Supervisor WIP
This commit is contained in:
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@ -8,11 +8,11 @@
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"\n",
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"\n",
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"- [X] Read a YAML recipe\n",
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"- [X] Read a YAML recipe\n",
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"- [X] Brew recipe\n",
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"- [X] Brew recipe\n",
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"- [] Compute hashes\n",
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"- [X] Compute hashes\n",
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"- [] Write hashes\n",
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"- [X] Write hashes\n",
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"- [] Time metrics\n",
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"- [X] Time metrics\n",
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"- [] Result metrics\n",
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"- [X] Result metrics\n",
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"- [] Write metrics\n",
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"- [X] Write metrics\n",
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"- [] Write/move results\n",
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"- [] Write/move results\n",
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"- [] Watch folder\n",
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"- [] Watch folder\n",
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"- [] Main loop\n",
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"- [] Main loop\n",
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@ -34,6 +34,13 @@
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"import numpy as np\n",
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"import numpy as np\n",
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"import importlib\n",
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"import importlib\n",
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"import sys\n",
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"import sys\n",
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"import hashlib\n",
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"from collections import OrderedDict\n",
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"import time\n",
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"import os\n",
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"import datetime\n",
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"from sklearn import metrics\n",
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"from pathlib import Path\n",
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"\n",
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"\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"\n",
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"\n",
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@ -45,6 +52,21 @@
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"import triskele"
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"import triskele"
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]
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]
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},
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"### Keep yaml ordered\n",
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"\n",
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"def setup_yaml():\n",
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" \"\"\" https://stackoverflow.com/a/8661021 \"\"\"\n",
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" represent_dict_order = lambda self, data: self.represent_mapping('tag:yaml.org,2002:map', data.items())\n",
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" yaml.add_representer(OrderedDict, represent_dict_order) \n",
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"setup_yaml()"
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]
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},
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{
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{
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"cell_type": "markdown",
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {},
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@ -52,6 +74,16 @@
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"## Serial Classifier"
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"## Serial Classifier"
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]
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]
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},
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"expe_in = '../test.yml'\n",
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"expe_out = '../test_out.yml'"
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]
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": null,
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"execution_count": null,
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@ -60,32 +92,301 @@
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"source": [
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"source": [
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"%load_ext autoreload\n",
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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"%autoreload 2\n",
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"with open('../test.yml') as f:\n",
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"with open(expe_in) as f:\n",
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" expe = yaml.safe_load(f)['expe']\n",
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" expe = OrderedDict(yaml.safe_load(f)['expe'])\n",
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"display(expe)\n",
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"display(expe)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Compute hashes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def compute_hashes(expe):\n",
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" glob = hashlib.sha1()\n",
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"\n",
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"\n",
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"# Ground truth\n",
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" expe_hashes = OrderedDict()\n",
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"gt = triskele.read(expe['ground_truth'])\n",
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"\n",
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"\n",
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"# Descriptors\n",
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" for k in ['ground_truth', 'descriptors_script', 'cross_validation', 'classifier']:\n",
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"script = expe['descriptors_script']\n",
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" v = str(expe[k]).encode('utf-8')\n",
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"desc = importlib.import_module(script['name'], package=Descriptors)\n",
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" expe_hashes[k] = hashlib.sha1(v).hexdigest()\n",
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"importlib.reload(Descriptors)\n",
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" glob.update(v)\n",
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"att = desc.run(**script['parameters'])\n",
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" expe_hashes['global'] = glob.hexdigest()\n",
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" return expe_hashes\n",
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"\n",
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"\n",
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"# CrossVal and ML\n",
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"expe_hashes = compute_hashes(expe)\n",
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"cv = expe['cross_validation']\n",
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"expe_hashes"
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"cl = expe['classifier']\n",
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]
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"\n",
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},
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"prediction = np.zeros_like(gt)\n",
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{
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"\n",
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"cell_type": "markdown",
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"for xt, xv, yt, yv, ti in APsCVG(gt, att, **cv['parameters']):\n",
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"metadata": {},
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" rfc = RandomForestClassifier(**cl['parameters'])\n",
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"source": [
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" rfc.fit(xt, yt)\n",
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"### Write hashes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(expe_out, 'w') as of:\n",
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" yaml.dump({'expe': expe, 'expe_hashes': expe_hashes}, of, default_flow_style=False, encoding=None, allow_unicode=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Keep track of time"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Kronos(object):\n",
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" def __init__(self):\n",
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" self._pt = time.process_time()\n",
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" self._times = OrderedDict()\n",
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" \n",
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" def time(self, name):\n",
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" self._times[name + '_process_time'] = time.process_time() - self._pt\n",
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" self._pt = time.process_time()\n",
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" \n",
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" def get_times(self):\n",
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" return self._times\n",
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" \n",
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" \n",
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" ypred = rfc.predict(xv)\n",
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"kronos = Kronos()\n",
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"start_time = time.time()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Compute descriptors"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def compute_descriptors(expe):\n",
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" \"\"\"Compute descriptors from a standard expe recipe\"\"\"\n",
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" script = expe['descriptors_script']\n",
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" desc = importlib.import_module(script['name'], package=Descriptors)\n",
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" #importlib.reload(Descriptors)\n",
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" att = desc.run(**script['parameters'])\n",
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" \n",
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" \n",
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" prediction[ti] = ypred"
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" return att\n",
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"\n",
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"att = compute_descriptors(expe)\n",
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"kronos.time('description')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Compute classification"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def compute_classification(expe, att):\n",
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" \"\"\"Read a standard expe recipe and attributes, return the result classification\"\"\"\n",
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" # Ground truth\n",
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" gt = triskele.read(expe['ground_truth'])\n",
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"\n",
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"\n",
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" # CrossVal and ML\n",
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" cv = expe['cross_validation']\n",
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" cl = expe['classifier']\n",
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"\n",
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" prediction = np.zeros_like(gt)\n",
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"\n",
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" for xt, xv, yt, yv, ti in APsCVG(gt, att, **cv['parameters']):\n",
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" rfc = RandomForestClassifier(**cl['parameters'])\n",
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" rfc.fit(xt, yt)\n",
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"\n",
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" ypred = rfc.predict(xv)\n",
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"\n",
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" prediction[ti] = ypred\n",
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" \n",
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" return prediction"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"classification = compute_classification(expe, att)\n",
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"kronos.time('classification')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Metrics"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def compute_metrics(ground_truth, classication):\n",
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" \"\"\"Return dict of metrics for ground_truth and classification prediction in parameters\"\"\"\n",
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" f = np.nonzero(classification)\n",
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" pred = classification[f].ravel()\n",
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" gt = ground_truth[f].ravel()\n",
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" \n",
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" results = OrderedDict() \n",
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" results['overall_accuracy'] = float(metrics.accuracy_score(gt, pred))\n",
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" results['cohen_kappa'] = float(metrics.cohen_kappa_score(gt, pred))\n",
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" \n",
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" return results\n",
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"\n",
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"def run_metrics(expe, classification):\n",
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" \"\"\"Compute the metrics from a standard expe recipe and an given classification\"\"\"\n",
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" \n",
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" ### Extensible: meta-classes\n",
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" gt = triskele.read(expe['ground_truth'])\n",
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" return compute_metrics(gt, classification)\n",
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"\n",
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"expe_results = run_metrics(expe, classification)\n",
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"expe_results"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"kronos.time('metrics')\n",
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"end_time = time.time()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Report"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def create_report(kronos):\n",
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" expe_report = OrderedDict()\n",
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"\n",
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" expe_report['supervisor'] = os.uname()[1]\n",
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"\n",
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" for timev, datek in zip((start_time, end_time), ('start_date', 'end_date')):\n",
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" expe_report[datek] = datetime.datetime.fromtimestamp(timev).strftime('Le %d/%m/%Y à %H:%M:%S')\n",
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"\n",
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" ressources = kronos.get_times()\n",
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" ressources['ram'] = None\n",
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"\n",
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" expe_report['ressources'] = ressources\n",
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" return expe_report\n",
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"\n",
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"expe_report = create_report(kronos)\n",
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"expe_report"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**TODO**\n",
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"\n",
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"améliorer kronos pour le start et le end time (build and get_times)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Name and write prediction"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"oname = '{}_{}'.format(Path(expe_in).stem, expe_hashes['global'][:6])\n",
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"oname_tif = oname + '.tif'\n",
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"oname_yml = oname + '.yml'\n",
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"\n",
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"triskele.write(oname_tif, classification)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Write report and results"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(oname_yml, 'w') as of:\n",
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" yaml.dump(OrderedDict({'expe': expe, \n",
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" 'expe_hashes': expe_hashes, \n",
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" 'expe_report': expe_report,\n",
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" 'expe_classification': oname_tif,\n",
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" 'expe_results': expe_results}), of, default_flow_style=False, encoding=None, allow_unicode=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"att.dtype"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import watchdog"
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]
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]
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},
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},
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{
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{
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156
supervisor.py
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156
supervisor.py
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#!/usr/bin/python
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# -*- coding: utf-8 -*-
|
||||||
|
# \file supervisor.py
|
||||||
|
# \brief TODO
|
||||||
|
# \author Florent Guiotte <florent.guiotte@gmail.com>
|
||||||
|
# \version 0.1
|
||||||
|
# \date 25 juil. 2018
|
||||||
|
#
|
||||||
|
# TODO details
|
||||||
|
|
||||||
|
import yaml
|
||||||
|
import numpy as np
|
||||||
|
import importlib
|
||||||
|
import sys
|
||||||
|
import hashlib
|
||||||
|
from collections import OrderedDict
|
||||||
|
import time
|
||||||
|
import os
|
||||||
|
import datetime
|
||||||
|
from sklearn import metrics
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
from sklearn.ensemble import RandomForestClassifier
|
||||||
|
|
||||||
|
#sys.path.append('.')
|
||||||
|
import Descriptors
|
||||||
|
from CrossValidationGenerator import APsCVG
|
||||||
|
|
||||||
|
sys.path.append('./triskele/python')
|
||||||
|
import triskele
|
||||||
|
|
||||||
|
|
||||||
|
### Keep yaml ordered
|
||||||
|
def setup_yaml():
|
||||||
|
""" https://stackoverflow.com/a/8661021 """
|
||||||
|
represent_dict_order = lambda self, data: self.represent_mapping('tag:yaml.org,2002:map', data.items())
|
||||||
|
yaml.add_representer(OrderedDict, represent_dict_order)
|
||||||
|
setup_yaml()
|
||||||
|
|
||||||
|
|
||||||
|
def run(expe_file):
|
||||||
|
with open(expe_file) as f:
|
||||||
|
expe = OrderedDict(yaml.safe_load(f)['expe'])
|
||||||
|
|
||||||
|
### Compute hashes
|
||||||
|
expe_hashes = compute_hashes(expe)
|
||||||
|
|
||||||
|
### Keep track of time
|
||||||
|
kronos = Kronos()
|
||||||
|
start_time = time.time()
|
||||||
|
|
||||||
|
### Compute descriptors
|
||||||
|
descriptors = compute_descriptors(expe)
|
||||||
|
kronos.time('description')
|
||||||
|
|
||||||
|
### Compute classification
|
||||||
|
classification = compute_classification(expe, descriptors)
|
||||||
|
kronos.time('classification')
|
||||||
|
|
||||||
|
### Metrics
|
||||||
|
metrics = run_metrics(expe, classification)
|
||||||
|
kronos.time('metrics')
|
||||||
|
|
||||||
|
### Create report WIP WIP WIP WIP WIP WIP WIP
|
||||||
|
expe_report = OrderedDict()
|
||||||
|
|
||||||
|
expe_report['supervisor'] = os.uname()[1]
|
||||||
|
|
||||||
|
for timev, datek in zip((start_time, end_time), ('start_date', 'end_date')):
|
||||||
|
expe_report[datek] = datetime.datetime.fromtimestamp(timev).strftime('Le %d/%m/%Y à %H:%M:%S')
|
||||||
|
|
||||||
|
ressources = kronos.get_times()
|
||||||
|
ressources['ram'] = None
|
||||||
|
|
||||||
|
expe_report['ressources'] = ressources
|
||||||
|
|
||||||
|
|
||||||
|
def compute_hashes(expe):
|
||||||
|
glob = hashlib.sha1()
|
||||||
|
|
||||||
|
expe_hashes = OrderedDict()
|
||||||
|
|
||||||
|
for k in ['ground_truth', 'descriptors_script', 'cross_validation', 'classifier']:
|
||||||
|
v = str(expe[k]).encode('utf-8')
|
||||||
|
expe_hashes[k] = hashlib.sha1(v).hexdigest()
|
||||||
|
glob.update(v)
|
||||||
|
expe_hashes['global'] = glob.hexdigest()
|
||||||
|
return expe_hashes
|
||||||
|
|
||||||
|
|
||||||
|
def compute_descriptors(expe):
|
||||||
|
"""Compute descriptors from a standard expe recipe"""
|
||||||
|
script = expe['descriptors_script']
|
||||||
|
desc = importlib.import_module(script['name'], package=Descriptors)
|
||||||
|
#importlib.reload(Descriptors)
|
||||||
|
att = desc.run(**script['parameters'])
|
||||||
|
|
||||||
|
return att
|
||||||
|
|
||||||
|
|
||||||
|
def compute_classification(expe, descriptors):
|
||||||
|
"""Read a standard expe recipe and descriptors, return the result classification"""
|
||||||
|
# Ground truth
|
||||||
|
gt = triskele.read(expe['ground_truth'])
|
||||||
|
|
||||||
|
|
||||||
|
# CrossVal and ML
|
||||||
|
cv = expe['cross_validation']
|
||||||
|
cl = expe['classifier']
|
||||||
|
|
||||||
|
prediction = np.zeros_like(gt)
|
||||||
|
|
||||||
|
for xt, xv, yt, yv, ti in APsCVG(gt, descriptors, **cv['parameters']):
|
||||||
|
rfc = RandomForestClassifier(**cl['parameters'])
|
||||||
|
rfc.fit(xt, yt)
|
||||||
|
|
||||||
|
ypred = rfc.predict(xv)
|
||||||
|
|
||||||
|
prediction[ti] = ypred
|
||||||
|
|
||||||
|
return prediction
|
||||||
|
|
||||||
|
|
||||||
|
def compute_metrics(ground_truth, classication):
|
||||||
|
"""Return dict of metrics for ground_truth and classification prediction in parameters"""
|
||||||
|
f = np.nonzero(classification)
|
||||||
|
pred = classification[f].ravel()
|
||||||
|
gt = ground_truth[f].ravel()
|
||||||
|
|
||||||
|
results = OrderedDict()
|
||||||
|
results['overall_accuracy'] = float(metrics.accuracy_score(gt, pred))
|
||||||
|
results['cohen_kappa'] = float(metrics.cohen_kappa_score(gt, pred))
|
||||||
|
|
||||||
|
return results
|
||||||
|
|
||||||
|
|
||||||
|
def run_metrics(expe, classification):
|
||||||
|
"""Compute the metrics from a standard expe recipe and an given classification"""
|
||||||
|
|
||||||
|
### Extensible: meta-classes
|
||||||
|
gt = triskele.read(expe['ground_truth'])
|
||||||
|
return compute_metrics(gt, classification)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
class Kronos(object):
|
||||||
|
def __init__(self):
|
||||||
|
self._pt = time.process_time()
|
||||||
|
self._times = OrderedDict()
|
||||||
|
|
||||||
|
def time(self, name):
|
||||||
|
self._times[name + '_process_time'] = time.process_time() - self._pt
|
||||||
|
self._pt = time.process_time()
|
||||||
|
|
||||||
|
def get_times(self):
|
||||||
|
return self._times
|
||||||
11
test.yml
11
test.yml
@ -10,21 +10,18 @@ expe:
|
|||||||
rasters:
|
rasters:
|
||||||
- '../Data/phase1_rasters/DEM+B_C123/UH17_GEM051_TR.tif'
|
- '../Data/phase1_rasters/DEM+B_C123/UH17_GEM051_TR.tif'
|
||||||
- '../Data/phase1_rasters/DEM_C123_3msr/UH17_GEG051_TR.tif'
|
- '../Data/phase1_rasters/DEM_C123_3msr/UH17_GEG051_TR.tif'
|
||||||
areas:
|
areas: [100, 1000]
|
||||||
- 10
|
moi: [.5, .9]
|
||||||
- 100
|
|
||||||
- 1e4
|
|
||||||
moi: [.5, .7, .9]
|
|
||||||
cross_validation:
|
cross_validation:
|
||||||
name: CrossValidationGenerator.APsCVG
|
name: CrossValidationGenerator.APsCVG
|
||||||
parameters:
|
parameters:
|
||||||
n_test: 5
|
n_test: 2
|
||||||
classifier:
|
classifier:
|
||||||
name: sklearn.ensemble.RandomForestClassifier
|
name: sklearn.ensemble.RandomForestClassifier
|
||||||
parameters:
|
parameters:
|
||||||
n_jobs: -1
|
n_jobs: -1
|
||||||
random_state: 0
|
random_state: 0
|
||||||
n_estimators: 100
|
n_estimators: 50
|
||||||
min_samples_leaf: 10
|
min_samples_leaf: 10
|
||||||
expe_hashes:
|
expe_hashes:
|
||||||
ground_truth: XXX
|
ground_truth: XXX
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user