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plot setup and netconfig #22

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plot setup and netconfig #22

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5cdd413
plot setup and netconfig
Nov 27, 2019
b1b9381
legacy vars
Jan 7, 2020
660cf5a
pt130
pekraem Jan 7, 2020
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9 changes: 7 additions & 2 deletions pyrootsOfTheCaribbean/plot_configs/setupPlots.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,8 +6,13 @@
# dictionary for colors
def GetPlotColor( cls ):
color_dict = {
"ttZ": ROOT.kCyan,
"ttH": ROOT.kBlue+1,
"ttH_1": 830,
"ttH_2": 418,
"ttH_3": 433,
"ttH_4": 867,
"ttH_5": 602,
"ttZ": ROOT.kCyan,
"ttlf": ROOT.kRed-7,
"ttcc": ROOT.kRed+1,
"ttbb": ROOT.kRed+3,
Expand All @@ -24,7 +29,7 @@ def GetPlotColor( cls ):
}

if "ttZ" in cls: cls = "ttZ"
if "ttH" in cls: cls = "ttH"
if "ttH" in cls and cls not in color_dict: cls = "ttH"
return color_dict[cls]

def GetyTitle(privateWork = False):
Expand Down
154 changes: 154 additions & 0 deletions train_scripts/net_configs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -135,6 +135,20 @@
"earlystopping_epochs": 100,
}

config_dict["binary_config_v2"] = {
"layers": [200,100],
"loss_function": "binary_crossentropy",
"Dropout": 0.30,
"L2_Norm": 1e-3,
"L1_Norm": 1e-4,
"batch_size": 64,
"optimizer": optimizers.Adam(1e-3),
"activation_function": "elu",
"output_activation": "Sigmoid",
"earlystopping_percentage": 0.02,
"earlystopping_epochs": 100,
}

config_dict["binary_squared_Adadelta"] = {
"layers": [200,100],
"loss_function": "squared_hinge",
Expand Down Expand Up @@ -162,3 +176,143 @@
"earlystopping_percentage": 0.02,
"earlystopping_epochs": 100,
}

config_dict["STXS_2017"] = {
"layers": [500,200,50],
"loss_function": "categorical_crossentropy",
"Dropout": 0.50,
"L1_Norm": 0.,
"L2_Norm": 1e-5,
"batch_size": 1000,
"optimizer": optimizers.Adam(1e-4),
"activation_function": "elu",
"output_activation": "Softmax",
"earlystopping_percentage": 0.05,
"earlystopping_epochs": 100,
}

config_dict["STXS_2017_1"] = {
"layers": [50,50],
"loss_function": "categorical_crossentropy",
"Dropout": 0.50,
"L1_Norm": 0.,
"L2_Norm": 1e-5,
"batch_size": 1000,
"optimizer": optimizers.Adam(1e-4),
"activation_function": "elu",
"output_activation": "Softmax",
"earlystopping_percentage": 0.05,
"earlystopping_epochs": 100,
}

config_dict["STXS_2017_2"] = {
"layers": [50,50,50],
"loss_function": "categorical_crossentropy",
"Dropout": 0.50,
"L1_Norm": 0.,
"L2_Norm": 1e-5,
"batch_size": 1000,
"optimizer": optimizers.Adam(1e-4),
"activation_function": "elu",
"output_activation": "Softmax",
"earlystopping_percentage": 0.05,
"earlystopping_epochs": 100,
}

config_dict["STXS_2017_3"] = {
"layers": [100,75,50,25,10],
"loss_function": "categorical_crossentropy",
"Dropout": 0.50,
"L1_Norm": 0.,
"L2_Norm": 1e-5,
"batch_size": 1000,
"optimizer": optimizers.Adam(1e-4),
"activation_function": "elu",
"output_activation": "Softmax",
"earlystopping_percentage": 0.05,
"earlystopping_epochs": 100,
}

config_dict["STXS_2017_4"] = {
"layers": [50,50],
"loss_function": "categorical_crossentropy",
"Dropout": 0.50,
"L1_Norm": 0.,
"L2_Norm": 1e-5,
"batch_size": 200,
"optimizer": optimizers.Adagrad(),
"activation_function": "leakyrelu",
"output_activation": "Softmax",
"earlystopping_percentage": 0.05,
"earlystopping_epochs": 100,
}

config_dict["STXS_2017_5"] = {
"layers": [50,50,50],
"loss_function": "categorical_crossentropy",
"Dropout": 0.50,
"L1_Norm": 0.,
"L2_Norm": 1e-5,
"batch_size": 200,
"optimizer": optimizers.Adagrad(),
"activation_function": "leakyrelu",
"output_activation": "Softmax",
"earlystopping_percentage": 0.05,
"earlystopping_epochs": 100,
}

config_dict["STXS_2017_6"] = {
"layers": [100,75,50,25,10],
"loss_function": "categorical_crossentropy",
"Dropout": 0.50,
"L1_Norm": 0.,
"L2_Norm": 1e-5,
"batch_size": 200,
"optimizer": optimizers.Adagrad(),
"activation_function": "leakyrelu",
"output_activation": "Softmax",
"earlystopping_percentage": 0.05,
"earlystopping_epochs": 100,
}

config_dict["STXS_2017_7"] = {
"layers": [1000],
"loss_function": "categorical_crossentropy",
"Dropout": 0.50,
"L1_Norm": 0.,
"L2_Norm": 1e-5,
"batch_size": 200,
"optimizer": optimizers.Adagrad(),
"activation_function": "leakyrelu",
"output_activation": "Softmax",
"earlystopping_percentage": 0.05,
"earlystopping_epochs": 100,
}

config_dict["STXS_2017_8"] = {
"layers": [10,10,10,10,10,10,10],
"loss_function": "categorical_crossentropy",
"Dropout": 0.50,
"L1_Norm": 0.,
"L2_Norm": 1e-5,
"batch_size": 200,
"optimizer": optimizers.Adagrad(),
"activation_function": "leakyrelu",
"output_activation": "Softmax",
"earlystopping_percentage": 0.05,
"earlystopping_epochs": 100,
}

config_dict["STXS_2017_9"] = {
"layers": [10,25,50,75,100],
"loss_function": "categorical_crossentropy",
"Dropout": 0.50,
"L1_Norm": 0.,
"L2_Norm": 1e-5,
"batch_size": 200,
"optimizer": optimizers.Adagrad(),
"activation_function": "leakyrelu",
"output_activation": "Softmax",
"earlystopping_percentage": 0.05,
"earlystopping_epochs": 100,
}
41 changes: 41 additions & 0 deletions utils/mergeVariableSets.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,41 @@
import sys
import os
import optparse
import imp

parser = optparse.OptionParser()
parser.add_option("--jt", dest="jtregions",
help = "comma separated list of jet tag regions",
default = "4j_ge3t,5j_ge3t,ge6j_ge3t")
parser.add_option("-o", dest = "output",
help = "output path of new variable set",
default = "merged_variableset.py")
(opts, args) = parser.parse_args()


varsets = []
for i, vset in enumerate(args):
varsets.append(imp.load_source("module{}".format(i),vset))


string = "variables = {}\n"
for jt in opts.jtregions.split(","):
string += "\nvariables[\"{}\"] = [\n".format(jt)

variables = []
for vset in varsets:
variables += vset.variables[jt]

variables = sorted(list(set(variables)))

for v in variables:
string += " \"{}\",\n".format(v)

string += " ]\n\n"

string += "all_variables = list(set( [v for key in variables for v in variables[key] ] ))\n"

with open(opts.output,"w") as f:
f.write(string)
print("wrote variable set to {}".format(opts.output))

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