def test_create_flat_names_2d():
shape = 2, 3
result = ttab.create_flat_names('x', shape)
expected = ['x__0_0', 'x__0_1', 'x__0_2',
'x__1_0', 'x__1_1', 'x__1_2']
assert result == expected
assert ttab._create_shape(result) == shape
def extract_bounds_from_summary(summary, varname, shape, roundto=None):
"""
Extract lower and upper bound of random variable.
Returns
-------
list of num.Ndarray
"""
def do_nothing(value):
return value
indexes = ttab.create_flat_names(varname, shape)
lower_quant = 'hpd_2.5'
upper_quant = 'hpd_97.5'
bounds = []
for quant in [lower_quant, upper_quant]:
values = num.empty(shape, 'float64')
for i, idx in enumerate(indexes):
adjust = 10.**roundto
if roundto is not None:
if quant == lower_quant:
operation = num.floor
elif quant == upper_quant:
operation = num.ceil
else:
operation = do_nothing
values[i] = operation(summary[quant][idx] * adjust) / adjust
bounds.append(values)
return bounds
def load(con_str, model=None):
"""Load ODBC database.
Parameters
----------
con_str : str
ODBC Connection string including database
model : Model
If None, the model is taken from the `with` context.
Returns
-------
A MultiTrace instance
"""
db = _ODBCDB(con_str)
db.connect()
name = _get_db_name(con_str)
varnames = _get_table_list(db.cursor)
if len(varnames) == 0:
raise ValueError(('Can not get variable list for database'
'`{}`'.format(name)))
chains = _get_chain_list(db.cursor, varnames[0])
print(chains)
straces = []
for chain in chains:
strace = ODBC(con_str, model=model)
strace.chain = chain
strace._var_cols = {varname: ttab.create_flat_names('v', shape)
for varname, shape in strace.var_shapes.items()}
strace._is_setup = True
strace.db = db # Share the db with all traces.
straces.append(strace)
return base.MultiTrace(straces)
def dump(name, trace, chains=None):
"""
Store values from NDArray trace as CSV files.
Parameters
----------
name : str
Name of directory to store CSV files in
trace : :class:`pymc3.backend.base.MultiTrace` of NDArray traces
Result of MCMC run with default NDArray backend
chains : list
Chains to dump. If None, all chains are dumped.
"""
if not os.path.exists(name):
os.mkdir(name)
if chains is None:
chains = trace.chains
var_shapes = trace._straces[chains[0]].var_shapes
flat_names = {
v: ttab.create_flat_names(v, shape)
for v, shape in var_shapes.items()
}
for chain in chains:
filename = os.path.join(name, 'chain-{}.csv'.format(chain))
df = ttab.trace_to_dataframe(trace,
chains=chain,
flat_names=flat_names)
df.to_csv(filename, index=False)
def __init__(self, name, model=None, vars=None):
if not os.path.exists(name):
os.mkdir(name)
super(TextChain, self).__init__(name, model, vars)
self.flat_names = {v: ttab.create_flat_names(v, shape)
for v, shape in self.var_shapes.items()}
self.filename = None
self.df = None
self.corrupted_flag = False
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:
self.flat_names = OrderedDict()
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 v, shape in self.var_shapes.items():
self.flat_names[v] = ttab.create_flat_names(v, 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 dict2pd(statdict, labelname):
"""Small helper function to transform a diagnostics output dict into a
pandas Series.
"""
var_dfs = []
for key, value in statdict.items():
var_df = pd.Series(value.flatten())
var_df.index = ttab.create_flat_names(key, value.shape)
var_dfs.append(var_df)
statpd = pd.concat(var_dfs, axis=0)
statpd = statpd.rename(labelname)
return statpd
def __init__(self,
name,
model=None,
vars=None,
buffer_size=5000,
progressbar=False,
k=None):
if not os.path.exists(name):
os.mkdir(name)
super(TextChain, self).__init__(name, model, vars)
self.flat_names = None
if self.var_shapes is not None:
if k is not None:
self.flat_names = {}
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:
self.flat_names = {
v: ttab.create_flat_names(v, shape)
for v, shape in self.var_shapes.items()
}
self.k = k
self.filename = None
self.df = None
self.corrupted_flag = False
self.progressbar = progressbar
self.buffer_size = buffer_size
self.stored_samples = 0
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 setup(self, draws, chain):
"""Perform chain-specific setup.
Parameters
----------
draws : int
Expected number of draws
chain : int
Chain number
"""
self.db.connect()
self.chain = chain
if self._is_setup:
self.draw_idx = self._get_max_draw(chain) + 1
self._len = None
else: # Table has not been created.
self._var_cols = {varname: ttab.create_flat_names('v', shape)
for varname, shape in self.var_shapes.items()}
self._create_table()
self._is_setup = True
self._create_insert_queries()
def test_create_flat_names_1d():
shape = (2, )
result = ttab.create_flat_names("x", shape)
expected = ["x__0", "x__1"]
assert result == expected
assert ttab._create_shape(result) == shape
def summary(trace,
varnames=None,
transform=lambda x: x,
stat_funcs=None,
extend=False,
include_transformed=False,
alpha=0.05,
start=0,
batches=None):
R"""Create a data frame with summary statistics.
Parameters
----------
trace : MultiTrace instance
varnames : list
Names of variables to include in summary
transform : callable
Function to transform data (defaults to identity)
stat_funcs : None or list
A list of functions used to calculate statistics. By default,
the mean, standard deviation, simulation standard error, and
highest posterior density intervals are included.
The functions will be given one argument, the samples for a
variable as a 2 dimensional array, where the first axis
corresponds to sampling iterations and the second axis
represents the flattened variable (e.g., x__0, x__1,...). Each
function should return either
1) A `pandas.Series` instance containing the result of
calculating the statistic along the first axis. The name
attribute will be taken as the name of the statistic.
2) A `pandas.DataFrame` where each column contains the
result of calculating the statistic along the first axis.
The column names will be taken as the names of the
statistics.
extend : boolean
If True, use the statistics returned by `stat_funcs` in
addition to, rather than in place of, the default statistics.
This is only meaningful when `stat_funcs` is not None.
include_transformed : bool
Flag for reporting automatically transformed variables in addition
to original variables (defaults to False).
alpha : float
The alpha level for generating posterior intervals. Defaults
to 0.05. This is only meaningful when `stat_funcs` is None.
start : int
The starting index from which to summarize (each) chain. Defaults
to zero.
batches : None or int
Batch size for calculating standard deviation for non-independent
samples. Defaults to the smaller of 100 or the number of samples.
This is only meaningful when `stat_funcs` is None.
Returns
-------
`pandas.DataFrame` with summary statistics for each variable Defaults one
are: `mean`, `sd`, `mc_error`, `hpd_2.5`, `hpd_97.5`, `n_eff` and `Rhat`.
Last two are only computed for traces with 2 or more chains.
Examples
--------
.. code:: ipython
>>> import pymc3 as pm
>>> trace.mu.shape
(1000, 2)
>>> pm.summary(trace, ['mu'])
mean sd mc_error hpd_5 hpd_95
mu__0 0.106897 0.066473 0.001818 -0.020612 0.231626
mu__1 -0.046597 0.067513 0.002048 -0.174753 0.081924
n_eff Rhat
mu__0 487.0 1.00001
mu__1 379.0 1.00203
Other statistics can be calculated by passing a list of functions.
.. code:: ipython
>>> import pandas as pd
>>> def trace_sd(x):
... return pd.Series(np.std(x, 0), name='sd')
...
>>> def trace_quantiles(x):
... return pd.DataFrame(pm.quantiles(x, [5, 50, 95]))
...
>>> pm.summary(trace, ['mu'], stat_funcs=[trace_sd, trace_quantiles])
sd 5 50 95
mu__0 0.066473 0.000312 0.105039 0.214242
mu__1 0.067513 -0.159097 -0.045637 0.062912
"""
if varnames is None:
varnames = get_default_varnames(
trace.varnames, include_transformed=include_transformed)
if batches is None:
batches = min([100, len(trace)])
funcs = [
lambda x: pd.Series(np.mean(x, 0), name='mean'),
lambda x: pd.Series(np.std(x, 0), name='sd'),
lambda x: pd.Series(mc_error(x, batches), name='mc_error'),
lambda x: _hpd_df(x, alpha)
]
if stat_funcs is not None:
if extend:
funcs = funcs + stat_funcs
else:
funcs = stat_funcs
var_dfs = []
for var in varnames:
vals = transform(trace.get_values(var, burn=start, combine=True))
flat_vals = vals.reshape(vals.shape[0], -1)
var_df = pd.concat([f(flat_vals) for f in funcs], axis=1)
var_df.index = ttab.create_flat_names(var, vals.shape[1:])
var_dfs.append(var_df)
dforg = pd.concat(var_dfs, axis=0)
if (stat_funcs is not None) and (not extend):
return dforg
elif trace.nchains < 2:
return dforg
else:
n_eff = pm.effective_n(trace,
varnames=varnames,
include_transformed=include_transformed)
n_eff_pd = dict2pd(n_eff, 'n_eff')
rhat = pm.gelman_rubin(trace,
varnames=varnames,
include_transformed=include_transformed)
rhat_pd = dict2pd(rhat, 'Rhat')
#import pdb; pdb.set_trace()
# return pd.concat([dforg, n_eff_pd, rhat_pd],
# axis=1, join_axes=[dforg.index])
return pd.concat([dforg, n_eff_pd, rhat_pd],
axis=1).reindex(dforg.index)
def test_create_flat_names_3d():
shape = 2, 3, 4
assert ttab._create_shape(ttab.create_flat_names('x', shape)) == shape
def test_create_flat_names_2d():
shape = 2, 3
result = ttab.create_flat_names('x', shape)
expected = ['x__0_0', 'x__0_1', 'x__0_2', 'x__1_0', 'x__1_1', 'x__1_2']
assert result == expected
assert ttab._create_shape(result) == shape
def test_create_flat_names_0d():
shape = ()
result = ttab.create_flat_names('x', shape)
expected = ['x']
assert result == expected
assert ttab._create_shape(result) == shape
def test_create_flat_names_0d():
shape = ()
result = ttab.create_flat_names('x', shape)
expected = ['x']
assert result == expected
assert ttab._create_shape(result) == shape
def test_create_flat_names_3d():
shape = 2, 3, 4
assert ttab._create_shape(ttab.create_flat_names('x', shape)) == shape
def test_create_flat_names_2d():
shape = 2, 3
result = ttab.create_flat_names("x", shape)
expected = ["x__0_0", "x__0_1", "x__0_2", "x__1_0", "x__1_1", "x__1_2"]
assert result == expected
assert ttab._create_shape(result) == shape
