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#!/usr/bin/env python
import os
import math
import matplotlib.pyplot as plt
import matplotlib.colors
from matplotlib.ticker import LogFormatter
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import numpy as np
import meme
"""
Some further plotting functions
"""
def get_mean_event(x, y, class_label):
return [np.mean(x[y==class_label][:,var_index]) for var_index in range(x.shape[1])]
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
print("Creating varied events (1d)")
sequence = np.arange(*var_range)
events = np.tile(means, len(sequence)).reshape(-1, len(means))
events[:,var_index] = sequence
print("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")
fig.savefig(plotname)
def plot_NN_vs_var_2D(plotname, means,
scorefun,
var1_index, var1_range,
var2_index, var2_range,
var1_label=None,
var2_label=None,
logscale=False,
ncontours=20,
black_contourlines=False):
print("Creating varied events (2d)")
# example: vary var1 vs var2
sequence1 = np.arange(*var1_range)
sequence2 = np.arange(*var2_range)
# 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][var1_index] = x
events[i][j][var2_index] = y
# convert back into 1d array
events = events.reshape(-1, len(means))
print("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:
lvls = np.logspace(math.log10(zmin), math.log10(zmax), ncontours)
pcm = ax.contourf(sequence1, sequence2, scores, levels=lvls, norm=matplotlib.colors.LogNorm(vmin=zmin, vmax=zmax))
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:
pcm = ax.contourf(sequence1, sequence2, scores, ncontours, norm=matplotlib.colors.Normalize(vmin=0, vmax=1))
if black_contourlines:
ax.contour(sequence1, sequence2, scores, ncontours, colors="k", linewidths=1)
cbar = fig.colorbar(pcm, ax=ax, extend='max')
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cbar.set_label("NN output")
if var1_label is not None:
ax.set_xlabel(var1_label)
if var2_label is not None:
ax.set_ylabel(var2_label)
fig.savefig(plotname)
if __name__ == "__main__":
from .toolkit import ClassificationProject
c = ClassificationProject(os.path.expanduser("~/p/scripts/keras/008-allhighlevel/all_highlevel_985"))
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.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]")