def make_plot(points, truth, label_truth, label_pred, fname):
truth_cent = truth.mean()
truth_std = truth.std()
points = sorted(points)
smooth = True
window = 15
points = np.array(points)
if not smooth:
xvals = points[:, 0]
yvals = points[:, 1]
ydown = points[:, 2]
yup = points[:, 2]
else:
xvals = points[:, 0][window - 1:]
yvals = moving_average(points[:, 1], n=window)
ydown = moving_average(points[:, 2], n=window)
yup = moving_average(points[:, 2], n=window)
# hpred = r.TH1F("hpred",100,truth.min(),truth.max())
# htruth = r.TH1F("htruth",100,truth.min(),truth.max())
# fill_fast(hpred, yvals)
# fill_fast(htruth, truth)
ply.plot_graph(
[
([0., max(xvals)], [truth_cent, truth_cent],
[truth_std, truth_std], [truth_std, truth_std]),
(xvals, yvals, ydown, yup),
],
colors=[r.kAzure + 2, r.kRed - 2],
legend_labels=[label_truth, label_pred],
options={
"legend_alignment": "bottom right",
"legend_scalex": 0.7,
"xaxis_label": "epoch",
"yaxis_label": label_pred,
"output_name": fname,
"output_ic": True,
})
for v1, v2 in zip(vals[:len(vals) / 2], vals[len(vals) / 2:])], [])
legend_labels = sum(
[[l1, l2]
for l1, l2 in zip(legend_labels[:len(vals) / 2], legend_labels[len(vals) /
2:])], [])
colors = sum([[t, t] for t in temp], [])
draw_styles = [0, 2] * (len(legend_labels) / 2)
ply.plot_graph(
vals,
colors=colors,
legend_labels=legend_labels,
draw_styles=draw_styles,
options={
"legend_alignment": "bottom right",
"legend_scalex": 1.45,
"legend_scaley": 1.5,
"xaxis_label": "bkg. eff.",
"yaxis_label": "sig. eff.",
"title": "DY: prompt vs unmatched e & b/c#rightarrow e",
"xaxis_log": True,
"yaxis_log": True,
"xaxis_range": [0.005, 0.8],
"yaxis_range": [0.7, 1.0],
# "xaxis_range": [0.005,0.8],
# "yaxis_range": [0.5,1.0],
# "output_name": "plots/roc.pdf",
"output_name": fname,
"output_ic": True,
})
quantities_real = get_quantities(data[:, range(1, 9)])
epoch_chi2 = []
fnames = sorted(fnames, key=lambda x: int(x.rsplit("_", 1)[-1].split(".")[0]))
for fname in fnames:
data = np.load(fname)
epoch = int(fname.rsplit("_", 1)[-1].split(".")[0])
if epoch < 500: continue
quantities_gen = get_quantities(data)
tot_chi2 = 0.
for key in quantities_gen:
tot_chi2 += get_redchi2(quantities_real[key], quantities_gen[key])
print epoch, tot_chi2
epoch_chi2.append([epoch, tot_chi2])
epoch_chi2 = np.array(epoch_chi2)
ply.plot_graph(
[(epoch_chi2[:, 0], epoch_chi2[:, 1])],
colors=[r.kAzure + 2],
legend_labels=["model1"],
options={
"legend_alignment": "top right",
"legend_scalex": 0.7,
"xaxis_label": "epoch",
"yaxis_label": "#Sigma(#chi^{2}/ndof)",
"yaxis_log": True,
"title": "#Sigma(#chi^{2}/ndof) vs epoch",
"output_name": "plots/chi2_vs_epoch.pdf",
"output_ic": True,
})
models = []
for modeldir,name in modelinfos:
fname_history = "{}/history.pkl".format(modeldir)
if not os.path.exists(fname_history): continue
data = pickle.load(open(fname_history,"r"))
models.append( Model(data,name) )
varinfo = [
["d_loss", "discriminator loss"],
["g_loss", "generator loss"],
["d_acc", "discriminator accuracy"],
["mass_mu", "m_{ll} mean"],
["mass_sig", "m_{ll} width"],
["time", "cumulative time [minutes]"],
]
for varname, varlabel in varinfo:
ply.plot_graph(
[m.get_points(varname) for m in models],
legend_labels = [m.name for m in models],
options = {
"legend_alignment": "top left",
# "legend_scalex": 0.7,
"xaxis_label": "epoch",
"yaxis_label": varlabel,
"output_name": "plots/{}.pdf".format(varname),
"output_ic": True,
}
)