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new tests including for gp2Scale
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@MarcusMNoack
MarcusMNoack committed Feb 24, 2023
1 parent 70b28a1 commit 65da0c7
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229 changes: 91 additions & 138 deletions examples/run_gp2ScaleCPU.py
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Expand Up @@ -11,28 +11,7 @@
import time
import sys
from dask.distributed import performance_report



def client_is_ready(ip, n_workers):
ready = False
print("checking the client", flush = True)
counter = 0
while ready is False:
try:
c = Client(ip)
n = len(list(c.scheduler_info()["workers"]))
if n >= n_workers:
ready=True
return ready
else:
print("only ",n," of desired",n_workers," workers available", flush = True)
except: pass
time.sleep(5)
counter += 1
if counter > 20: print("getting the client is taking a long time: ", counter * 5, "seconds", flush = True)


from fvgp.advanced_kernels import kernel_cpu

def normalize(v):
v = v - np.min(v)
Expand All @@ -42,126 +21,100 @@ def normalize(v):


def main():

start_time = time.time()
print("inputs to the run script: ",sys.argv, flush = True)
print("port: ", str(sys.argv[1]), flush = True)
if client_is_ready(str(sys.argv[1]),int(sys.argv[2])/2):
client = Client(str(sys.argv[1]))
print("Client is ready", flush = True)
print(datetime.datetime.now().isoformat())
print("client received: ", client, flush = True)

print("Everything is ready to call gp2Scale", flush = True)

with performance_report(filename="dask-report.html"):
input_dim = 3
target_worker_number = int(sys.argv[2])



#station_locations = np.load("station_coord.npy")
#temperatures = np.load("data.npy")
#N = len(station_locations) * len(temperatures)
#x_data = np.zeros((N,3))
#y_data = np.zeros((N))
#count = 0
#for i in range(len(temperatures)):
# for j in range(len(temperatures[0])):
# x_data[count] = np.array([station_locations[j,0],station_locations[j,1],float(i)])
# y_data[count] = temperatures[i,j]
# count += 1

#non_nan_indices = np.where(y_data == y_data) ###nans in data
#x_data = x_data[non_nan_indices]
#y_data = y_data[non_nan_indices]
#x_data = x_data[::10] ##1000: about 50 000 points; 100: 500 000; 10: 5 million
#y_data = y_data[::10]
#x_data[:,0] = normalize(x_data[:,0])
#x_data[:,1] = normalize(x_data[:,1])
#x_data[:,2] = normalize(x_data[:,2])
#print(np.min(x_data[:,0]),np.max(x_data[:,0]))
#print(np.min(x_data[:,1]),np.max(x_data[:,1]))
#print(np.min(x_data[:,2]),np.max(x_data[:,2]))
#N = 3200000
#N = 1000000

x_data = np.random.rand(N,input_dim)
y_data = np.sin(np.linalg.norm(x_data,axis = 1) * 5.0)

#N = len(x_data)
hps_n = 42

hps_bounds = np.array([
[0.,1.], ##pos bump 1 f comp 1
[0.,1.], ##pos bump 1 f comp 2
[0.,1.], ##pos bump 1 f comp 3
#
[0.,1.], ##pos bump 2 f
[0.,1.], ##pos bump 2 f
[0.,1.], ##pos bump 2 f
#
[0.,1.], ##pos bump 3 f
[0.,1.], ##pos bump 3 f
[0.,1.], ##pos bump 3 f
#
[0.,1.], ##pos bump 4 f
[0.,1.], ##pos bump 4 f
[0.,1.], ##pos bump 4 f
#
[0.01,0.1], ##radius bump 1 f
[0.01,0.1], ##...2
[0.01,0.1], ##...3
[0.01,0.1], ##...4
[0.1,1.], ##ampl bump 1 f
[0.1,1.], ##...2
[0.1,1.], ##...3
[0.1,1.], ##...4
#
[0.,1.], ##pos bump 1 g comp 1
[0.,1.], ##pos bump 1 g comp 2
[0.,1.], ##pos bump 1 g comp 3
#
[0.,1.], ##pos bump 2 g comp 1
[0.,1.], ##pos bump 2 g comp 2
[0.,1.], ##pos bump 2 g comp 3
#
[0.,1.], ##pos bump 3 g comp 1
[0.,1.], ##pos bump 3 g comp 2
[0.,1.], ##pos bump 3 g comp 3
#
[0.,1.], ##pos bump 4 g comp 1
[0.,1.], ##pos bump 4 g comp 2
[0.,1.], ##pos bump 4 g comp 3
#
[0.01,0.1], ##radius bump 1 g
[0.01,0.1], ##...2
[0.01,0.1], ##...3
[0.01,0.1], ##...4
[0.1,1.], ##ampl bump 1 g
[0.1,1.], ##...2
[0.1,1.], ##...3
[0.1,1.], ##...4
[0.1,10.], ##signal var of stat kernel
[0.001,0.02] ##length scale for stat kernel
])


init_hps = np.random.uniform(size = len(hps_bounds), low = hps_bounds[:,0], high = hps_bounds[:,1])

print(init_hps)
print(hps_bounds)
print("INITIALIZED")
#print(client.get_versions())
st = time.time()

my_gp = gp2Scale(input_dim, x_data, y_data, init_hps, 10000, target_worker_number,
gp_kernel_function = kernel, ram_limit = 400e9, info = True,
client = Client(str(sys.argv[1]))
client.wait_for_workers(int(sys.argv[2]))
print("Client is ready", flush = True)
print(datetime.datetime.now().isoformat())
print("client received: ", client, flush = True)

print("Everything is ready to call gp2Scale after ", time.time() - start_time, flush = True)



input_dim = 3
N = 20000
x_data = np.random.rand(N,input_dim)
y_data = np.sin(np.linalg.norm(x_data,axis = 1) * 5.0)
hps_n = 42

hps_bounds = np.array([
[0.,1.], ##pos bump 1 f comp 1
[0.,1.], ##pos bump 1 f comp 2
[0.,1.], ##pos bump 1 f comp 3
#
[0.,1.], ##pos bump 2 f
[0.,1.], ##pos bump 2 f
[0.,1.], ##pos bump 2 f
#
[0.,1.], ##pos bump 3 f
[0.,1.], ##pos bump 3 f
[0.,1.], ##pos bump 3 f
#
[0.,1.], ##pos bump 4 f
[0.,1.], ##pos bump 4 f
[0.,1.], ##pos bump 4 f
#
[0.01,0.1], ##radius bump 1 f
[0.01,0.1], ##...2
[0.01,0.1], ##...3
[0.01,0.1], ##...4
[0.1,1.], ##ampl bump 1 f
[0.1,1.], ##...2
[0.1,1.], ##...3
[0.1,1.], ##...4
#
[0.,1.], ##pos bump 1 g comp 1
[0.,1.], ##pos bump 1 g comp 2
[0.,1.], ##pos bump 1 g comp 3
#
[0.,1.], ##pos bump 2 g comp 1
[0.,1.], ##pos bump 2 g comp 2
[0.,1.], ##pos bump 2 g comp 3
#
[0.,1.], ##pos bump 3 g comp 1
[0.,1.], ##pos bump 3 g comp 2
[0.,1.], ##pos bump 3 g comp 3
#
[0.,1.], ##pos bump 4 g comp 1
[0.,1.], ##pos bump 4 g comp 2
[0.,1.], ##pos bump 4 g comp 3
#
[0.01,0.1], ##radius bump 1 g
[0.01,0.1], ##...2
[0.01,0.1], ##...3
[0.01,0.1], ##...4
[0.1,1.], ##ampl bump 1 g
[0.1,1.], ##...2
[0.1,1.], ##...3
[0.1,1.], ##...4
[0.1,10.], ##signal var of stat kernel
[0.001,0.02] ##length scale for stat kernel
])


init_hps = np.random.uniform(size = len(hps_bounds), low = hps_bounds[:,0], high = hps_bounds[:,1])

print(init_hps)
print(hps_bounds)
print("INITIALIZED")
st = time.time()


my_gp = gp2Scale(input_dim, x_data, y_data, init_hps, 10000,
gp_kernel_function = kernel_cpu, info = True,
covariance_dask_client = client)
print("initialization done after: ",time.time() - st," seconds")
print("===============")
print("Log Likelihood: ", my_gp.log_likelihood(my_gp.hyperparameters, recompute_xK = False))
print("all done after: ",time.time() - st," seconds")

#my_gp.train(hps_bounds, max_iter = 10, init_hyperparameters = init_hps)

print("initialization done after: ",time.time() - st," seconds")
print("===============")
print("Log Likelihood: ", my_gp.log_likelihood(my_gp.hyperparameters))
print("all done after: ",time.time() - st," seconds")

my_gp.train(hps_bounds, max_iter = 2, init_hyperparameters = init_hps)


if __name__ == '__main__':
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100 changes: 100 additions & 0 deletions examples/run_gp2ScaleCPU_local.py
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from dask.distributed import Client
import socket
import time
import numpy
from fvgp import gp
import numpy as np
import time
from fvgp.gp2Scale import gp2Scale
import argparse
import datetime
import time
import sys
from dask.distributed import performance_report
from fvgp.advanced_kernels import kernel_cpu


def main():
client = Client()
input_dim = 3
N = 10000
x_data = np.random.rand(N,input_dim)
y_data = np.sin(np.linalg.norm(x_data,axis = 1) * 5.0)
hps_n = 42

hps_bounds = np.array([
[0.,1.], ##pos bump 1 f comp 1
[0.,1.], ##pos bump 1 f comp 2
[0.,1.], ##pos bump 1 f comp 3
#
[0.,1.], ##pos bump 2 f
[0.,1.], ##pos bump 2 f
[0.,1.], ##pos bump 2 f
#
[0.,1.], ##pos bump 3 f
[0.,1.], ##pos bump 3 f
[0.,1.], ##pos bump 3 f
#
[0.,1.], ##pos bump 4 f
[0.,1.], ##pos bump 4 f
[0.,1.], ##pos bump 4 f
#
[0.01,0.1], ##radius bump 1 f
[0.01,0.1], ##...2
[0.01,0.1], ##...3
[0.01,0.1], ##...4
[0.1,1.], ##ampl bump 1 f
[0.1,1.], ##...2
[0.1,1.], ##...3
[0.1,1.], ##...4
#
[0.,1.], ##pos bump 1 g comp 1
[0.,1.], ##pos bump 1 g comp 2
[0.,1.], ##pos bump 1 g comp 3
#
[0.,1.], ##pos bump 2 g comp 1
[0.,1.], ##pos bump 2 g comp 2
[0.,1.], ##pos bump 2 g comp 3
#
[0.,1.], ##pos bump 3 g comp 1
[0.,1.], ##pos bump 3 g comp 2
[0.,1.], ##pos bump 3 g comp 3
#
[0.,1.], ##pos bump 4 g comp 1
[0.,1.], ##pos bump 4 g comp 2
[0.,1.], ##pos bump 4 g comp 3
#
[0.01,0.1], ##radius bump 1 g
[0.01,0.1], ##...2
[0.01,0.1], ##...3
[0.01,0.1], ##...4
[0.1,1.], ##ampl bump 1 g
[0.1,1.], ##...2
[0.1,1.], ##...3
[0.1,1.], ##...4
[0.1,10.], ##signal var of stat kernel
[0.001,0.02] ##length scale for stat kernel
])


init_hps = np.random.uniform(size = len(hps_bounds), low = hps_bounds[:,0], high = hps_bounds[:,1])

print(init_hps)
print(hps_bounds)
print("INITIALIZED")
st = time.time()

my_gp = gp2Scale(input_dim, x_data, y_data, init_hps, 1000,
gp_kernel_function = kernel_cpu, info = True,
covariance_dask_client = client)
print("initialization done after: ",time.time() - st," seconds")
print("===============")
print("Log Likelihood: ", my_gp.log_likelihood(my_gp.hyperparameters))
print("all done after: ",time.time() - st," seconds")

my_gp.train(hps_bounds, max_iter = 5, init_hyperparameters = init_hps)


if __name__ == '__main__':
main()

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