plotting.py

+#!/usr/bin/env python
+
+import os
+import math
+
+import matplotlib.pyplot as plt
+import matplotlib.colors
+from matplotlib.ticker import LogFormatter
+from mpl_toolkits.axes_grid1 import ImageGrid, make_axes_locatable
+import numpy as np
+
+from .keras_visualize_activations.read_activations import get_activations
+from .utils import get_grad_function, max_activation_wrt_input, create_random_event
+
+import logging
+logger = logging.getLogger(__name__)
+logger.addHandler(logging.NullHandler())
+
+"""
+Some further plotting functions
+"""
+
+def save_show(plt, fig, filename, **kwargs):
+    "Save a figure and show it in case we are in ipython or jupyter notebook."
+    fig.savefig(filename, **kwargs)
+    try:
+        get_ipython
+        plt.show()
+        return fig
+    except NameError:
+        plt.close(fig)
+        return None
+
+
+def get_mean_event(x, y, class_label, mask_value=None):
+    means = []
+    for var_index in range(x.shape[1]):
+        if mask_value is not None:
+            masked_values = np.where(x[:,var_index] != mask_value)[0]
+            x = x[masked_values]
+            y = y[masked_values]
+        means.append(np.mean(x[y==class_label][:,var_index]))
+    return means
+
+
+def plot_NN_vs_var_1D(plotname, means, scorefun, var_index, var_range, var_label=None):
+    "Plot the NN output vs one variable with the other variables set to the given mean values"
+
+    # example: vary var1
+    logger.info("Creating varied events (1d)")
+    sequence = np.arange(*var_range)
+    events = np.tile(means, len(sequence)).reshape(-1, len(means))
+    events[:,var_index] = sequence
+
+    logger.info("Predicting scores")
+    scores = scorefun(events)
+
+    fig, ax = plt.subplots()
+    ax.plot(sequence, scores)
+    if var_label is not None:
+        ax.set_xlabel(var_label)
+    ax.set_ylabel("NN output")
+    save_show(plt, fig, plotname)
+
+
+def plot_NN_vs_var_2D(plotname, means,
+                      scorefun,
+                      varx_index,
+                      vary_index,
+                      nbinsx, xmin, xmax,
+                      nbinsy, ymin, ymax,
+                      varx_label=None,
+                      vary_label=None,
+                      logscale=False,
+                      ncontours=20,
+                      only_pixels=False,
+                      black_contourlines=False,
+                      cmap="inferno"):
+
+    logger.info("Creating varied events (2d)")
+
+    sequence1 = np.linspace(xmin, xmax, nbinsx)
+    sequence2 = np.linspace(ymin, ymax, nbinsy)
+
+    # the following is a 2d array of events (so effectively 3D)
+    events = np.tile(means, len(sequence1)*len(sequence2)).reshape(len(sequence2), len(sequence1), -1)
+
+    # fill in the varied values
+    # (probably there is a more clever way, but sufficient here)
+    for i, y in enumerate(sequence2):
+        for j, x in enumerate(sequence1):
+            events[i][j][varx_index] = x
+            events[i][j][vary_index] = y
+
+    # convert back into 1d array
+    events = events.reshape(-1, len(means))
+
+    logger.info("Predicting scores")
+    scores = scorefun(events)
+
+    # convert scores into 2d array
+    scores = scores.reshape(len(sequence2), len(sequence1))
+
+    fig, ax = plt.subplots()
+
+    zmin = np.min(scores)
+    zmax = np.max(scores)
+
+    if logscale:
+        if zmin <= 0:
+            zmin = 1e-5
+            logger.info("Setting zmin to {}".format(zmin))
+        lvls = np.logspace(math.log10(zmin), math.log10(zmax), ncontours)
+        if only_pixels:
+            pcm = ax.pcolormesh(sequence1, sequence2, scores, norm=matplotlib.colors.LogNorm(vmin=zmin, vmax=zmax), cmap=cmap, linewidth=0, rasterized=True)
+        else:
+            pcm = ax.contourf(sequence1, sequence2, scores, levels=lvls, norm=matplotlib.colors.LogNorm(vmin=zmin, vmax=zmax), cmap=cmap)
+        if black_contourlines:
+            ax.contour(sequence1, sequence2, scores, levels=lvls, colors="k", linewidths=1)
+        l_f = LogFormatter(10, labelOnlyBase=False, minor_thresholds=(np.inf, np.inf))
+        cbar = fig.colorbar(pcm, ax=ax, extend='max', ticks=lvls, format=l_f)
+    else:
+        if only_pixels:
+            pcm = ax.pcolormesh(sequence1, sequence2, scores, norm=matplotlib.colors.Normalize(vmin=zmin, vmax=zmax), cmap=cmap, linewidth=0, rasterized=True)
+        else:
+            pcm = ax.contourf(sequence1, sequence2, scores, ncontours, norm=matplotlib.colors.Normalize(vmin=zmin, vmax=zmax), cmap=cmap)
+        if black_contourlines:
+            ax.contour(sequence1, sequence2, scores, ncontours, colors="k", linewidths=1)
+        cbar = fig.colorbar(pcm, ax=ax, extend='max')
+
+    cbar.set_label("NN output")
+    if varx_label is not None:
+        ax.set_xlabel(varx_label)
+    if vary_label is not None:
+        ax.set_ylabel(vary_label)
+    save_show(plt, fig, plotname)
+
+
+def plot_NN_vs_var_2D_all(plotname, model, means,
+                          varx_index,
+                          vary_index,
+                          nbinsx, xmin, xmax,
+                          nbinsy, ymin, ymax,
+                          transform_function=None,
+                          varx_label=None,
+                          vary_label=None,
+                          zrange=None, logz=False,
+                          plot_last_layer=False,
+                          log_default_ymin=1e-5,
+                          cmap="inferno"):
+
+    "Similar to plot_NN_vs_var_2D, but creates a grid of plots for all neurons."
+
+    varx_vals = np.linspace(xmin, xmax, nbinsx)
+    vary_vals = np.linspace(ymin, ymax, nbinsy)
+
+    # create the events for which we want to fetch the activations
+    events = np.tile(means, len(varx_vals)*len(vary_vals)).reshape(len(vary_vals), len(varx_vals), -1)
+    for i, y in enumerate(vary_vals):
+        for j, x in enumerate(varx_vals):
+            events[i][j][varx_index] = x
+            events[i][j][vary_index] = y
+
+    # convert back into 1d array
+    events = events.reshape(-1, len(means))
+
+    # transform
+    if transform_function is not None:
+        events = transform_function(events)
+
+    acts = get_activations(model, events, print_shape_only=True)
+
+    if plot_last_layer:
+        n_neurons = [len(i[0]) for i in acts]
+    else:
+        n_neurons = [len(i[0]) for i in acts[:-1]]
+    layers = len(n_neurons)
+
+    nrows_ncols = (layers, max(n_neurons))
+    fig = plt.figure(1, figsize=nrows_ncols)
+    grid = ImageGrid(fig, 111, nrows_ncols=nrows_ncols[::-1], axes_pad=0,
+                     label_mode="1",
+                     aspect=False,
+                     cbar_location="top",
+                     cbar_mode="single",
+                     cbar_pad=.2,
+                     cbar_size="5%",)
+    grid_array = np.array(grid)
+    grid_array = grid_array.reshape(*nrows_ncols[::-1])
+
+    # leave out the last layer
+    global_min = min([np.min(ar_layer) for ar_layer in acts[:-1]])
+    global_max = max([np.max(ar_layer) for ar_layer in acts[:-1]])
+
+    logger.info("global_min: {}".format(global_min))
+    logger.info("global_max: {}".format(global_max))
+
+    output_min_default = 0
+    output_max_default = 1
+
+    if global_min <= 0 and logz:
+        global_min = log_default_ymin
+        logger.info("Changing global_min to {}".format(log_default_ymin))
+
+    ims = []
+    for layer in range(layers):
+        for neuron in range(len(acts[layer][0])):
+            acts_neuron = acts[layer][:,neuron]
+            acts_neuron = acts_neuron.reshape(len(vary_vals), len(varx_vals))
+            ax = grid_array[neuron][layer]
+            extra_opts = {}
+            if not (plot_last_layer and layer == layers-1):
+                # for hidden layers, plot the same z-scale
+                if logz:
+                    norm = matplotlib.colors.LogNorm
+                else:
+                    norm = matplotlib.colors.Normalize
+                if zrange is not None:
+                    extra_opts["norm"] = norm(vmin=zrange[0], vmax=zrange[1])
+                else:
+                    extra_opts["norm"] = norm(vmin=global_min, vmax=global_max)
+            im = ax.pcolormesh(varx_vals, vary_vals, acts_neuron, cmap=cmap, linewidth=0, rasterized=True, **extra_opts)
+            ax.set_facecolor("black")
+            if varx_label is not None:
+                ax.set_xlabel(varx_label)
+            if vary_label is not None:
+                ax.set_ylabel(vary_label)
+            ax.text(0., 0.5, "{}, {}".format(layer, neuron), transform=ax.transAxes, color="white")
+
+    cb = fig.colorbar(im, cax=grid[0].cax, orientation="horizontal")
+    cb.ax.xaxis.set_ticks_position('top')
+    cb.ax.xaxis.set_label_position('top')
+
+    save_show(plt, fig, plotname, bbox_inches='tight')
+
+
+def plot_profile_2D_all(plotname, model, events,
+                        valsx, valsy,
+                        nbinsx, xmin, xmax,
+                        nbinsy, ymin, ymax,
+                        transform_function=None,
+                        varx_label=None,
+                        vary_label=None,
+                        zrange=None, logz=False,
+                        plot_last_layer=False,
+                        log_default_ymin=1e-5,
+                        global_norm=True,
+                        cmap="inferno", **kwargs):
+
+    "Similar to plot_profile_2D, but creates a grid of plots for all neurons."
+
+    # transform
+    if transform_function is not None:
+        events = transform_function(events)
+
+    logger.info("Reading activations for all neurons")
+    acts = get_activations(model, events, print_shape_only=True)
+    logger.info("Done")
+
+    if plot_last_layer:
+        n_neurons = [len(i.reshape(i.shape[0], -1)[0]) for i in acts]
+    else:
+        n_neurons = [len(i.reshape(i.shape[0], -1)[0]) for i in acts[:-1]]
+    layers = len(n_neurons)
+
+    nrows_ncols = (layers, max(n_neurons))
+    fig = plt.figure(1, figsize=nrows_ncols)
+    grid = ImageGrid(fig, 111, nrows_ncols=nrows_ncols[::-1], axes_pad=0,
+                     label_mode="1",
+                     aspect=False,
+                     cbar_location="top",
+                     cbar_mode="single",
+                     cbar_pad=.2,
+                     cbar_size="5%",)
+    grid_array = np.array(grid)
+    grid_array = grid_array.reshape(*nrows_ncols[::-1])
+
+    global_min = None
+    global_max = None
+
+    logger.info("Creating profile histograms")
+    ims = []
+    reg_plots = []
+    for layer in range(layers):
+        neurons_acts = acts[layer]
+        neurons_acts = neurons_acts.reshape(neurons_acts.shape[0], -1)
+        for neuron in range(len(neurons_acts[0])):
+            acts_neuron = neurons_acts[:,neuron]
+            ax = grid_array[neuron][layer]
+            extra_opts = {}
+            if not (plot_last_layer and layer == layers-1):
+                # for hidden layers, plot the same z-scale
+                if logz:
+                    norm = matplotlib.colors.LogNorm
+                else:
+                    norm = matplotlib.colors.Normalize
+                if zrange is not None:
+                    extra_opts["norm"] = norm(vmin=zrange[0], vmax=zrange[1])
+                else:
+                    extra_opts["norm"] = norm(vmin=global_min, vmax=global_max)
+            hist, xedges, yedges = get_profile_2D(
+                valsx, valsy, acts_neuron,
+                nbinsx, xmin, xmax,
+                nbinsy, ymin, ymax,
+                **kwargs
+            )
+            if global_min is None or hist.min() < global_min:
+                global_min = hist.min()
+            if global_max is None or hist.max() > global_max:
+                global_max = hist.max()
+            X, Y = np.meshgrid(xedges, yedges)
+            reg_plots.append((layer, neuron, ax, (X, Y, hist), dict(cmap="inferno", linewidth=0, rasterized=True, **extra_opts)))
+    logger.info("Done")
+
+    logger.info("global_min: {}".format(global_min))
+    logger.info("global_max: {}".format(global_max))
+
+    if global_min <= 0 and logz:
+        global_min = log_default_ymin
+        logger.info("Changing global_min to {}".format(log_default_ymin))
+
+    for layer, neuron, ax, args, kwargs in reg_plots:
+        if zrange is None:
+            kwargs["norm"].vmin = global_min
+            kwargs["norm"].vmax = global_max
+        if not global_norm:
+            kwargs["norm"] = None
+        im = ax.pcolormesh(*args, **kwargs)
+        ax.set_facecolor("black")
+        if varx_label is not None:
+            ax.set_xlabel(varx_label)
+        if vary_label is not None:
+            ax.set_ylabel(vary_label)
+        ax.text(0., 0.5, "{}, {}".format(layer, neuron), transform=ax.transAxes, color="white")
+
+    cb = fig.colorbar(im, cax=grid[0].cax, orientation="horizontal")
+    cb.ax.xaxis.set_ticks_position('top')
+    cb.ax.xaxis.set_label_position('top')
+
+    logger.info("Rendering")
+    save_show(plt, fig, plotname, bbox_inches='tight')
+    logger.info("Done")
+
+
+def plot_hist_2D(plotname, xedges, yedges, hist, varx_label=None, vary_label=None, log=False, zlabel="# of events"):
+    X, Y = np.meshgrid(xedges, yedges)
+
+    fig, ax = plt.subplots()
+
+    extraopts = dict()
+    if log:
+        extraopts.update(norm=matplotlib.colors.LogNorm(vmin=np.min(hist[hist>0]), vmax=np.max(hist)))
+
+    ax.set_facecolor("black")
+    pcm = ax.pcolormesh(X, Y, hist, cmap="inferno", linewidth=0, rasterized=True, **extraopts)
+    cbar = fig.colorbar(pcm, ax=ax)
+    cbar.set_label(zlabel)
+    ax.set_ylabel(vary_label)
+    ax.set_xlabel(varx_label)
+    save_show(plt, fig, plotname)
+
+
+def plot_hist_2D_events(plotname, valsx, valsy, nbinsx, xmin, xmax, nbinsy, ymin, ymax,
+                        weights=None,
+                        varx_label=None, vary_label=None, log=False):
+
+    xedges = np.linspace(xmin, xmax, nbinsx)
+    yedges = np.linspace(ymin, ymax, nbinsy)
+
+    hist, xedges, yedges = np.histogram2d(valsx, valsy, bins=(xedges, yedges), weights=weights)
+
+    hist = hist.T
+
+    plot_hist_2D(plotname, xedges, yedges, hist, varx_label, vary_label, log)
+
+
+def plot_cond_avg_actmax_2D(plotname, model, layer, neuron, ranges,
+                            varx_index, vary_index,
+                            nbinsx, xmin, xmax, nbinsy, ymin, ymax,
+                            transform=None, inverse_transform=None,
+                            ntries=20,
+                            step=1,
+                            maxit=1,
+                            **kwargs):
+
+    transform_given = [fn is not None for fn in [transform, inverse_transform]]
+    if any(transform_given) and not all(transform_given):
+        raise ValueError("Need to pass both transform and inverse_transform if data should be transformed")
+
+    xedges = np.linspace(xmin, xmax, nbinsx)
+    yedges = np.linspace(ymin, ymax, nbinsy)
+
+    hist = np.zeros(int(nbinsx*nbinsy)).reshape(int(nbinsx), int(nbinsy))
+
+    gradient_function = get_grad_function(model, layer, neuron)
+
+    for ix, x in enumerate(xedges):
+        for iy, y in enumerate(yedges):
+            random_event = create_random_event(ranges)
+            if inverse_transform is not None:
+                random_event = inverse_transform(random_event)
+            for index, val in [(varx_index, x), (vary_index, y)]:
+                random_event[0][index] = val
+            if transform is not None:
+                random_event = transform(random_event)
+            act = np.mean([max_activation_wrt_input(gradient_function, random_event, maxit=maxit, step=step, const_indices=[varx_index, vary_index])[0][0] for i in range(ntries)])
+            hist[ix][iy] = act
+
+    hist = hist.T
+
+    plot_hist_2D(plotname, xedges, yedges, hist, zlabel="Neuron output", **kwargs)
+
+
+def get_profile_2D(valsx, valsy, scores,
+                   nbinsx, xmin, xmax,
+                   nbinsy, ymin, ymax,
+                   metric=np.mean, weights=None):
+    xedges = np.linspace(xmin, xmax, nbinsx)
+    yedges = np.linspace(ymin, ymax, nbinsy)
+
+    binindices_x = np.digitize(valsx, xedges)
+    binindices_y = np.digitize(valsy, yedges)
+
+    # create profile histogram
+    hist = []
+    for binindex_x in range(1, len(xedges)+1):
+        line = []
+        for binindex_y in range(1, len(yedges)+1):
+            binindices_xy = (binindices_x == binindex_x) & (binindices_y == binindex_y)
+            scores_bin = scores[binindices_xy]
+            if len(scores_bin) > 0:
+                metric_kwargs = dict()
+                if weights is not None:
+                    metric_kwargs["weights"] = weights[binindices_xy]
+                prof_score = metric(scores_bin, **metric_kwargs)
+            else:
+                prof_score = 0
+            line.append(prof_score)
+        hist.append(line)
+    hist = np.array(hist)
+    hist = hist.T # had a list of columns - needs to be list of rows
+
+    return hist, xedges, yedges
+
+
+def plot_profile_2D(plotname, valsx, valsy, scores,
+                    nbinsx, xmin, xmax,
+                    nbinsy, ymin, ymax,
+                    metric=np.mean,
+                    weights=None,
+                    **kwargs):
+
+    kwargs["zlabel"] = kwargs.get("zlabel", "Profile")
+
+    hist, xedges, yedges = get_profile_2D(
+        valsx, valsy, scores,
+        nbinsx, xmin, xmax,
+        nbinsy, ymin, ymax,
+        metric=metric, weights=weights
+    )
+
+    plot_hist_2D(plotname, xedges, yedges, hist, **kwargs)
+
+
+if __name__ == "__main__":
+
+    import sys
+
+    from .toolkit import ClassificationProject
+
+    import logging
+    logging.basicConfig()
+    logging.getLogger().setLevel(logging.DEBUG)
+
+    from .utils import get_single_neuron_function, get_max_activation_events
+
+    import meme
+    # meme.setOptions(overrideCache="/scratch-local/nhartmann/meme_cache")
+
+    if len(sys.argv) < 2:
+        c = ClassificationProject("/project/etp/nhartmann/p/keras/021-check-low-vs-high-fewvar/all_high")
+    else:
+        c = ClassificationProject(sys.argv[1])
+
+    def test_mean_signal():
+
+        c._load_data() # untransformed
+
+        mean_signal = get_mean_event(c.x_test, c.y_test, 1)
+
+        print("Mean signal: ")
+        for branch_index, val in enumerate(mean_signal):
+            print("{:>20}: {:<10.3f}".format(c.fields[branch_index], val))
+
+        plot_NN_vs_var_1D("met.pdf", mean_signal,
+                          scorefun=c.evaluate,
+                          var_index=c.fields.index("met"),
+                          var_range=(0, 1000, 10),
+                          var_label="met [GeV]")
+
+        plot_NN_vs_var_1D("mt.pdf", mean_signal,
+                          scorefun=c.evaluate,
+                          var_index=c.fields.index("mt"),
+                          var_range=(0, 500, 10),
+                          var_label="mt [GeV]")
+
+        plot_NN_vs_var_2D("mt_vs_met.pdf", means=mean_signal,
+                          scorefun=c.evaluate,
+                          varx_index=c.fields.index("met"),
+                          vary_index=c.fields.index("mt"),
+                          nbinsx=100, xmin=0, xmax=1000,
+                          nbinsy=100, ymin=0, ymax=500,
+                          varx_label="met [GeV]", vary_label="mt [GeV]")
+
+
+        plot_NN_vs_var_2D_all("mt_vs_met_all.pdf", means=mean_signal,
+                              model=c.model, transform_function=c.transform,
+                              varx_index=c.fields.index("met"),
+                              vary_index=c.fields.index("mt"),
+                              nbinsx=100, xmin=0, xmax=1000,
+                              nbinsy=100, ymin=0, ymax=500,
+                              varx_label="met [GeV]", vary_label="mt [GeV]")
+
+        input_transform = c.transform
+        if hasattr(c, "get_input_list"):
+            input_transform = lambda x : c.get_input_list(c.transform(x))
+
+        plot_NN_vs_var_2D("mt_vs_met_crosscheck.pdf", means=mean_signal,
+                          scorefun=get_single_neuron_function(c.model, layer=3, neuron=0, input_transform=input_transform),
+                          varx_index=c.fields.index("met"),
+                          vary_index=c.fields.index("mt"),
+                          nbinsx=100, xmin=0, xmax=1000,
+                          nbinsy=100, ymin=0, ymax=500,
+                          varx_label="met [GeV]", vary_label="mt [GeV]")
+
+
+    def test_max_act():
+
+        # transformed events
+        c.load(reload=True)
+        ranges = [np.percentile(c.x_test[:,var_index], [1,99]) for var_index in range(len(c.fields))]
+
+        losses, events = get_max_activation_events(c.model, ranges, ntries=100000, layer=3, neuron=0, threshold=0.2)
+
+        events = c.inverse_transform(events)
+
+        plot_hist_2D_events(
+            "mt_vs_met_actmaxhist.pdf",
+            events[:,c.fields.index("met")],
+            events[:,c.fields.index("mt")],
+            100, 0, 1000,
+            100, 0, 500,
+            varx_label="met [GeV]", vary_label="mt [GeV]",
+        )
+
+        plot_hist_2D_events(
+            "mt_vs_output_actmax.pdf",
+            events[:,c.fields.index("mt")],
+            losses,
+            100, 0, 500,
+            100, 0, 1,
+            varx_label="mt [GeV]", vary_label="NN output",
+            log=True,
+        )
+
+
+    def test_cond_max_act():
+
+        c.load(reload=True)
+        ranges = [np.percentile(c.x_test[:,var_index], [1,99]) for var_index in range(len(c.fields))]
+
+        plot_cond_avg_actmax_2D(
+            "mt_vs_met_cond_actmax.pdf",
+            c.model, 3, 0, ranges,
+            c.fields.index("met"),
+            c.fields.index("mt"),
+            30, 0, 1000,
+            30, 0, 500,
+            transform=c.transform, inverse_transform=c.inverse_transform,
+            varx_label="met [GeV]", vary_label="mt [GeV]",
+        )
+
+
+    def test_xtest_vs_output():
+
+        c.load(reload=True)
+
+        utrf_x_test = c.inverse_transform(c.x_test)
+
+        plot_hist_2D_events(
+            "mt_vs_output_signal_test.pdf",
+            utrf_x_test[c.y_test==1][:,c.fields.index("mt")],
+            c.scores_test[c.y_test==1].reshape(-1),
+            100, 0, 1000,
+            100, 0, 1,
+            varx_label="mt [GeV]", vary_label="NN output",
+            log=True,
+        )
+
+        plot_hist_2D_events(
+            "mt_vs_met_signal.pdf",
+            utrf_x_test[c.y_test==1][:,c.fields.index("met")],
+            utrf_x_test[c.y_test==1][:,c.fields.index("mt")],
+            100, 0, 1000,
+            100, 0, 500,
+            varx_label="met [GeV]",
+            vary_label="mt [GeV]",
+            log=True,
+        )
+
+        plot_hist_2D_events(
+            "mt_vs_met_backgound.pdf",
+            utrf_x_test[c.y_test==0][:,c.fields.index("met")],
+            utrf_x_test[c.y_test==0][:,c.fields.index("mt")],
+            100, 0, 1000,
+            100, 0, 500,
+            varx_label="met [GeV]",
+            vary_label="mt [GeV]",
+            log=True,
+        )
+
+
+        # plot_hist_2D_events(
+        #     "apl_vs_output_actmax.pdf",
+        #     events[:,c.fields.index("LepAplanarity")],
+        #     losses,
+        #     100, 0, 0.1,
+        #     100, 0, 1,
+        #     varx_label="Aplanarity", vary_label="NN output",
+        # )
+
+
+    def test_profile():
+
+        c.load(reload=True)
+        utrf_x_test = c.inverse_transform(c.x_test)
+
+        plot_profile_2D(
+            "mt_vs_met_profilemean_sig.pdf",
+            utrf_x_test[c.y_test==1][:,c.fields.index("met")],
+            utrf_x_test[c.y_test==1][:,c.fields.index("mt")],
+            c.scores_test[c.y_test==1].reshape(-1),
+            20, 0, 500,
+            20, 0, 1000,
+            varx_label="met [GeV]", vary_label="mt [GeV]",
+        )
+
+        plot_profile_2D(
+            "mt_vs_met_profilemax_sig.pdf",
+            utrf_x_test[c.y_test==1][:,c.fields.index("met")],
+            utrf_x_test[c.y_test==1][:,c.fields.index("mt")],
+            c.scores_test[c.y_test==1].reshape(-1),
+            20, 0, 500,
+            20, 0, 1000,
+            metric=np.max,
+            varx_label="met [GeV]", vary_label="mt [GeV]",
+        )
+
+    for obj in dir():
+        if obj.startswith("test_") and callable(locals()[obj]):
+            if (len(sys.argv) > 2) and (not sys.argv[2] == obj):
+                continue
+            print("Running {}".format(obj))
+            locals()[obj]()

scripts/eval_model.py

+#!/usr/bin/env python
+
+import os
+import argparse
+
+import keras
+import h5py
+from sklearn.metrics import roc_curve, auc
+import matplotlib.pyplot as plt
+import numpy as np
+
+from KerasROOTClassification import ClassificationProject
+
+parser = argparse.ArgumentParser(description='Evaluate a model from a classification project using the given '
+                                             'weights and plot the ROC curve and train/test overlayed scores')
+parser.add_argument("project_dir")
+parser.add_argument("weights")
+parser.add_argument("-p", "--plot-prefix", default="eval_nn")
+args = parser.parse_args()
+
+c = ClassificationProject(args.project_dir)
+
+c.model.load_weights(args.weights)
+
+print("Predicting for test sample ...")
+scores_test = c.evaluate(c.x_test)
+print("Done")
+
+fpr, tpr, threshold = roc_curve(c.y_test, scores_test, sample_weight = c.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(c.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(args.plot_prefix+"_ROC.pdf")
+plt.clf()
+

scripts/generate_actmax.py

+#!/usr/bin/env python
+
+import sys, argparse, re, random
+
+parser = argparse.ArgumentParser(description="generate events that maximise the activation for a given neuron")
+parser.add_argument("input_project")
+parser.add_argument("output_file")
+parser.add_argument("-n", "--nevents", default=100000, type=int)
+parser.add_argument("-j", "--mask-jets", action="store_true",
+                    help="mask variables called jet*Pt/Eta/Phi and generate a random uniform distribution of the number of jets (only nescessary for non-recurrent NN)")
+args = parser.parse_args()
+
+import logging
+logging.basicConfig()
+logging.getLogger().setLevel(logging.INFO)
+
+import h5py
+import numpy as np
+
+from KerasROOTClassification.utils import (
+    weighted_quantile,
+    get_max_activation_events,
+    create_random_event,
+    get_ranges
+)
+from KerasROOTClassification import load_from_dir
+import meme
+
+meme.setOptions(deactivated=True)
+
+input_project = args.input_project
+output_file = args.output_file
+
+c = load_from_dir(input_project)
+c._load_data()
+
+ranges, mask_probs = get_ranges(c.transform(c.x_train), [0.01, 0.99], c.w_train_tot, mask_value=c.mask_value, max_evts=10000)
+
+def mask_uniform(x, mask_value, recurrent_field_idx):
+    """
+    Mask recurrent fields with a random (uniform) number of objects. Works in place.
+    """
+    for rec_idx in recurrent_field_idx:
+        for evt in x:
+            masked = False
+            nobj = int(random.random()*(rec_idx.shape[1]+1))
+            for obj_number, line_idx in enumerate(rec_idx.reshape(*rec_idx.shape[1:])):
+                if obj_number == nobj:
+                    masked=True
+                if masked:
+                    evt[line_idx] = mask_value
+
+def get_input_flat(x):
+    return x[0].reshape(-1, len(c.fields))
+
+
+if args.mask_jets:
+    jet_fields = {}
+    for field_name in c.fields:
+        if any(field_name.startswith("jet") and field_name.endswith(suffix) for suffix in ["Pt", "Eta", "Phi"]):
+            jet_number = re.findall("[0-9]+", field_name)[0]
+            if not jet_number in jet_fields:
+                jet_fields[jet_number] = []
+            jet_fields[jet_number].append(c.fields.index(field_name))
+    jet_fields = [np.array([[v for k, v in sorted(jet_fields.items(), key=lambda l:l[0])]])]
+
+
+def input_transform(x):
+    x = np.array(x)
+    if hasattr(c, "mask_uniform"):
+        c.mask_uniform(x)
+        return c.get_input_list(x)
+    elif args.mask_jets:
+        mask_uniform(x, c.mask_value, jet_fields)
+        return x
+
+
+opt_kwargs = dict()
+if hasattr(c, "mask_uniform"):
+    opt_kwargs["input_transform"] = input_transform
+    opt_kwargs["input_inverse_transform"] = c.get_input_flat
+if args.mask_jets:
+    opt_kwargs["input_transform"] = input_transform
+    opt_kwargs["input_inverse_transform"] = get_input_flat
+
+
+evts = get_max_activation_events(
+    c.model, ranges,
+    ntries=args.nevents,
+    layer=len(c.model.layers)-1,
+    neuron=0,
+    maxit=10,
+    seed=45,
+    threshold=0,
+    **opt_kwargs
+)
+
+with h5py.File(output_file, "w") as f:
+    f.create_dataset("actmax", data=evts[1])

scripts/plot_NN_2D.py

+#!/usr/bin/env python
+
+import sys
+import argparse
+
+parser = argparse.ArgumentParser(description='Create various 2D plots for a single neuron')
+parser.add_argument("project_dir")
+parser.add_argument("output_filename")
+parser.add_argument("varx")
+parser.add_argument("vary")
+parser.add_argument("-m", "--mode",
+                    choices=["mean_sig", "mean_bkg", "profile_sig", "profile_bkg", "hist_sig", "hist_bkg", "hist_actmax", "cond_actmax"],
+                    default="mean_sig")
+parser.add_argument("-l", "--layer", type=int, help="Layer index (takes last layer by default)")
+parser.add_argument("-n", "--neuron", type=int, default=0, help="Neuron index (takes first neuron by default)")
+parser.add_argument("-a", "--all-neurons", action="store_true", help="Create a summary plot for all neurons in all hidden layers")
+parser.add_argument("--log", action="store_true", help="Plot in color in log scale")
+parser.add_argument("--contour", action="store_true", help="Interpolate with contours")
+parser.add_argument("-b", "--nbins", default=20, type=int, help="Number of bins in x and y direction")
+parser.add_argument("-x", "--xrange", type=float, nargs="+", help="xrange (low, high)")
+parser.add_argument("-y", "--yrange", type=float, nargs="+", help="yrange (low, high)")
+parser.add_argument("-p", "--profile-metric", help="metric for profile modes", default="average", choices=["mean", "average", "max"])
+parser.add_argument("--ntries-cond-actmax", help="number of random events to be maximised and averaged per bin", default=20, type=int)
+parser.add_argument("--nit-cond-actmax", help="number of iterations for maximisation per bin", default=1, type=int)
+parser.add_argument("--ntries-actmax", help="number of random events to be maximised for hist_actmax", default=10000, type=int)
+parser.add_argument("-t", "--threshold", help="minimum activation threshold", default=0.2, type=float)
+parser.add_argument("-v", "--verbose", action="store_true")
+parser.add_argument("-s", "--step-size", help="step size for activation maximisation", default=1., type=float)
+
+args = parser.parse_args()
+
+import logging
+logging.basicConfig()
+
+import numpy as np
+
+import ROOT
+ROOT.gROOT.SetBatch()
+ROOT.PyConfig.IgnoreCommandLineOptions = True
+
+from KerasROOTClassification import load_from_dir
+from KerasROOTClassification.plotting import (
+    get_mean_event,
+    plot_NN_vs_var_2D,
+    plot_profile_2D,
+    plot_hist_2D_events,
+    plot_cond_avg_actmax_2D,
+    plot_NN_vs_var_2D_all,
+)
+from KerasROOTClassification.utils import (
+    get_single_neuron_function,
+    get_max_activation_events,
+    weighted_quantile,
+    get_ranges
+)
+
+if args.all_neurons and (not args.mode.startswith("mean")):
+    parser.error("--all-neurons currently only supported for mean_sig and mean_bkg")
+
+if args.verbose:
+    logging.getLogger().setLevel(logging.DEBUG)
+
+c = load_from_dir(args.project_dir)
+
+plot_vs_activation = (args.vary == "activation")
+
+layer = args.layer
+neuron = args.neuron
+
+if layer is None:
+    layer = len(c.model.layers)-1
+
+varx_index = c.fields.index(args.varx)
+if not plot_vs_activation:
+    vary_index = c.fields.index(args.vary)
+else:
+    vary_index = 0 # dummy value in this case
+
+varx_label = args.varx
+vary_label = args.vary
+
+total_weights = c.w_test*np.array(c.class_weight)[c.y_test.astype(int)]
+
+try:
+    mask_value = c.mask_value
+except AttributeError:
+    mask_value = None
+
+# varx_test = c.x_test[:,varx_index]
+# vary_test = c.x_test[:,vary_index]
+
+# x_not_masked = np.where(varx_test != mask_value)[0]
+# y_not_masked = np.where(vary_test != mask_value)[0]
+
+# percentilesx = weighted_quantile(varx_test[x_not_masked], [0.01, 0.99], sample_weight=total_weights[x_not_masked])
+# percentilesy = weighted_quantile(vary_test[y_not_masked], [0.01, 0.99], sample_weight=total_weights[y_not_masked])
+
+percentilesx = get_ranges(c.x_test, [0.01, 0.99], total_weights, mask_value=mask_value, filter_index=varx_index, max_evts=10000)[0][0]
+percentilesy = get_ranges(c.x_test, [0.01, 0.99], total_weights, mask_value=mask_value, filter_index=vary_index, max_evts=10000)[0][0]
+
+
+if args.xrange is not None:
+    if len(args.xrange) < 3:
+        args.xrange.append(args.nbins)
+    varx_range = args.xrange
+else:
+    varx_range = (percentilesx[0], percentilesx[1], args.nbins)
+
+if args.yrange is not None:
+    if len(args.yrange) < 3:
+        args.yrange.append(args.nbins)
+    vary_range = args.yrange
+else:
+    vary_range = (percentilesy[0], percentilesy[1], args.nbins)
+    if plot_vs_activation:
+        vary_range = (0, 1, args.nbins)
+
+if args.mode.startswith("mean"):
+
+    if args.mode == "mean_sig":
+        means = get_mean_event(c.x_test, c.y_test, 1, mask_value=mask_value)
+    elif args.mode == "mean_bkg":
+        means = get_mean_event(c.x_test, c.y_test, 0, mask_value=mask_value)
+
+    print(means)
+
+    if hasattr(c, "get_input_list"):
+        input_transform = lambda x : c.get_input_list(c.transform(x))
+    else:
+        input_transform = c.transform
+
+    if not args.all_neurons:
+        plot_NN_vs_var_2D(
+            args.output_filename,
+            means=means,
+            varx_index=varx_index,
+            vary_index=vary_index,
+            scorefun=get_single_neuron_function(
+                c.model, layer, neuron,
+                input_transform=input_transform
+            ),
+            xmin=varx_range[0], xmax=varx_range[1], nbinsx=varx_range[2],
+            ymin=vary_range[0], ymax=vary_range[1], nbinsy=vary_range[2],
+            varx_label=varx_label, vary_label=vary_label,
+            logscale=args.log, only_pixels=(not args.contour)
+        )
+    else:
+        if hasattr(c, "get_input_list"):
+            transform_function = lambda inp : c.get_input_list(c.scaler.transform(inp))
+        else:
+            transform_function = c.scaler.transform
+        plot_NN_vs_var_2D_all(
+            args.output_filename,
+            means=means,
+            model=c.model,
+            transform_function=transform_function,
+            varx_index=varx_index,
+            vary_index=vary_index,
+            xmin=varx_range[0], xmax=varx_range[1], nbinsx=varx_range[2],
+            ymin=vary_range[0], ymax=vary_range[1], nbinsy=vary_range[2],
+            logz=args.log,
+            plot_last_layer=False,
+        )
+
+elif args.mode.startswith("profile"):
+
+    def my_average(x, weights):
+        if weights.sum() <= 0:
+            return np.nan
+        else:
+            return np.average(x, weights=weights)
+
+    metric_dict = {
+        "mean" : np.mean,
+        "max" : np.max,
+        "average" : my_average,
+    }
+
+    if args.mode == "profile_sig":
+        class_index = 1
+    else:
+        class_index = 0
+    valsx = c.x_test[c.y_test==class_index][:,varx_index]
+    valsy = c.x_test[c.y_test==class_index][:,vary_index]
+    scores = c.scores_test[c.y_test==class_index].reshape(-1)
+
+    opt_kwargs = dict()
+
+    if args.profile_metric == "average":
+        opt_kwargs["weights"] = c.w_test[c.y_test==class_index]
+
+    plot_profile_2D(
+        args.output_filename,
+        valsx, valsy, scores,
+        xmin=varx_range[0], xmax=varx_range[1], nbinsx=varx_range[2],
+        ymin=vary_range[0], ymax=vary_range[1], nbinsy=vary_range[2],
+        metric=metric_dict[args.profile_metric],
+        varx_label=varx_label, vary_label=vary_label,
+        log=args.log,
+        **opt_kwargs
+    )
+
+elif args.mode.startswith("hist"):
+
+    if not args.mode == "hist_actmax":
+        if args.mode == "hist_sig":
+            class_index = 1
+        else:
+            class_index = 0
+
+        valsx = c.x_test[c.y_test==class_index][:,varx_index]
+        if not plot_vs_activation:
+            valsy = c.x_test[c.y_test==class_index][:,vary_index]
+        else:
+            valsy = c.scores_test[c.y_test==class_index].reshape(-1)
+        weights = c.w_test[c.y_test==class_index]
+    else:
+        # ranges in which to sample the random events
+        x_test_scaled = c.transform(c.x_test)
+        ranges = get_ranges(x_test_scaled, [0.01, 0.99], total_weights, mask_value=mask_value)
+        kwargs = {}
+        if hasattr(c, "get_input_list"):
+            kwargs["input_transform"] = c.get_input_list
+            kwargs["input_inverse_transform"] = c.get_input_flat
+        losses, events = get_max_activation_events(c.model, ranges,
+                                                   ntries=args.ntries_actmax,
+                                                   step=args.step_size,
+                                                   layer=layer,
+                                                   neuron=neuron,
+                                                   threshold=args.threshold,
+                                                   **kwargs)
+        events = c.inverse_transform(events)
+        valsx = events[:,varx_index]
+        if not plot_vs_activation:
+            valsy = events[:,vary_index]
+        else:
+            valsy = losses
+        weights = None
+
+    plot_hist_2D_events(
+        args.output_filename,
+        valsx, valsy,
+        xmin=varx_range[0], xmax=varx_range[1], nbinsx=varx_range[2],
+        ymin=vary_range[0], ymax=vary_range[1], nbinsy=vary_range[2],
+        weights=weights,
+        varx_label=varx_label, vary_label=vary_label,
+        log=args.log,
+    )
+
+elif args.mode.startswith("cond_actmax"):
+
+    x_test_scaled = c.transform(c.x_test)
+
+    # ranges in which to sample the random events
+    ranges = get_ranges(x_test_scaled, [0.01, 0.99], total_weights, mask_value=mask_value)
+
+    plot_cond_avg_actmax_2D(
+        args.output_filename,
+        c.model, layer, neuron, ranges,
+        varx_index,
+        vary_index,
+        xmin=varx_range[0], xmax=varx_range[1], nbinsx=varx_range[2],
+        ymin=vary_range[0], ymax=vary_range[1], nbinsy=vary_range[2],
+        scaler=c.scaler,
+        ntries=args.ntries_cond_actmax,
+        maxit=args.nit_cond_actmax,
+        step=args.step_size,
+        varx_label=varx_label, vary_label=vary_label,
+        log=args.log,
+    )

scripts/write_parametrized.py

+#!/usr/bin/env python
+"""
+Write new TTrees with signal parameters as branches. For the
+backgrounds the parameters are generated following the total
+distribution for all signals. The discrete values for the whole ntuple
+of signal parameters are counted, such that correlations between
+signal parameters are taken into account.
+"""
+
+import argparse, re, os
+
+import ROOT
+
+from root_numpy import list_trees
+from root_pandas import read_root
+import numpy as np
+
+if __name__ == "__main__":
+
+    input_filename = "/project/etp4/nhartmann/trees/allTrees_m1.8_NoSys.root"
+    output_filename = "/project/etp4/nhartmann/trees/allTrees_m1.8_NoSys_parametrized.root"
+
+    param_names = ["mg", "mc", "mn"]
+
+    param_match = "GG_oneStep_(.*?)_(.*?)_(.*?)_NoSys"
+
+    output_signal_treename = "GG_oneStep_NoSys"
+
+    bkg_trees = [
+        "diboson_Sherpa221_NoSys",
+        "singletop_NoSys",
+        "ttbar_NoSys",
+        "ttv_NoSys",
+        "wjets_Sherpa221_NoSys",
+        "zjets_Sherpa221_NoSys",
+    ]
+
+    # read in the number of events for each combination of parameters
+    f = ROOT.TFile.Open(input_filename)
+    count_dict = {}
+    for key in f.GetListOfKeys():
+        tree_name = key.GetName()
+        match = re.match(param_match, tree_name)
+        if match is not None:
+            tree = f.Get(tree_name)
+            params = tuple([float(i) for i in match.groups()])
+            if not params in count_dict:
+                count_dict[params] = 0
+            # TODO: might be better to use sum of weights
+            count_dict[params] += tree.GetEntries()
+    f.Close()
+
+    # calculate cumulative sum of counts to sample signal parameters for background from
+    numbers = np.array(count_dict.keys(), dtype=np.float)
+    counts = np.array(count_dict.values(), dtype=np.float)
+    probs = counts/counts.sum()
+    prob_bins = np.cumsum(probs)
+
+    # read and write the rest in chunks
+    if os.path.exists(output_filename):
+        os.remove(output_filename)
+    for tree_name in list_trees(input_filename):
+        match_signal = re.match(param_match, tree_name)
+        if match_signal is not None or tree_name in bkg_trees:
+            print("Writing {}".format(tree_name))
+            nwritten = 0
+            for df in read_root(input_filename, tree_name, chunksize=100000):
+                print("Writing event {}".format(nwritten))
+                if match_signal is None:
+                    rnd = np.random.random(len(df))
+                    rnd_idx = np.digitize(rnd, prob_bins)
+                    param_values = numbers[rnd_idx]
+                    for param_idx, param_name in enumerate(param_names):
+                        df[param_name] = param_values[:,param_idx]
+                    df["training_weight"] = df["eventWeight"]*df["genWeight"]
+                else:
+                    for param_name, param_value in zip(param_names, match_signal.groups()):
+                        df[param_name] = float(param_value)
+                    df["training_weight"] = df["eventWeight"]
+                if match_signal is None:
+                    out_tree_name = tree_name
+                else:
+                    out_tree_name = output_signal_treename
+                df.to_root(output_filename, mode="a", key=out_tree_name)
+                nwritten += len(df)

test/test_toolkit.py

+import pytest
+import numpy as np
+import root_numpy
+import pandas as pd
+from sklearn.datasets import make_classification
+from keras.layers import GRU
+
+from KerasROOTClassification import ClassificationProject, ClassificationProjectRNN
+
+
+def create_dataset(path):
+
+    # create example dataset with (low-weighted) noise added
+    X, y = make_classification(n_samples=10000, random_state=1)
+    X2 = np.random.normal(size=20*10000).reshape(-1, 20)
+    y2 = np.concatenate([np.zeros(5000), np.ones(5000)])
+    X = np.concatenate([X, X2])
+    y = np.concatenate([y, y2])
+    w = np.concatenate([np.ones(10000), 0.01*np.ones(10000)])
+
+    # shift and scale randomly (to check if transformation is working)
+    shift = np.random.rand(20)*100
+    scale = np.random.rand(20)*1000
+    X *= scale
+    X += shift
+
+    # write to root files
+    branches = ["var_{}".format(i) for i in range(len(X[0]))]
+    df = pd.DataFrame(X, columns=branches)
+    df["class"] = y
+    df["weight"] = w
+    tree_path_bkg = str(path / "bkg.root")
+    tree_path_sig = str(path / "sig.root")
+    root_numpy.array2root(df[df["class"]==0].to_records(), tree_path_bkg)
+    root_numpy.array2root(df[df["class"]==1].to_records(), tree_path_sig)
+    return branches, tree_path_sig, tree_path_bkg
+
+
+def test_ClassificationProject(tmp_path):
+    branches, tree_path_sig, tree_path_bkg = create_dataset(tmp_path)
+    c = ClassificationProject(
+        str(tmp_path / "project"),
+        bkg_trees = [(tree_path_bkg, "tree")],
+        signal_trees = [(tree_path_sig, "tree")],
+        branches = branches,
+        weight_expr = "weight",
+        identifiers = ["index"],
+        optimizer="Adam",
+        earlystopping_opts=dict(patience=5),
+        dropout=0.5,
+        layers=3,
+        nodes=128,
+    )
+
+    c.train(epochs=200)
+    c.plot_all_inputs()
+    c.plot_loss()
+    assert min(c.history.history["val_loss"]) < 0.18
+
+
+def test_ClassificationProjectRNN(tmp_path):
+    branches, tree_path_sig, tree_path_bkg = create_dataset(tmp_path)
+    c = ClassificationProjectRNN(
+        str(tmp_path / "project"),
+        bkg_trees = [(tree_path_bkg, "tree")],
+        signal_trees = [(tree_path_sig, "tree")],
+        branches = branches,
+        recurrent_field_names=[
+            [
+                ["var_1", "var_2", "var_3"],
+                ["var_4", "var_5", "var_6"]
+            ],
+            [
+                ["var_10", "var_11", "var_12"],
+                ["var_13", "var_14", "var_15"]
+            ],
+        ],
+        weight_expr = "weight",
+        identifiers = ["index"],
+        optimizer="Adam",
+        earlystopping_opts=dict(patience=5),
+        dropout=0.5,
+        layers=3,
+        nodes=128,
+    )
+    assert sum([isinstance(layer, GRU) for layer in c.model.layers]) == 2
+    c.train(epochs=200)
+    c.plot_all_inputs()
+    c.plot_loss()
+    assert min(c.history.history["val_loss"]) < 0.18

toolkit.py

+#!/usr/bin/env python
+
+__all__ = ["load_from_dir", "ClassificationProject", "ClassificationProjectDataFrame", "ClassificationProjectRNN"]
+
+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 pickle
+import importlib
+import csv
+import math
+import glob
+import shutil
+import gc
+import random
+
+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 sklearn.utils.extmath import stable_cumsum
+from sklearn.model_selection import KFold
+from keras.models import Sequential, Model, model_from_json
+from keras.layers import Dense, Dropout, Input, Masking, GRU, LSTM, concatenate, SimpleRNN
+from keras.callbacks import History, EarlyStopping, CSVLogger, ModelCheckpoint, TensorBoard
+from keras.optimizers import SGD
+from keras.activations import relu
+import keras.initializers
+import keras.optimizers
+from keras.utils.vis_utils import model_to_dot
+from keras import backend as K
+import tensorflow as tf
+import matplotlib.pyplot as plt
+
+from .utils import WeightedRobustScaler, weighted_quantile, poisson_asimov_significance
+from .plotting import save_show
+
+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
+
+
+def set_session_threads(n_cpu=None):
+    "Set the number of threads based on OMP_NUM_THREADS or the given argument"
+
+    if n_cpu is None:
+        if os.environ.get('OMP_NUM_THREADS'):
+            n_cpu = int(os.environ.get('OMP_NUM_THREADS'))
+        else:
+            return
+
+    # not sure if this is the best configuration ...
+    config = tf.ConfigProto(intra_op_parallelism_threads=n_cpu,
+                            inter_op_parallelism_threads=1,
+                            allow_soft_placement=True,
+                            #log_device_placement=True,
+                            device_count = {'CPU': n_cpu})
+    session = tf.Session(config=config)
+    K.set_session(session)
+
+
+def load_from_dir(path):
+    "Load a project and the options from a directory"
+    try:
+        with open(os.path.join(path, "info.json")) as f:
+            info = json.load(f)
+        project_type = info["project_type"]
+        if project_type == "ClassificationProjectRNN":
+            return ClassificationProjectRNN(path)
+    except (KeyError, IOError):
+        pass
+    return ClassificationProject(path)
+
+
+class ClassificationProject(object):
+
+    verbose = 1 # verbosity of the fit method
+
+    """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 regression_branches: list of branch names to be used as regression targets
+
+    :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 use_bias: use bias constant for each neuron? (default: True). You can also pass a list of booleans for each layer.
+
+    :param batch_size: size of the training batches
+
+    :param validation_split: split off this fraction of training events for loss evaluation
+
+    :param kfold_splits: if given, split into this number of of subsets to perform KFold cross validation
+
+    :param kfold_index: index of the subset to leave out for kfold validation
+
+    :param activation_function: activation function in the hidden layers
+
+    :param activation_function_output: activation function in the output layer
+
+    :param leaky_relu_alpha: set this to a non-zero value to use the LeakyReLU variant with a slope in the negative part
+
+    :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 stop_train: stop after this number of events for reading in training events
+
+    :param stop_test: stop after this number of events for reading in test events
+
+    :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 use_tensorboard: if True, use the tensorboard callback to write logs for tensorboard
+
+    :param tensorboard_opts: options for the TensorBoard callback
+
+    :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.
+
+    :param shuffle_seed: use this seed for shuffling the training data
+                         the first time. This seed (increased by one) is used again before
+                         training when keras shuffling is used.
+
+    :param loss: loss function name (or list of names in case of regression targets)
+
+    :param loss_weights: (optional) list of weights to weight the individual losses (for multiple targets)
+
+    :param mask_value: value that is used for non-existent entries (e.g. 4th jet pt in events with 3 jets)
+
+    :param apply_class_weight: apply a weight that scales the events such that sumw(signal) = sumw(background)
+
+    :param normalize_weights: normalize the weights to mean 1
+
+    :param ignore_neg_weights: ignore events with negative weights in training - not recommended! (default: False)
+
+    :param kernel_initializer: weight initializer for the dense layers - if None (default) the keras defaults are used
+
+    :param shuffle: shuffle training data after (and before first) epoch
+
+    """
+
+
+    # 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,
+                        regression_branches=None,
+                        rename_branches=None,
+                        project_dir=None,
+                        data_dir=None,
+                        identifiers=None,
+                        selection=None,
+                        layers=3,
+                        nodes=64,
+                        dropout=None,
+                        dropout_input=None,
+                        use_bias=True,
+                        batch_size=128,
+                        validation_split=0.33,
+                        kfold_splits=None,
+                        kfold_index=0,
+                        activation_function='relu',
+                        leaky_relu_alpha=None,
+                        activation_function_output='sigmoid',
+                        scaler_type="WeightedRobustScaler",
+                        step_signal=2,
+                        step_bkg=2,
+                        stop_train=None,
+                        stop_test=None,
+                        optimizer="SGD",
+                        optimizer_opts=None,
+                        use_earlystopping=True,
+                        earlystopping_opts=None,
+                        use_modelcheckpoint=True,
+                        modelcheckpoint_opts=None,
+                        use_tensorboard=False,
+                        tensorboard_opts=None,
+                        random_seed=1234,
+                        shuffle_seed=42,
+                        balance_dataset=False,
+                        loss='binary_crossentropy',
+                        loss_weights=None,
+                        mask_value=None,
+                        apply_class_weight=True,
+                        normalize_weights=True,
+                        ignore_neg_weights=False,
+                        kernel_initializer=None,
+                        shuffle=True,
+    ):
+
+        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
+        if regression_branches is None:
+            regression_branches = []
+        self.regression_branches = regression_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)]
+        self.dropout_input = dropout_input
+        self.use_bias = use_bias
+        if not isinstance(self.use_bias, list):
+            self.use_bias = [self.use_bias 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!")
+        if len(self.use_bias) != self.layers:
+            raise ValueError("List biases has to be of equal size as the number of layers!")
+        self.batch_size = batch_size
+        self.validation_split = validation_split
+        self.kfold_splits = kfold_splits
+        self.kfold_index = kfold_index
+        self.activation_function = activation_function
+        self.leaky_relu_alpha = leaky_relu_alpha
+        if self.activation_function == "relu" and self.leaky_relu_alpha:
+            self.activation_function = lambda x : relu(x, alpha=self.leaky_relu_alpha)
+        self.activation_function_output = activation_function_output
+        self.scaler_type = scaler_type
+        self.step_signal = step_signal
+        self.step_bkg = step_bkg
+        self.stop_train = stop_train
+        self.stop_test = stop_test
+        self.optimizer = optimizer
+        self.use_earlystopping = use_earlystopping
+        self.use_modelcheckpoint = use_modelcheckpoint
+        self.use_tensorboard = use_tensorboard
+        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.tensorboard_opts = dict(
+            log_dir=os.path.join(self.project_dir, "tensorboard"),
+        )
+        if tensorboard_opts is not None:
+            self.tensorboard_opts.update(**tensorboard_opts)
+        self.random_seed = random_seed
+        self.shuffle_seed = shuffle_seed
+        self.balance_dataset = balance_dataset
+        self.loss = loss
+        self.loss_weights = loss_weights
+        if self.regression_branches and (not isinstance(self.loss, list)):
+            self.loss = [self.loss]+["mean_squared_error"]*len(self.regression_branches)
+
+        self.mask_value = mask_value
+        self.apply_class_weight = apply_class_weight
+        self.normalize_weights = normalize_weights
+        self.ignore_neg_weights = ignore_neg_weights
+        self.kernel_initializer = kernel_initializer
+        self.shuffle = shuffle
+
+        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 training data (divided by mean)
+        self._w_train_tot = None
+
+        self._s_eventlist_train = None
+        self._b_eventlist_train = None
+
+        self._scaler = None
+        self._scaler_target = None
+        self._class_weight = None
+        self._balanced_class_weight = None
+        self._model = None
+        self._history = None
+        self._callbacks_list = []
+        self._train_val_idx = None
+
+        # track the number of epochs this model has been trained
+        self.total_epochs = 0
+
+        self.data_loaded = False
+
+        # track if we are currently training
+        self.is_training = False
+
+        self._fields = None
+        self._target_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
+
+
+    @property
+    def target_fields(self):
+        "Renamed branch expressions for regression targets"
+        if self._target_fields is None:
+            self._target_fields = []
+            for branch_expr in self.regression_branches:
+                self._target_fields.append(self.rename_branches.get(branch_expr, branch_expr))
+        return self._target_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):
+
+        self._w_train_tot = None
+
+        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, IOError):
+
+            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=set(self.branches+self.regression_branches+[self.weight_expr]+self.identifiers),
+                                      selection=self.selection,
+                                      start=0, step=self.step_signal, stop=self.stop_train)
+            self.b_train = tree2array(bkg_chain,
+                                      branches=set(self.branches+self.regression_branches+[self.weight_expr]+self.identifiers),
+                                      selection=self.selection,
+                                      start=0, step=self.step_bkg, stop=self.stop_train)
+            self.s_test = tree2array(signal_chain,
+                                     branches=set(self.branches+self.regression_branches+[self.weight_expr]),
+                                     selection=self.selection,
+                                     start=1, step=self.step_signal, stop=self.stop_test)
+            self.b_test = tree2array(bkg_chain,
+                                     branches=set(self.branches+self.regression_branches+[self.weight_expr]),
+                                     selection=self.selection,
+                                     start=1, step=self.step_bkg, stop=self.stop_test)
+
+            if self.ignore_neg_weights:
+                self.s_train = self.s_train[self.s_train[self.weight_expr]>0]
+                self.b_train = self.b_train[self.b_train[self.weight_expr]>0]
+
+            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()
+
+            # 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.w_train = self.s_train[self.weight_expr]
+            self.w_train = np.concatenate((self.w_train, self.b_train[self.weight_expr]))
+
+            def fill_target(x, s, b):
+                if not self.target_fields:
+                    y = np.empty(len(x), dtype=np.bool)
+                    y[:len(s)] = 1
+                    y[len(s):] = 0
+                else:
+                    y = np.empty((len(x), 1+len(self.target_fields)), dtype=np.float)
+                    y[:len(s),0] = 1
+                    y[len(s):,0] = 0
+                    y[:len(s),1:] = rec2array(s[self.target_fields])
+                    y[len(s):,1:] = rec2array(b[self.target_fields])
+                return y
+
+            self.y_train = fill_target(self.x_train, self.s_train, self.b_train)
+            self.b_train = None
+            self.s_train = None
+
+            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_test = self.s_test[self.weight_expr]
+            self.w_test = np.concatenate((self.w_test, self.b_test[self.weight_expr]))
+
+            self.y_test = fill_target(self.x_test, self.s_test, self.b_test)
+            self.b_test = None
+            self.s_test = None
+
+            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, "r") 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))
+        if self.use_tensorboard:
+            self._callbacks_list.append(TensorBoard(**self.tensorboard_opts))
+        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_tot
+                    scaler_fit_kwargs["mask_value"] = self.mask_value
+                else:
+                    raise ValueError("Scaler type {} unknown".format(self.scaler_type))
+                logger.info("Fitting {} to training data".format(self.scaler_type))
+                orig_copy_setting = self.scaler.copy
+                self.scaler.copy = False
+                self._scaler.fit(self.x_train, **scaler_fit_kwargs)
+                self.scaler.copy = orig_copy_setting
+                joblib.dump(self._scaler, filename)
+        return self._scaler
+
+
+    @property
+    def scaler_target(self):
+        "same as scaler, but for scaling regression targets"
+        # create the scaler (and fit to training data) if not existent
+        if self._scaler_target is None:
+            filename = os.path.join(self.project_dir, "scaler_target.pkl")
+            try:
+                self._scaler_target = joblib.load(filename)
+                logger.info("Loaded existing scaler from {}".format(filename))
+            except IOError:
+                logger.info("Creating new {} for scaling the targets".format(self.scaler_type))
+                scaler_fit_kwargs = dict()
+                if self.scaler_type == "StandardScaler":
+                    self._scaler_target = StandardScaler()
+                elif self.scaler_type == "RobustScaler":
+                    self._scaler_target = RobustScaler()
+                elif self.scaler_type == "WeightedRobustScaler":
+                    self._scaler_target = WeightedRobustScaler()
+                    scaler_fit_kwargs["weights"] = self.w_train_tot
+                else:
+                    raise ValueError("Scaler type {} unknown".format(self.scaler_type))
+                logger.info("Fitting {} to training data".format(self.scaler_type))
+                orig_copy_setting = self.scaler.copy
+                self.scaler.copy = False
+                self._scaler_target.fit(self.y_train, **scaler_fit_kwargs)
+                # i don't want to scale the classification target here
+                self._scaler_target.center_[0] = 0.
+                self._scaler_target.scale_[0] = 1.
+                self.scaler.copy = orig_copy_setting
+                joblib.dump(self._scaler_target, filename)
+        return self._scaler_target
+
+
+    def _batch_transform(self, x, fn, batch_size):
+        "Transform array in batches, temporarily setting mask_values to nan"
+        transformed = np.empty(x.shape, dtype=x.dtype)
+        for start in range(0, len(x), batch_size):
+            stop = start+batch_size
+            x_batch = np.array(x[start:stop]) # copy
+            x_batch[x_batch == self.mask_value] = np.nan
+            x_batch = fn(x_batch)
+            x_batch[np.isnan(x_batch)] = self.mask_value
+            transformed[start:stop] = x_batch
+        return transformed
+
+
+    def transform(self, x, batch_size=10000):
+        if self.mask_value is not None:
+            return self._batch_transform(x, self.scaler.transform, batch_size)
+        else:
+            return self.scaler.transform(x)
+
+
+    def inverse_transform(self, x, batch_size=10000):
+        if self.mask_value is not None:
+            return self._batch_transform(x, self.scaler.inverse_transform, batch_size)
+        else:
+            return self.scaler.inverse_transform(x)
+
+
+    def transform_target(self, y, batch_size=10000):
+        if not self.target_fields:
+            return y
+        if self.mask_value is not None:
+            return self._batch_transform(y, self.scaler_target.transform, batch_size)
+        else:
+            return self.scaler_target.transform(y)
+
+
+    def inverse_transform_target(self, y, batch_size=10000):
+        if not self.target_fields:
+            return y
+        if self.mask_value is not None:
+            return self._batch_transform(y, self.scaler_target.inverse_transform, batch_size)
+        else:
+            return self.scaler_target.inverse_transform(y)
+
+
+    @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 _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")
+        if not os.path.exists(filename):
+            with open(filename, "w") as of:
+                json.dump({}, of)
+        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:
+
+
+            # input
+            input_layer = Input((len(self.fields),))
+
+            # optional dropout on inputs
+            if self.dropout_input is None:
+                hidden_layer = input_layer
+            else:
+                hidden_layer = Dropout(rate=self.dropout_input)(input_layer)
+
+            # densely connected hidden layers
+            for node_count, dropout_fraction, use_bias in zip(
+                    self.nodes,
+                    self.dropout,
+                    self.use_bias,
+            ):
+                extra_opts = dict()
+                if self.kernel_initializer is not None:
+                    extra_opts["kernel_initializer"] = getattr(keras.initializers, self.kernel_initializer)()
+                hidden_layer = Dense(node_count, activation=self.activation_function, use_bias=use_bias, **extra_opts)(hidden_layer)
+                if (dropout_fraction is not None) and (dropout_fraction > 0):
+                    hidden_layer = Dropout(rate=dropout_fraction)(hidden_layer)
+
+            # optional regression targets
+            extra_targets = []
+            for target_field in self.target_fields:
+                extra_target = Dense(1, activation="linear", name="target_{}".format(target_field))(hidden_layer)
+                extra_targets.append(extra_target)
+
+            if not self.target_fields:
+                # one output node for binary classification
+                output_layer = Dense(1, activation=self.activation_function_output)(hidden_layer)
+                outputs = [output_layer]
+            else:
+                # add another hidden layer on top of the regression targets and previous hidden layers
+                merge = concatenate([hidden_layer]+extra_targets)
+                hidden_layer2 = Dense(64, activation=self.activation_function)(merge)
+                output_class = Dense(1, activation=self.activation_function_output)(hidden_layer2)
+                outputs = [output_class]+extra_targets
+
+            self._model = Model(inputs=[input_layer], outputs=outputs)
+            self._compile_or_load_model()
+
+        return self._model
+
+
+    def _compile_or_load_model(self):
+        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,
+                            loss_weights=self.loss_weights,
+                            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())
+
+        # plot model
+        with open(os.path.join(self.project_dir, "model.svg"), "wb") as of:
+            of.write(model_to_dot(self._model, show_shapes=True).create("dot", format="svg"))
+
+
+    @property
+    def class_weight(self):
+        if self._class_weight is None:
+            sumw_bkg = np.sum(self.w_train[self.l_train == 0])
+            sumw_sig = np.sum(self.w_train[self.l_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.l_train == 0])
+            sumw_sig = np.sum(self.w_train[self.l_train == 1])
+            # use sumw *per event* in this case
+            sumw_bkg /= len(self.w_train[self.l_train == 0])
+            sumw_sig /= len(self.w_train[self.l_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
+
+        if not self.data_loaded:
+            self._load_data()
+
+
+    @property
+    def l_train(self):
+        "labels (in case y contains regression targets)"
+        if not self.target_fields:
+            return self.y_train
+        else:
+            return self.y_train[:,0]
+
+
+    @property
+    def l_test(self):
+        "labels (in case y contains regression targets)"
+        if not self.target_fields:
+            return self.y_test
+        else:
+            return self.y_test[:,0]
+
+
+    @property
+    def w_train_tot(self):
+        "(sample weight * class weight), divided by mean"
+        if not self.balance_dataset:
+            class_weight = self.class_weight
+        else:
+            class_weight = self.balanced_class_weight
+        if not self.data_loaded:
+            raise ValueError("Data not loaded! can't calculate total weight")
+        if self._w_train_tot is None:
+            if self.apply_class_weight:
+                self._w_train_tot = self.w_train*np.array(class_weight)[self.l_train.astype(int)]
+            else:
+                self._w_train_tot = np.array(self.w_train)
+            if self.normalize_weights:
+                self._w_train_tot /= np.mean(self._w_train_tot)
+        return self._w_train_tot
+
+
+    @property
+    def validation_data(self):
+        "(Transformed) validation data for loss evaluation"
+        idx = self.train_val_idx[1]
+        x_val, y_val, w_val = self.x_train[idx], self.y_train[idx], self.w_train_tot[idx]
+        x_val_input = self.get_input_list(self.transform(x_val))
+        y_val_output = self.get_output_list(self.transform_target(y_val))
+        w_val_list = self.get_weight_list(w_val)
+        return x_val_input, y_val_output, w_val_list
+
+
+    @property
+    def training_data(self):
+        "(Transformed) Training data with validation data split off"
+        idx = self.train_val_idx[0]
+        x_train, y_train, w_train =  self.x_train[idx], self.y_train[idx], self.w_train_tot[idx]
+        x_train_input = self.get_input_list(self.transform(x_train))
+        y_train_output = self.get_output_list(self.transform_target(y_train))
+        w_train_list = self.get_weight_list(w_train)
+        return x_train_input, y_train_output, w_train_list
+
+
+    @property
+    def train_val_idx(self):
+        if self._train_val_idx is None:
+            if self.kfold_splits is not None:
+                kfold = KFold(self.kfold_splits, shuffle=self.shuffle, random_state=self.shuffle_seed)
+                for i, train_val_idx in enumerate(kfold.split(self.x_train)):
+                    if i == self.kfold_index:
+                        self._train_val_idx = train_val_idx
+                        break
+                else:
+                    raise IndexError("Index {} out of range for kfold (requested {} splits)".format(self.kfold_index, self.kfold_splits))
+            else:
+                split_index = int((1-self.validation_split)*len(self.x_train))
+                np.random.seed(self.shuffle_seed)
+                if self.shuffle:
+                    shuffled_idx = np.random.permutation(len(self.x_train))
+                else:
+                    shuffled_idx = np.arange(len(self.x_train))
+                self._train_val_idx = (shuffled_idx[:split_index], shuffled_idx[split_index:])
+        return self._train_val_idx
+
+
+    @property
+    def steps_per_epoch(self):
+        return int(float(len(self.train_val_idx[0]))/float(self.batch_size))
+
+
+    def get_input_list(self, x):
+        "For the standard Dense models with single input, this does nothing"
+        return x
+
+
+    def get_output_list(self, y):
+        "Split target vector column wise in case of regression targets"
+        if not self.target_fields:
+            return y
+        else:
+            return np.hsplit(y, len(self.target_fields)+1)
+
+
+    def get_weight_list(self, w):
+        "Repeat weight n times for regression targets"
+        if not self.target_fields:
+            return w
+        else:
+            return [w]*(len(self.target_fields)+1)
+
+
+    def yield_batch(self):
+        "Batch generator - optionally shuffle the indices after each epoch"
+        x_train, y_train, w_train = self.x_train, self.y_train, self.w_train_tot
+        train_idx = list(self.train_val_idx[0])
+        np.random.seed(self.shuffle_seed+1)
+        logger.info("Generating training batches from {} signal and {} background events"
+                    .format(len(np.where(self.l_train[train_idx]==1)[0]),
+                            len(np.where(self.l_train[train_idx]==0)[0])))
+        while True:
+            if self.shuffle:
+                shuffled_idx = np.random.permutation(train_idx)
+            else:
+                shuffled_idx = train_idx
+            for start in range(0, len(shuffled_idx), int(self.batch_size)):
+                x_batch = x_train[shuffled_idx[start:start+int(self.batch_size)]]
+                y_batch = y_train[shuffled_idx[start:start+int(self.batch_size)]]
+                w_batch = w_train[shuffled_idx[start:start+int(self.batch_size)]]
+                x_input = self.get_input_list(self.transform(x_batch))
+                y_output = self.get_output_list(self.transform_target(y_batch))
+                w_list = self.get_weight_list(w_batch)
+                yield (x_input, y_output, w_list)
+
+
+    def yield_single_class_batch(self, class_label):
+        """
+        Generate batches of half batch size, containing only entries for the given class label.
+        The weights are multiplied by balanced_class_weight.
+        """
+        x_train, y_train, w_train = self.training_data
+        l_train = y_train[:,0] if self.target_fields else y_train
+        class_idx = np.where(l_train==class_label)[0]
+        while True:
+            # shuffle the indices for this class label
+            if self.shuffle:
+                shuffled_idx = np.random.permutation(class_idx)
+            else:
+                shuffled_idx = class_idx
+            # yield them batch wise
+            for start in range(0, len(shuffled_idx), int(self.batch_size/2)):
+                yield (x_train[shuffled_idx[start:start+int(self.batch_size/2)]],
+                       y_train[shuffled_idx[start:start+int(self.batch_size/2)]],
+                       w_train[shuffled_idx[start:start+int(self.batch_size/2)]])
+
+
+    def yield_balanced_batch(self):
+        "generate batches with equal amounts of both classes"
+        logcounter = 0
+        for batch_0, batch_1 in izip(self.yield_single_class_batch(0),
+                                     self.yield_single_class_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, skip_checkpoint=False):
+
+        self.load()
+
+        self.total_epochs = self._read_info("epochs", 0)
+
+        set_session_threads()
+
+        logger.info("Train model")
+        if not self.balance_dataset:
+            try:
+                self.is_training = True
+                self.model.fit_generator(self.yield_batch(),
+                                         steps_per_epoch=self.steps_per_epoch,
+                                         epochs=epochs,
+                                         validation_data=self.validation_data,
+                                         callbacks=self.callbacks_list,
+                                         verbose=self.verbose)
+                self.is_training = False
+            except KeyboardInterrupt:
+                logger.info("Interrupt training - continue with rest")
+        else:
+            try:
+                self.is_training = True
+                labels, label_counts = np.unique(self.l_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.validation_data,
+                                         callbacks=self.callbacks_list,
+                                         verbose=self.verbose)
+                self.is_training = False
+            except KeyboardInterrupt:
+                logger.info("Interrupt training - continue with rest")
+
+        if not skip_checkpoint:
+            self.checkpoint_model()
+
+
+    def checkpoint_model(self):
+
+        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 += self.history.epoch[-1]+1
+        self._write_info("epochs", self.total_epochs)
+
+
+    def evaluate_train_test(self, do_train=True, do_test=True, batch_size=10000, mode=None):
+        "Calculate scores for training and test sample"
+
+        if mode is not None:
+            self._write_info("scores_mode", mode)
+
+        def eval_score(data_name):
+            logger.info("Create/Update scores for {} sample".format(data_name))
+            n_events = len(getattr(self, "x_"+data_name))
+            setattr(self, "scores_"+data_name, np.empty(n_events))
+            for start in range(0, n_events, batch_size):
+                stop = start+batch_size
+                outputs =  self.predict(
+                    self.get_input_list(self.transform(getattr(self, "x_"+data_name)[start:stop])),
+                    mode=mode
+                )
+                if not self.target_fields:
+                    scores_batch = outputs.reshape(-1)
+                else:
+                    scores_batch = outputs[0].reshape(-1)
+                getattr(self, "scores_"+data_name)[start:stop] = scores_batch
+            self._dump_to_hdf5("scores_"+data_name)
+
+        if do_test:
+            eval_score("test")
+        if do_train:
+            eval_score("train")
+
+
+    def predict(self, x, mode=None):
+        """
+        Calculate the scores for a (transformed) array of input values.
+        If the array is not transformed, use `evaluate` instead
+        """
+        if mode is None:
+            # normal output - after activation function output layer
+            return self.model.predict(x)
+        elif mode == "skip_activation":
+            # output before applying activation function
+            # (after weighted sum + bias of last hidden layer)
+            if isinstance(self.model.input, list):
+                feed_dict={tuple(self.model.input) : x}
+            else:
+                feed_dict={self.model.input : x}
+            return K.get_session().run(
+                self.model.output.op.inputs[0],
+                feed_dict=feed_dict
+            )
+        else:
+            raise ValueError("Unknown mode {}".format(mode))
+
+
+    def evaluate(self, x_eval, mode=None):
+        """
+        Calculate the scores for an array of input values.
+        All nescessary transformations are applied.
+        """
+        logger.debug("Evaluate score for {}".format(x_eval))
+        x_eval = self.transform(x_eval)
+        logger.debug("Evaluate for transformed array: {}".format(x_eval))
+        return self.predict(x_eval, mode=mode)
+
+
+    def write_friend_tree(self, score_name,
+                          source_filename, source_treename,
+                          target_filename, target_treename,
+                          batch_size=100000,
+                          score_mode=None,
+                          fixed_params=None):
+        "TODO: doesn't work for regression targets"
+        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.branches+self.identifiers,
+                                     start=start, stop=start+batch_size)
+            # for parametrized classifiers
+            if fixed_params is not None:
+                for param_name, value in fixed_params.items():
+                    x_from_tree[param_name] = value
+            x_eval = rec2array(x_from_tree[self.branches])
+            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, mode=score_mode).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, ax=None, from_training_batches=False, max_n_batches=None):
+        """
+        plot a single input variable as a histogram (signal vs background)
+
+        :param from_training_batches: use data from training batch generator
+        :param max_n_batches: if training batch generator is used, just use
+                              this number of batches (otherwise steps_per_epoch is used)
+        """
+        branch = self.fields[var_index]
+        if ax is None:
+            fig, ax = plt.subplots()
+        else:
+            fig = None
+
+        if not from_training_batches:
+            bkg = self.x_train[:,var_index][self.l_train == 0]
+            sig = self.x_train[:,var_index][self.l_train == 1]
+            bkg_weights = self.w_train_tot[self.l_train == 0]
+            sig_weights = self.w_train_tot[self.l_train == 1]
+        else:
+            bkg = None
+            sig = None
+            bkg_weights = None
+            sig_weights = None
+            if max_n_batches is not None:
+                n_batches = max_n_batches
+            else:
+                n_batches = self.steps_per_epoch
+            for i_batch, (x, y, w) in enumerate(self.yield_batch()):
+                if i_batch > n_batches:
+                    break
+                if self.target_fields:
+                    y = y[0]
+                try:
+                    x = self.get_input_flat(x)
+                except NameError:
+                    pass
+                bkg_batch = x[:,var_index][y==0]
+                sig_batch = x[:,var_index][y==1]
+                bkg_weights_batch = w[y==0]
+                sig_weights_batch = w[y==1]
+                if bkg is None:
+                    bkg = bkg_batch
+                    sig = sig_batch
+                    bkg_weights = bkg_weights_batch
+                    sig_weights = sig_weights_batch
+                else:
+                    bkg = np.concatenate([bkg, bkg_batch])
+                    sig = np.concatenate([sig, sig_batch])
+                    bkg_weights = np.concatenate([bkg_weights, bkg_weights_batch])
+                    sig_weights = np.concatenate([sig_weights, sig_weights_batch])
+
+        if hasattr(self, "mask_value"):
+            bkg_not_masked = np.where(bkg != self.mask_value)[0]
+            sig_not_masked = np.where(sig != self.mask_value)[0]
+            bkg = bkg[bkg_not_masked]
+            sig = sig[sig_not_masked]
+            bkg_weights = bkg_weights[bkg_not_masked]
+            sig_weights = sig_weights[sig_not_masked]
+
+        if self.balance_dataset:
+            if len(sig) < len(bkg):
+                logger.warning("Plotting only up to {} bkg events, since we use balance_dataset".format(len(sig)))
+                bkg = bkg[0:len(sig)]
+                bkg_weights = bkg_weights[0:len(sig)]
+            else:
+                logger.warning("Plotting only up to {} sig events, since we use balance_dataset".format(len(bkg)))
+                sig = sig[0:len(bkg)]
+                sig_weights = sig_weights[0:len(bkg)]
+
+        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
+        x_total = np.concatenate([bkg, sig])
+        w_total = np.concatenate([bkg_weights, sig_weights])
+        plot_range = weighted_quantile(
+            x_total,
+            [0.01, 0.99],
+            sample_weight=w_total,
+        )
+        logger.debug("Calculated range based on percentiles: {}".format(plot_range))
+
+        bins = 50
+
+        # check if we have a distribution of integer numbers (e.g. njet or something categorical)
+        # in that case we want to have a bin for each number
+        if (x_total == x_total.astype(int)).all():
+            plot_range = (math.floor(plot_range[0])-0.5, math.ceil(plot_range[1])+0.5)
+            bins = int(plot_range[1]-plot_range[0])
+
+        try:
+            centers_sig, hist_sig, _ = self.get_bin_centered_hist(sig, bins=bins, range=plot_range, weights=sig_weights)
+            centers_bkg, hist_bkg, _ = self.get_bin_centered_hist(bkg, bins=bins, 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, bins=bins, range=plot_range, weights=sig_weights)
+            centers_bkg, hist_bkg, _ = self.get_bin_centered_hist(bkg, bins=bins, range=plot_range, weights=bkg_weights)
+
+        if bins > 1:
+            width = centers_sig[1]-centers_sig[0]
+        else:
+            width = 1.
+        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)
+
+        label = branch
+        ax.set_xlabel(label)
+        if fig is not None:
+            plot_dir = os.path.join(self.project_dir, "plots")
+            if not os.path.exists(plot_dir):
+                os.mkdir(plot_dir)
+            return save_show(plt, fig, os.path.join(plot_dir, "var_{}.pdf".format(var_index)))
+
+
+    def plot_all_inputs(self, **kwargs):
+        nrows = math.ceil(math.sqrt(len(self.fields)))
+        fig, axes = plt.subplots(nrows=int(nrows), ncols=int(nrows),
+                                 figsize=(3*nrows, 3*nrows),
+                                 gridspec_kw=dict(wspace=0.4, hspace=0.4))
+        for i in range(len(self.fields)):
+            self.plot_input(i, ax=axes.reshape(-1)[i], **kwargs)
+        return save_show(plt, fig, os.path.join(self.project_dir, "all_inputs.pdf"))
+
+
+    def plot_weights(self, bins=100, range=None):
+        fig, ax = plt.subplots()
+        bkg = self.w_train_tot[self.l_train == 0]
+        sig = self.w_train_tot[self.l_train == 1]
+        ax.hist(bkg, bins=bins, range=range, color="b", alpha=0.5)
+        ax.set_yscale("log")
+        save_show(plt, fig, os.path.join(self.project_dir, "eventweights_bkg.pdf"))
+        fig, ax = plt.subplots()
+        ax.hist(sig, bins=bins, range=range, color="r", alpha=0.5)
+        ax.set_yscale("log")
+        save_show(plt, fig, os.path.join(self.project_dir, "eventweights_sig.pdf"))
+
+
+    def plot_ROC(self, xlim=(0,1), ylim=(0,1)):
+
+        logger.info("Plot ROC curve")
+
+        fig, ax = plt.subplots()
+        ax.grid(color='gray', linestyle='--', linewidth=1)
+
+        for y, scores, weight, label in [
+                (self.l_train, self.scores_train, self.w_train, "train"),
+                (self.l_test, self.scores_test, self.w_test, "test")
+        ]:
+            fpr, tpr, threshold = roc_curve(y, scores, sample_weight = weight)
+            fpr = 1.0 - fpr # background rejection
+            try:
+                roc_auc = auc(tpr, fpr)
+            except ValueError:
+                logger.warning("Got a value error from auc - trying to rerun with reorder=True")
+                roc_auc = auc(tpr, fpr, reorder=True)
+            ax.plot(tpr,  fpr, label=str(self.name + " {} (AUC = {:.3f})".format(label, roc_auc)))
+
+        ax.plot([0,1],[1,0], linestyle='--', color='black', label='Luck')
+        ax.set_ylabel("Background rejection")
+        ax.set_xlabel("Signal efficiency")
+        ax.set_title('Receiver operating characteristic')
+        ax.set_xlim(*xlim)
+        ax.set_ylim(*ylim)
+        # plt.xticks(np.arange(0,1,0.1))
+        # plt.yticks(np.arange(0,1,0.1))
+        ax.legend(loc='lower left', framealpha=1.0)
+        return save_show(plt, fig, os.path.join(self.project_dir, "ROC.pdf"))
+
+
+    def plot_score(self, log=True, plot_opts=dict(bins=50, range=(0,1)),
+                   ylim=None, xlim=None, density=True,
+                   lumifactor=None, apply_class_weight=True,
+                   invert_activation=False):
+        if invert_activation:
+            trf = self.get_inverse_act_fn()
+        else:
+            trf = lambda y : y
+        fig, ax = plt.subplots()
+        for scores, weights, y, class_label, fn, opts in [
+                (self.scores_train, self.w_train, self.l_train, 1, ax.bar, dict(color="r", label="signal train")),
+                (self.scores_train, self.w_train, self.l_train, 0, ax.bar, dict(color="b", label="background train")),
+                (self.scores_test, self.w_test, self.l_test, 1, ax.errorbar, dict(fmt="ro", label="signal test")),
+                (self.scores_test, self.w_test, self.l_test, 0, ax.errorbar, dict(fmt="bo", label="background test")),
+        ]:
+            weights = weights[y==class_label]
+            if apply_class_weight is True and (lumifactor is not None):
+                logger.warning("not applying class weight, since lumifactor given")
+            if apply_class_weight and (lumifactor is None):
+                weights = weights*self.class_weight[class_label]
+            if lumifactor is not None:
+                weights = weights*lumifactor
+            centers, hist, rel_errors = self.get_bin_centered_hist(
+                trf(scores[y==class_label].reshape(-1)),
+                weights=weights,
+                **plot_opts
+            )
+            width = centers[1]-centers[0]
+            if density:
+                hist = hist/width
+            if fn == ax.errorbar:
+                errors = rel_errors*hist
+                opts.update(yerr=errors)
+            else:
+                opts.update(width=width, alpha=0.5)
+            fn(centers, hist, **opts)
+        if log:
+            ax.set_yscale("log")
+        if ylim is not None:
+            ax.set_ylim(*ylim)
+        if xlim is not None:
+            ax.set_xlim(*xlim)
+        ax.set_xlabel("NN output")
+        if density:
+            ax.set_ylabel("dN / d(NN output)")
+        else:
+            ax.set_ylabel("Events / {:.2f}".format(width))
+        if apply_class_weight:
+            ax.set_title("Class weights applied")
+        ax.legend(loc='upper center', framealpha=0.5)
+        return save_show(plt, fig, os.path.join(self.project_dir, "scores.pdf"))
+
+
+    def plot_significance_hist(self, lumifactor=1., significance_function=None, plot_opts=dict(bins=50, range=(0, 1)), invert_activation=False):
+
+        """
+        Plot significances based on a histogram of scores
+        """
+
+        logger.info("Plot significances")
+
+        if invert_activation:
+            trf = self.get_inverse_act_fn()
+        else:
+            trf = lambda y : y
+
+        centers_sig_train, hist_sig_train, rel_errors_sig_train = self.get_bin_centered_hist(trf(self.scores_train[self.l_train==1].reshape(-1)), weights=self.w_train[self.l_train==1], **plot_opts)
+        centers_bkg_train, hist_bkg_train, rel_errors_bkg_train = self.get_bin_centered_hist(trf(self.scores_train[self.l_train==0].reshape(-1)), weights=self.w_train[self.l_train==0], **plot_opts)
+        centers_sig_test, hist_sig_test, rel_errors_sig_test = self.get_bin_centered_hist(trf(self.scores_test[self.l_test==1].reshape(-1)), weights=self.w_test[self.l_test==1], **plot_opts)
+        centers_bkg_test, hist_bkg_test, rel_errors_bkg_test = self.get_bin_centered_hist(trf(self.scores_test[self.l_test==0].reshape(-1)), weights=self.w_test[self.l_test==0], **plot_opts)
+
+        significances_train = []
+        significances_test = []
+        for hist_sig, hist_bkg, rel_errors_sig, rel_errors_bkg, significances in [
+                (hist_sig_train, hist_bkg_train, rel_errors_sig_train, rel_errors_bkg_train, significances_train),
+                (hist_sig_test, hist_bkg_test, rel_errors_sig_test, rel_errors_bkg_test, significances_test),
+        ]:
+            # factor to rescale due to using only a fraction of events (training and test samples)
+            # normfactor_sig = (np.sum(self.w_train[self.y_train==1])+np.sum(self.w_test[self.y_test==1]))/np.sum(w[y==1])
+            # normfactor_bkg = (np.sum(self.w_train[self.y_train==0])+np.sum(self.w_test[self.y_test==0]))/np.sum(w[y==0])
+            normfactor_sig = self.step_signal
+            normfactor_bkg = self.step_bkg
+            # first set nan values to 0 and multiply by lumi
+            for arr in hist_sig, hist_bkg, rel_errors_bkg:
+                arr[np.isnan(arr)] = 0
+            hist_sig *= lumifactor*normfactor_sig
+            hist_bkg *= lumifactor*normfactor_bkg
+            for i in range(len(hist_sig)):
+                s = sum(hist_sig[i:])
+                b = sum(hist_bkg[i:])
+                db = math.sqrt(sum((rel_errors_bkg[i:]*hist_bkg[i:])**2))
+                if significance_function is None:
+                    try:
+                        z = poisson_asimov_significance(s, 0, b, db)
+                    except (ZeroDivisionError, ValueError) as e:
+                        z = 0
+                else:
+                    z = significance_function(s, b, db)
+                if z == float('inf'):
+                    z = 0
+                logger.debug("s, b, db, z = {}, {}, {}, {}".format(s, b, db, z))
+                significances.append(z)
+
+        fig, ax = plt.subplots()
+        width = centers_sig_train[1]-centers_sig_train[0]
+        ax.plot(centers_bkg_train, significances_train, label="train, Z_max={}".format(np.amax(significances_train)))
+        ax.plot(centers_bkg_test, significances_test, label="test, Z_max={}".format(np.amax(significances_test)))
+        ax.set_xlabel("Cut on NN score")
+        ax.set_ylabel("Significance")
+        ax.legend(loc='lower center', framealpha=0.5)
+        return save_show(plt, fig, os.path.join(self.project_dir, "significances_hist.pdf"))
+
+
+    @staticmethod
+    def calc_s_ds_b_db(scores, y, w):
+
+        """
+        Calculate the sum of weights of signal (s), background (b) and the
+        sqrt of the squared sum of weights for all possible threshold
+        of the output score.
+        Following the implementation from sklearn.metrics.ranking._binary_clf_curve
+        """
+
+        desc_score_indices = np.argsort(scores, kind="mergesort")[::-1]
+        scores_sorted = scores[desc_score_indices]
+        y_sorted = y[desc_score_indices]
+        w_sorted = w[desc_score_indices]
+        distinct_value_indices = np.where(np.diff(scores_sorted))[0]
+        threshold_idxs = np.r_[distinct_value_indices, y_sorted - 1]
+        s_sumw = stable_cumsum(y_sorted * w_sorted)[threshold_idxs]
+        s_sumw2 = stable_cumsum(y_sorted * (w_sorted**2))[threshold_idxs]
+        b_sumw = stable_cumsum(np.logical_not(y_sorted) * w_sorted)[threshold_idxs]
+        b_sumw2 = stable_cumsum(np.logical_not(y_sorted) * (w_sorted**2))[threshold_idxs]
+
+        return s_sumw, np.sqrt(s_sumw2), b_sumw, np.sqrt(b_sumw2), scores_sorted[threshold_idxs]
+
+
+    def get_inverse_act_fn(self):
+        if not self.activation_function_output == "sigmoid":
+            raise NotImplementedError("Inverse function of {} not supported yet - "
+                                      "currently only sigmoid"
+                                      .format(self.activation_function_output))
+        return lambda y : np.log(y/(1-y))
+
+
+    def plot_significance(self, significance_function=None, maxsteps=None, lumifactor=1., vectorized=False, invert_activation=False):
+        """
+        Plot the significance when cutting on all posible thresholds and plot against signal efficiency.
+        """
+
+        if significance_function is None:
+            vectorized = True
+            significance_function = poisson_asimov_significance
+
+        if invert_activation:
+            trf = self.get_inverse_act_fn()
+        else:
+            trf = lambda y : y
+
+        fig, ax = plt.subplots()
+        ax2 = ax.twinx()
+        prop_cycle = plt.rcParams['axes.prop_cycle']
+        colors = prop_cycle.by_key()['color']
+        for (scores, y, w, label), col in zip(
+                [(self.scores_train, self.l_train, self.w_train, "train"),
+                 (self.scores_test, self.l_test, self.w_test, "test")],
+                colors
+        ):
+            scores = trf(scores)
+            s_sumws, s_errs, b_sumws, b_errs, thresholds = self.calc_s_ds_b_db(scores, y, w)
+            if maxsteps is not None:
+                stepsize = int(len(s_sumws))/int(maxsteps)
+            else:
+                stepsize = 1
+            if stepsize == 0:
+                stepsize = 1
+            s_sumws = s_sumws[::stepsize]*lumifactor*self.step_signal
+            s_errs = s_errs[::stepsize]*lumifactor*self.step_signal
+            b_sumws = b_sumws[::stepsize]*lumifactor*self.step_bkg
+            b_errs = b_errs[::stepsize]*lumifactor*self.step_bkg
+            nonzero_b = np.where(b_sumws!=0)[0]
+            s_sumws = s_sumws[nonzero_b]
+            s_errs = s_errs[nonzero_b]
+            b_sumws = b_sumws[nonzero_b]
+            b_errs = b_errs[nonzero_b]
+            thresholds = thresholds[nonzero_b]
+            if not vectorized:
+                zs = []
+                for s, ds, b, db in zip(s_sumws, s_errs, b_sumws, b_errs):
+                    zs.append(significance_function(s, ds, b, db))
+            else:
+                zs = significance_function(s_sumws, s_errs, b_sumws, b_errs)
+            ax.plot(s_sumws/s_sumws[-1], zs, label=label, color=col)
+            ax2.plot(s_sumws/s_sumws[-1], thresholds, "--", color=col)
+        ax.set_xlabel("Signal efficiency")
+        ax.set_ylabel("Significance")
+        ax.set_xlim(0, 1)
+        ax2.set_ylabel("Threshold")
+        ax.legend()
+        return save_show(plt, fig, os.path.join(self.project_dir, "significances.pdf"))
+
+
+    @property
+    def csv_hist(self):
+        with open(os.path.join(self.project_dir, "training.log")) as f:
+            reader = csv.reader(f)
+            history_list = list(reader)
+        hist_dict = {}
+        for hist_key_index, hist_key in enumerate(history_list[0]):
+            hist_dict[hist_key] = [float(line[hist_key_index]) for line in history_list[1:]]
+        return hist_dict
+
+    def plot_loss(self, all_trainings=False, log=False, ylim=None, xlim=None):
+        """
+        Plot the value of the loss function for each epoch
+
+        :param all_trainings: set to true if you want to plot all trainings (otherwise the previous history is used)
+        """
+
+        if all_trainings:
+            hist_dict = self.csv_hist
+        else:
+            hist_dict = self.history.history
+
+        if (not 'loss' in hist_dict) or (not 'val_loss' in hist_dict):
+            logger.warning("No previous history found for plotting, try global history")
+            hist_dict = self.csv_hist
+
+        logger.info("Plot losses")
+        fig, ax = plt.subplots()
+        ax.plot(hist_dict['loss'])
+        ax.plot(hist_dict['val_loss'])
+        ax.set_ylabel('loss')
+        ax.set_xlabel('epoch')
+        ax.legend(['training data','validation data'], loc='upper left')
+        if log:
+            ax.set_yscale("log")
+        if xlim is not None:
+            ax.set_xlim(*xlim)
+        if ylim is not None:
+            ax.set_ylim(*ylim)
+        return save_show(plt, fig, os.path.join(self.project_dir, "losses.pdf"))
+
+
+    def plot_accuracy(self, all_trainings=False, log=False, acc_suffix="weighted_acc"):
+        """
+        Plot the value of the accuracy metric for each epoch
+
+        :param all_trainings: set to true if you want to plot all trainings (otherwise the previous history is used)
+        """
+
+        if all_trainings:
+            hist_dict = self.csv_hist
+        else:
+            hist_dict = self.history.history
+
+        if (not acc_suffix in hist_dict) or (not 'val_'+acc_suffix in hist_dict):
+            logger.warning("No previous history found for plotting, try global history")
+            hist_dict = self.csv_hist
+
+        logger.info("Plot accuracy")
+
+        plt.plot(hist_dict[acc_suffix])
+        plt.plot(hist_dict['val_'+acc_suffix])
+        plt.title('model accuracy')
+        plt.ylabel('accuracy')
+        plt.xlabel('epoch')
+        plt.legend(['training data', 'validation data'], loc='upper left')
+        if log:
+            plt.yscale("log")
+        plt.savefig(os.path.join(self.project_dir, "accuracy.pdf"))
+        plt.clf()
+
+
+    def plot_all(self):
+        self.plot_ROC()
+        # self.plot_accuracy()
+        self.plot_loss()
+        self.plot_score()
+        self.plot_weights()
+        # self.plot_significance()
+
+
+    def to_DataFrame(self):
+        df = pd.DataFrame(np.concatenate([self.x_train, self.x_test]), columns=self.fields)
+        df["weight"] = np.concatenate([self.w_train, self.w_test])
+        df["labels"] = pd.Categorical.from_codes(
+            np.concatenate([self.l_train, self.l_test]),
+            categories=["background", "signal"]
+        )
+        for identifier in self.identifiers:
+            try:
+                df[identifier] = np.concatenate([self.s_eventlist_train[identifier],
+                                                 self.b_eventlist_train[identifier],
+                                                 -1*np.ones(len(self.x_test), dtype="i8")])
+            except IOError:
+                logger.warning("Can't find eventlist - DataFrame won't contain identifiers")
+        df["is_train"] = np.concatenate([np.ones(len(self.x_train), dtype=np.bool),
+                                         np.zeros(len(self.x_test), dtype=np.bool)])
+        return df
+
+
+def create_getter(dataset_name):
+    def getx(self):
+        if getattr(self, "_"+dataset_name) is None:
+            self._load_from_hdf5(dataset_name)
+        return getattr(self, "_"+dataset_name)
+    return getx
+
+def create_setter(dataset_name):
+    def setx(self, value):
+        setattr(self, "_"+dataset_name, value)
+    return setx
+
+# define getters and setters for all datasets
+for dataset_name in ClassificationProject.dataset_names:
+    setattr(ClassificationProject, dataset_name, property(create_getter(dataset_name),
+                                                          create_setter(dataset_name)))
+
+
+class ClassificationProjectDataFrame(ClassificationProject):
+
+    """
+    A little hack to initialize a ClassificationProject from a pandas DataFrame instead of ROOT TTrees
+    """
+
+    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:
+                # don't put the dataframe into options.pickle!
+                if len(args) > 1:
+                    args = args[1:]
+                else:
+                    args = []
+                pickle.dump(dict(args=args, kwargs=kwargs), of)
+
+
+    def _init_from_args(self,
+                        name,
+                        df,
+                        input_columns,
+                        weight_column="weights",
+                        label_column="labels",
+                        signal_label="signal",
+                        background_label="background",
+                        split_mode="split_column",
+                        split_column="is_train",
+                        **kwargs):
+
+        self.df = df
+        self.input_columns = input_columns
+        self.weight_column = weight_column
+        self.label_column = label_column
+        self.signal_label = signal_label
+        self.background_label = background_label
+        if split_mode != "split_column":
+            raise NotImplementedError("'split_column' is the only currently supported split mode")
+        self.split_mode = split_mode
+        self.split_column = split_column
+        super(ClassificationProjectDataFrame, self)._init_from_args(
+            name,
+            signal_trees=[], bkg_trees=[], branches=[], weight_expr="1",
+            **kwargs
+        )
+        self._x_train = None
+        self._x_test = None
+        self._y_train = None
+        self._y_test = None
+        self._w_train = None
+        self._w_test = None
+
+    @property
+    def x_train(self):
+        if self._x_train is None:
+            self._x_train = self.df[self.df[self.split_column]][self.input_columns].values
+        return self._x_train
+
+    @x_train.setter
+    def x_train(self, value):
+        self._x_train = value
+
+    @property
+    def x_test(self):
+        if self._x_test is None:
+            self._x_test = self.df[~self.df[self.split_column]][self.input_columns].values
+        return self._x_test
+
+    @x_test.setter
+    def x_test(self, value):
+        self._x_test = value
+
+    @property
+    def y_train(self):
+        if self._y_train is None:
+            self._y_train = (self.df[self.df[self.split_column]][self.label_column] == self.signal_label).values
+        return self._y_train
+
+    @y_train.setter
+    def y_train(self, value):
+        self._y_train = value
+
+    @property
+    def y_test(self):
+        if self._y_test is None:
+            self._y_test = (self.df[~self.df[self.split_column]][self.label_column] == self.signal_label).values
+        return self._y_test
+
+    @y_test.setter
+    def y_test(self, value):
+        self._y_test = value
+
+    @property
+    def w_train(self):
+        if self._w_train is None:
+            self._w_train = self.df[self.df[self.split_column]][self.weight_column].values
+        return self._w_train
+
+    @w_train.setter
+    def w_train(self, value):
+        self._w_train = value
+
+    @property
+    def w_test(self):
+        if self._w_test is None:
+            self._w_test = self.df[~self.df[self.split_column]][self.weight_column].values
+        return self._w_test
+
+    @w_test.setter
+    def w_test(self, value):
+        self._w_test = value
+
+    @property
+    def fields(self):
+        return self.input_columns
+
+
+    def load(self, reload=False):
+
+        if reload:
+            self.data_loaded = False
+            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._w_train_tot = None
+
+        self.data_loaded = True
+
+
+class ClassificationProjectRNN(ClassificationProject):
+
+    """
+    A little wrapper to use recurrent units for things like jet collections
+    """
+
+    def _init_from_args(self, name,
+                        recurrent_field_names=None,
+                        rnn_layer_nodes=32,
+                        mask_value=-999,
+                        recurrent_unit_type="GRU",
+                        **kwargs):
+        """
+        recurrent_field_names example:
+        [["jet1Pt", "jet1Eta", "jet1Phi"],
+         ["jet2Pt", "jet2Eta", "jet2Phi"],
+         ["jet3Pt", "jet3Eta", "jet3Phi"]],
+        [["lep1Pt", "lep1Eta", "lep1Phi", "lep1flav"],
+         ["lep2Pt", "lep2Eta", "lep2Phi", "lep2flav"]],
+        """
+        super(ClassificationProjectRNN, self)._init_from_args(name, **kwargs)
+
+        self._write_info("project_type", "ClassificationProjectRNN")
+
+        self.recurrent_field_names = recurrent_field_names
+        if self.recurrent_field_names is None:
+            self.recurrent_field_names = []
+        self.rnn_layer_nodes = rnn_layer_nodes
+        self.mask_value = mask_value
+        self.recurrent_unit_type = recurrent_unit_type
+
+        # convert to  of indices
+        self.recurrent_field_idx = []
+        for field_name_list in self.recurrent_field_names:
+            field_names = np.array([field_name_list])
+            if field_names.dtype == np.object:
+                raise ValueError(
+                    "Invalid entry for recurrent fields: {} - "
+                    "please ensure that the length for all elements in the list is equal"
+                    .format(field_names)
+                )
+            field_idx = (
+                np.array([self.fields.index(field_name)
+                          for field_name in field_names.reshape(-1)])
+                .reshape(field_names.shape)
+            )
+            self.recurrent_field_idx.append(field_idx)
+        self.flat_fields = []
+        for field in self.fields:
+            if any(self.fields.index(field) in field_idx.reshape(-1) for field_idx in self.recurrent_field_idx):
+                continue
+            self.flat_fields.append(field)
+
+        if self.scaler_type != "WeightedRobustScaler":
+            raise NotImplementedError(
+                "Invalid scaler '{}' - only WeightedRobustScaler is currently supported for RNN"
+                .format(self.scaler_type)
+            )
+
+
+    @property
+    def model(self):
+        if self._model is None:
+            # following the setup from the tutorial:
+            # https://github.com/YaleATLAS/CERNDeepLearningTutorial
+            rnn_inputs = []
+            rnn_channels = []
+            for field_idx in self.recurrent_field_idx:
+                chan_inp = Input(field_idx.shape[1:])
+                channel = Masking(mask_value=self.mask_value)(chan_inp)
+                if self.recurrent_unit_type == "GRU":
+                    channel = GRU(self.rnn_layer_nodes)(channel)
+                elif self.recurrent_unit_type == "SimpleRNN":
+                    channel = SimpleRNN(self.rnn_layer_nodes)(channel)
+                elif self.recurrent_unit_type == "LSTM":
+                    channel = LSTM(self.rnn_layer_nodes)(channel)
+                else:
+                    raise NotImplementedError("{} not implemented".format(self.recurrent_unit_type))
+                logger.info("Added {} unit".format(self.recurrent_unit_type))
+                # TODO: configure dropout for recurrent layers
+                #channel = Dropout(0.3)(channel)
+                rnn_inputs.append(chan_inp)
+                rnn_channels.append(channel)
+            flat_input = Input((len(self.flat_fields),))
+            if self.dropout_input is not None:
+                flat_channel = Dropout(rate=self.dropout_input)(flat_input)
+            else:
+                flat_channel = flat_input
+            combined = concatenate(rnn_channels+[flat_channel])
+            for node_count, dropout_fraction in zip(self.nodes, self.dropout):
+                extra_opts = dict()
+                if self.kernel_initializer is not None:
+                    extra_opts["kernel_initializer"] = getattr(keras.initializers, self.kernel_initializer)()
+                combined = Dense(node_count, activation=self.activation_function, **extra_opts)(combined)
+                if (dropout_fraction is not None) and (dropout_fraction > 0):
+                    combined = Dropout(rate=dropout_fraction)(combined)
+            combined = Dense(1, activation=self.activation_function_output)(combined)
+            outputs = [combined]
+
+            # optional regression targets
+            for target_field in self.target_fields:
+                extra_target = Dense(1, activation="linear", name="target_{}".format(target_field))(combined)
+                outputs.append(extra_target)
+
+            self._model = Model(inputs=rnn_inputs+[flat_input], outputs=outputs)
+            self._compile_or_load_model()
+        return self._model
+
+
+    def clean_mask(self, x):
+        """
+        Mask recurrent fields such that once a masked value occurs,
+        all values corresponding to the same and following objects are
+        masked as well. Works in place.
+        """
+        for recurrent_field_idx in self.recurrent_field_idx:
+            for evt in x:
+                masked = False
+                for line_idx in recurrent_field_idx.reshape(*recurrent_field_idx.shape[1:]):
+                    if (evt[line_idx] == self.mask_value).any():
+                        masked=True
+                    if masked:
+                        evt[line_idx] = self.mask_value
+
+
+    def mask_uniform(self, x):
+        """
+        Mask recurrent fields with a random (uniform) number of objects. Works in place.
+        """
+        for recurrent_field_idx in self.recurrent_field_idx:
+            for evt in x:
+                masked = False
+                nobj = int(random.random()*(recurrent_field_idx.shape[1]+1))
+                for obj_number, line_idx in enumerate(recurrent_field_idx.reshape(*recurrent_field_idx.shape[1:])):
+                    if obj_number == nobj:
+                        masked=True
+                    if masked:
+                        evt[line_idx] = self.mask_value
+
+
+    def get_input_list(self, x):
+        """
+        Returns a list of 3-dimensional inputs for each
+        recurrent layer and a 2-dimensional one for the normal flat inputs.
+        """
+        x_input = []
+        for field_idx in self.recurrent_field_idx:
+            x_recurrent = x[:,field_idx.reshape(-1)].reshape(-1, *field_idx.shape[1:])
+            x_input.append(x_recurrent)
+        x_flat = x[:,[self.fields.index(field_name) for field_name in self.flat_fields]]
+        x_input.append(x_flat)
+        return x_input
+
+
+    def get_input_flat(self, x):
+        "Transform the multiple inputs back to flat ntuple"
+        nevent = x[0].shape[0]
+        x_flat = np.empty((nevent, len(self.fields)), dtype=np.float)
+        # recurrent fields
+        for rec_ar, idx in zip(x, self.recurrent_field_idx):
+            idx = idx.reshape(-1)
+            for source_idx, target_idx in enumerate(idx):
+                x_flat[:,target_idx] = rec_ar.reshape(nevent, -1)[:,source_idx]
+        # flat fields
+        for source_idx, field_name in enumerate(self.flat_fields):
+            target_idx = self.fields.index(field_name)
+            x_flat[:,target_idx] = x[-1][:,source_idx]
+        return x_flat
+
+
+    def evaluate(self, x_eval, mode=None):
+        logger.debug("Evaluate score for {}".format(x_eval))
+        x_eval = self.transform(x_eval)
+        logger.debug("Evaluate for transformed array: {}".format(x_eval))
+        return self.predict(self.get_input_list(x_eval), mode=mode)
+
+
+if __name__ == "__main__":
+
+    logging.basicConfig()
+    logging.getLogger("KerasROOTClassification").setLevel(logging.INFO)
+    #logging.getLogger("KerasROOTClassification").setLevel(logging.DEBUG)
+
+    filename = "/project/etp4/nhartmann/trees/allTrees_m1.8_NoSys.root"
+
+
+
+    c = ClassificationProject("test4",
+                              signal_trees = [(filename, "GG_oneStep_1705_1105_505_NoSys")],
+                              bkg_trees = [(filename, "ttbar_NoSys"),
+                                           (filename, "wjets_Sherpa221_NoSys")
+                              ],
+                              optimizer="Adam",
+                              #optimizer="SGD",
+                              #optimizer_opts=dict(lr=100., decay=1e-6, momentum=0.9),
+                              earlystopping_opts=dict(monitor='val_loss',
+                                                      min_delta=0, patience=2, verbose=0, mode='auto'),
+                              selection="1",
+                              branches = ["met", "mt"],
+                              weight_expr = "eventWeight*genWeight",
+                              identifiers = ["DatasetNumber", "EventNumber"],
+                              step_bkg = 100)
+
+    np.random.seed(42)
+    c.train(epochs=20)
+    #c.plot_all()
+
+    # c.write_friend_tree("test4_score",
+    #                     source_filename=filename, source_treename="GG_oneStep_1705_1105_505_NoSys",
+    #                     target_filename="friend.root", target_treename="test4_score")
+
+    # c.write_friend_tree("test4_score",
+    #                     source_filename=filename, source_treename="ttbar_NoSys",
+    #                     target_filename="friend_ttbar_NoSys.root", target_treename="test4_score")

utils.py

+"Helper functions using keras or tensorflow"
+
+import logging
+
+import numpy as np
+
+import keras.backend as K
+from sklearn.preprocessing import RobustScaler
+from sklearn.preprocessing.data import _handle_zeros_in_scale
+
+from meme import cache
+
+logger = logging.getLogger(__name__)
+logger.addHandler(logging.NullHandler())
+
+def get_single_neuron_function(model, layer, neuron, input_transform=None):
+
+    inp = model.input
+    if not isinstance(inp, list):
+        inp = [inp]
+
+    f = K.function(inp+[K.learning_phase()], [model.layers[layer].output[:,neuron]])
+
+    def eval_single_neuron(x):
+        if input_transform is not None:
+            x_eval = input_transform(x)
+        else:
+            x_eval = x
+        if not isinstance(x_eval, list):
+            x_eval = [x_eval]
+        return f(x_eval)[0]
+
+    return eval_single_neuron
+
+
+def create_random_event(ranges, mask_probs=None, mask_value=None):
+    random_event = np.array([p[0]+(p[1]-p[0])*np.random.rand() for p in ranges])
+    random_event = random_event.reshape(-1, len(random_event))
+    # if given, mask values with a certain probability
+    if mask_probs is not None:
+        if mask_value is None:
+            raise ValueError("Need to provide mask_value if random events should be masked")
+        for var_index, mask_prob in enumerate(mask_probs):
+            random_event[:,var_index][np.random.rand(len(random_event)) < mask_prob] = mask_value
+    return random_event
+
+
+def get_ranges(x, quantiles, weights, mask_value=None, filter_index=None, max_evts=None):
+    "Get ranges for plotting or random event generation based on quantiles"
+    ranges = []
+    mask_probs = []
+    if max_evts is not None:
+        rnd_idx = np.random.permutation(np.arange(len(x)))
+        rnd_idx = rnd_idx[:max_evts]
+    for var_index in range(x.shape[1]):
+        if (filter_index is not None) and (var_index != filter_index):
+            continue
+        x_var = x[:,var_index]
+        if max_evts is not None:
+            x_var = x_var[rnd_idx]
+        not_masked = np.where(x_var != mask_value)[0]
+        masked = np.where(x_var == mask_value)[0]
+        ranges.append(weighted_quantile(x_var[not_masked], quantiles, sample_weight=weights[not_masked]))
+        mask_probs.append(float(len(masked))/float(len(x_var)))
+    return ranges, mask_probs
+
+
+def max_activation_wrt_input(gradient_function, random_event, threshold=None, maxthreshold=None, maxit=100, step=1, const_indices=[],
+                             input_transform=None, input_inverse_transform=None):
+    if input_transform is not None:
+        random_event = input_transform(random_event)
+    if not isinstance(random_event, list):
+        random_event = [random_event]
+
+    def iterate(random_event):
+        for i in range(maxit):
+            grads_out = gradient_function(random_event)
+            loss_value = grads_out[0][0]
+            grads_values = grads_out[1:]
+            # follow gradient for all inputs
+            for i, (grads_value, input_event) in enumerate(zip(grads_values, random_event)):
+                for const_index in const_indices:
+                    grads_value[0][const_index] = 0
+                if threshold is not None:
+                    if loss_value > threshold and (maxthreshold is None or loss_value < maxthreshold):
+                        # found an event within the thresholds
+                        return loss_value, random_event
+                    elif (maxthreshold is not None and loss_value > maxthreshold):
+                        random_event[i] -= grads_value*step
+                    else:
+                        random_event[i] += grads_value*step
+                else:
+                    random_event[i] += grads_value*step
+        else:
+            if threshold is not None:
+                # no event found for the given threshold
+                return None, None
+        # otherwise return last status
+        return loss_value, random_event
+
+    loss_value, random_event = iterate(random_event)
+    if input_inverse_transform is not None and random_event is not None:
+        random_event = input_inverse_transform(random_event)
+    elif random_event is None:
+        return None
+    return loss_value, random_event
+
+
+def get_grad_function(model, layer, neuron):
+
+    loss = model.layers[layer].output[:,neuron]
+
+    grads = K.gradients(loss, model.input)
+
+    # trick from https://blog.keras.io/how-convolutional-neural-networks-see-the-world.html
+    norm_grads = [grad/(K.sqrt(K.mean(K.square(grad))) + 1e-5) for grad in grads]
+
+    inp = model.input
+    if not isinstance(inp, list):
+        inp = [inp]
+
+    return K.function(inp, [loss]+norm_grads)
+
+
+@cache(useJSON=True,
+       argHashFunctions=[
+           lambda model: [hash(i.tostring()) for i in model.get_weights()],
+           lambda ranges: [hash(i.tostring()) for i in ranges],
+       ],
+       ignoreKwargs=["input_transform", "input_inverse_transform"],
+)
+def get_max_activation_events(model, ranges, ntries, layer, neuron, seed=42, mask_probs=None, mask_value=None, **kwargs):
+
+    gradient_function = get_grad_function(model, layer, neuron)
+
+    events = None
+    losses = None
+    np.random.seed(seed)
+    for i in range(ntries):
+        if not (i%100):
+            logger.info(i)
+        res = max_activation_wrt_input(
+            gradient_function,
+            create_random_event(
+                ranges,
+                mask_probs=mask_probs,
+                mask_value=mask_value
+            ),
+            **kwargs
+        )
+        if res is not None:
+            loss, event = res
+            loss = np.array([loss])
+        else:
+            continue
+        if events is None:
+            events = event
+            losses = loss
+        else:
+            events = np.concatenate([events, event])
+            losses = np.concatenate([losses, loss])
+
+    return losses, events
+
+"from https://stackoverflow.com/questions/21844024/weighted-percentile-using-numpy#29677616"
+def weighted_quantile(values, quantiles, sample_weight=None, values_sorted=False, old_style=False):
+    """ Very close to np.percentile, but supports weights.
+    NOTE: quantiles should be in [0, 1]!
+    :param values: np.array with data
+    :param quantiles: array-like with many quantiles needed
+    :param sample_weight: array-like of the same length as `array`
+    :param values_sorted: bool, if True, then will avoid sorting of initial array
+    :param old_style: if True, will correct output to be consistent with np.percentile.
+    :return: np.array with computed quantiles.
+    """
+    values = np.array(values)
+    quantiles = np.array(quantiles)
+    if sample_weight is None:
+        sample_weight = np.ones(len(values))
+    sample_weight = np.array(sample_weight)
+    assert np.all(quantiles >= 0) and np.all(quantiles <= 1), 'quantiles should be in [0, 1]'
+
+    if not values_sorted:
+        sorter = np.argsort(values)
+        values = values[sorter]
+        sample_weight = sample_weight[sorter]
+
+    weighted_quantiles = np.cumsum(sample_weight) - 0.5 * sample_weight
+    if old_style:
+        # To be convenient with np.percentile
+        weighted_quantiles -= weighted_quantiles[0]
+        weighted_quantiles /= weighted_quantiles[-1]
+    else:
+        weighted_quantiles /= np.sum(sample_weight)
+    return np.interp(quantiles, weighted_quantiles, values)
+
+
+class WeightedRobustScaler(RobustScaler):
+
+    def fit(self, X, y=None, weights=None, mask_value=None):
+        if not np.isnan(X).any() and mask_value is not None and weights is None:
+            # these checks don't work for nan values
+            return super(WeightedRobustScaler, self).fit(X, y)
+        else:
+            if weights is None:
+                weights = np.ones(len(self.X))
+            wqs = []
+            for i in range(X.shape[1]):
+                mask = ~np.isnan(X[:,i])
+                if mask_value is not None:
+                    mask &= (X[:,i] != mask_value)
+                wqs.append(
+                    weighted_quantile(
+                        X[:,i][mask],
+                        [0.25, 0.5, 0.75],
+                        sample_weight=weights[mask]
+                    )
+                )
+            wqs = np.array(wqs)
+            self.center_ = wqs[:,1]
+            self.scale_ = wqs[:,2]-wqs[:,0]
+            self.scale_ = _handle_zeros_in_scale(self.scale_, copy=False)
+            return self
+
+
+    def transform(self, X):
+        if np.isnan(X).any():
+            # we'd like to ignore nan values, so lets calculate without further checks
+            X -= self.center_
+            X /= self.scale_
+            return X
+        else:
+            return super(WeightedRobustScaler, self).transform(X)
+
+
+    def inverse_transform(self, X):
+        if np.isnan(X).any():
+            X *= self.scale_
+            X += self.center_
+            return X
+        else:
+            return super(WeightedRobustScaler, self).inverse_transform(X)
+
+
+def poisson_asimov_significance(s, ds, b, db):
+    "see `<http://www.pp.rhul.ac.uk/~cowan/stat/medsig/medsigNote.pdf>`_)"
+    db = np.sqrt(db**2+ds**2)
+    return np.sqrt(2*((s+b)*np.log(((s+b)*(b+db**2))/(b**2+(s+b)*db**2))-(b**2)/(db**2)*np.log(1+(db**2*s)/(b*(b+db**2)))))
+