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41a_beta_phase.py
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import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
seed = 0
os.environ['PYTHONHASHSEED'] = str(seed)
import pickle
import rompy
import numpy as np
import tensorflow as tf
import random as rn
np.random.seed(seed)
rn.seed(seed)
tf.random.set_seed(seed)
# #put the these lines before importing any module from keras.
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
from keras.callbacks import Callback
from keras.callbacks import ReduceLROnPlateau
from keras.callbacks import EarlyStopping
from keras import backend
from keras.optimizers import Adam, Adamax, SGD
from sklearn.preprocessing import MinMaxScaler, StandardScaler
# from tensorflow.keras import initializers
from keras.models import Sequential
from keras.layers import Dense
import matplotlib.pyplot as plt
# monitor the learning rate
class LearningRateMonitor(Callback):
# start of training
def on_train_begin(self, logs={}):
self.lrates = list()
# end of each training epoch
def on_epoch_end(self, epoch, logs={}):
# get and store the learning rate
lrate = float(backend.get_value(self.model.optimizer.lr))
self.lrates.append(lrate)
def beta_function(q, s1,s2):
beta = ((113.0 / 12.0) + (25.0 / (4.0*q))) * s1 * ((q*q)/np.square(1+q))+\
((113.0 / 12.0) + ((25.0 *q)/ 4.0)) * s2 * (1.0/np.square(1+q))
return beta
with open('q1to8_s0.99_both/phi_rel_sur/tol_1e-10.pkl', 'rb') as f:
[lambda_values, coeffs, eim_basis, eim_indices] = pickle.load(f)
original = np.zeros((200000,4))
original[:,0] = lambda_values[:, 0]
original[:,1] = lambda_values[:, 1]
original[:,2] = lambda_values[:, 2]
# Mtot=60
beta=[]
for i in range(200000):
print(i)
spin_1 = lambda_values[i, 1]
spin_2 = lambda_values[i, 2]
mass_ratio = lambda_values[i, 0]
beta_i = beta_function(mass_ratio, spin_1, spin_2)
beta.append(beta_i)
original[i, 3] = beta_i
print(original[:,3].min(), original[:,3].max())
# SPLIT DATASETS
original[:,0]=np.log10(original[:,0])
scaler_x=StandardScaler()
lambda_values_scaled = scaler_x.fit_transform(original)
phi_train_x = lambda_values_scaled
phi_train_y = coeffs
print(phi_train_x.shape)
print(phi_train_y.shape)
phi_scaler = MinMaxScaler() # scaling data to (0,1)
phi_train_y_scaled = phi_scaler.fit_transform(phi_train_y) # fitting scaler to training data .fit_transform
with open('q1to8_s0.99_both/phi_rel_sur/tol_1e-10_val.pkl', 'rb') as f:
[lambda_values_val, coeffs_val, eim_basis_val, eim_indices_val] = pickle.load(f)
original_val = np.zeros((30000, 4))
original_val[:, 0] = lambda_values_val[:, 0]
original_val[:, 1] = lambda_values_val[:, 1]
original_val[:, 2] = lambda_values_val[:, 2]
beta_val = []
for i in range(30000):
print(i)
spin_1 = lambda_values_val[i, 1]
spin_2 = lambda_values_val[i, 2]
mass_ratio = lambda_values_val[i, 0]
beta_i = beta_function(mass_ratio, spin_1, spin_2)
beta_val.append(beta_i)
original_val[i, 3] = beta_i
print(original_val[:, 3].min(), original_val[:, 3].max())
original_val[:, 0] = np.log10(original_val[:, 0])
lambda_values_val_scaled = scaler_x.transform(original_val)
phi_val_x = lambda_values_val_scaled
phi_val_y = coeffs_val
print(phi_val_x.shape)
print(phi_val_y.shape)
phi_val_y_scaled = phi_scaler.transform(phi_val_y) # fitting validation data according to training data .transform
# constructing the model
phi_model = Sequential()
phi_model.add(Dense(320, activation= 'softplus'))
phi_model.add(Dense(320, activation='softplus'))
phi_model.add(Dense(320, activation='softplus'))
phi_model.add(Dense(320, activation='softplus'))
phi_model.add(Dense(8, activation=None))
# compile model
opt = Adamax(lr=0.01)
phi_model.compile(loss='mse', optimizer=opt, metrics=['mse'])
# fit model
# phi_checkpoint_filepath = './Model_checkpoint_phi/weights.{epoch:02d}.hdf5'
# phi_modelcheckpoint = tf.keras.callbacks.ModelCheckpoint(filepath=phi_checkpoint_filepath,
# save_best_only=False, verbose=1,
# monitor='mse', save_freq=20 * 40)
phi_rlrp = ReduceLROnPlateau(monitor='mse', factor=0.9, patience=15)
phi_lrm = LearningRateMonitor()
phi_stop = EarlyStopping(monitor='mse', patience=1000)
phi_history = phi_model.fit(phi_train_x, phi_train_y_scaled, validation_data=(phi_val_x, phi_val_y_scaled),
epochs=1000, batch_size=1000,
verbose=1, callbacks=[phi_rlrp, phi_lrm, phi_stop])
# from tensorflow import keras
# # phi_model.save('phase baseline model')
# phi_model = keras.models.load_model('phase baseline model')
#
# Plot training & validation learning curves
plt.figure()
plt.plot(phi_history.history['mse'])
plt.plot(phi_history.history['val_mse'])
plt.yscale("log")
plt.title('Phase Model Mean Square Error')
plt.ylabel('mse')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper right')
plt.savefig('Fig1: Phi Model Train MSE')
plt.show()
# Learning Rate
plt.figure()
plt.plot(phi_lrm.lrates)
plt.yscale("log")
plt.title('Phase Learning Rate')
plt.xlabel('Epoch')
plt.savefig('Fig2: Phi Model Learning Rate ')
plt.show()
train_evaluation = phi_model.evaluate(phi_train_x, phi_train_y_scaled, batch_size=1000)
#
# # TESTING
# with open('q1to8_s0.99_both/phi_rel_sur/tol_1e-10_test.pkl', 'rb') as f:
# [lambda_values_test, coeffs_test, eim_basis_test, eim_indices_test] = pickle.load(f)
#
# # print(lambda_values_test[712,:])
#
# lambda_values_test[:, 0] = np.log10(lambda_values_test[:, 0])
# lambda_values_test_scaled = scaler_x.transform(lambda_values_test)
# phi_test_x = lambda_values_test_scaled
# phi_test_y = coeffs_test
# print(phi_test_x.shape)
# print(phi_test_y.shape)
# phi_test_y_scaled = phi_scaler.transform(phi_test_y)
phi_val_norm=np.square(phi_val_y_scaled-phi_model.predict(phi_val_x))
print('Validation mse:',phi_val_norm.mean())
# phi_test_norm=np.square(phi_test_y_scaled-phi_model.predict(phi_test_x))
# print('Test mse:',phi_test_norm.mean())
phi_val_predictions = phi_scaler.inverse_transform(phi_model.predict(phi_val_x))
# phi_test_predictions = phi_scaler.inverse_transform(phi_model.predict(phi_test_x)) #inverse prin ta kanw save
# phi_test_mse = np.square(phi_test_y-phi_test_predictions).mean()
# print(phi_test_mse)
phi_val_mse = np.square(phi_val_y-phi_val_predictions).mean()
print(phi_val_mse)
# with open('Phase_nn_predictions.pkl', 'wb') as f:
# pickle.dump([phi_val_predictions, phi_test_predictions], f)
# ########################################################################################
# errors from philitude training
phi_y_pred = phi_model.predict(phi_train_x)
phi_y_pred = np.float64(phi_y_pred)
phi_errors = phi_train_y_scaled - phi_y_pred
phi_mse_train = np.mean(phi_errors ** 2)
print(phi_mse_train)
phi_y_pred_val = phi_model.predict(phi_val_x)
phi_y_pred_val = np.float64(phi_y_pred_val)
phi_errors_val = phi_val_y_scaled - phi_y_pred_val
phi_mse_val = np.mean(phi_errors_val ** 2)
print(phi_mse_val)
# input for the residual error network
phi_res_scaler = MinMaxScaler()
phi_errors_new = phi_res_scaler.fit_transform(phi_errors)
phi_errors_val_new = phi_res_scaler.transform(phi_errors_val)
print(phi_errors_new)
# Residual Error
phi_res_model = Sequential()
phi_res_model.add(Dense(320,activation= 'softplus'))
phi_res_model.add(Dense(320,activation= 'softplus'))
phi_res_model.add(Dense(320,activation= 'softplus'))
phi_res_model.add(Dense(320,activation= 'softplus'))
# phi_res_model.add(Dense(320,activation=tf.keras.layers.PReLU()))
# phi_res_model.add(Dense(320, activation=tf.keras.layers.PReLU()))
# phi_res_model.add(Dense(320, activation=tf.keras.layers.PReLU()))
# phi_res_model.add(Dense(320, activation=tf.keras.layers.PReLU()))
phi_res_model.add(Dense(8, activation=None))
#
# opt_res = Adam(lr=0.001)
opt_res = Adamax(lr=0.01)
phi_res_model.compile(loss='mse', optimizer=opt_res, metrics=['mse'])
phi_res_rlrp = ReduceLROnPlateau(monitor='mse', factor=0.9, patience=15)
phi_res_lrm = LearningRateMonitor()
phi_res_stop = EarlyStopping(monitor='mse', patience=100)
phi_res_history = phi_res_model.fit(phi_train_x, phi_errors_new, validation_data=(phi_val_x, phi_errors_val_new),
epochs=400, batch_size=1000,
verbose=1,
callbacks=[phi_res_rlrp, phi_res_lrm, phi_res_stop])
# Plot training & validation accuracy values
plt.figure()
plt.plot(phi_res_history.history['mse'])
plt.plot(phi_res_history.history['val_mse'])
plt.yscale("log")
plt.title('Residual phi Model Mean Square Error')
plt.ylabel('mse')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper right')
plt.savefig('Fig3: phi Res Model Train MSE')
plt.show()
# Learning Rate
plt.figure()
plt.plot(phi_res_lrm.lrates)
plt.yscale("log")
plt.title('Residual phi Learning Rate')
plt.xlabel('Epoch')
plt.savefig('Fig4: phi Res Model Learning Rate')
plt.show()
#
# # FINAL MSE
#
# predictions from residual and reverse transform to the scaled data space
phi_train_y_error = phi_res_scaler.inverse_transform(phi_res_model.predict(phi_train_x))
phi_val_y_error = phi_res_scaler.inverse_transform(phi_res_model.predict(phi_val_x))
# adding corrections from residual
phi_train_y_pred_corrected = phi_model.predict(phi_train_x) + phi_train_y_error
phi_val_y_pred_corrected = phi_model.predict(phi_val_x) + phi_val_y_error
phi_val_predictions_before_residual = phi_scaler.inverse_transform(phi_model.predict(phi_val_x))
#
# reverse transform to the initial data space
phi_train_y_predictions = phi_scaler.inverse_transform(phi_train_y_pred_corrected)
phi_val_predictions = phi_scaler.inverse_transform(phi_val_y_pred_corrected)
phi_final_val_mse_before_res = np.square((phi_model.predict(phi_val_x)) - phi_val_y_scaled).mean()
phi_final_train_mse_before_res = np.square((phi_model.predict(phi_train_x)) - phi_train_y_scaled).mean()
#
phi_final_train_mse = np.square(phi_train_y_pred_corrected - phi_train_y_scaled).mean()
phi_final_val_mse = np.square(phi_val_y_pred_corrected - phi_val_y_scaled).mean()
print('Phase')
print('Train mse: ',phi_final_train_mse)
print('Train mse before residual : ',phi_final_train_mse_before_res)
print('Validation mse: ', phi_final_val_mse)
print('Validation mse before residual: ', phi_final_val_mse_before_res)
with open('Phase_nn_predictions#5.pkl', 'wb') as f:
pickle.dump([phi_val_predictions_before_residual, phi_val_predictions ], f)