diff --git a/keras.py b/keras.py
index 15645ec2a9102c79b0498fcad95353e134408925..16c94fec825dc8325da7cb020e2ef6208dadc527 100644
--- a/keras.py
+++ b/keras.py
@@ -392,20 +392,25 @@ class TFSummaryCallback(tf.keras.callbacks.Callback):
class PlotMulticlass(TFSummaryCallback):
def __init__(
self,
+ x,
+ y,
+ sample_weight=None,
class_names=["signal", "background"],
to_file=False,
columns=None,
plot_inputs=False,
+ signalvsbkg=False,
**kwargs,
):
super().__init__(**kwargs)
- self.x = kwargs["x"]
- self.truth = kwargs["y"]
- self.sample_weight = kwargs.get("sample_weight", None)
+ self.x = x
+ self.truth = y
+ self.sample_weight = sample_weight
self.class_names = class_names
self.plot_inputs = plot_inputs
self.columns = columns
self.to_file = to_file
+ self.signalvsbkg = signalvsbkg
def on_test_begin(self, logs=None):
self.on_train_begin(logs=logs)
@@ -419,26 +424,27 @@ class PlotMulticlass(TFSummaryCallback):
inps = [inps]
for part, inp in zip(self.columns.keys(), inps):
- imgs[f"inputs_merged_{part}"] = figure_to_image(
+ imgs[f"inp_xmerged_{part}"] = figure_to_image(
figure_multihist(inp, columns=self.columns[part])
)
if self.sample_weight is not None:
for part, inp in zip(self.columns.keys(), inps):
- imgs[f"inputs_{part}"] = figure_to_image(
+ imgs[f"inp_x_{part}"] = figure_to_image(
figure_inputs(
inp,
self.truth,
sample_weight=self.sample_weight,
columns=self.columns[part],
class_names=self.class_names,
+ signalvsbkg=self.signalvsbkg,
)
)
- imgs["weights"] = figure_to_image(
+ imgs["inp_weights"] = figure_to_image(
figure_weights(self.sample_weight, self.truth, class_names=self.class_names)
)
- imgs["processes"] = figure_to_image(figure_y(self.truth, class_names=self.class_names))
- imgs["processes_relative"] = figure_to_image(
+ imgs["inp_y"] = figure_to_image(figure_y(self.truth, class_names=self.class_names))
+ imgs["inp_yrelative"] = figure_to_image(
figure_y(self.truth, class_names=self.class_names, relative=True)
)
for name, img in imgs.items():
diff --git a/plotting.py b/plotting.py
index acc8aae946501ca597febf3b783d4a9e5de073e1..1dad79930a3a80d877cbcffdbd344b70b6143127 100644
--- a/plotting.py
+++ b/plotting.py
@@ -1,6 +1,7 @@
# -*- coding: utf-8 -*-
import io
+import warnings
import tensorflow as tf
import itertools
@@ -12,21 +13,32 @@ import pandas as pd
from .numpy import one_hot
-class Quadrature:
+class Multiplot:
def __init__(self, n):
- self.n = n
- self.cols = np.ceil(np.sqrt(n)).astype(int)
- self.rows = np.ceil(n / self.cols).astype(int)
+ try:
+ self.cols, self.rows = [i for i in n]
+ except TypeError:
+ self.n = n
+ self.cols = np.ceil(np.sqrt(n)).astype(int)
+ self.rows = np.ceil(n / self.cols).astype(int)
def lenghts(self):
return self.rows, self.cols
- def index(self, n):
- row = int(n / self.cols)
- col = n - row * self.cols
+ def index(self, i):
+ row = int(i / self.cols)
+ col = i - row * self.cols
return row, col
+def saveplot(f):
+ def helper(*args, **kwargs):
+ plt.close("all")
+ return f(*args, **kwargs)
+
+ return helper
+
+
def figure_confusion_matrix(
truth,
prediction,
@@ -149,9 +161,9 @@ def figure_node_activations(
activations, truth, class_names=None, disjoint=False, sample_weight=None
):
n_b, n_p = activations.shape
- quad = Quadrature(n_p)
+ multiplot = Multiplot(n_p)
- rows, cols = quad.lenghts()
+ rows, cols = multiplot.lenghts()
fig, ax = plt.subplots(rows, cols, figsize=(15, 15 * rows / cols))
bins = np.linspace(0, 1.0, 10)
@@ -171,7 +183,7 @@ def figure_node_activations(
old_err = np.seterr(divide="ignore", invalid="ignore")
for node in range(n_p):
- ax_index = quad.index(node)
+ ax_index = multiplot.index(node)
for process in range(n_p):
label = "%i" % process if class_names is None else class_names[process]
plot_kwargs = {"histtype": "step", "label": label, "range": (0.0, 1.0)}
@@ -187,9 +199,11 @@ def figure_node_activations(
va="top",
transform=ax[ax_index].transAxes,
)
- ax[quad.index(node)].set_yscale("log")
+ ax[multiplot.index(node)].set_yscale("log")
np.seterr(**old_err)
- ax[quad.index(cols - 1)].legend(title="processes", bbox_to_anchor=(1.05, 1.0), loc="upper left")
+ ax[multiplot.index(cols - 1)].legend(
+ title="processes", bbox_to_anchor=(1.05, 1.0), loc="upper left"
+ )
fig.tight_layout()
return fig
@@ -218,9 +232,11 @@ def figure_roc_curve(
return fig
-def figure_inputs(inps, truth, sample_weight=None, columns=None, class_names=None):
- quad = Quadrature(len(columns))
- rows, cols = quad.lenghts()
+def figure_inputs(
+ inps, truth, sample_weight=None, columns=None, class_names=None, signalvsbkg=False, bins=20
+):
+ multiplot = Multiplot(inps.shape[1:][::-1])
+ rows, cols = multiplot.lenghts()
size = len(columns)
fig, ax = plt.subplots(rows, cols, figsize=(size, size * rows / cols))
@@ -228,25 +244,100 @@ def figure_inputs(inps, truth, sample_weight=None, columns=None, class_names=Non
order = np.argsort(-(sample_weight[:, None] * truth).sum(axis=0))
class_names = np.array(class_names)[order]
for feat, name in enumerate(columns):
- ax_index = quad.index(feat)
- mask = np.argmax(truth, axis=-1) != 0
- bins = ax[ax_index].hist(
- inps[:, feat][mask],
- histtype="stepfilled",
- weights=sample_weight[mask],
- label="Background",
- density=True,
- )[1]
- mask = np.argmax(truth, axis=-1) == 0
- ax[ax_index].hist(
- inps[:, feat][mask],
- histtype="step",
- bins=bins,
- weights=sample_weight[mask],
- label="HH",
- density=True,
- linewidth=2,
- )
+ ax_index = multiplot.index(feat)
+ if signalvsbkg:
+ mask = np.argmax(truth, axis=-1) != 0
+ bins = ax[ax_index].hist(
+ inps[:, feat][mask],
+ histtype="stepfilled",
+ weights=sample_weight[mask],
+ label="Background",
+ density=True,
+ )[1]
+ mask = np.argmax(truth, axis=-1) == 0
+ ax[ax_index].hist(
+ inps[:, feat][mask],
+ histtype="step",
+ bins=bins,
+ weights=sample_weight[mask],
+ label="HH",
+ density=True,
+ linewidth=2,
+ )
+ else:
+ for i in range(len(class_names)):
+ mask = np.argmax(truth, axis=-1) == i
+ ax[ax_index].hist(
+ inps[:, feat][mask],
+ histtype="step",
+ bins=bins,
+ weights=sample_weight[mask],
+ label=class_names[i],
+ density=True,
+ linewidth=2,
+ )
+ ax[ax_index].set_title(name)
+ ax[ax_index].legend()
+ fig.tight_layout()
+ return fig
+
+
+def figure_weight_study(
+ class_inps, sample_weights=None, columns=None, label=None, log=False, mode="plain", **kwargs
+):
+ multiplot = Multiplot(class_inps[0].shape[1:][::-1])
+ rows, cols = multiplot.lenghts()
+ size = 3
+ fig, ax = plt.subplots(rows, cols, figsize=(cols * size, rows * size))
+
+ class_inps = [inps.reshape(inps.shape[0], -1) for inps in class_inps]
+ for feat, name in enumerate(columns):
+ ax_index = multiplot.index(feat)
+
+ bins = 25
+ ref = None
+
+ for i, inps in enumerate(class_inps):
+ if mode == "plain":
+ val, bins, _ = ax[ax_index].hist(
+ inps[:, feat],
+ histtype="step",
+ bins=bins,
+ weights=sample_weights[i],
+ density=True,
+ label=label[i] if label else None,
+ **kwargs,
+ )
+ if mode == "rel":
+ val, bins = np.histogram(
+ inps[:, feat], bins=bins, weights=sample_weights[i], density=True
+ )
+ if ref is None:
+ ref = val
+ ax[ax_index].bar(
+ bins[:-1],
+ height=(val - ref) / ref,
+ width=bins[1:] - bins[:-1],
+ align="edge",
+ label=label[i] if label else None,
+ alpha=0.5,
+ )
+ if mode == "weight":
+ mask = sample_weights[0] > 0
+ pos_feat, pos_weight = class_inps[0][:, feat][mask], sample_weights[0][mask]
+ neg_feat, neg_weight = class_inps[0][:, feat][~mask], sample_weights[0][~mask]
+
+ val_pos, bins = np.histogram(pos_feat, bins=bins, weights=pos_weight)
+ val_neg, bins = np.histogram(neg_feat, bins=bins, weights=neg_weight)
+ ax[ax_index].bar(
+ bins[:-1],
+ height=np.abs(val_neg) / (val_pos + np.abs(val_neg)),
+ width=bins[1:] - bins[:-1],
+ align="edge",
+ )
+
+ if mode in ["weight", "rel"]:
+ ax[ax_index].set_yscale("log")
ax[ax_index].set_title(name)
ax[ax_index].legend()
fig.tight_layout()
@@ -256,7 +347,8 @@ def figure_inputs(inps, truth, sample_weight=None, columns=None, class_names=Non
def figure_multihist(data, columns=None):
fig, ax = plt.subplots()
df = pd.DataFrame(np.reshape(data, (data.shape[0], -1)), columns=columns)
- df.hist(figsize=(20, 20))
+ warnings.simplefilter("ignore"): # temporary fix outdated pandas
+ df.hist(figsize=(20, 20))
fig.tight_layout()
return fig