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L1AnomalyEncoder.py
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import datetime
import matplotlib.pyplot as plt
import h5py
import numpy as np
from sklearn.metrics import roc_curve, auc
#Class that handles AE and VAE and bagging models
import tensorflow as tf
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.initializers import HeUniform
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense, BatchNormalization, LeakyReLU, Concatenate
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, TerminateOnNaN, TensorBoard
from sklearn.model_selection import train_test_split
import L1AnomalyBase
import L1AnomalyPlot
class L1AnomalyEncoder(L1AnomalyBase):
def __init__(self, background_file, signal_files, signal_labels, blackbox_file, classVar = False, latent_dim=3, variational = False):
super().__init__(self, background_file, signal_files, signal_labels, blackbox_file, classVar)
self.background_data = super.load_data(self, type = 0)
#returns an array with four signals chronologically
self.signal_data = super.load_data(self, type = 1)
self.blackbox_data = super.load_data(self, type = 2)
self.latent_dim = latent_dim
self.variational = variational
def dnnae_architecture(self, inputs):
x = BatchNormalization()(inputs)
x = Dense(32, kernel_initializer=HeUniform())(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.3)(x)
x = Dense(16, kernel_initializer=HeUniform())(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.3)(x)
x = Dense(self.latent_dim, kernel_initializer=HeUniform())(x)
self.intermediate = x
x = Dense(16, kernel_initializer=HeUniform())(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.3)(x)
x = Dense(32, kernel_initializer=HeUniform())(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.3)(x)
outputs = Dense(self.X_train.shape[1], kernel_initializer=HeUniform())(x)
return outputs
def dnnvae_architecture(self, inputs):
#Batch Normalization
x = BatchNormalization()(inputs)
#Block 1
x = Dense(32, kernel_initializer=HeUniform())(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.3)(x)
#Block 2
x = Dense(16, kernel_initializer=HeUniform())(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.3)(x)
#Block 3
x = Dense(self.latent_dim, kernel_initializer=HeUniform())(x)
meanLatentSpaceVector = Dense(3, activation='linear')(x)
logVarVector = Dense(3, activation='linear')(x)
epsilon = tf.random.normal(tf.shape(meanLatentSpaceVector), mean=0.0, stddev=1.0)
z = meanLatentSpaceVector + tf.exp(0.5 * logVarVector) * epsilon
self.intermediate = z
# Block 4
z = Dense(16, kernel_initializer=HeUniform())(z)
z = BatchNormalization()(z)
z = LeakyReLU(alpha=0.3)(z)
# Block 5
z = Dense(32, kernel_initializer=HeUniform())(z)
z = BatchNormalization()(z)
z = LeakyReLU(alpha=0.3)(z)
decoderEpsilon = tf.random.normal(tf.shape(meanLatentSpaceVector), mean=0.0, stddev=1.0)
decoderZ = meanLatentSpaceVector + tf.exp(0.5 * logVarVector) * decoderEpsilon
# Output Layer
outputs = Dense(self.X_train.shape[1], kernel_initializer=HeUniform())(decoderZ)
outputs = Concatenate(axis=1)([meanLatentSpaceVector, outputs, logVarVector])
print("Model architecture setup")
return outputs
def build_model(self):
super.preprocess_data()
callbacks = [
ReduceLROnPlateau(
monitor="val_loss",
factor=0.1,
patience=2,
verbose=1,
mode="auto",
min_delta=0.0001,
cooldown=2,
min_lr=1e-6,
),
TerminateOnNaN(),
EarlyStopping(
monitor="val_loss", verbose=1, patience=10, restore_best_weights=True
),
TensorBoard(
log_dir=("./VAELOGS" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
),
]
self.inputs = Input(shape=(self.X_train.shape[1],))
#self.intermediate will be set here
if self.variational == False:
outputs = self.dnnae_architecture(self, self.inputs)
else:
outputs = self.dnnvae_architecture(self, self.inputs)
self.model = Model(inputs=self.inputs, outputs=outputs)
#Use inheritance to grab the loss function from L1AnomalyBase
self.model.compile(optimizer=Adam(lr=0.00001), loss=self.make_mse)
self.model.fit(
self.X_train,
self.X_train_scaled,
epochs=10,
batch_size=1024,
validation_data=(self.X_val, self.X_val_scaled),
callbacks=callbacks,
)
def plot_bokeh(self, signal=3, RS = 42, test_size = 0.20):
L1AnomalyPlot.StandardBokehSplit(self, RS=42, background_data = self.background_data,
signal_data = self.signal_data[signal], blackbox_data = self.blackbox_data)
self.X_train_val = self.X_sample
self.X_sample, self.X_val = train_test_split(
self.X_train_val,
test_size=test_size,
shuffle=True,
random_state=RS,
)
self.X_sample_unscaled = self.X_sample
self.X_val_unscaled = self.X_val
self.X_sample, self.x_pt_scaler = L1AnomalyBase.scale_pt(self.X_sample)
self.X_val, self.x_val_pt_scaler = L1AnomalyBase.scale_pt(self.X_val)
self.S_sample, self.s_pt_scaler = L1AnomalyBase.scale_pt(self.S_sample)
self.B_sample, self.b_pt_scaler = L1AnomalyBase.scale_pt(self.B_sample)
self.T_sample, self.t_pt_scaler = L1AnomalyBase.scale_pt(self.T_sample)
#Every event has different color
self.X_sample_pt = self.X_sample[:, 0::4]
self.X_sample_eta = self.X_sample[:, 1::4]
self.X_sample_phi = self.X_sample[:, 2::4]
self.X_sample_class = self.X_sample[:, 3::4]
self.S_sample_pt = self.S_sample[:, 0::4]
self.S_sample_eta = self.S_sample[:, 1::4]
self.S_sample_phi = self.S_sample[:, 2::4]
self.S_sample_class = self.S_sample[:, 3::4]
self.B_sample_pt = self.B_sample[:, 0::4]
self.B_sample_eta = self.B_sample[:, 1::4]
self.B_sample_phi = self.B_sample[:, 2::4]
self.B_sample_class = self.B_sample[:, 3::4]
self.T_sample_pt = self.T_sample[:, 0::4]
self.T_sample_eta = self.T_sample[:, 1::4]
self.T_sample_phi = self.T_sample[:, 2::4]
self.T_sample_class = self.T_sample[:, 3::4]
self.reducer = Model(inputs=self.inputs, outputs=self.intermediate)
if self.variational == False:
L1AnomalyPlot.Bokeh(self, reducer = self.reducer, reducer_string = "DNNAE")
else:
L1AnomalyPlot.Bokeh(self, reducer = self.reducer, reducer_string = "DNNVAE")
def build_bagging_model(self):
input_a = Input(shape=(self.X_train.shape[1],))
input_b = Input(shape=(self.X_train.shape[1],))
output_a = self.dnnae_architecture(input_a)
output_b = self.dnnae_architecture(input_b)
self.model = Model(inputs=[input_a, input_b], outputs=[output_a, output_b])
self.model.compile(optimizer=Adam(lr=0.00001), loss=self.make_mse)
def train_model(self, epochs=10, batch_size=1024):
callbacks = [
ReduceLROnPlateau(monitor="val_loss", factor=0.1, patience=2, verbose=1, mode="auto", min_delta=0.0001, cooldown=2, min_lr=1e-6),
TerminateOnNaN(),
EarlyStopping(monitor="val_loss", verbose=1, patience=10, restore_best_weights=True)
]
self.model.fit([self.X_train, self.X_train], [self.X_train_scaled, self.X_train_scaled],
epochs=epochs, batch_size=batch_size,
validation_data=([self.X_val, self.X_val], [self.X_val_scaled, self.X_val_scaled]),
callbacks=callbacks)
def generate_roc_curve(self):
background_loss = self.get_loss([self.X_test, self.X_test], [self.X_test_scaled, self.X_test_scaled])
plt.figure()
for signal_file, signal_label in zip(self.signal_files, self.signal_labels):
with h5py.File(signal_file, "r") as f:
signal_data = f["Particles"][:, :, :-1]
signal_data = signal_data.reshape(signal_data.shape[0], -1)
signal_data_scaled, _ = self.scale_pt(signal_data, self.pt_scaler)
merged_labels = np.concatenate([np.zeros(self.X_test.shape[0]), np.ones(signal_data.shape[0])], axis=0)
signal_loss = self.get_loss([signal_data, signal_data], [signal_data_scaled, signal_data_scaled])
merged_loss = np.concatenate([background_loss, signal_loss], axis=0)
fpr, tpr, thresholds = roc_curve(merged_labels, merged_loss)
tpr_1em5 = L1AnomalyBase.find_nearest(fpr, 1e-5)
plt.plot(fpr, tpr, label=f"{signal_label}, AUC={auc(fpr, tpr)*100:.2f}%, TPR@FPR $10^{{-5}}$={tpr[tpr_1em5]*100:.3f}%")
plt.legend(title="DNNAE baseline")
plt.plot([1e-6, 1], [1e-6, 1], "k--")
plt.plot([1e-5, 1e-5], [1e-6, 1], "r-.")
plt.xlim([1e-6, 1])
plt.ylim([1e-6, 1])
plt.xlabel("Background Efficiency (FPR)")
plt.ylabel("Signal Efficiency (TPR)")
plt.xscale("log")
plt.yscale("log")
plt.grid(True)
plt.show()