def _setup_design(self):
"""
Create a callable object to evaluate the design matrix
at a given set of parameter values to be specified by
a recarray and observed Term values, also specified
by a recarray.
"""
d = self.design_expr
# Before evaluating, we recreate the formula
# with numbered terms, and numbered parameters.
# This renaming has no impact on the
# final design matrix as the
# callable, self._f below, is a lambda
# that does not care about the names of the terms.
# First, find all terms in the mean expression,
# and rename them in the form "__t%d__" with a
# random offset.
# This may cause a possible problem
# when there are parameters named something like "__t%d__".
# Using the random offset will minimize the possibility
# of this happening.
# This renaming is here principally because of the
# intercept.
random_offset = np.random.random_integers(low=0, high=2**30)
terms = getterms(self.mean)
newterms = []
for i, t in enumerate(terms):
newt = sympy.DeferredVector("__t%d__" % (i + random_offset))
for j, _ in enumerate(d):
d[j] = d[j].subs(t, newt)
newterms.append(newt)
# Next, find all parameters that remain in the design expression.
# In a standard regression model, there will be no parameters
# because they will all be differentiated away in computing
# self.design_expr. In nonlinear models, parameters will remain.
params = getparams(self.design_expr)
newparams = []
for i, p in enumerate(params):
newp = sympy.Symbol("__p%d__" % (i + random_offset), dummy=True)
for j, _ in enumerate(d):
d[j] = d[j].subs(p, newp)
newparams.append(newp)
# If there are any aliased functions, these need to be added
# to the name space before sympy lambdifies the expression
# These "aliased" functions are used for things like
# the natural splines, etc. You can represent natural splines
# with sympy but the expression is pretty awful.
_namespace = {}
_add_aliases_to_namespace(_namespace, *d)
self._f = sympy.lambdify(newparams + newterms, d,
(_namespace, "numpy"))
# The input to self.design will be a recarray of that must
# have field names that the Formula will expect to see.
# However, if any of self.terms are FactorTerms, then the field
# in the recarray will not actually be in the Term.
#
# For example, if there is a Factor 'f' with levels ['a','b'],
# there will be terms 'f_a' and 'f_b', though the input to
# design will have a field named 'f'. In this sense,
# the recarray used in the call to self.design
# is not really made up of terms, but "preterms".
# In this case, the callable
preterm = []
for t in terms:
if not is_factor_term(t):
preterm.append(str(t))
else:
preterm.append(t.factor_name)
preterm = list(set(preterm))
# There is also an argument for parameters that are not
# Terms.
self._dtypes = {
'param': np.dtype([(str(p), np.float) for p in params]),
'term': np.dtype([(str(t), np.float) for t in terms]),
'preterm': np.dtype([(n, np.float) for n in preterm])
}
self.__terms = terms
def _getdiff(self):
p = list(set(getparams(self.mean)))
p.sort()
return [s.doit() for s in sympy.diff(self.mean, p)]
def params(self):
"""
The parameters in the Formula.
"""
return getparams(self.mean)
def _setup_design(self):
"""
Create a callable object to evaluate the design matrix
at a given set of parameter values to be specified by
a recarray and observed Term values, also specified
by a recarray.
"""
d = self.design_expr
# Before evaluating, we recreate the formula
# with numbered terms, and numbered parameters.
# This renaming has no impact on the
# final design matrix as the
# callable, self._f below, is a lambda
# that does not care about the names of the terms.
# First, find all terms in the mean expression,
# and rename them in the form "__t%d__" with a
# random offset.
# This may cause a possible problem
# when there are parameters named something like "__t%d__".
# Using the random offset will minimize the possibility
# of this happening.
# This renaming is here principally because of the
# intercept.
random_offset = np.random.random_integers(low=0, high=2**30)
terms = getterms(self.mean)
newterms = []
for i, t in enumerate(terms):
newt = sympy.DeferredVector("__t%d__" % (i + random_offset))
for j, _ in enumerate(d):
d[j] = d[j].subs(t, newt)
newterms.append(newt)
# Next, find all parameters that remain in the design expression.
# In a standard regression model, there will be no parameters
# because they will all be differentiated away in computing
# self.design_expr. In nonlinear models, parameters will remain.
params = getparams(self.design_expr)
newparams = []
for i, p in enumerate(params):
newp = sympy.Symbol("__p%d__" % (i + random_offset), dummy=True)
for j, _ in enumerate(d):
d[j] = d[j].subs(p, newp)
newparams.append(newp)
# If there are any aliased functions, these need to be added
# to the name space before sympy lambdifies the expression
# These "aliased" functions are used for things like
# the natural splines, etc. You can represent natural splines
# with sympy but the expression is pretty awful.
_namespace = {};
_add_aliases_to_namespace(_namespace, *d)
self._f = sympy.lambdify(newparams + newterms, d, (_namespace, "numpy"))
# The input to self.design will be a recarray of that must
# have field names that the Formula will expect to see.
# However, if any of self.terms are FactorTerms, then the field
# in the recarray will not actually be in the Term.
#
# For example, if there is a Factor 'f' with levels ['a','b'],
# there will be terms 'f_a' and 'f_b', though the input to
# design will have a field named 'f'. In this sense,
# the recarray used in the call to self.design
# is not really made up of terms, but "preterms".
# In this case, the callable
preterm = []
for t in terms:
if not is_factor_term(t):
preterm.append(str(t))
else:
preterm.append(t.factor_name)
preterm = list(set(preterm))
# There is also an argument for parameters that are not
# Terms.
self._dtypes = {'param':np.dtype([(str(p), np.float) for p in params]),
'term':np.dtype([(str(t), np.float) for t in terms]),
'preterm':np.dtype([(n, np.float) for n in preterm])}
self.__terms = terms
def _getdiff(self):
p = list(set(getparams(self.mean)))
p.sort()
return [s.doit() for s in sympy.diff(self.mean, p)]
def params(self):
"""
The parameters in the Formula.
"""
return getparams(self.mean)
