ld2daps/Notebooks/YAML Serialization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Serialize Attribute Profiles Classification\n",
    "\n",
    "- [X] Read a YAML recipe\n",
    "- [X] Brew recipe\n",
    "- [] Compute hashes\n",
    "- [] Write hashes\n",
    "- [] Time metrics\n",
    "- [] Result metrics\n",
    "- [] Write metrics\n",
    "- [] Write/move results\n",
    "- [] Watch folder\n",
    "- [] Main loop\n",
    "- [] Logs\n",
    "- [] Catch errors\n",
    "- [] Custom CVG\n",
    "\n",
    "\n",
    "## Init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yaml\n",
    "import numpy as np\n",
    "import importlib\n",
    "import sys\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "sys.path.append('..')\n",
    "import Descriptors\n",
    "from CrossValidationGenerator import APsCVG\n",
    "\n",
    "sys.path.append('../triskele/python')\n",
    "import triskele"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Serial Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "with open('../test.yml') as f:\n",
    "    expe = yaml.safe_load(f)['expe']\n",
    "display(expe)\n",
    "\n",
    "# Ground truth\n",
    "gt = triskele.read(expe['ground_truth'])\n",
    "\n",
    "# Descriptors\n",
    "script = expe['descriptors_script']\n",
    "desc = importlib.import_module(script['name'], package=Descriptors)\n",
    "importlib.reload(Descriptors)\n",
    "att = desc.run(**script['parameters'])\n",
    "\n",
    "# CrossVal and ML\n",
    "cv = expe['cross_validation']\n",
    "cl = expe['classifier']\n",
    "\n",
    "prediction = np.zeros_like(gt)\n",
    "\n",
    "for xt, xv, yt, yv, ti in APsCVG(gt, att, **cv['parameters']):\n",
    "    rfc = RandomForestClassifier(**cl['parameters'])\n",
    "    rfc.fit(xt, yt)\n",
    "    \n",
    "    ypred = rfc.predict(xv)\n",
    "    \n",
    "    prediction[ti] = ypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(16, 9))\n",
    "plt.imshow(prediction)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import from string module, class and instantiate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import importlib\n",
    "module = importlib.import_module(module_name)\n",
    "class_ = getattr(module, class_name)\n",
    "instance = class_()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run(rasters, treshold=1e4, areas=None, sd=None, moi=None):\n",
    "    treshold = float(treshold)\n",
    "    areas = None if areas is None else np.array(areas).astype(np.float).astype(np.int)\n",
    "    sd = None if sd is None else np.array(sd).astype(np.float)\n",
    "    moi = None if moi is None else np.array(moi).astype(np.float)\n",
    "    return treshold, areas, sd, moi\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run(**expe['descriptors_param'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "desc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sha1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hashlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hashlib.md5()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted(expe.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "expe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "expe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.array(expe['descriptors_param']['areas']).astype(np.float).astype(np.int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.array(None).astype(np.float).astype(np.int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "desc = importlib.import_module(expe['descriptors_script']['path'])\n",
    "desc.run(**expe['descriptors_param'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}