def related(
name=None,
sample_count=loom.preql.SAMPLE_COUNT,
debug=False,
profile='time'):
'''
Run related query.
'''
loom.store.require(name, [
'ingest.schema',
'ingest.encoding',
'samples.0.config',
'samples.0.model',
'samples.0.groups',
])
inputs, results = get_paths(name, 'related')
loom.config.config_dump({}, inputs['query']['config'])
root = inputs['root']
encoding = inputs['ingest']['encoding']
features = sorted(json_load(inputs['ingest']['schema']).keys())
print 'starting server'
with loom.preql.get_server(root, encoding, debug, profile) as preql:
print 'querying {} features'.format(len(features))
preql.relate(features, sample_count=sample_count)
def test_relate(root, encoding, **unused):
with loom.query.get_server(root, debug=True) as query_server:
with tempdir(cleanup_on_error=CLEANUP_ON_ERROR):
result_out = 'related_out.csv'
preql = loom.preql.PreQL(query_server, encoding)
preql.relate(preql.feature_names, result_out, sample_count=10)
with open(result_out, 'r') as f:
reader = csv.reader(f)
for row in reader:
pass
def test_relate(root, **unused):
with tempdir(cleanup_on_error=CLEANUP_ON_ERROR):
with loom.preql.get_server(root, debug=True) as preql:
result_out = 'related_out.csv'
preql.relate(preql.feature_names, result_out, sample_count=10)
with open(result_out, 'r') as f:
reader = csv.reader(f)
header = reader.next()
columns = header[1:]
assert_equal(columns, preql.feature_names)
zmatrix = numpy.zeros((len(columns), len(columns)))
for i, row in enumerate(reader):
column = row.pop(0)
assert_equal(column, preql.feature_names[i])
for j, score in enumerate(row):
score = float(score)
zmatrix[i][j] = score
assert_close(zmatrix, zmatrix.T)
def test_relate(root, **unused):
with tempdir(cleanup_on_error=CLEANUP_ON_ERROR):
with loom.preql.get_server(root, debug=True) as preql:
result_out = 'related_out.csv'
preql.relate(preql.feature_names, result_out, sample_count=10)
with open(result_out, 'r') as f:
reader = csv.reader(f)
header = reader.next()
columns = header[1:]
assert_equal(columns, preql.feature_names)
zmatrix = numpy.zeros((len(columns), len(columns)))
for i, row in enumerate(reader):
column = row.pop(0)
assert_equal(column, preql.feature_names[i])
for j, score in enumerate(row):
score = float(score)
zmatrix[i][j] = score
assert_close(zmatrix, zmatrix.T)
def test_relate_pandas(root, rows_csv, schema, **unused):
feature_count = len(json_load(schema))
with loom.preql.get_server(root, debug=True) as preql:
result_string = preql.relate(preql.feature_names)
result_df = pandas.read_csv(StringIO(result_string), index_col=0)
print 'result_df ='
print result_df
assert_equal(result_df.ndim, 2)
assert_equal(result_df.shape[0], feature_count)
assert_equal(result_df.shape[1], feature_count)
def test_relate_pandas(root, rows_csv, schema, **unused):
feature_count = len(json_load(schema))
with loom.preql.get_server(root, debug=True) as preql:
result_string = preql.relate(preql.feature_names)
result_df = pandas.read_csv(StringIO(result_string), index_col=0)
print 'result_df ='
print result_df
assert_equal(result_df.ndim, 2)
assert_equal(result_df.shape[0], feature_count)
assert_equal(result_df.shape[1], feature_count)
