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Rewrite complete, need packages import before test

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This commit is contained in:
Florent Guiotte 2018-09-10 17:30:26 +02:00
parent 5eb4180bae
commit 2943cc4a22
3 changed files with 183 additions and 41 deletions
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27
mockup.yml → result_mockup.yml
View File

@ -7,13 +7,16 @@ detail: |
générique. Il faut ajouter le chargement dynamique du protocole puis
réusiner le fonctionnement du supervisor pour respecter l'esprit universel
de minigrida.
protocol: Jurse
protocol:
name: Jurse
package: protocols.jurse
expe:
ground_truth:
raster: ./Data/ground_truth/2018_IEEE_GRSS_DFC_GT_TR.tif
meta_labels: GT/jurse_idx.csv
meta_labels: ./Data/ground_truth/jurse_idx.csv
descriptors_script:
name: Descriptors.dfc_aps
name: dfc_aps
package: descriptors
parameters:
areas:
- 100
@ -26,23 +29,25 @@ expe:
- ./Data/phase1_rasters/DEM_C123_3msr/UH17_GEG051_TR.tif
treshold: 1e4
cross_validation:
name: CrossValidationGenerator.APsCVG
name: APsCVG
package: CVGenerators
parameters:
n_test: 2
classifier:
name: sklearn.ensemble.RandomForestClassifier
name: RandomForestClassifier
package: sklearn.ensemble
parameters:
min_samples_leaf: 10
n_estimators: 50
n_jobs: -1
random_state: 0
expe_hashes:
hashes:
ground_truth: 2c5ecaddcb8c4a1c8863bc65e7440de4a1b4962c
descriptors_script: cfdcc84d9d9c47177930257f286d850db446812b
cross_validation: 4a61b34fda812fe717890b25d75430023335a7a6
classifier: 40e6741ef8cc4b4fbe188b8ca0563eb5195b88ad
global: b8219fab322bf11ec1aac14a1f51466dd94ddbdd
expe_report:
report:
supervisor: thecomedian
start_date: Le 27/07/2018 à 16:28:52
end_date: Le 27/07/2018 à 16:29:54
@ -52,10 +57,10 @@ expe_report:
description: 0.6744262149950373
classification: 168.82905034400028
metrics: 1.1557443889978458
expe_results:
classification: test_b8219f.tif
dimensions: 42
scores:
results:
classification: ./Enrichment/Results/test_b8219f.tif
metrics:
dimensions: 42
overall_accuracy: 0.5550408093111998
cohen_kappa: 0.41714275852261407

154
supervisor.py
View File

@ -53,7 +53,7 @@ def update_queue():
tmp_queue = list()
for child in TEST_DIR.iterdir():
if child.is_file() and child.suffix == '.yml':
tmp_queue.append({'expe_file': child,
tmp_queue.append({'expe_file': ExpePath(child),
'priority': get_priority(child)})
queue = sorted(tmp_queue, key=itemgetter('priority'))
@ -67,30 +67,56 @@ def get_priority(yml_file):
def run(expe_file):
start_time = time.time()
log.info('Run test {}'.format(expe_file))
with open(expe_file) as f:
test = OrderedDict(yaml.safe_load(f))
test = expe_file.read()
### Stage test
expe_file.stage(test)
### Load protocol
protocol = getattr(importlib.import_module('protocols.jurse'), test['protocol'])
experience = protocol(test['expe'])
try:
protocol = getattr(importlib.import_module(test['protocol']['package']),
test['protocol']['name'])
experience = protocol(test['expe'])
except Exception as e:
err = 'Could not load protocol from test {}'.format(expe_file)
log.warning(err)
expe_file.error(test, 'loading protocol', e)
raise TestError(err)
log.info('{} test protocol loaded'.format(experience))
### Write hahes
hashes = experience.get_hashes()
log.info(hashes)
test['hashes'] = experience.get_hashes()
test['report'] = create_report(start_time)
expe_file.stage(test)
### Run test
try:
experience.run()
except Exception as e:
err = 'Experience error'
log.warning(err)
expe_file.error(test, 'testing', e)
raise TestError(err)
### Write report
end_time = time.time()
### Write complete report
report = create_report(start_time, end_time)
ressources = OrderedDict()
ressouces['ram'] = None
ressouces['proccess_time'] = experience.get_process_time()
report['ressources'] = ressouces
test['report'] = report
### Write results
test['results'] = experience.get_results()
expe_file.result(test)
log.info('Additional results in {}'.format(expe_file.get_result_path()))
### End of test
log.info('Test complete')
return
@ -149,20 +175,71 @@ def run(expe_file):
(STAGING_DIR / oname_yml).unlink()
write_expe_file(RESULT_DIR / oname_yml, expe, expe_hashes, expe_report, oname_tif, metrics)
log.info('Test complete')
class ExpePath:
"""Utility wrapper for expe files.
Extend pathlib Path with staging, result and errors function to move the
test report through the Enrichment center.
"""
def __init__(self, path, hash_length=6):
self._actual = Path(path)
self._base_name = self._actual.stem
self._hash_length = hash_length
self._hash = None
def __str__(self):
return self._get_complete_name()
def read(self):
with open(self._actual) as f:
return OrderedDict(yaml.safe_load(f))
def _get_hash_name(self):
return '{}{}'.format(self._base_name,
'_' + self._hash[:self._hash_length] if self._hash is not None else '')
def _get_complete_name(self):
return self._get_hash_name() + '.yml'
def stage(self, expe):
log.info('Staging {}'.format(self._base_name))
self._check_hash(expe)
self._write(STAGING_DIR, expe)
def result(self, expe):
log.info('Write results for test {}'.format(self._base_name))
self._check_hash(expe)
self._write(RESULT_DIR, expe)
def error(self, expe, when='', e=Exception):
error = OrderedDict()
error['when'] = when
error['what'] = str(e)
error['where'] = traceback.format_exc()
expe['error'] = error
self._write(FAILED_DIR, expe)
def get_result_path(self):
return Path(RESULT_DIR) / self._get_hash_name()
def _check_hash(self, expe):
if self._hash is None:
if 'hashes' in expe:
self._hash = expe['hashes']['global']
def _write(self, path, expe):
new_path = Path(path) / self._get_complete_name()
with open(new_path, 'w') as of:
yaml.dump(expe, of,
default_flow_style=False,
encoding=None,
allow_unicode=True)
self._actual.unlink()
self._actual = new_path
def write_error(file, expe, hashes=None, report=None, when='', e=Exception):
error = OrderedDict()
error['when'] = when
error['what'] = str(e)
error['where'] = traceback.format_exc()
with open(file, 'w') as of:
yaml.dump(OrderedDict({'expe': expe,
'expe_hashes': hashes,
'expe_report': report,
'expe_error': error}),
of, default_flow_style=False, encoding=None, allow_unicode=True)
def write_expe_file(file, expe, hashes=None, report=None, classification=None, results=None):
with open(file, 'w') as of:
@ -186,24 +263,41 @@ def compute_hashes(expe):
expe_hashes['global'] = glob.hexdigest()
return expe_hashes
def create_report(kronos):
def create_report(stime=None, etime=None):
expe_report = OrderedDict()
expe_report['supervisor'] = os.uname()[1]
for timev, datek in zip((kronos.get_start_date(), kronos.get_end_date()), ('start_date', 'end_date')):
for datek, timev in zip(('start_date', 'end_date'), (stime, etime)):
expe_report[datek] = datetime.datetime.fromtimestamp(timev).strftime('Le %d/%m/%Y à %H:%M:%S') if timev is not None else None
ressources = kronos.get_times()
ressources['ram'] = None
expe_report['ressources'] = ressources
return expe_report
def watch_folder():
log.info('Waiting for test')
while not list(TEST_DIR.glob('*.yml')):
time.sleep(10)
class Kronos(object):
def __init__(self):
self._pt = time.process_time()
self._stime = time.time()
self._etime = None
def time(self, name):
self._times[name + '_process_time'] = time.process_time() - self._pt
self._pt = time.process_time()
self._etime = time.time()
def get_times(self):
return self._times
def get_start_date(self):
return self._stime
def get_end_date(self):
return self._etime
def main():
while(True):

43
test_mockup.yml Normal file
View File

@ -0,0 +1,43 @@
name: Première expérience
date: 9 juillet 2018
priority: 1
detail: |
Maquette pour la création du supervisor de minigrida. Par rapport à la
version legacy du projet LD2DAPs, le choix du protocole de test est
générique. Il faut ajouter le chargement dynamique du protocole puis
réusiner le fonctionnement du supervisor pour respecter l'esprit universel
de minigrida.
protocol:
name: Jurse
package: protocols.jurse
expe:
ground_truth:
raster: ./Data/ground_truth/2018_IEEE_GRSS_DFC_GT_TR.tif
meta_labels: ./Data/ground_truth/jurse_idx.csv
descriptors_script:
name: dfc_aps
package: descriptors
parameters:
areas:
- 100
- 1000
moi:
- 0.5
- 0.9
rasters:
- ./Data/phase1_rasters/DEM+B_C123/UH17_GEM051_TR.tif
- ./Data/phase1_rasters/DEM_C123_3msr/UH17_GEG051_TR.tif
treshold: 1e4
cross_validation:
name: APsCVG
package: CVGenerators
parameters:
n_test: 2
classifier:
name: RandomForestClassifier
package: sklearn.ensemble
parameters:
min_samples_leaf: 10
n_estimators: 50
n_jobs: -1
random_state: 0