From 35d84c6569960cf9fb46fb60121122363e2130e0 Mon Sep 17 00:00:00 2001
From: Nikolai <osterei33@gmx.de>
Date: Fri, 27 Apr 2018 09:59:52 +0200
Subject: [PATCH] Transform and plot data before training
---
toolkit.py | 78 +++++++++++++++++++++++++++++++++++++++++++++++++++---
1 file changed, 75 insertions(+), 3 deletions(-)
diff --git a/toolkit.py b/toolkit.py
index 6d86077..0432498 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -13,10 +13,10 @@ import pandas as pd
import h5py
from sklearn.preprocessing import StandardScaler
from sklearn.externals import joblib
-
from keras.models import Sequential
from keras.layers import Dense
from keras.models import model_from_json
+import matplotlib.pyplot as plt
# configure number of cores
# this doesn't seem to work, but at least with these settings keras only uses 4 processes
@@ -75,12 +75,15 @@ class KerasROOTClassification:
self._scaler = None
self._class_weight = None
+ self._bkg_weights = None
+ self._sig_weights = None
self._model = None
# track the number of epochs this model has been trained
self.total_epochs = 0
self.data_loaded = False
+ self.data_transformed = False
def _load_data(self):
@@ -177,6 +180,18 @@ class KerasROOTClassification:
return self._scaler
+ def _transform_data(self):
+ if not self.data_transformed:
+ # todo: what to do about the outliers? Where do they come from?
+ logger.debug("training data before transformation: {}".format(self.x_train))
+ logger.debug("minimum values: {}".format([np.min(self.x_train[:,i]) for i in range(self.x_train.shape[1])]))
+ logger.debug("maximum values: {}".format([np.max(self.x_train[:,i]) for i in range(self.x_train.shape[1])]))
+ self.x_train = self.scaler.transform(self.x_train)
+ logger.debug("training data after transformation: {}".format(self.x_train))
+ self.x_test = self.scaler.transform(self.x_test)
+ self.data_transformed = True
+
+
def _read_info(self, key, default):
filename = os.path.join(self.project_dir, "info.json")
if not os.path.exists(filename):
@@ -222,6 +237,7 @@ class KerasROOTClassification:
return self._model
+
@property
def class_weight(self):
if self._class_weight is None:
@@ -230,11 +246,18 @@ class KerasROOTClassification:
self._class_weight = [(sumw_sig+sumw_bkg)/(2*sumw_bkg), (sumw_sig+sumw_bkg)/(2*sumw_sig)]
return self._class_weight
+
def train(self, epochs=10):
if not self.data_loaded:
self._load_data()
+ if not self.data_transformed:
+ self._transform_data()
+
+ for branch_index, branch in enumerate(self.branches):
+ self.plot_input(branch_index)
+
try:
self.model.load_weights(os.path.join(self.project_dir, "weights.h5"))
logger.info("Weights found and loaded")
@@ -244,7 +267,9 @@ class KerasROOTClassification:
self.total_epochs = self._read_info("epochs", 0)
- self.model.fit(self.x_train, self.y_train,
+ self.model.fit(self.x_train,
+ # the reshape might be unnescessary here
+ self.y_train.reshape(-1, 1),
epochs=epochs,
class_weight=self.class_weight,
shuffle=True,
@@ -261,6 +286,52 @@ class KerasROOTClassification:
def writeFriendTree(self):
pass
+
+ @property
+ def bkg_weights(self):
+ """
+ class weights multiplied by event weights (for plotting)
+ TODO: find a better way to do this
+ """
+ if self._bkg_weights is None:
+ logger.debug("Calculating background weights for plotting")
+ self._bkg_weights = np.empty(sum(self.y_train == 0))
+ self._bkg_weights.fill(self.class_weight[0])
+ self._bkg_weights *= self.w_train[self.y_train == 0]
+ return self._bkg_weights
+
+
+ @property
+ def sig_weights(self):
+ """
+ class weights multiplied by event weights (for plotting)
+ TODO: find a better way to do this
+ """
+ if self._sig_weights is None:
+ logger.debug("Calculating signal weights for plotting")
+ self._sig_weights = np.empty(sum(self.y_train == 1))
+ self._sig_weights.fill(self.class_weight[1])
+ self._sig_weights *= self.w_train[self.y_train == 1]
+ return self._sig_weights
+
+
+ def plot_input(self, var_index):
+ "plot a single input variable"
+ branch = self.branches[var_index]
+ fig, ax = plt.subplots()
+ bkg = self.x_train[:,var_index][self.y_train == 0]
+ sig = self.x_train[:,var_index][self.y_train == 1]
+ logger.debug("Plotting bkg (min={}, max={}) from {}".format(np.min(bkg), np.max(bkg), bkg))
+ logger.debug("Plotting sig (min={}, max={}) from {}".format(np.min(sig), np.max(sig), sig))
+ ax.hist(bkg, color="b", alpha=0.5, bins=50, weights=self.bkg_weights)
+ ax.hist(sig, color="r", alpha=0.5, bins=50, weights=self.sig_weights)
+ ax.set_xlabel(branch+" (transformed)")
+ plot_dir = os.path.join(self.project_dir, "plots")
+ if not os.path.exists(plot_dir):
+ os.mkdir(plot_dir)
+ fig.savefig(os.path.join(plot_dir, "var_{}.pdf".format(var_index)))
+
+
def plotROC(self):
pass
@@ -272,7 +343,8 @@ class KerasROOTClassification:
if __name__ == "__main__":
logging.basicConfig()
- logging.getLogger("KerasROOTClassification").setLevel(logging.INFO)
+ #logging.getLogger("KerasROOTClassification").setLevel(logging.INFO)
+ logging.getLogger("KerasROOTClassification").setLevel(logging.DEBUG)
filename = "/project/etp4/nhartmann/trees/allTrees_m1.8_NoSys.root"
--
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