From 9ee7e3277b913f2a7b14a54424ac1cc0646cd1a4 Mon Sep 17 00:00:00 2001
From: Nikolai Hartmann <Nikolai.Hartmann@physik.uni-muenchen.de>
Date: Tue, 21 Aug 2018 15:20:41 +0200
Subject: [PATCH] weighted quantiles for all functions in plot_NN_2D
---
scripts/plot_NN_2D.py | 34 +++++++++++++++++++++++-----------
1 file changed, 23 insertions(+), 11 deletions(-)
diff --git a/scripts/plot_NN_2D.py b/scripts/plot_NN_2D.py
index a5b65b0..680d657 100755
--- a/scripts/plot_NN_2D.py
+++ b/scripts/plot_NN_2D.py
@@ -80,14 +80,26 @@ try:
except NameError:
mask_value = None
-varx_test = c.x_test[:,varx_index]
-vary_test = c.x_test[:,vary_index]
+# varx_test = c.x_test[:,varx_index]
+# vary_test = c.x_test[:,vary_index]
-x_not_masked = np.where(varx_test != mask_value)[0]
-y_not_masked = np.where(vary_test != mask_value)[0]
+# x_not_masked = np.where(varx_test != mask_value)[0]
+# y_not_masked = np.where(vary_test != mask_value)[0]
+
+# percentilesx = weighted_quantile(varx_test[x_not_masked], [0.01, 0.99], sample_weight=total_weights[x_not_masked])
+# percentilesy = weighted_quantile(vary_test[y_not_masked], [0.01, 0.99], sample_weight=total_weights[y_not_masked])
+
+def get_ranges(x, quantiles, weights, mask_value=None, filter_index=None):
+ ranges = []
+ for var_index in range(x.shape[1]):
+ x_var = x[:,var_index]
+ not_masked = np.where(x_var != mask_value)[0]
+ ranges.append(weighted_quantile(x_var[not_masked], quantiles, sample_weight=weights[not_masked]))
+ return ranges
+
+percentilesx = get_ranges(c.x_test, [0.01, 0.99], total_weights, mask_value=mask_value, filter_index=varx_index)[0]
+percentilesy = get_ranges(c.x_test, [0.01, 0.99], total_weights, mask_value=mask_value, filter_index=vary_index)[0]
-percentilesx = weighted_quantile(varx_test[x_not_masked], [0.01, 0.99], sample_weight=total_weights[x_not_masked])
-percentilesy = weighted_quantile(vary_test[y_not_masked], [0.01, 0.99], sample_weight=total_weights[y_not_masked])
if args.xrange is not None:
if len(args.xrange) < 3:
@@ -206,10 +218,10 @@ elif args.mode.startswith("hist"):
weights = c.w_test[c.y_test==class_index]
else:
# ranges in which to sample the random events
- x_test_scaled = c.scaler.transform(c.x_test)
- ranges = [np.percentile(x_test_scaled[:,var_index], [1,99]) for var_index in range(len(c.fields))]
+ x_test_scaled = c.transform(c.x_test)
+ ranges = get_ranges(x_test_scaled, [0.01, 0.99], total_weights, mask_value=mask_value)
losses, events = get_max_activation_events(c.model, ranges, ntries=args.ntries_actmax, step=args.step_size, layer=layer, neuron=neuron, threshold=args.threshold)
- events = c.scaler.inverse_transform(events)
+ events = c.inverse_transform(events)
valsx = events[:,varx_index]
if not plot_vs_activation:
valsy = events[:,vary_index]
@@ -229,10 +241,10 @@ elif args.mode.startswith("hist"):
elif args.mode.startswith("cond_actmax"):
- x_test_scaled = c.scaler.transform(c.x_test)
+ x_test_scaled = c.transform(c.x_test)
# ranges in which to sample the random events
- ranges = [np.percentile(x_test_scaled[:,var_index], [1,99]) for var_index in range(len(c.fields))]
+ ranges = get_ranges(x_test_scaled, [0.01, 0.99], total_weights, mask_value=mask_value)
plot_cond_avg_actmax_2D(
args.output_filename,
--
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