def test_save_model(self):
X, y, _ = datasets.csv2numpy(config.get('sklearn_SVC_test', 'traindata'))
self.svc.fit(X, y)
newX, _, _ = datasets.csv2numpy(config.get('sklearn_SVC_test', 'noveldata'))
self.assertTrue(len(self.svc.predict(newX)) == 20)
self.svc.save_model(config.get('sklearn_SVC_test', 'modelfile'))
os.remove(config.get('sklearn_SVC_test', 'modelfile'))
def test_predict(self):
X, y, _ = datasets.csv2numpy(
config.get('RandomForest_test', 'traindata'))
self.rf.fit(X, y)
newX, _, _ = datasets.csv2numpy(
config.get('RandomForest_test', 'noveldata'))
self.assertTrue(len(self.rf.predict(newX)) == 20)
def test_decision_function(self):
X, y, _ = datasets.csv2numpy(
config.get('RandomForest_test', 'traindata'))
self.rf.fit(X, y)
newX, _, _ = datasets.csv2numpy(
config.get('RandomForest_test', 'noveldata'))
self.assertTrue(len(self.rf.decision_function(newX)) == 20)
def __init__(self):
'''
Constructor.
'''
self.submit_url = config.get('pdfrateproxy', 'submit_url')
self.report_url = config.get('pdfrateproxy', 'report_url')
self.metadata_url = config.get('pdfrateproxy', 'metadata_url')
def __init__(self):
'''
Constructor.
'''
self.submit_url = config.get('pdfrateproxy', 'submit_url')
self.report_url = config.get('pdfrateproxy', 'report_url')
self.metadata_url = config.get('pdfrateproxy', 'metadata_url')
def test_save_model(self):
X, y, _ = datasets.csv2numpy(config.get('RandomForest_test', 'traindata'))
self.rf.fit(X, y)
newX, _, _ = datasets.csv2numpy(config.get('RandomForest_test', 'noveldata'))
self.assertTrue(len(self.rf.predict(newX)) == 20)
self.rf.save_model(config.get('RandomForest_test', 'modelfile'))
os.remove(config.get('RandomForest_test', 'modelfile'))
def test_load_model(self):
X, y, _ = datasets.csv2numpy(config.get('RandomForest_test', 'traindata'))
self.rf.fit(X, y)
newX, _, _ = datasets.csv2numpy(config.get('RandomForest_test', 'noveldata'))
prediction = self.rf.predict(newX)
self.rf.save_model(config.get('RandomForest_test', 'modelfile'))
newrf = RandomForest()
newrf.load_model(config.get('RandomForest_test', 'modelfile'))
self.assertTrue(numpy.array_equal(prediction, newrf.predict(newX)))
os.remove(config.get('RandomForest_test', 'modelfile'))
def test_load_model(self):
X, y, _ = datasets.csv2numpy(config.get('sklearn_SVC_test', 'traindata'))
self.svc.fit(X, y)
newX, _, _ = datasets.csv2numpy(config.get('sklearn_SVC_test', 'noveldata'))
prediction = self.svc.predict(newX)
self.svc.save_model(config.get('sklearn_SVC_test', 'modelfile'))
newsvc = sklearn_SVC()
newsvc.load_model(config.get('sklearn_SVC_test', 'modelfile'))
self.assertTrue(numpy.array_equal(prediction, newsvc.predict(newX)))
os.remove(config.get('sklearn_SVC_test', 'modelfile'))
def test_load_model(self):
X, y, _ = datasets.csv2numpy(
config.get('RandomForest_test', 'traindata'))
self.rf.fit(X, y)
newX, _, _ = datasets.csv2numpy(
config.get('RandomForest_test', 'noveldata'))
prediction = self.rf.predict(newX)
self.rf.save_model(config.get('RandomForest_test', 'modelfile'))
newrf = RandomForest()
newrf.load_model(config.get('RandomForest_test', 'modelfile'))
self.assertTrue(numpy.array_equal(prediction, newrf.predict(newX)))
os.remove(config.get('RandomForest_test', 'modelfile'))
def test_fit(self):
X, y, _ = datasets.csv2numpy(
config.get('RandomForest_test', 'traindata'))
self.rf.fit(X, y)
def test_decision_function(self):
X, y, _ = datasets.csv2numpy(config.get('sklearn_SVC_test', 'traindata'))
self.svc.fit(X, y)
newX, _, _ = datasets.csv2numpy(config.get('sklearn_SVC_test', 'noveldata'))
self.assertTrue(len(self.svc.decision_function(newX)) == 20)
def __init__(self):
'''
Constructor
'''
self.query_dir = config.get('pdfratequeryscheduler', 'query_dir')
self.reply_dir = config.get('pdfratequeryscheduler', 'reply_dir')
def test_decision_function(self):
X, y, _ = datasets.csv2numpy(config.get('RandomForest_test', 'traindata'))
self.rf.fit(X, y)
newX, _, _ = datasets.csv2numpy(config.get('RandomForest_test', 'noveldata'))
self.assertTrue(len(self.rf.decision_function(newX)) == 20)
def main():
sys.stdout.write('PDFrate Query Scheduler running! [{0}]\n'.format(datetime.datetime.now()))
parser = ArgumentParser(description = 'PDFrate Query Scheduler')
parser.parse_args()
QUERY_DIR = config.get('pdfratequeryscheduler', 'query_dir')
REPLY_DIR = config.get('pdfratequeryscheduler', 'reply_dir')
queries = []
max_priority = 0
sys.stdout.write('Queries found: ')
# Process all files in the QUERY_DIR
for f in os.listdir(QUERY_DIR):
f = os.path.join(QUERY_DIR, f)
if not os.path.isfile(f) or os.path.splitext(f)[1] != '.json':
continue
try:
queries.append(json.load(open(f, 'r')))
except Exception as ex:
sys.stderr.write('Error reading query file \'{f}\': {ex}\n'.format(f=f, ex=ex))
continue
# Keep track of max priority
queries[-1]['queryfile'] = f
if queries[-1]['priority'] > max_priority:
max_priority = queries[-1]['priority']
#sys.stdout.write('{0}\n'.format(queries[-1]))
# In case of no queries
if not queries:
sys.stdout.write("None\nExiting.\n")
return
else:
sys.stdout.write('{}\n'.format(len(queries)))
# Filter for max priority queries
sys.stdout.write('Max priority: {0}\n'.format(max_priority))
if max_priority != 0:
queries = [q for q in queries if q['priority'] == max_priority]
# The oldest one is next
top_query = min(queries, key=itemgetter('datetime'))
del queries
sys.stdout.write('Next query: {0}\n'.format(top_query))
# Submit query to PDFrate and save the reply
proxy = pdfrateproxy.PdfrateProxy()
sleep_time = randint(0, int(config.get('pdfratequeryscheduler', 'sleep_time')))
sys.stdout.write('Sleeping for {0} seconds...\n'.format(sleep_time))
sleep(sleep_time)
sys.stdout.write('Getting report...\n')
reply = proxy.get_report(utility.file_sha256_hash(top_query['filename']))
if reply['status'] == 'noreport':
sys.stdout.write('No report, submitting file...\n')
reply = proxy.submit_file(top_query['filename'])
if top_query['get_metadata'] == True and reply['status'] == 'success':
# Also get metadata
file_hash = os.path.splitext(os.path.basename(top_query['queryfile']))[0]
sys.stdout.write('Getting metadata...\n')
metadata_reply = proxy.get_metadata(file_hash)
reply['metadata'] = metadata_reply['metadata']
reply['status'] = metadata_reply['status']
reply_filename = os.path.join(REPLY_DIR, os.path.basename(top_query['queryfile']))
reply['filename'] = top_query['filename']
sys.stdout.write('Writing reply to disk...\n')
json.dump(reply, open(reply_filename, 'w+'))
# Remove query file
sys.stdout.write('Removing query file...\n')
os.remove(top_query['queryfile'])
sys.stdout.write('Exiting.\n')
def test_standardize_csv(self):
datasets.standardize_csv(config.get('datasets_test', 'csv_in'),
config.get('datasets_test', 'csv_temp'))
os.remove(config.get('datasets_test', 'csv_temp'))
def test_standardize_csv(self):
datasets.standardize_csv(config.get("datasets_test", "csv_in"), config.get("datasets_test", "csv_temp"))
os.remove(config.get("datasets_test", "csv_temp"))
def test_fit(self):
X, y, _ = datasets.csv2numpy(config.get('RandomForest_test', 'traindata'))
self.rf.fit(X, y)
def __init__(self):
'''
Constructor
'''
self.query_dir = config.get('pdfratequeryscheduler', 'query_dir')
self.reply_dir = config.get('pdfratequeryscheduler', 'reply_dir')
def test_predict(self):
X, y, _ = datasets.csv2numpy(config.get('RandomForest_test', 'traindata'))
self.rf.fit(X, y)
newX, _, _ = datasets.csv2numpy(config.get('RandomForest_test', 'noveldata'))
self.assertTrue(len(self.rf.predict(newX)) == 20)
def main():
sys.stdout.write('PDFrate Query Scheduler running! [{0}]\n'.format(
datetime.datetime.now()))
parser = ArgumentParser(description='PDFrate Query Scheduler')
parser.parse_args()
QUERY_DIR = config.get('pdfratequeryscheduler', 'query_dir')
REPLY_DIR = config.get('pdfratequeryscheduler', 'reply_dir')
queries = []
max_priority = 0
sys.stdout.write('Queries found: ')
# Process all files in the QUERY_DIR
for f in os.listdir(QUERY_DIR):
f = os.path.join(QUERY_DIR, f)
if not os.path.isfile(f) or os.path.splitext(f)[1] != '.json':
continue
try:
queries.append(json.load(open(f, 'r')))
except Exception as ex:
sys.stderr.write('Error reading query file \'{f}\': {ex}\n'.format(
f=f, ex=ex))
continue
# Keep track of max priority
queries[-1]['queryfile'] = f
if queries[-1]['priority'] > max_priority:
max_priority = queries[-1]['priority']
#sys.stdout.write('{0}\n'.format(queries[-1]))
# In case of no queries
if not queries:
sys.stdout.write("None\nExiting.\n")
return
else:
sys.stdout.write('{}\n'.format(len(queries)))
# Filter for max priority queries
sys.stdout.write('Max priority: {0}\n'.format(max_priority))
if max_priority != 0:
queries = [q for q in queries if q['priority'] == max_priority]
# The oldest one is next
top_query = min(queries, key=itemgetter('datetime'))
del queries
sys.stdout.write('Next query: {0}\n'.format(top_query))
# Submit query to PDFrate and save the reply
proxy = pdfrateproxy.PdfrateProxy()
sleep_time = randint(
0, int(config.get('pdfratequeryscheduler', 'sleep_time')))
sys.stdout.write('Sleeping for {0} seconds...\n'.format(sleep_time))
sleep(sleep_time)
sys.stdout.write('Getting report...\n')
reply = proxy.get_report(utility.file_sha256_hash(top_query['filename']))
if reply['status'] == 'noreport':
sys.stdout.write('No report, submitting file...\n')
reply = proxy.submit_file(top_query['filename'])
if top_query['get_metadata'] == True and reply['status'] == 'success':
# Also get metadata
file_hash = os.path.splitext(os.path.basename(
top_query['queryfile']))[0]
sys.stdout.write('Getting metadata...\n')
metadata_reply = proxy.get_metadata(file_hash)
reply['metadata'] = metadata_reply['metadata']
reply['status'] = metadata_reply['status']
reply_filename = os.path.join(REPLY_DIR,
os.path.basename(top_query['queryfile']))
reply['filename'] = top_query['filename']
sys.stdout.write('Writing reply to disk...\n')
json.dump(reply, open(reply_filename, 'w+'))
# Remove query file
sys.stdout.write('Removing query file...\n')
os.remove(top_query['queryfile'])
sys.stdout.write('Exiting.\n')
def test_fit(self):
X, y, _ = datasets.csv2numpy(config.get('sklearn_SVC_test', 'traindata'))
self.svc.fit(X, y)
