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refactor #33

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4 changes: 2 additions & 2 deletions src/MaCh3PythonUtils/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,2 +1,2 @@
__version__="2.0.1"
__author__="Henry Wallace"
__version__ = "2.0.1"
__author__ = "Henry Wallace"
4 changes: 3 additions & 1 deletion src/MaCh3PythonUtils/apps/__main_mach3_python_utils__.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,9 @@
import argparse

def main()->None:

def main() -> None:
from MaCh3PythonUtils.config_reader.config_reader import ConfigReader

parser = argparse.ArgumentParser(usage="python make_plots -c <config_name>.yaml")
parser.add_argument("-c", "--config", help="yaml config file", required=True)

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175 changes: 98 additions & 77 deletions src/MaCh3PythonUtils/config_reader/config_reader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,15 +5,16 @@
from MaCh3PythonUtils.fitters.multi_mcmc_gpu import MCMCMultGPU
from deepmerge import always_merger


class ConfigReader:
# Strictly unecessary but nice conceptually
_file_handler = None
_interface = None
_interface = None
_plot_interface = None

__default_settings = {
# Settings for file and I/O
"FileSettings" : {
"FileSettings": {
# Name of input file
"FileName": "",
# Skip loading a file? Useful for ML
Expand All @@ -25,134 +26,154 @@ class ConfigReader:
# Run an LLH Scan?
"RunLLHScan": False,
# Run MCMC?
"RunMCMC": False
"RunMCMC": False,
},

"ParameterSettings":{
"CircularParameters" : [],
"ParameterSettings": {
"CircularParameters": [],
# List of parameter names
"ParameterNames":[],
"ParameterNames": [],
# List of cuts
"ParameterCuts":[],
"ParameterCuts": [],
# Name of label branch, used in ML
"LabelName": "",
# Any parameters we want to ignore
"IgnoredParameters":[]
"IgnoredParameters": [],
},

# Specific Settings for ML Applications
"MLSettings": {
# Name of plots
"PlotOutputName": "ml_output.pdf",
# Fitter package either SciKit or TensorFlow
"FitterPackage": "",
# Fitter Model
# Fitter Model
"FitterName": "",
# Keyword arguments for fitter
"FitterKwargs" : {},
#Use an external model that's already been trained?
"FitterKwargs": {},
# Use an external model that's already been trained?
"AddFromExternalModel": False,
# Proportion of input data set used for testing (range of 0-1 )
"TestSize": 0.0,
# Name to save ML model in
"MLOutputFile": "mlmodel.pkl"
"MLOutputFile": "mlmodel.pkl",
},

# Settings for LLH Scan
"LikelihoodScanSettings": {
"NDivisions": 100
},


"LikelihoodScanSettings": {"NDivisions": 100},
# Settings for MCMC
"MCMCSettings": {
"NSteps": 100000,
"NChains": 1,
"UpdateStep": 100,
"MaxUpdateSteps": 500000
}
"MaxUpdateSteps": 500000,
},
}




def __init__(self, config: str):
"""Constructor

:param config: Name of yaml config
:type config: str
"""
with open(config, 'r') as c:
yaml_config = yaml.safe_load(c)
"""
with open(config, "r") as c:
yaml_config = yaml.safe_load(c)

# Update default settings
self.__chain_settings = always_merger.merge(self.__default_settings, yaml_config)

def make_file_handler(self)->None:
"""Sets up file handler object
"""
# Process MCMC chain
self._file_handler = ChainHandler(self.__chain_settings["FileSettings"]["FileName"],
self.__chain_settings["FileSettings"]["ChainName"],
self.__chain_settings["FileSettings"]["Verbose"])

self._file_handler.ignore_plots(self.__chain_settings["ParameterSettings"]["IgnoredParameters"])
self._file_handler.add_additional_plots(self.__chain_settings["ParameterSettings"]["ParameterNames"])

self._file_handler.add_additional_plots(self.__chain_settings["ParameterSettings"]["LabelName"], True)

self._file_handler.add_new_cuts(self.__chain_settings["ParameterSettings"]["ParameterCuts"])
self.__chain_settings = always_merger.merge(
self.__default_settings, yaml_config
)

def make_file_handler(self) -> None:
"""Sets up file handler object"""
# Process MCMC chain
self._file_handler = ChainHandler(
self.__chain_settings["FileSettings"]["FileName"],
self.__chain_settings["FileSettings"]["ChainName"],
self.__chain_settings["FileSettings"]["Verbose"],
)

self._file_handler.ignore_plots(
self.__chain_settings["ParameterSettings"]["IgnoredParameters"]
)
self._file_handler.add_additional_plots(
self.__chain_settings["ParameterSettings"]["ParameterNames"]
)

self._file_handler.add_additional_plots(
self.__chain_settings["ParameterSettings"]["LabelName"], True
)

self._file_handler.add_new_cuts(
self.__chain_settings["ParameterSettings"]["ParameterCuts"]
)

self._file_handler.convert_ttree_to_array()


def make_ml_interface(self)->None:
"""Generates ML interface objects
"""
def make_ml_interface(self) -> None:
"""Generates ML interface objects"""
if self._file_handler is None:
raise Exception("Cannot make interface without opening a file!")

factory = MLFactory(self._file_handler, self.__chain_settings["ParameterSettings"]["LabelName"], self.__chain_settings["MLSettings"]["PlotOutputName"])

self._interface = factory.make_interface(self.__chain_settings["MLSettings"]["FitterPackage"],
self.__chain_settings["MLSettings"]["FitterName"],
**self.__chain_settings["MLSettings"]["FitterKwargs"])

factory = MLFactory(
self._file_handler,
self.__chain_settings["ParameterSettings"]["LabelName"],
self.__chain_settings["MLSettings"]["PlotOutputName"],
)

self._interface = factory.make_interface(
self.__chain_settings["MLSettings"]["FitterPackage"],
self.__chain_settings["MLSettings"]["FitterName"],
**self.__chain_settings["MLSettings"]["FitterKwargs"]
)

if self.__chain_settings["MLSettings"].get("AddFromExternalModel"):
external_model = self.__chain_settings["MLSettings"]["ExternalModel"]
external_scaler = self.__chain_settings["MLSettings"]["ExternalScaler"]
self._interface.load_model(external_model)
self._interface.load_scaler(external_scaler)

else:
self._interface.set_training_test_set(self.__chain_settings["MLSettings"]["TestSize"])
self._interface.set_training_test_set(
self.__chain_settings["MLSettings"]["TestSize"]
)
self._interface.train_model()
self._interface.test_model()
self._interface.save_model(self.__chain_settings["MLSettings"]["MLOutputFile"])
self._interface.save_scaler(self.__chain_settings['MLSettings']['MLScalerOutputName'])

self._interface.save_model(
self.__chain_settings["MLSettings"]["MLOutputFile"]
)
self._interface.save_scaler(
self.__chain_settings["MLSettings"]["MLScalerOutputName"]
)

def run_mcmc(self):
print("WARNING: MCMC HAS ONLY BEEN TESTED WITH TENSORFLOW INTERFACES!")
mcmc = MCMCMultGPU(self._interface,
self.__chain_settings["MCMCSettings"]["NChains"],
self.__chain_settings["ParameterSettings"]["CircularParameters"],
self.__chain_settings["MCMCSettings"]["UpdateStep"],
self.__chain_settings["MCMCSettings"]["MaxUpdateSteps"])
mcmc = MCMCMultGPU(
self._interface,
self.__chain_settings["MCMCSettings"]["NChains"],
self.__chain_settings["ParameterSettings"]["CircularParameters"],
self.__chain_settings["MCMCSettings"]["UpdateStep"],
self.__chain_settings["MCMCSettings"]["MaxUpdateSteps"],
)

mcmc(self.__chain_settings["MCMCSettings"]["NSteps"],
self.__chain_settings["MCMCSettings"]["MCMCOutput"],)
mcmc(
self.__chain_settings["MCMCSettings"]["NSteps"],
self.__chain_settings["MCMCSettings"]["MCMCOutput"],
)


def __call__(self) -> None:
"""Runs over all files from config
"""
"""Runs over all files from config"""
self.make_file_handler()

self.make_ml_interface()

if self.__chain_settings["FileSettings"]["RunLLHScan"] and self._interface is not None:
self._interface.run_likelihood_scan(self.__chain_settings["LikelihoodScanSettings"]["NDivisions"])

if self.__chain_settings["FileSettings"]["RunMCMC"] and self._interface is not None:
self.run_mcmc()

if (
self.__chain_settings["FileSettings"]["RunLLHScan"]
and self._interface is not None
):
self._interface.run_likelihood_scan(
self.__chain_settings["LikelihoodScanSettings"]["NDivisions"]
)

if (
self.__chain_settings["FileSettings"]["RunMCMC"]
and self._interface is not None
):
self.run_mcmc()
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