def callThirdPartyProgram(self, should_write_sim0_output):
current_directory = os.getcwd()
directory_of_files = self.files_directory + "/GenomeFiles"
OperatingSystemUtil.changeWorkingDirectory(directory_of_files)
outputs = collections.OrderedDict()
if self.file_type == SupportedFileTypes.MATLAB:
try:
import matlab.engine
outputs = self.callMatlabAPI(outputs)
except ImportError as error:
self.log.warn(
"Unable to find MATLAB API hook. Starting program once per trial file.\n%s",
error)
for file in self.file_list:
self.logAndIncrementProgress()
file_result = self.callMATLAB(directory_of_files, file)
outputs[file.split(".")[0]] = file_result
if self.number_of_trials != 0:
output_file_name = self.SIM1OUTPUT_FILE_NAME + '_' + file.split(
".")[0]
self.writeOutputFile(file_result, output_file_name)
self.writeSim1Matrix(outputs)
elif self.file_type == SupportedFileTypes.R:
for file in self.file_list:
self.logAndIncrementProgress()
file_result = self.callR(directory_of_files, file)
outputs[file.split(".")[0]] = file_result
if self.number_of_trials != 0:
output_file_name = self.SIM1OUTPUT_FILE_NAME + '_' + file.split(
".")[0]
self.writeOutputFile(file_result, output_file_name)
self.writeSim1Matrix(outputs)
elif self.file_type == SupportedFileTypes.OCTAVE:
for file in self.file_list:
self.logAndIncrementProgress()
file_result = self.callOctave(directory_of_files, file)
outputs[file.split(".")[0]] = file_result
if self.number_of_trials != 0:
output_file_name = self.SIM1OUTPUT_FILE_NAME + '_' + file.split(
".")[0]
self.writeOutputFile(file_result, output_file_name)
self.writeSim1Matrix(outputs)
if should_write_sim0_output:
output_list = []
for file in outputs.keys():
output_list.append(outputs[file])
self.writeOutputFile(output_list, self.SIM0OUTPUT_FILE_NAME)
elif self.number_of_trials != 0:
sim1matrix_service = MatrixService(outputs, self.number_of_genomes,
self.number_of_trials)
sim1matrix_service.generateSimilarityMatrix('final')
if self.response_type == SupportedThirdPartyResponses.FLOAT or self.response_type == SupportedThirdPartyResponses.INTEGER:
sim1matrix_service.generateResponseMatrix()
OperatingSystemUtil.changeWorkingDirectory(current_directory)
return outputs
def writeDataFile(self, genomes_matrix):
current_directory = os.getcwd()
OperatingSystemUtil.changeWorkingDirectory(self.path + "/GenomeFiles")
with open(self.OUTPUT_FILE_NAME, 'w') as csv_file:
try:
data_writer = csv.writer(csv_file, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)
for i in range(0, self.number_of_genomes):
data_writer.writerow(genomes_matrix[i])
finally:
csv_file.close()
OperatingSystemUtil.changeWorkingDirectory(current_directory)
def writeDataFile(self, matrix, file_name):
path = os.getcwd()
OperatingSystemUtil.changeWorkingDirectory(path +
self.OUTPUT_FOLDER_NAME)
n = len(matrix)
with open(file_name, 'w') as csv_file:
try:
data_writer = csv.writer(csv_file)
for i in range(0, n):
data_writer.writerow(matrix[i])
finally:
csv_file.close()
os.chdir(path)
def writeOutputFile(self, outputs, output_file_name):
if type(outputs) != list:
outputs = [outputs]
path = os.getcwd()
OperatingSystemUtil.changeWorkingDirectory(path +
self.OUTPUT_FOLDER_NAME)
file_name = output_file_name + ".csv"
with open(file_name, 'w') as csv_file:
try:
outputs_writer = csv.writer(csv_file)
outputs_writer.writerow(outputs)
except ValueError as error:
self.log.error("Error writing %s to file %s. %s", outputs,
file_name, error)
finally:
csv_file.close()
os.chdir(path)
def makeMultiBarPlotWithMultipleAnalysis(self, full_data, output_path,
analysis_type, title):
basic_plot = matplotlib.figure()
full_data_sorted_by_percentage = self.sortByPercentage(full_data)
location_on_plot = 1
x_ticks = []
for percentage in full_data_sorted_by_percentage.keys():
data = full_data_sorted_by_percentage[percentage].values()
keys = full_data_sorted_by_percentage[percentage].keys()
positions = SafeCastUtil.safeCast(
range(location_on_plot, location_on_plot + len(keys)), list)
plot = matplotlib.boxplot(data, positions=positions, widths=0.6)
self.color_by_analysis(keys, plot)
x_ticks.append(location_on_plot + (len(keys) / 2))
location_on_plot += 4
matplotlib.title(title)
# set axes limits and labels
if analysis_type == SupportedAnalysisTypes.CLASSIFICATION:
matplotlib.ylim(0, 1)
else:
matplotlib.ylim(-1, 1)
matplotlib.ylabel("Accuracy distribution")
matplotlib.xlim(0, location_on_plot - 1)
matplotlib.xlabel("% Train")
matplotlib.xticks(x_ticks, [
SafeCastUtil.safeCast(k * 100, int)
for k in full_data_sorted_by_percentage.keys()
])
self.createLegend(full_data)
basic_plot.show()
current_path = os.getcwd()
OperatingSystemUtil.changeWorkingDirectory(output_path)
basic_plot.savefig(self.DEFAULT_PLOT_FILENAME, bbox_inches='tight')
OperatingSystemUtil.changeWorkingDirectory(current_path)