def run(cls, rank, mpi_comm, parameters, workspace_handler, timer,
generated_files):
DataDriver.timer = timer
# Wait until the trajectories are loaded.
# Ideally this and a lot of other things may be done
# in parallel too.
# TODO: Atomic looks not to be pickable, which doesn't allow
# us to directly share the data_handler
data_handler = cls.load_data(parameters)
mpi_comm.Barrier()
# Load the trajectories
if rank == 0:
matrix_handler = cls.calc_matrix(data_handler,
parameters["matrix"])
matrix_contents = matrix_handler.distance_matrix.get_data()
else:
matrix_contents = None
matrix_handler = MatrixHandler(None, parameters["matrix"])
mpi_comm.Barrier()
# Wait until matrix is calculated.
# Then broadcast its contents
matrix_contents = mpi_comm.bcast(matrix_contents, root=0)
if rank != 0:
matrix_handler.distance_matrix = CondensedMatrix(matrix_contents)
# We expect to have at least 2 processes running (if not, the process will freeze)
if rank == 0:
# Save statistics
statistics_file_path = matrix_handler.save_statistics(
workspace_handler["matrix"])
generated_files.append({
"description":
"Matrix statistics",
"path":
os.path.abspath(statistics_file_path),
"type":
"text"
})
# Save matrix contents
if "filename" in parameters["matrix"]:
cls.save_matrix(matrix_handler, workspace_handler,
parameters["matrix"])
if rank == 1:
# Plot matrix
if "image" in parameters["matrix"]:
cls.plot_matrix(matrix_handler, workspace_handler,
parameters["matrix"], generated_files)
return data_handler, matrix_handler
def testSilhouetteSpecialCase(self):
clustering = Clustering.from_dic(data.clustering_01)
mh = MatrixHandler({
"method": "load",
"parameters":{
"path": "data/example_clustering_1_matrix"
}
}
)
s = SilhouetteCoefficientCalculator()
matrix = mh.create_matrix(None)
print s.evaluate(clustering, matrix)
def run(cls, rank, mpi_comm, parameters, workspace_handler, timer, generated_files):
DataDriver.timer = timer
# Wait until the trajectories are loaded.
# Ideally this and a lot of other things may be done
# in parallel too.
# TODO: Atomic looks not to be pickable, which doesn't allow
# us to directly share the data_handler
data_handler = cls.load_data(parameters)
mpi_comm.Barrier()
# Load the trajectories
if rank == 0:
matrix_handler = cls.calc_matrix(data_handler,
parameters["matrix"])
matrix_contents = matrix_handler.distance_matrix.get_data()
else:
matrix_contents = None
matrix_handler = MatrixHandler(None, parameters["matrix"])
mpi_comm.Barrier()
# Wait until matrix is calculated.
# Then broadcast its contents
matrix_contents = mpi_comm.bcast(matrix_contents, root=0)
if rank != 0:
matrix_handler.distance_matrix = CondensedMatrix(matrix_contents)
# We expect to have at least 2 processes running (if not, the process will freeze)
if rank == 0:
# Save statistics
statistics_file_path = matrix_handler.save_statistics(workspace_handler["matrix"])
generated_files.append({
"description":"Matrix statistics",
"path":os.path.abspath(statistics_file_path),
"type":"text"
})
# Save matrix contents
if "filename" in parameters["matrix"]:
cls.save_matrix(matrix_handler,
workspace_handler,
parameters["matrix"])
if rank == 1:
# Plot matrix
if "image" in parameters["matrix"]:
cls.plot_matrix(matrix_handler,
workspace_handler,
parameters["matrix"],
generated_files)
return data_handler, matrix_handler
class MatrixCalculator(object):
CALCULATION_METHOD = "None" # Hack that allow us to work with matrix combination.
# in skip_list, matrixCalculator has been removed.
# TODO: understand why this is necessary
def __init__(self):
pass
@classmethod
def calculate(cls, data_handler, matrix_params):
calculator_class = cls.get_calculator(matrix_params)
try:
distance_matrix = calculator_class.calculate(
data_handler, matrix_params["parameters"])
except Exception, e:
print "[ERROR][Driver::postprocess] Impossible to perform matrix calculation for method: %s" % (
calculator_class.CALCULATION_METHOD)
print "Message: %s" % str(e)
traceback.print_exc()
exit()
return MatrixHandler(distance_matrix, matrix_params)
from pyproct.driver.parameters import ProtocolParameters
from pyproct.driver.observer.observer import Observer
from pyproct.driver.driver import Driver
from pyproct.tools.commonTools import convert_to_utf8
from pyproct.clustering.clustering import Clustering
from pyproct.data.matrix.matrixHandler import MatrixHandler
if __name__ == '__main__':
create_directory("./clustering_images")
create_directory("./matrices")
create_directory("./tmp")
condensed_matrices, all_observations = vtools.create_matrices(data)
# Saving matrices
for dataset_name in data.all_datasets:
handler = MatrixHandler(condensed_matrices[dataset_name],
{"method": "load"})
handler.save_matrix("./matrices/%s" % dataset_name)
# Run pyProCT for each of them
base_script = "".join(open("base_script.json", "r").readlines())
for dataset_name in ['concentric_circles'
]: #data.all_datasets: #["spaeth_06"]:#
print dataset_name
# Change placeholders
script_str = base_script % (os.path.abspath(
"./tmp/%s" % dataset_name), "./matrices/%s" % dataset_name)
parameters = ProtocolParameters.get_params_from_json(script_str)
# And change another hypothesis stuff
parameters["clustering"]["evaluation"]["maximum_noise"] = data.noise[
dataset_name]
parameters["clustering"]["evaluation"][
from pyproct.driver.parameters import ProtocolParameters
from pyproct.driver.observer.observer import Observer
from pyproct.driver.driver import Driver
from pyproct.tools.commonTools import convert_to_utf8
from pyproct.clustering.clustering import Clustering
from pyproct.data.matrix.matrixHandler import MatrixHandler
if __name__ == '__main__':
create_directory("./clustering_images")
create_directory("./matrices")
create_directory("./tmp")
condensed_matrices, all_observations = vtools.create_matrices(data)
# Saving matrices
for dataset_name in data.all_datasets:
handler = MatrixHandler(condensed_matrices[dataset_name], {"method":"load"})
handler.save_matrix("./matrices/%s"%dataset_name)
# Run pyProCT for each of them
base_script = "".join(open("base_script.json","r").readlines())
for dataset_name in ['concentric_circles']: #data.all_datasets: #["spaeth_06"]:#
print dataset_name
# Change placeholders
script_str = base_script%(os.path.abspath("./tmp/%s"%dataset_name),"./matrices/%s"%dataset_name)
parameters = ProtocolParameters.get_params_from_json(script_str)
# And change another hypothesis stuff
parameters["clustering"]["evaluation"]["maximum_noise"] = data.noise[dataset_name]
parameters["clustering"]["evaluation"]["minimum_cluster_size"] = data.minsize[dataset_name]
parameters["clustering"]["evaluation"]["minimum_clusters"] = data.num_cluster_ranges[dataset_name][0]
parameters["clustering"]["evaluation"]["maximum_clusters"] = data.num_cluster_ranges[dataset_name][1]
print parameters["clustering"]["evaluation"]["minimum_clusters"], parameters["clustering"]["evaluation"]["maximum_clusters"]
