Merge pull request #19 from longyangking/main

Add interactive PCA and Kmeans

examples/interactive_Kmeans.py

@@ -0,0 +1,18 @@
+import sys
+sys.path.append('..')
+
+import numpy as np
+from vidar import interactive_kmeans
+
+rng = np.random.RandomState(0)
+n_samples = 1000
+cov = [[0.4, 0], [0, 0.4]]
+X = np.concatenate([
+    rng.multivariate_normal(mean=[-2, 0], cov=cov, size=n_samples), 
+    rng.multivariate_normal(mean=[2, 0], cov=cov, size=n_samples),
+    rng.multivariate_normal(mean=[0.3, 1], cov=cov, size=n_samples)
+    ])
+
+n_clusters = 2
+app = interactive_kmeans(X, n_clusters)
+app.show()

examples/interactive_PCA.py

@@ -0,0 +1,17 @@
+import sys
+sys.path.append('..')
+
+import numpy as np
+from vidar import interactive_PCA
+
+rng = np.random.RandomState(0)
+n_samples = 1000
+cov = [[1, 0], [0, 1]]
+X = np.concatenate([
+    rng.multivariate_normal(mean=[-2, 0], cov=cov, size=n_samples), 
+    rng.multivariate_normal(mean=[2, 0], cov=cov, size=n_samples)])
+
+
+n_components = 1
+app = interactive_PCA(X, n_components)
+app.show()

notebooks/yang/Kmeans interactive _ more clusters.ipynb

@@ -0,0 +1,130 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# K-means interactive\n",
+    "\n",
+    "> Yang"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib qt\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from matplotlib.widgets import Button\n",
+    "from matplotlib.widgets import PolygonSelector\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "def colors_from_lbs(lbs, colors=None):\n",
+    "    mpl_20 = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n",
+    "          '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf',\n",
+    "          '#3397dc', '#ff993e', '#3fca3f', '#df5152', '#a985ca',\n",
+    "          '#ad7165', '#e992ce', '#999999', '#dbdc3c', '#35d8e9']\n",
+    "    \n",
+    "    if colors is None:\n",
+    "        colors = np.array(mpl_20)\n",
+    "    else:\n",
+    "        colors = np.array(colors)\n",
+    "    lbs = np.array(lbs) % len(colors)\n",
+    "    return colors[lbs]\n",
+    "\n",
+    "rng = np.random.RandomState(0)\n",
+    "n_samples = 1000\n",
+    "cov = [[0.4, 0], [0, 0.4]]\n",
+    "X = np.concatenate([\n",
+    "    rng.multivariate_normal(mean=[-2, 0], cov=cov, size=n_samples), \n",
+    "    rng.multivariate_normal(mean=[2, 0], cov=cov, size=n_samples),\n",
+    "    rng.multivariate_normal(mean=[0.5, 1], cov=cov, size=n_samples)\n",
+    "    ])\n",
+    "\n",
+    "n_clusters=3\n",
+    "kmeans = KMeans(n_clusters=n_clusters, random_state=0, n_init=\"auto\")\n",
+    "labels = kmeans.fit_predict(X)\n",
+    "\n",
+    "centers = kmeans.cluster_centers_\n",
+    "\n",
+    "fig, (ax_orig, ax_redim) = plt.subplots(1, 2, figsize=(12, 6))\n",
+    "\n",
+    "def plot_figure(axe_list, X, centers):\n",
+    "    ax_orig, ax_redim = axe_list\n",
+    "\n",
+    "    kmeans.cluster_centers_ = np.array(centers, dtype=np.float64)\n",
+    "    labels = kmeans.predict(X)    \n",
+    "\n",
+    "    ax_orig.clear()\n",
+    "    ax_orig.scatter(X[:, 0], X[:, 1], alpha=0.3, label=\"samples\", c=colors_from_lbs(labels))\n",
+    "    ax_orig.scatter(centers[:,0], centers[:,1], s=50, c='black', edgecolors='r')\n",
+    "    ax_orig.set(\n",
+    "        aspect=\"auto\", \n",
+    "        title=\"Interactive K-means\",\n",
+    "        xlabel=\"first feature\",\n",
+    "        ylabel=\"second feature\",\n",
+    "    )\n",
+    "\n",
+    "    ax_redim.clear()\n",
+    "    class_name = ['class {0}'.format(i+1) for i in range(len(centers))]\n",
+    "\n",
+    "    # update labels\n",
+    "    counts = [np.sum(labels==i) for i in range(len(centers))]\n",
+    "    \n",
+    "\n",
+    "    ax_redim.bar(class_name, counts, label=class_name, color=colors_from_lbs(range(len(centers))))\n",
+    "    ax_redim.set(\n",
+    "        aspect=\"auto\",\n",
+    "        title=\"Clustering results\",\n",
+    "        xlabel=\"Main feature\",\n",
+    "        ylabel=\"Number of samples\",\n",
+    "    )\n",
+    "    fig.canvas.draw_idle()\n",
+    "\n",
+    "plot_figure((ax_orig, ax_redim), X, centers)\n",
+    "\n",
+    "def onselect(verts):\n",
+    "    centers = np.array(verts)\n",
+    "    plot_figure((ax_orig, ax_redim), X, centers)\n",
+    "\n",
+    "selector = PolygonSelector(ax_orig, onselect=onselect, \n",
+    "                           props=dict(color='r', linestyle='', linewidth=3, alpha=0.6, label=f\"Component\"))\n",
+    "selector.verts = centers\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/yang/Kmeans interactive _ more.ipynb

@@ -0,0 +1,124 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# K-means interactive\n",
+    "\n",
+    "> Yang"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib qt\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from matplotlib.widgets import Button\n",
+    "from matplotlib.widgets import PolygonSelector\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "def colors_from_lbs(lbs, colors=None):\n",
+    "    mpl_20 = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n",
+    "          '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf',\n",
+    "          '#3397dc', '#ff993e', '#3fca3f', '#df5152', '#a985ca',\n",
+    "          '#ad7165', '#e992ce', '#999999', '#dbdc3c', '#35d8e9']\n",
+    "    \n",
+    "    if colors is None:\n",
+    "        colors = np.array(mpl_20)\n",
+    "    else:\n",
+    "        colors = np.array(colors)\n",
+    "    lbs = np.array(lbs) % len(colors)\n",
+    "    return colors[lbs]\n",
+    "\n",
+    "rng = np.random.RandomState(0)\n",
+    "n_samples = 1000\n",
+    "cov = [[0.4, 0], [0, 0.4]]\n",
+    "X = np.concatenate([\n",
+    "    rng.multivariate_normal(mean=[-2, 0], cov=cov, size=n_samples), \n",
+    "    rng.multivariate_normal(mean=[2, 0], cov=cov, size=n_samples),\n",
+    "    rng.multivariate_normal(mean=[0.3, 1], cov=cov, size=n_samples)\n",
+    "    ])\n",
+    "\n",
+    "kmeans = KMeans(n_clusters=2, random_state=0, n_init=\"auto\")\n",
+    "labels = kmeans.fit_predict(X)\n",
+    "\n",
+    "centers = kmeans.cluster_centers_\n",
+    "\n",
+    "fig, (ax_orig, ax_redim) = plt.subplots(1, 2, figsize=(12, 6))\n",
+    "\n",
+    "def plot_figure(axe_list, X, centers):\n",
+    "    ax_orig, ax_redim = axe_list\n",
+    "\n",
+    "    kmeans.cluster_centers_ = np.array(centers, dtype=np.float64)\n",
+    "    labels = kmeans.predict(X)    \n",
+    "\n",
+    "    ax_orig.clear()\n",
+    "    ax_orig.scatter(X[:, 0], X[:, 1], alpha=0.3, label=\"samples\", c=colors_from_lbs(labels))\n",
+    "    ax_orig.scatter(centers[:,0], centers[:,1], s=50, c='black', edgecolors='r')\n",
+    "    ax_orig.set(\n",
+    "        aspect=\"auto\", \n",
+    "        title=\"Interactive K-means\",\n",
+    "        xlabel=\"first feature\",\n",
+    "        ylabel=\"second feature\",\n",
+    "    )\n",
+    "\n",
+    "    ax_redim.clear()\n",
+    "    class_name = ['class {0}'.format(i+1) for i in range(len(centers))]\n",
+    "\n",
+    "    # update labels\n",
+    "    counts = [np.sum(labels==i) for i in range(len(centers))]\n",
+    "    \n",
+    "\n",
+    "    ax_redim.bar(class_name, counts, \n",
+    "                label=class_name,\n",
+    "                color=colors_from_lbs(range(len(centers))))\n",
+    "    ax_redim.set(\n",
+    "        aspect=\"auto\",\n",
+    "        title=\"Clustering results\",\n",
+    "        xlabel=\"Main feature\",\n",
+    "        ylabel=\"Number of samples\",\n",
+    "    )\n",
+    "    fig.canvas.draw_idle()\n",
+    "\n",
+    "plot_figure((ax_orig, ax_redim), X, centers)\n",
+    "\n",
+    "def onselect(verts):\n",
+    "    centers = np.array(verts)\n",
+    "    plot_figure((ax_orig, ax_redim), X, centers)\n",
+    "\n",
+    "selector = PolygonSelector(ax_orig, onselect=onselect, \n",
+    "                           props=dict(color='r', linestyle='', linewidth=3, alpha=0.6, label=f\"Component\"))\n",
+    "selector.verts = centers\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/yang/Kmeans interactive.ipynb

@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,13 +63,13 @@
     "    ax_orig.scatter(centers[:,0], centers[:,1], s=50, c='black', edgecolors='r')\n",
     "    ax_orig.set(\n",
     "        aspect=\"auto\", \n",
-    "        title=\"K-means\",\n",
+    "        title=\"Interactive K-means\",\n",
     "        xlabel=\"first feature\",\n",
     "        ylabel=\"second feature\",\n",
     "    )\n",
     "\n",
     "    ax_redim.clear()\n",
-    "    class_name = ['class {0}'.format(i) for i in range(len(centers))]\n",
+    "    class_name = ['class {0}'.format(i+1) for i in range(len(centers))]\n",
     "\n",
     "    # update labels\n",
     "    counts = [np.sum(labels==i) for i in range(len(centers))]\n",
@@ -95,17 +95,6 @@
     "                           props=dict(color='r', linestyle='', linewidth=3, alpha=0.6, label=f\"Component\"))\n",
     "selector.verts = centers\n",
     "\n",
-    "\n",
-    "# ax_redim.hist((X @ component.T - x_center @ component.T),50)\n",
-    "# ax_redim.set(\n",
-    "#     aspect=\"auto\",\n",
-    "#     title=\"1-dimensional dataset after dimension reduction\",\n",
-    "#     xlabel=\"Main feature\",\n",
-    "#     ylabel=\"Number of samples\",\n",
-    "# )\n",
-    "#_asp = np.diff(ax_orig.get_ylim())[0] / np.diff(ax_orig.get_xlim())[0]\n",
-    "#ax_redim.set_aspect(_asp)\n",
-    "\n",
     "plt.tight_layout()\n",
     "plt.show()\n"
    ]
@@ -115,31 +104,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "# pca = PCA(n_components=1).fit(X)\n",
-    "# component = pca.components_.reshape(-1)\n",
-    "\n",
-    "# # print(pca.components_)\n",
-    "# # print(pca.explained_variance_)\n",
-    "# # print(list(zip(pca.components_, pca.explained_variance_)))\n",
-    "\n",
-    "# # fig, (ax_orig, ax_redim) = plt.subplots(1, 2, figsize=(12, 6))\n",
-    "# # ax_orig.scatter(X[:, 0], X[:, 1], alpha=0.3, label=\"samples\")\n",
-    "# # x_center = np.mean(X, axis=0)\n",
-    "\n",
-    "# comp_vector = [component, x_center]\n",
-    "\n",
-    "# ax_orig.set(\n",
-    "#     aspect=\"auto\", \n",
-    "#     title=\"2-dimensional dataset with principal components\",\n",
-    "#     xlabel=\"first feature\",\n",
-    "#     ylabel=\"second feature\",\n",
-    "# )\n",
-    "\n",
-    "\n"
-   ]
+   "source": []
   }
  ],
  "metadata": {