33           184.60      2019.0   \n",
      "1  ...         24.99          23.41           158.80      1956.0   \n",
      "2  ...         23.57          25.53           152.50      1709.0   \n",
      "3  ...         14.91          26.50            98.87       567.7   \n",
      "4  ...         22.54          16.67           152.20      1575.0   \n",
      "\n",
      "   Worst Smoothness  Worst Compactness  Worst Concavity  Worst Concave Points  \\\n",
      "0            0.1622             0.6656           0.7119                0.2654   \n",
      "1            0.1238             0.1866           0.2416                0.1860   \n",
      "2            0.1444             0.4245           0.4504                0.2430   \n",
      "3            0.2098             0.8663           0.6869                0.2575   \n",
      "4            0.1374             0.2050           0.4000                0.1625   \n",
      "\n",
      "   Worst Symmetry  Worst Fractal Dimension  \n",
      "0          0.4601                  0.11890  \n",
      "1          0.2750                  0.08902  \n",
      "2          0.3613                  0.08758  \n",
      "3          0.6638                  0.17300  \n",
      "4          0.2364                  0.07678  \n",
      "\n",
      "[5 rows x 32 columns]\n",
      "No missing values detected.\n",
      "cosine idx idx new\n",
      "[[  0  77 108 ... 458 457 334]\n",
      " [  1 365 164 ...  66 103  61]\n",
      " [  2 352 393 ...  46 425 548]\n",
      " ...\n",
      " [566 441 233 ... 502 380 130]\n",
      " [567 181  30 ... 144 159 428]\n",
      " [568 550 494 ... 252 432  45]]\n",
      "[[  0  77 108  25  45 300]\n",
      " [  1 365 164   6 373  23]\n",
      " [  2 352 393 535 162 521]\n",
      " ...\n",
      " [566 441 233 219 239 237]\n",
      " [567 181  30 393  72 108]\n",
      " [568 550 494  50 305 425]]\n",
      "dump new\n",
      "[[1.         0.92866291 0.90333421 0.89138364 0.84905829 0.82994818]\n",
      " [1.         0.89855248 0.89466272 0.87529851 0.87016243 0.86673432]\n",
      " [1.         0.93581252 0.93072617 0.9187624  0.91293458 0.91222069]\n",
      " ...\n",
      " [1.         0.82191075 0.79922729 0.7827156  0.78181637 0.69338024]\n",
      " [1.         0.95057158 0.93058747 0.91807226 0.90405084 0.90354406]\n",
      " [1.         0.92318683 0.90096331 0.86927603 0.86902834 0.86780742]]\n",
      "<Compressed Sparse Column sparse matrix of dtype 'float64'\n",
      "\twith 3414 stored elements and shape (569, 569)>\n",
      "  Coords\tValues\n",
      "  (0, 0)\t1.0000000000000002\n",
      "  (22, 0)\t0.8109908245225569\n",
      "  (25, 0)\t0.8913836401572595\n",
      "  (77, 0)\t0.9286629082563826\n",
      "  (108, 0)\t0.9033342129679833\n",
      "  (1, 1)\t0.9999999999999999\n",
      "  (164, 1)\t0.8946627220257407\n",
      "  (321, 1)\t0.8573481888185246\n",
      "  (2, 2)\t1.0000000000000004\n",
      "  (45, 2)\t0.9042682444124793\n",
      "  (77, 2)\t0.8573984224246173\n",
      "  (250, 2)\t0.8970063673609581\n",
      "  (323, 2)\t0.8561943295582465\n",
      "  (393, 2)\t0.930726167118997\n",
      "  (408, 2)\t0.8881174041215885\n",
      "  (432, 2)\t0.8565572162724193\n",
      "  (3, 3)\t1.0\n",
      "  (31, 3)\t0.8780397130030972\n",
      "  (146, 3)\t0.8347288787180561\n",
      "  (190, 3)\t0.8274389406554876\n",
      "  (208, 3)\t0.7306881084305693\n",
      "  (214, 3)\t0.7563795302085382\n",
      "  (351, 3)\t0.8517557886093415\n",
      "  (501, 3)\t0.8413432255124483\n",
      "  (4, 4)\t0.9999999999999994\n",
      "  :\t:\n",
      "  (461, 564)\t0.8971871454764203\n",
      "  (564, 564)\t1.0\n",
      "  (335, 565)\t0.8531803742520461\n",
      "  (565, 565)\t0.9999999999999999\n",
      "  (239, 566)\t0.7818163749415615\n",
      "  (441, 566)\t0.8219107510474084\n",
      "  (566, 566)\t1.0\n",
      "  (28, 567)\t0.8756333585806877\n",
      "  (30, 567)\t0.9305874719831145\n",
      "  (32, 567)\t0.8836916521011379\n",
      "  (72, 567)\t0.9040508397589483\n",
      "  (108, 567)\t0.9035440584948512\n",
      "  (118, 567)\t0.8722856790399593\n",
      "  (177, 567)\t0.8341212765703869\n",
      "  (181, 567)\t0.9505715797609744\n",
      "  (370, 567)\t0.8119335777702431\n",
      "  (392, 567)\t0.8752348197118002\n",
      "  (400, 567)\t0.8798974437509574\n",
      "  (567, 567)\t1.0\n",
      "  (305, 568)\t0.869028335952329\n",
      "  (494, 568)\t0.9009633087307676\n",
      "  (549, 568)\t0.8538509894847786\n",
      "  (550, 568)\t0.9231868297340949\n",
      "  (557, 568)\t0.8147333724574463\n",
      "  (568, 568)\t1.0000000000000002\n",
      "<Compressed Sparse Column sparse matrix of dtype 'float64'\n",
      "\twith 4841 stored elements and shape (569, 569)>\n",
      "  Coords\tValues\n",
      "  (0, 0)\t1.0000000000000002\n",
      "  (22, 0)\t0.8109908245225569\n",
      "  (25, 0)\t0.8913836401572595\n",
      "  (45, 0)\t0.8490582870881729\n",
      "  (77, 0)\t0.9286629082563826\n",
      "  (108, 0)\t0.9033342129679833\n",
      "  (300, 0)\t0.8299481818427947\n",
      "  (1, 1)\t0.9999999999999999\n",
      "  (6, 1)\t0.875298513471844\n",
      "  (23, 1)\t0.8667343230790894\n",
      "  (164, 1)\t0.8946627220257407\n",
      "  (321, 1)\t0.8573481888185246\n",
      "  (365, 1)\t0.8985524781422052\n",
      "  (373, 1)\t0.8701624338518402\n",
      "  (2, 2)\t1.0000000000000004\n",
      "  (45, 2)\t0.9042682444124793\n",
      "  (77, 2)\t0.8573984224246173\n",
      "  (162, 2)\t0.9129345797716962\n",
      "  (250, 2)\t0.8970063673609581\n",
      "  (323, 2)\t0.8561943295582465\n",
      "  (352, 2)\t0.9358125155656944\n",
      "  (393, 2)\t0.930726167118997\n",
      "  (408, 2)\t0.8881174041215885\n",
      "  (432, 2)\t0.8565572162724193\n",
      "  (521, 2)\t0.9122206915457696\n",
      "  :\t:\n",
      "  (237, 566)\t0.6933802402266226\n",
      "  (239, 566)\t0.7818163749415615\n",
      "  (441, 566)\t0.8219107510474084\n",
      "  (566, 566)\t1.0\n",
      "  (28, 567)\t0.8756333585806877\n",
      "  (30, 567)\t0.9305874719831145\n",
      "  (32, 567)\t0.8836916521011379\n",
      "  (72, 567)\t0.9040508397589483\n",
      "  (108, 567)\t0.9035440584948512\n",
      "  (118, 567)\t0.8722856790399593\n",
      "  (177, 567)\t0.8341212765703869\n",
      "  (181, 567)\t0.9505715797609744\n",
      "  (370, 567)\t0.8119335777702431\n",
      "  (392, 567)\t0.8752348197118002\n",
      "  (393, 567)\t0.9180722611691647\n",
      "  (400, 567)\t0.8798974437509574\n",
      "  (567, 567)\t1.0\n",
      "  (50, 568)\t0.8692760312044946\n",
      "  (305, 568)\t0.869028335952329\n",
      "  (425, 568)\t0.867807416304043\n",
      "  (494, 568)\t0.9009633087307676\n",
      "  (549, 568)\t0.8538509894847786\n",
      "  (550, 568)\t0.9231868297340949\n",
      "  (557, 568)\t0.8147333724574463\n",
      "  (568, 568)\t1.0000000000000002\n",
      "\n",
      "Adjusted Rand Index (ARI): 0.7073\n",
      "\n",
      "Confusion Matrix:\n",
      "[[330  27]\n",
      " [ 18 194]]\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Benign       0.95      0.92      0.94       357\n",
      "   Malignant       0.88      0.92      0.90       212\n",
      "\n",
      "    accuracy                           0.92       569\n",
      "   macro avg       0.91      0.92      0.92       569\n",
      "weighted avg       0.92      0.92      0.92       569\n",
      "\n",
      "\n",
      "Top selected features:\n",
      "1. Worst Perimeter (Weight: 0.5039)\n",
      "2. Worst Radius (Weight: 0.4560)\n",
      "3. SE Perimeter (Weight: 0.2045)\n",
      "4. SE Radius (Weight: 0.1907)\n",
      "5. SE Area (Weight: 0.0318)\n",
      "6. Worst Compactness (Weight: 0.0310)\n",
      "7. Mean Concavity (Weight: 0.0272)\n",
      "8. Mean Area (Weight: 0.0247)\n",
      "9. Worst Area (Weight: 0.0195)\n",
      "10. Mean Concave Points (Weight: 0.0080)\n",
      "11. SE Compactness (Weight: 0.0080)\n",
      "12. Worst Concave Points (Weight: 0.0063)\n",
      "13. Mean Compactness (Weight: 0.0054)\n",
      "14. Worst Symmetry (Weight: 0.0050)\n",
      "15. Worst Smoothness (Weight: 0.0048)\n",
      "16. Mean Texture (Weight: 0.0046)\n",
      "17. Worst Texture (Weight: 0.0003)\n",
      "18. SE Concave Points (Weight: 0.0000)\n",
      "19. Worst Fractal Dimension (Weight: 0.0000)\n",
      "20. Worst Concavity (Weight: 0.0000)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "    \n",
    "\n",
    "# Define the column names based on the dataset description\n",
    "column_names = [\n",
    "    'ID', 'Diagnosis',\n",
    "    'Mean Radius', 'Mean Texture', 'Mean Perimeter', 'Mean Area', 'Mean Smoothness',\n",
    "    'Mean Compactness', 'Mean Concavity', 'Mean Concave Points', 'Mean Symmetry', 'Mean Fractal Dimension',\n",
    "    'SE Radius', 'SE Texture', 'SE Perimeter', 'SE Area', 'SE Smoothness',\n",
    "    'SE Compactness', 'SE Concavity', 'SE Concave Points', 'SE Symmetry', 'SE Fractal Dimension',\n",
    "    'Worst Radius', 'Worst Texture', 'Worst Perimeter', 'Worst Area', 'Worst Smoothness',\n",
    "    'Worst Compactness', 'Worst Concavity', 'Worst Concave Points', 'Worst Symmetry', 'Worst Fractal Dimension'\n",
    "]\n",
    "\n",
    "print(len(column_names))\n",
    "# Load the data\n",
    "data = pd.read_csv('wdbc.data', names=column_names)\n",
    "\n",
    "# Display the first few rows (optional)\n",
    "print(\"First few rows of the dataset:\")\n",
    "print(data.head())\n",
    "\n",
    "# Map Diagnosis to numerical values: M=1 (malignant), B=0 (benign)\n",
    "data['Diagnosis'] = data['Diagnosis'].map({'M': 1, 'B': 0})\n",
    "\n",
    "# Check for any missing values\n",
    "if data.isnull().sum().any():\n",
    "    print(\"Data contains missing values. Please handle them before proceeding.\")\n",
    "    # Optionally, handle missing values here\n",
    "else:\n",
    "    print(\"No missing values detected.\")\n",
    "\n",
    "# Separate features and labels\n",
    "X = data.drop(['ID', 'Diagnosis'], axis=1).values\n",
    "y = data['Diagnosis'].values\n",
    "\n",
    "# Preprocess the features\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# Set parameters\n",
    "n_clusters = 2  # Number of clusters (malignant and benign)\n",
    "n_selected_features = 20  # Number of features to select\n",
    "k = 5  # Number of neighbors for graph construction\n",
    "\n",
    "# Construct the affinity matrix W\n",
    "kwargs_W = {\n",
    "    \"metric\": \"cosine\",\n",
    "    \"neighbor_mode\": \"knn\",\n",
    "    \"weight_mode\": \"cosine\",\n",
    "    \"k\": k,\n",
    "    \"t\": 1.2\n",
    "}\n",
    "W = construct_W2(X_scaled, **kwargs_W)\n",
    "\n",
    "# Perform MCFS feature selection\n",
    "# Note: Ensure that MCFS in your library version supports supervised feature selection\n",
    "#Weight = MCFS.mcfs(X_scaled, y=y, n_selected_features=n_selected_features, W=W, n_clusters=n_clusters)\n",
    "weights = mcfs2(X_scaled, W=W, n_selected_features=n_selected_features, n_clusters=n_clusters, mode=\"raw\")\n",
    "ranking = feature_ranking(weights)\n",
    "\n",
    "\n",
    "# Manually rank the features based on the Weight matrix\n",
    "feature_ranking_indices = ranking[:n_selected_features]\n",
    "\n",
    "# Select the top features\n",
    "selected_features_indices = feature_ranking_indices[:n_selected_features]\n",
    "X_selected = X_scaled[:, selected_features_indices]\n",
    "\n",
    "# Perform KMeans clustering using the selected features\n",
    "kmeans = KMeans(n_clusters=n_clusters, random_state=42)\n",
    "cluster_labels = kmeans.fit_predict(X_selected)\n",
    "\n",
    "# Map cluster labels to match the original labels (optional step)\n",
    "acc_original = accuracy_score(y, cluster_labels)\n",
    "acc_inverted = accuracy_score(y, 1 - cluster_labels)\n",
    "\n",
    "if acc_inverted > acc_original:\n",
    "    cluster_labels = 1 - cluster_labels\n",
    "\n",
    "# Evaluate clustering performance\n",
    "ari = adjusted_rand_score(y, cluster_labels)\n",
    "print(f\"\\nAdjusted Rand Index (ARI): {ari:.4f}\")\n",
    "\n",
    "# Print confusion matrix and classification report\n",
    "print(\"\\nConfusion Matrix:\")\n",
    "print(confusion_matrix(y, cluster_labels))\n",
    "\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y, cluster_labels, target_names=['Benign', 'Malignant']))\n",
    "\n",
    "# Print the top selected features with their weights\n",
    "print(\"\\nTop selected features:\")\n",
    "for rank, idx in enumerate(selected_features_indices, start=1):\n",
    "    feature_name = column_names[idx + 2]  # Adjust index for 'ID' and 'Diagnosis' columns\n",
    "    feature_weight = np.abs(weights).max(1)[idx]\n",
    "    print(f\"{rank}. {feature_name} (Weight: {feature_weight:.4f})\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tKioqtGTJEl144YWaPHmyHnjgAa1du1YlJSVNHmc2m/XCCy/o5ptv1o033qjU1FQ9+eSTiouL0/Llyxu0HzhwoIYOHer4SfCBD+mEBHIFOoa63FFUVKT169dL8u/ccf/99+vCCy8kd/igqqoqpaenq7y8XC+99FKjS6WfSlJSknbv3u1Y5rjO7t27nb5o1RYGg0FRUVF+81NXBA1LTlb4mWc6/YSdKPRHRkZ6Pc76sfpKPIH405ZrHBkZqaCgIAUHBzf6U/cFlsbajB492pE73n//fQUHB+u5557TkCFDnNq9/fbbuummmxw54Ve/+pVef/31BuczGAx67rnndNFFF2n48OG68cYbtXv3bl122WX605/+5Gj3zjvvaMiQIfrmm2+0aNEijR49Wueee67uuecelZaWNjjv2rVr9etf/1rnnHOOLrroIs2fP18VFRVObfbu3avMzEylpaVp6NChuuSSS3T//fersrLS0aYujrqieHV1tZ5//nmNGzdOQ4cO1ahRozR16lT961//avJ61r+ujf05tXd1k8D8WhuAgDBhwgQ9+eST+vTTT3XNNdc02mbNmjUaOHCgxo4dq5CQEH388ceaO3eu7Ha7fve73znaPfHEE3rppZd0ySWX6MILL9R///tfTZ8+vcmlPebPny+j0ai77rpL+/fv16pVq/Too4/q6aefdrTJzs7WkiVLNGrUKP32t7/V7t27tWbNGn3//fdas2aNQkNDZbFYNH36dFksFk2dOlVdu3ZVSUmJPvnkkyafASRJS5Ys0QsvvKCrr75aZ599to4fP64ffvhB27dv1+jRo9t+UQEgwLkydzz11FNavnw5uQMAApw/jzteeuklRUREkDt8yJYtW5Samqq4uDjHtnHjxmnOnDnaunWrJjaxtOvXX3+t48ePa9y4cY5tYWFhuuyyy7Rp0yZ3hw2glepyx7/+9S9dd911jbbx1dxRf9xx2223yWw263e/+526d+9O7vAh1dXVyszMVH5+vl577TX16NHjlMdUVlbqk08+cVr1IC0tTc8995w+//xzjRo1SlJtcfzHH3/ULbfc4rb4ATTkz+OO9t6zevbZZ7Vs2TKfyh0UyAH4rJ49eyomJkZ79+5tss2rr77qtBzJ1KlTNX36dL388suOhHH48GGtXLlSl156qZ599llH2yVLlig7O7vR88bFxWnFihWObyHV1NQoJydH5eXliomJ0dGjR/XCCy9ozJgxWrZsmePbYUlJSXr00Uf17rvvatKkSdq1a5f27dunZ555RldccYXj/HfddVezff/kk0900UUXad68eae4SgCA+lyZO1atWqVLLrlEzz77rIKDgyWROwAgEPnzuGP9+vW69tpryR0+JD8/X5MmTXLaZjQa1a1btyafnVh3nKQGs/kGDBigVatWqaqqyunv4G233aZjx46pW7duuvLKK3XvvfcG/FKdgC+pyx379u1rso2v5o6Txx2LFi3SVVdd5RjzkDt8w9y5c/Xxxx9r5syZOn78uL755hvHviFDhui7777TSy+9pMsuu0x9+vTRwYMH9fLLL+vQoUN65plnHG2HDRumMWPGaNasWXrwwQcVHh6up556SoMHD9Yvf/lLL/QM6Lj8edzR3ntWmzdv9rncwRLrAHxaVFSUKioqmtxfP1mUl5fr6NGjGjFihPbu3etYDurzzz9XdXV1g2/0Tp06tcnzXnPNNU5LdJx33nmy2Wzav3+/JOmzzz6T1WrVDTfc4PTsn6uvvlqdOnXS5s2bJUmdOnWSJH366acymUwt7baMRqPy8vJUUFDQ4mMAALVcmTuuvvpqp2PJHQAQmPx13PHpp59KInf4krKyMhmNxgbbY2NjVVpa2uxxYWFhDZ6taDQaZbfbHcfGxMTolltu0YIFC7RixQr95je/0auvvqp77723XXHb7XZVVlY2+1P3d8tkMjW63WIxy2IxN9rGV36a6oM//QRCH3yhHyaTSTU1NbLZbI3+1NTUSFKzbepyh91ud7Svvz80NNTx38eOHdPhw4d13nnnae/evTp27JhsNpu2bt2q6upqTZkyxenY3/72t47/N0+OafLkyU5xDR8+XDabTXv37pXNZtOnn34qq9WqqVOnOh0/ceJEderUSZ988okjfqk2f1VWVjbZT7vd7nSemJgY5eXladeuXU0e09x1bezP/OTlvyHHkvULFy7Utdde6/Rz8OBBdevWTVarVU899ZRuueUWzZs3T926ddPq1at19tlnO53r6aef1qhRozR79mz9/ve/V2Jiol588UWFhDB/EvA0fx13BOI9Kz4BAfi0yspKdenSpcn9X331lbKzs/XNN980+ECu+/ZTUVGRJKlfv35O++Pi4hQbG9voeXv37u30uu4GR92zmurOefK3+8PCwtS3b19HYunbt69uuukmvfzyy3rvvfd03nnnaezYsfr1r3/d7HOD7rnnHt155526/PLLNWjQII0ZM0YTJkzQ6aef3uQxAIBarswdffv2ddpP7gCAwOSP4474+HgVFxdLInd0JEOGDNGQIUMcr1NTU9W9e3c9+uij+u677xoURVrKarUqNze3RW1PvrFZUBApaYjj76OUqIKC3YqIaPlNU0/zpZuzbRUIfZC824+QkJAml6K1Wq2SJIvFoqqqqkbbVFRU6LTTTpPZbFZ1dbUkObX95ptvtHTpUn333XcNznH48GGFhoZqz549kqQePXo4tYmIiJDRaJTNZnNsr4upS5cuDdrWnTMhIcFxzt69ezd43z59+mjfvn2qqqpS165dNXXqVL366qvasGGDhg4dqosuukjjx493yh11xfG6c91222267777NH78eCUnJys1NVVXXnmlBg0a1Oh1qlN3nZpaTSMsLKzZ4zuajz766JRtli9f3qJzxcTEKCsrS1lZWe0NC0A7+eO4wxX3rO6++27dddddPjXuoEAOwGcdOHBA5eXlDT7o6+zZs0c33nijkpKSNHPmTPXq1UuhoaHavHmzVq5c6fhmbVvU/5ZUfW35RuvMmTP1m9/8Rv/85z+1detWzZ8/Xy+88ILeeOMN9ezZs9Fjzj//fG3atMlxzF//+letWrVKc+fObTCbEQDwP+QOcoc37Nq1S/Pnz9e2bdsUHR2tCRMmKDMzs0U3+UpKSvTkk09q8+bNqqysVJ8+fXTHHXfo17/+tQciByCRO8gdrmU0Gh2ze+orLS1t8oZl3XEWi0Vms9lpFnlZWZkMBkOzx44bN06PPvqofvjhhzYXyENDQ5WcnNxsG5PJpIKCAiUmJioyMtKxvaqqdjZSr169HNsSE/srJcX3ZoQ21Qd/Egh9kLzfD7PZrKKiIoWHhzf6eILQ0FBJtYWBxvYfOHBAx48fV9++fRUeHu6YiVvXds+ePbr99tvVv39/Pfjgg+rZs6dCQ0P1//7f/9OqVasc5607rqk4goODHdvrYoqMjHRqW/dvztDQ0FOe02AwKCgoyLH9j3/8o371q1/p008/1WeffabHH39cK1eu1Jo1axy5w2AwOMUxevRo/eMf/9BHH32kzz77TO+8845Wr16tOXPmaPLkyc1e95CQEPXr16/Bahk7d+5s9jgACAQdedxx3nnn+dy4gwI5AJ/1zjvvSJLGjBnT6P6PPvpIFotFzz//vNM3oL744gundnX79uzZ4zQT8Oeff252ibvm1J0zPz/f6ZwWi0X79u3TqFGjnNoPHjxYgwcP1p133qmvv/5av/3tb7VmzRrNmDGjyfeIi4vTpEmTNGnSJFVUVGjq1KnKzs7mRhUANMPVuWPv3r0aMGCAYzu5AycrLS3VtGnTlJiYqOzsbJWUlGjhwoWqqqrS7Nmzmz324MGDuvbaa9W/f3/NmzdPnTp1Ul5eniwWi4eiByD577hj//79GjFihFN7cof3JSUlNZgdWV5erkOHDjWYkXPycZK0e/dup5k0+fn56t27t9ufL24wGBzLLZ9KZGSkU9u62mZYWPhJbVwaokud3Ad/FAh9kLzXj6CgIAUFBSk4ONjx7O2T99f9bmz/unXrJNWu4lBXdJbkaLt582ZZLBYtXbrUKXd8+eWXTueNj4+XJO3bt08JCQmOdj///LPjCzJ152wqppO3152zsLBQiYmJjnZ1uWPUqFGO4202mwYOHKizzjpLd911lyN3vPHGG47cYTAYnOKQamexX3311br66qsduePZZ5/Vtdde2+Q1Dw4OVlBQUIMCf917AECg89dxR6Des+IZ5AEmN1f6+uv//eTleTsioG0+//xzPffcc4qPj29yBlXdP8zrf8upvLxcb731llO71NRUhYSEaM2aNU7bX3vttTbHN2rUKIWGhionJ8fp/f/617+qvLxcF110kSTp+PHjjmW26gwaNEhBQUHN3vz++eefnV5HR0erX79+3DAHgGa4I3e8+eabTtvJHTjZ2rVrVVFRoSVLlujCCy/U5MmT9cADD2jt2rUqKSlp9tjHH39cPXv21EsvvaTLLrtMqampuuGGG0458waA6/j7uKPu5hq5w3ekpaXps88+cyxXKUkbN25UUFCQRo8e3eRxw4cPV6dOnbRhwwbHNqvVqg8++EBpaWnNvuf7778vSTrrrLPaGT2AlqifO8aNG9doG1/OHYw7AMDz/H3c0d7ccezYMafXvpA7mEEeIOq+aDl1asN9O3ZIAwd6Nh6gNbZs2aL8/HzZbDYdPnxYX3zxhbZu3arevXvr+eefb7DsUp3Ro0crNDRUt99+u6ZMmaKKigq9+eab6tKliw4dOuRo17VrV91www1asWKFbr/9dl144YX66aeftGXLFp122mlt+pZq586dlZ6eriVLluiWW27R2LFjtXv3bq1evVpnnXWWI8n961//0qOPPqorrrhCiYmJstlseueddxQcHKzLL7+8yfNfeeWVGjFihM444wzFxcXp+++/1z/+8Q9Nbex/cgDogDyRO6ZOnaqVK1cqIyOD3IEmbdmyRampqYqLi3NsGzdunObMmaOtW7dq4sS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wwANmhmy4WP38IzrMziNZWVlqaWkJ+vihikYe6Vz36uOPPw4u2AAZUadOI0aM0Je+9CXt3bu3x/Ej+Rp1Pme4ddq/f78++ugjffOb3wyovNmvUyhi9XNklFj7LMFciXZdEquMzAnoXyJdm8a6cM91dna2pk6dqjlz5qi0tFRz5szRQw89ZEaoiBOJ+PlNtM9JIt0zk8y/Zou0RLoPiNgVjeuHZLnmNPuaIV7vURghGtdjyfC+Nfu7Wyy8Z2kgN8HYsWN7zZHf3Nyszz//vNec+qfuJ/nm4++uqqpKI0eOjFpvvlDr02nXrl265557dOutt+rb3/62WWEGJNS6fPbZZ/qf//kfnXvuuV3/PvjgA/3hD3/Queeeq3feecfs0HsJtS7jx4/v97infpGJhFDrsm/fPqWmpmrixIk9thcXF+vo0aNyOBymxGuGWP38IzrMziP+ju/1evXZZ58F9Hc9WImURzqFW6dQjm/ma9TXcwZbp1dffVUpKSn6+te/bkaIERGrnyMjxOJnCeZKtOuSWGV2TsA/JOJ3ilhl9Pt6ypQpNMIkuUT8/Cba5ySR7plJiZefE+k+IGIX1w/mScT7SLEi0f7exwqzv7vFwnuWBnITzJkzR++8846ampq6tu3cuVMpKSk6//zz+9zv7LPP1pAhQ7Rjx46ubR6PR6+99prmzJljasz9CbU+kvTuu+/q+9//vi6//HItW7bM7FAHFGpdVq1apWeffbbHvzPPPFMzZszQs88+G5VpNEKty6hRozRx4sReFyjvvPOO0tPTB/zibIZw6tLe3q5PPvmkx/a9e/dq+PDhstvtpsVstFj9/CM6zM4jc+bM0d///ndVV1d3bfvjH/+o48ePmzKdXzTyyG9/+1tJ0tSpU0MLegDh1OlUR44c0fvvv98j1ki/Rp3PGW6dfvvb3+q8887TiBEjAi4vmfc6hSJWP0fhitXPEsyVaNclscrInID+JdK1aawz+n39/vvvR23dXsSGRPz8JtrnJJHumUnmX7NFWiLdB0Tsisb1Q7Jcc5p9zRBv9yiMFI3rsWR435r93S0W3rOsQW6CK6+8UmVlZVq2bJmWLl2qI0eO6MEHH9SVV16p3NzcrnKLFi3S4cOHtWvXLkmSzWbT0qVLtXbtWmVnZ2vixIn65S9/qePHj+v666+PVnVCrs+nn36qZcuWqaioSPPnz9eHH37YVTY7O1tnnHFGpKsScl2Ki4t7HSsrK0sZGRkqKSmJWPzdhVoXSVqxYoW+973v6Sc/+Ym+8pWv6K9//as2bdqk66+/PirrQIRalzlz5mjkyJG69dZbtWzZMo0YMUJ/+MMf9Jvf/Ea33HJLxOvRyeFw6K233pIkHTp0SC0tLdq5c6ck6bzzzlN2dnbcfP4RHWbnkUsuuURPPvmkbrnlFn3/+9+Xw+HQgw8+qK985Sum3LwwO4/cfvvtKiws1OTJk2Wz2fSnP/1Jmzdv1kUXXWTaF9VQ67Rt2za9+eabuuCCCzRixAgdOHBA69evV2pqqq677rqu/SL9GoVTp05/+9vf9Omnn/aoR3fReJ3M/Hscjdco1DrF8mcJ5kq065JYFc7fz7/+9a86dOiQGhoaJEl79uyR5PtsnnfeeZGtSBxIpGvTWBfquf7973+vl156SV/5yleUn5+vxsZGbdu2TX/4wx/0i1/8IlrVQQxIxM9von1OEumemWT+NVukJdJ9QMQus68fkvma0+xrhmjdo4gFZp/bZH3fmv3dLRbeszSQm2Do0KF65plndO+992rZsmUaPHiwvv3tb2vFihU9yp08eVLt7e09ti1ZskRer1ebNm1SQ0ODiouLtXHjxqj2IA21Pnv27FFzc7Oam5v1f/7P/+lR9lvf+lZU1ogO57WJNeHUZe7cufrFL36hxx9/XL/85S81YsQI3XLLLbrxxhsjWYUuodZlyJAh2rx5s/7jP/5DP/vZz9Tc3KyCggKtXLlSCxYsiHQ1utTX1+u2227rsa3z92effVYlJSVx8/lHdJidR9LS0vTUU0/pvvvu0/e//30NGjRIF198sVatWhVT9Qk0j0yYMEGvvvqqNm3aJI/Ho1GjRummm24y9W9aqHUqKCjQ0aNHVVpaqubmZmVmZmrWrFm69dZbo/oahVOnTq+++qqsVqsuueQSv8ePxutk5t/jaLxGodYplj9LMFeiXZfEqnDO85YtW/Sb3/ym6/dNmzZJ8nV4KSsrMz/4OJNI16axLtRzPXr0aLndbv385z/XsWPHdNppp2nSpEkqKyuj00eSS8TPb6J9ThLpnplk/jVbpCXSfUDELrOvH5L5mtPsa4Zo3aOIBWaf22R935r93S0W3rMWr9frjdizAQAAAAAAAAAAAAAQJaxBDgAAAAAAAAAAAABICjSQAwAAAAAAAAAAAACSAg3kAAAAAAAAAAAAAICkQAM5AAAAAAAAAAAAACAp0EAOAAAAAAAAAAAAAEgKNJADAAAAAAAAAAAAAJICDeQAAAAAAAAAAAAAgKRAAzkAAAAAAAAAAAAAICnQQA4AAAAAAAAAAAAASAo0kAMAAAAAAAAAAAAAkgIN5AAAAAAAAAAAAACApEADOQAAAAAAAAAAAAAgKfx//JWUc4BGx7AAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x2400 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Define the column names based on the dataset description \n",
    "column_names = [\n",
    "    'ID', 'Diagnosis',\n",
    "    'Mean Radius', 'Mean Texture', 'Mean Perimeter', 'Mean Area', 'Mean Smoothness',\n",
    "    'Mean Compactness', 'Mean Concavity', 'Mean Concave Points', 'Mean Symmetry', 'Mean Fractal Dimension',  \n",
    "    'SE Radius', 'SE Texture', 'SE Perimeter', 'SE Area', 'SE Smoothness',\n",
    "    'SE Compactness', 'SE Concavity', 'SE Concave Points', 'SE Symmetry', 'SE Fractal Dimension',\n",
    "    'Worst Radius', 'Worst Texture', 'Worst Perimeter', 'Worst Area', 'Worst Smoothness',\n",
    "    'Worst Compactness', 'Worst Concavity', 'Worst Concave Points', 'Worst Symmetry', 'Worst Fractal Dimension'\n",
    "]\n",
    "\n",
    "# Load the data\n",
    "data = pd.read_csv('wdbc.data', names=column_names)\n",
    "\n",
    "# Map Diagnosis to numerical values: M=1 (malignant), B=0 (benign)\n",
    "data['Diagnosis'] = data['Diagnosis'].map({'M': 1, 'B': 0})\n",
    "\n",
    "# Drop the ID column as it's not useful for visualization\n",
    "data.drop('ID', axis=1, inplace=True)\n",
    "\n",
    "# Set the style for seaborn\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# List of features to plot\n",
    "features = data.columns.drop('Diagnosis')\n",
    "\n",
    "# Number of features\n",
    "num_features = len(features)\n",
    "\n",
    "# Determine the number of rows and columns for the subplot grid\n",
    "num_cols = 5\n",
    "num_rows = int(np.ceil(num_features / num_cols))\n",
    "\n",
    "# Create a figure and axes\n",
    "fig, axes = plt.subplots(num_rows, num_cols, figsize=(20, num_rows * 4))\n",
    "\n",
    "# Flatten the axes array for easy iteration\n",
    "axes = axes.flatten()\n",
    "\n",
    "# Plot each feature\n",
    "for idx, feature in enumerate(features):\n",
    "    ax = axes[idx]\n",
    "    sns.histplot(data=data, x=feature, hue='Diagnosis', element='step', stat='density', common_norm=False, palette={0: 'blue', 1: 'red'}, ax=ax)\n",
    "    ax.set_title(feature)\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_ylabel('Density')\n",
    "\n",
    "# Remove any empty subplots\n",
    "for i in range(idx + 1, len(axes)):\n",
    "    fig.delaxes(axes[i])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}