simple.py

#!/usr/bin/env python3
 
 
 
import numpy as np
import tensorflow as tf
import basf2_mva
import basf2_mva_util
import time
 
from basf2_mva_python_interface.tensorflow import State
 
 
def get_model(number_of_features, number_of_spectators, number_of_events, training_fraction, parameters):
 
    tf.reset_default_graph()
    x = tf.placeholder(tf.float32, [None, number_of_features])
    y = tf.placeholder(tf.float32, [None, 1])
 
    def layer(x, shape, name, unit=tf.sigmoid):
        with tf.name_scope(name):
            weights = tf.Variable(tf.truncated_normal(shape, stddev=1.0 / np.sqrt(float(shape[0]))), name='weights')
            biases = tf.Variable(tf.constant(0.0, shape=[shape[1]]), name='biases')
            layer = unit(tf.matmul(x, weights) + biases)
        return layer
 
    inference_hidden1 = layer(x, [number_of_features, number_of_features + 1], 'inference_hidden1')
    inference_activation = layer(inference_hidden1, [number_of_features + 1, 1], 'inference_sigmoid', unit=tf.sigmoid)
 
    epsilon = 1e-5
    inference_loss = -tf.reduce_sum(y * tf.log(inference_activation + epsilon) +
                                    (1.0 - y) * tf.log(1 - inference_activation + epsilon))
 
    inference_optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
    inference_minimize = inference_optimizer.minimize(inference_loss)
 
    init = tf.global_variables_initializer()
 
    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    session = tf.Session(config=config)
 
    session.run(init)
    state = State(x, y, inference_activation, inference_loss, inference_minimize, session)
    return state
 
 
def partial_fit(state, X, S, y, w, epoch):
    """
    Pass received data to tensorflow session
    """
    feed_dict = {state.x: X, state.y: y}
    state.session.run(state.optimizer, feed_dict=feed_dict)
 
    if epoch % 100 == 0:
        avg_cost = state.session.run(state.cost, feed_dict=feed_dict)
        print("Epoch:", '%04d' % (epoch), "cost=", "{:.9f}".format(avg_cost))
    return True
 
 
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 Any results obtained like this WILL NOT BE PUBLISHED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 = "Simple"
    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 = "tensorflow"
    specific_options.m_steering_file = 'mva/examples/tensorflow/simple.py'
    specific_options.m_nIterations = 100
    specific_options.m_mini_batch_size = 100
    specific_options.m_normalize = True
    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)