tensorflow_adversary.py

#!/usr/bin/env python3
 
 
 
import numpy as np
import tensorflow as tf
import basf2_mva
 
from basf2_mva_python_interface.tensorflow import State
 
 
def get_variables(variables):
    result = [tv for v in variables for tv in tf.all_variables() if v == tv.name]
    if len(result) != len(variables):
        print([tv.name for tv in tf.all_variables()])
        print([v for v in variables if v not in list(tf.all_variables())])
        raise RuntimeError("Did not find all variables in tensorflow scope")
    return result
 
 
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])
    z = tf.placeholder(tf.float32, [None, number_of_spectators])
    w = tf.placeholder(tf.float32, [None, 1])
 
    if parameters['lambda'] <= 0:
        number_of_spectators = 0
 
    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_hidden2 = layer(inference_hidden1, [number_of_features+1, number_of_features+1], 'inference_hidden2')
    # inference_hidden3 = layer(inference_hidden2, [number_of_features+1, number_of_features+1], 'inference_hidden3')
    # inference_hidden4 = layer(inference_hidden3, [number_of_features+1, number_of_features+1], 'inference_hidden4')
    inference_activation = layer(inference_hidden1, [number_of_features + 1, 1], 'inference_sigmoid', unit=tf.sigmoid)
 
    epsilon = 1e-5
    inference_loss = -tf.reduce_sum(y * w * tf.log(inference_activation + epsilon) +
                                    (1.0 - y) * w * tf.log(1 - inference_activation + epsilon)) / tf.reduce_sum(w)
    inference_loss = tf.reduce_sum((y - inference_activation) * (y - inference_activation))
    for i in range(number_of_spectators):
        for c in ['signal', 'background']:
            z_single = tf.slice(z, [0, i], [-1, 1])
            adversary_hidden1 = layer(inference_activation, [1, number_of_features + 1],
                                      'adversary_hidden1_{}_{}'.format(i, c), unit=tf.tanh)
            # adversary_hidden2 = layer(adversary_hidden1, [number_of_features+1, number_of_features+1],
            #                           'adversary_hidden2_{}_{}'.format(i,c), unit=tf.nn.relu)
            adversary_means = layer(adversary_hidden1, [number_of_features + 1, 4],
                                    'adversary_means_{}_{}'.format(i, c), unit=tf.identity)
            adversary_widths = layer(adversary_hidden1, [number_of_features + 1, 4],
                                     'adversary_width_{}_{}'.format(i, c), unit=tf.exp)
            adversary_fractions_not_normed = layer(adversary_hidden1, [number_of_features + 1, 4],
                                                   'adversary_fractions_{}_{}'.format(i, c), unit=tf.identity)
            adversary_fractions = tf.nn.softmax(adversary_fractions_not_normed)
            adversary_activation = tf.reduce_sum(adversary_fractions *
                                                 tf.exp(-(adversary_means - z_single) * (adversary_means - z_single) /
                                                        (2 * adversary_widths)) / tf.sqrt(2 * np.pi * adversary_widths), axis=1)
            if c == 'signal':
                adversary_loss = -tf.reduce_sum(y * w * tf.log(adversary_activation + epsilon)) / tf.reduce_sum(y * w)
            else:
                adversary_loss = -tf.reduce_sum((1 - y) * w * tf.log(adversary_activation + epsilon)) / tf.reduce_sum((1 - y) * w)
            tf.add_to_collection('adversary_losses', adversary_loss)
 
    if number_of_spectators > 0:
        adversary_loss = tf.add_n(tf.get_collection('adversary_losses'), name='adversary_loss')
        total_loss = inference_loss - parameters.get('lambda', 50) * adversary_loss
    else:
        adversary_loss = None
        total_loss = inference_loss
 
    adversary_vars = [
        'adversary_hidden1_{}_{}/weights:0',
        'adversary_hidden1_{}_{}/biases:0',
        # 'adversary_hidden2_{}_{}/weights:0',
        # 'adversary_hidden2_{}_{}/biases:0',
        'adversary_means_{}_{}/weights:0',
        'adversary_width_{}_{}/biases:0',
        'adversary_fractions_{}_{}/weights:0',
        'adversary_fractions_{}_{}/biases:0',
    ]
    adversary_vars = [v.format(i, c) for i in range(number_of_spectators) for c in ['signal', 'background'] for v in adversary_vars]
 
    if number_of_spectators > 0:
        adversary_optimizer = tf.train.AdamOptimizer(learning_rate=parameters.get('learning_rate', 0.001))
        adversary_global_step = tf.Variable(0, name='adversary_global_step', trainable=False)
        adversary_minimize = adversary_optimizer.minimize(adversary_loss, global_step=adversary_global_step,
                                                          var_list=get_variables(adversary_vars))
    else:
        adversary_minimize = None
 
    inference_vars = [
        'inference_hidden1/weights:0',
        'inference_hidden1/biases:0',
        # 'inference_hidden2/weights:0',
        # 'inference_hidden2/biases:0',
        # 'inference_hidden3/weights:0',
        # 'inference_hidden3/biases:0',
        # 'inference_hidden4/weights:0',
        # 'inference_hidden4/biases:0',
        'inference_sigmoid/weights:0',
        'inference_sigmoid/biases:0',
    ]
 
    inference_optimizer = tf.train.AdamOptimizer(learning_rate=parameters.get('learning_rate', 0.001))
    inference_global_step = tf.Variable(0, name='inference_global_step', trainable=False)
    inference_minimize = inference_optimizer.minimize(total_loss, global_step=inference_global_step,
                                                      var_list=get_variables(inference_vars))
 
    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, total_loss, inference_minimize, session)
    state.adversary_cost = adversary_loss
    state.adversary_optimizer = adversary_minimize
    state.w = w
    state.K = parameters.get('adversary_steps', 10)
    state.z = z
 
    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.w: w, state.z: S}
    if epoch % state.K == 0 or state.adversary_optimizer is None:
        state.session.run(state.optimizer, feed_dict=feed_dict)
        avg_cost = state.session.run(state.cost, feed_dict=feed_dict)
        if epoch % 100 == 0:
            print("Epoch:", '%04d' % (epoch), "cost=", "{:.9f}".format(avg_cost))
    else:
        state.session.run(state.adversary_optimizer, feed_dict=feed_dict)
    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'
    ]
 
    variables = ['p', 'pt', 'pz', 'phi',
                 'daughter(0, p)', 'daughter(0, pz)', 'daughter(0, pt)', 'daughter(0, phi)',
                 'daughter(1, p)', 'daughter(1, pz)', 'daughter(1, pt)', 'daughter(1, phi)',
                 'daughter(2, p)', 'daughter(2, pz)', 'daughter(2, pt)', 'daughter(2, phi)',
                 'chiProb', 'dr', 'dz', 'dphi',
                 'daughter(0, dr)', 'daughter(1, dr)', 'daughter(0, dz)', 'daughter(1, dz)',
                 'daughter(0, dphi)', 'daughter(1, dphi)',
                 'daughter(0, chiProb)', 'daughter(1, chiProb)', 'daughter(2, chiProb)',
                 'daughter(0, kaonID)', 'daughter(0, pionID)', 'daughter(1, kaonID)', 'daughter(1, pionID)',
                 'daughterAngle(0, 1)', 'daughterAngle(0, 2)', 'daughterAngle(1, 2)',
                 'daughter(2, daughter(0, E))', 'daughter(2, daughter(1, E))',
                 'daughter(2, daughter(0, clusterTiming))', 'daughter(2, daughter(1, clusterTiming))',
                 'daughter(2, daughter(0, clusterE9E25))', 'daughter(2, daughter(1, clusterE9E25))',
                 'daughter(2, daughter(0, minC2TDist))', 'daughter(2, daughter(1, minC2TDist))',
                 'M']
 
    variables2 = ['p', 'pt', 'pz', 'phi',
                  'chiProb', 'dr', 'dz', 'dphi',
                  'daughter(2, chiProb)',
                  'daughter(0, kaonID)', 'daughter(0, pionID)', 'daughter(1, kaonID)', 'daughter(1, pionID)',
                  'daughter(2, daughter(0, E))', 'daughter(2, daughter(1, E))',
                  'daughter(2, daughter(0, clusterTiming))', 'daughter(2, daughter(1, clusterTiming))',
                  'daughter(2, daughter(0, clusterE9E25))', 'daughter(2, daughter(1, clusterE9E25))',
                  'daughter(2, daughter(0, minC2TDist))', 'daughter(2, daughter(1, minC2TDist))']
 
    general_options = basf2_mva.GeneralOptions()
    general_options.m_datafiles = basf2_mva.vector("train.root")
    general_options.m_treename = "tree"
    general_options.m_variables = basf2_mva.vector(*variables)
    general_options.m_spectators = basf2_mva.vector('daughterInvariantMass(0, 1)', 'daughterInvariantMass(0, 2)')
    general_options.m_target_variable = "isSignal"
    general_options.m_identifier = "tensorflow"
 
    specific_options = basf2_mva.PythonOptions()
    specific_options.m_framework = "tensorflow"
    specific_options.m_steering_file = 'mva/examples/orthogonal_discriminators/tensorflow_adversary.py'
    specific_options.m_normalize = True
    specific_options.m_nIterations = 1000
    specific_options.m_mini_batch_size = 400
    specific_options.m_config = '{"adversary_steps": 13, "learning_rate": 0.001, "lambda": 0.1}'
    basf2_mva.teacher(general_options, specific_options)
 
    general_options.m_identifier = "tensorflow_baseline"
    specific_options.m_nIterations = 100
    specific_options.m_config = '{"adversary_steps": 1, "learning_rate": 0.001, "lambda": -1.0}'
    basf2_mva.teacher(general_options, specific_options)
 
    general_options.m_variables = basf2_mva.vector(*variables2)
    general_options.m_identifier = "tensorflow_feature_drop"
    basf2_mva.teacher(general_options, specific_options)