diff --git a/plotting.py b/plotting.py
index db24def073d5f9aaa7a1eb55f86e3e508ec3bb11..ae25dfc096feffa92f3470e134155b7439638f6c 100644
--- a/plotting.py
+++ b/plotting.py
@@ -120,11 +120,13 @@ def plot_NN_vs_var_2D(plotname, means,
def plot_NN_vs_var_2D_all(plotname, model, means,
- var1_index, var1_range,
- var2_index, var2_range,
+ varx_index,
+ vary_index,
+ nbinsx, xmin, xmax,
+ nbinsy, ymin, ymax,
transform_function=None,
- var1_label=None,
- var2_label=None,
+ varx_label=None,
+ vary_label=None,
zrange=None, logz=False,
plot_last_layer=False,
log_default_ymin=1e-5,
@@ -132,15 +134,15 @@ def plot_NN_vs_var_2D_all(plotname, model, means,
"Similar to plot_NN_vs_var_2D, but creates a grid of plots for all neurons."
- var1_vals = np.arange(*var1_range)
- var2_vals = np.arange(*var2_range)
+ 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(var1_vals)*len(var2_vals)).reshape(len(var2_vals), len(var1_vals), -1)
- for i, y in enumerate(var2_vals):
- for j, x in enumerate(var1_vals):
- events[i][j][var1_index] = x
- events[i][j][var2_index] = y
+ 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))
@@ -187,7 +189,7 @@ def plot_NN_vs_var_2D_all(plotname, model, means,
for layer in range(layers):
for neuron in range(len(acts[layer][0])):
acts_neuron = acts[layer][:,neuron]
- acts_neuron = acts_neuron.reshape(len(var2_vals), len(var1_vals))
+ 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):
@@ -200,12 +202,12 @@ def plot_NN_vs_var_2D_all(plotname, model, means,
extra_opts["norm"] = norm(vmin=zrange[0], vmax=zrange[1])
else:
extra_opts["norm"] = norm(vmin=global_min, vmax=global_max)
- im = ax.pcolormesh(var1_vals, var2_vals, acts_neuron, cmap=cmap, linewidth=0, rasterized=True, **extra_opts)
+ im = ax.pcolormesh(varx_vals, vary_vals, acts_neuron, cmap=cmap, linewidth=0, rasterized=True, **extra_opts)
ax.set_facecolor("black")
- if var1_label is not None:
- ax.set_xlabel(var1_label)
- if var2_label is not None:
- ax.set_ylabel(var2_label)
+ 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")
@@ -342,6 +344,8 @@ if __name__ == "__main__":
def test_mean_signal():
+ c._load_data() # untransformed
+
mean_signal = get_mean_event(c.x_test, c.y_test, 1)
print("Mean signal: ")
@@ -371,9 +375,11 @@ if __name__ == "__main__":
plot_NN_vs_var_2D_all("mt_vs_met_all.pdf", means=mean_signal,
model=c.model, transform_function=c.scaler.transform,
- var1_index=c.fields.index("met"), var1_range=(0, 1000, 10),
- var2_index=c.fields.index("mt"), var2_range=(0, 500, 10),
- var1_label="met [GeV]", var2_label="mt [GeV]")
+ 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("mt_vs_met_crosscheck.pdf", means=mean_signal,
scorefun=get_single_neuron_function(c.model, layer=3, neuron=0, scaler=c.scaler),
diff --git a/scripts/plot_NN_2D.py b/scripts/plot_NN_2D.py
index ba54b1eb6551d2bb2baefb985d9cbfc7b43557b3..460148f0cb802b1417382ef5edf731401a07494c 100755
--- a/scripts/plot_NN_2D.py
+++ b/scripts/plot_NN_2D.py
@@ -7,13 +7,18 @@ logging.basicConfig()
import numpy as np
+import ROOT
+ROOT.gROOT.SetBatch()
+ROOT.PyConfig.IgnoreCommandLineOptions = True
+
from KerasROOTClassification import ClassificationProject
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_cond_avg_actmax_2D,
+ plot_NN_vs_var_2D_all,
)
from KerasROOTClassification.utils import get_single_neuron_function, get_max_activation_events
@@ -27,6 +32,7 @@ parser.add_argument("-m", "--mode",
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")
@@ -42,6 +48,9 @@ parser.add_argument("-s", "--step-size", help="step size for activation maximisa
args = parser.parse_args()
+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)
@@ -90,17 +99,31 @@ if args.mode.startswith("mean"):
elif args.mode == "mean_bkg":
means = get_mean_event(c.x_test, c.y_test, 0)
- 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, scaler=c.scaler),
- 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)
- )
+ 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, scaler=c.scaler),
+ 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:
+ plot_NN_vs_var_2D_all(
+ args.output_filename,
+ means=means,
+ model=c.model,
+ transform_function=c.scaler.transform,
+ 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"):