{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing Iris Dataset using Clustergrammer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "import pandas as pd\n",
    "from clustergrammer_widget import *\n",
    "from copy import deepcopy\n",
    "net = Network(clustergrammer_widget)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import iris data \n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make DataFrame from Iris\n",
    "First make row and col names, then build the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_cat_names = iris.target_names.tolist()\n",
    "cols = []\n",
    "for i in range(len(iris.target)):\n",
    "    inst_col = iris.target[i]\n",
    "    inst_name = 'flowers: flower-'+str(i)\n",
    "    inst_cat = 'flower-type: ' + unique_cat_names[inst_col]\n",
    "    inst_tuple = (inst_name, inst_cat)\n",
    "    cols.append(inst_tuple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = []\n",
    "for i in range(len(iris.feature_names)):\n",
    "    inst_name = 'feature: ' + iris.feature_names[i]\n",
    "    rows.append(inst_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a DataFrame with flower-samples as columns and dimensions (e.g. peteal width) as rows. Column categories (e.g. setosa) have been added to the column names using tuples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 150)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat = iris.data.transpose()\n",
    "df = pd.DataFrame(data=mat, columns=cols, index = rows)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize Iris Dataframe using Clustergrammer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Below we Z-score normalize the rows (dimensions) to make them more easily comparable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.load_df(df)\n",
    "net.set_cat_color(axis='col', cat_index=1, cat_name='flower-type: versicolor', inst_color='red')\n",
    "net.set_cat_color(axis='col', cat_index=1, cat_name='flower-type: virginica', inst_color='blue')\n",
    "net.set_cat_color(axis='col', cat_index=1, cat_name='flower-type: setosa', inst_color='yellow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Widget Javascript not detected.  It may not be installed or enabled properly.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "802ad756c66644bf94f740432717a6e3"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.load_df(df)\n",
    "net.normalize(axis='row', norm_type='zscore', keep_orig=True)\n",
    "net.cluster()\n",
    "net.widget()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We see that flowers still largely cluster according to the categories. We also get a smoother breakdown ot the flowers into hierarchical clusters (toggle the column dendrogram level using the triangle/circle slider). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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     "802ad756c66644bf94f740432717a6e3": {
      "model_module": "clustergrammer_widget",
      "model_module_version": "*",
      "model_name": "hello_model",
      "state": {
       "_dom_classes": [],
       "_model_module_version": "*",
       "_view_module_version": "*",
       "layout": "IPY_MODEL_03476d2dfccf41d59ed93bf94af664e2",
       "mat_string": "",
       "msg_throttle": 1,
       "network": "{\"row_nodes\": [{\"name\": \"feature: sepal length (cm)\", \"ini\": 4, \"clust\": 1, \"rank\": 2, \"rankvar\": 2, \"group\": [3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"feature: sepal width (cm)\", \"ini\": 3, \"clust\": 0, \"rank\": 0, \"rankvar\": 1, \"group\": [4.0, 3.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0]}, {\"name\": \"feature: petal length (cm)\", \"ini\": 2, \"clust\": 2, \"rank\": 1, \"rankvar\": 3, \"group\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"feature: petal width (cm)\", \"ini\": 1, \"clust\": 3, \"rank\": 3, \"rankvar\": 0, \"group\": [2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}], \"col_nodes\": [{\"name\": \"flowers: flower-0\", \"ini\": 150, \"clust\": 34, \"rank\": 31, \"rankvar\": 127, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 0, \"group\": [33.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-1\", \"ini\": 149, \"clust\": 27, \"rank\": 7, \"rankvar\": 56, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 1, \"group\": [24.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-2\", \"ini\": 148, \"clust\": 19, \"rank\": 14, \"rankvar\": 97, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 2, \"group\": [21.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-3\", \"ini\": 147, \"clust\": 16, \"rank\": 6, \"rankvar\": 84, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 3, \"group\": [17.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-4\", \"ini\": 146, \"clust\": 36, \"rank\": 34, \"rankvar\": 134, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 4, \"group\": [37.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-5\", \"ini\": 145, \"clust\": 11, \"rank\": 61, \"rankvar\": 143, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 5, \"group\": [9.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-6\", \"ini\": 144, \"clust\": 46, \"rank\": 20, \"rankvar\": 124, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 6, \"group\": [47.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-7\", \"ini\": 143, \"clust\": 32, \"rank\": 26, \"rankvar\": 113, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 7, \"group\": [30.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-8\", \"ini\": 142, \"clust\": 13, \"rank\": 2, \"rankvar\": 57, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 8, \"group\": [16.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-9\", \"ini\": 141, \"clust\": 23, \"rank\": 11, \"rankvar\": 78, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 9, \"group\": [23.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-10\", \"ini\": 140, \"clust\": 0, \"rank\": 49, \"rankvar\": 136, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 10, \"group\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-11\", \"ini\": 139, \"clust\": 47, \"rank\": 24, \"rankvar\": 118, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 11, \"group\": [45.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-12\", \"ini\": 138, \"clust\": 28, \"rank\": 4, \"rankvar\": 67, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 12, \"group\": [25.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-13\", \"ini\": 137, \"clust\": 14, \"rank\": 1, \"rankvar\": 86, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 13, \"group\": [14.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-14\", \"ini\": 136, \"clust\": 7, \"rank\": 68, \"rankvar\": 146, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 14, \"group\": [7.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-15\", \"ini\": 135, \"clust\": 6, \"rank\": 85, \"rankvar\": 149, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 15, \"group\": [13.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-16\", \"ini\": 134, \"clust\": 12, \"rank\": 58, \"rankvar\": 145, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 16, \"group\": [10.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-17\", \"ini\": 133, \"clust\": 37, \"rank\": 35, \"rankvar\": 125, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 17, \"group\": [35.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-18\", \"ini\": 132, \"clust\": 8, \"rank\": 62, \"rankvar\": 140, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 18, \"group\": [8.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-19\", \"ini\": 131, \"clust\": 4, \"rank\": 48, 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\"rankvar\": 112, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 28, \"group\": [32.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-29\", \"ini\": 121, \"clust\": 20, \"rank\": 17, \"rankvar\": 94, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 29, \"group\": [19.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-30\", \"ini\": 120, \"clust\": 17, \"rank\": 15, \"rankvar\": 74, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 30, \"group\": [18.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-31\", \"ini\": 119, \"clust\": 40, \"rank\": 40, \"rankvar\": 104, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 31, \"group\": [38.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-32\", \"ini\": 118, \"clust\": 10, \"rank\": 57, \"rankvar\": 147, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 32, \"group\": [11.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-33\", \"ini\": 117, \"clust\": 9, \"rank\": 71, \"rankvar\": 148, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 33, \"group\": [12.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-34\", \"ini\": 116, \"clust\": 24, \"rank\": 12, \"rankvar\": 79, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 34, \"group\": [23.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-35\", \"ini\": 115, \"clust\": 29, \"rank\": 18, \"rankvar\": 93, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 35, \"group\": [28.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-36\", \"ini\": 114, \"clust\": 41, \"rank\": 39, \"rankvar\": 129, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 36, \"group\": [39.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-37\", \"ini\": 113, \"clust\": 22, \"rank\": 13, \"rankvar\": 80, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 37, \"group\": [23.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-38\", \"ini\": 112, \"clust\": 15, \"rank\": 3, \"rankvar\": 77, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 38, \"group\": [15.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-39\", \"ini\": 111, \"clust\": 33, \"rank\": 28, \"rankvar\": 111, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 39, \"group\": [31.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-40\", \"ini\": 110, \"clust\": 38, \"rank\": 30, \"rankvar\": 128, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 40, \"group\": [36.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"flowers: flower-41\", \"ini\": 109, \"clust\": 146, \"rank\": 0, \"rankvar\": 7, \"cat-0\": \"flower-type: setosa\", \"cat_0_index\": 41, \"group\": [148.0, 10.0, 5.0, 3.0, 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       "viz_title": "updating python by restarting kernel"
      }
     }
    },
    "version_major": 1,
    "version_minor": 0
   }
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 "nbformat_minor": 2
}