simple_deep.py

#!/usr/bin/env python3
 
 
 
# This example shows the implementation of a simple MLP in keras.
 
import basf2_mva
import basf2_mva_util
import time
 
from basf2_mva_python_interface.keras import State
 
 
from tensorflow.keras.layers import Input, Dense, Dropout, BatchNormalization
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import binary_crossentropy
from tensorflow.keras.activations import sigmoid, tanh
from tensorflow.keras.callbacks import Callback
 
 
old_time = time.time()
 
 
def get_model(number_of_features, number_of_spectators, number_of_events, training_fraction, parameters):
    """
    Build feed forward keras model
    """
    input = Input(shape=(number_of_features,))
 
    net = Dense(units=number_of_features, activation=tanh)(input)
    for i in range(7):
        net = Dense(units=number_of_features, activation=tanh)(net)
        net = BatchNormalization()(net)
    for i in range(7):
        net = Dense(units=number_of_features, activation=tanh)(net)
        net = Dropout(rate=0.4)(net)
 
    output = Dense(units=1, activation=sigmoid)(net)
 
    state = State(Model(input, output))
 
    state.model.compile(optimizer=Adam(lr=0.01), loss=binary_crossentropy, metrics=['accuracy'])
 
    state.model.summary()
 
    return state
 
 
def begin_fit(state, Xtest, Stest, ytest, wtest, nBatches):
    """
    Returns just the state object
    """
    state.Xtest = Xtest
    state.ytest = ytest
 
    return state
 
 
def partial_fit(state, X, S, y, w, epoch, batch):
    """
    Pass received data to tensorflow
    """
    class TestCallback(Callback):
 
        def on_epoch_end(self, epoch, logs=None):
            loss, acc = state.model.evaluate(state.Xtest, state.ytest, verbose=0, batch_size=1000)
            loss2, acc2 = state.model.evaluate(X[:10000], y[:10000], verbose=0, batch_size=1000)
            print('\nTesting loss: {}, acc: {}'.format(loss, acc))
            print('Training loss: {}, acc: {}'.format(loss2, acc2))
 
    state.model.fit(X, y, batch_size=500, epochs=10, callbacks=[TestCallback()])
    return False
 
 
if __name__ == "__main__":
    from basf2 import conditions
    # NOTE: do not use testing payloads in production! Any results obtained like this WILL NOT BE PUBLISHED
    conditions.testing_payloads = [
        'localdb/database.txt'
    ]
 
    general_options = basf2_mva.GeneralOptions()
    general_options.m_datafiles = basf2_mva.vector("train.root")
    general_options.m_identifier = "deep_keras"
    general_options.m_treename = "tree"
    variables = ['M', 'p', 'pt', 'pz',
                 'daughter(0, p)', 'daughter(0, pz)', 'daughter(0, pt)',
                 'daughter(1, p)', 'daughter(1, pz)', 'daughter(1, pt)',
                 'daughter(2, p)', 'daughter(2, pz)', 'daughter(2, pt)',
                 'chiProb', 'dr', 'dz',
                 'daughter(0, dr)', 'daughter(1, dr)',
                 'daughter(0, dz)', 'daughter(1, dz)',
                 'daughter(0, chiProb)', 'daughter(1, chiProb)', 'daughter(2, chiProb)',
                 'daughter(0, kaonID)', 'daughter(0, pionID)',
                 'daughterInvM(0, 1)', 'daughterInvM(0, 2)', 'daughterInvM(1, 2)']
    general_options.m_variables = basf2_mva.vector(*variables)
    general_options.m_target_variable = "isSignal"
 
    specific_options = basf2_mva.PythonOptions()
    specific_options.m_framework = "keras"
    specific_options.m_steering_file = 'mva/examples/keras/simple_deep.py'
    specific_options.m_normalize = True
    specific_options.m_training_fraction = 0.9
 
    training_start = time.time()
    basf2_mva.teacher(general_options, specific_options)
    training_stop = time.time()
    training_time = training_stop - training_start
 
    method = basf2_mva_util.Method(general_options.m_identifier)
    inference_start = time.time()
    test_data = ["test.root"] * 10
    p, t = method.apply_expert(basf2_mva.vector(*test_data), general_options.m_treename)
    inference_stop = time.time()
    inference_time = inference_stop - inference_start
    auc = basf2_mva_util.calculate_auc_efficiency_vs_background_retention(p, t)
    print("Tensorflow.keras", training_time, inference_time, auc)