SOM Color.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multivariate Data Visualisation using Self-Organising Maps\n",
    "\n",
    "This workbook is an attempt to reproduce some unpublished research which I undertook in a previous life when working for the [Shidmadzu Research Laboratory](http://www.srlab.co.uk/) in the late '90s. Although similar research has subsequently been published, as far as I am aware, this was original research at the time. I used this as a learning exercise for parts of Pandas, NumPy and sklearn.\n",
    "\n",
    "The context of this research is the visualisation of multi- or hyper-spectral images, where each pixel of an image consists of a spectrum. Originally conceived as part of the LandSat programme, multi-spectral imaging was taken into the field of surface analytics by my PhD supervisor, the late Prof. Martin Prutton.\n",
    "\n",
    "Visualisation of multi-spectral data presents a significant challenge, not least because of the need to be able to visualise data in considerably more than three dimensions. My original approach was to quantise the data using a Self-Organising Map (SOM), and then use a Sammon Mapping to organise the map vectors into a two-dimensional space where the distance between the vectors in the reduced data space approximates their (Euclidian) distances in the original data space. This two-dimensional mapping can be transferred to two of the three dimensions of a colour space in order to visualise the original data.\n",
    "\n",
    "___\n",
    "\n",
    "We start with some `import`s etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import somoclu\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn import manifold\n",
    "import sklearn.cluster as cluster\n",
    "\n",
    "get_ipython().magic('matplotlib inline')\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import colorsys\n",
    "\n",
    "import os\n",
    "os.chdir(r'C:\\Users\\User\\OneDrive\\komoot\\data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data used is from the [NASA PMH08 Prognistics Challenge dataset](https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/#phm08_challenge). This is space-delimited data in a plain text file.\n",
    "\n",
    "We import the first 26 columns only as the remainder contain no data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(filepath_or_buffer=\"train.txt\", sep=' ', header=None, usecols = range(26))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now manually add column headings. `op` columns are operational parameters; `sense` columns are sensor columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.columns = ['unit','cycles','op1','op2','op3'\n",
    "              ,'sense1','sense2','sense3','sense4','sense5','sense6'\n",
    "              ,'sense7','sense8','sense9','sense10','sense11','sense12'\n",
    "              ,'sense13','sense14','sense15','sense16','sense17'\n",
    "              ,'sense18','sense19','sense20','sense21']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now normalise the data into a new dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unit</th>\n",
       "      <th>cycles</th>\n",
       "      <th>op1</th>\n",
       "      <th>op2</th>\n",
       "      <th>op3</th>\n",
       "      <th>sense1</th>\n",
       "      <th>sense2</th>\n",
       "      <th>sense3</th>\n",
       "      <th>sense4</th>\n",
       "      <th>sense5</th>\n",
       "      <th>...</th>\n",
       "      <th>sense12</th>\n",
       "      <th>sense13</th>\n",
       "      <th>sense14</th>\n",
       "      <th>sense15</th>\n",
       "      <th>sense16</th>\n",
       "      <th>sense17</th>\n",
       "      <th>sense18</th>\n",
       "      <th>sense19</th>\n",
       "      <th>sense20</th>\n",
       "      <th>sense21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.946633</td>\n",
       "      <td>-1.032857</td>\n",
       "      <td>-0.897512</td>\n",
       "      <td>0.611799</td>\n",
       "      <td>0.656125</td>\n",
       "      <td>0.747996</td>\n",
       "      <td>0.872271</td>\n",
       "      <td>0.686528</td>\n",
       "      <td>...</td>\n",
       "      <td>0.768702</td>\n",
       "      <td>0.420043</td>\n",
       "      <td>0.650642</td>\n",
       "      <td>-0.945465</td>\n",
       "      <td>1.407503</td>\n",
       "      <td>0.707894</td>\n",
       "      <td>0.622245</td>\n",
       "      <td>0.419839</td>\n",
       "      <td>0.787906</td>\n",
       "      <td>0.792200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.622814</td>\n",
       "      <td>-1.836240</td>\n",
       "      <td>1.563749</td>\n",
       "      <td>1.731217</td>\n",
       "      <td>1.672411</td>\n",
       "      <td>1.548658</td>\n",
       "      <td>1.658630</td>\n",
       "      <td>1.816899</td>\n",
       "      <td>...</td>\n",
       "      <td>1.851910</td>\n",
       "      <td>0.420198</td>\n",
       "      <td>0.792348</td>\n",
       "      <td>-1.252581</td>\n",
       "      <td>1.407503</td>\n",
       "      <td>1.534115</td>\n",
       "      <td>1.095841</td>\n",
       "      <td>0.419839</td>\n",
       "      <td>1.839028</td>\n",
       "      <td>1.833676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.742866</td>\n",
       "      <td>0.864646</td>\n",
       "      <td>0.333118</td>\n",
       "      <td>-0.885167</td>\n",
       "      <td>-0.646592</td>\n",
       "      <td>-0.487151</td>\n",
       "      <td>-0.695764</td>\n",
       "      <td>-0.703003</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.598445</td>\n",
       "      <td>0.418641</td>\n",
       "      <td>-0.020112</td>\n",
       "      <td>0.030947</td>\n",
       "      <td>-0.710463</td>\n",
       "      <td>-0.513475</td>\n",
       "      <td>-0.036670</td>\n",
       "      <td>0.419839</td>\n",
       "      <td>-0.600465</td>\n",
       "      <td>-0.607676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.270777</td>\n",
       "      <td>0.415677</td>\n",
       "      <td>-1.512828</td>\n",
       "      <td>0.692676</td>\n",
       "      <td>0.733684</td>\n",
       "      <td>0.644408</td>\n",
       "      <td>0.363952</td>\n",
       "      <td>0.363958</td>\n",
       "      <td>...</td>\n",
       "      <td>0.353904</td>\n",
       "      <td>0.419576</td>\n",
       "      <td>-0.155934</td>\n",
       "      <td>-0.145421</td>\n",
       "      <td>-0.710463</td>\n",
       "      <td>0.564204</td>\n",
       "      <td>0.656564</td>\n",
       "      <td>0.419839</td>\n",
       "      <td>0.367860</td>\n",
       "      <td>0.389925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1.216413</td>\n",
       "      <td>0.865932</td>\n",
       "      <td>-0.282197</td>\n",
       "      <td>-1.052966</td>\n",
       "      <td>-0.804384</td>\n",
       "      <td>-0.615953</td>\n",
       "      <td>-0.680456</td>\n",
       "      <td>-1.135852</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.980745</td>\n",
       "      <td>0.418174</td>\n",
       "      <td>0.213280</td>\n",
       "      <td>-0.045000</td>\n",
       "      <td>-0.710463</td>\n",
       "      <td>-0.657166</td>\n",
       "      <td>-0.112171</td>\n",
       "      <td>0.419839</td>\n",
       "      <td>-0.988199</td>\n",
       "      <td>-1.020503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   unit  cycles       op1       op2       op3    sense1    sense2    sense3  \\\n",
       "0     1       1 -0.946633 -1.032857 -0.897512  0.611799  0.656125  0.747996   \n",
       "1     1       2 -1.622814 -1.836240  1.563749  1.731217  1.672411  1.548658   \n",
       "2     1       3  0.742866  0.864646  0.333118 -0.885167 -0.646592 -0.487151   \n",
       "3     1       4 -0.270777  0.415677 -1.512828  0.692676  0.733684  0.644408   \n",
       "4     1       5  1.216413  0.865932 -0.282197 -1.052966 -0.804384 -0.615953   \n",
       "\n",
       "     sense4    sense5    ...      sense12   sense13   sense14   sense15  \\\n",
       "0  0.872271  0.686528    ...     0.768702  0.420043  0.650642 -0.945465   \n",
       "1  1.658630  1.816899    ...     1.851910  0.420198  0.792348 -1.252581   \n",
       "2 -0.695764 -0.703003    ...    -0.598445  0.418641 -0.020112  0.030947   \n",
       "3  0.363952  0.363958    ...     0.353904  0.419576 -0.155934 -0.145421   \n",
       "4 -0.680456 -1.135852    ...    -0.980745  0.418174  0.213280 -0.045000   \n",
       "\n",
       "    sense16   sense17   sense18   sense19   sense20   sense21  \n",
       "0  1.407503  0.707894  0.622245  0.419839  0.787906  0.792200  \n",
       "1  1.407503  1.534115  1.095841  0.419839  1.839028  1.833676  \n",
       "2 -0.710463 -0.513475 -0.036670  0.419839 -0.600465 -0.607676  \n",
       "3 -0.710463  0.564204  0.656564  0.419839  0.367860  0.389925  \n",
       "4 -0.710463 -0.657166 -0.112171  0.419839 -0.988199 -1.020503  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_norm = df.copy()\n",
    "norm_cols = list(df.columns[2:])\n",
    "for col in norm_cols:\n",
    "    df_norm[col] = (df_norm[col] - df[col].mean()) / df[col].std()\n",
    "df_norm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ratio of the first _n_ eigenvalues obtained from PCA of the data can be used to set the ratio of the size of the SOM. We perform PCA here to establish this ratio is approximately 4.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.13936504547\n"
     ]
    }
   ],
   "source": [
    "X = df_norm[norm_cols] #.astype('float32')\n",
    "pca = PCA()\n",
    "X_reduced = pca.fit_transform(X)\n",
    "print(pca.explained_variance_ratio_[0] / pca.explained_variance_ratio_[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now train a Self-Organising Map on the normalised data using the [somoclu library](http://somoclu.readthedocs.io/en/stable/example.html), a Python wrapper for a very fast and parallelised SOM library.\n",
    "\n",
    "The number of rows and columns in the map has been set from the ratio of the first two eigenvalues of PCA as performed above.\n",
    "\n",
    "We use a PCA initialisation of the map to provide (almost) reproducable results. The `compactsupport` flag turns off an approximation method which can be use to speed up the training of the map. Using `astype('float32')` converts to single-precision values as required my somoclu, and `fillna(0.0)` ensures that missing values in the (normalised / centred) data are effectively replaced by the mean for the series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_rows, n_columns = 20, 82\n",
    "som = somoclu.Somoclu(n_columns, n_rows\n",
    "                      , compactsupport=False \n",
    "                      , initialization=\"pca\"\n",
    "                      , verbose = 2\n",
    "                     )\n",
    "som.train(df_norm[norm_cols].fillna(0.0).values.astype('float32'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create 2D histogram data into `sizes` of the SOM cell occupancy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sizes = np.zeros((n_columns, n_rows), dtype=int)\n",
    "for col, row in som.bmus: \n",
    "    sizes[col, row] += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use [multidimensional scaling](https://en.wikipedia.org/wiki/Multidimensional_scaling) from sklearn as an alternative to Sammon Mapping. With 1680 data points, this is unfortunately somewhat time-consuming."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "breaking at iteration 8 with stress 19357742.7971\n",
      "breaking at iteration 157 with stress 132616.547766\n",
      "breaking at iteration 155 with stress 117119.060575\n",
      "breaking at iteration 215 with stress 125068.136796\n",
      "breaking at iteration 170 with stress 129970.984464\n",
      "breaking at iteration 235 with stress 112881.59814\n",
      "breaking at iteration 382 with stress 117109.347005\n",
      "breaking at iteration 122 with stress 115717.868693\n",
      "breaking at iteration 218 with stress 132791.323187\n",
      "breaking at iteration 181 with stress 120222.895338\n"
     ]
    }
   ],
   "source": [
    "codebook_reshape = np.reshape(som.codebook, (n_rows*n_columns,len(norm_cols))).astype('float64')\n",
    "mds = manifold.MDS(2, max_iter=1000, n_init=10, verbose=1, random_state=123)\n",
    "Y = mds.fit_transform(codebook_reshape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the _I_ and _Q_ components of the [YIQ colour space](https://en.wikipedia.org/wiki/YIQ) with _Y_ fixed at 0.5. The `I_RANGE` and `Q_RANGE` constants are taken from the linked Wikipedia page."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "colours = []\n",
    "minY0 = min(Y[:, 0])\n",
    "minY1 = min(Y[:, 1])\n",
    "rangeY0 = max(Y[:, 0]) - minY0\n",
    "rangeY1 = max(Y[:, 1]) - minY1\n",
    "I_RANGE = 0.5957\n",
    "Q_RANGE = 0.5226\n",
    "for x, y in Y:\n",
    "    colours.append(\n",
    "        colorsys.yiq_to_rgb(\n",
    "            0.5 # Fixed value of Y colour space component\n",
    "            , (2.0 * I_RANGE * (x-minY0) / rangeY0) - I_RANGE\n",
    "            , (2.0 * Q_RANGE * (y-minY1) / rangeY1) - Q_RANGE\n",
    "        ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2wAAAJCCAYAAAC8vUSjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x249b50a2ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.scatter(Y[:, 0], Y[:, 1], marker='.', s=sizes*16, alpha=0.2, c=colours)\n",
    "plt.axis('tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}