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non_keras_gauss_predictor.py
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non_keras_gauss_predictor.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Jul 27 15:01:49 2020
@author: Martin Sanner
"""
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import sys
import logging
from normal_dist_calculator import generate_vector_gauss_mixture,generate_vector_random_gauss_mixture
from datetime import datetime
import pickle
from sklearn.model_selection import train_test_split
import argparse
import trainingAddons as trad
np.random.seed(42)
tf.random.set_seed(42)
now = datetime.now()
d_string = now.strftime("%d/%m/%Y, %H:%M:%S")
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s [%(levelname)s] %(message)s",
handlers=[
logging.FileHandler("logfile_{}_{}.log".format(now.day,now.month)),
logging.StreamHandler(sys.stdout)
]
)
if __name__ == "__main__":
'''
Restricted float from https://stackoverflow.com/questions/12116685/how-can-i-require-my-python-scripts-argument-to-be-a-float-between-0-0-1-0-usin
'''
def restricted_float(x):
try:
x = float(x)
except ValueError:
raise argparse.ArgumentTypeError("%r not a floating-point literal" % (x,))
return x
'''
Argument defaults
'''
def_num_profiles = 2000
def_train_ratio = 0.5
def_lr = 1e-3
def_k = 4
def_kg = 1
def_epochs = 100
parser = argparse.ArgumentParser()
parser.add_argument("--num_profile",type = int,default = def_num_profiles, help = "Number of profiles - default {}".format(def_num_profiles))
parser.add_argument("--train_ratio",type = restricted_float, default = def_train_ratio, help = "Ratio of training to test samples - default {}".format(def_train_ratio))
parser.add_argument("--lr",type = restricted_float,default = def_lr,help = "Learning rate - default {}".format(def_lr))
parser.add_argument("--k",type = int, default = def_k, help = "k - default {}".format(def_k))
parser.add_argument("--kg",type = int, default = def_kg, help = "k-generator - default {}".format(def_kg))
parser.add_argument("--epochs",type = int, default = def_epochs, help = "Epochs - default {}".format(def_epochs))
args = parser.parse_args()
run_file = "./runID_gauss.txt"
run_id = -1
if not os.path.isfile(run_file):
with open(run_file,"w") as f:
run_id = 1
f.write(str(run_id))
else:
with open(run_file,"r") as f:
run_id = int(f.read())
logging.info("="*20)
logging.info("Gauss Run {}".format(run_id))
logging.info("="*20)
num_profiles = args.num_profile
kg = args.kg
r = np.linspace(-10,10,1001)
rs = [r for i in range(num_profiles)]
logging.info("Generating {} normals based on {} distribution for training".format(num_profiles, kg))
generated_gaussians, parameters, gaussMixtures = generate_vector_random_gauss_mixture(rs,kg)
gaussians,test_gaussians,X_full,X_tt = train_test_split(generated_gaussians,parameters,test_size = float(args.train_ratio))
logging.info("Defining backend type: TF.float64")
tf.keras.backend.set_floatx("float64")
X_full = np.asarray(X_full).astype(np.float64)
EPOCHS = args.epochs
l = len(X_full[0])
#output dimension
out_dim = 1
# Number of gaussians to represent the multimodal distribution
k = args.k
logging.info("Running {} dimensions on {} distributions".format(out_dim,k))
'''
Define model manually
'''
lr = args.lr
n_hid_1 = 20
n_hid_2 = 20
initial_nodes,best_nodes = trad.create_initial_nodes(l,n_hid_1,n_hid_2,k,out_dim)
optimizer = tf.optimizers.Adam(lr)#tf.optimizers.Adadelta(lr)#tfa.optimizers.AdamW(lr,wd)
logging.info("Training with optimizer: {}".format(optimizer.__class__.__name__))
'''
Create Dataset
'''
N = np.asarray(X_full).shape[0]
num_batches = 10
batchsize = N//num_batches
logging.info("Employing {} batches with size {}".format(num_batches,batchsize))
dataset = tf.data.Dataset \
.from_tensor_slices((X_full, gaussians)) \
.shuffle(N).batch(batchsize)
'''
Initialize training
'''
start_parameters = {"minDelta":1e-5}
min_epoch_train_pre_div = 100
normalize = True
max_loss_divergence = 10
patience_disabled = False
wd = 0
best_nodes,losses,MSEs,counters,test_MAEs = trad.train_model(initial_nodes,
optimizer,
dataset,
rs,
EPOCHS,
X_tt,
test_gaussians,
rs,
min_epoch_train_pre_div,
start_parameters,
5,
lr,
normalize,
max_loss_divergence,
patience_disabled,
wd)
data_folder = ".//data//gauss_{}//".format(run_id)
if not os.path.exists(data_folder):
os.makedirs(data_folder)
logging.info("Dumping data to {}".format(data_folder))
now = datetime.now()
with open(data_folder+"MAE_Losses.dat","wb") as f:
pickle.dump(losses,f)
with open(data_folder+"MSE_Losses.dat","wb") as f:
pickle.dump(MSEs,f)
with open(data_folder+"Patience.dat","wb") as f:
pickle.dump(counters,f)
with open(data_folder+"mae_test_losses.dat","wb") as f:
pickle.dump(test_MAEs,f)
n_test_profiles = 10
rs = [r for i in range(n_test_profiles)]
test_gauss,test_params,generators = generate_vector_random_gauss_mixture(rs,kg)#EinastoSim.generate_n_k_gaussian_parameters(rs,n_test_profiles,kg)
X_test = test_params
pi_test,mu_test,var_test = trad.model(np.asarray(X_test),best_nodes,True)
sample_preds, sample_mixtures = generate_vector_gauss_mixture(rs,pi_test,mu_test,var_test)#generate_tensor_mixture_model(rs, pi_test,mu_test, var_test)
test_data = {"Profiles":test_gauss, "STDParams":{"Pi":pi_test,"Mu":mu_test,"Var":var_test},"Xtest":X_test, "r":rs}
with open(data_folder+"test_data.dat","wb") as f:
pickle.dump(test_data,f)
with open(run_file,"w") as f:
f.write(str(run_id +1))