{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Hierarchical clustering basics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Go to:\n", " \n", " - [Notebook configuration](hierarchical_clustering_basics.ipynb#Notebook-configuration)\n", " - [Dissimilar blobs showcase](hierarchical_clustering_basics.ipynb#Dissimilar-blobs-showcase)\n", " - [Parameter scan](hierarchical_clustering_basics.ipynb#Parameter-scan)\n", " - [Manual hierarchical clustering](hierarchical_clustering_basics.ipynb#Manual-hierarchical-clustering)\n", " - [Label prediction](hierarchical_clustering_basics.ipynb#Label-prediction)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Notebook configuration" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2022-11-27T19:22:20.942205Z", "start_time": "2022-11-27T19:22:18.278589Z" } }, "outputs": [], "source": [ "import sys\n", "\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import sklearn\n", "from sklearn import datasets\n", "from sklearn.metrics import pairwise_distances\n", "from sklearn.preprocessing import StandardScaler\n", "from tqdm.notebook import tqdm\n", "\n", "import commonnn\n", "from commonnn import cluster, plot\n", "from commonnn import _fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print Python and package version information:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:07:38.324409Z", "start_time": "2022-11-24T11:07:38.318016Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python: 3.10.7 (main, Sep 27 2022, 11:41:38) [GCC 10.2.1 20210110]\n", "Packages:\n", " matplotlib: 3.6.0\n", " numpy: 1.23.3\n", " pandas: 1.5.0\n", " sklearn: 1.1.2\n", " commonnn: 0.0.2\n" ] } ], "source": [ "# Version information\n", "print(\"Python: \", *sys.version.split(\"\\n\"))\n", "\n", "print(\"Packages:\")\n", "for package in [mpl, np, pd, sklearn, commonnn]:\n", " print(f\" {package.__name__}: {package.__version__}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use [Matplotlib](https://matplotlib.org/) to create plots. The `matplotlibrc` file in the root directory of the `CommonNNClustering` repository is used to customize the appearance of the plots." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:07:38.347120Z", "start_time": "2022-11-24T11:07:38.327081Z" } }, "outputs": [], "source": [ "# Matplotlib configuration\n", "mpl.rc_file(\n", " \"../../matplotlibrc\",\n", " use_default_template=False\n", ")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:07:38.359826Z", "start_time": "2022-11-24T11:07:38.350865Z" } }, "outputs": [], "source": [ "# Axis property defaults for the plots\n", "ax_props = {\n", " \"xlabel\": None,\n", " \"ylabel\": None,\n", " \"xlim\": (-2.5, 2.5),\n", " \"ylim\": (-2.5, 2.5),\n", " \"xticks\": (),\n", " \"yticks\": (),\n", " \"aspect\": \"equal\"\n", "}\n", "\n", "# Line plot property defaults\n", "line_props = {\n", " \"linewidth\": 0,\n", " \"marker\": '.',\n", "}" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:07:38.377863Z", "start_time": "2022-11-24T11:07:38.362511Z" } }, "outputs": [], "source": [ "# Pandas DataFrame print options\n", "pd.set_option('display.max_rows', 1000)" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2019-09-11T14:38:11.051577Z", "start_time": "2019-09-11T14:38:11.034144Z" } }, "source": [ "## Dissimilar blobs showcase" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2021-03-22T13:19:52.308965Z", "start_time": "2021-03-22T13:19:52.302030Z" } }, "source": [ "Learn in this tutorial how to use the `commonnn.cluster` module for step-wise hierarchical clusterings, where\n", "one cluster step does not deliver a satisfactory result. We will also show how to use a data set of reduced size for cluster exploration and how we can transfer the result to the original full size data set." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will generate a sample data set of three clusters that have very different point densities and are spatially not very well separated. As we will see,\n", "it can be non-trivial (if not impossible) to extract all three clusters with a single set of cluster parameters. We will solve the problem by extracting the clusters in a two step procedure. We refer to this kind of hierarchical approach as *manual* hierarchical clustering." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:07:38.432791Z", "start_time": "2022-11-24T11:07:38.380379Z" } }, "outputs": [], "source": [ "# Generate blobs with quite different point densities\n", "dblobs, _ = datasets.make_blobs(\n", " n_samples=int(1e5),\n", " cluster_std=[3.5, 0.32, 1.8],\n", " random_state=1\n", " )\n", "\n", "dblobs = StandardScaler().fit_transform(dblobs)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:07:38.459832Z", "start_time": "2022-11-24T11:07:38.435610Z" } }, "outputs": [], "source": [ "# Initialise clustering\n", "clustering = cluster.Clustering(dblobs)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:07:38.475857Z", "start_time": "2022-11-24T11:07:38.462126Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "alias: 'root'\n", "hierarchy_level: 0\n", "access: components\n", "points: 100000\n", "children: 0\n" ] } ], "source": [ "# Get basic information about the clustering instance\n", "print(clustering.root.info())" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:07:39.180724Z", "start_time": "2022-11-24T11:07:38.478648Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the original data\n", "fig, ax = plt.subplots(1, 2)\n", "_ = clustering.evaluate(ax=ax[0], ax_props=ax_props)\n", "plotted = clustering.evaluate(\n", " ax=ax[1], ax_props=ax_props,\n", " plot_style=\"contourf\",\n", " plot_props={\"levels\": range(8)}\n", ")\n", "\n", "rect = ax[1].get_position().bounds\n", "cax = fig.add_axes([rect[0] + rect[2] + 0.02, rect[1], 0.025, rect[3]])\n", "cbar = fig.colorbar(plotted[-1], ax=ax, cax=cax, ticks=(0, 7))\n", "cbar.set_label(\"density\", labelpad=-15)\n", "cbar.ax.set_yticklabels((\"high\", \"low\"))\n", "for i in range(2):\n", " ax[i].set(**{\"xlabel\": \"$x$\", \"ylabel\": \"$y$\"})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at these 2D plots of the generated points above, we can already tell that this is probably not the easiest\n", "of all clustering problems. One of the three clusters is hardly visible when the data points are drawn directly (left plot). More importantly, the point density between the two narrower clusters is as high as the density within the broader cluster (right plot).\n", "\n", "We also have generated a fairly large amount of data points. Although we can attempt to cluster the 100,000 points directly, this can be rather slow and memory intensive. For quick data exploration, it might be a good idea to perform the clustering on a reduced data set. We can predict the clustering later for the full sized set on the basis of the reduced result." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:07:39.188927Z", "start_time": "2022-11-24T11:07:39.185224Z" } }, "outputs": [], "source": [ "reduced_dblobs = dblobs[::100]\n", "rclustering = cluster.Clustering(reduced_dblobs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we created a cluster object holding a smaller data set by using a point stride of 100, leaving us with only 1000 points that can be clustered very quickly. By visual inspection, we can now also clearly spot three point clouds." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:07:39.469483Z", "start_time": "2022-11-24T11:07:39.190815Z" } }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, '1000 points')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the reduced data\n", "fig, Ax = plt.subplots(1, 2)\n", "_ = clustering.evaluate(ax=Ax[0], ax_props=ax_props)\n", "_ = rclustering.evaluate(ax=Ax[1], ax_props=ax_props)\n", "Ax[0].set_title(f\"{clustering.root._input_data.n_points} points\", fontsize=10, pad=4)\n", "Ax[1].set_title(f\"{rclustering.root._input_data.n_points} points\", fontsize=10, pad=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we pre-calculate pairwise point distances, we can plot the distribution of distances. This can give us a very basic estimate for a reasonable radius cutoff as one of the cluster parameters. For globular clusters, each cluster should be visible as a peak in the distance distribution around a value that is very roughly equivalent to the radius of the point cloud. We also expect a peak for each pair of clusters, roughly corresponding to the distance between the cluster centers. For more complicated data sets this approximation is not valid, but we can still get a feeling for the value range of meaningful radius cutoffs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In general, we want to pick a rather small radius that allows us to estimate point density with a comparably high resolution. If we pick a too small radius, however, the clustering can become sensitive to (random) fluctuations in the point density which yields probably meaningless clusters." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:07:39.617916Z", "start_time": "2022-11-24T11:07:39.471147Z" } }, "outputs": [ { "data": { "image/png": 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oUe6x0nPnzp0DAEyaNAn9+/fHQw89hH379uHatWv48ccf8eKLL+Ldd99F69atrVo7ERGRrbLYPWpm7TmcanaPWiEXE8hOXl4eAOOI3J1cXV0BABqNBgDg6OiI33//HTNmzEDfvn3Fc2vWrMGjjz5qpYqJiIhsnyxXfeazPYfsFBQUAACUyvKDsCqV8b9vSUmJeG7hwoX4+uuvMX/+fERGRmLz5s145plnEBgYiJiYGKvUTEREZOssF9RQ+1WfhcU6GAwClEqFRWqjmnNzcwMA6HTlRzu1WmOwdnd3B2BcdPDWW29h+/btiI2NBQA89NBDcHNzw4gRI3Dp0iX4+vpaqXIiIiLbZblVnyZ9Tauz16e7S1lQEwSgqJjTn3JSuvggMzOz3GO5ubkAAC8vLwDA8uXLERYWJoa0UpMnT0ZOTg7Wr19v4WqJiIgaBqus+qxOew43Z0ezYza9lRcHBwcEBAQgMTGx3GOliwgaNWoEALhy5QqaN29e7nml52qyEwIREZE9s1xQM/m6OlOfjg5KqB3LyuF9avIzZMgQHDp0SJzqLHX48GFERkYiNDQUABAREYErV66Uu740oEVERFi8ViIioobAKiNqLtUIagDg7sxtpOTsoYceQlpaGr7//nvx3Llz57BlyxYMHz5cPDdq1ChcunQJW7duNbt+6dKlcHV1xZAhQ6xWMxERkS2zyqpPJ3X13sbNxQGZecUAgAL2UrOYjRs3AgBOnToFANiyZQsCAwMREBCAfv36AQB2796N9PR0PPLII+J1I0eORO/evTF16lRcu3YNSqUSS5cuRWBgIKZPny4+7/HHH8cPP/yAhx9+GC+++CJatWqF33//HRs2bMCyZcsQEhJixU9LRERkuywW1ExVZ+oTML9PrYC7E1jM6NGjzY6nTp0KAOjXrx927doFAHj33Xexe/dus5FRlUqFrVu34vXXX8eiRYug0WgQGxuLjz/+GAEBAeLzFAoF/ve//2HBggVYv349Ll++jI4dO+K3337DsGHDLP8BiYiIGgiZBTXTbaQ4omYppuGrMqWB7U4eHh5YtmwZli1bVuX1KpUKM2fOxMyZM2tTIhEREcEKQU2lVMBBVb1+aKYtOgq4mKBeaEp0WLXlPHIKSjB1RFt4uqmlLomIiIiqyeJBzUmtgkJRvaDmxsUE9UoQBMz/7iS2HUkBAOQVajF3cleJqyIiIqLqstiqz1LVXfEJGBcTlGJQq7sNu5LEkAYAu0+m4Vp6gYQVERERUU1YPKhVp9ltKdMRNfZRq5uTFzOw5Kd4s3OCAGzYdUmiioiIiKimLB7UqruQADC/R62QI2q1ditHg7e+PAK9wbhoQGWyZ+rm/VeRV8gQTEREZAssH9Sqsc9nKVfTVZ8cUasVnd6At788goxcYz86B5UCi6b2gIercbSyqESPX/8uv2sAERERyY/MRtS4mKCulv58BicTyzZOf/GRdugSGYCRfZqL5zbuToJOb5CgOiIiIqoJmd2jxvYcdbHtyDX8sLPsHrSh3UMwsm9zAMCofmFim5QbWUXYeTxNihKJiIioBqyw6rP6HUA4olZ7aRmFmP/dSfG4VRNPvDa2vdgaJcDbGbGdm4iPr/szsVqNb4mIiEg6spr6NL9HjUGtJtb9mQhNiR4A4OHiiLn/7grnO0LymAHh4tcJV7NxymSKlIiIiORHVlOfpiNqxVo976OqptyCEmw+cFU8fubBSDTxdyv3vNZNvdApwl88/v6vRKvUR0RERLUjqxE103vUAKCAo2rV8r99V8TRNG93NYb1aFrpcx8b0EL8es+p62yAS0REJGOyCmquTncENQ0XFNxNiVZv1sT24b7Ny015muoZFYTQQONomyDAbPEBERERyYuspj6VSgXvU6uhbUdSxJ5pagclHu4XVuXzlUqF2b1qvx24itzCEovWSERERLUjq70+AcDdtEUHR9SqJAgC1v1Zdp/Zfd2bwtfD6a7XDe0eAk+3sga4Ww8mW6xGIiIiqj1ZTX0CgBtbdFTboTPpSErLE4/Hmtx/VhVntQMe6BkqHu+Pv1nvtREREVHdyWrqEzBfUMBtpKq27q+L4te92wWhebBHta/t1S5I/PpkYgaKtfp6rY2IiIjqTlZ7fQJseltd55Nz8E/CLfF4XGx4Fc8ur12YrzgtXaI1sKcaERGRDMlv6tNsGykGtcqY9kCLDPVCh5Z+Nbre0UGJjiY91Q4npNdbbURERFQ/5BfUzEbUOPVZkZtZRdh+JEU8fmxgS3GrqJroGhkgfn34LO9TIyIikhsrBLXq7/UJ3HmPGkfUKrJh1yXoDcZ9OoN8XNC/Y6NavU73NmVB7cK1XGTmFddLfURERFQ/ZDei5u7C9hxVyS/S4pe/r4jHYwa0gIOqdv8ZmwW5I8DbWTw+co7Tn0RERHIiu6Dm6mwy9ckRtXLWbL8ojjS6uzjggV7Nav1aCoXijulPBjUiIiI5kV1QM294y6BmKj27COv/KtvyaUz/8HL7o9ZUN5Og9k9COgRBqNPrERERUf2RXx8106lP9lEz88Vv58R+Z74eTnhsYM1aclSki0lQS8/W4MqN/Dq/JhEREdUPiwY1hcK4/2RNuDuzj1pFktLy8NuBq+LxpGERZvui1pavhxNahXiKx5z+JCIikg+LBjUXtarGbSNcTUbU8rmYQPTZr2dxe6Enmga64cHetb837U6m05/sp0ZERCQfFg1qNZ32BO4YUSvS8Z4pAKcSM7D31HXx+JkH2tR6pWdFTBcUHD9/C1qdod5em4iIiGrPokGtpj3UAPM+anqDYPd7UAqCgKU/nxGPo5r71LpvWmWiw/2gdjT+USgq0SMuKateX5+IiIhqx7JBrYb7fALme30CbNGx59R1s+A0ZUTbWu1CUBUntQrR4WVbUP3D6U8iIiJZsFhQ83JTY0i3kBpfp3ZUQqUsCyL5drygQKc34LNfzorHvdsFoWOrmu3pWV3duJ0UERGR7FgsqPl6OuGJIa1qfJ1CoTAbVbPn3Qk2H7gqtstQKoD/PNTGYu9lep9awtVs5BaWWOy9iIiIqHos3ketNkzvU7PXqc8CjQ5f/nZOPB7avSnCG3tWcUXdtGziCR8PNQDAIABHz92y2HsRERFR9cgzqHG/T3z7xwVk5Bo3SVc7KvH08EiLvp9SqUDX1pVvJ1VUrONIGxERkZXVvWOqBbiZtOjIt8MRtWvpBVj3V6J4PG5gSwT5uFj8fbtGBmDbkRQAxn5q+05fx4kLGThxMQPnknOgNwjw93LG56/2QbCvq8XrISIisneyDGruLva93+eSn+LFXmaB3s6YMLilVd63W5uyEbW0jEJM/+xwuefcytFg4bpT+GhK93pffUpERETm5Dn1adb01r6mPg+fvWnW3HbKyLZwcbJOng7wdkFYsMddn3fwzE38fviaFSoiIiKyb/IMaqbbSNlRUNPpDfhkY7x43L6FLwZ1bmLVGsYPKtvo3dtdjZgOjTDtkXZY/Xo/9GgbKD72yY9xyMzVWLU2IiIieyPPqU873Zj9pz2XkXQ9D4BxQ/uXRrez+vTisB6h6BThjxKtAU0D3cze/7Wx7THh/Z0oLNYjt0CLRRvi8N5TXaxaHxERkT2R5Yia6cbs9tKeIyuv2Kwdx/CeoWgd6i1JLcG+rggNci8XEhv5ueLZh9qKx38eS8Xuk2nWLo+IiMhuyDKouTvbX3uOLzYnIO/2NK+bswOeedByzW3rYmTf5ogO9xWPP/z+FFt2EBERWYgsg5pZew47mPq8cC0Hv/x9RTyeNKw1fD2cJKyockqlAq+P7wC1g/GPTkZuMZaZbBpPRERE9UeWQc20PUdhA5/6FAQBn2yMg0EwHocGueORfmHSFnUXzYLcMen+1uLxpv1XuZE7ERGRBcgyqLmajag17KnPfadv4NiFDPF42qgoODrI8j+LmXEDwxHR1Es8XrD2JEq0egkrIiIianhkmQjspeGtTm/Asv+VTRt2bxOAnlFBElZUfQ4qJWaO7wCV0rjgIDWjEH8dT5W4KiIiooZFlkHNdFP2Qo0O+tJ5wQbml7+v4OqNfACAUgE893CUxBXVTERTL9zXLUQ83rgrScJqiIiIGh5ZBjV3F0ez46Lihjeqll+kNWvHcX/PUIQ39pSwotp5JKaF+PWZK9mIT8qSsBoiIqKGRZZBzdXZvA9vQ9ydYM22i8jON7a1cFarMHl4pMQV1U7rpl5m7To27L4kYTVEREQNiyyDmoNKCWe1SjxuaPepXc8sxPc7E8Xj8bEt4e/lLGFFdTPaZFTtr2OpuJXDraWIiIjqgyyDGmB+n1pD25j9800JKNEaAAD+Xk4YFxt+lyvk7d7oYAR6G4OmTi/gf/suS1sQERFRAyHfoObSMPf7PHc1G78fviYeTx4eCRcnWW65Wm0OKiVG9m0uHv9v7xVodQbpCiIiImog5BvUTEbU8htI01tBELDkp3jxuEVjDwzrESphRfXnwd7NxN0KMvOK8dcxtuogIiKqK9kGNfNearY/9SkIAn47mGzW3Pa5kVFiHzJb5+PhhNguTcTjjbvZqoOIiKiuZBvUTPf7zCu03aAmCAIOnb2Jf3+0D3PXnBDPd28TgB5tA6UrzAJGm2x9FX85C2cus1UHERFRXcj25ig/z7JNyW1xFaEgCDhy7ha++C0Bpy+ZBxZntcrmmttWR+tQb7Rv4YtTlzIBABt2JeHdJ30kroqIiMh2yXZELdDHRfz6RlaRhJXU3M2sIjz3yX5MW3KgXEjr0TYQn73cxyab21bH6JiyUbU/j6UgwwZDNhERkVzIdkQtyCSo3bShoKbTG/DGyn9w9kq22fmukQF4+v7WuKeFb8UXNhD9OjRCgLcz0rM1t1t1XMFT97eWuiwiIiKbJNsRtSAbHVFb+2eiWUjrHOGP/3upNz55vmeDD2lABa069l1GiVYvXUFEREQ2TLZBzXTqMyuvBMU28MP+Umqu2f6dw7o3xZJpvRDd0k/CqqzvIZNWHRm5xWZ948i+aDQaZGdnV/irpKRE6vKIiGRPtkEtwNsZCpPOFekyH1XT6Q2Y8+0JsdGrv5czpj3STuKqpOHj4YRhPZqKx2t3XITeIEhYEdXV0aNHMXDgQHh7eyMyMhJTpkxBdnb2Xa+bP38+fHx8Kvz166+/lnt+UVERXnnlFYSEhMDb2xuxsbHYuXOnBT4REZFtkO09ag4qJfy9jPc6AcCNbA1CAt0lrqpya/9MRMLVbPH49XHR8HB1rPyCBm7cwHD8+vcVGATg6s0C7D2VhpgOjaUui2rh9OnT6N27N8aOHYtx48YhLi4Oy5cvx7Zt23Do0CH4+d19xLiisBUVZb7yOSMjAw8++CDOnDmDiRMnon379khMTERmZma9fRYiIlsj26AGGKc/xaCWWShxNZUrN+XZoyl6tQuSsCLphQS6I6ZDY/x13LhDwZptF9EvuhEUiobR4NeeTJs2Dc888ww++eQT8dzw4cMRGxuL2bNnm52vTExMzF2fM3HiRCQkJGDXrl2Ijo6uS8lERA2GbKc+ASDI23RBgTzbPNw55Rng7Yxpo+xzyvNOjw9uKX595kq22a4MZBuys7OxZ88ezJw50+z8wIEDMWDAAKxbt65e3ufo0aPYtGkTZsyYwZBGRGRC3kHNV/4tOtbu4JRnZSJDvdGltb94vGbbBQmrodrw9vbGqlWrEBRUfoQ4Ojoa6enpyMioewD/6KOP4OjoiGeeeabOr0VE1JDIOqjJveltYmouvtxSNuV5f4+m6Bll31Oed3p8UNmo2qGz6TifnCNhNVQbTzzxRIXn09LSoFKp4O5+93tHFy1ahBYtWiAoKAhPPfUUcnNzzR4/deoUWrdujaysLIwaNQq+vr5o1aoVVq9eXS+fgYjIVsk6qAX5OItfyy2oFWh0eOuLI2ZTni9wyrOcrpEBiGjqJR5/t+OihNVQfdHpdNi/fz/69u0LJyenuz5/9uzZGDVqFN588038+uuvuPfee1FcXAwAMBgMSExMBAAMHjwYkZGRmDdvHry9vTFp0iRs3rzZop+FiEjOZL2YIMjHVfxaTlOfgiBg/toTuHIjXzz3xrgOnPKsgEKhwOODWuKdVUcBAH8eTcEzD0Sisb+bxJVRXSxYsABpaWnVvkdt8+bN6N27NwAgMjISQ4YMwRdffIGpU6fixo0b0Gg0iIuLw969e9GnTx8AwJgxY9CxY0fMmzcPw4cPt9hnISKSM5sZUSvQ6JBfpJWwmjIbdyfhz6Op4vG/7muFHlGBElYkbzEdGqGxvzF0GwRg3Z+JEldEdXHs2DHMnj0bq1evRq9evap87quvvoobN26IIQ0ABg0aBD8/P6xduxYA4OBg/Pdit27dxJAGGO+PGzt2LPbv31+tnm1ERA2RrIOat7sTHB3KSpTD9GdcUiaW/BQvHneO8MfT90dKWJH8OaiUGDcwXDzefCAZWXnFElZEtXXp0iUMHz4cc+fOxfjx4+/6fHd3dwQGmv8jRqFQIDQ0FAkJCQAAT09PAECrVq3KXR8WFgYASEpKqmvpREQ2SdZBTalUINC7bFRN6unP7PxivPXFEej0xi77/l7O+O+kzlAp2Rvsbu7vEQofDzUAoFirx8bd/MFra9LS0jBo0CBMnjwZr7zySp1eq7i4GI6OxlsFnJycEBYWhrS0tHLP0+uNW8eVjroREdkbWQc14I7N2TOlC2p6g4BZXx3DzdsNeFVKBeY81Rm+Hne/kZoAJ7UKj8a0EI837k5CoUYnYUVUE5mZmRg8eDBGjhyJWbNmVfu65OTkcueKi4uRmJiI9u3bi+d69uyJQ4cOQacz/zNROupWOrJGRGRvZB/UzFp0ZEsX1FZvPYfDZ9PF4ykj2qJ9uH1ttl5XI+9tDlcnFQAgr1CLX/dfkbgiqo78/HwMGzYMffr0wQcffFDt3SXi4uLQu3dvXL582ez8p59+iuLiYkyZMkU898QTT6CgoADffvuteC4jIwNr1qzBhAkTqtUChIioIZL9fIJp01upRtTik7Kweut58TimQyOMHdCiiiuoIp6uaozo0xxrby8mWPdnIkbdG2Z2HyLJz8iRI5GRkYH+/fvjxx9/LPf40KFD4ebmhi1btgAAhg0bBgCIiIhAREQEOnTogBEjRqBPnz44evQoVqxYgQkTJuChhx4SX2Pw4MF45JFH8MILLyAtLQ0uLi5YuXIlXFxcMG/ePOt8UCIiGZJ/UPOWfneCn/dehmC8LQ0hAW6Y+XgH7llZS2MGhGPD7iRodQakZ2vwxz/XMLxnaI1fJ69QC5VSAVdn2f8Rtnk7duwAYGyXUZGkpCS4ubmJI2SlI2hqtRq//PILFi5ciI0bN2LDhg1o164dlixZgilTppj9P6RQKPD9999j1qxZWLFiBTQaDWJiYrB48WI0atTIsh+QiEjGFIJQGkHqT1RUFAAgPj7+Ls+8u/1xN/Dq/x0CADTxd8WG/8bW+TVroqhYh+Gv/4GiEuNNzbMndUZs5yZWraGhmffdCWzafxUAEBrkjrVv9YeymgsyktLy8PUf57HjSAocHVR48r5WGBfbkqNyREQkW3XJRbIfjjDb7zNbA4NBqPYP9fqw5+R1MaS5OTug7z3BVnvvhmp8bEtsPnAVggBcvZGPvaeuo1+HqkdNziXn4Ovfz2P3yTRxdLNYq8eKTQn4459reG1se3Rs5V/la1D1nLmchf9+fQy5BVpEhfmgfbgvolv4IrKZN5wcVVKXR0RkV+Qf1EwWE2h1BmTnF8PX07mKK+rX74fLVq0N6NQYTmr+oKqr0CB3xHRohJ3Hje0Yvt12AfdGB1c4nRyflIXVv5/H/rgblb7e5ev5mLp4P4Z1b4qpI9vChytxa+1UYgZeXn5IXJG7P+6G+L13dFAiMtQLve8Jxpj+LRjaiIisQPbzRe4ujmb3Id3I0ljtvW/laPBPQtlKz/u6hVjtvRu6xweVNTc9cyUbxy5kmD0uCAJWbz2PyR/uLRfSwhp54O0nOuLhe5vDNNttOZSMx977C38cvmbR2huq4xdu4aWlByttm6LVGXD6UhY+++UsJs7fjTNXsqxcIRGR/ZF9UAPu6KWWVWi19932zzUYhLIaotmOo960aeaNrpFlU5Xfbrsgfl2i1WP218excnOC2TWtm3ph3uSu+HZmDIZ2b4pXx7THylf7mm36nlugxexvjuH0pUzLf4gG5EhCOl5edkic5lc7KPHy6HswcWgEOkX4lxs9u3w9H898uA8rNp2FVmeQomQiIrsg+6lPwBiSktLyAFh3RM10ZOa+7iFWvTfOHkwY1Ar/JNwCABw+m45zV7MR6OOCNz7/B6dMglZIgBteGt0OPdoGlpsebdvcB1+81hc/7rmMlZvOorBYD0EANh+4inta+Fr189iqg/E38frKwyjRGgOXk6MKC57pim5tyrZ+0ukNOH8tB78fuoYf9yRBEIxNoL/+/QL+Pn0Db03oaBaYiYioftjEiJpp01trtei4mJKDCym54vF93Zpa5X3tSefW/ogM9RaPl/wUj8kf7DULaZ1a+WHlq33RMyqo0pYoDiolxvRvgVfHlnW633vqOvSGel/Q3ODsPXUdMz4vC2kuahU+mtLdLKQBxu9x22Y+ePnRe7B0Wm809ncVH7uYkounFu7Bmu0XYIFF5EREds0mgpoU20j9fqhsNK1NM280C2Jn9PqmUCgwYXBL8fjYhQykZpRNbQ/vGYpFz/WEl7u6Wq/Xu10wHFTGMJedX4JTiRl3ucI+pWUUYs32C3hy/m7MWHFYnLp0dVLh46k90Cmi6tWzHVv54Zs3YvDwvc3Fc3qDgOX/O4sdR1MtWToRkd2xkanPslWe1thGSm8QsO1IWVAbytE0i7k3uhFCA91w9WaBeE6hAKaOaIvHBobXqLGwh6sjurYOwIEzNwEAO4+nsWXHbVl5xdhxNAU7jqbg9KXyiwDcXRzw8dQeaBdWveliV2cHvDqmPfpFN8LcNSdw4/ZI96c/xqFnVCDcXRzrtX4iIntlEyNq1p76PHIuHbdyigEYN1+P7dLY4u9pr1RKhdkKUGe1CvMmd8W42Ja12v3BtB/b7pNpMNj59GdRsQ5f/JaAUe/swKINcRWGtMhQLyx5oVe1Q5qprpEBWPpiL6gdjX+VZOQW4/NNCXe5ioiIqssmRtSCTYLarRwNdHoDHFSWy5im0549owLh7c6+XJZ0f8+muJldhMvX8zFhcEu0Cqn9Tel92gdDue4kDAKQnq1BwtVstG3uU4/V2ga9QcDWg8n4fPNZ8R8dpkID3RDbpQliOzdB82CPOr1XE383TLwvAituB7Qf9yRhaPemaNPMu06vS0RENhLUAkyCmnD7B3AjP9cqrqi9Qo0Ou0+micdcRGB5CoUCk4a1rpfX8vVwQnRLPxy/3Zdt14k0WQU1vUHA/rgbuHw9Dzn5JcgpMP7Kzi9BQZEO94T74j8PRtbpHweHz97E0p/P4KLJYhgA8HZX4/4eTRHbJQQRIZ71ul/tuNiW+P3wNVy5kQ9BABauO4kvpt8LFVdKExHViU0ENSdHFbzd1cjOLwFgnP60VFDbfTINmtu9pNxdHND7niCLvA9ZTr/oRmZB7dmH2tRrKKmtc8k5WLD2JBKuZlf6nKTreTgYfwNznu5So6nIrLxi/HU8FX8cvoa4JPPpTbWDEmMHhOPxwS0tdu+Yo4MSr41tj+c+2Q/A+Fl/2pOE0TEtLPJ+RET2wiaCGgAE+7qIQe2GBe9TM98yqgm3ybFB/To0wuKNcQCAa+kFuJSah/AmnpLVU1Ssw6ot5/D9X5eq1TLkZrYGUxb9jedGRmF0TFilIbNAo8Oek2nYfiQF/ySkV/jaQ7qG4JkHIxHsa5l/2JjqFOGPod1DsPX2rQMrNiUgpkNjBHhbb8s3IqKGxmaCWqCPCxKu5gCwXFC7eiMfR87dEo+HcssomxTk44K2zbxx5ko2AOOomlRB7eCZm/jg+1NIyzDfUaNbmwA09nOFl7sa3m5qeLqpcT2zEKu2nIfeIECnF7B4YxxOJWbijfHRcHNxhMEgIDE1F0fO3cKRhHQcvXBL7H92pw4t/fD8w1FWv09s6sgo7Dt9A3mFWhRqdPj0pzi8N6mLVWsgImpIbCeoeVt25Wd+kRavf34Ypf06G/u5on04O9vbqn4dGpkEtVQ8dX/93ANXXenZGiz7OR7bjqSYnW/i74rpj0Wja2RAhdd1auWPt1cdxa0c4w4cfx1PxcWUHEQ09caRc+niqHJF/L2cMahLEwzq0gStm3pJMt3r6+GEKSPaYsHakwCAP4+m4v4eN9GjbeBdriQioorYTFAL9jXd77N+g5reIODd1Udx+Xq+eG7KiLayuK+JaiemQyP83y9nAQCJqXlIvpmPpoGWb1pcXKLHur8S8e0fF8R9MwFjG5LxsS0xcWgEnNSVT6dHt/TDV2/0w6zVR8XR3as3C8z6zJnycHVE/46NMbhLE0S39JPFzfsP9AzFbweuivfKffj9Kax8rS98PLh6moiopmwmqAX6WC6offbLGRyIvyke/+u+VhjQib3TbFnTQHeEN/ZAYqpxj9jdJ9PM+rXVN0EQsPN4Gpb+HI/rd+yeEdXcBzPGtUfLJtVrO+Lr4YRFz/XEqi3nsHrrebPHFAogMtQbXVsHoEtrf0S39IOjg7zaISqVCrw2tj0mLdgDvUFAakYhXlxyAEum9YKnW/V2mSAiIiObCWpBZk1v629j9q2HkvHdjkTx+N7oYEy+P7LeXp+k069DIzGo7TpuuaB2PjkHizacxsnETLPz3u5q/Ht4JB7o3azGI10qpQKTh0ciOtwXG3dfRoC3M7pFBqBjhB88XeUfdlqFeOHp+1uLvdUupOTixaUH8MnzveDhyl0LiIiqyyaDWk5BCTQlOjir61Z+XFIm5t++lwYAwht74J0nOkEpg+kjqruYDo2waotxROrMlWzcyCoy+3NUH44kpOPl5Qeh05etuFQpFRgdE4aJQ1vXOZR0axNYboN0W/HEkFYo0OiwZvtFAEDC1Ry8vOwgFj/XA27cYoqIqFrkNWdSBT8vZ7NRiRt1HFW7mVWE11f8I25I7e2uxsL/dIers81kV7qL8MaeaOJf1pZij0kj4/qQllGIt1cdNQtpfe4Jwndv9ccLo9rZ/ciRQqHAsw+1wZj+Zb3U4i9n4dX/O4RCjU7CyoiIbIfNBDWVUgF/r7J+THVZ+anTG/D654eRmVe2n+f7T3e1WBNdkoZCoUBMx7J7DXefqL+gVlyixxsr/0FOgXEVptpRiQ+f7Y6F/+mO0CDLL1qwFQqFAi+MisLD9zYXz51MzMT0zw5BU8KwRkR0NzYT1ADz6c8bmbUPauv+TBR7sgHAq2Pbo2MrvzrVRvIUE122SfuJixn10tpFEAQs/P4kzieX/Rma8Vg0erXjLhYVUSgUeHn0PXigV6h47tiFDLzx+T/Q6SvuA0dEREY2FdTqY+VnWkah2Uq6B3qF4qHezepcG8lTm2beCLzdGd8gAB+uPw1BuPvuAFX5cfdlsfs+ADzSLwxDu3NP2KoolQrMeCwaQ7uXNZE+dDYdC9edqvN/DyKihsymglqQj8nUZ3btgtqiDXHiXp7e7mpMHdm2XmojeVIqFZhosuH7vtPXsf1oShVXVO3ExQx88mOceBwd7osXRkXVqUZ7oVQqMPPxjhhgMh29+cBVfPX7BQmrIiKSNxsLamX3kN3Zq6o69pxMw77T18Xj5x+OsolWB1Q3D/YKRZfW/uLxoh/ikJlb88Uo6dlFeOuLI+Kemv5ezpjzVBc4qGzqfyNJqZQKvP2vjog22fVj5eYEsz12iYiojE39hAn0qf1igqJiHRZtKBsJ6dTKD/dxL0+7oFAo8Mb4DnC5vSNATkEJPvrhdI1eQ6c3YOYXR8QFKA4qBeZO7gI/L244XlNOjirM/3c3hAa6iefmrjmBo+dvVXEVEZF9sqmgZt70tqhG97as2npevK/NQaXAq2Pbc4soO9LIzxVTRpRNc+88noa/jqVW+/p1fyYi/vaWSADwypj2aBfGvWBry8tdjQ+n9IC3u3FEW6cX8Mbnh5GUlidxZURE8mJbQc1kv8+iEj3yCrXVui4xNRff/1m2+8C4gS3RPNij3usjeRvZt7nZ6t4P159Cdn7xXa9LvVUgNs4FgGE9mnIBSj0ICXDDwv90g9rR+NdQfpEOryw/KG5IT0RENhbUvNzU4l/qQPVWfhoMAj78/pR4X1EjP1c8OdRyez6SfCmVCrwxLhpOjsYp0Oz8ErPp8IoIgoCPfjiNYq1xAYqPh5qLB+pRuzBf/PfJzigd3L6eWYTHZv+FzzclICe/RNriiIhkwKaCmkKhMJv+jDOZiqrMlkPJZnswvvzoPXXeeopsV0igO/7zYNlertuPpFS5Y8HO42k4EH9TPH7h4XZcgFLP+nVohBceLgu/BRodvvr9PEa9sx3L/ndGvC+QiMge2VRQA4B2YT7i15/9chbp2ZVPk1zPLMSSn+LF437RwejNpqR275GYFrinRdmfo7nfncDxC+VvZC8o0mLxxrIRt66R/hjctYlVarQ3YwaEY9oj7eDuUvaPqMJiPb7bfhGj3t6BTzbGIbeQI2xEZH9sLqhNHh4p7seZV6TFgnUnK1xUoNUZ8PaXR8X72FzUKrw4+h6r1krypLrdz6t0Gj23QIsXPj2ATfuvmD1vxaYE8X4ptYMSr47hAhRLGtO/BX56bxD+/UAkPN3K9kkt1uqxfuclTJq/x2w3CCIie2BzQS3Y19VsmmR/3A38fvhauect/Tke8ZfLpkZfHdvebNqU7FuzIHe880QnqB2M/wvoDQLmfXcSn/wYB71BwNkr2fhxT5L4/CeGtELTQO7haWnuLo548r4I/DR7EKaOaAsfj7Jp5tSMQjzz0T72XCMiu2JzQQ0wbvvUrU2AeLx4Q5zZFOhfx1KxYVfZD9kHezfjFj9UzoBOjbH0xV7w9XASz63/6xKm/98hLFx3EqUDtaFB7nh8UEuJqrRPrs4OGD+oJX6cHYvJw1tDeXsgs1irx+yvj2PRhtPcJ5SI7IJNBjWFQoHXx0VXOAV69UY+5n53QnxuqxBPvDS6nUSVkty1C/PFF9P7olWIp3juwJmbOGcyxfba2PZQ314pStblrHbAxKGt8fHUHmbToRt2JeH5T/Yjg608iKiBs8mgBlQ8BfrLvit468sjKNToAABuzg54/+muYjsGoooE+7ris5f7oF90cLnHhnYPQecI/wquImvq1iYQq2f0Q0RTL/HcycRMTFywB3+bbAtHRNTQ2GxQA8pPgS78/hQupuSKx29O6ICQALeKLiUy4+JkDPVP3hchnvN0c8RzI9kzTS4a+blixct9MLR72dZvt3I0eO2zw3jt/w7hWnqBhNUREVmGQqjJPkzVFBVl/OEWHx9/l2fW3fXMQjz+/i5xFK3UmAEtMG0Upzyp5g7E38CB+JsY3jPUbASH5EEQBPy09zIWb4gTG1kDxpW54we1xITBLdkrkYhkpS65yOaDGgD8+vcVzF97UjxuF+aD5S/1hoPKpgcMiagKiam5+PiH0zh+IcPsfLCvC54bGYV7o4P5dwARyYLdBzVBEPDmF0ew60QaAr2dseLVvmzFQWQHBEHAjqOpWPJTfLk9Qr3d1ejfsTEGdWmC9i18oVSyBx4RScPugxpg7IOVcCUboUHu8HB1vPsFRNRgFGh0+GrrOXz/1yWz6dBSQT4uGNipMYb3CkXzYA8JKiQie8agRkQE4PL1PKzcnIB9p29AqyvfZ81BpcD7T3dF3/blV/gSEVkKgxoRkYm8Qi12n0zD9iMpOHouHaaDbGpHJT59vifah/tJVyAR2RUGNSKiSmTmavDXsTR8vvks8ouMq8M9XB3xfy/1RovGnne5moio7uqSi7gkiogaNF9PZzwSE4YFz3QT93bNK9Ti5WUHcSOrSOLqiIiqxqBGRHahYyt/vPtkJyhuL/68ma3By0sPIregRNrCiIiqwKBGRHajf8fGeOXRe8TjpOt5mP7ZYRSX6CWsioiocgxqRGRXHr43zGyrsFOXMvH2qqPQ6cuvEiUikhqDGhHZncnDW+OBXqHi8b7T1zFr9TGGNSKSHQY1IrI7CoUCr41tjz73lPVT++t4KmZ/fZxhjYhkhUGNiOySg0qJ957qjB5tA8VzO46mYM43xyvc3YCISAoMakRkt5wcVZj3767oGhkgntt2JAXvf8uwRkTywKBGRHbNyVGFhc90Q5fW/uK53w9fw7zvTsDAsEZEEmNQIyK756RWYeF/uqFTRFlY23IwGbO/OYZCjU7CyojI3jGoEREBcFY74IP/dEOnVmV7gG77JwVPzN2F4xduSVgZEdkzBjUiottcnBzwwbPd0dEkrKVmFGLq4v1YtOE0NCUcXSMi62JQIyIy4eLkgMXP9cTEoRFQKRXi+Q27kvDE3N04lZghYXVEZG8Y1IiI7uDooMTk4ZFY+VpfhDXyEM9fSy/As4v+xqyvjuLIuXQuNiAii1MIglDvf9NERUUBAOLj4+v7pYmIrKpEq8eXW87hu+0XcWcua+TniqHdQ3B/j1A08nOVpkAikr265CIGNSKiaohPysKcb4/jyo38Ch/vHOGP5x+OQkRTLytXRkRyV5dcxKlPIqJqiArzwTczYzDnqS7oGRUIk9vXAABHz9/Cc5/8jYspOdIUSEQNEkfUiIhqIT1bg98PJ+O3g8m4ajLK5ufphM9e6YMm/m4SVkdEcsIRNSIiKwvwdsaEwa2w7u3+eG9SZ3GFaEZuMV5ccgCZuRqJKySihoBBjYioDhQKBQZ2boIZ46LFcym3CvHSsoPIL9JKWBkRNQQMakRE9WB4z1BMGdFGPL5wLRczVhxGsVYvYVVEZOsY1IiI6snjg1ph3MBw8fj4hQy8s+ookm/mM7ARUa04SF0AEVFDMmVEW2Tnl2DLoWQAwN5T17H31HUAxoUGQT4uCPZ1RasQTzx8bxg8XB2lLJeIZI4jakRE9UipVOD18dHo3S6o3GMZucU4cyUbfx1PxYpNCXj8/Z04GH9TgiqJyFYwqBER1TMHlRJznuqCR/u3QIC3c7mea6XSszV4eflBzF97EgUabvhOROWxjxoRkYXp9AakZ2twPbMQ1zOLcOFaDjbuToJOX/bXb7CvC2Y+3gFdWgdIWCkRWQK3kCIisjGJKbl479vjOJ9svpPBqHub4/mHo6B2VElUGRHVNza8JSKyMeFNPPHFa33x1LDWYrNcAPhxz2VMX3EYmhJOhRIRgxoRkWQcVEo8dX9rfPFaX4Q39hDPHz6bjpeWsmEuETGoERFJrnWoN76cfi8Gdm4snjuZmIkXPt2PnPwSCSsjIqkxqBERyYDaUYVZT3bG8J6h4rmEqzmYsvhv3MrhvqFE9opBjYhIJlRKBV4fF41H+7cQzyWl5eHZj/chLaNQwsqISCoMakREMqJUKjBtVBSevC9CPJdyqxDPfrwPN7OKJKyMiKTAoEZEJDMKhQL/fiDSbJP3m9kavPnFEZRwz1Aiu8KgRkQkU48PaoUXH2knHsdfzsKiDXESVkRE1sagRkQkY4/2b4GHejcTj3/5+wp+/fuKhBURkTUxqBERydxLo9uhbTNv8fijH07jzOUs6QoiIqthUCMikjm1owpzJ3eFt7saAKDVGTBz5T/IzCuWuDIisjQGNSIiGxDo44I5T3URt5u6ma3BO6uOQqc3SFwZEVkSgxoRkY3oFOGPqSPbisfHzt/C//1yVsKKiMjSGNSIiGzImP4tENu5iXi87s9E/Lz3snQFEZFFMagREdkQhUKBN8ZHm23i/uH6U9h5PFXCqojIUhjUiIhsjIuTAz54tjsCvJ0BAIIAzPrqGI6evyVxZURU3xjUiIhsULCvKxZN7QEPV0cAxpWgM1YcxrnkHIkrI6L6xKBGRGSjWjT2xIfPdoeTowoAUKjR4ZVlB3EtvUDiyoiovjCoERHZsHta+GLO02VtOzLzivHS0gPIzNVIXBkR1QcGNSIiG9e7XRDeGB8tHqfcKsQTc3fj/345g+Sb+RJWRkR15SB1AUREVHfDeoQiK68Ey/53BoBxZO3bbRfx7baL6NjKDw/0CkX/Do3hpFZJXCkR1QSDGhFRAzF+UEto9Qas2nIOOr0gnj9+IQPHL2Tg4x9Oo12YL1qFeKJlEy+0CvFE00B3cdqUiORHIQiCcPen1UxUVBQAID4+vr5fmoiI7iI7vxi/H76GTX9fRdL1vCqf6+SoQmSoF566vzW6tA6wUoVE9qUuuYhBjYiogRIEAfGXs7Bp/1XsOJKCohJ9pc9VKRWY8Vg0hvcKtWKFRPahLrmIU59ERA2UQqFAuzBftAvzxbRR7XAyMQMXUnJx8VouLlzLQfLNfBhu/1NdbxAw97sTuJFVhEnDIqBQcDqUSA4Y1IiI7ICrswN6RgWhZ1SQeE5TosPpS1mY/fUxZOQWAwC+3HION7KKMP2x9nBQsTEAkdT4fyERkZ1yVjuga2QAPn+1L5oFuYvnNx+4iumfHUahRidhdUQEMKgREdm9Rn6uWPFKH0SH+4rnDp65iamL/0ZGDhvnEkmJQY2IiODppsbi53uif8dG4rlzyTl4YckB5OSXSFgZkX1jUCMiIgDGVh3vTeqCMQNaiOeS0vLwyvKDKOA0KNWDkpISZGdnV/hLo+HobUUY1IiISKRUKjBtVDv8675W4rkzV7Ix47NDKK6ivQdRdaxduxY+Pj4V/lq6dGmNXmvLli2YNWsWTpw4UeHjer0e77//PsLDw+Hh4YG+ffvihx9+qIdPYV1c9UlEROX8e3gkCop02Lg7CQBw7EIG3l51BHMnd+VqUKqzdevWITg42OxceHh4ta/Pzs7Gk08+ifT0dDRv3hwdOnQwe7yoqAjjx4/Hli1bMHHiRHTr1g2pqanIysqqj/KtikGNiIjKUSgUePGRdijQaLH10DUAwL7TN/DeN8fxzr86cdspqpMePXqgefPmtb5+7ty5aNeuHXbu3Fnh4zNmzMCWLVuwefNmxMbG1vp95ID/LCIiogoplQq8Mb4D7o0uG/nYfiQFH68/BQtsakNULVeuXMGnn36KUaNGVfh4SkoKPv/8c0yaNMnmQxrAoEZERFVwUCkxe2JndI30F8/9vO8KXvvsMG6xdQdJ4K233sI999yDbt26Vfj4smXLUFxcjOeff97KlVkGgxoREVVJ7ajCvMnd0C7MRzy3P+4Gxs/ZiW3/XOPoGtXY2rVr0aZNG/j5+WH06NFITU2t1nXHjx/HmjVrMGPGjEq3OTt16hTc3d0REBCAiRMnIjAwECEhIZg/f75N/lllUCMiortydXbAh1O6o0fbQPFcXqEWs746hje/OIKsvGIJqyNbM3v2bAwYMABz5szBsWPH0KNHD9y6davKawRBwGuvvYYOHTrg4YcfrvR5Fy9ehI+PD/r37w8vLy/MnTsXbdu2xRtvvIHly5fX90exOAY1IiKqFk9XNT6a0h0zxkXD1Uklnt91Ig3j5+zEzuOpNjliQda3atUqLFu2DM8++yx+++03JCcnY8GCBVVe88cff+DPP//EvHnzoFRWHl+SkpKQnJyMGTNmYPHixXj66aexadMmdO/eHQsWLIBOZ1s9ARnUiIio2hQKBR7q3QzfvtkfnVr5ieez80vw5hdH8MryQ7h8PU/CCknOxowZg7S0NDz22GPiucjISHTq1Alr166t9Dq9Xo/p06fj3nvvxZAhQ6p8DwcHBwQHB2PcuHHiOScnJ0ycOBHJycmIi4ur+wexIgY1IiKqsUZ+rvj0hV54aXQ7ODmWja4dPHMTE97fhU9+jEN+kVbCCkmOXFxcEBwcXO7+smbNmiE1NRW5ubkVXvfNN9/g9OnTePPNN1FQUID8/HwUFhYCADQaDfLz81FSYtzqzMvLC+Hh4eVG3cLCwgAYR9xsCYMaERHVilKpwOiYFvhmZj90jihbFao3CFj/1yU8OutP/Pr3FegNnA6lqhUXF0OhUEClUlX4+O7duwEAQ4YMgYeHBzw8PNCvXz8AwLPPPgsPDw/MnTsXgHGELi0trdxr6PXGnTUcHGyrhaxtVUtERLLTNNAdn77QE7tPpmHJT2eQlmEc6cjOL8H8tSfxzR8X0CnCHx1a+qJDSz808nOtdMUeNWzJyclo2rSp2TlBEHD27Fm0bNkSbm5uFV43c+ZMPP3002bnEhISMHnyZLzxxhsYNmwYQkNDAQA9e/bEzp07cePGDQQFBZk9HwBatGgBW8KgRkREdaZQKBDToTF6RgVh3Z+J+OaPC9Dc3hs0NaMQqQeuYvOBqwCAAG9ndGjph8hQb4Q39kCLxp7w83RieGvg0tPT0bVrV2zduhUdO3YUz2/cuBFJSUlYtGhRpddGREQgIiLC7Jyzs7P4WJ8+fcTzjz/+OObNm4fly5fjv//9LwDjllKff/45+vbti7Zt29bnx7I4BjUiIqo3To4qPHlfBIZ1b4rlv5zBtn9Syj0nPVuD7UdSsP1I2WOebo5o0cgTYcHucHVxhINKAQel0vi7Sgm1oxK+Hk7w83KGv5cz/L2c4KzmjzBb4u/vjyFDhqBXr1548MEHMWDAAFy6dAmLFy9GTEwMnnvuOfG5W7ZsAQAMGzasxu/Tpk0bvPLKK3j//fdRUlKCpk2b4ptvvkFaWhq+//57m/sHAf+UExFRvQv0ccGsJzvjPw+2wfELGThxMQMnEzNx9UZ+hc/PLdDixEXj86rL3cUB3u5OcFQpoVQCKpUSKqUCKqUCagcl3F0d4e7iCA9XR3i4GL/2dlfD38sZAd7GXwx71qNQKPDFF1+gU6dO+Oqrr/Dbb78hPDwcb775JmbOnGl279iUKVMAAJcvX67Vey1cuBChoaH49NNPkZGRgZ49e+LgwYOIjIysj49iVQrBAk1voqKiAADx8fH1/dJERGTDMnM1OJmYiVOXMnEpNReXUvOQkStds1wPF0f4ezvDy00NF7UKzk4quKgdbv+ugrPaAc5qFZzVKjiV/u6oglKpgFIBKKAAFIBSYVxc4ePhhAAvZ3i4OtrcyA1ZTl1yEf8pQUREVuPr6Yz+HRujf8fG4rns/GIkpeUhMSUPyen5KNEaoNMboNML0BkM0OsFaEr0yMjV4FaOBtn5JaivIYa8Ii3yLNBGRO2oNI7ceTnD39sZTfzcEBLghiYBbmga6Nbg78nT6gzILShBTkEJcgu1UCkV8Pdyhp+nE9SOFa/spIoxqBERkaS83Z3QsZUTOrbyv/uTAej0BmTmFuNWrga5+VroDQboDYL4y2AwBrv8Ii3yi3TIL9Iir9AYyLLyipGerUFmrgaW7BpSojUg9VYhUm8VVvi4s1qFxv6uCPJ2QYCPCwK9nRHo7YIAH2f4uDvBSa2Ck6MSTo7GETwntQpKBcTPZxAE6A2A3mCAAgoolQo4KI2/i6N9tQyCOr0BhcU6FBXrUXT7d02JHpoS49fFWj0KinTIuR3ExF/5t4NZQQkKi/WVvr6Xmxp+Xk5ikA30cUHA7c8f6GP83d3FEUplww2yNcGgRkRENsVBpUSgjwsCfVxq/Ro6vUEMbenZGuQXaaEp0aOoRAdNsR6Fxbrb4cT4q7hED4227FgQBAgCIADi11qd8TVLdIa7vr+mRI9LqXm4lGq5XRwUxllZKBQK49cKBRSA8T6+24s0Su/pUygUxs9frKtW/XVRGuzu9tld1Cq4ODnA1dkBLk4quDo5QO2ogtrBuLjE0cEYZB1UJi1hBQFC2ZcmX5uncgelwnhPo0ohfq1UKCBAKButFYDSVys9Z/oqCgBKRenUd1k4Ln0NgyBAMBh/z8wrhq+HU22+XZa5R83DwwNarRbh4eH1/dJERESyZjAI0BkE6E2mbrU6A7R6A3Q6g101AFbcvncPAuzqc99Jk5MGN1dn5OXVPJhbZETNzc0NBQUFlnhpqkBiYiIAMBhLhN9/6fB7Lx1+7yunVCqgVipu/4St//ux+L2XTm2/91e1WZU2870bi4yokXVxla20+P2XDr/30uH3Xjr83ktHiu899/okIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikimu+iQiIiKSKY6oEREREckUgxoRERGRTDGoEREREckUgxoRERGRTDGoEREREckUgxoRERGRTDlIXQARERHdXUlJCQoLCyt8zNnZGc7OzlauiKyBI2oNhFarxbBhw3D58mWpS7EbR48excCBA+Ht7Y3IyEhMmTIF2dnZUpdlNy5evIjx48cjMDAQfn5+eOCBB3D69Gmpy7Ir+/fvx6xZs7Br1y6pS7ELa9euhY+PT4W/li5dKnV5dqGoqAivvPIKQkJC4O3tjdjYWOzcudOi78kRNRsmCAKuXbuGkydPYv78+fj777+lLslunD59Gr1798bYsWMxbtw4xMXFYfny5di2bRsOHToEPz8/qUts0E6dOoWYmBhERETg7bffhlqtxqeffopevXrhyJEjaN26tdQlNnharRZPP/00zp49CwCIiYmRtiA7sm7dOgQHB5udCw8Pl6ga+5GRkYEHH3wQZ86cwcSJE9G+fXskJiYiMzPTou/LoGbDkpOT0axZMyiVSgQFBUldjl2ZNm0annnmGXzyySfiueHDhyM2NhazZ882O0/1b86cORg5ciRWrlwJpdI4MfDAAw8gJCQES5Ys4eiCFaxcuRLBwcFiUCPr6dGjB5o3by51GXZn4sSJSEhIwK5duxAdHW219+XUpw0LDAzEiRMnkJWVhZdfflnqcuxGdnY29uzZg5kzZ5qdHzhwIAYMGIB169ZJVJn9ePfdd/HJJ5+IIQ0AGjduDB8fH07/W0FeXh5mzZqFUaNGSV0KkVUcPXoUmzZtwowZM6wa0gAGNZvm7OyM6OhoeHp6Sl2KXfH29saqVasqHMWMjo5Geno6MjIyJKjMfkRFRcHd3d3sXEJCAjIzM9GxY0eJqrIfCxcuhKOjI0aMGCF1KURW8dFHH8HR0RHPPPOM1d+bQY2oFp544okKz6elpUGlUpULEWRZ6enpmDhxIvz8/DBlyhSpy2nQUlNT8dFHH+Gll16Ck5OT1OXYpbVr16JNmzbw8/PD6NGjkZqaKnVJDd6pU6fQunVrZGVlYdSoUfD19UWrVq2wevVqi783gxpRPdHpdNi/fz/69u3LH2BWsmLFCnTr1g2hoaG4cuUKNm/ejEaNGkldVoP2zjvvwNvbm4FYQrNnz8aAAQMwZ84cHDt2DD169MCtW7ekLqvBMhgMSExMBAAMHjwYkZGRmDdvHry9vTFp0iRs3rzZou/PxQRE9WTBggVIS0vjPWpW5OPjg44dO8Lb2xtHjx7Fzz//jC5dusDBgX+1WUJcXBxWr16NZcuWwdXVtdKeXmRZq1atwrhx4wAA/fv3R5s2bbBgwQJ88MEHElfWMN24cQMajQZxcXHYu3cv+vTpAwAYM2YMOnbsiHnz5mH48OEWe3+OqBHVg2PHjmH27NlYvXo1evXqJXU5duPRRx/FihUrsG3bNuzYsQPLly+X5B4SezFjxgyEhYXhqaeekroUuzRmzBikpaXhscceE89FRkaiU6dOWLt2rYSVNWyl//Dr1q2bGNIA4/3KY8eOxf79+y3aQ5NBjaiOLl26hOHDh2Pu3LkYP3681OXYrY4dO2LChAlYtWoVLly4IHU5Dc7OnTuxZcsWzJgxA8XFxcjPz0d+fj4AY8f8/Px8aDQaiats2FxcXBAcHAyFQmF2vlmzZkhNTUVubq5ElTVspQv2WrVqVe6xsLAwAEBSUpLF3p9BjagO0tLSMGjQIEyePBmvvPKK1OXYvXbt2gEATp48KXElDc/u3bsBAP/+97/h4eEBDw8P8YfUvHnz4OHhgf/85z9Slmi3iouLoVAooFKppC6lQXJyckJYWBjS0tLKPabX6wHAordb8EYOolrKzMzE4MGDMXLkSMyaNUvqcuxKQkICIiMjy50vXf0WGhpq7ZIavEmTJiE2NtbsXHZ2Nh544AFMnDgRkyZNQmBgoETV2Yfk5GQ0bdrU7JwgCDh79ixatmwJNzc3iSpr+Hr27IlffvkFOp3OLJQlJCQAKBtZswSOqBHVQn5+PoYNG4Y+ffrggw8+KDcVQZZTWFiIQYMG4ciRI2bn8/Pz8c0336Bp06ZWb0hpD0JDQ9GnTx+zXz169DB7LCIiQuIqG6709HR07doVx48fNzu/ceNGJCUlcRWuhT3xxBMoKCjAt99+K57LyMjAmjVrMGHCBIu2ZOKImo3buHEjAGOPFwDYsmULAgMDERAQgH79+klZWoM2cuRIZGRkoH///vjxxx/LPT506FD+69ZCXF1d8c4776BXr14YOXIkBgwYAK1Wi88//xzXr1/Hpk2b2B6FGhx/f38MGTIEvXr1woMPPogBAwbg0qVLWLx4MWJiYvDcc89JXWKDNnjwYDzyyCN44YUXkJaWBhcXF6xcuRIuLi6YN2+eRd9bIQiCYNF3IIuqbCSnX79+2LVrl3WLsSN3G0FLSkriXnwWduDAASxatAiHDx9GQUEBunfvjjlz5qBDhw5Sl2Y3bt26hYCAALz77ruc/rcCrVaL5cuX46uvvsKFCxcQHh6OUaNGYebMmWxJYwV6vR6zZs3CN998A41Gg5iYGCxevNjivRsZ1IiIiIhkiveoEREREckUgxoRERGRTDGoEREREckUgxoRERGRTDGoEREREckUgxoRERGRTDGoEREREckUgxoRERGRTDGoEREREckUgxoRERGRTDGoEREREckUgxoRNUhz5szBwYMHpS6DiKhOuCk7ETU4giCge/fu2Lt3L5ycnKQuh4io1jiiRkSyMnz4cDRv3rxOr3HixAm0bduWIY2IbB6DGhE1OD/88AMeffRRqcsgIqozBjUialAEQcCOHTsQGxsrdSlERHXGoEZEDcqRI0fQvn17qNVqqUshIqozBjUialB++OEHjBkzRuoyiIjqBYMaEUlm37596Nu3Lzw9PdG9e3f8/PPPqMtCdEEQsHPnTvTv379G1+3cuRMPPPAAQkJC4O3tjeHDhyMxMdHsOV999RUUCgWOHDlidr4+Fj8QEVXGQeoCiMg+HT16FIMGDYKTkxNeeuklNGrUCIsWLcKBAwfQpEmTWr3moUOH0LlzZzg6OtbouokTJ2LgwIGYPn063NzcsGTJEvTt2xcJCQnw9PSsVS1ERPWBQY2IJPH2229DEAQcPHgQkZGRAIBJkyahQ4cOKCwsrNVrrl+/vlbTnmfOnIGrq6t4PHDgQISFheHbb7/F1KlTa1ULEVF94NQnEVldQUEBtm7din/9619iSAMAtVqNwMDAWr2mwWDAnj17cO+999b4WtOQZjAYEBgYCBcXF8TFxdWqFiKi+sKgRkRWV3r/V3R0dL295v79+9GjRw84ONR8okCj0WDOnDno3r07nJyc4ObmhqKiIhQVFdVbfUREtcGpTyKyutIFAyqVqt5ec/369bVqcpuSkoKBAwfi4sWLGDVqFEaMGIGWLVtiwoQJ9VYbEVFtMagRkdWFh4cDAOLj4+vl9fR6Pfbv34/FixfX+Nply5bh3Llz2LdvH3r37i2e/9e//lWt6wsKCmr8nkRE1cWpTyKyOnd3dwwaNAirV69GSkqKeD4lJQVnz56t8evt3bsXvXv3rtUIXX5+Ptzc3NClSxfx3MWLF8tNe5auJM3JyRHP5eTk4MSJEzV+TyKi6uKIGhFJ4r///S9iYmLQtWtXvPTSSwCMvcqKi4vh4uJSo9dav349Hn/88VrVMWTIECxZsgQxMTEYN24cioqKsHTp0nLPa9myJQBg5cqV8Pb2RkZGBubMmQOtVlur9yUiqg6OqBGRJHr27IlNmzYhJCQEc+bMwe+//47vvvsOHTp0qNHr6HQ6HD58GD179qxVHcOGDcPrr7+O8+fPY8GCBdi1axe++eYb+Pn5mT2vW7dumDp1KrZu3YrY2FjMnj0b06ZNw3333Ver9yUiqg6FUJc24EREEtuxYwe2bNmCjz/+WOpSiIjqHUfUiMim1bbJLRGRLWBQIyKbpdVqcezYMXTr1k3qUoiILIJBjYhs1p9//omBAwdCoVBIXQoRkUUwqBGRzdq7dy/Gjh0rdRlERBbDxQREREREMsURNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikqn/B+peZQNYae3CAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "distances = pairwise_distances(rclustering.input_data)\n", "distance_rclustering = cluster.Clustering(distances, recipe=\"distances\")\n", "\n", "fig, ax = plt.subplots()\n", "_ = plot.plot_histogram(ax, distances.flatten(), maxima=True, maxima_props={\"order\": 5})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a handy rational for a first guess on a suitable radius cutoff, we can use the first maximum in the just plotted distance distribution. For larger numbers of points, the radius can be usually smaller than for poorly sampled data sets. In this particular example, we could start with a radius cutoff of say $r\\approx 0.3$ considering that we have a low number of points in the reduced data set. With this radius fixed, we can than vary the common-nearest-neighbours cutoff $c$ to tune the density threshold for the clustering." ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2019-09-19T10:35:37.964861Z", "start_time": "2019-09-19T10:35:37.962263Z" } }, "source": [ "### Parameter scan" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Blindly starting to cluster a data set in a happy-go-lucky attempt may already lead to a satisfactory result in some cases, but let's tackle this problem in a more systematic way to see how different cluster parameters effect the outcome. A scan of a few parameters shows that it is difficult to extract the three clusters at once with one parameter set. We also apply a member cutoff of 10 to prevent that we obtain clusters that are definitely too small." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:07:42.599415Z", "start_time": "2022-11-24T11:07:39.619845Z" }, "scrolled": true }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "32dc0ae75b6b4306bcb80a07029d0742", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/101 [00:00\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
n_pointsradius_cutoffsimilarity_cutoffmember_cutoffmax_clustersn_clustersratio_largestratio_noiseexecution_time
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2110000.32110<NA>30.6090.3360.02796
2210000.32210<NA>30.6020.360.028592
2510000.32510<NA>30.3410.3990.027648
\n", "" ], "text/plain": [ " n_points radius_cutoff similarity_cutoff member_cutoff max_clusters \\\n", "4 1000 0.3 4 10 \n", "7 1000 0.3 7 10 \n", "8 1000 0.3 8 10 \n", "9 1000 0.3 9 10 \n", "10 1000 0.3 10 10 \n", "18 1000 0.3 18 10 \n", "20 1000 0.3 20 10 \n", "21 1000 0.3 21 10 \n", "22 1000 0.3 22 10 \n", "25 1000 0.3 25 10 \n", "\n", " n_clusters ratio_largest ratio_noise execution_time \n", "4 3 0.665 0.087 0.023985 \n", "7 3 0.656 0.15 0.024447 \n", "8 3 0.652 0.182 0.02423 \n", "9 3 0.65 0.198 0.024639 \n", "10 3 0.645 0.21 0.024605 \n", "18 3 0.62 0.298 0.026852 \n", "20 3 0.614 0.326 0.02807 \n", "21 3 0.609 0.336 0.02796 \n", "22 3 0.602 0.36 0.028592 \n", "25 3 0.341 0.399 0.027648 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get summary sorted by number of identified clusters\n", "df = distance_rclustering.summary.to_DataFrame().sort_values('n_clusters')\n", "\n", "# Show cluster results where we have 3 clusters\n", "df[(df.n_clusters == 3)].sort_values([\"radius_cutoff\", \"similarity_cutoff\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The summary shows indeed that we got a clustering with 3 clusters (as desired) for some parameter combinations. Apart from the number of clusters, it is, however often also of interest, how many data points ended up in the clusters and how many are considered outliers (noise). In this case we expect 3 clusters of more or less equal size (member wise) and we may be interested in keeping the outliers-level low. In the results giving 3 clusters, in one case the largest cluster entails one third of the data points (which is good), but the noise level is around 40 % (which is most likely not what we want). Let's plot a few results to see what is going on here." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:07:43.008189Z", "start_time": "2022-11-24T11:07:42.650504Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Cluster attempts in comparison\n", "fig, ax = plt.subplots(2, 2)\n", "Ax = ax.flatten()\n", "for i, pair in enumerate([(0.3, 4), (0.3, 10), (0.3, 20), (0.3, 25)]):\n", " distance_rclustering.fit(\n", " radius_cutoff=pair[0],\n", " similarity_cutoff=pair[1],\n", " member_cutoff=10, record=False, v=False\n", " )\n", " rclustering.labels = distance_rclustering.labels\n", " \n", " _ = rclustering.evaluate(ax=Ax[i], ax_props=ax_props)\n", " Ax[i].set_title(f'{pair[0]}, {pair[1]}', fontsize=10, pad=4)\n", "\n", "fig.subplots_adjust(\n", " left=0.2, right=0.8, bottom=0, top=1, wspace=0, hspace=0.3 \n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "None of the above attempts was able to achieve a split into 3 clusters as we wanted it. One could now try to tinker around with the parameters a bit more (which is probably not very fruitful), or resort to hierarchical clustering. As we see in the plots above (lower right and upper right), two different parameter pairs are leading to splits in different regions of the data, separating either the lowest density cluster from the rest or separating the two denser clusters. So why not apply both of them, one after the other.\n", "\n", "Before we do this let's have another close look at the cluster results we obtained. Using the `summarize` method of a cluster object, we can visualize a summary table in a 2D contour plot, to evaluate a few quality measures versus the input parameters *radius cutoff* (*r*) and *similarity criterion* (*c*):\n", "\n", " - number of identified clusters\n", " - members in the largest cluster\n", " - points classified as outliers\n", " - computational time of the fit\n", " \n", "Let's also throw in a few more different values for the radius cutoff into the mix." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:08:18.882584Z", "start_time": "2022-11-24T11:07:43.009953Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c9302fb69d664f2793f1a3a0288c8de1", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/9 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Computing time\n", "fig, ax = plt.subplots()\n", "contour = distance_rclustering.summarize(\n", " ax=ax, quantity=\"execution_time\",\n", " contour_props={\n", " \"levels\": 50,\n", " }\n", " )[0]\n", "colorbar = fig.colorbar(mappable=contour, ax=ax)\n", "colorbar.set_label(\"time / s\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the *time vs. r/c* plot we can see how the total clustering time depends in particular on the neighbour search radius. Larger radii result in larger neighbour lists for each point, increasing the processing time, so if one has the choice, smaller values for *r* should be preferred." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:08:19.693003Z", "start_time": "2022-11-24T11:08:19.370185Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Noise level\n", "fig, ax = plt.subplots()\n", "contour = distance_rclustering.summarize(\n", " ax=ax, quantity=\"ratio_noise\",\n", " contour_props={\n", " \"levels\": [x/100 for x in range(101)],\n", " }\n", " )[0]\n", "colorbar = fig.colorbar(mappable=contour, ax=ax, ticks=(0, 0.5, 1))\n", "colorbar.set_label(\"noise / %\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ratio of outlier-points depends on the search radius *r* as well as on the similarity criterion *c* and increases continuously with the set density threshold. For high density thresholds (low *r* and high *c*) the noise ratio is comparably large. The desired amount of noise depends much on the nature of the underlying data set." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:08:20.008370Z", "start_time": "2022-11-24T11:08:19.694832Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Largest cluster\n", "fig, ax = plt.subplots()\n", "contour = distance_rclustering.summarize(\n", " ax=ax, quantity=\"ratio_largest\",\n", " contour_props={\n", " \"levels\": [x/100 for x in range(101)],\n", " }\n", ")[0]\n", "colorbar = fig.colorbar(mappable=contour, ax=ax, ticks=(0, 0.5, 1))\n", "colorbar.set_label(\"largest / %\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ratio of points assigned to the largest cluster shows a similar trend as the noise-ratio, only that it changes in a rather step-wise manner. This view could give a good hint towards reasonable parameter combinations if one already has an idea about the expected cluster size. It also shows for which parameters we do not observe any splitting (about 100 % of the points are in the largest cluster). " ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:08:20.261269Z", "start_time": "2022-11-24T11:08:20.010240Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Number of clusters\n", "show_n = 5\n", "\n", "fig, ax = plt.subplots()\n", "contour = distance_rclustering.summarize(\n", " ax=ax, quantity=\"n_clusters\",\n", " contour_props={\n", " \"levels\": np.arange(-0.5, show_n + 1.5, 1),\n", " \"vmin\": 0,\n", " \"vmax\": show_n,\n", " \"extend\": \"max\"\n", " }\n", ")[0]\n", "colorbar = fig.colorbar(mappable=contour, ax=ax, ticks=range(0, show_n + 1, 1))\n", "colorbar.set_label(\"# clusters\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The probably most telling view is given by the *number of clusters* plots. The analysis demonstrates nicely that for this data set a splitting into 3 meaningful clusters is hard to achieve. With increased density threshold, the number of obtained clusters does at first increase before it drops again when low density clusters become part of the noise. In general, clusterings that are stable over a wider range of parameter combinations tend to be more meaningful." ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2019-09-19T10:35:00.015683Z", "start_time": "2019-09-19T10:34:59.968481Z" } }, "source": [ "### Manual hierarchical clustering" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use the (manual) hierarchical approach on clustering this data set, we will apply a pair of cluster parameters that will extract the lesser dense region of the data as an isolated cluster in a first step. That means we choose a comparably low value for the similarity criterion *c*. We can refer to these parameters as *soft* parameters, leaving the more dense regions of the data untouched and in one cluster. Remember that density in terms of common-nearest-neighbour clustering is estimated as the number of common neighbours within the neighbourhood intersection of two points with respect to a radius *r*. More common neighbours (higher similarity cutoff *c*) and/or a smaller neighbourhood radius (smaller *r*) will result in a higher density requirement. To make this more clear let's have a look again at some clusterings." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:08:20.573716Z", "start_time": "2022-11-24T11:08:20.263117Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Cluster attempts in comparison\n", "fig, ax = plt.subplots(2, 2)\n", "Ax = ax.flatten()\n", "for i, pair in enumerate([(0.3, 1), (0.3, 3), (0.3, 40), (0.3, 100)]):\n", " distance_rclustering.fit(\n", " radius_cutoff=pair[0],\n", " similarity_cutoff=pair[1],\n", " member_cutoff=10, record=False, v=False\n", " )\n", " rclustering.labels = distance_rclustering.labels\n", " _ = rclustering.evaluate(ax=Ax[i], ax_props=ax_props)\n", " Ax[i].set_title(f'{pair[0]}, {pair[1]}', fontsize=10, pad=4)\n", "\n", "fig.subplots_adjust(\n", " left=0.2, right=0.8, bottom=0, top=1, wspace=0, hspace=0.3 \n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For these clusterings, we again keep the radius fixed while we increase the similarity cutoff and therefore the density threshold. \n", "\n", "__Upper left__: Choosing a low (soft) threshold, results in no cluster splitting. The data set as a whole forms a cluster in which the point density is *at least* as high as our density requirement.\n", "\n", "__Upper right__: When we increase the density requirement just a bit, we observe a splitting between the broadly distributed lower density cluster and the more dense clusters. Within both the resulting clusters the point density is higher than required by the parameters but they are separated by a low density region and therefore split. This parameter set can be used for the first step of the hierarchical clustering.\n", "\n", "__Lower left__: Increasing the density requirement further, eventually leads to a splitting between the two denser clusters. At his point, the broader low density cluster has already vanished into noise because the density within this cluster is lower than the density between the just split denser clusters. We could use this parameter set in a second hierarchical clustering step.\n", "\n", "__lower right__: Choosing even harder parameters leaves only the densest cluster. The second densest cluster falls into the noise region." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The central element of the manual hierarchical cluster functionality is the `isolate` method of a `Clustering` object. After a clustering (with soft parameters) we can *freeze* the result before we start to re-cluster. " ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:08:20.619089Z", "start_time": "2022-11-24T11:08:20.575658Z" } }, "outputs": [], "source": [ "# After the first step, we need to isolate the cluster result\n", "rclustering.fit(\n", " radius_cutoff=0.3, similarity_cutoff=3,\n", " member_cutoff=10, record=False, v=False\n", ")\n", "rclustering.isolate()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `isolate` method will create a new `Bundle` object (a child) for every cluster of a cluster result.\n", "In our case we get two new child clusters (plus one for outliers). The clusters are stored in a dictionary under the `children` attribute of the parent `Bundle` object. The children dictionary of the root data after isolation holds a `Bundle` object for each cluster found in the last step." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:08:20.639145Z", "start_time": "2022-11-24T11:08:20.621039Z" } }, "outputs": [ { "data": { "text/plain": [ "{1: 'root.1', 2: 'root.2'}" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "{k: v.alias for k, v in rclustering.children.items()}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we access the chidren dictionary from our `Clustering` object, we are actually looking at the children of the respective root bundle." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:08:20.653860Z", "start_time": "2022-11-24T11:08:20.641259Z" } }, "outputs": [ { "data": { "text/plain": [ "{1: Bundle(alias='root.1', hierarchy_level=1),\n", " 2: Bundle(alias='root.2', hierarchy_level=1)}" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rclustering.root.children" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we will use different cluster parameters now for different clusters it would be nice to have something\n", "to keep the overview of which cluster has been identified under which parameters. This information is provided by the `meta` attribute on the object that stores the cluster label assignments on the `Bundle` which we get with `Bundle._labels`. Note that this is different to `Clustering.labels` which only return the actual labels in form of a NumPy array. The label meta information tells us three things:\n", "\n", " - `origin`: How have these labels been assigned? The entry `\"fit\"` means, they were obtained by a regular clustering.\n", " - `reference`: This is a related clustering object, i.e. the object that is associated to the data for which the labels are valid. For `\"fitted\"` labels this is a reference to the bundle object itself that carries the labels.\n", " - `params`: This is a dictionary stating the cluster parameters (*r*, *c*) that led to each cluster. For `\"fitted\"` labels, each cluster has the same parameters." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:08:20.667956Z", "start_time": "2022-11-24T11:08:20.656050Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "params: {1: (0.3, 3), 2: (0.3, 3)}\n", "reference: \n", "origin: 'fit'\n" ] } ], "source": [ "print(*(f\"{k}: {v!r}\" for k, v in rclustering.root._labels.meta.items()), sep=\"\\n\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Every single isolated child bundle is represented by a full-fledged, completely functional `Bundle` object itself. When we want to re-cluster a child, this is no different to clustering a parent. " ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:08:20.686214Z", "start_time": "2022-11-24T11:08:20.670556Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "alias: 'root.1'\n", "hierarchy_level: 1\n", "access: components\n", "points: 667\n", "children: 0\n" ] } ], "source": [ "child1 = rclustering[1] # rclustering.children[1]\n", "print(child1.info()) # Child cluster 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note, that the `hierarchy_level` in the above overview has increased to 1. We can again plot the distance distribution within this child cluster to see how this changed." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:08:20.841807Z", "start_time": "2022-11-24T11:08:20.694133Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "distances_child1 = pairwise_distances(child1.input_data)\n", "fig, ax = plt.subplots()\n", "_ = plot.plot_histogram(ax, distances_child1.flatten(), maxima=True, maxima_props={\"order\": 5})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then we can attempt the second clustering step. Note that we need to pass the\n", "child bundle to the fit function as the first argument (or the `bundle` keyword argument)." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:08:20.937208Z", "start_time": "2022-11-24T11:08:20.843478Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-----------------------------------------------------------------------------------------------\n", "#points r nc min max #clusters %largest %noise time \n", "667 0.300 30 10 None 2 0.505 0.153 00:00:0.032\n", "-----------------------------------------------------------------------------------------------\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Now cluster the child cluster\n", "fig, ax = plt.subplots()\n", "rclustering.fit(\n", " child1, # bundle\n", " radius_cutoff=0.3,\n", " similarity_cutoff=30, member_cutoff=10)\n", "_ = rclustering.evaluate(ax=ax, ax_props=ax_props, bundle=child1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a little extra feature a `Clustering` object has a `pie` method that allows to visualize the current state and splitting on the different hierarchy levels as a pie-diagram (from level 0 in the middle to lower levels to the outside)." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:08:21.092656Z", "start_time": "2022-11-24T11:08:20.938917Z" } }, "outputs": [ { "data": { "text/plain": [ "(-0.3, 0.3)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# The cluster hierarchy can be visualised as pie diagram\n", "rclustering.pie(\n", " pie_props={\n", " \"radius\": 0.15,\n", " \"wedgeprops\": dict(width=0.15, edgecolor='w')\n", " }\n", ")\n", "plt.xlim(-0.3, 0.3)\n", "plt.ylim(-0.3, 0.3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, a hierarchical tree can be drawn using the `tree` method." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:08:21.220533Z", "start_time": "2022-11-24T11:08:21.094288Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Tree shows only isolated bundles\n", "child1.isolate()\n", "rclustering.tree()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have isolated one cluster in the first step and two others in a further splitting on a lower hierarchy level, the last step that remains, is to put everything back together. This can be done automatically by calling `reel` on the parent cluster object into which the child cluster results should be integrated." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:08:21.226175Z", "start_time": "2022-11-24T11:08:21.222159Z" } }, "outputs": [], "source": [ "# Wrap up the hierarchical clustering and integrate the child clusters into\n", "# the parent cluster\n", "rclustering.reel()\n", "\n", "# Manually sort clusters by size (make largest be cluster number 1)\n", "rclustering.root._labels.sort_by_size()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:08:21.328707Z", "start_time": "2022-11-24T11:08:21.228419Z" } }, "outputs": [ { "data": { "text/plain": [ "(-0.3, 0.3)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Remove children\n", "rclustering.root._children = None\n", "rclustering.pie(\n", " pie_props={\n", " \"radius\": 0.3,\n", " \"wedgeprops\": dict(width=0.3, edgecolor='w')\n", " }\n", ")\n", "plt.xlim(-0.3, 0.3)\n", "plt.ylim(-0.3, 0.3)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:08:21.396614Z", "start_time": "2022-11-24T11:08:21.330758Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "_ = rclustering.evaluate(ax=ax, ax_props=ax_props)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Label prediction" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2021-03-22T17:46:20.686581Z", "start_time": "2021-03-22T17:46:20.680283Z" } }, "source": [ "Now, the parameter information saved for the labels becomes even more useful as they differ for the clusters from different clustering steps." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:08:21.402648Z", "start_time": "2022-11-24T11:08:21.398248Z" } }, "outputs": [ { "data": { "text/plain": [ "{'params': {2: (0.3, 3), 3: (0.3, 30), 1: (0.3, 30)},\n", " 'reference': ,\n", " 'origin': 'reel'}" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rclustering.root._labels.meta" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:08:21.415590Z", "start_time": "2022-11-24T11:08:21.404251Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Label\tr\tc\n", "--------------------\n", "1\t0.3\t30\n", "2\t0.3\t3\n", "3\t0.3\t30\n" ] } ], "source": [ "print(\"Label\", \"r\", \"c\", sep=\"\\t\")\n", "print(\"-\" * 20)\n", "for k, v in sorted(rclustering.root._labels.meta[\"params\"].items()):\n", " print(k, *v, sep=\"\\t\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The cluster result is satisfying. But we have clustered only a reduced set of 1000 points. We would like to predict the cluster label assignment for the full 100,000 points on the basis of the reduced set assignments. This we can do with the `predict` method of a cluster object. We call `predict` with another cluster object for which labels should be predicted as an argument. Similar to a regular `fit` we need to compute neighbourhoods for the points that need assignment, but this time we need relative neighbourhoods between two data sets. We want to compute the neighbouring points in the small set `rclustering` for the points in the big set `clustering`. When we predict labels we should do the prediction separately for the clusters because the assignment parameters differ. This is where the label info comes in handy showing us the parameters used for the fit as an orientation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "**Info:** A cluster label prediction requires a `Predictor` in the same way as a clustering requires a `Fitter`. See tutorial [Advanced usage](advanced_usage.ipynb) for details.\n", "
" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:08:28.555029Z", "start_time": "2022-11-24T11:08:21.417286Z" }, "run_control": { "marked": true } }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, '3 (0.3, 30)')" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rclustering._predictor = _fit.PredictorCommonNNFirstmatch(\n", " rclustering._fitter._neighbours_getter,\n", " clustering._fitter._neighbours_getter,\n", " rclustering._fitter._neighbours,\n", " rclustering._fitter._neighbour_neighbours,\n", " rclustering._fitter._similarity_checker,\n", ")\n", "\n", "fig, Ax = plt.subplots(1, 3)\n", "\n", "r = 0.3; c = 30\n", "rclustering.predict(\n", " clustering.root,\n", " radius_cutoff=r, similarity_cutoff=c,\n", " clusters=[1], purge=True\n", " )\n", "_ = clustering.evaluate(ax=Ax[0], ax_props=ax_props)\n", "Ax[0].set_title(f'1 {(r, c)}', fontsize=10, pad=4)\n", "\n", "r = 0.3; c = 3\n", "rclustering.predict(\n", " clustering.root,\n", " radius_cutoff=r, similarity_cutoff=c,\n", " clusters=[2], purge=False)\n", "_ = clustering.evaluate(ax=Ax[1], ax_props=ax_props)\n", "Ax[1].set_title(f'2 {(r, c)}', fontsize=10, pad=4)\n", "\n", "r = 0.3; c = 30\n", "rclustering.predict(\n", " clustering.root,\n", " radius_cutoff=r, similarity_cutoff=c,\n", " clusters=[3], purge=False)\n", "_ = clustering.evaluate(ax=Ax[2], ax_props=ax_props)\n", "Ax[2].set_title(f'3 {(r, c)}', fontsize=10, pad=4)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "ExecuteTime": { "end_time": "2022-11-24T11:08:28.561724Z", "start_time": "2022-11-24T11:08:28.557159Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Label\tr\tc\n", "--------------------\n", "1\t0.3\t30\n", "2\t0.3\t3\n", "3\t0.3\t30\n" ] } ], "source": [ "print(\"Label\", \"r\", \"c\", sep=\"\\t\")\n", "print(\"-\" * 20)\n", "for k, v in sorted(clustering.root._labels.meta[\"params\"].items()):\n", " print(k, *v, sep=\"\\t\")" ] } ], "metadata": { "kernelspec": { "display_name": "labcommonnn_3.10.7", "language": "python", "name": "labcommonnn_3.10.7" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.7" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": true, "title_cell": "Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": { "height": "calc(100% - 180px)", "left": "10px", "top": "150px", "width": "165px" }, "toc_section_display": true, "toc_window_display": true }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }