diff --git a/toolkit.py b/toolkit.py
index dd7651a03b9428fe790f71dc9fad1952ba489a12..cb32d90c675ebd333999c09a1295c123a9480bde 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -441,19 +441,19 @@ class ClassificationProject(object):
for filename, treename in self.bkg_trees:
bkg_chain.AddFile(filename, -1, treename)
self.s_train = tree2array(signal_chain,
- branches=self.branches+self.regression_branches+[self.weight_expr]+self.identifiers,
+ branches=set(self.branches+self.regression_branches+[self.weight_expr]+self.identifiers),
selection=self.selection,
start=0, step=self.step_signal, stop=self.stop_train)
self.b_train = tree2array(bkg_chain,
- branches=self.branches+self.regression_branches+[self.weight_expr]+self.identifiers,
+ branches=set(self.branches+self.regression_branches+[self.weight_expr]+self.identifiers),
selection=self.selection,
start=0, step=self.step_bkg, stop=self.stop_train)
self.s_test = tree2array(signal_chain,
- branches=self.branches+self.regression_branches+[self.weight_expr],
+ branches=set(self.branches+self.regression_branches+[self.weight_expr]),
selection=self.selection,
start=1, step=self.step_signal, stop=self.stop_test)
self.b_test = tree2array(bkg_chain,
- branches=self.branches+self.regression_branches+[self.weight_expr],
+ branches=set(self.branches+self.regression_branches+[self.weight_expr]),
selection=self.selection,
start=1, step=self.step_bkg, stop=self.stop_test)
@@ -466,10 +466,6 @@ class ClassificationProject(object):
self.b_eventlist_train = self.b_train[self.identifiers].astype(dtype=[(branchName, "u8") for branchName in self.identifiers])
self._dump_training_list()
- # now we don't need the identifiers anymore
- self.s_train = self.s_train[self.fields+[self.weight_expr]]
- self.b_train = self.b_train[self.fields+[self.weight_expr]]
-
# create x (input), y (target) and w (weights) arrays
# the first block will be signals, the second block backgrounds
self.x_train = rec2array(self.s_train[self.fields])
@@ -486,8 +482,8 @@ class ClassificationProject(object):
y = np.empty((len(x), 1+len(self.target_fields)), dtype=np.float)
y[:len(s),0] = 1
y[len(s):,0] = 0
- y[:len(s),1:] = s[self.target_fields]
- y[len(s):,1:] = b[self.target_fields]
+ y[:len(s),1:] = rec2array(s[self.target_fields])
+ y[len(s):,1:] = rec2array(b[self.target_fields])
return y
self.y_train = fill_target(self.x_train, self.s_train, self.b_train)
@@ -784,8 +780,8 @@ class ClassificationProject(object):
@property
def class_weight(self):
if self._class_weight is None:
- sumw_bkg = np.sum(self.w_train[self.y_train == 0])
- sumw_sig = np.sum(self.w_train[self.y_train == 1])
+ sumw_bkg = np.sum(self.w_train[self.l_train == 0])
+ sumw_sig = np.sum(self.w_train[self.l_train == 1])
self._class_weight = [(sumw_sig+sumw_bkg)/(2*sumw_bkg), (sumw_sig+sumw_bkg)/(2*sumw_sig)]
logger.debug("Calculated class_weight: {}".format(self._class_weight))
return self._class_weight
@@ -800,11 +796,11 @@ class ClassificationProject(object):
event with class weights
"""
if self._balanced_class_weight is None:
- sumw_bkg = np.sum(self.w_train[self.y_train == 0])
- sumw_sig = np.sum(self.w_train[self.y_train == 1])
+ sumw_bkg = np.sum(self.w_train[self.l_train == 0])
+ sumw_sig = np.sum(self.w_train[self.l_train == 1])
# use sumw *per event* in this case
- sumw_bkg /= len(self.w_train[self.y_train == 0])
- sumw_sig /= len(self.w_train[self.y_train == 1])
+ sumw_bkg /= len(self.w_train[self.l_train == 0])
+ sumw_sig /= len(self.w_train[self.l_train == 1])
self._balanced_class_weight = [(sumw_sig+sumw_bkg)/(2*sumw_bkg), (sumw_sig+sumw_bkg)/(2*sumw_sig)]
logger.debug("Calculated balanced_class_weight: {}".format(self._balanced_class_weight))
return self._balanced_class_weight
@@ -820,6 +816,24 @@ class ClassificationProject(object):
self._load_data()
+ @property
+ def l_train(self):
+ "labels (in case y contains regression targets)"
+ if not self.target_fields:
+ return self.y_train
+ else:
+ return self.y_train[:,0]
+
+
+ @property
+ def l_test(self):
+ "labels (in case y contains regression targets)"
+ if not self.target_fields:
+ return self.y_test
+ else:
+ return self.y_test[:,0]
+
+
@property
def w_train_tot(self):
"(sample weight * class weight), divided by mean"
@@ -831,7 +845,7 @@ class ClassificationProject(object):
raise ValueError("Data not loaded! can't calculate total weight")
if self._w_train_tot is None:
if self.apply_class_weight:
- self._w_train_tot = self.w_train*np.array(class_weight)[self.y_train.astype(int)]
+ self._w_train_tot = self.w_train*np.array(class_weight)[self.l_train.astype(int)]
else:
self._w_train_tot = np.array(self.w_train)
if self.normalize_weights:
@@ -889,14 +903,22 @@ class ClassificationProject(object):
return x
+ def get_output_list(self, y):
+ "Split target vector column wise in case of regression targets"
+ if not self.target_fields:
+ return y
+ else:
+ return np.hsplit(y, len(self.target_fields)+1)
+
+
def yield_batch(self):
"Batch generator - optionally shuffle the indices after each epoch"
x_train, y_train, w_train = self.x_train, self.y_train, self.w_train_tot
train_idx = list(self.train_val_idx[0])
np.random.seed(self.shuffle_seed+1)
logger.info("Generating training batches from {} signal and {} background events"
- .format(len(np.where(self.y_train[train_idx]==1)[0]),
- len(np.where(self.y_train[train_idx]==0)[0])))
+ .format(len(np.where(self.l_train[train_idx]==1)[0]),
+ len(np.where(self.l_train[train_idx]==0)[0])))
while True:
if self.shuffle:
shuffled_idx = np.random.permutation(train_idx)
@@ -907,7 +929,8 @@ class ClassificationProject(object):
y_batch = y_train[shuffled_idx[start:start+int(self.batch_size)]]
w_batch = w_train[shuffled_idx[start:start+int(self.batch_size)]]
x_input = self.get_input_list(self.transform(x_batch))
- yield (x_input, y_batch, w_batch)
+ y_output = self.get_output_list(y_batch)
+ yield (x_input, y_output, w_batch)
def yield_single_class_batch(self, class_label):
@@ -916,7 +939,8 @@ class ClassificationProject(object):
The weights are multiplied by balanced_class_weight.
"""
x_train, y_train, w_train = self.training_data
- class_idx = np.where(y_train==class_label)[0]
+ l_train = y_train[:,0] if self.target_fields else y_train
+ class_idx = np.where(l_train==class_label)[0]
while True:
# shuffle the indices for this class label
if self.shuffle:
@@ -925,9 +949,12 @@ class ClassificationProject(object):
shuffled_idx = class_idx
# yield them batch wise
for start in range(0, len(shuffled_idx), int(self.batch_size/2)):
- yield (x_train[shuffled_idx[start:start+int(self.batch_size/2)]],
- y_train[shuffled_idx[start:start+int(self.batch_size/2)]],
- w_train[shuffled_idx[start:start+int(self.batch_size/2)]])
+ x_batch = x_train[shuffled_idx[start:start+int(self.batch_size/2)]]
+ y_batch = y_train[shuffled_idx[start:start+int(self.batch_size/2)]]
+ w_batch = w_train[shuffled_idx[start:start+int(self.batch_size/2)]]
+ x_input = self.get_input_list(self.transform(x_batch))
+ y_output = self.get_output_list(y_batch)
+ yield (x_input, y_output, w_batch)
def yield_balanced_batch(self):
@@ -967,7 +994,7 @@ class ClassificationProject(object):
else:
try:
self.is_training = True
- labels, label_counts = np.unique(self.y_train, return_counts=True)
+ labels, label_counts = np.unique(self.l_train, return_counts=True)
logger.info("Training on balanced batches")
# note: the batches have balanced_class_weight already applied
self.model.fit_generator(self.yield_balanced_batch(),
@@ -1084,6 +1111,7 @@ class ClassificationProject(object):
batch_size=100000,
score_mode=None,
fixed_params=None):
+ "TODO: doesn't work for regression targets"
f = ROOT.TFile.Open(source_filename)
tree = f.Get(source_treename)
entries = tree.GetEntries()
@@ -1161,10 +1189,10 @@ class ClassificationProject(object):
fig, ax = plt.subplots()
else:
fig = None
- bkg = self.x_train[:,var_index][self.y_train == 0]
- sig = self.x_train[:,var_index][self.y_train == 1]
- bkg_weights = self.w_train_tot[self.y_train == 0]
- sig_weights = self.w_train_tot[self.y_train == 1]
+ bkg = self.x_train[:,var_index][self.l_train == 0]
+ sig = self.x_train[:,var_index][self.l_train == 1]
+ bkg_weights = self.w_train_tot[self.l_train == 0]
+ sig_weights = self.w_train_tot[self.l_train == 1]
if hasattr(self, "mask_value"):
bkg_not_masked = np.where(bkg != self.mask_value)[0]
@@ -1240,8 +1268,8 @@ class ClassificationProject(object):
def plot_weights(self, bins=100, range=None):
fig, ax = plt.subplots()
- bkg = self.w_train_tot[self.y_train == 0]
- sig = self.w_train_tot[self.y_train == 1]
+ bkg = self.w_train_tot[self.l_train == 0]
+ sig = self.w_train_tot[self.l_train == 1]
ax.hist(bkg, bins=bins, range=range, color="b", alpha=0.5)
ax.set_yscale("log")
save_show(plt, fig, os.path.join(self.project_dir, "eventweights_bkg.pdf"))
@@ -1259,8 +1287,8 @@ class ClassificationProject(object):
ax.grid(color='gray', linestyle='--', linewidth=1)
for y, scores, weight, label in [
- (self.y_train, self.scores_train, self.w_train, "train"),
- (self.y_test, self.scores_test, self.w_test, "test")
+ (self.l_train, self.scores_train, self.w_train, "train"),
+ (self.l_test, self.scores_test, self.w_test, "test")
]:
fpr, tpr, threshold = roc_curve(y, scores, sample_weight = weight)
fpr = 1.0 - fpr # background rejection
@@ -1293,10 +1321,10 @@ class ClassificationProject(object):
trf = lambda y : y
fig, ax = plt.subplots()
for scores, weights, y, class_label, fn, opts in [
- (self.scores_train, self.w_train, self.y_train, 1, ax.bar, dict(color="r", label="signal train")),
- (self.scores_train, self.w_train, self.y_train, 0, ax.bar, dict(color="b", label="background train")),
- (self.scores_test, self.w_test, self.y_test, 1, ax.errorbar, dict(fmt="ro", label="signal test")),
- (self.scores_test, self.w_test, self.y_test, 0, ax.errorbar, dict(fmt="bo", label="background test")),
+ (self.scores_train, self.w_train, self.l_train, 1, ax.bar, dict(color="r", label="signal train")),
+ (self.scores_train, self.w_train, self.l_train, 0, ax.bar, dict(color="b", label="background train")),
+ (self.scores_test, self.w_test, self.l_test, 1, ax.errorbar, dict(fmt="ro", label="signal test")),
+ (self.scores_test, self.w_test, self.l_test, 0, ax.errorbar, dict(fmt="bo", label="background test")),
]:
weights = weights[y==class_label]
if apply_class_weight is True and (lumifactor is not None):
@@ -1349,16 +1377,16 @@ class ClassificationProject(object):
else:
trf = lambda y : y
- centers_sig_train, hist_sig_train, rel_errors_sig_train = self.get_bin_centered_hist(trf(self.scores_train[self.y_train==1].reshape(-1)), weights=self.w_train[self.y_train==1], **plot_opts)
- centers_bkg_train, hist_bkg_train, rel_errors_bkg_train = self.get_bin_centered_hist(trf(self.scores_train[self.y_train==0].reshape(-1)), weights=self.w_train[self.y_train==0], **plot_opts)
- centers_sig_test, hist_sig_test, rel_errors_sig_test = self.get_bin_centered_hist(trf(self.scores_test[self.y_test==1].reshape(-1)), weights=self.w_test[self.y_test==1], **plot_opts)
- centers_bkg_test, hist_bkg_test, rel_errors_bkg_test = self.get_bin_centered_hist(trf(self.scores_test[self.y_test==0].reshape(-1)), weights=self.w_test[self.y_test==0], **plot_opts)
+ centers_sig_train, hist_sig_train, rel_errors_sig_train = self.get_bin_centered_hist(trf(self.scores_train[self.l_train==1].reshape(-1)), weights=self.w_train[self.l_train==1], **plot_opts)
+ centers_bkg_train, hist_bkg_train, rel_errors_bkg_train = self.get_bin_centered_hist(trf(self.scores_train[self.l_train==0].reshape(-1)), weights=self.w_train[self.l_train==0], **plot_opts)
+ centers_sig_test, hist_sig_test, rel_errors_sig_test = self.get_bin_centered_hist(trf(self.scores_test[self.l_test==1].reshape(-1)), weights=self.w_test[self.l_test==1], **plot_opts)
+ centers_bkg_test, hist_bkg_test, rel_errors_bkg_test = self.get_bin_centered_hist(trf(self.scores_test[self.l_test==0].reshape(-1)), weights=self.w_test[self.l_test==0], **plot_opts)
significances_train = []
significances_test = []
- for hist_sig, hist_bkg, rel_errors_sig, rel_errors_bkg, significances, w, y in [
- (hist_sig_train, hist_bkg_train, rel_errors_sig_train, rel_errors_bkg_train, significances_train, self.w_train, self.y_train),
- (hist_sig_test, hist_bkg_test, rel_errors_sig_test, rel_errors_bkg_test, significances_test, self.w_test, self.y_test),
+ for hist_sig, hist_bkg, rel_errors_sig, rel_errors_bkg, significances in [
+ (hist_sig_train, hist_bkg_train, rel_errors_sig_train, rel_errors_bkg_train, significances_train),
+ (hist_sig_test, hist_bkg_test, rel_errors_sig_test, rel_errors_bkg_test, significances_test),
]:
# factor to rescale due to using only a fraction of events (training and test samples)
# normfactor_sig = (np.sum(self.w_train[self.y_train==1])+np.sum(self.w_test[self.y_test==1]))/np.sum(w[y==1])
@@ -1447,8 +1475,8 @@ class ClassificationProject(object):
prop_cycle = plt.rcParams['axes.prop_cycle']
colors = prop_cycle.by_key()['color']
for (scores, y, w, label), col in zip(
- [(self.scores_train, self.y_train, self.w_train, "train"),
- (self.scores_test, self.y_test, self.w_test, "test")],
+ [(self.scores_train, self.l_train, self.w_train, "train"),
+ (self.scores_test, self.l_test, self.w_test, "test")],
colors
):
scores = trf(scores)
@@ -1570,7 +1598,7 @@ class ClassificationProject(object):
df = pd.DataFrame(np.concatenate([self.x_train, self.x_test]), columns=self.fields)
df["weight"] = np.concatenate([self.w_train, self.w_test])
df["labels"] = pd.Categorical.from_codes(
- np.concatenate([self.y_train, self.y_test]),
+ np.concatenate([self.l_train, self.l_test]),
categories=["background", "signal"]
)
for identifier in self.identifiers: