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predict_strength.py
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predict_strength.py
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from pymks import PrimitiveBasis
from pymks.stats import correlate
from sklearn.externals import joblib
import numpy as np
#import h5py
# import logging
from bqapi.comm import BQCommError
from bqapi.comm import BQSession
import logging
import os
logging.basicConfig(filename='PythonScript.log',filemode='a',level=logging.DEBUG)
log = logging.getLogger('bq.modules')
#logging.basicConfig(filename='prediction.log',level=logging.DEBUG)
def predict(bq, log, table_url, predictor_url, reducer_url, ms_path, **kw):
'''
Predicts effective strength of 3-D RVE of a 2-phase composite with strength contrast s2/s1 = 5
Args:
- table_path - path to dream3d file containing microstructure data (phase labels)
- predictor_path - path to sav file containing calibrated model (LinearRegression)
- reducer_path - path to sav file containing dimensionality reducer (Principal Component Basis)
- ms_path - path to microstructure data (phase lables) inside dream3d file
Returns:
- y - predicted effective strength
'''
log.debug('kw is: %s', str(kw))
predictor_uniq = predictor_url.split('/')[-1]
reducer_uniq = reducer_url.split('/')[-1]
table_uniq = table_url.split('/')[-1]
predictor_url = bq.service_url('blob_service', path=predictor_uniq)
predictor_path = os.path.join(kw.get('stagingPath', ''), 'predictor.sav')
predictor_path = bq.fetchblob(predictor_url, path=predictor_path)
reducer_url = bq.service_url('blob_service', path=reducer_uniq)
reducer_path = os.path.join(kw.get('stagingPath', ''), 'reducer.sav')
reducer_path = bq.fetchblob(reducer_url, path=reducer_path)
# ms_path default: '/DataContainers/SyntheticVolumeDataContainer/CellData/Phases'
# Default settings for 2-pt stats
p_axes = (0,1,2)
corrs = [(1,1)]
# Read hdf5 table
table_service = bq.service ('table')
# Get dataset
data = table_service.load_array(table_uniq, ms_path.lstrip('/'))
ms = np.squeeze(data)
# f = h5py.File(table_path, 'r')
# data = f[ms_path].value
# ms = np.squeeze(data)
# Get phase labels as local states
states = np.unique(ms)
if len(states) > 2 :
log.warn('WARNING: Model is only for two-phase materials! All extra phases will be considered as the second (hard) phase')
ms[ms > states[0]] = states[0]
ph_1 = np.min(states)
ph_2 = np.max(states)
s1 = 0.2
s2 = 1.0
eta = s2/s1
f1 = np.count_nonzero(ms==ph_1)*1.0 / np.prod(ms.shape)
f2 = np.count_nonzero(ms==ph_2)*1.0 / np.prod(ms.shape)
sbar_up = (f1*s1) + (f2*s2)
sbar_low = (f1/s1) + (f2/s2)
sbar_low = 1.0/sbar_low
# Get the size of the RVE
if len(ms.shape) == 4:
dims = ms.shape[1:4]
elif len(ms.shape) == 3:
dims = ms.shape
ms = np.expand_dims(ms,0)
else:
log.error('ERROR: 3-D RVE(s) are expected!')
return None
# Load model and dimensionality reducer
predictor = joblib.load(predictor_path)
reducer = joblib.load(reducer_path)
# Get the number of PC components used
n_comps = predictor.named_steps['poly'].n_input_features_
# Get the size of the calibration RVE
nx_cal = int(np.round((reducer.components_.shape[1])**(1.0/3.0)))
dims_cal = np.array((nx_cal,nx_cal,nx_cal))
# Compute 2-pt stats
n_states = len(states)
p_basis = PrimitiveBasis(n_states=n_states, domain=states)
tps = correlate(ms, p_basis, periodic_axes=p_axes, correlations=corrs)
# Check size of the provided MVE: truncate if large, pad if small
if np.prod(dims) > reducer.components_.shape[1]:
tps = truncate(tps, [len(ms),dims_cal[0],dims_cal[1],dims_cal[2],1])
dims = dims_cal
log.info('Microstructure volume is larger than calibration RVE. 2-pt correlation function is truncated')
elif np.prod(dims) < reducer.components_.shape[1]:
tps = pad(tps, [len(ms),dims_cal[0],dims_cal[1],dims_cal[2],1])
dims = dims_cal
log.info('Microstructure volume is smaller than calibration RVE. 2-pt correlation function is padded')
# Convert 2-pt stats to a vector
tps_v = np.reshape(tps,(len(ms), np.prod(dims)))
# Get low-dimensional representation
x = reducer.transform(tps_v)
# Get the property prediction
y = predictor.predict(x[:,0:n_comps])
# outtable_xml = table_service.store_array(y, name='predicted_strength')
# return [ outtable_xml ]
out_strength_xml = """<tag name="Strength">
<tag name="Strength" type="string" value="%s"/>
<tag name="sbar_up" type="string" value="%s"/>
<tag name="sbar_low" type="string" value="%s"/>
<tag name="Volume Fraction" type="string" value="%s"/>
<tag name="link" type="resource" value="%s"/>
</tag>""" %(str(y[0]*eta),str(sbar_up*eta),str(sbar_low*eta),str(f1)+', '+str(f2), table_url)
return [out_strength_xml]
def truncate(a, shape):
'''truncates the edges of the array based on the shape. '''
a_shape = np.array(a.shape)
n = len(shape)
new_shape = a_shape.copy()
new_shape[:n] = shape
diff_shape = a_shape - new_shape
index0 = (diff_shape + (diff_shape % 2) * (new_shape % 2)) // 2
index1 = index0 + new_shape
multi_slice = tuple(slice(index0[ii], index1[ii]) for ii in range(n))
return a[multi_slice]
def pad(a, shape):
'''pads the array with zeros to make for the shape'''
a_shape = np.array(a.shape)
diff = shape-a_shape
pad_1 = (diff/2.0).astype(int)
pad_2 = diff - pad_1
padding = []
for ii in range(len(pad_1)):
padding.append((pad_1[ii],pad_2[ii]))
return np.pad(a,padding,'constant',constant_values=(0,0))
# predictor_path = 'predictor.sav'
# reducer_path = 'reducer.sav'
# # table_path = 'example_27x27x27.dream3d'
# table_path = 'example_55x55x55.dream3d'
# y_hat = predict(table_path,predictor_path,reducer_path)
# print y_hat
# if __name__ == "__main__":
# import optparse
# parser = optparse.OptionParser()
# parser.add_option("-c", "--credentials", dest="credentials",
# help="credentials are in the form user:password")
# #parser.add_option('--table_path')
# #parser.add_option('--mex_url')
# #parser.add_option('--auth_token')
# (options, args) = parser.parse_args()
# if options.credentials is None:
# table_path, mex_url, auth_token = args[:3]
# bq = BQSession().init_mex(mex_url, auth_token)
# else:
# table_path = args.pop(0)
# if not options.credentials:
# parser.error('need credentials')
# user,pwd = options.credentials.split(':')
# bq = BQSession().init_local(user, pwd)
# logging.debug("Path is {}".format(table_path))
# table_uniq = table_path.split('/')[-1]
# predict(bq, table_uniq)