]
# if SPDhits: variables += ['SPDhits']
# if isolationc: variables += ['isolationc']
# if IPSig: variables += ['IPSig']
# if IP: variables += ['IP']
if np.round(hyperparameters.pop("SPDhits")):
variables += ["SPDhits"]
if np.round(hyperparameters.pop("isolationc")):
variables += ["isolationc"]
if np.round(hyperparameters.pop("IPSig")):
variables += ["IPSig"]
if np.round(hyperparameters.pop("IP")):
variables += ["IP"]
hyperparameters["numFeatures"] = len(variables)
data = loadTrainingData(variables, rng, valPart=0.3, verbose=True)
optAlgs = ["RMSProp", "adaDelta", "vSGDfd"]
patience = 12000
validationFrequency = 1
batchSize = int(data[0].get_value(borrow=True).shape[0] / 12.0)
# totNumSamples = 67553
numTrainBatches = data[0].get_value(borrow=True).shape[0] / batchSize
numEpochs = int(patience / numTrainBatches) * 15
hyperparameters["optAlg"] = optAlgs[1]
errorDict = {}
AUCDict = {}
mlp = initalizeModel(**hyperparameters)
score = trainWithEarlyStopping(
mlp,
import theano
folder = "saves\\16L500LS0.85FD0.9D0SPD1ISO1IPS1IP5.00C0.00L10.50L20.75RHO1e-10EPS13002242\\"
hyperparameters = {'c': 5, 'SPDhits': 0.49, 'dropout': 0.9, 'firstLayerDropout': 0.85,
'layerSize': 500, 'rho': 0.75, 'epsilon': 1e-10, 'numLayers': 16, 'lambda1': 1e-4, 'lambda2': 0.5}
hyperparameters["isolationc"] = 1
hyperparameters["IPSig"] = 1
hyperparameters["IP"] = 1
rng = np.random.RandomState(123)
hyperparameters["rng"] = rng
variables = ['FlightDistance','FlightDistanceError', 'LifeTime', 'VertexChi2','pt','dira','DOCAone',
'DOCAtwo','DOCAthree','IP_p0p2','IP_p1p2', 'isolationa', 'isolationb', 'isolationd',
'isolatione', 'isolationf', 'iso', 'CDF1', 'CDF2', 'CDF3', 'ISO_SumBDT', 'p0_IsoBDT', 'p1_IsoBDT',
'p2_IsoBDT', 'p0_track_Chi2Dof','p1_track_Chi2Dof', 'p2_track_Chi2Dof','p0_pt','p0_p','p0_eta',
'p0_IP','p0_IPSig','p1_pt','p1_p', 'p1_eta','p1_IP','p1_IPSig','p2_pt','p2_p','p2_eta','p2_IP',
'p2_IPSig']
if np.round(hyperparameters.pop('SPDhits')): variables += ['SPDhits']
if np.round(hyperparameters.pop('isolationc')): variables += ['isolationc']
if np.round(hyperparameters.pop('IPSig')): variables += ['IPSig']
if np.round(hyperparameters.pop('IP')): variables += ['IP']
hyperparameters["numFeatures"] = len(variables)
optAlgs = ["RMSProp", "adaDelta", "vSGDfd"]
hyperparameters["optAlg"] = optAlgs[1]
Xtrain, Ytrain, Xval, Yval, mu, sig, AUCindices = loadTrainingData(variables, rng, valPart = 0.3, verbose = True)
model = initalizeModel(**hyperparameters)
model.loadModel("", folder, verbose = True)
generateSubmission(model, variables, mu, sig)
