diff --git a/plotting.py b/plotting.py
index 74de03259cc92dad8fc13d7cba09e587c37f4bf3..f13a461a0030b66d1b9d20544fbd61facbfdf636 100644
--- a/plotting.py
+++ b/plotting.py
@@ -273,117 +273,198 @@ def plot_cond_avg_actmax_2D(plotname, model, layer, neuron, ranges,
plot_hist_2D(plotname, xedges, yedges, hist, zlabel="Neuron output", **kwargs)
+def plot_profile_2D(plotname, valsx, valsy, scores,
+ nbinsx, xmin, xmax,
+ nbinsy, ymin, ymax,
+ metric=np.mean,
+ **kwargs):
+ kwargs["zlabel"] = kwargs.get("zlabel", "Profile")
-if __name__ == "__main__":
-
- from .toolkit import ClassificationProject
+ xedges = np.linspace(xmin, xmax, nbinsx)
+ yedges = np.linspace(ymin, ymax, nbinsy)
- c = ClassificationProject(os.path.expanduser("~/p/scripts/keras/008-allhighlevel/all_highlevel_985"))
+ binindices_x = np.digitize(valsx, xedges)
+ binindices_y = np.digitize(valsy, yedges)
- mean_signal = get_mean_event(c.x_test, c.y_test, 1)
+ # create profile histogram
+ hist = []
+ for binindex_x in range(1, len(xedges)+1):
+ line = []
+ for binindex_y in range(1, len(xedges)+1):
+ try:
+ prof_score = metric(scores[(binindices_x == binindex_x) & (binindices_y == binindex_y)])
+ except ValueError:
+ prof_score = 0
+ line.append(prof_score)
+ hist.append(line)
+ hist = np.array(hist)
- print("Mean signal: ")
- for branch_index, val in enumerate(mean_signal):
- print("{:>20}: {:<10.3f}".format(c.branches[branch_index], val))
+ plot_hist_2D(plotname, xedges, yedges, hist, **kwargs)
- plot_NN_vs_var_1D("met.pdf", mean_signal,
- scorefun=c.evaluate,
- var_index=c.branches.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.branches.index("mt"),
- var_range=(0, 500, 10),
- var_label="mt [GeV]")
+if __name__ == "__main__":
- plot_NN_vs_var_2D("mt_vs_met.pdf", means=mean_signal,
- scorefun=c.evaluate,
- var1_index=c.branches.index("met"), var1_range=(0, 1000, 10),
- var2_index=c.branches.index("mt"), var2_range=(0, 500, 10),
- var1_label="met [GeV]", var2_label="mt [GeV]")
+ import sys
+ from .toolkit import ClassificationProject
- # 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.branches.index("met"), var1_range=(0, 1000, 10),
- # var2_index=c.branches.index("mt"), var2_range=(0, 500, 10),
- # var1_label="met [GeV]", var2_label="mt [GeV]")
+ import logging
+ logging.basicConfig()
+ logging.getLogger("tfhelpers").setLevel(logging.DEBUG)
- import keras.backend as K
from .tfhelpers import get_single_neuron_function, get_max_activation_events
import meme
- meme.setOptions(overrideCache="/scratch-local/nhartmann/meme_cache")
+ # meme.setOptions(overrideCache="/scratch-local/nhartmann/meme_cache")
- import logging
- logging.basicConfig()
- logging.getLogger("tfhelpers").setLevel(logging.DEBUG)
+ if len(sys.argv) < 2:
+ c = ClassificationProject(os.path.expanduser("~/p/scripts/keras/008-allhighlevel/all_highlevel_985"))
+ else:
+ c = ClassificationProject(sys.argv[1])
+
+ def test_mean_signal():
+
+ 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.branches[branch_index], val))
+
+ plot_NN_vs_var_1D("met.pdf", mean_signal,
+ scorefun=c.evaluate,
+ var_index=c.branches.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.branches.index("mt"),
+ var_range=(0, 500, 10),
+ var_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),
- var1_index=c.branches.index("met"), var1_range=(0, 1000, 10),
- var2_index=c.branches.index("mt"), var2_range=(0, 500, 10),
- var1_label="met [GeV]", var2_label="mt [GeV]")
-
-
- # transformed events
- c.load(reload=True)
- ranges = [np.percentile(c.x_test[:,var_index], [1,99]) for var_index in range(len(c.branches))]
-
- losses, events = get_max_activation_events(c.model, ranges, ntries=100000, layer=3, neuron=0, threshold=0.2)
-
- events = c.scaler.inverse_transform(events)
-
- plot_hist_2D_events(
- "mt_vs_met_actmaxhist.pdf",
- events[:,c.branches.index("met")],
- events[:,c.branches.index("mt")],
- 100, 0, 1000,
- 100, 0, 500,
- varx_label="met [GeV]", vary_label="mt [GeV]",
- )
-
- plot_cond_avg_actmax_2D(
- "mt_vs_met_cond_actmax.pdf",
- c.model, 3, 0, ranges,
- c.branches.index("met"),
- c.branches.index("mt"),
- 30, 0, 1000,
- 30, 0, 500,
- scaler=c.scaler,
- varx_label="met [GeV]", vary_label="mt [GeV]",
- )
-
- plot_hist_2D_events(
- "mt_vs_output_actmax.pdf",
- events[:,c.branches.index("mt")],
- losses,
- 100, 0, 500,
- 100, 0, 1,
- varx_label="mt [GeV]", vary_label="NN output",
- log=True,
- )
-
- utrf_x_test = c.scaler.inverse_transform(c.x_test)
-
- plot_hist_2D_events(
- "mt_vs_output_signal_test.pdf",
- utrf_x_test[c.y_test==1][:,c.branches.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(
- "apl_vs_output_actmax.pdf",
- events[:,c.branches.index("LepAplanarity")],
- losses,
- 100, 0, 0.1,
- 100, 0, 1,
- varx_label="Aplanarity", vary_label="NN output",
- )
+ plot_NN_vs_var_2D("mt_vs_met.pdf", means=mean_signal,
+ scorefun=c.evaluate,
+ var1_index=c.branches.index("met"), var1_range=(0, 1000, 10),
+ var2_index=c.branches.index("mt"), var2_range=(0, 500, 10),
+ var1_label="met [GeV]", var2_label="mt [GeV]")
+
+
+ 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.branches.index("met"), var1_range=(0, 1000, 10),
+ var2_index=c.branches.index("mt"), var2_range=(0, 500, 10),
+ var1_label="met [GeV]", var2_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),
+ var1_index=c.branches.index("met"), var1_range=(0, 1000, 10),
+ var2_index=c.branches.index("mt"), var2_range=(0, 500, 10),
+ var1_label="met [GeV]", var2_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.branches))]
+
+ losses, events = get_max_activation_events(c.model, ranges, ntries=100000, layer=3, neuron=0, threshold=0.2)
+
+ events = c.scaler.inverse_transform(events)
+
+ plot_hist_2D_events(
+ "mt_vs_met_actmaxhist.pdf",
+ events[:,c.branches.index("met")],
+ events[:,c.branches.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.branches.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.branches))]
+
+ plot_cond_avg_actmax_2D(
+ "mt_vs_met_cond_actmax.pdf",
+ c.model, 3, 0, ranges,
+ c.branches.index("met"),
+ c.branches.index("mt"),
+ 30, 0, 1000,
+ 30, 0, 500,
+ scaler=c.scaler,
+ varx_label="met [GeV]", vary_label="mt [GeV]",
+ )
+
+
+ def test_xtest_vs_output():
+
+ c.load(reload=True)
+
+ utrf_x_test = c.scaler.inverse_transform(c.x_test)
+
+ plot_hist_2D_events(
+ "mt_vs_output_signal_test.pdf",
+ utrf_x_test[c.y_test==1][:,c.branches.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(
+ # "apl_vs_output_actmax.pdf",
+ # events[:,c.branches.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.scaler.inverse_transform(c.x_test)
+
+ plot_profile_2D(
+ "mt_vs_met_profilemean_sig.pdf",
+ utrf_x_test[c.y_test==1][:,c.branches.index("met")],
+ utrf_x_test[c.y_test==1][:,c.branches.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.branches.index("met")],
+ utrf_x_test[c.y_test==1][:,c.branches.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]()