From 660b4beb75b7ef29782ae946647d49e0dc660571 Mon Sep 17 00:00:00 2001
From: Thomas Weber <Thomas.Weber@physik.uni-muenchen.de>
Date: Thu, 26 Apr 2018 20:07:32 +0200
Subject: [PATCH] Added plotROC function and scaler function call
---
toolkit.py | 46 ++++++++++++++++++++++++++++++++++++++++++----
1 file changed, 42 insertions(+), 4 deletions(-)
diff --git a/toolkit.py b/toolkit.py
index 6d86077..7b5892b 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -13,11 +13,14 @@ import pandas as pd
import h5py
from sklearn.preprocessing import StandardScaler
from sklearn.externals import joblib
+from sklearn.metrics import roc_curve
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
import tensorflow as tf
@@ -77,6 +80,9 @@ class KerasROOTClassification:
self._class_weight = None
self._model = None
+ self.score_train = None
+ self.score_test = None
+
# track the number of epochs this model has been trained
self.total_epochs = 0
@@ -173,6 +179,8 @@ class KerasROOTClassification:
self._scaler = StandardScaler()
logger.info("Fitting StandardScaler to training data")
self._scaler.fit(self.x_train)
+ logger.info("Fitting StandardScaler to test data")
+ self._scaler.fit(self.x_test)
joblib.dump(self._scaler, filename)
return self._scaler
@@ -211,7 +219,8 @@ class KerasROOTClassification:
self._model.add(Dense(self.nodes, activation=self.activation_function))
# last layer is one neuron (binary classification)
self._model.add(Dense(1, activation='sigmoid'))
-
+
+ logger.info("Compile model")
self._model.compile(optimizer='SGD',
loss='binary_crossentropy',
metrics=['accuracy'])
@@ -234,6 +243,8 @@ class KerasROOTClassification:
if not self.data_loaded:
self._load_data()
+
+ self.scaler
try:
self.model.load_weights(os.path.join(self.project_dir, "weights.h5"))
@@ -243,17 +254,25 @@ class KerasROOTClassification:
logger.info("No weights found, starting completely new training")
self.total_epochs = self._read_info("epochs", 0)
-
+
+ logger.info("Train model")
self.model.fit(self.x_train, self.y_train,
epochs=epochs,
class_weight=self.class_weight,
shuffle=True,
batch_size=self.batch_size)
-
+
+ logger.info("Save weights")
self.model.save_weights(os.path.join(self.project_dir, "weights.h5"))
self.total_epochs += epochs
self._write_info("epochs", self.total_epochs)
+
+ logger.info("Create scores for ROC curve")
+ self.scores_test = self.model.predict(self.x_test)
+ self.scores_train = self.model.predict(self.x_train)
+
+
def evaluate(self):
pass
@@ -262,7 +281,25 @@ class KerasROOTClassification:
pass
def plotROC(self):
- pass
+
+ logger.info("Plot ROC curve")
+ fpr, tpr, threshold = roc_curve(self.y_test, self.scores_test, sample_weight = self.w_test)
+
+ plt.grid(color='gray', linestyle='--', linewidth=1)
+ plt.plot(fpr, tpr, label='NN')
+ plt.plot([0,1],[0,1], linestyle='--', color='black', label='Luck')
+ plt.xlabel("False positive rate (background rejection)")
+ plt.ylabel("True positive rate (signal efficiency)")
+ plt.title('Receiver operating characteristic')
+ plt.xlim(0,1)
+ plt.ylim(0,1)
+ plt.xticks(np.arange(0,1,0.1))
+ plt.yticks(np.arange(0,1,0.1))
+ plt.legend(loc='lower left', framealpha=1.0)
+
+ plt.savefig(os.path.join(self.project_dir, "ROC.pdf"))
+ plt.clf()
+
def plotScore(self):
pass
@@ -286,3 +323,4 @@ if __name__ == "__main__":
identifiers = ["DatasetNumber", "EventNumber"])
c.train(epochs=1)
+ c.plotROC()
--
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