"""
Text file trace backend modified from pymc3 to work efficiently with
SMC
Store sampling values as CSV files.
File format
-----------
Sampling values for each chain are saved in a separate file (under a
directory specified by the `dir_path` argument). The rows correspond to
sampling iterations. The column names consist of variable names and
index labels. For example, the heading
x,y__0_0,y__0_1,y__1_0,y__1_1,y__2_0,y__2_1
represents two variables, x and y, where x is a scalar and y has a
shape of (3, 2).
"""
import copy
import itertools
import json
import logging
import os
import shutil
from glob import glob
from time import time
from collections import OrderedDict
import numpy as num
import pandas as pd
from pandas.errors import EmptyDataError
# pandas version control
try:
from pandas.io.common import CParserError
except ImportError:
from pandas.errors import ParserError as CParserError
from pymc3.backends import base, ndarray
from pymc3.backends import tracetab as ttab
from pymc3.blocking import DictToArrayBijection, ArrayOrdering
from pymc3.model import modelcontext
from pymc3.step_methods.arraystep import BlockedStep
from pyrocko import util
from beat.config import (sample_p_outname, transd_vars_dist, mt_components,
dc_components)
from beat.covariance import calc_sample_covariance
from beat.utility import load_objects, dump_objects, \
ListArrayOrdering, ListToArrayBijection, list2string
logger = logging.getLogger('backend')
derived_variables_mapping = {
'MTQTSource': mt_components + dc_components,
'MTSource': dc_components,
'RectangularSource': ['magnitude'],
}
[docs]def thin_buffer(buffer, buffer_thinning, ensure_last=True):
"""
Reduce a list of objects by a given value.
Parameters
----------
buffer : list
of objects to be thinned
buffer_thinning : int
every nth object in list is returned
ensure_last : bool
enable to ensure that last object in list is returned
"""
if ensure_last:
write_buffer = buffer[-1::-buffer_thinning]
write_buffer.reverse()
else:
write_buffer = buffer[::buffer_thinning]
return write_buffer
[docs]class ArrayStepSharedLLK(BlockedStep):
"""
Modified ArrayStepShared To handle returned larger point including the
likelihood values.
Takes additionally a list of output vars including the likelihoods.
Parameters
----------
vars : list
variables to be sampled
out_vars : list
variables to be stored in the traces
shared : dict
theano variable -> shared variables
blocked : boolen
(default True)
"""
def __init__(self, vars, out_vars, shared, blocked=True):
self.vars = vars
self.ordering = ArrayOrdering(vars)
self.lordering = ListArrayOrdering(out_vars, intype='tensor')
lpoint = [var.tag.test_value for var in out_vars]
self.shared = {var.name: shared for var, shared in shared.items()}
self.blocked = blocked
self.bij = DictToArrayBijection(self.ordering, self.population[0])
blacklist = list(set(self.lordering.variables) -
set([var.name for var in vars]))
self.lij = ListToArrayBijection(
self.lordering, lpoint, blacklist=blacklist)
def __getstate__(self):
return self.__dict__
def __setstate__(self, state):
self.__dict__.update(state)
def step(self, point):
for var, share in self.shared.items():
share.container.storage[0] = point[var]
apoint, alist = self.astep(self.bij.map(point))
return self.bij.rmap(apoint), alist
[docs]class BaseChain(object):
"""
Base chain object, independent of file or memory output.
Parameters
----------
model : Model
If None, the model is taken from the `with` context.
vars : list of variables
Sampling values will be stored for these variables. If None,
`model.unobserved_RVs` is used.
"""
def __init__(self, model=None, vars=None, buffer_size=5000,
buffer_thinning=1):
self.model = None
self.vars = None
self.var_shapes = None
self.chain = None
self.buffer_size = buffer_size
self.buffer_thinning = buffer_thinning
self.buffer = []
self.count = 0
self.cov_counter = 0
if model is not None:
self.model = modelcontext(model)
if vars is None and self.model is not None:
vars = self.model.unobserved_RVs
if vars is not None:
self.vars = vars
if self.vars is not None:
# Get variable shapes. Most backends will need this
# information.
self.var_shapes = OrderedDict()
self.var_dtypes = OrderedDict()
self.varnames = []
for var in self.vars:
self.var_shapes[var.name] = var.tag.test_value.shape
self.var_dtypes[var.name] = var.tag.test_value.dtype
self.varnames.append(var.name)
else:
logger.debug('No model or variables given!')
def __getitem__(self, idx):
if isinstance(idx, slice):
return self._slice(idx)
try:
return self.point(int(idx))
except (ValueError, TypeError): # Passed variable or variable name.
raise ValueError('Can only index with slice or integer')
def __getstate__(self):
return self.__dict__
def __setstate__(self, state):
self.__dict__.update(state)
[docs] def buffer_write(self, lpoint, draw):
"""
Write sampling results into buffer.
If buffer is full trow an error.
"""
self.count += 1
self.buffer.append((lpoint, draw))
if self.count == self.buffer_size:
raise BufferError(
'Buffer is full! Needs recording!!')
def empty_buffer(self):
self.buffer = []
self.count = 0
[docs] def get_sample_covariance(self, step):
"""
Return sample Covariance matrix from buffer.
"""
sample_difference = self.count - self.buffer_size
if sample_difference < 0:
raise ValueError('Covariance has been updated already!')
elif sample_difference > 0:
raise BufferError(
'Buffer is not full and sample covariance may be biased')
else:
logger.info(
'Evaluating sampled trace covariance of worker %i at '
'sample %i' % (self.chain, step.cumulative_samples))
cov = calc_sample_covariance(
self.buffer, lij=step.lij, bij=step.bij, beta=step.beta)
self.cov_counter += 1
return cov
[docs]class FileChain(BaseChain):
"""
Base class for a trace written to a file with buffer functionality and
rogressbar. Buffer is a list of tuples of lpoints and a draw index. Inheriting classes
must define the methods: '_write_data_to_file' and '_load_df'
"""
def __init__(
self, dir_path='', model=None, vars=None, buffer_size=5000,
buffer_thinning=1, progressbar=False, k=None):
super(FileChain, self).__init__(
model=model, vars=vars, buffer_size=buffer_size,
buffer_thinning=buffer_thinning)
if not os.path.exists(dir_path):
os.mkdir(dir_path)
self.dir_path = dir_path
self.flat_names = OrderedDict()
if self.var_shapes is not None:
if k is not None:
for var, shape in self.var_shapes.items():
if var in transd_vars_dist:
shape = (k,)
self.flat_names[var] = ttab.create_flat_names(var, shape)
else:
for var, shape in self.var_shapes.items():
self.flat_names[var] = ttab.create_flat_names(var, shape)
self.k = k
self.corrupted_flag = False
self.progressbar = progressbar
self.stored_samples = 0
self.draws = 0
self._df = None
self.filename = None
def __len__(self):
if self.filename is None:
return 0
self._load_df()
if self._df is None:
return 0
else:
return self._df.shape[0] + len(self.buffer)
def add_derived_variables(self, source_type, n_sources=1):
try:
varnames = derived_variables_mapping[source_type]
logger.info(
'Adding derived variables %s to '
'trace.' % list2string(varnames))
except KeyError:
logger.info('No derived variables for %s' % source_type)
varnames = []
for varname in varnames:
shape = (n_sources,)
self.flat_names[varname] = ttab.create_flat_names(varname, shape)
self.var_shapes[varname] = shape
self.var_dtypes[varname] = 'float64'
self.varnames.append(varname)
def _load_df(self):
raise ValueError('This method must be defined in inheriting classes!')
def _write_data_to_file(self):
raise ValueError('This method must be defined in inheriting classes!')
def data_file(self):
return self._df
def record_buffer(self):
if self.chain is None:
raise ValueError(
'Chain has not been setup. Saving samples not possible!')
else:
n_samples = len(self.buffer)
self.stored_samples += n_samples
if not self.progressbar:
if n_samples > self.buffer_size // 2:
logger.info(
'Writing %i / %i samples of chain %i to disk...' %
(self.stored_samples, self.draws, self.chain))
t0 = time()
logger.debug(
'Start Record: Chain_%i' % self.chain)
self._write_data_to_file()
t1 = time()
logger.debug('End Record: Chain_%i' % self.chain)
logger.debug('Writing to file took %f' % (t1 - t0))
self.empty_buffer()
[docs] def write(self, lpoint, draw):
"""
Write sampling results into buffer.
If buffer is full write samples to file.
"""
self.count += 1
self.buffer.append((lpoint, draw))
if self.count == self.buffer_size:
self.record_buffer()
[docs] def clear_data(self):
"""
Clear the data loaded from file.
"""
self._df = None
[docs]class MemoryChain(BaseChain):
"""
Slim memory trace object. Keeps points in a list in memory.
Parameters
----------
draws : int
Number of samples
chain : int
Chain number
"""
def __init__(self, buffer_size=5000):
super(MemoryChain, self).__init__(buffer_size=buffer_size)
def setup(self, draws, chain, overwrite=False):
self.draws = draws
self.chain = chain
if self.buffer is None:
self.buffer = []
if overwrite:
self.buffer = []
def record_buffer(self):
logger.debug('Emptying buffer of trace %i' % self.chain)
self.empty_buffer()
[docs]class TextChain(FileChain):
"""
Text trace object based on '.csv' files. Slow in reading and writing.
Good for debugging.
Parameters
----------
dir_path : str
Name of directory to store text files
model : Model
If None, the model is taken from the `with` context.
vars : list of variables
Sampling values will be stored for these variables. If None,
`model.unobserved_RVs` is used.
buffer_size : int
this is the number of samples after which the buffer is written to disk
or if the chain end is reached
buffer_thinning : int
every nth sample of the buffer is written to disk
progressbar : boolean
flag if a progressbar is active, if not a logmessage is printed
everytime the buffer is written to disk
k : int, optional
if given dont use shape from testpoint as size of transd variables
"""
def __init__(
self, dir_path, model=None, vars=None,
buffer_size=5000, buffer_thinning=1, progressbar=False, k=None):
super(TextChain, self).__init__(
dir_path, model, vars, buffer_size=buffer_size,
progressbar=progressbar, k=k, buffer_thinning=buffer_thinning)
[docs] def setup(self, draws, chain, overwrite=False):
"""
Perform chain-specific setup.
Parameters
----------
draws : int
Expected number of draws
chain : int
Chain number
"""
logger.debug('SetupTrace: Chain_%i step_%i' % (chain, draws))
self.chain = chain
self.draws = draws
self.filename = os.path.join(
self.dir_path, 'chain-{}.csv'.format(chain))
cnames = [fv for v in self.varnames for fv in self.flat_names[v]]
if os.path.exists(self.filename) and not overwrite:
logger.debug('Found existing trace, appending!')
else:
self.count = 0
# writing header
with open(self.filename, 'w') as fh:
fh.write(','.join(cnames) + '\n')
def _write_data_to_file(self, lpoint=None):
"""
Write the lpoint to file. If lpoint is None it
will try to write from buffer.
Parameters
----------
lpoint: list
of numpy arrays
"""
def lpoint2file(filehandle, lpoint):
columns = itertools.chain.from_iterable(
map(str, value.ravel()) for value in lpoint)
filehandle.write(','.join(columns) + '\n')
# Write binary
if lpoint is None and len(self.buffer) == 0:
logger.debug("There is no data to write into file.")
try:
with open(self.filename, mode="a+") as fh:
if lpoint is None:
# write out thinned buffer starting with last sample
write_buffer = thin_buffer(
self.buffer, self.buffer_thinning, ensure_last=True)
for lpoint, draw in write_buffer:
lpoint2file(fh, lpoint)
else:
lpoint2file(fh, lpoint)
except EnvironmentError as e:
print("Error on write file: ", e)
def _load_df(self):
if self._df is None:
try:
self._df = pd.read_csv(self.filename)
except EmptyDataError:
logger.warning(
'Trace %s is empty and needs to be resampled!' %
self.filename)
os.remove(self.filename)
self.corrupted_flag = True
except CParserError:
logger.warning(
'Trace %s has wrong size!' % self.filename)
self.corrupted_flag = True
os.remove(self.filename)
if len(self.flat_names) == 0 and not self.corrupted_flag:
self.flat_names, self.var_shapes = extract_variables_from_df(
self._df)
self.varnames = list(self.var_shapes.keys())
[docs] def get_values(self, varname, burn=0, thin=1):
"""
Get values from trace.
Parameters
----------
varname : str
Variable name for which values are to be retrieved.
burn : int
Burn-in samples from trace. This is the number of samples to be
thrown out from the start of the trace
thin : int
Nuber of thinning samples. Throw out every 'thin' sample of the
trace.
Returns
-------
:class:`numpy.array`
"""
self._load_df()
try:
var_df = self._df[self.flat_names[varname]]
shape = (self._df.shape[0],) + self.var_shapes[varname]
vals = var_df.values.ravel().reshape(shape)
return vals[burn::thin]
except(KeyError):
raise ValueError(
'Did not find varname "%s" in sampling '
'results! Fixed?' % varname)
def _slice(self, idx):
if idx.stop is not None:
raise ValueError('Stop value in slice not supported.')
return ndarray._slice_as_ndarray(self, idx)
[docs] def point(self, idx):
"""
Get point of current chain with variables names as keys.
Parameters
----------
idx : int
Index of the nth step of the chain
Returns
-------
dictionary of point values
"""
idx = int(idx)
self._load_df()
pt = {}
for varname in self.varnames:
# needs deepcopy otherwise reference to df is kept repetead calls
# lead to memory leak
vals = self._df[self.flat_names[varname]].iloc[idx]
pt[varname] = copy.deepcopy(
vals.values.reshape(self.var_shapes[varname]))
del vals
return pt
[docs]class NumpyChain(FileChain):
"""
Numpy binary trace object based on '.bin' files. Fast in reading and
writing. Bad for debugging.
Parameters
----------
dir_path : str
Name of directory to store text files
model : Model
If None, the model is taken from the `with` context.
vars : list of variables
Sampling values will be stored for these variables. If None,
`model.unobserved_RVs` is used.
buffer_size : int
this is the number of samples after which the buffer is written to disk
or if the chain end is reached
buffer_thinning : int
every nth sample of the buffer is written to disk
progressbar : boolean
flag if a progressbar is active, if not a logmessage is printed
everytime the buffer is written to disk
k : int, optional
if given dont use shape from testpoint as size of transd variables
"""
flat_names_tag = "flat_names"
var_shape_tag = "var_shapes"
var_dtypes_tag = "var_dtypes"
__data_structure = None
def __init__(
self, dir_path, model=None, vars=None, buffer_size=5000,
progressbar=False, k=None, buffer_thinning=1):
super(NumpyChain, self).__init__(
dir_path, model, vars, progressbar=progressbar,
buffer_size=buffer_size, buffer_thinning=buffer_thinning, k=k)
self.k = k
def __repr__(self):
return "NumpyChain({},{},{},{},{},{})".format(
self.dir_path, self.model, self.vars, self.buffer_size,
self.progressbar, self.k)
@property
def data_structure(self):
return self.__data_structure
@property
def file_header(self):
with open(self.filename, mode="rb") as file:
# read header.
file_header = file.readline().decode()
return file_header
[docs] def setup(self, draws, chain, overwrite=False):
"""
Perform chain-specific setup. Creates file with header.
If exist not overwritten again unless flag is set.
Parameters
----------
draws: int.
Expected number of draws
chain:
int. Chain number
overwrite:
Bool (optional). True(default) if file need to be overwrite,
false otherwise.
"""
logger.debug('SetupTrace: Chain_%i step_%i' % (chain, draws))
self.chain = chain
self.draws = draws
self.filename = os.path.join(
self.dir_path, 'chain-{}.bin'.format(chain))
self.__data_structure = self.construct_data_structure()
if os.path.exists(self.filename) and not overwrite:
logger.info('Found existing trace, appending!')
else:
logger.debug('Setup new "bin" trace for chain %i' % chain)
self.count = 0
data_type = OrderedDict()
with open(self.filename, 'wb') as fh:
for k, v in self.var_dtypes.items():
data_type[k] = "{}".format(v)
header_data = {
self.flat_names_tag: self.flat_names,
self.var_shape_tag: self.var_shapes,
self.var_dtypes_tag: data_type}
header = (json.dumps(header_data) + '\n').encode()
fh.write(header)
def extract_variables_from_header(self, file_header):
header_data = json.loads(file_header, object_pairs_hook=OrderedDict)
flat_names = header_data[self.flat_names_tag]
var_shapes = OrderedDict()
for k, v in header_data[self.var_shape_tag].items():
var_shapes[k] = tuple(v)
var_dtypes = header_data[self.var_dtypes_tag]
varnames = list(flat_names.keys())
return flat_names, var_shapes, var_dtypes, varnames
[docs] def construct_data_structure(self):
"""
Create a dtype to store the data based on varnames in a numpy array.
Returns
-------
A numpy.dtype
"""
if len(self.flat_names) == 0 and not self.corrupted_flag:
self.flat_names, self.var_shapes, self.var_dtypes, self.varnames = \
self.extract_variables_from_header(self.file_header)
formats = [
"{shape}{dtype}".format(
shape=self.var_shapes[name], dtype=self.var_dtypes[name])
for name in self.varnames]
# set data structure
return num.dtype({'names': self.varnames, 'formats': formats})
def _write_data_to_file(self, lpoint=None):
"""
Writes lpoint to file. If lpoint is None it
will try to write from buffer.
Parameters
----------
lpoint: list
of numpy arrays.
"""
def lpoint2file(filehandle, varnames, data, lpoint):
for names, array in zip(varnames, lpoint):
data[names] = array
data.tofile(filehandle)
# Write binary
if lpoint is None and len(self.buffer) == 0:
logger.debug("There is no data to write into file.")
try:
# create initial data using the data structure.
data = num.zeros(1, dtype=self.data_structure)
with open(self.filename, mode="ab+") as fh:
if lpoint is None:
write_buffer = thin_buffer(
self.buffer, self.buffer_thinning, ensure_last=True)
for lpoint, draw in write_buffer:
lpoint2file(fh, self.varnames, data, lpoint)
else:
lpoint2file(fh, self.varnames, data, lpoint)
except EnvironmentError as e:
print("Error on write file: ", e)
def _load_df(self):
if not self.__data_structure:
try:
self.__data_structure = self.construct_data_structure()
except json.decoder.JSONDecodeError:
logger.warning(
'File header of %s is corrupted!'
' Resampling!' % self.filename)
self.corrupted_flag = True
if self._df is None and not self.corrupted_flag:
try:
with open(self.filename, mode="rb") as file:
# skip header.
next(file)
# read data
self._df = num.fromfile(
file, dtype=self.data_structure)
except EOFError as e:
print(e)
self.corrupted_flag = True
def get_values(self, varname, burn=0, thin=1):
self._load_df()
try:
data = self._df[varname]
shape = (self._df.shape[0],) + self.var_shapes[varname]
vals = data.ravel().reshape(shape)
return vals[burn::thin]
except(ValueError):
raise ValueError(
'Did not find varname "%s" in sampling '
'results! Fixed?' % varname)
[docs] def point(self, idx):
"""
Get point of current chain with variables names as keys.
Parameters
----------
idx : int
Index of the nth step of the chain
Returns
-------
dictionary of point values
"""
idx = int(idx)
self._load_df()
pt = {}
for varname in self.varnames:
data = self._df[varname][idx]
pt[varname] = data.reshape(self.var_shapes[varname])
return pt
backend_catalog = {
'csv': TextChain,
'bin': NumpyChain
}
[docs]class TransDTextChain(object):
"""
Result Trace object for trans-d problems.
Manages several TextChains one for each dimension.
"""
def __init__(
self, name, model=None, vars=None,
buffer_size=5000, progressbar=False):
self._straces = {}
self.buffer_size = buffer_size
self.progressbar = progressbar
if vars is None:
vars = model.unobserved_RVs
transd, dims_idx = istransd(model)
if transd:
self.dims_idx
else:
raise ValueError(
'Model is not trans-d but TransD Chain initialized!')
dimensions = model.unobserved_RVs[self.dims_idx]
for k in range(dimensions.lower, dimensions.upper + 1):
self._straces[k] = TextChain(
dir_path=name,
model=model,
buffer_size=buffer_size,
progressbar=progressbar,
k=k)
# init indexing chain
self._index = TextChain(
dir_path=name,
vars=[],
buffer_size=self.buffer_size,
progressbar=self.progressbar)
self._index.flat_names = {
'draw__0': (1,), 'k__0': (1,), 'k_idx__0': (1,)}
def setup(self, draws, chain):
self.draws = num.zeros(1, dtype='int32')
for k, trace in self._straces.items():
trace.setup(draws=draws, chain=k)
self._index.setup(draws, chain=0)
def write(self, lpoint, draw):
self.draws[0] = draw
ipoint = [self.draws, lpoint[self.dims_idx]]
self._index.write(ipoint, draw)
self._straces[lpoint[self.dims_idx]].write(lpoint, draw)
def __len__(self):
return int(self._index[-1])
def record_buffer(self):
for trace in self._straces:
trace.record_buffer()
self._index.record_buffer()
[docs] def point(self, idx):
"""
Get point of current chain with variables names as keys.
Parameters
----------
idx : int
Index of the nth step of the chain
Returns
-------
dict : of point values
"""
ipoint = self._index.point(idx)
return self._straces[ipoint['k']].point(ipoint['k_idx'])
def get_values(self, varname):
raise NotImplementedError()
class SampleStage(object):
def __init__(self, base_dir, backend='csv'):
self.base_dir = base_dir
self.project_dir = os.path.dirname(base_dir)
self.mode = os.path.basename(base_dir)
self.backend = backend
util.ensuredir(self.base_dir)
def stage_path(self, stage):
return os.path.join(self.base_dir, 'stage_{}'.format(stage))
def trans_stage_path(self, stage):
return os.path.join(self.base_dir, 'trans_stage_{}'.format(stage))
def stage_number(self, stage_path):
"""
Inverse function of SampleStage.path
"""
return int(os.path.basename(stage_path).split('_')[-1])
def highest_sampled_stage(self):
"""
Return stage number of stage that has been sampled before the final
stage.
Returns
-------
stage number : int
"""
return max(self.stage_number(s) for s in glob(self.stage_path('*')))
def get_stage_indexes(self, load_stage=None):
"""
Return indexes to all sampled stages.
Parameters
----------
load_stage : int, optional
if specified only return a list with this stage_index
Returns
-------
list of int, stage_index that have been sampled
"""
if load_stage is not None and isinstance(load_stage, int):
return [load_stage]
elif load_stage is not None and not isinstance(load_stage, int):
raise ValueError('Requested stage_number has to be of type "int"')
else:
stage_number = self.highest_sampled_stage()
if os.path.exists(self.atmip_path(-1)):
list_indexes = [i for i in range(-1, stage_number + 1)]
else:
list_indexes = [i for i in range(stage_number)]
return list_indexes
def atmip_path(self, stage_number):
"""
Consistent naming for atmip params.
"""
return os.path.join(
self.stage_path(stage_number), sample_p_outname)
def load_sampler_params(self, stage_number):
"""
Load saved parameters from last sampled stage.
Parameters
----------
stage number : int
of stage number or -1 for last stage
"""
if stage_number == -1:
if not os.path.exists(self.atmip_path(stage_number)):
prev = self.highest_sampled_stage()
else:
prev = stage_number
elif stage_number == -2:
prev = stage_number + 1
else:
prev = stage_number - 1
logger.info('Loading parameters from completed stage {}'.format(prev))
sampler_state, updates = load_objects(self.atmip_path(prev))
sampler_state['stage'] = stage_number
return sampler_state, updates
def dump_atmip_params(self, stage_number, outlist):
"""
Save atmip params to file.
"""
dump_objects(self.atmip_path(stage_number), outlist)
def clean_directory(self, stage, chains, rm_flag):
"""
Optionally remove directory for the stage.
Does nothing if rm_flag is False.
"""
stage_path = self.stage_path(stage)
if rm_flag:
if os.path.exists(stage_path):
logger.info(
'Removing previous sampling results ... %s' % stage_path)
shutil.rmtree(stage_path)
chains = None
elif not os.path.exists(stage_path):
chains = None
return chains
def load_multitrace(self, stage, chains=None, varnames=None):
"""
Load TextChain database.
Parameters
----------
stage : int
number of stage that should be loaded
chains : list, optional
of result chains to load, -1 is the summarized trace
varnames : list
of varnames in the model
Returns
-------
A :class:`pymc3.backend.base.MultiTrace` instance
"""
dirname = self.stage_path(stage)
return load_multitrace(
dirname=dirname, chains=chains,
varnames=varnames, backend=self.backend)
def recover_existing_results(
self, stage, draws, step,
buffer_thinning=1, varnames=None, update=None):
if stage > 0:
prev = stage - 1
if update is not None:
prev_stage_path = self.trans_stage_path(prev)
else:
prev_stage_path = self.stage_path(prev)
logger.info('Loading end points of last completed stage: '
'%s' % prev_stage_path)
mtrace = load_multitrace(
dirname=prev_stage_path,
varnames=varnames,
backend=self.backend)
step.population, step.array_population, step.likelihoods = \
step.select_end_points(mtrace)
stage_path = self.stage_path(stage)
if os.path.exists(stage_path):
# load incomplete stage results
logger.info('Reloading existing results ...')
mtrace = self.load_multitrace(stage, varnames=varnames)
if len(mtrace.chains):
# continue sampling if traces exist
logger.info('Checking for corrupted files ...')
return check_multitrace(
mtrace, draws=draws, n_chains=step.n_chains,
buffer_thinning=buffer_thinning)
else:
logger.info('Found no sampling results under %s ' % stage_path)
logger.info('Init new trace!')
return None
def istransd(varnames):
dims = 'dimensions'
if dims in varnames:
dims_idx = varnames.index(dims)
return True, dims_idx
else:
logger.debug('Did not find "%s" random variable in model!' % dims)
return False, None
[docs]def load_multitrace(dirname, varnames=[], chains=None, backend='csv'):
"""
Load TextChain database.
Parameters
----------
dirname : str
Name of directory with files (one per chain)
varnames : list
of strings with variable names
chains : list optional
Returns
-------
A :class:`pymc3.backend.base.MultiTrace` instance
"""
if not istransd(varnames)[0]:
logger.info('Loading multitrace from %s' % dirname)
if chains is None:
files = glob(os.path.join(dirname, 'chain-*.%s' % backend))
chains = [
int(os.path.splitext(
os.path.basename(f))[0].replace('chain-', ''))
for f in files]
final_chain = -1
if final_chain in chains:
idx = chains.index(final_chain)
files.pop(idx)
chains.pop(idx)
else:
files = [
os.path.join(
dirname, 'chain-%i.%s' % (
chain, backend)) for chain in chains]
for f in files:
if not os.path.exists(f):
raise IOError(
'File %s does not exist! Please run:'
' "beat summarize <project_dir>"!' % f)
straces = []
for chain, f in zip(chains, files):
strace = backend_catalog[backend](dirname)
strace.chain = chain
strace.filename = f
straces.append(strace)
return base.MultiTrace(straces)
else:
logger.info('Loading trans-d trace from %s' % dirname)
raise NotImplementedError('Loading trans-d trace is not implemented!')
[docs]def check_multitrace(mtrace, draws, n_chains, buffer_thinning=1):
"""
Check multitrace for incomplete sampling and return indexes from chains
that need to be resampled.
Parameters
----------
mtrace : :class:`pymc3.backend.base.MultiTrace`
Multitrace object containing the sampling traces
draws : int
Number of steps (i.e. chain length for each Markov Chain)
n_chains : int
Number of Markov Chains
Returns
-------
list of indexes for chains that need to be resampled
"""
not_sampled_idx = []
# apply buffer thinning
draws = int(num.ceil(draws / buffer_thinning))
for chain in range(n_chains):
if chain in mtrace.chains:
chain_len = len(mtrace._straces[chain])
if chain_len != draws:
logger.warn(
'Trace number %i incomplete: (%i / %i)' % (
chain, chain_len, draws))
mtrace._straces[chain].corrupted_flag = True
else:
not_sampled_idx.append(chain)
flag_bool = [
mtrace._straces[chain].corrupted_flag for chain in mtrace.chains]
corrupted_idx = [i for i, x in enumerate(flag_bool) if x]
return corrupted_idx + not_sampled_idx
[docs]def get_highest_sampled_stage(homedir, return_final=False):
"""
Return stage number of stage that has been sampled before the final stage.
Parameters
----------
homedir : str
Directory to the sampled stage results
Returns
-------
stage number : int
"""
stages = glob(os.path.join(homedir, 'stage_*'))
stagenumbers = []
for s in stages:
stage_ending = os.path.splitext(s)[0].rsplit('_', 1)[1]
try:
stagenumbers.append(int(stage_ending))
except ValueError:
logger.debug('string - Thats the final stage!')
if return_final:
return stage_ending
return max(stagenumbers)
[docs]def load_sampler_params(project_dir, stage_number, mode):
"""
Load saved parameters from given ATMIP stage.
Parameters
----------
project_dir : str
absolute path to directory of BEAT project
stage number : string
of stage number or 'final' for last stage
mode : str
problem mode that has been solved ('geometry', 'static', 'kinematic')
"""
stage_path = os.path.join(
project_dir, mode, 'stage_%s' % stage_number, sample_p_outname)
return load_objects(stage_path)
[docs]def concatenate_traces(mtraces):
"""
Concatenate a List of MultiTraces with same chain indexes.
"""
base_traces = copy.deepcopy(mtraces)
cat_trace = base_traces.pop(0)
cat_dfs = []
for chain in cat_trace.chains:
cat_trace._straces[chain]._load_df()
cat_dfs.append(cat_trace._straces[chain].df)
for mtrace in base_traces:
for chain in cat_trace.chains:
mtrace._straces[chain]._load_df()
cat_dfs[chain] = cat_dfs[chain].append(mtrace._straces[chain].df)
for chain in cat_trace.chains:
cat_trace._straces[chain].df = cat_dfs[chain]
return cat_trace
