simple_deep.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
 
# Dennis Weyland 2017
 
# 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.contrib_keras import State
 
import tensorflow as tf
import tensorflow.contrib.keras as keras
 
from keras.layers import Input, Dense, Dropout
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from keras.optimizers import adam
from keras.losses import binary_crossentropy
from keras.activations import sigmoid, tanh
from 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):
    """
    Returns just the state object
    """
    state.Xtest = Xtest
    state.ytest = ytest
 
    return state
 
 
def partial_fit(state, X, S, y, w, epoch):
    """
    Pass received data to tensorflow session
    """
    class TestCallback(Callback):
 
        def on_epoch_end(self, epoch, logs={}):
            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 Any results obtained like this WILL NOT BE PUBLISHED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)',
                 'daughterInvariantMass(0, 1)', 'daughterInvariantMass(0, 2)', 'daughterInvariantMass(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 = "contrib_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_roc_auc(p, t)
    print("Tensorflow", training_time, inference_time, auc)