From 5dc5d14989e877d526ef4aa60d3db04732e12a3b Mon Sep 17 00:00:00 2001
From: Nikolai Hartmann <Nikolai.Hartmann@physik.uni-muenchen.de>
Date: Mon, 30 Jul 2018 11:01:46 +0200
Subject: [PATCH] prototype for plot_NN_vs_var_2D_all
---
plotting.py | 70 +++++++++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 70 insertions(+)
diff --git a/plotting.py b/plotting.py
index 4d071fd..ecad660 100644
--- a/plotting.py
+++ b/plotting.py
@@ -6,8 +6,11 @@ import math
import matplotlib.pyplot as plt
import matplotlib.colors
from matplotlib.ticker import LogFormatter
+from mpl_toolkits.axes_grid1 import ImageGrid
import numpy as np
+from .keras-visualize-activations.read_activations import get_activations
+
import meme
"""
@@ -97,6 +100,66 @@ def plot_NN_vs_var_2D(plotname, means,
fig.savefig(plotname)
+def plot_NN_vs_var_2D_all(plotname, model, means,
+ var1_index, var1_range,
+ var2_index, var2_range,
+ transform_function=None,
+ var1_label=None,
+ var2_label=None):
+
+ "Similar to plot_NN_vs_var_2D, but creates a grid of plots for all neurons."
+
+ # var1 = "lep1Phi"
+ # var2 = "met_Phi"
+ # # var1_vals = np.arange(-3.15,3.15,0.1)
+ # # var2_vals = np.arange(-3.15,3.15,0.1)
+ var1_vals = np.arange(*var1_range)
+ var2_vals = np.arange(*var2_range)
+
+ # 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][index1] = x
+ events[i][j][index2] = y
+
+ # convert back into 1d array
+ events = events.reshape(-1, len(means))
+
+ # transform
+ if transform_function is not None:
+ #events = c.scaler.transform(events)
+ events = transform_function(events)
+
+ acts = get_activations(model, events, print_shape_only=True)
+
+ aspect = (var1_vals[-1]-var1_vals[0])/(var2_vals[-1]-var2_vals[0])
+
+ n_neurons = [len(i[0]) for i in acts]
+ layers = len(n_neurons)
+
+ nrows_ncols = (layers, max(n_neurons))
+ fig = plt.figure(1, figsize=nrows_ncols)
+ grid = ImageGrid(fig, 111, nrows_ncols=nrows_ncols[::-1], axes_pad=0, label_mode="1")
+ grid = np.array(grid)
+ grid = grid.reshape(*nrows_ncols[::-1])
+
+ 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))
+ ax = grid[neuron][layer]
+ ax.imshow(acts_neuron, origin="lower", extent=[var1_vals[0], var1_vals[-1], var2_vals[0], var2_vals[-1]], aspect=aspect, cmap="jet")
+ if var1_label is not None:
+ ax.set_xlabel(var1_label)
+ if var2_label is not None:
+ ax.set_ylabel(var2_label)
+ ax.text(0., 0.5, "{}, {}".format(layer, neuron), transform=ax.transAxes)
+
+ fig.savefig(plotname, bbox_inches='tight')
+
+
+
if __name__ == "__main__":
@@ -128,3 +191,10 @@ if __name__ == "__main__":
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]")
+
--
GitLab