toolkit.py

#!/usr/bin/env python

from sys import version_info

if version_info[0] > 2:
    raw_input = input
    izip = zip
else:
    from itertools import izip

import os
import json
import yaml
import pickle
import importlib
import csv
import math
import glob
import shutil

import logging
logger = logging.getLogger("KerasROOTClassification")
logger.addHandler(logging.NullHandler())

from root_numpy import tree2array, rec2array, array2root
import numpy as np
import pandas as pd
import h5py
from sklearn.preprocessing import StandardScaler, RobustScaler
from sklearn.externals import joblib
from sklearn.metrics import roc_curve, auc
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.models import model_from_json
from keras.callbacks import History, EarlyStopping, CSVLogger, ModelCheckpoint
from keras.optimizers import SGD
import keras.optimizers
import matplotlib.pyplot as plt

from .utils import WeightedRobustScaler, weighted_quantile

# configure number of cores
# this doesn't seem to work, but at least with these settings keras only uses 4 processes
import tensorflow as tf
from keras import backend as K
num_cores = 1
config = tf.ConfigProto(intra_op_parallelism_threads=num_cores,
                        inter_op_parallelism_threads=num_cores,
                        allow_soft_placement=True,
                        device_count = {'CPU': num_cores})
session = tf.Session(config=config)
K.set_session(session)

import ROOT


def byteify(input):
    "From stackoverflow https://stackoverflow.com/a/13105359"
    if isinstance(input, dict):
        return {byteify(key): byteify(value)
                for key, value in input.iteritems()}
    elif isinstance(input, list):
        return [byteify(element) for element in input]
    elif isinstance(input, unicode):
        return input.encode('utf-8')
    else:
        return input

if version_info[0] > 2:
    byteify = lambda input : input

class ClassificationProject(object):

    """Simple framework to load data from ROOT TTrees and train Keras
    neural networks for classification according to some global settings.

    See the `Keras documentation <https://keras.io>` for further information

    All needed data that is created is stored in a project dir and can
    be used again later without the need to be recreated.

    :param name: Name of the project - this will also be the name of
                 the project directory in the output dir. If no further arguments
                 are given, this argument is interpreted as a directory name, from
                 which a previously created project should be initialised

    :param signal_trees: list of tuples (filename, treename) for the data that should be used as signal

    :param bkg_trees: list of tuples (filename, treename) for the data that should be used as background

    :param branches: list of branch names or expressions to be used as input values for training

    :param rename_branches: dictionary that maps branch expressions to names for better readability

    :param weight_expr: expression to weight the events in the loss function

    :param data_dir: if given, load the data from a previous project with the given name
                     instead of creating it from trees. If the data is on the same
                     disk (and the filesystem supports it), hard links will be used,
                     otherwise symlinks.

    :param identifiers: list of branches or expressions that uniquely
                        identify events. This is used to store the list of training
                        events, such that they can be marked later on, for example when
                        creating friend trees with output score

    :param selection: selection expression that events have to fulfill to be considered for training

    :param layers: number of layers in the neural network

    :param nodes: list number of nodes in each layer. If only a single number is given, use this number for every layer

    :param dropout: dropout fraction after each hidden layer. You can also pass a list for dropout fractions for each layer. Set to None for no Dropout.

    :param dropout_input: dropout fraction for the input layer. Set to None for no Dropout.

    :param batch_size: size of the training batches

    :param validation_split: split off this fraction of training events for loss evaluation

    :param activation_function: activation function in the hidden layers

    :param activation_function_output: activation function in the output layer

    :param out_dir: base directory in which the project directories should be stored

    :param scaler_type: sklearn scaler class name to transform the data before training (options: "StandardScaler", "RobustScaler")

    :param step_signal: step size when selecting signal training events (e.g. 2 means take every second event)

    :param step_bkg: step size when selecting background training events (e.g. 2 means take every second event)

    :param optimizer: name of optimizer class in keras.optimizers

    :param optimizer_opts: dictionary of options for the optimizer

    :param use_earlystopping: set true to use the keras EarlyStopping callback

    :param earlystopping_opts: options for the keras EarlyStopping callback

    :param use_modelcheckpoint: save model weights after each epoch and don't save after no validation loss improvement (except if the options are set otherwise).

    :param modelcheckpoint_opts: options for the Keras ModelCheckpoint
                                 callback. After training, the newest saved weight will be used. If
                                 you change the format of the saved model weights it has to be of
                                 the form "weights*.h5"

    :param balance_dataset: if True, balance the dataset instead of
                            applying class weights. Only a fraction of the overrepresented
                            class will be used in each epoch, but different subsets of the
                            overrepresented class will be used in each epoch.

    :param random_seed: use this seed value when initialising the model and produce consistent results. Note:
                        random data is also used for shuffling the training data, so results may vary still. To
                        produce consistent results, set the numpy random seed before training.

    :param loss: loss function name

    """


    # Datasets that are stored to (and dynamically loaded from) hdf5
    dataset_names = ["x_train", "x_test", "y_train", "y_test", "w_train", "w_test", "scores_train", "scores_test"]

    # Datasets that are retrieved from ROOT trees the first time
    dataset_names_tree = ["x_train", "x_test", "y_train", "y_test", "w_train", "w_test"]

    def __init__(self, name, *args, **kwargs):
        if len(args) < 1 and len(kwargs) < 1:
            # if no further arguments given, interpret as directory name
            self._init_from_dir(name)
        else:
            # otherwise initialise new project
            self._init_from_args(name, *args, **kwargs)
            with open(os.path.join(self.project_dir, "options.pickle"), "wb") as of:
                pickle.dump(dict(args=args, kwargs=kwargs), of)


    def _init_from_dir(self, dirname):
        if not os.path.exists(os.path.join(dirname, "options.pickle")):
            # for backward compatibility
            with open(os.path.join(dirname, "options.json")) as f:
                options = byteify(json.load(f))
        else:
            with open(os.path.join(dirname, "options.pickle"), "rb") as f:
                options = pickle.load(f)
        options["kwargs"]["project_dir"] = dirname
        self._init_from_args(os.path.basename(dirname), *options["args"], **options["kwargs"])


    def _init_from_args(self, name,
                        signal_trees, bkg_trees, branches, weight_expr,
                        rename_branches=None,
                        project_dir=None,
                        data_dir=None,
                        identifiers=None,
                        selection=None,
                        layers=3,
                        nodes=64,
                        dropout=None,
                        dropout_input=None,
                        batch_size=128,
                        validation_split=0.33,
                        activation_function='relu',
                        activation_function_output='sigmoid',
                        scaler_type="WeightedRobustScaler",
                        step_signal=2,
                        step_bkg=2,
                        optimizer="SGD",
                        optimizer_opts=None,
                        use_earlystopping=True,
                        earlystopping_opts=None,
                        use_modelcheckpoint=True,
                        modelcheckpoint_opts=None,
                        random_seed=1234,
                        balance_dataset=False,
                        loss='binary_crossentropy'):

        self.name = name
        self.signal_trees = signal_trees
        self.bkg_trees = bkg_trees
        self.branches = branches
        if rename_branches is None:
            rename_branches = {}
        self.rename_branches = rename_branches
        self.weight_expr = weight_expr
        self.selection = selection

        self.project_dir = project_dir
        if self.project_dir is None:
            self.project_dir = name

        if not os.path.exists(self.project_dir):
            os.mkdir(self.project_dir)

        self.data_dir = data_dir
        if identifiers is None:
            identifiers = []
        self.identifiers = identifiers
        self.layers = layers
        self.nodes = nodes
        if not isinstance(self.nodes, list):
            self.nodes = [self.nodes for i in range(self.layers)]
        if len(self.nodes) != self.layers:
            self.layers = len(self.nodes)
            logger.warning("Number of layers not equal to the given nodes "
                           "per layer - adjusted to " + str(self.layers))
        self.dropout = dropout
        if not isinstance(self.dropout, list):
            self.dropout = [self.dropout for i in range(self.layers)]
        if len(self.dropout) != self.layers:
            raise ValueError("List of dropout fractions has to be of equal size as the number of layers!")
        self.dropout_input = dropout_input
        self.batch_size = batch_size
        self.validation_split = validation_split
        self.activation_function = activation_function
        self.activation_function_output = activation_function_output
        self.scaler_type = scaler_type
        self.step_signal = step_signal
        self.step_bkg = step_bkg
        self.optimizer = optimizer
        self.use_earlystopping = use_earlystopping
        self.use_modelcheckpoint = use_modelcheckpoint
        if optimizer_opts is None:
            optimizer_opts = dict()
        self.optimizer_opts = optimizer_opts
        if earlystopping_opts is None:
            earlystopping_opts = dict()
        self.earlystopping_opts = earlystopping_opts
        if modelcheckpoint_opts is None:
            modelcheckpoint_opts = dict(
                save_best_only=True,
                verbose=True,
                filepath="weights.h5"
            )
        self.modelcheckpoint_opts = modelcheckpoint_opts
        self.random_seed = random_seed
        self.balance_dataset = balance_dataset
        self.loss = loss

        self.s_train = None
        self.b_train = None
        self.s_test = None
        self.b_test = None

        self._x_train = None
        self._x_test = None
        self._y_train = None
        self._y_test = None
        self._w_train = None
        self._w_test = None
        self._scores_train = None
        self._scores_test = None

        # class weighted validation data
        self._w_validation = None

        self._s_eventlist_train = None
        self._b_eventlist_train = None

        self._scaler = None
        self._class_weight = None
        self._balanced_class_weight = None
        self._model = None
        self._history = None
        self._callbacks_list = []

        # track the number of epochs this model has been trained
        self.total_epochs = 0

        self.data_loaded = False
        self.data_transformed = False

        # track if we are currently training
        self.is_training = False

        self._fields = None


    @property
    def fields(self):
        "Renamed branch expressions"
        if self._fields is None:
            self._fields = []
            for branch_expr in self.branches:
                self._fields.append(self.rename_branches.get(branch_expr, branch_expr))
        return self._fields


    def rename_fields(self, ar):
        "Rename fields of structured array"
        fields = list(ar.dtype.names)
        renamed_fields = []
        for old_name in fields:
            renamed_fields.append(self.rename_branches.get(old_name, old_name))
        ar.dtype.names = tuple(renamed_fields)


    def _load_data(self):

        try:

            # if those don't exist, we need to load them from ROOT trees first
            self._load_from_hdf5(*self.dataset_names_tree)

        except KeyError:

            logger.info("Couldn't load all datasets - reading from ROOT trees")

            # Read signal and background trees into structured numpy arrays
            signal_chain = ROOT.TChain()
            bkg_chain = ROOT.TChain()
            for filename, treename in self.signal_trees:
                signal_chain.AddFile(filename, -1, treename)
            for filename, treename in self.bkg_trees:
                bkg_chain.AddFile(filename, -1, treename)
            self.s_train = tree2array(signal_chain,
                                      branches=self.branches+[self.weight_expr]+self.identifiers,
                                      selection=self.selection,
                                      start=0, step=self.step_signal)
            self.b_train = tree2array(bkg_chain,
                                      branches=self.branches+[self.weight_expr]+self.identifiers,
                                      selection=self.selection,
                                      start=0, step=self.step_bkg)
            self.s_test = tree2array(signal_chain,
                                     branches=self.branches+[self.weight_expr],
                                     selection=self.selection,
                                     start=1, step=self.step_signal)
            self.b_test = tree2array(bkg_chain,
                                     branches=self.branches+[self.weight_expr],
                                     selection=self.selection,
                                     start=1, step=self.step_bkg)

            self.rename_fields(self.s_train)
            self.rename_fields(self.b_train)
            self.rename_fields(self.s_test)
            self.rename_fields(self.b_test)

            self.s_eventlist_train = self.s_train[self.identifiers].astype(dtype=[(branchName, "u8") for branchName in self.identifiers])
            self.b_eventlist_train = self.b_train[self.identifiers].astype(dtype=[(branchName, "u8") for branchName in self.identifiers])
            self._dump_training_list()

            # now we don't need the identifiers anymore
            self.s_train = self.s_train[self.fields+[self.weight_expr]]
            self.b_train = self.b_train[self.fields+[self.weight_expr]]

            # create x (input), y (target) and w (weights) arrays
            # the first block will be signals, the second block backgrounds
            self.x_train = rec2array(self.s_train[self.fields])
            self.x_train = np.concatenate((self.x_train, rec2array(self.b_train[self.fields])))
            self.x_test = rec2array(self.s_test[self.fields])
            self.x_test = np.concatenate((self.x_test, rec2array(self.b_test[self.fields])))
            self.w_train = self.s_train[self.weight_expr]
            self.w_train = np.concatenate((self.w_train, self.b_train[self.weight_expr]))
            self.w_test = self.s_test[self.weight_expr]
            self.w_test = np.concatenate((self.w_test, self.b_test[self.weight_expr]))

            self.y_train = np.empty(len(self.x_train))
            self.y_train[:len(self.s_train)] = 1
            self.y_train[len(self.s_train):] = 0
            self.y_test = np.empty(len(self.x_test))
            self.y_test[:len(self.s_test)] = 1
            self.y_test[len(self.s_test):] = 0

            self._dump_to_hdf5(*self.dataset_names_tree)

        self.data_loaded = True


    def _dump_training_list(self):
        s_eventlist_df = pd.DataFrame(self.s_eventlist_train)
        b_eventlist_df = pd.DataFrame(self.b_eventlist_train)

        s_eventlist_df.to_csv(os.path.join(self.project_dir, "s_eventlist_train.csv"))
        b_eventlist_df.to_csv(os.path.join(self.project_dir, "b_eventlist_train.csv"))


    @property
    def s_eventlist_train(self):
        if self._s_eventlist_train is None:
            df = pd.read_csv(os.path.join(self.project_dir, "s_eventlist_train.csv"))
            self._s_eventlist_train = df.to_records()[self.identifiers]
        return self._s_eventlist_train


    @s_eventlist_train.setter
    def s_eventlist_train(self, value):
        self._s_eventlist_train = value


    @property
    def b_eventlist_train(self):
        if self._b_eventlist_train is None:
            df = pd.read_csv(os.path.join(self.project_dir, "b_eventlist_train.csv"))
            self._b_eventlist_train = df.to_records()[self.identifiers]
        return self._b_eventlist_train


    @b_eventlist_train.setter
    def b_eventlist_train(self, value):
        self._b_eventlist_train = value


    def _dump_to_hdf5(self, *dataset_names):
        if len(dataset_names) < 1:
            dataset_names = self.dataset_names
        for dataset_name in dataset_names:
            filename = os.path.join(self.project_dir, dataset_name+".h5")
            logger.info("Writing {} to {}".format(dataset_name, filename))
            with h5py.File(filename, "w") as hf:
                hf.create_dataset(dataset_name, data=getattr(self, dataset_name))


    def _load_from_hdf5(self, *dataset_names):
        if len(dataset_names) < 1:
            dataset_names = self.dataset_names
        for dataset_name in dataset_names:
            filename = os.path.join(self.project_dir, dataset_name+".h5")
            if (self.data_dir is not None) and (not os.path.exists(filename)):
                srcpath = os.path.abspath(os.path.join(self.data_dir, dataset_name+".h5"))
                try:
                    os.link(srcpath, filename)
                    logger.info("Created hardlink from {} to {}".format(srcpath, filename))
                except OSError:
                    os.symlink(srcpath, filename)
                    logger.info("Created symlink from {} to {}".format(srcpath, filename))
            logger.info("Trying to load {} from {}".format(dataset_name, filename))
            with h5py.File(filename) as hf:
                setattr(self, dataset_name,  hf[dataset_name][:])
        logger.info("Data loaded")


    @property
    def callbacks_list(self):
        self._callbacks_list = []
        self._callbacks_list.append(self.history)
        if self.use_earlystopping:
            self._callbacks_list.append(EarlyStopping(**self.earlystopping_opts))
        if self.use_modelcheckpoint:
            mc = ModelCheckpoint(**self.modelcheckpoint_opts)
            self._callbacks_list.append(mc)
            if not os.path.dirname(mc.filepath) == self.project_dir:
                mc.filepath = os.path.join(self.project_dir, mc.filepath)
                logger.debug("Prepending project dir to ModelCheckpoint filepath: {}".format(mc.filepath))
        self._callbacks_list.append(CSVLogger(os.path.join(self.project_dir, "training.log"), append=True))
        return self._callbacks_list


    @property
    def scaler(self):
        # create the scaler (and fit to training data) if not existent
        if self._scaler is None:
            filename = os.path.join(self.project_dir, "scaler.pkl")
            try:
                self._scaler = joblib.load(filename)
                logger.info("Loaded existing scaler from {}".format(filename))
            except IOError:
                logger.info("Creating new {}".format(self.scaler_type))
                scaler_fit_kwargs = dict()
                if self.scaler_type == "StandardScaler":
                    self._scaler = StandardScaler()
                elif self.scaler_type == "RobustScaler":
                    self._scaler = RobustScaler()
                elif self.scaler_type == "WeightedRobustScaler":
                    self._scaler = WeightedRobustScaler()
                    scaler_fit_kwargs["weights"] = self.w_train*np.array(self.class_weight)[self.y_train.astype(int)]
                else:
                    raise ValueError("Scaler type {} unknown".format(self.scaler_type))
                logger.info("Fitting {} to training data".format(self.scaler_type))
                self._scaler.fit(self.x_train, **scaler_fit_kwargs)
                joblib.dump(self._scaler, filename)
        return self._scaler


    @property
    def history(self):
        params_file = os.path.join(self.project_dir, "history_params.json")
        history_file = os.path.join(self.project_dir, "history_history.json")
        if self._history is None:
            self._history = History()
            if os.path.exists(params_file) and os.path.exists(history_file):
                try:
                    with open(params_file) as f:
                        self._history.params = json.load(f)
                    with open(history_file) as f:
                        self._history.history = json.load(f)
                except ValueError:
                    logger.warning("Couldn't load history - starting with empty one")
        return self._history


    @history.setter
    def history(self, value):
        self._history = value


    def _dump_history(self):
        params_file = os.path.join(self.project_dir, "history_params.json")
        history_file = os.path.join(self.project_dir, "history_history.json")
        with open(params_file, "w") as of:
            json.dump(self.history.params, of)
        with open(history_file, "w") as of:
            json.dump(self.history.history, of)


    def _transform_data(self):
        if not self.data_transformed:
            # todo: what to do about the outliers? Where do they come from?
            logger.debug("training data before transformation: {}".format(self.x_train))
            logger.debug("minimum values: {}".format([np.min(self.x_train[:,i]) for i in range(self.x_train.shape[1])]))
            logger.debug("maximum values: {}".format([np.max(self.x_train[:,i]) for i in range(self.x_train.shape[1])]))
            self.x_train = self.scaler.transform(self.x_train)
            logger.debug("training data after transformation: {}".format(self.x_train))
            self.x_test = self.scaler.transform(self.x_test)
            self.data_transformed = True
            logger.info("Training and test data transformed")


    def _read_info(self, key, default):
        filename = os.path.join(self.project_dir, "info.json")
        if not os.path.exists(filename):
            with open(filename, "w") as of:
                json.dump({}, of)
        with open(filename) as f:
            info = json.load(f)
        return info.get(key, default)


    def _write_info(self, key, value):
        filename = os.path.join(self.project_dir, "info.json")
        with open(filename) as f:
            info = json.load(f)
        info[key] = value
        with open(filename, "w") as of:
            json.dump(info, of)


    @staticmethod
    def query_yn(text):
        result = None
        while result is None:
            input_text = raw_input(text)
            if len(input_text) > 0:
                if input_text.upper()[0] == "Y":
                    result = True
                elif input_text.upper()[0] == "N":
                    result = False
        return result


    @property
    def model(self):
        "Simple MLP"

        if self._model is None:

            self._model = Sequential()

            if self.dropout_input is None:
                self._model.add(Dense(self.nodes[0], input_dim=len(self.fields), activation=self.activation_function))
                # in case of no Dropout we already have the first hidden layer
                start_layer = 1
            else:
                self._model.add(Dropout(rate=self.dropout_input, input_shape=(len(self.fields),)))
                start_layer = 0
            # the (other) hidden layers
            for node_count, dropout_fraction in zip(self.nodes[start_layer:], self.dropout[start_layer:]):
                self._model.add(Dense(node_count, activation=self.activation_function))
                if dropout_fraction > 0:
                    self._model.add(Dropout(rate=dropout_fraction))
            # last layer is one neuron (binary classification)
            self._model.add(Dense(1, activation=self.activation_function_output))

            logger.info("Using {}(**{}) as Optimizer".format(self.optimizer, self.optimizer_opts))
            Optimizer = getattr(keras.optimizers, self.optimizer)
            optimizer = Optimizer(**self.optimizer_opts)
            logger.info("Compile model")
            rn_state = np.random.get_state()
            np.random.seed(self.random_seed)
            self._model.compile(optimizer=optimizer,
                                loss=self.loss,
                                weighted_metrics=['accuracy']
            )
            np.random.set_state(rn_state)

            if os.path.exists(os.path.join(self.project_dir, "weights.h5")):
                if self.is_training:
                    continue_training = self.query_yn("Found previously trained weights - "
                                                      "continue training (choosing N will restart)? (Y/N) ")
                else:
                    continue_training = True
                if continue_training:
                    self.model.load_weights(os.path.join(self.project_dir, "weights.h5"))
                    logger.info("Found and loaded previously trained weights")
                else:
                    logger.info("Starting completely new model")
            else:
                logger.info("No weights found, starting completely new model")

            # dump to json for documentation
            with open(os.path.join(self.project_dir, "model.json"), "w") as of:
                of.write(self._model.to_json())

        return self._model


    @property
    def class_weight(self):
        if self._class_weight is None:
            sumw_bkg = np.sum(self.w_train[self.y_train == 0])
            sumw_sig = np.sum(self.w_train[self.y_train == 1])
            self._class_weight = [(sumw_sig+sumw_bkg)/(2*sumw_bkg), (sumw_sig+sumw_bkg)/(2*sumw_sig)]
            logger.debug("Calculated class_weight: {}".format(self._class_weight))
        return self._class_weight


    @property
    def balanced_class_weight(self):
        """
        Class weight for the balance_dataset method
        Since we have equal number of signal and background events in
        each batch, we need to balance the ratio of sum of weights per
        event with class weights
        """
        if self._balanced_class_weight is None:
            sumw_bkg = np.sum(self.w_train[self.y_train == 0])
            sumw_sig = np.sum(self.w_train[self.y_train == 1])
            # use sumw *per event* in this case
            sumw_bkg /= len(self.w_train[self.y_train == 0])
            sumw_sig /= len(self.w_train[self.y_train == 1])
            self._balanced_class_weight = [(sumw_sig+sumw_bkg)/(2*sumw_bkg), (sumw_sig+sumw_bkg)/(2*sumw_sig)]
            logger.debug("Calculated balanced_class_weight: {}".format(self._balanced_class_weight))
        return self._balanced_class_weight


    def load(self, reload=False):
        "Load all data needed for plotting and training"

        if reload:
            self.data_loaded = False
            self.data_transformed = False

        if not self.data_loaded:
            self._load_data()

        if not self.data_transformed:
            self._transform_data()


    def shuffle_training_data(self):
        rn_state = np.random.get_state()
        np.random.shuffle(self.x_train)
        np.random.set_state(rn_state)
        np.random.shuffle(self.y_train)
        np.random.set_state(rn_state)
        np.random.shuffle(self.w_train)
        if self._scores_train is not None:
            logger.info("Shuffling scores, since they are also there")
            np.random.set_state(rn_state)
            np.random.shuffle(self._scores_train)


    @property
    def w_validation(self):
        "class weighted validation data weights"
        split_index = int((1-self.validation_split)*len(self.x_train))
        if self._w_validation is None:
            self._w_validation = np.array(self.w_train[split_index:])
            self._w_validation[self.y_train[split_index:]==0] *= self.class_weight[0]
            self._w_validation[self.y_train[split_index:]==1] *= self.class_weight[1]
        return self._w_validation


    @property
    def class_weighted_validation_data(self):
        "class weighted validation data. Attention: Shuffle training data before using this!"
        split_index = int((1-self.validation_split)*len(self.x_train))
        return self.x_train[split_index:], self.y_train[split_index:], self.w_validation


    @property
    def training_data(self):
        "training data with validation data split off. Attention: Shuffle training data before using this!"
        split_index = int((1-self.validation_split)*len(self.x_train))
        return self.x_train[:split_index], self.y_train[:split_index], self.w_train[:split_index]


    def yield_batch(self, class_label):
        while True:
            x_train, y_train, w_train = self.training_data
            # shuffle the entries for this class label
            rn_state = np.random.get_state()
            x_train[y_train==class_label] = np.random.permutation(x_train[y_train==class_label])
            np.random.set_state(rn_state)
            w_train[y_train==class_label] = np.random.permutation(w_train[y_train==class_label])
            # yield them batch wise
            for start in range(0, len(x_train[y_train==class_label]), int(self.batch_size/2)):
                yield (x_train[y_train==class_label][start:start+int(self.batch_size/2)],
                       y_train[y_train==class_label][start:start+int(self.batch_size/2)],
                       w_train[y_train==class_label][start:start+int(self.batch_size/2)]*self.balanced_class_weight[class_label])
            # restart


    def yield_balanced_batch(self):
        "generate batches with equal amounts of both classes"
        logcounter = 0
        for batch_0, batch_1 in izip(self.yield_batch(0), self.yield_batch(1)):
            if logcounter == 10:
                logger.debug("\rSumw sig*balanced_class_weight[1]: {}".format(np.sum(batch_1[2])))
                logger.debug("\rSumw bkg*balanced_class_weight[0]: {}".format(np.sum(batch_0[2])))
                logcounter = 0
            logcounter += 1
            yield (np.concatenate((batch_0[0], batch_1[0])),
                   np.concatenate((batch_0[1], batch_1[1])),
                   np.concatenate((batch_0[2], batch_1[2])))


    def train(self, epochs=10):

        self.load()

        for branch_index, branch in enumerate(self.fields):
            self.plot_input(branch_index)

        self.total_epochs = self._read_info("epochs", 0)

        logger.info("Train model")
        if not self.balance_dataset:
            try:
                self.shuffle_training_data()
                self.is_training = True
                self.model.fit(self.x_train,
                               # the reshape might be unnescessary here
                               self.y_train.reshape(-1, 1),
                               epochs=epochs,
                               validation_split = self.validation_split,
                               class_weight=self.class_weight,
                               sample_weight=self.w_train,
                               shuffle=True,
                               batch_size=self.batch_size,
                               callbacks=self.callbacks_list)
                self.is_training = False
            except KeyboardInterrupt:
                logger.info("Interrupt training - continue with rest")
        else:
            try:
                self.shuffle_training_data() # needed here too, in order to get correct validation data
                self.is_training = True
                labels, label_counts = np.unique(self.y_train, return_counts=True)
                logger.info("Training on balanced batches")
                # note: the batches have balanced_class_weight already applied
                self.model.fit_generator(self.yield_balanced_batch(),
                                         steps_per_epoch=int(min(label_counts)/self.batch_size),
                                         epochs=epochs,
                                         validation_data=self.class_weighted_validation_data,
                                         callbacks=self.callbacks_list)
                self.is_training = False
            except KeyboardInterrupt:
                logger.info("Interrupt training - continue with rest")

        logger.info("Save history")
        self._dump_history()

        if not self.use_modelcheckpoint:
            logger.info("Save weights")
            self.model.save_weights(os.path.join(self.project_dir, "weights.h5"))
        else:
            weight_file = sorted(glob.glob(os.path.join(self.project_dir, "weights*.h5")), key=lambda f:os.path.getmtime(f))[-1]
            if not os.path.basename(weight_file) == "weights.h5":
                logger.info("Copying latest weight file {} to weights.h5".format(weight_file))
                shutil.copy(weight_file, os.path.join(self.project_dir, "weights.h5"))
            logger.info("Reloading weights file since we are using model checkpoint!")
            self.model.load_weights(os.path.join(self.project_dir, "weights.h5"))

        self.total_epochs += epochs
        self._write_info("epochs", self.total_epochs)

        logger.info("Reloading (and re-transforming) unshuffled training data")
        self.load(reload=True)

        logger.info("Create/Update scores for ROC curve")
        self.scores_test = self.model.predict(self.x_test)
        self.scores_train = self.model.predict(self.x_train)

        self._dump_to_hdf5("scores_train", "scores_test")

        logger.info("Creating all validation plots")
        self.plot_all()



    def evaluate(self, x_eval):
        logger.debug("Evaluate score for {}".format(x_eval))
        x_eval = self.scaler.transform(x_eval)
        logger.debug("Evaluate for transformed array: {}".format(x_eval))
        return self.model.predict(x_eval)


    def write_friend_tree(self, score_name,
                          source_filename, source_treename,
                          target_filename, target_treename,
                          batch_size=100000):
        f = ROOT.TFile.Open(source_filename)
        tree = f.Get(source_treename)
        entries = tree.GetEntries()
        logger.info("Write friend tree for {} in {}".format(source_treename, source_filename))
        if os.path.exists(target_filename):
            raise IOError("{} already exists, if you want to recreate it, delete it first".format(target_filename))
        for start in range(0, entries, batch_size):
            logger.info("Evaluating score for entry {}/{}".format(start, entries))
            logger.debug("Loading next batch")
            x_from_tree = tree2array(tree,
                                     branches=self.fields+self.identifiers,
                                     start=start, stop=start+batch_size)
            x_eval = rec2array(x_from_tree[self.fields])

            if len(self.identifiers) > 0:
                # create list of booleans that indicate which events where used for training
                df_identifiers = pd.DataFrame(x_from_tree[self.identifiers])
                total_train_list = self.s_eventlist_train
                total_train_list = np.concatenate((total_train_list, self.b_eventlist_train))
                merged = df_identifiers.merge(pd.DataFrame(total_train_list), on=tuple(self.identifiers), indicator=True, how="left")
                is_train = np.array(merged["_merge"] == "both")
            else:
                is_train = np.zeros(len(x_eval))

            # join scores and is_train array
            scores = self.evaluate(x_eval).reshape(-1)
            friend_df = pd.DataFrame(np.array(scores, dtype=[(score_name, np.float64)]))
            friend_df[score_name+"_is_train"] = is_train
            friend_tree = friend_df.to_records()[[score_name, score_name+"_is_train"]]
            if start == 0:
                mode = "recreate"
            else:
                mode = "update"
            logger.debug("Write to root file")
            array2root(friend_tree, target_filename, treename=target_treename, mode=mode)
            logger.debug("Done")

    def write_all_friend_trees(self):
        pass


    @staticmethod
    def get_bin_centered_hist(x, scale_factor=None, **np_kwargs):
        "Return bin centers, histogram and relative (!) errors"
        hist, bins = np.histogram(x, **np_kwargs)
        centers = (bins[:-1] + bins[1:]) / 2
        if "weights" in np_kwargs:
            bin_indices = np.digitize(x, bins)
            sumw2 = np.array([np.sum(np_kwargs["weights"][bin_indices==i]**2)
                              for i in range(1, len(bins)+1)])
            sumw = np.array([np.sum(np_kwargs["weights"][bin_indices==i])
                             for i in range(1, len(bins)+1)])
            # move overflow to last bin
            # (since thats what np.histogram gives us)
            sumw2[-2] += sumw2[-1]
            sumw2 = sumw2[:-1]
            sumw[-2] += sumw[-1]
            sumw = sumw[:-1]
            # calculate relative error
            errors = np.sqrt(sumw2)/sumw
        else:
            errors = np.sqrt(hist)/hist
        if scale_factor is not None:
            hist *= scale_factor
        return centers, hist, errors


    def plot_input(self, var_index):
        "plot a single input variable"
        branch = self.fields[var_index]
        fig, ax = plt.subplots()
        bkg = self.x_train[:,var_index][self.y_train == 0]
        sig = self.x_train[:,var_index][self.y_train == 1]
        bkg_weights = self.w_train[self.y_train == 0]
        sig_weights = self.w_train[self.y_train == 1]

        logger.debug("Plotting bkg (min={}, max={}) from {}".format(np.min(bkg), np.max(bkg), bkg))
        logger.debug("Plotting sig (min={}, max={}) from {}".format(np.min(sig), np.max(sig), sig))

        # calculate percentiles to get a heuristic for the range to be plotted
        # (should in principle also be done with weights, but for now do it unweighted)
        # range_sig = np.percentile(sig, [1, 99])
        # range_bkg = np.percentile(sig, [1, 99])
        # plot_range = (min(range_sig[0], range_bkg[0]), max(range_sig[1], range_sig[1]))
        plot_range = weighted_quantile(self.x_train[:,var_index], [0.1, 0.99], sample_weight=self.w_train*np.array(self.class_weight)[self.y_train.astype(int)])

        logger.debug("Calculated range based on percentiles: {}".format(plot_range))

        try:
            centers_sig, hist_sig, _ = self.get_bin_centered_hist(sig, scale_factor=self.class_weight[1], bins=50, range=plot_range, weights=sig_weights)
            centers_bkg, hist_bkg, _ = self.get_bin_centered_hist(bkg, scale_factor=self.class_weight[0], bins=50, range=plot_range, weights=bkg_weights)
        except ValueError:
            # weird, probably not always working workaround for a numpy bug
            plot_range = (float("{:.3f}".format(plot_range[0])), float("{:.3f}".format(plot_range[1])))
            logger.warn("Got a value error during plotting, maybe this is due to a numpy bug - changing range to {}".format(plot_range))
            centers_sig, hist_sig, _ = self.get_bin_centered_hist(sig, scale_factor=self.class_weight[1], bins=50, range=plot_range, weights=sig_weights)
            centers_bkg, hist_bkg, _ = self.get_bin_centered_hist(bkg, scale_factor=self.class_weight[0], bins=50, range=plot_range, weights=bkg_weights)

        width = centers_sig[1]-centers_sig[0]
        ax.bar(centers_bkg, hist_bkg, color="b", alpha=0.5, width=width)
        ax.bar(centers_sig, hist_sig, color="r", alpha=0.5, width=width)

        ax.set_xlabel(branch+" (transformed)")
        plot_dir = os.path.join(self.project_dir, "plots")
        if not os.path.exists(plot_dir):
            os.mkdir(plot_dir)
        fig.savefig(os.path.join(plot_dir, "var_{}.pdf".format(var_index)))
        plt.close(fig)


    def plot_weights(self):
        fig, ax = plt.subplots()
        bkg = self.w_train[self.y_train == 0]
        sig = self.w_train[self.y_train == 1]
        ax.hist(bkg, bins=100, color="b", alpha=0.5)
        fig.savefig(os.path.join(self.project_dir, "eventweights_bkg.pdf"))
        plt.close(fig)
        fig, ax = plt.subplots()
        ax.hist(sig, bins=100, color="r", alpha=0.5)
        fig.savefig(os.path.join(self.project_dir, "eventweights_sig.pdf"))
        plt.close(fig)


    def plot_ROC(self):

        logger.info("Plot ROC curve")
        fpr, tpr, threshold = roc_curve(self.y_test, self.scores_test, sample_weight = self.w_test)
        fpr = 1.0 - fpr
        try:
            roc_auc = auc(tpr, fpr, reorder=True)
        except ValueError:
            logger.warning("Got a value error from auc - trying to rerun with reorder=True")
            roc_auc = auc(tpr, fpr, reorder=True)

        plt.grid(color='gray', linestyle='--', linewidth=1)
        plt.plot(tpr,  fpr, label=str(self.name + " (AUC = {})".format(roc_auc)))
        plt.plot([0,1],[1,0], linestyle='--', color='black', label='Luck')
        plt.ylabel("Background rejection")
        plt.xlabel("Signal efficiency")
        plt.title('Receiver operating characteristic')
        plt.xlim(0,1)
        plt.ylim(0,1)
        plt.xticks(np.arange(0,1,0.1))
        plt.yticks(np.arange(0,1,0.1))
        plt.legend(loc='lower left', framealpha=1.0)
        plt.savefig(os.path.join(self.project_dir, "ROC.pdf"))
        plt.clf()


    def plot_score(self, log=True, plot_opts=dict(bins=50, range=(0, 1)), ylim=None, xlim=None):
        centers_sig_train, hist_sig_train, _ = self.get_bin_centered_hist(self.scores_train[self.y_train==1].reshape(-1), density=True, weights=self.w_train[self.y_train==1], **plot_opts)
        centers_bkg_train, hist_bkg_train, _ = self.get_bin_centered_hist(self.scores_train[self.y_train==0].reshape(-1), density=True, weights=self.w_train[self.y_train==0], **plot_opts)
        centers_sig_test, hist_sig_test, rel_errors_sig_test = self.get_bin_centered_hist(self.scores_test[self.y_test==1].reshape(-1), density=True, weights=self.w_test[self.y_test==1], **plot_opts)
        centers_bkg_test, hist_bkg_test, rel_errors_bkg_test = self.get_bin_centered_hist(self.scores_test[self.y_test==0].reshape(-1), density=True, weights=self.w_test[self.y_test==0], **plot_opts)
        errors_sig_test = hist_sig_test*rel_errors_sig_test
        errors_bkg_test = hist_bkg_test*rel_errors_bkg_test
        fig, ax = plt.subplots()
        width = centers_sig_train[1]-centers_sig_train[0]