def __init__(self,
data=None,
default_priors=None,
auto_scale=True,
dropna=False,
taylor=None,
noncentered=True):
if isinstance(data, string_types):
data = pd.read_table(data, sep=None)
self.default_priors = PriorFactory(default_priors)
obj_cols = data.select_dtypes(['object']).columns
data[obj_cols] = data[obj_cols].apply(lambda x: x.astype('category'))
self.data = data
# Some random effects stuff later requires us to make guesses about
# column groupings into terms based on patsy's naming scheme.
if re.search("[\[\]]+", ''.join(data.columns)):
warnings.warn("At least one of the column names in the specified "
"dataset contain square brackets ('[' or ']')."
"This may cause unexpected behavior if you specify "
"models with random effects. You are encouraged to "
"rename your columns to avoid square brackets.")
self.reset()
self.auto_scale = auto_scale
self.dropna = dropna
self.taylor = taylor
self.noncentered = noncentered
self._backend_name = None
def test_prior_factory_init_from_default_config():
pf = PriorFactory()
for d in ['dists', 'terms', 'families']:
assert hasattr(pf, d)
assert isinstance(getattr(pf, d), dict)
assert 'normal' in pf.dists
assert 'fixed' in pf.terms
assert 'gaussian' in pf.families
def test_prior_factory_init_from_default_config():
pf = PriorFactory()
for d in ["dists", "terms", "families"]:
assert hasattr(pf, d)
assert isinstance(getattr(pf, d), dict)
assert "normal" in pf.dists
assert "fixed" in pf.terms
assert "gaussian" in pf.families
def __init__(
self,
data=None,
default_priors=None,
auto_scale=True,
dropna=False,
taylor=None,
noncentered=True,
):
if isinstance(data, string_types):
data = pd.read_csv(data, sep=None, engine="python")
self.default_priors = PriorFactory(default_priors)
obj_cols = data.select_dtypes(["object"]).columns
data[obj_cols] = data[obj_cols].apply(lambda x: x.astype("category"))
self.data = data
# Some random effects stuff later requires us to make guesses about
# column groupings into terms based on patsy's naming scheme.
if re.search("[\[\]]+", "".join(data.columns)):
warnings.warn(
"At least one of the column names in the specified "
"dataset contain square brackets ('[' or ']')."
"This may cause unexpected behavior if you specify "
"models with random effects. You are encouraged to "
"rename your columns to avoid square brackets."
)
self.reset()
self.auto_scale = auto_scale
self.dropna = dropna
self.taylor = taylor
self.noncentered = noncentered
self._backend_name = None
# build() will loop over these, calling _add() and _set_priors()
self.added_terms = []
self._added_priors = {}
# if dropna=True, completes gets updated by add() to track complete cases
self.completes = []
self.clean_data = None
def test_prior_factory_init_from_config():
config_file = join(dirname(__file__), 'data', 'sample_priors.json')
pf = PriorFactory(config_file)
for d in ['dists', 'terms', 'families']:
assert hasattr(pf, d)
assert isinstance(getattr(pf, d), dict)
config_dict = json.load(open(config_file, 'r'))
pf = PriorFactory(config_dict)
for d in ['dists', 'terms', 'families']:
assert hasattr(pf, d)
assert isinstance(getattr(pf, d), dict)
assert 'feta' in pf.dists
assert 'hard' in pf.families
assert 'yellow' in pf.terms
pf = PriorFactory(dists=config_dict['dists'])
assert 'feta' in pf.dists
pf = PriorFactory(terms=config_dict['terms'])
assert 'yellow' in pf.terms
pf = PriorFactory(families=config_dict['families'])
assert 'hard' in pf.families
def test_prior_factory_init_from_config():
config_file = join(dirname(__file__), "data", "sample_priors.json")
pf = PriorFactory(config_file)
for d in ["dists", "terms", "families"]:
assert hasattr(pf, d)
assert isinstance(getattr(pf, d), dict)
config_dict = json.load(open(config_file, "r"))
pf = PriorFactory(config_dict)
for d in ["dists", "terms", "families"]:
assert hasattr(pf, d)
assert isinstance(getattr(pf, d), dict)
assert "feta" in pf.dists
assert "hard" in pf.families
assert "yellow" in pf.terms
pf = PriorFactory(dists=config_dict["dists"])
assert "feta" in pf.dists
pf = PriorFactory(terms=config_dict["terms"])
assert "yellow" in pf.terms
pf = PriorFactory(families=config_dict["families"])
assert "hard" in pf.families
def __init__(self,
data=None,
intercept=False,
backend='pymc3',
default_priors=None,
auto_scale=True):
if isinstance(data, string_types):
data = pd.read_table(data, sep=None)
self.default_priors = PriorFactory(default_priors)
obj_cols = data.select_dtypes(['object']).columns
data[obj_cols] = data[obj_cols].apply(lambda x: x.astype('category'))
self.data = data
# Some random effects stuff later requires us to make guesses about
# column groupings into terms based on patsy's naming scheme.
if re.search("[\[\]]+", ''.join(data.columns)):
warnings.warn("At least one of the column names in the specified "
"dataset contain square brackets ('[' or ']')."
"This may cause unexpected behavior if you specify "
"models with random effects. You are encouraged to "
"rename your columns to avoid square brackets.")
self.reset()
if backend.lower() == 'pymc3':
from bambi.backends import PyMC3BackEnd
self.backend = PyMC3BackEnd()
else:
raise ValueError(
"At the moment, only the PyMC3 backend is supported.")
if intercept:
self.add_intercept()
self.auto_scale = auto_scale
class Model(object):
'''
Args:
data (DataFrame, str): the dataset to use. Either a pandas
DataFrame, or the name of the file containing the data, which
will be passed to pd.read_table().
default_priors (dict, str): An optional specification of the
default priors to use for all model terms. Either a dict
containing named distributions, families, and terms (see the
documentation in priors.PriorFactory for details), or the name
of a JSON file containing the same information.
auto_scale (bool): If True (default), priors are automatically rescaled
to the data (to be weakly informative) any time default priors are
used. Note that any priors explicitly set by the user will always
take precedence over default priors.
dropna (bool): When True, rows with any missing values in either the
predictors or outcome are automatically dropped from the dataset in
a listwise manner.
taylor (int): Order of Taylor expansion to use in approximate variance
when constructing the default priors. Should be between 1 and 13.
Lower values are less accurate, tending to undershoot the correct
prior width, but are faster to compute and more stable. Odd-
numbered values tend to work better. Defaults to 5 for Normal
models and 1 for non-Normal models. Values higher than the defaults
are generally not recommended as they can be unstable.
noncentered (True): If True (default), uses a non-centered
parameterization for normal hyperpriors on grouped parameters.
If False, naive (centered) parameterization is used.
'''
def __init__(self,
data=None,
default_priors=None,
auto_scale=True,
dropna=False,
taylor=None,
noncentered=True):
if isinstance(data, string_types):
data = pd.read_table(data, sep=None)
self.default_priors = PriorFactory(default_priors)
obj_cols = data.select_dtypes(['object']).columns
data[obj_cols] = data[obj_cols].apply(lambda x: x.astype('category'))
self.data = data
# Some random effects stuff later requires us to make guesses about
# column groupings into terms based on patsy's naming scheme.
if re.search("[\[\]]+", ''.join(data.columns)):
warnings.warn("At least one of the column names in the specified "
"dataset contain square brackets ('[' or ']')."
"This may cause unexpected behavior if you specify "
"models with random effects. You are encouraged to "
"rename your columns to avoid square brackets.")
self.reset()
self.auto_scale = auto_scale
self.dropna = dropna
self.taylor = taylor
self.noncentered = noncentered
self._backend_name = None
# build() will loop over these, calling _add() and _set_priors()
self.added_terms = []
self.added_priors = []
# if dropna=True, completes gets updated by add() to track complete cases
self.completes = []
self.clean_data = None
def reset(self):
'''
Reset list of terms and y-variable.
'''
self.terms = OrderedDict()
self.y = None
self.backend = None
self.added_terms = []
self.added_priors = []
self.completes = []
self.clean_data = None
def _set_backend(self, backend):
backend = backend.lower()
if backend.startswith('pymc'):
from bambi.backends import PyMC3BackEnd
self.backend = PyMC3BackEnd()
elif backend == 'stan':
from bambi.backends import StanBackEnd
self.backend = StanBackEnd()
else:
raise ValueError(
"At the moment, only the PyMC3 and Stan backends are "
"supported.")
self._backend_name = backend
def build(self, backend=None):
''' Set up the model for sampling/fitting. Performs any steps that
require access to all model terms (e.g., scaling priors on each term),
then calls the BackEnd's build() method.
Args:
backend (str): The name of the backend to use for model fitting.
Currently, 'pymc' and 'stan' are supported. If None, assume
that fit() has already been called (possibly without building),
and look in self._backend_name.
'''
# retain only the complete cases
n_total = len(self.data.index)
if len(self.completes):
completes = [set(x) for x in sum(self.completes, [])]
completes = set.intersection(*completes)
else:
completes = [x for x in range(len(self.data.index))]
self.clean_data = self.data.iloc[list(completes), :]
# warn the user about any dropped rows
if len(completes) < n_total:
msg = "Automatically removing {}/{} rows from the dataset."
msg = msg.format(n_total - len(completes), n_total)
warnings.warn(msg)
# loop over the added terms and actually _add() them
for term_args in self.added_terms:
self._add(**term_args)
# loop over the added priors, now that the terms are added
for prior_args in self.added_priors:
self._set_priors(**prior_args)
# check for backend
if backend is None:
if self._backend_name is None:
raise ValueError("Error: no backend was passed or set in the "
"Model; did you forget to call fit()?")
backend = self._backend_name
# check for outcome
if self.y is None:
raise ValueError("No outcome (y) variable is set! Please specify "
"an outcome variable using the formula interface "
"before build() or fit().")
# X = fixed effects design matrix (excluding intercept/constant term)
# r2_x = 1 - 1/VIF, i.e., R2 for predicting each x from all other x's.
# only compute these stats if there are multiple terms in the model
terms = [t for t in self.fixed_terms.values() if t.name != 'Intercept']
if len(self.fixed_terms) > 1:
X = [pd.DataFrame(x.data, columns=x.levels) for x in terms]
X = pd.concat(X, axis=1)
self.dm_statistics = {
'r2_x':
pd.Series({
x: sm.OLS(endog=X[x],
exog=sm.add_constant(X.drop(x, axis=1))
if 'Intercept' in self.term_names else X.drop(
x, axis=1)).fit().rsquared
for x in list(X.columns)
}),
'sd_x':
X.std(),
'mean_x':
X.mean(axis=0)
}
# save potentially useful info for diagnostics, send to
# ModelResults.
# mat = correlation matrix of X, w/ diagonal replaced by X means
mat = X.corr()
for x in list(mat.columns):
mat.loc[x, x] = self.dm_statistics['mean_x'][x]
self._diagnostics = {
# the Variance Inflation Factors (VIF), which is possibly
# useful for diagnostics
'VIF': 1 / (1 - self.dm_statistics['r2_x']),
'corr_mean_X': mat
}
# throw informative error if perfect collinearity among fixed fx
if any(self.dm_statistics['r2_x'] > .999):
raise ValueError(
"There is perfect collinearity among the fixed effects!\n"
"Printing some design matrix statistics:\n" +
str(self.dm_statistics) + '\n' + str(self._diagnostics))
# throw informative error message if any categorical predictors have 1
# category
num_cats = [x.data.size for x in self.fixed_terms.values()]
if any(np.array(num_cats) == 0):
raise ValueError(
"At least one categorical predictor contains only 1 category!")
# only set priors if there is at least one term in the model
if len(self.terms) > 0:
# Get and scale default priors if none are defined yet
if self.taylor is not None:
taylor = self.taylor
else:
taylor = 5 if self.family.name == 'gaussian' else 1
scaler = PriorScaler(self, taylor=taylor)
scaler.scale()
# For bernoulli models with n_trials = 1 (most common use case),
# tell user which event is being modeled
if self.family.name == 'bernoulli' and np.max(self.y.data) < 1.01:
event = next(i for i, x in enumerate(self.y.data.flatten())
if x > .99)
warnings.warn('Modeling the probability that {}==\'{}\''.format(
self.y.name, str(self.clean_data[self.y.name].iloc[event])))
self._set_backend(backend)
self.backend.build(self)
self.built = True
def fit(self,
fixed=None,
random=None,
priors=None,
family='gaussian',
link=None,
run=True,
categorical=None,
backend=None,
**kwargs):
'''
Fit the model using the specified BackEnd.
Args:
fixed (str): Optional formula specification of fixed effects.
random (list): Optional list-based specification of random effects.
priors (dict): Optional specification of priors for one or more
terms. A dict where the keys are the names of terms in the
model, and the values are either instances of class Prior or
ints, floats, or strings that specify the width of the priors
on a standardized scale.
family (str, Family): A specification of the model family
(analogous to the family object in R). Either a string, or an
instance of class priors.Family. If a string is passed, a
family with the corresponding name must be defined in the
defaults loaded at Model initialization. Valid pre-defined
families are 'gaussian', 'bernoulli', 'poisson', and 't'.
link (str): The model link function to use. Can be either a string
(must be one of the options defined in the current backend;
typically this will include at least 'identity', 'logit',
'inverse', and 'log'), or a callable that takes a 1D ndarray
or theano tensor as the sole argument and returns one with
the same shape.
run (bool): Whether or not to immediately begin fitting the model
once any set up of passed arguments is complete.
categorical (str, list): The names of any variables to treat as
categorical. Can be either a single variable name, or a list
of names. If categorical is None, the data type of the columns
in the DataFrame will be used to infer handling. In cases where
numeric columns are to be treated as categoricals (e.g., random
factors coded as numerical IDs), explicitly passing variable
names via this argument is recommended.
backend (str): The name of the BackEnd to use. Currently only
'pymc' and 'stan' backends are supported. Defaults to PyMC3.
'''
if fixed is not None or random is not None:
self.add(fixed=fixed,
random=random,
priors=priors,
family=family,
link=link,
categorical=categorical,
append=False)
''' Run the BackEnd to fit the model. '''
if backend is None:
backend = 'pymc' if self._backend_name is None else self._backend_name
if run:
if not self.built or backend != self._backend_name:
self.build(backend)
return self.backend.run(**kwargs)
self._backend_name = backend
def add(self,
fixed=None,
random=None,
priors=None,
family='gaussian',
link=None,
categorical=None,
append=True):
'''
Adds one or more terms to the model via an R-like formula syntax.
Args:
fixed (str): Optional formula specification of fixed effects.
random (list): Optional list-based specification of random effects.
priors (dict): Optional specification of priors for one or more
terms. A dict where the keys are the names of terms in the
model, and the values are either instances of class Prior or
ints, floats, or strings that specify the width of the priors
on a standardized scale.
family (str, Family): A specification of the model family
(analogous to the family object in R). Either a string, or an
instance of class priors.Family. If a string is passed, a
family with the corresponding name must be defined in the
defaults loaded at Model initialization. Valid pre-defined
families are 'gaussian', 'bernoulli', 'poisson', and 't'.
link (str): The model link function to use. Can be either a string
(must be one of the options defined in the current backend;
typically this will include at least 'identity', 'logit',
'inverse', and 'log'), or a callable that takes a 1D ndarray
or theano tensor as the sole argument and returns one with
the same shape.
categorical (str, list): The names of any variables to treat as
categorical. Can be either a single variable name, or a list
of names. If categorical is None, the data type of the columns
in the DataFrame will be used to infer handling. In cases where
numeric columns are to be treated as categoricals (e.g., random
factors coded as numerical IDs), explicitly passing variable
names via this argument is recommended.
append (bool): if True, terms are appended to the existing model
rather than replacing any existing terms. This allows
formula-based specification of the model in stages.
'''
data = self.data
# Primitive values (floats, strs) can be overwritten with Prior objects
# so we need to make sure to copy first to avoid bad things happening
# if user is re-using same prior dict in multiple models.
if priors is None:
priors = {}
else:
priors = deepcopy(priors)
if not append:
self.reset()
# Explicitly convert columns to category if desired--though this
# can also be done within the formula using C().
if categorical is not None:
data = data.copy()
cats = listify(categorical)
data[cats] = data[cats].apply(lambda x: x.astype('category'))
# Custom patsy.missing.NAAction class. Similar to patsy drop/raise
# defaults, but changes the raised message and logs any dropped rows
NA_handler = Custom_NA(dropna=self.dropna)
# screen fixed terms
if fixed is not None:
if '~' in fixed:
clean_fix = re.sub(r'\[.+\]', '', fixed)
dmatrices(clean_fix, data=data, NA_action=NA_handler)
else:
dmatrix(fixed, data=data, NA_action=NA_handler)
# screen random terms
if random is not None:
for term in listify(random):
for side in term.split('|'):
dmatrix(side, data=data, NA_action=NA_handler)
# update the running list of complete cases
if len(NA_handler.completes):
self.completes.append(NA_handler.completes)
# save arguments to pass to _add()
args = dict(
zip(['fixed', 'random', 'priors', 'family', 'link', 'categorical'],
[fixed, random, priors, family, link, categorical]))
self.added_terms.append(args)
self.built = False
def _add(self,
fixed=None,
random=None,
priors=None,
family='gaussian',
link=None,
categorical=None,
append=True):
'''
Internal version of add(), with the same arguments.
Runs during Model.build()
'''
# use cleaned data with NAs removed (if user requested)
data = self.clean_data
# alter this pandas flag to avoid false positive SettingWithCopyWarnings
data.is_copy = False
# Explicitly convert columns to category if desired--though this
# can also be done within the formula using C().
if categorical is not None:
data = data.copy()
cats = listify(categorical)
data[cats] = data[cats].apply(lambda x: x.astype('category'))
if fixed is not None:
if '~' in fixed:
# check to see if formula is using the 'y[event] ~ x' syntax
# (for bernoulli models). If so, chop it into groups:
# 1 = 'y[event]', 2 = 'y', 3 = 'event', 4 = 'x'
# If this syntax is not being used, event = None
event = re.match(r'^((\S+)\[(\S+)\])\s*~(.*)$', fixed)
if event is not None:
fixed = '{}~{}'.format(event.group(2), event.group(4))
y, X = dmatrices(fixed, data=data, NA_action='raise')
y_label = y.design_info.term_names[0]
if event is not None:
# pass in new Y data that has 1 if y=event and 0 otherwise
y_data = y[:,
y.design_info.column_names.index(event.group(1)
)]
y_data = pd.DataFrame({event.group(3): y_data})
self._add_y(y_label, family=family, link=link, data=y_data)
else:
# use Y as-is
self._add_y(y_label, family=family, link=link)
else:
X = dmatrix(fixed, data=data, NA_action='raise')
# Loop over predictor terms
for _name, _slice in X.design_info.term_name_slices.items():
cols = X.design_info.column_names[_slice]
term_data = pd.DataFrame(X[:, _slice], columns=cols)
prior = priors.pop(_name, priors.get('fixed', None))
_type = 'intercept' if _name == 'Intercept' else 'fixed'
prior = self._prepare_prior(prior, _type)
self.terms[_name] = Term(_name, term_data, prior=prior)
# Random effects
if random is not None:
random = listify(random)
for f in random:
f = f.strip()
# Split specification into intercept, predictor, and grouper
patt = r'^([01]+)*[\s\+]*([^\|]+)*\|(.*)'
intcpt, pred, grpr = re.search(patt, f).groups()
label = '{}|{}'.format(pred, grpr) if pred else grpr
prior = priors.pop(label, priors.get('random', None))
prior = self._prepare_prior(prior, 'random')
# Treat all grouping variables as categoricals, regardless of
# their dtype and what the user may have specified in the
# 'categorical' argument.
var_names = re.findall('(\w+)', grpr)
for v in var_names:
if v in data.columns:
data.loc[:, v] = data.loc[:, v].astype('category')
self.clean_data.loc[:, v] = data.loc[:, v]
# Default to including random intercepts
intcpt = 1 if intcpt is None else int(intcpt)
grpr_df = dmatrix('0+%s' % grpr,
data,
return_type='dataframe',
NA_action='raise')
# If there's no predictor, we must be adding random intercepts
if not pred and grpr not in self.terms:
name = '1|' + grpr
pred = np.ones((len(grpr_df), 1))
term = RandomTerm(name,
grpr_df,
pred,
grpr_df.values,
categorical=True,
prior=prior)
self.terms[name] = term
else:
pred_df = dmatrix('%s+%s' % (intcpt, pred),
data,
return_type='dataframe',
NA_action='raise')
# determine value of the 'constant' attribute
const = np.atleast_2d(pred_df.T).T.sum(1).var() == 0
for col, i in pred_df.design_info.column_name_indexes.items(
):
pred_data = pred_df.iloc[:, i]
lev_data = grpr_df.multiply(pred_data, axis=0)
# Also rename intercepts and skip if already added.
# This can happen if user specifies something like
# random=['1|school', 'student|school'].
if col == 'Intercept':
if grpr in self.terms:
continue
label = '1|%s' % grpr
else:
label = col + '|' + grpr
prior = priors.pop(label, priors.get('random', None))
prior = self._prepare_prior(prior, 'random')
# Categorical or continuous is determined from data
ld = lev_data.values
if ((ld == 0) | (ld == 1)).all():
lev_data = lev_data.astype(int)
cat = True
else:
cat = False
pred_data = pred_data[:, None] # Must be 2D later
term = RandomTerm(label,
lev_data,
pred_data,
grpr_df.values,
categorical=cat,
constant=const if const else None,
prior=prior)
self.terms[label] = term
def _add_y(self,
variable,
prior=None,
family='gaussian',
link=None,
*args,
**kwargs):
'''
Add a dependent (or outcome) variable to the model.
Args:
variable (str): the name of the dataset column containing the
y values.
prior (Prior, int, float, str): Optional specification of prior.
Can be an instance of class Prior, a numeric value, or a string
describing the width. In the numeric case, the distribution
specified in the defaults will be used, and the passed value
will be used to scale the appropriate variance parameter. For
strings (e.g., 'wide', 'narrow', 'medium', or 'superwide'),
predefined values will be used.
family (str, Family): A specification of the model family
(analogous to the family object in R). Either a string, or an
instance of class priors.Family. If a string is passed, a
family with the corresponding name must be defined in the
defaults loaded at Model initialization. Valid pre-defined
families are 'gaussian', 'bernoulli', 'poisson', and 't'.
link (str): The model link function to use. Can be either a string
(must be one of the options defined in the current backend;
typically this will include at least 'identity', 'logit',
'inverse', and 'log'), or a callable that takes a 1D ndarray
or theano tensor as the sole argument and returns one with
the same shape.
args, kwargs: Optional positional and keyword arguments to pass
onto Term initializer.
'''
if isinstance(family, string_types):
family = self.default_priors.get(family=family)
self.family = family
# Override family's link if another is explicitly passed
if link is not None:
self.family.link = link
if prior is None:
prior = self.family.prior
# implement default Uniform [0, sd(Y)] prior for residual SD
if self.family.name == 'gaussian':
prior.update(sd=Prior(
'Uniform', lower=0, upper=self.clean_data[variable].std()))
data = kwargs.pop('data', self.clean_data[variable])
term = Term(variable, data, prior=prior, *args, **kwargs)
self.y = term
self.built = False
def _match_derived_terms(self, name):
''' Returns all (random) terms whose named are derived from the
specified string. For example, 'condition|subject' should match the
terms with names '1|subject', 'condition[T.1]|subject', and so on.
Only works for strings with grouping operator ('|').
'''
if '|' not in name:
return None
patt = r'^([01]+)*[\s\+]*([^\|]+)*\|(.*)'
intcpt, pred, grpr = re.search(patt, name).groups()
intcpt = '1|%s' % grpr
if not pred:
return [self.terms[intcpt]] if intcpt in self.terms else None
source = '%s|%s' % (pred, grpr)
found = [
t for (n, t) in self.terms.items()
if n == intcpt or re.sub('(\[.*?\])', '', n) == source
]
# If only the intercept matches, return None, because we want to err
# on the side of caution and not consider '1|subject' to be a match for
# 'condition|subject' if no slopes are found (e.g., the intercept could
# have been set by some other specification like 'gender|subject').
return found if found and (len(found) > 1 or found[0].name != intcpt) \
else None
def set_priors(self,
priors=None,
fixed=None,
random=None,
match_derived_names=True):
'''
Set priors for one or more existing terms.
Args:
priors (dict): Dict of priors to update. Keys are names of terms
to update; values are the new priors (either a Prior instance,
or an int or float that scales the default priors). Note that
a tuple can be passed as the key, in which case the same prior
will be applied to all terms named in the tuple.
fixed (Prior, int, float, str): a prior specification to apply to
all fixed terms currently included in the model.
random (Prior, int, float, str): a prior specification to apply to
all random terms currently included in the model.
match_derived_names (bool): if True, the specified prior(s) will be
applied not only to terms that match the keyword exactly,
but to the levels of random effects that were derived from
the original specification with the passed name. For example,
`priors={'condition|subject':0.5}` would apply the prior
to the terms with names '1|subject', 'condition[T.1]|subject',
and so on. If False, an exact match is required for the
prior to be applied.
'''
# save arguments to pass to _set_priors() at build time
args = dict(
zip(['priors', 'fixed', 'random', 'match_derived_names'],
[priors, fixed, random, match_derived_names]))
self.added_priors.append(args)
self.built = False
def _set_priors(self,
priors=None,
fixed=None,
random=None,
match_derived_names=True):
'''
Internal version of set_priors(), with same arguments.
Runs during Model.build().
'''
targets = {}
if fixed is not None:
targets.update({name: fixed for name in self.fixed_terms.keys()})
if random is not None:
targets.update({name: random for name in self.random_terms.keys()})
if priors is not None:
for k, prior in priors.items():
for name in listify(k):
term_names = list(self.terms.keys())
msg = "No terms in model match '%s'." % name
if name not in term_names:
terms = self._match_derived_terms(name)
if not match_derived_names or terms is None:
raise ValueError(msg)
for t in terms:
targets[t.name] = prior
else:
targets[name] = prior
for name, prior in targets.items():
_type = 'intercept' if name == 'Intercept' else \
'random' if self.terms[name].random else 'fixed'
self.terms[name].prior = self._prepare_prior(prior, _type)
if fixed is not None or random is not None or priors is not None:
self.built = False
# helper function to correctly set default priors, auto_scaling, etc.
def _prepare_prior(self, prior, _type):
'''
prior: Prior object, or float, or None
type (string): 'intercept, 'fixed', or 'random'
'''
if prior is None and not self.auto_scale:
prior = self.default_priors.get(term=_type + '_flat')
if isinstance(prior, Prior):
prior._auto_scale = False
else:
_scale = prior
prior = self.default_priors.get(term=_type)
prior.scale = _scale
if prior.scale is not None: prior._auto_scale = False
return prior
def plot(self, varnames=None):
self.plot_priors(varnames)
def plot_priors(self, varnames=None):
if not self.built:
raise ValueError("Cannot plot priors until model is built!")
with pm.Model():
# get priors for fixed fx, separately for each level of each
# predictor
dists = []
for t in self.fixed_terms.values():
if varnames is not None and t.name not in varnames:
continue
for i, l in enumerate(t.levels):
params = {
k: v[i % len(v)] if isinstance(v, np.ndarray) else v
for k, v in t.prior.args.items()
}
dists += [getattr(pm, t.prior.name)(l, **params)]
# get priors for random effect SDs
for t in self.random_terms.values():
if varnames is not None and t.name not in varnames:
continue
prior = t.prior.args['sd'].name
params = t.prior.args['sd'].args
dists += [getattr(pm, prior)(t.name + '_sd', **params)]
# add priors on Y params if applicable
y_prior = [(k, v) for k, v in self.y.prior.args.items()
if isinstance(v, Prior)]
if len(y_prior):
for p in y_prior:
dists += [
getattr(pm, p[1].name)('_'.join([self.y.name, p[0]]),
**p[1].args)
]
# make the plot!
p = float(len(dists))
fig, axes = plt.subplots(int(np.ceil(p / 2)),
2,
figsize=(12, np.ceil(p / 2) * 2))
# in case there is only 1 row
if int(np.ceil(p / 2)) < 2:
axes = axes[None, :]
for i, d in enumerate(dists):
dist = d.distribution if isinstance(d, pm.model.FreeRV) else d
samp = pd.Series(dist.random(size=1000).flatten())
samp.plot(kind='hist',
ax=axes[divmod(i, 2)[0],
divmod(i, 2)[1]],
normed=True)
samp.plot(kind='kde',
ax=axes[divmod(i, 2)[0],
divmod(i, 2)[1]],
color='b')
axes[divmod(i, 2)[0], divmod(i, 2)[1]].set_title(d.name)
fig.tight_layout()
return axes
@property
def term_names(self):
''' Return names of all terms in order of addition to model. '''
return list(self.terms.keys())
@property
def fixed_terms(self):
''' Return dict of all and only fixed effects in model. '''
return {k: v for (k, v) in self.terms.items() if not v.random}
@property
def random_terms(self):
''' Return dict of all and only random effects in model. '''
return {k: v for (k, v) in self.terms.items() if v.random}
def test_prior_retrieval():
config_file = join(dirname(__file__), 'data', 'sample_priors.json')
pf = PriorFactory(config_file)
prior = pf.get(dist='asiago')
assert prior.name == 'Asiago'
assert isinstance(prior, Prior)
assert prior.args['hardness'] == 10
with pytest.raises(KeyError):
assert prior.args['holes'] == 4
family = pf.get(family='hard')
assert isinstance(family, Family)
assert family.link == 'grate'
backup = family.prior.args['backup']
assert isinstance(backup, Prior)
assert backup.args['flavor'] == 10000
prior = pf.get(term='yellow')
assert prior.name == 'Swiss'
# Test exception raising
with pytest.raises(ValueError):
pf.get(dist='apple')
with pytest.raises(ValueError):
pf.get(term='banana')
with pytest.raises(ValueError):
pf.get(family='cantaloupe')
def test_prior_retrieval():
config_file = join(dirname(__file__), "data", "sample_priors.json")
pf = PriorFactory(config_file)
prior = pf.get(dist="asiago")
assert prior.name == "Asiago"
assert isinstance(prior, Prior)
assert prior.args["hardness"] == 10
with pytest.raises(KeyError):
assert prior.args["holes"] == 4
family = pf.get(family="hard")
assert isinstance(family, Family)
assert family.link == "grate"
backup = family.prior.args["backup"]
assert isinstance(backup, Prior)
assert backup.args["flavor"] == 10000
prior = pf.get(term="yellow")
assert prior.name == "Swiss"
# Test exception raising
with pytest.raises(ValueError):
pf.get(dist="apple")
with pytest.raises(ValueError):
pf.get(term="banana")
with pytest.raises(ValueError):
pf.get(family="cantaloupe")
class Model(object):
'''
Args:
data (DataFrame, str): the dataset to use. Either a pandas
DataFrame, or the name of the file containing the data, which
will be passed to pd.read_table().
intercept (bool): If True, an intercept term is added to the model
at initialization. Defaults to False, as both fixed and random
effect specifications will add an intercept by default.
backend (str): The name of the BackEnd to use. Currently only
'pymc3' is supported.
default_priors (dict, str): An optional specification of the
default priors to use for all model terms. Either a dict
containing named distributions, families, and terms (see the
documentation in priors.PriorFactory for details), or the name
of a JSON file containing the same information.
auto_scale (bool): If True (default), priors are automatically rescaled
to the data (to be weakly informative) any time default priors are
used. Note that any priors explicitly set by the user will always
take precedence over default priors.
'''
def __init__(self,
data=None,
intercept=False,
backend='pymc3',
default_priors=None,
auto_scale=True):
if isinstance(data, string_types):
data = pd.read_table(data, sep=None)
self.default_priors = PriorFactory(default_priors)
obj_cols = data.select_dtypes(['object']).columns
data[obj_cols] = data[obj_cols].apply(lambda x: x.astype('category'))
self.data = data
# Some random effects stuff later requires us to make guesses about
# column groupings into terms based on patsy's naming scheme.
if re.search("[\[\]]+", ''.join(data.columns)):
warnings.warn("At least one of the column names in the specified "
"dataset contain square brackets ('[' or ']')."
"This may cause unexpected behavior if you specify "
"models with random effects. You are encouraged to "
"rename your columns to avoid square brackets.")
self.reset()
if backend.lower() == 'pymc3':
from bambi.backends import PyMC3BackEnd
self.backend = PyMC3BackEnd()
else:
raise ValueError(
"At the moment, only the PyMC3 backend is supported.")
if intercept:
self.add_intercept()
self.auto_scale = auto_scale
def reset(self):
'''
Reset list of terms and y-variable.
'''
self.terms = OrderedDict()
self.y = None
def build(self):
''' Set up the model for sampling/fitting. Performs any steps that
require access to all model terms (e.g., scaling priors on each term),
then calls the BackEnd's build() method.
'''
if self.y is None:
raise ValueError("No outcome (y) variable is set! Please call "
"add_y() or specify an outcome variable using the"
" formula interface before build() or fit().")
# compute information used to set the default priors
# X = fixed effects design matrix (excluding intercept/constant term)
# r2_x = 1 - 1/VIF for each x, i.e., R2 for predicting each x from all
# other x's r2_y = R2 for predicting y from all x's *other than* the
# current x.
# only compute these stats if there are multiple terms in the model
terms = [t for t in self.fixed_terms.values() if t.name != 'Intercept']
if len(self.fixed_terms) > 1:
X = [pd.DataFrame(x.data, columns=x.levels) for x in terms]
X = pd.concat(X, axis=1)
# interim solution for handling non-normal models
sd_y_defaults = {
'gaussian': {
'identity': self.y.data.std(),
'logit': self.y.data.std(),
'probit': self.y.data.std(),
'inverse': self.y.data.std(),
'exp': self.y.data.std()
},
'binomial': {
'identity': self.y.data.std(),
'logit': np.pi / 3**.5,
'probit': 1,
'inverse': self.y.data.std(),
'exp': self.y.data.std()
},
'poisson': {
'identity': self.y.data.std(),
'logit': self.y.data.std(),
'probit': self.y.data.std(),
'inverse': self.y.data.std(),
'exp': self.y.data.std()
},
't': {
'identity': self.y.data.std(),
'logit': self.y.data.std(),
'probit': self.y.data.std(),
'inverse': self.y.data.std(),
'exp': self.y.data.std()
}
}
self.dm_statistics = {
'r2_x':
pd.Series({
x: pd.stats.api.ols(y=X[x],
x=X.drop(x, axis=1),
intercept=True if 'Intercept'
in self.term_names else False).r2
for x in list(X.columns)
}),
'r2_y':
pd.Series({
x: pd.stats.api.ols(y=self.y.data.squeeze(),
x=X.drop(x, axis=1),
intercept=True if 'Intercept'
in self.term_names else False).r2
for x in list(X.columns)
}),
'sd_x':
X.std(),
'sd_y':
sd_y_defaults[self.family.name][self.family.link],
'mean_x':
X.mean(axis=0)
}
# save potentially useful info for diagnostics and send to ModelResults
# mat = correlation matrix of X, w/ diagonal replaced by X means
mat = X.corr()
for x in list(mat.columns):
mat.loc[x, x] = self.dm_statistics['mean_x'][x]
self._diagnostics = {
# the Variance Inflation Factors (VIF), which is possibly useful
# for diagnostics
'VIF': 1 / (1 - self.dm_statistics['r2_x']),
'corr_mean_X': mat
}
# throw informative error if there is perfect collinearity among the fixed effects
if any(self.dm_statistics['r2_x'] > .999):
raise ValueError("There is perfect collinearity among the fixed effects!\n" + \
"Printing some design matrix statistics:\n" + \
str(self.dm_statistics) + '\n' + \
str(self._diagnostics))
# only set priors if there is at least one term in the model
if len(self.terms) > 0:
# Get and scale default priors if none are defined yet
scaler = PriorScaler(self)
for t in self.terms.values():
if not isinstance(t.prior, Prior):
scaler.scale(t)
self.backend.build(self)
self.built = True
def fit(self,
fixed=None,
random=None,
priors=None,
family='gaussian',
link=None,
run=True,
categorical=None,
**kwargs):
'''
Fit the model using the current BackEnd.
Args:
fixed (str): Optional formula specification of fixed effects.
random (list): Optional list-based specification of random effects.
priors (dict): Optional specification of priors for one or more
terms. A dict where the keys are the names of terms in the
model, and the values are either instances of class Prior or
ints, floats, or strings that specify the width of the priors
on a standardized scale.
family (str, Family): A specification of the model family
(analogous to the family object in R). Either a string, or an
instance of class priors.Family. If a string is passed, a
family with the corresponding name must be defined in the
defaults loaded at Model initialization. Valid pre-defined
families are 'gaussian', 'binomial', 'poisson', and 't'.
link (str): The model link function to use. Can be either a string
(must be one of the options defined in the current backend;
typically this will include at least 'identity', 'logit',
'inverse', and 'exp'), or a callable that takes a 1D ndarray
or theano tensor as the sole argument and returns one with
the same shape.
run (bool): Whether or not to immediately begin fitting the model
once any set up of passed arguments is complete.
categorical (str, list): The names of any variables to treat as
categorical. Can be either a single variable name, or a list
of names. If categorical is None, the data type of the columns
in the DataFrame will be used to infer handling. In cases where
numeric columns are to be treated as categoricals (e.g., random
factors coded as numerical IDs), explicitly passing variable
names via this argument is recommended.
'''
if fixed is not None or random is not None:
self.add_formula(fixed=fixed,
random=random,
priors=priors,
family=family,
link=link,
categorical=categorical,
append=False)
''' Run the BackEnd to fit the model. '''
if run:
if not self.built:
warnings.warn("Current Bayesian model has not been built yet; "
"building it first before sampling begins.")
self.build()
return self.backend.run(**kwargs)
def add_intercept(self):
'''
Adds a constant term to the model. Generally unnecessary when using the
formula interface, but useful when specifying the model via add_term().
'''
n = len(self.data)
df = pd.DataFrame(np.ones((n, 1)), columns=['Intercept'])
self.add_term('Intercept', df)
def add_formula(self,
fixed=None,
random=None,
priors=None,
family='gaussian',
link=None,
categorical=None,
append=False):
'''
Adds one or more terms to the model via an R-like formula syntax.
Args:
fixed (str): Optional formula specification of fixed effects.
random (list): Optional list-based specification of random effects.
priors (dict): Optional specification of priors for one or more
terms. A dict where the keys are the names of terms in the
model, and the values are either instances of class Prior or
ints, floats, or strings that specify the width of the priors
on a standardized scale.
family (str, Family): A specification of the model family
(analogous to the family object in R). Either a string, or an
instance of class priors.Family. If a string is passed, a
family with the corresponding name must be defined in the
defaults loaded at Model initialization. Valid pre-defined
families are 'gaussian', 'binomial', 'poisson', and 't'.
link (str): The model link function to use. Can be either a string
(must be one of the options defined in the current backend;
typically this will include at least 'identity', 'logit',
'inverse', and 'exp'), or a callable that takes a 1D ndarray
or theano tensor as the sole argument and returns one with
the same shape.
categorical (str, list): The names of any variables to treat as
categorical. Can be either a single variable name, or a list
of names. If categorical is None, the data type of the columns
in the DataFrame will be used to infer handling. In cases where
numeric columns are to be treated as categoricals (e.g., random
factors coded as numerical IDs), explicitly passing variable
names via this argument is recommended.
append (bool): if True, terms are appended to the existing model
rather than replacing any existing terms. This allows
formula-based specification of the model in stages.
'''
data = self.data
if priors is None:
priors = {}
if not append:
self.reset()
if fixed is not None:
# Explicitly convert columns to category if desired--though this
# can also be done within the formula using C().
if categorical is not None:
data = data.copy()
cats = listify(categorical)
data[cats] = data[cats].apply(lambda x: x.astype('category'))
if '~' in fixed:
y, X = dmatrices(fixed, data=data)
y_label = y.design_info.term_names[0]
self.add_y(y_label, family=family, link=link)
else:
X = dmatrix(fixed, data=data)
# Loop over predictor terms
for _name, _slice in X.design_info.term_name_slices.items():
cols = X.design_info.column_names[_slice]
term_data = pd.DataFrame(X[:, _slice], columns=cols)
prior = priors.pop(_name, priors.pop('fixed', None))
self.add_term(_name, data=term_data, prior=prior)
# Random effects
if random is not None:
random = listify(random)
for f in random:
f = f.strip()
kwargs = {'random': True}
if re.search('[\*\(\)]+', f):
raise ValueError("Random term '%s' contains an invalid "
"character. Note that only the | and + "
"operators are currently supported in "
"random effects specifications.")
# replace explicit intercept terms like '1|subj' with just 'subj'
f = re.sub(r'^1\s*\|(.*)', r'\1', f).strip()
# Split specification into intercept, predictor, and grouper
patt = r'^([01]+)*[\s\+]*([^\|]+)\|*(.*)'
intcpt, pred, grpr = re.search(patt, f).groups()
label = '{}|{}'.format(pred, grpr) if grpr else pred
# Default to including random intercepts
if intcpt is None:
intcpt = 1
intcpt = int(intcpt)
# If there's no grouper, we must be adding random intercepts
if not grpr:
kwargs.update(dict(categorical=True, drop_first=False))
variable = pred
else:
# If we're adding slopes, add random intercepts as well,
# unless they were explicitly excluded
if intcpt and grpr not in self.terms:
self.add_term(variable=grpr,
categorical=True,
random=True,
drop_first=False)
if self.data[pred].dtype.name in ['object', 'category']:
kwargs['categorical'] = True
if not intcpt:
kwargs['drop_first'] = False
variable, kwargs['over'] = pred, grpr
prior = priors.pop(label, priors.pop('random', None))
self.add_term(variable=variable, label=label, **kwargs)
def add_y(self,
variable,
prior=None,
family='gaussian',
link=None,
*args,
**kwargs):
'''
Add a dependent (or outcome) variable to the model.
Args:
variable (str): the name of the dataset column containing the
y values.
prior (Prior, int, float, str): Optional specification of prior.
Can be an instance of class Prior, a numeric value, or a string
describing the width. In the numeric case, the distribution
specified in the defaults will be used, and the passed value
will be used to scale the appropriate variance parameter. For
strings (e.g., 'wide', 'narrow', 'medium', or 'superwide'),
predefined values will be used.
family (str, Family): A specification of the model family
(analogous to the family object in R). Either a string, or an
instance of class priors.Family. If a string is passed, a
family with the corresponding name must be defined in the
defaults loaded at Model initialization. Valid pre-defined
families are 'gaussian', 'binomial', 'poisson', and 't'.
link (str): The model link function to use. Can be either a string
(must be one of the options defined in the current backend;
typically this will include at least 'identity', 'logit',
'inverse', and 'exp'), or a callable that takes a 1D ndarray
or theano tensor as the sole argument and returns one with
the same shape.
args, kwargs: Optional positional and keyword arguments to pass
onto add_term().
'''
if isinstance(family, string_types):
family = self.default_priors.get(family=family)
self.family = family
# Override family's link if another is explicitly passed
if link is not None:
self.family.link = link
if prior is None:
prior = self.family.prior
# implement default Uniform [0, sd(Y)] prior for residual SD
if self.family.name == 'gaussian':
prior.update(
sd=Prior('Uniform', lower=0, upper=self.data[variable].std()))
self.add_term(variable, prior=prior, *args, **kwargs)
# use last-added term name b/c it could have been changed by add_term
name = list(self.terms.values())[-1].name
self.y = self.terms.pop(name)
self.built = False
def add_term(self,
variable,
data=None,
label=None,
categorical=False,
random=False,
over=None,
prior=None,
drop_first=True):
'''
Add a term to the model.
Args:
variable (str): The name of the dataset column to use; also used
as the Term instance label if not otherwise specified using
the label argument.
data (DataFrame): Optional pandas DataFrame containing the term
values to use. If None (default), the correct column will be
extracted from the dataset currently loaded into the model
(based on the name passed in the variable argument).
label (str): Optional label/name to use for the term. If None, the
label will be automatically generated based on the variable
name and additional arguments.
categorical (bool): Whether or not the input variable should be
treated as categorical (defaults to False).
random (bool): If True, the predictor variable is modeled as a
random effect; if False, the predictor is modeled as a fixed
effect.
over (str): When adding random slopes, the name of the variable the
slopes are randomly distributed over. For example, if
variable='condition', categorical=True, random=True, and
over='subject', a separate set of random subject slopes will be
added for each level of the condition variable. This is
analogous to the lme4 specification of 'condition|subject'.
prior (Prior, int, float, str): Optional specification of prior.
Can be an instance of class Prior, a numeric value, or a string
describing the width. In the numeric case, the distribution
specified in the defaults will be used, and the passed value
will be used to scale the appropriate variance parameter. For
strings (e.g., 'wide', 'narrow', 'medium', or 'superwide'),
predefined values will be used.
drop_first (bool): indicates whether to use full rank or N-1 coding
when the predictor is categorical. If True, the N levels of the
categorical variable will be represented using N dummy columns.
If False, the predictor will be represented using N-1 binary
indicators, where each indicator codes the contrast between
the N_j and N_0 columns, for j = {1..N-1}.
Notes: One can think of bambi's split_by operation as a sequence of two
steps. First, the target variable is multiplied by the splitting
variable. This is equivalent to a formula call like 'A:B'. Second,
the columns of the resulting matrix are "grouped" by the levels
of the split_by variable.
'''
if data is None:
data = self.data.copy()
# Make sure user didn't forget to set categorical=True
if variable in data.columns and \
data.loc[:, variable].dtype.name in ['object', 'category']:
categorical = True
else:
# If all columns have identical names except for levels in [],
# assume they've already been contrast-coded, and pass data as-is
cols = [re.sub('\[.*?\]', '', c) for c in data.columns]
if len(set(cols)) > 1:
X = data[[variable]]
if categorical:
X = pd.get_dummies(data[variable], drop_first=drop_first)
elif variable in data.columns:
X = data[[variable]]
else:
X = data
if random and over is not None:
id_var = pd.get_dummies(data[over], drop_first=False)
data = {over: id_var.values, variable: X.values}
f = '0 + %s:%s' % (over, variable)
data = dmatrix(f, data=data)
cols = data.design_info.column_names
data = pd.DataFrame(data, columns=cols)
# For categorical effects, one variance term per predictor level
if categorical:
split_data = {}
groups = list(set([re.sub(r'^.*?\:', '', c) for c in cols]))
for g in groups:
patt = re.escape(r':%s' % g) + '$'
level_data = data.filter(regex=patt)
level_data.columns = [
c.split(':')[0] for c in level_data.columns
]
level_data = level_data.loc[:,
(level_data != 0).any(axis=0)]
split_data[g] = level_data.values
data = split_data
else:
data = X
if label is None:
label = variable
if over is not None:
label += '|%s' % over
term = Term(name=label,
data=data,
categorical=categorical,
random=random,
prior=prior)
self.terms[term.name] = term
self.built = False
def set_priors(self, priors=None, fixed=None, random=None):
'''
Set priors for one or more existing terms.
Args:
priors (dict): Dict of priors to update. Keys are names of terms
to update; values are the new priors (either a Prior instance,
or an int or float that scales the default priors). Note that
a tuple can be passed as the key, in which case the same prior
will be applied to all terms named in the tuple.
fixed (Prior, int, float, str): a prior specification to apply to
all fixed terms currently included in the model.
random (Prior, int, float, str): a prior specification to apply to
all random terms currently included in the model.
'''
targets = {}
if fixed is not None:
targets.update({name: fixed for name in self.fixed_terms.keys()})
if random is not None:
targets.update({name: random for name in self.random_terms.keys()})
if priors is not None:
for k, prior in priors.items():
for name in listify(k):
if name not in self.terms:
raise ValueError("The model contains no term with "
"the name '%s'." % name)
targets[name] = prior
for name, prior in targets.items():
self.terms[name].prior = prior
def plot(self, kind='priors'):
# Currently this only supports plotting priors for fixed effects
if not self.built:
raise ValueError("Cannot plot priors until model is built!")
with pm.Model():
# get priors separately for each level of each predictor
dists = []
for t in self.fixed_terms.values():
for i, l in enumerate(t.levels):
params = {
k: v[i % len(v)] if isinstance(v, np.ndarray) else v
for k, v in t.prior.args.items()
}
dists += [getattr(pm, t.prior.name)(l, **params)]
# add priors on Y params if applicable
y_prior = [(k, v) for k, v in self.y.prior.args.items()
if isinstance(v, Prior)]
if len(y_prior):
for p in y_prior:
dists += [
getattr(pm, p[1].name)('_'.join([self.y.name, p[0]]),
**p[1].args)
]
# make the plot!
p = len(dists)
fig, axes = plt.subplots(int(np.ceil(p / 2)),
2,
figsize=(12, np.ceil(p / 2) * 2))
# in case there is only 1 row
if int(np.ceil(p / 2)) < 2: axes = axes[None, :]
for i, d in enumerate(dists):
dist = d.distribution if isinstance(d, FreeRV) else d
samp = pd.Series(dist.random(size=1000).flatten())
samp.plot(kind='hist',
ax=axes[divmod(i, 2)[0],
divmod(i, 2)[1]],
normed=True)
samp.plot(kind='kde',
ax=axes[divmod(i, 2)[0],
divmod(i, 2)[1]],
color='b')
axes[divmod(i, 2)[0], divmod(i, 2)[1]].set_title(d.name)
fig.tight_layout()
return axes
@property
def term_names(self):
''' Return names of all terms in order of addition to model. '''
return list(self.terms.keys())
@property
def fixed_terms(self):
''' Return dict of all and only fixed effects in model. '''
return {k: v for (k, v) in self.terms.items() if not v.random}
@property
def random_terms(self):
''' Return dict of all and only random effects in model. '''
return {k: v for (k, v) in self.terms.items() if v.random}
