From db706fce036d0a568b7974589b95c3397dc199f0 Mon Sep 17 00:00:00 2001
From: Nikolai Hartmann <Nikolai.Hartmann@physik.uni-muenchen.de>
Date: Wed, 28 Nov 2018 13:21:28 +0100
Subject: [PATCH] weights and layers
---
toolkit.py | 87 +++++++++++++++++++++++++++++++++++++++++++++++++-----
1 file changed, 80 insertions(+), 7 deletions(-)
diff --git a/toolkit.py b/toolkit.py
index 89e3e89..6098d68 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -936,7 +936,7 @@ class ClassificationProject(object):
x_val, y_val, w_val = self.x_train[idx], self.y_train[idx], self.w_train_tot[idx]
x_val_input = self.get_input_list(self.transform(x_val))
y_val_output = self.get_output_list(self.transform_target(y_val))
- w_val_list = self.get_weight_list(w_val)
+ w_val_list = self.get_weight_list(w_val, y_val)
return x_val_input, y_val_output, w_val_list
@@ -947,7 +947,7 @@ class ClassificationProject(object):
x_train, y_train, w_train = self.x_train[idx], self.y_train[idx], self.w_train_tot[idx]
x_train_input = self.get_input_list(self.transform(x_train))
y_train_output = self.get_output_list(self.transform_target(y_train))
- w_train_list = self.get_weight_list(w_train)
+ w_train_list = self.get_weight_list(w_train, y_train)
return x_train_input, y_train_output, w_train_list
@@ -991,7 +991,7 @@ class ClassificationProject(object):
return np.hsplit(y, len(self.target_fields)+1)
- def get_weight_list(self, w):
+ def get_weight_list(self, w, y=None):
"Repeat weight n times for regression targets"
if not self.target_fields:
return w
@@ -1018,7 +1018,7 @@ class ClassificationProject(object):
w_batch = w_train[shuffled_idx[start:start+int(self.batch_size)]]
x_input = self.get_input_list(self.transform(x_batch))
y_output = self.get_output_list(self.transform_target(y_batch))
- w_list = self.get_weight_list(w_batch)
+ w_list = self.get_weight_list(w_batch, y_batch)
yield (x_input, y_output, w_list)
@@ -2023,17 +2023,22 @@ class ClassificationProjectDecorr(ClassificationProject):
self.decorr_binnings = []
self.decorr_bins = 10
self._write_info("project_type", "ClassificationProjectDecorr")
+ self._class_layers = None
+ self._adv_hidden_layers = None
+ self._adv_target_layers = None
def load(self, *args, **kwargs):
super(ClassificationProjectDecorr, self).load(*args, **kwargs)
bin_frac = 1./float(self.decorr_bins)
+ print(bin_frac)
+ print(np.arange(bin_frac, 1+bin_frac, bin_frac))
for idx, field_name in enumerate(self.target_fields):
# adversary target is fit as multiclass problem with bin indices
# (self.decorr_bins quantiles) as labels like in arXiv:1703.03507
self.decorr_binnings.append(
weighted_quantile(
- self.y_train[self.l_train==0][:,idx], # bkg only
+ self.y_train[self.l_train==0][:,idx+1], # bkg only
np.arange(bin_frac, 1+bin_frac, bin_frac),
sample_weight=self.w_train[self.l_train==0]
)
@@ -2047,8 +2052,76 @@ class ClassificationProjectDecorr(ClassificationProject):
):
bin_idx = np.digitize(out, binning)
# include overflow into last bin
- bin_idx[bin_idx==len(deciles)] = len(deciles)-1
- out_list[i] = keras.utils.to_categorical(bin_idx)
+ bin_idx[bin_idx==len(binning)] = len(binning)-1
+ out_list[i+1] = keras.utils.to_categorical(bin_idx)
+ return out_list
+
+
+ def get_weight_list(self, w, y):
+ w_list = super(ClassificationProjectDecorr, self).get_weight_list(w)
+ # copy first entry (the others might be references)
+ w_list[0] = np.array(w_list[0])
+ for w in w_list[1:]:
+ # set signal weights to 0 for decorr target
+ w[y[:,0]==1] = 0.
+ return w_list
+
+
+ @property
+ def class_layers(self):
+ """
+ Layers for the classification model
+
+ This should be generalised to avoid code duplication with the model
+ functions of the base classes
+ """
+ if self._class_layers is None:
+ layers = []
+ self._class_layers = layers
+ layers.append(Input((len(self.fields),)))
+ if self.dropout_input is not None:
+ layers.append(Dropout(rate=self.dropout_input))
+ for node_count, dropout_fraction, use_bias in zip(
+ self.nodes,
+ self.dropout,
+ self.use_bias,
+ ):
+ layers.append(
+ Dense(
+ node_count,
+ activation=self.activation_function,
+ use_bias=use_bias
+ )
+ )
+ if (dropout_fraction is not None) and (dropout_fraction > 0):
+ layers.append(Dropout(rate=dropout_fraction))
+ layers.append(Dense(1, activation=self.activation_function_output))
+ return self._class_layers
+
+
+ @property
+ def adv_layers(self):
+ """
+ Layers for the adversary
+ """
+ if self._adv_hidden_layers is None:
+ self._adv_hidden_layers = []
+ self._adv_target_layers = []
+ self._adv_hidden_layers.append(Dense(128, activation="tanh"))
+ for binning in self.decorr_binnings:
+ layer = Dense(len(binning), activation="softmax")
+ self._adv_target_layers.append(layer)
+ return self._adv_hidden_layers+self._adv_target_layers
+
+
+ @property
+ def class_input(self):
+ pass
+
+
+ @property
+ def model(self):
+ pass
if __name__ == "__main__":
--
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