{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Toy dataset\n",
"\n",
"We used here an artificial toy dataset composed of 3 sets of points with 2D random coordinates around an arbitrary center.\n",
"\n",
"To go through this example, you need to install AutoClassWrapper:\n",
"```bash\n",
"$ python3 -m pip install autoclasswrapper\n",
"```\n",
"\n",
"[AutoClass C](https://ti.arc.nasa.gov/tech/rse/synthesis-projects-applications/autoclass/autoclass-c/) also needs to be installed locally and available in path.\n",
"\n",
"Here is a quick solution for a Linux Bash shell:\n",
"```bash\n",
"wget https://ti.arc.nasa.gov/m/project/autoclass/autoclass-c-3-3-6.tar.gz\n",
"tar zxvf autoclass-c-3-3-6.tar.gz\n",
"rm -f autoclass-c-3-3-6.tar.gz\n",
"export PATH=$PATH:$(pwd)/autoclass-c\n",
"\n",
"# if you use a 64-bit operating system,\n",
"# you also need to install the standard 32-bit C libraries:\n",
"# sudo apt-get install -y libc6-i386\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Python: 3.7.1 | packaged by conda-forge | (default, Feb 26 2019, 04:48:14) \n",
"[GCC 7.3.0]\n",
"matplotlib: 3.0.3\n",
"numpy: 1.16.2\n",
"pandas: 0.24.1\n",
"AutoClassWrapper: 1.4.1\n"
]
}
],
"source": [
"from pathlib import Path\n",
"import sys\n",
"import time\n",
"\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.lines import Line2D\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"%matplotlib inline\n",
"\n",
"print(\"Python:\", sys.version)\n",
"print(\"matplotlib:\", matplotlib.__version__)\n",
"print(\"numpy:\", np.__version__)\n",
"print(\"pandas:\", pd.__version__)\n",
"\n",
"import autoclasswrapper as wrapper\n",
"print(\"AutoClassWrapper:\", wrapper.__version__)\n",
"\n",
"version = sys.version_info \n",
"if not ((version.major >= 3) and (version.minor >= 6)):\n",
" sys.exit(\"Need Python>=3.6\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dataset generation"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" x \n",
" y \n",
" color \n",
" \n",
" \n",
" name \n",
" \n",
" \n",
" \n",
" \n",
" id000 \n",
" 4.268207 \n",
" 4.857065 \n",
" blue \n",
" \n",
" \n",
" id001 \n",
" 3.079047 \n",
" 3.985566 \n",
" blue \n",
" \n",
" \n",
" id002 \n",
" 3.704671 \n",
" 4.374195 \n",
" blue \n",
" \n",
" \n",
" id003 \n",
" 2.681800 \n",
" 3.745056 \n",
" blue \n",
" \n",
" \n",
" id004 \n",
" 3.012738 \n",
" 3.704896 \n",
" blue \n",
" \n",
" \n",
"
\n",
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(df[\"x\"], df[\"y\"], color=df[\"color\"], s=10)\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"y\")\n",
"plt.xlim(0, 10)\n",
"plt.ylim(-5, 10);"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# verify all x are > 0\n",
"assert min(df[\"x\"]) > 0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Save `x` and `y` in 2 different files (that will be later merged)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df[\"x\"].to_csv(\"demo_real_scalar.tsv\", sep=\"\\t\", header=True)\n",
"df[\"y\"].to_csv(\"demo_real_location.tsv\", sep=\"\\t\", header=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1 - prepare input files\n",
"\n",
"[AutoClass C](https://ti.arc.nasa.gov/tech/rse/synthesis-projects-applications/autoclass/autoclass-c/) can handle different types of data:\n",
"\n",
"- *real scalar*: numerical values bounded by 0. Examples: length, weight, age...\n",
"- *real location*: numerical values, positive and negative. Examples: position, microarray log ratio, elevation...\n",
"- *discrete*: qualitative data. Examples: color, phenotype, name...\n",
"\n",
"Each data type must be entered in separate input file.\n",
"\n",
"AutoClass C handles very well missing data. Missing data must be represented by nothing (no `NA`, `?`, `None`...)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-07 19:07:11 INFO Reading data file 'demo_real_scalar.tsv' as 'real scalar' with error 0.01\n",
"2019-07-07 19:07:11 INFO Detected encoding: ascii\n",
"2019-07-07 19:07:11 INFO Found 300 rows and 2 columns\n",
"2019-07-07 19:07:11 DEBUG Checking column names\n",
"2019-07-07 19:07:11 DEBUG Index name 'name'\n",
"2019-07-07 19:07:11 DEBUG Column name 'x'\n",
"2019-07-07 19:07:11 INFO Checking data format\n",
"2019-07-07 19:07:11 INFO Column 'x'\n",
"2019-07-07 19:07:11 INFO count 300.000000\n",
"2019-07-07 19:07:11 INFO mean 4.331423\n",
"2019-07-07 19:07:11 INFO std 1.316879\n",
"2019-07-07 19:07:11 INFO min 1.616267\n",
"2019-07-07 19:07:11 INFO 50% 4.038210\n",
"2019-07-07 19:07:11 INFO max 7.776609\n",
"2019-07-07 19:07:11 INFO ---\n",
"2019-07-07 19:07:11 INFO Reading data file 'demo_real_location.tsv' as 'real location' with error 0.01\n",
"2019-07-07 19:07:11 INFO Detected encoding: ascii\n",
"2019-07-07 19:07:11 INFO Found 300 rows and 2 columns\n",
"2019-07-07 19:07:11 DEBUG Checking column names\n",
"2019-07-07 19:07:11 DEBUG Index name 'name'\n",
"2019-07-07 19:07:11 DEBUG Column name 'y'\n",
"2019-07-07 19:07:11 INFO Checking data format\n",
"2019-07-07 19:07:11 INFO Column 'y'\n",
"2019-07-07 19:07:11 INFO count 300.000000\n",
"2019-07-07 19:07:11 INFO mean 3.018801\n",
"2019-07-07 19:07:11 INFO std 2.263316\n",
"2019-07-07 19:07:11 INFO min -1.607160\n",
"2019-07-07 19:07:11 INFO 50% 3.882919\n",
"2019-07-07 19:07:11 INFO max 6.949139\n",
"2019-07-07 19:07:11 INFO ---\n",
"2019-07-07 19:07:11 INFO Preparing input data\n",
"2019-07-07 19:07:11 INFO Final dataframe has 300 lines and 3 columns\n",
"2019-07-07 19:07:11 INFO Searching for missing values\n",
"2019-07-07 19:07:11 INFO No missing values found\n",
"2019-07-07 19:07:11 INFO Writing autoclass.db2 file\n",
"2019-07-07 19:07:11 INFO If any, missing values will be encoded as '?'\n",
"2019-07-07 19:07:11 DEBUG Writing autoclass.tsv file [for later use]\n",
"2019-07-07 19:07:11 INFO Writing .hd2 file\n",
"2019-07-07 19:07:11 INFO Writing .model file\n",
"2019-07-07 19:07:11 INFO Writing .s-params file\n",
"2019-07-07 19:07:11 INFO Writing .r-params file\n"
]
}
],
"source": [
"# Create object to prepare dataset.\n",
"clust = wrapper.Input()\n",
"\n",
"# Load datasets from tsv files.\n",
"clust.add_input_data(\"demo_real_scalar.tsv\", \"real scalar\")\n",
"clust.add_input_data(\"demo_real_location.tsv\", \"real location\")\n",
"\n",
"# Prepare input data:\n",
"# - create a final dataframe\n",
"# - merge datasets if multiple inputs\n",
"clust.prepare_input_data()\n",
"\n",
"# Create files needed by AutoClass.\n",
"clust.create_db2_file()\n",
"clust.create_hd2_file()\n",
"clust.create_model_file()\n",
"clust.create_sparams_file()\n",
"clust.create_rparams_file()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 2 - prepare run script & run autoclass\n",
"\n",
"The file `autoclass-run-success` is created if AutoClass C has run without any issue. \n",
"Otherwise, the file `autoclass-run-failure` is created."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-07 19:07:14 INFO AutoClass C executable found in /home/pierre/.soft/bin/autoclass\n",
"2019-07-07 19:07:14 INFO Writing run file\n",
"2019-07-07 19:07:14 INFO AutoClass C executable found in /home/pierre/.soft/bin/autoclass\n",
"2019-07-07 19:07:14 INFO AutoClass C version: AUTOCLASS C (version 3.3.6unx)\n",
"2019-07-07 19:07:14 INFO Running clustering...\n"
]
}
],
"source": [
"# Clean previous status file and results if a classification has already been performed.\n",
"!rm -f autoclass-run-* *.results-bin\n",
"\n",
"# Search autoclass in path.\n",
"wrapper.search_autoclass_in_path()\n",
"\n",
"# Create object to run AutoClass.\n",
"run = wrapper.Run()\n",
"\n",
"# Prepare run script.\n",
"run.create_run_file()\n",
"\n",
"# Run AutoClass.\n",
"run.run()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3 - parse and format results\n",
"\n",
"AutoClass C results are parsed and formated for an easier use :\n",
"\n",
"- `.cdt`: cluster data (CDT) files can be open with [Java Treeview](http://jtreeview.sourceforge.net/)\n",
"- `.tsv`: Tab-separated values (TSV) file can be easily open and process with Microsoft Excel, R, Python...\n",
"- `_stats.tsv`: basic statistics for all classes\n",
"- `_dendrogram.png`: figure with a dendrogram showing relationship between classes\n",
"\n",
"Note that the $n$ classes are numbered from 1 to $n$.\n",
"\n",
"\n",
"Results are analyzed only when classification is completed.\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Time: 2 sec."
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-07 19:07:18 INFO Extracting autoclass results\n",
"2019-07-07 19:07:18 INFO Found 300 cases classified in 3 classes\n",
"2019-07-07 19:07:18 INFO Aggregating input data\n",
"2019-07-07 19:07:18 INFO Writing classes + probabilities .tsv file\n",
"2019-07-07 19:07:18 INFO Writing .cdt file\n",
"2019-07-07 19:07:18 INFO Writing .cdt file (with probabilities)\n",
"2019-07-07 19:07:18 INFO Writing class statistics\n",
"2019-07-07 19:07:18 INFO Writing dendrogram\n"
]
},
{
"data": {
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Ak5M8lm4c1W50Qep6umB2KF34m3Js1hocpx+mG/f1ILr73t5AN9/cQcCHBqYLmW47quqrSR5Ad+X13nQ/C66gO67ez02T5ULXRf9YumPvSX270+mmvfnvVb9jWiwycHxJkhaQJJ8C/hbYqap+1boeSVNzDJkkzXOTxyH2yx5KNznpmXRnjyTNYXZZStL8959Jbk83TvIKurGTE2PHDiy7QqQ5zy5LSZrnkjyX7nZcd6O7CvRyuln6D66q41rWJml6DGSSJEmNOYZMkiSpsXk1hmyLLbao7bbbrnUZkiRJq3XyySdfVFVT3drsZuZVINtuu+046aSTWpchSZK0WknOnW5buywlSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1Ni8urm4xuv888/n2GOPZcWKFa1LkbTALFmyhN13351tttmmdSnSnGAg05SOPfZYzjjjjNZlSFrA9t1339YlSHOCgUxTmjgztmTJErbaaqvG1UhaKJYtW8aKFSs8+y4NMJBptbbaaiv222+/1mVIWiCWLl3Kueee27oMaU5xUL8kSVJjBjJJkqTGDGSSJEmNGcgkSZIaG3sgS7JHkhryddm4a5EkSZoLWl5leSBw4sD317cqRJIkqaWWgey0qvpxw/1LkiTNCY4hkyRJaqxlIPtMkhuSXJzks0m2bViLJElSMy26LC8HDgGOAa4A7ge8Djg+yf2q6o8NapIkSWpm7IGsqn4K/HRg0TFJjgV+QjfQ/w2D7ZMcABwAsO22nkSTJEkLz5wYQ1ZVpwBnALsOWXdYVe1SVbtsueWW4y9OkiRpls2JQNYLUK2LkCRJGrc5EciS7ALcFTihdS2SJEnjNvYxZEk+A5wNnAJcRjeo/7XA+cAHxl2PJElSay2usvwFsC/w98AGwDLgS8Abq+qiBvVIkiQ11eIqy4OBg8e9X0mSpLlqTowhkyRJWswMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqbHmgSzJt5JUkre1rkWSJKmFpoEsyb7Azi1rkCRJaq1ZIEtya+DfgFe2qkGSJGkuaHmG7N3AL6vq8IY1SJIkNbdei50meSjwXOyulCRJGv8ZsiTrA/8BvKeqfj3u/UuSJM01LbosDwJuBbx9Oo2THJDkpCQnLV++fHYrkyRJamCsgSzJtsDrgX8GliS5dT+4n4Hv1x18TlUdVlW7VNUuW2655TjLlSRJGotxnyHbAbgl8Gng0oEvgFf1/773mGuSJElqatyD+n8G7Dlk+ffoQtrHgLPGWpEkSVJjYw1kVXUZ8P3Jy5MAnFtVK62TJEla6JrfOkmSJGmxazIP2WRVldY1SJIkteIZMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1tkaBLMk6Se6V5OFJNpzpoiRJkhaTkQNZkpcBy4CfA0cDd+uXfyXJgTNbniRJ0sI3UiBL8iLg/cBXgGcAGVj9A+CpM1eaJEnS4jDqGbJXAodU1QHAlyetO53+bJkkSZKmb9RAtj3w7SnWXQ3ceu3KkSRJWnxGDWQXAdtNse5uwPlrVY0kSdIiNGog+xrwL0l2GFhWSbYAXkE3tkySJEkjGDWQvQFYAfwCOAoo4N+B04AbgLfMaHWSJEmLwEiBrKouBnYBDgbWB34DrAd8EHhwVV0+4xVKkiQtcOuN+oSquhJ4a/8lSfPa+eefz7HHHsuKFStal7JoLFu27MbHpUuXti1mEVmyZAm7774722yzTetSNMRIgSzJXYGtq+qYIet2By6sqjNXs41HAwcB9wQ2A5YDxwFvqqpfjVKPJK2tY489ljPOOKN1GYvSihUrOPfcc1uXsejsu+++rUvQEKOeIXsf8CtgpUAG7EMXsvZZzTY2B04GDqULY9sCrwF+nOTeVeX/TkljM3FmbMmSJWy11VaNq5Fmx7Jly1ixYoVnguewUQPZLsBHplh3LPC81W2gqg4HDh9cluQndBPLPg04ZMSaJGmtbbXVVuy3336ty5BmxdKlSz0bOceNepXlxsC1U6z7M7DpGtZx8cA2JEmSFpVRA9lvgb2nWLcXcM50N5Rk3SS3SLIj8B90Nyz/3Ij1SJIkzXujBrJPAq9I8rIkSwCSLEnyMuAfgE+MsK0T6OY0OwO4D7BXVf1xxHokSZLmvVED2XuArwIfAK5O8ke6e1h+oF/+rhG29RxgN+BZwBXAkUm2m9woyQFJTkpy0vLly0csV5Ikae4baVB/Vd0APC3JXsAjgdvQ3d/yO1X1/RG3dVr/zxOS/C9dd+drgJdMancYcBjALrvsUqPsQ5IkaT4YeWJYgKo6Gjh6poqoqsuSnAXcZaa2KUmSNF+sUSADSHJb4JaTl1fVeWuwrdsBdwc+s6b1SJIkzVejztS/CfB+4BnAkimarbuabXwZOAX4P7qxY3cFXgFcj3OQSZKkRWjUM2QfAp4KfAw4le4qyVH9GHg68I/ALYDfAd8HDq6qc9Zge5IkSfPaqIHs0cA/VdWH1nSHVfUuRrsaU5IkaUEbddqLAL+ejUIkSZIWq1ED2eeAx89GIZIkSYvVqF2W3wHel2Rj4JvAJZMb9FNiSJIkaZpGDWRH9I/bA/sNLC+67sxiNVdZSpIk6eZGDWR7zkoVkiRJi9iot046ZrYKkSRJWqxGHdQvSZKkGTbyrZOS3At4AXA3Vr51UlXV3jNRmCRJ0mIx6q2THgQcA5wD7Eh3+6PNgG2B3wNnzXB9kiRJC96oXZbvAL4E7ER3VeULqmo74BF0V1e+bUarkyRJWgRGDWT3AT5NN70F9FNc9HOPvQ04eOZKkyRJWhxGDWTrA1dX1V/oJoXdemDdr4F7zVRhkiRJi8Wogew3wDb9v/8PeH6SdZKsA+wPLJvJ4iRJkhaDUa+y/BqwB/BZuvFk3wCuAG4ANgIOnMniJEmSFoNRJ4Z908C/j0qyG/BUYAPgW1X1nZktT5IkaeEbeR6yQVX1U+CnM1SLJEnSojTSGLIkNyR54BTrHpDkhpkpS5IkafEYdVB/VrFuXW6aDkOSJEnTNK0uy/4qyokwNnFV5aBbAY8BLprB2iRJkhaF1QayJG8E/qX/toAfraL5oTNRlCRJ0mIynTNk3+8fQxfMPkZ338pBK4BfAV+fscokSZIWidUGsqo6hu6G4iQp4KNVdf5sFyZJkrRYjDoP2ZsnL0tyT+AewPFVdcFMFSZJkrRYjDrtxQeTfGTg+6cAPwe+APwqya4zXJ8kSdKCN+q0F48Bjhv4/s1048Z2Bn4CvHGG6pIkSVo0Rg1kWwHnACS5A7ATcHBVnQr8O+AZMkmSpBGNGsiuobuJOMDD6W4sflL//VXAxjNUlyRJ0qIx6r0sTwFeluQ84GXAkVX1l37d9sCFM1mcJEnSYjBqIHs98C26gfyXAS8ZWPckunFkkiRJGsGo016cmGRb4O7AmVV1xcDqw4AzZ7I4SZKkxWDUM2RU1dXAyUOWf2NGKpIkSVpkpnMvy+cC36iqi/t/r1JVfXJGKpMkSVokpnOGbCmwG3Bx/+9VKcBAJkmSNILpBLLBqye3n8VaJEmSFqXp3Fz83IFv/wDsAmxNdzbsQuDkqrp2dsqTJEla+KY1qD/JEuDdwIuAJUD6VQVcm+TDwOuq6rpZqVKSJGkBm86g/tDdr3Iv4Ajgm8B5dKHsjsA+wCuAewKPnbVKJUmSFqjpnCF7GrAn8LSq+vKQ9R9N8hTg80meUlVfmtEKJUmSFrjp3MtyX+DzU4QxAPoQ9gXg2TNVmCRJ0mIxnUB2P2A6k75+Hbj/2pUjSZK0+EwnkG1JN2Zsdc4Dbrt25UiSJC0+0wlkGwArptHuOuCWa1eOJEnS4jPde1luk2SH1bS5w9oWI0mStBhNN5D9zzTahG5eMkmSJI1gOoFs/1mvQpIkaRGbzq2TPjGOQiRJkhar6QzqlyRJ0iwaayBL8rQkX0xybpJrkvw6ycFJNh5nHZIkSXPJuM+QvQq4AXgd8NfAh4GXAkcm8WydJElalKZ7leVMeXxVLR/4/pgklwCfAPYAjh5zPZIkSc2N9azUpDA24cT+cZtx1iJJkjRXzIVuwof3j6c1rUKSJKmRpoEsyTbAW4CjquqklrVIkiS10iyQJdkIOAK4nlVMPpvkgCQnJTlp+fJhPZ6SJEnzW5NAluSWwFeBHYBHV9Xvp2pbVYdV1S5VtcuWW245tholSZLGZdxXWZJkfeCLwAOBR1TVqeOuQZIkaS4ZayDr5xr7DLA38Liq+vE49y9JkjQXjfsM2YeAvwHeDlydZLeBdb9fVdelJEnSQjXuMWSP6R9fDxw/6euFY65FkiRpThjrGbKq2m6c+5MkSZoP5sLEsJIkSYuagUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDU29kCW5A5JPpDk+CR/SlJJtht3HZIkSXNFizNkdwGeDlwK/KDB/iVJkuaUFoHs2Kq6XVU9FvhCg/1LkiTNKWMPZFX1l3HvU5IkaS5zUL8kSVJjcz6QJTkgyUlJTlq+fHnrciRJkmbcnA9kVXVYVe1SVbtsueWWrcuRJEmacXM+kEmSJC10BjJJkqTGDGSSJEmNrddip0me1v/zAf3jY5IsB5ZX1TEtapIkSWqlSSBj5QlhD+0fjwH2GG8pkiRJbTUJZFWVFvuVJEmaixxDJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGht7IEtyxyT/k+TyJFck+VKSbcddhyRJ0lwx1kCWZAPgaODuwPOA5wA7At9LsuE4a5EkSZor1hvz/l4E7ADcrarOAkjyf8CZwIuB9465HkmSpObG3WXTDF88AAALn0lEQVT5BODHE2EMoKrOBn4EPHHMtUiSJM0J4w5kOwG/GLL8l8A9x1yLJEnSnDDuQLY5cOmQ5ZcAm425FkmSpDkhVTW+nSXXAYdU1WsnLX87cFBVrTSmLckBwAH9t3cDfj3rhUqSJK29O1XVltNpOO5B/ZfSnSWbbDOGnzmjqg4DDpvNoiRJkload5flL+nGkU12T+BXY65FkiRpThh3IPsqsFuSHSYWJNkO+Kt+nSRJ0qIz7jFkGwI/B64B3gAU8FZgY+A+VXXV2IqRJEmaI8Z6hqyqrgb2As4APgV8Bjgb2MswJkmSFquxniGTJEnSysZ+c3FJkiTdnIFMkiSpsXHPQ6Y5Ksle02x6Q1UdM6vFSLMkybHTbHptVT1qVouRZkmST06z6YqqetGsFqNpcwyZAEhyA3AukNU03bKqNhpDSdKMS3IN8JLVNQPeX1WbjqEkacYlWQG8YxpN/7GqNpntejQ9BjIBkOTKqtp4Gu0urSrvO6p5Kcl3q2rvabT7jmfINF8lOauq7jKNdqdX1d3HUZNWz0AmAJLsXVXfnUa7varq6HHUJEnSYmEgkyRpAUuyGd0E7FdW1dD7Rqs9A5luJsmmwFOAewEbAL8HflJVRzYtTBqDJLcATq+qHVbbWJrDkqwPvBnYH7gt3djIAv4AfBx4U1X9uV2FmsxAphsl+Svga8Byuv+8dwaOBO4O/A54clVd1K5CaXYlWQJcU1VOCaR5LclH6X6Gv5XuloWXA5sA9wVeD5xdVS9sV6EmM5DpRkl+BvxrVX2m//55wKOA5wKHALerqn0bliittf6K4ilXA1VV646rHmk2JLkU2K6qLh+ybjO6QHbr8VemqRjIdKMkVwCbVn9QJFkPWFZVWyTZBDjXKyw13yVZDjwf+NWQ1UuAUw1kmu+SXAjsWVWnD1l3D+B7VbXV+CvTVJwYVoNOA54MfKn//qnAb/t//4nVz1EmzQcnA1tU1W8mr+i7LD3OtRC8G/heko9x8y7LnYEXAO9sWJuG8AyZbpTkIcARwDK6X0q3B55YVT9Icl/g1VX1rJY1SmsryU7An6vqjCnW36mqzh1zWdKMS/JouiEnOwEbAVcBvwQ+WVXfblmbVmYg0830YwseQhfIjquqSxqXJEnSgmcgk7ToJLkNXZf8TvTzM9GdOfhiVV3csjZppniczy9e2q2bSfK8JMckuTjJNUnOTPKZJN5eQwtCkr2Bs4C/pfsZeAHdGeFnA2cm2bNhedKM8DiffzxDphsleSPwHOBjdP+B9wc+C6wLvAh4VlUd1a5Cae0l+RXwhqr60pB1TwbeUVX3GH9l0szxOJ9/DGS6UZJlwIMmBjQnuQvw5aq6d5LHAG+vqvs3LVJaS0muBjavqhVD1i0BLq2qDcZfmTRzPM7nH7ssNSh0V1hOWAZs0f/728Bdx16RNPNOAN6WZMPBhf33b+3XS/Odx/k84zxkGvS/wCeTvIsurL8G+G6/7tZ089hI893+wOHARUl+y03zM+0A/Ax4ZsPapJnicT7P2GWpG/U3Fn8fsE+/6FvA31fVZUl2AB5QVV9oVqA0g5LsyKT5marqzLZVSTPL43z+MJBJkiQ15hgyAZDkVjPZTpqPkqyf5OjWdUizyeN8bvIMmYDuxuJVtck02l1SVZuPoyZp3Pqrz/7kzcW1kHmcz00O6teEWyb55DTarT/rlUizqB/gPBV7DbQgeJzPPwYyTXj7NNu9c1arkGbf5sCrgLOHrLsF8PXxliPNCo/zecZAJgCq6s2ta5DG5BTgmqr67uQVfVdOxl+SNOM8zucZA5mkxeYtwNVTrLsO8B5/Wgg8zucZB/VLkiQ15sA+SYuG07toMfA4n58MZJIWkz9Ms935s1qFNLs8zuchx5DpZpJsCzyA7vYaZ0xat29VHd6mMmlGOL2LFgOP83nIMWS6UZK/Bj5Pd5n0jsBSuntZ3tCvn9bksdJcleSN02x6fVVNdyoYaU7xOJ+fDGS6UZKTgX+pqm8kuR3waWAF8JSqui7JlVW1cdsqJUlaeAxkulGSy6tq04Hv16MLZVsATwD+YCCTJGnmOahfgy5NcseJb6rqemBf4DzgKMD7nkmSNAsMZBp0FLD/4ILqPB/4P+CWTaqSJGmBs8tSN0pyC2C9qvrTFOu3rarzxlyWJEkLnmfIdKOqum6qMNavN4xpQUuybpJ/aV2HNBOS3C/J05Js0B/bf5fk35Ls07o2rcwzZJLU62+6/Keqcryk5rUkLwDeBhRwAfAl4I50848+E3h5Vf1Xuwo1mYFM0qKSZFW/hNYDnm0g03yX5HS6q+MDnAY8tKqO69c9Gnh3Ve3csERNYiCTtKgkuRb4GHDJkNXrAgcZyDTfDU5jlORqYKPqf+EnWQe4pKpu3bJG3Zy3TpK02JwKfLuqvjp5RZJbAq8Zf0nSjLs6yfpV9Wdgad387MutgL80qktTcFC/pMVmKVP/7Psz8ObxlSLNmu8CdwGoqpdNWrcP3VRGmkPsspQkaRFJsiXdNJMXta5FNzGQSZIkNWaXpSRJUmMGMkmSpMYMZJLmrCQPTvL5JBckuS7JxUmOTPK8fubx/ZJUku1a1ypJa8NpLyTNSUn+AXgvcDRwEHAusBnwKODDwGXtqpOkmeWgfklzTpLdge8DH6yqA4esvzOwIXB/4OPA9lV1zjhrlKSZZJelpLnoNXQz6b962Mqq+k1VDZ1HKckzkxydZHmSq5L8NMnzhrR7eZLTklyT5NIkJyV58sD6Ryc5Lsnl/XZ+PfnG40l2TvLV/vnXJPlRkodNarNr3816cZI/JfltkkPX5E2RtHDZZSlpTkmyLrAH8JWqunYNNrED8D/AO+lmI98d+GiSW1XVR/p9PBs4BHgL8AO6mcvvA2zer98B+Gq/nbcA1wE79tueqPP+/XN/CrwI+BPwEuCoJA+pqpOTbAR8G/gJsB9wJbAd8JA1eF2SFjC7LCXNKUluBywD3llVr11N2/1YRZdlf8++dejGnD1w4mbKST4IPKSq7j/Fdp8GfAHYtKqumKLNd4HbAztX1XX9snWBXwC/rqonJdkFOLFv48zokqZkl6WkBSXJjkkOT3I+3a2Q/gy8ELjbQLMTgfsm+UCSRyTZYNJmftY/73NJnpbktpP2cSvg4XSh7S9J1kuyHhDgKLqzcgBn0l188B9J/jbJHWf21UpaKAxkkuaai4FrgDuN+sS+i/BIYGe6cWgPA3YF/gtYMtD0k8BLgQfRdSlekuRLE9NnVNVZwKPpfkZ+CliW5IQkD++fvzmwLvDP3BT6Jr7+DtgsyTpVdTmwJ3ABcChwXpJfJHnqqK9N0sJml6WkOSfJN+mC1B2qasUq2u3HQJdlkkcC3wEeVlU/HGj3CeC5VZUh25iYSuMQ4PyqetCk9UuAv6IbS3ZfujFg1wBXAB+iC3crqaqTJm1nPWAX4LV0N3feuap+MfW7IGkx8QyZpLnoncBtgH8dtjLJ9knuM2TVRNfjnwfabgY8caodVdWlVfXfwOeBew1Zv6KqjgbeTTfVxvZVdTXdgP6dgVOq6qTJX0O2c31V/ZjurNo6wD2mqknS4uNVlpLmnKo6NskrgfcmuQewFDiPbmLYvenGhD1ryFOPoz9zleSNdAHqDcBFwKYTjZIcRnfF4/HAH4G7As+hO7tGkpfQjQP7JvA7YAu6M1sX0A3aB3glcCzw7SQfAy7s290fWLeqXpNkH+AA4CvA2X09Bw7sW5IAA5mkOaqq3pfkJ8ArgPfQhZ0rgZOAFwNfA5476TnL+7nEDqGbsuIC4P10Y77eOND0R8D+dCFs077dpwfa/Bx4DHAwcFu6OdF+CDy7qq7p93VKkl375/x7v53lwCnAR/rtnEnXvfnPwNZ9/ScCj6yq36/VGyRpQXEMmSRJUmOOIZMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElq7P8DWAIo7ZwT0/4AAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"timer = 0\n",
"step = 2\n",
"while not Path(\"autoclass-run-success\").exists():\n",
" timer += step\n",
" sys.stdout.write(\"\\r\")\n",
" sys.stdout.write(f\"Time: {timer} sec.\")\n",
" sys.stdout.flush()\n",
" time.sleep(step)\n",
"\n",
"results = wrapper.Output()\n",
"results.extract_results()\n",
"results.aggregate_input_data()\n",
"results.write_cdt()\n",
"results.write_cdt(with_proba=True)\n",
"results.write_class_stats()\n",
"results.write_dendrogram()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The dendrogram exhibits relationship between classes.\n",
"\n",
"Numbers in brakets are the number of cases (genes, proteins) in a class.\n",
"\n",
"In the above plot, classes 1 and 3 are closer to each other than to class 2. Class 1 has 101 cases, class 3 has 99 and class 2 has 100.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Results exploration\n",
"\n",
"All results are combined in `*_out.tsv` file.\n",
"\n",
"In addition to the original data (columns `name`, `x` and `y`), the class assigned to a particular case (gene, protein) is given (`main-class`) along with its probability (`main-class-proba`). Probability to belong to all classes (`class-x-proba`) are also provided."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" name \n",
" x \n",
" y \n",
" main-class \n",
" main-class-proba \n",
" class-1-proba \n",
" class-2-proba \n",
" class-3-proba \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" id000 \n",
" 4.268207 \n",
" 4.857065 \n",
" 1 \n",
" 0.964 \n",
" 0.964 \n",
" 0.0 \n",
" 0.036 \n",
" \n",
" \n",
" 1 \n",
" id001 \n",
" 3.079047 \n",
" 3.985566 \n",
" 1 \n",
" 1.000 \n",
" 1.000 \n",
" 0.0 \n",
" 0.000 \n",
" \n",
" \n",
" 2 \n",
" id002 \n",
" 3.704671 \n",
" 4.374195 \n",
" 1 \n",
" 1.000 \n",
" 1.000 \n",
" 0.0 \n",
" 0.000 \n",
" \n",
" \n",
" 3 \n",
" id003 \n",
" 2.681800 \n",
" 3.745056 \n",
" 1 \n",
" 1.000 \n",
" 1.000 \n",
" 0.0 \n",
" 0.000 \n",
" \n",
" \n",
" 4 \n",
" id004 \n",
" 3.012738 \n",
" 3.704896 \n",
" 1 \n",
" 1.000 \n",
" 1.000 \n",
" 0.0 \n",
" 0.000 \n",
" \n",
" \n",
"
\n",
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(df_res[\"x\"], df_res[\"y\"], color=df_res[\"main-class\"], s=10)\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"y\")\n",
"plt.xlim(0, 10)\n",
"legend_elements = [Line2D([0], [0], marker='o', color=value, label=str(key), markersize=8) \n",
" for key, value in class_to_color.items()]\n",
"plt.legend(handles=legend_elements)\n",
"plt.ylim(-5, 10);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Compare to original distribution"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(df[\"x\"], df[\"y\"], color=df[\"color\"], s=10)\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"y\")\n",
"plt.xlim(0, 10)\n",
"plt.ylim(-5, 10);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Slight differences could appear at the margin between groups of points but the overall groups are found."
]
}
],
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"display_name": "Python 3",
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