class DesignInfo(object):
"""A DesignInfo object holds metadata about a design matrix.
This is the main object that Patsy uses to pass information to
statistical libraries. Usually encountered as the `.design_info` attribute
on design matrices.
"""
def __init__(self,
column_names,
term_slices=None,
term_name_slices=None,
builder=None):
self.column_name_indexes = OrderedDict(
zip(column_names, range(len(column_names))))
if term_slices is not None:
#: An OrderedDict mapping :class:`Term` objects to Python
#: func:`slice` objects. May be None, for design matrices which
#: were constructed directly rather than by using the patsy
#: machinery. If it is not None, then it
#: is guaranteed to list the terms in order, and the slices are
#: guaranteed to exactly cover all columns with no overlap or
#: gaps.
self.term_slices = OrderedDict(term_slices)
if term_name_slices is not None:
raise ValueError("specify only one of term_slices and "
"term_name_slices")
term_names = [term.name() for term in self.term_slices]
#: And OrderedDict mapping term names (as strings) to Python
#: :func:`slice` objects. Guaranteed never to be None. Guaranteed
#: to list the terms in order, and the slices are
#: guaranteed to exactly cover all columns with no overlap or
#: gaps. Name overlap is allowed between term names and column
#: names, but it is guaranteed that if it occurs, then they refer
#: to exactly the same column.
self.term_name_slices = OrderedDict(
zip(term_names, self.term_slices.values()))
else: # term_slices is None
self.term_slices = None
if term_name_slices is None:
# Make up one term per column
term_names = column_names
slices = [slice(i, i + 1) for i in range(len(column_names))]
term_name_slices = zip(term_names, slices)
self.term_name_slices = OrderedDict(term_name_slices)
self.builder = builder
# Guarantees:
# term_name_slices is never None
# The slices in term_name_slices are in order and exactly cover the
# whole range of columns.
# term_slices may be None
# If term_slices is not None, then its slices match the ones in
# term_name_slices.
# If there is any name overlap between terms and columns, they refer
# to the same columns.
assert self.term_name_slices is not None
if self.term_slices is not None:
assert (list(self.term_slices.values()) == list(
self.term_name_slices.values()))
covered = 0
for slice_ in six.itervalues(self.term_name_slices):
start, stop, step = slice_.indices(len(column_names))
if start != covered:
raise ValueError("bad term slices")
if step != 1:
raise ValueError("bad term slices")
covered = stop
if covered != len(column_names):
raise ValueError("bad term indices")
for column_name, index in six.iteritems(self.column_name_indexes):
if column_name in self.term_name_slices:
slice_ = self.term_name_slices[column_name]
if slice_ != slice(index, index + 1):
raise ValueError("term/column name collision")
__repr__ = repr_pretty_delegate
def _repr_pretty_(self, p, cycle):
assert not cycle
if self.term_slices is None:
kwargs = [("term_name_slices", self.term_name_slices)]
else:
kwargs = [("term_slices", self.term_slices)]
if self.builder is not None:
kwargs.append(("builder", self.builder))
repr_pretty_impl(p, self, [self.column_names], kwargs)
@property
def column_names(self):
"A list of the column names, in order."
return list(self.column_name_indexes)
@property
def terms(self):
"A list of :class:`Terms`, in order, or else None."
if self.term_slices is None:
return None
return list(self.term_slices)
@property
def term_names(self):
"A list of terms, in order."
return list(self.term_name_slices)
def slice(self, columns_specifier):
"""Locate a subset of design matrix columns, specified symbolically.
A patsy design matrix has two levels of structure: the individual
columns (which are named), and the :ref:`terms <formulas>` in
the formula that generated those columns. This is a one-to-many
relationship: a single term may span several columns. This method
provides a user-friendly API for locating those columns.
(While we talk about columns here, this is probably most useful for
indexing into other arrays that are derived from the design matrix,
such as regression coefficients or covariance matrices.)
The `columns_specifier` argument can take a number of forms:
* A term name
* A column name
* A :class:`Term` object
* An integer giving a raw index
* A raw slice object
In all cases, a Python :func:`slice` object is returned, which can be
used directly for indexing.
Example::
y, X = dmatrices("y ~ a", demo_data("y", "a", nlevels=3))
betas = np.linalg.lstsq(X, y)[0]
a_betas = betas[X.design_info.slice("a")]
(If you want to look up a single individual column by name, use
``design_info.column_name_indexes[name]``.)
"""
if isinstance(columns_specifier, slice):
return columns_specifier
if np.issubsctype(type(columns_specifier), np.integer):
return slice(columns_specifier, columns_specifier + 1)
if (self.term_slices is not None
and columns_specifier in self.term_slices):
return self.term_slices[columns_specifier]
if columns_specifier in self.term_name_slices:
return self.term_name_slices[columns_specifier]
if columns_specifier in self.column_name_indexes:
idx = self.column_name_indexes[columns_specifier]
return slice(idx, idx + 1)
raise PatsyError("unknown column specified '%s'" %
(columns_specifier, ))
def linear_constraint(self, constraint_likes):
"""Construct a linear constraint in matrix form from a (possibly
symbolic) description.
Possible inputs:
* A dictionary which is taken as a set of equality constraint. Keys
can be either string column names, or integer column indexes.
* A string giving a arithmetic expression referring to the matrix
columns by name.
* A list of such strings which are ANDed together.
* A tuple (A, b) where A and b are array_likes, and the constraint is
Ax = b. If necessary, these will be coerced to the proper
dimensionality by appending dimensions with size 1.
The string-based language has the standard arithmetic operators, / * +
- and parentheses, plus "=" is used for equality and "," is used to
AND together multiple constraint equations within a string. You can
If no = appears in some expression, then that expression is assumed to
be equal to zero. Division is always float-based, even if
``__future__.true_division`` isn't in effect.
Returns a :class:`LinearConstraint` object.
Examples::
di = DesignInfo(["x1", "x2", "x3"])
# Equivalent ways to write x1 == 0:
di.linear_constraint({"x1": 0}) # by name
di.linear_constraint({0: 0}) # by index
di.linear_constraint("x1 = 0") # string based
di.linear_constraint("x1") # can leave out "= 0"
di.linear_constraint("2 * x1 = (x1 + 2 * x1) / 3")
di.linear_constraint(([1, 0, 0], 0)) # constraint matrices
# Equivalent ways to write x1 == 0 and x3 == 10
di.linear_constraint({"x1": 0, "x3": 10})
di.linear_constraint({0: 0, 2: 10})
di.linear_constraint({0: 0, "x3": 10})
di.linear_constraint("x1 = 0, x3 = 10")
di.linear_constraint("x1, x3 = 10")
di.linear_constraint(["x1", "x3 = 0"]) # list of strings
di.linear_constraint("x1 = 0, x3 - 10 = x1")
di.linear_constraint([[1, 0, 0], [0, 0, 1]], [0, 10])
# You can also chain together equalities, just like Python:
di.linear_constraint("x1 = x2 = 3")
"""
return linear_constraint(constraint_likes, self.column_names)
def describe(self):
"""Returns a human-readable string describing this design info.
Example:
.. ipython::
In [1]: y, X = dmatrices("y ~ x1 + x2", demo_data("y", "x1", "x2"))
In [2]: y.design_info.describe()
Out[2]: 'y'
In [3]: X.design_info.describe()
Out[3]: '1 + x1 + x2'
.. warning::
There is no guarantee that the strings returned by this
function can be parsed as formulas. They are best-effort descriptions
intended for human users.
"""
names = []
for name in self.term_names:
if name == "Intercept":
names.append("1")
else:
names.append(name)
return " + ".join(names)
@classmethod
def from_array(cls, array_like, default_column_prefix="column"):
"""Find or construct a DesignInfo appropriate for a given array_like.
If the input `array_like` already has a ``.design_info``
attribute, then it will be returned. Otherwise, a new DesignInfo
object will be constructed, using names either taken from the
`array_like` (e.g., for a pandas DataFrame with named columns), or
constructed using `default_column_prefix`.
This is how :func:`dmatrix` (for example) creates a DesignInfo object
if an arbitrary matrix is passed in.
:arg array_like: An ndarray or pandas container.
:arg default_column_prefix: If it's necessary to invent column names,
then this will be used to construct them.
:returns: a DesignInfo object
"""
if hasattr(array_like, "design_info") and isinstance(
array_like.design_info, cls):
return array_like.design_info
arr = atleast_2d_column_default(array_like, preserve_pandas=True)
if arr.ndim > 2:
raise ValueError("design matrix can't have >2 dimensions")
columns = getattr(arr, "columns", range(arr.shape[1]))
if (isinstance(columns, np.ndarray)
and not np.issubdtype(columns.dtype, np.integer)):
column_names = [str(obj) for obj in columns]
else:
column_names = [
"%s%s" % (default_column_prefix, i) for i in columns
]
return DesignInfo(column_names)
class DesignInfo(object):
"""A DesignInfo object holds metadata about a design matrix.
This is the main object that Patsy uses to pass metadata about a design
matrix to statistical libraries, in order to allow further downstream
processing like intelligent tests, prediction on new data, etc. Usually
encountered as the `.design_info` attribute on design matrices.
"""
def __init__(self, column_names,
factor_infos=None, term_codings=None):
self.column_name_indexes = OrderedDict(zip(column_names,
range(len(column_names))))
if (factor_infos is None) != (term_codings is None):
raise ValueError("Must specify either both or neither of "
"factor_infos= and term_codings=")
self.factor_infos = factor_infos
self.term_codings = term_codings
# factor_infos is a dict containing one entry for every factor
# mentioned in our terms
# and mapping each to FactorInfo object
if self.factor_infos is not None:
if not isinstance(self.factor_infos, dict):
raise ValueError("factor_infos should be a dict")
if not isinstance(self.term_codings, OrderedDict):
raise ValueError("term_codings must be an OrderedDict")
for term, subterms in six.iteritems(self.term_codings):
if not isinstance(term, Term):
raise ValueError("expected a Term, not %r" % (term,))
if not isinstance(subterms, list):
raise ValueError("term_codings must contain lists")
term_factors = set(term.factors)
for subterm in subterms:
if not isinstance(subterm, SubtermInfo):
raise ValueError("expected SubtermInfo, "
"not %r" % (subterm,))
if not term_factors.issuperset(subterm.factors):
raise ValueError("unexpected factors in subterm")
all_factors = set()
for term in self.term_codings:
all_factors.update(term.factors)
if all_factors != set(self.factor_infos):
raise ValueError("Provided Term objects and factor_infos "
"do not match")
for factor, factor_info in six.iteritems(self.factor_infos):
if not isinstance(factor_info, FactorInfo):
raise ValueError("expected FactorInfo object, not %r"
% (factor_info,))
if factor != factor_info.factor:
raise ValueError("mismatched factor_info.factor")
for term, subterms in six.iteritems(self.term_codings):
for subterm in subterms:
exp_cols = 1
cat_factors = set()
for factor in subterm.factors:
fi = self.factor_infos[factor]
if fi.type == "numerical":
exp_cols *= fi.num_columns
else:
assert fi.type == "categorical"
cm = subterm.contrast_matrices[factor].matrix
if cm.shape[0] != len(fi.categories):
raise ValueError("Mismatched contrast matrix "
"for factor %r" % (factor,))
cat_factors.add(factor)
exp_cols *= cm.shape[1]
if cat_factors != set(subterm.contrast_matrices):
raise ValueError("Mismatch between contrast_matrices "
"and categorical factors")
if exp_cols != subterm.num_columns:
raise ValueError("Unexpected num_columns")
if term_codings is None:
# Need to invent term information
self.term_slices = None
# We invent one term per column, with the same name as the column
term_names = column_names
slices = [slice(i, i + 1) for i in range(len(column_names))]
self.term_name_slices = OrderedDict(zip(term_names, slices))
else:
# Need to derive term information from term_codings
self.term_slices = OrderedDict()
idx = 0
for term, subterm_infos in six.iteritems(self.term_codings):
term_columns = 0
for subterm_info in subterm_infos:
term_columns += subterm_info.num_columns
self.term_slices[term] = slice(idx, idx + term_columns)
idx += term_columns
if idx != len(self.column_names):
raise ValueError("mismatch between column_names and columns "
"coded by given terms")
self.term_name_slices = OrderedDict(
[(term.name(), slice_)
for (term, slice_) in six.iteritems(self.term_slices)])
# Guarantees:
# term_name_slices is never None
# The slices in term_name_slices are in order and exactly cover the
# whole range of columns.
# term_slices may be None
# If term_slices is not None, then its slices match the ones in
# term_name_slices.
assert self.term_name_slices is not None
if self.term_slices is not None:
assert (list(self.term_slices.values())
== list(self.term_name_slices.values()))
# These checks probably aren't necessary anymore now that we always
# generate the slices ourselves, but we'll leave them in just to be
# safe.
covered = 0
for slice_ in six.itervalues(self.term_name_slices):
start, stop, step = slice_.indices(len(column_names))
assert start == covered
assert step == 1
covered = stop
assert covered == len(column_names)
# If there is any name overlap between terms and columns, they refer
# to the same columns.
for column_name, index in six.iteritems(self.column_name_indexes):
if column_name in self.term_name_slices:
slice_ = self.term_name_slices[column_name]
if slice_ != slice(index, index + 1):
raise ValueError("term/column name collision")
__repr__ = repr_pretty_delegate
def _repr_pretty_(self, p, cycle):
assert not cycle
repr_pretty_impl(p, self,
[self.column_names],
[("factor_infos", self.factor_infos),
("term_codings", self.term_codings)])
@property
def column_names(self):
"A list of the column names, in order."
return list(self.column_name_indexes)
@property
def terms(self):
"A list of :class:`Terms`, in order, or else None."
if self.term_slices is None:
return None
return list(self.term_slices)
@property
def term_names(self):
"A list of terms, in order."
return list(self.term_name_slices)
@property
def builder(self):
".. deprecated:: 0.4.0"
warnings.warn(DeprecationWarning(
"The DesignInfo.builder attribute is deprecated starting in "
"patsy v0.4.0; distinct builder objects have been eliminated "
"and design_info.builder is now just a long-winded way of "
"writing 'design_info' (i.e. the .builder attribute just "
"returns self)"), stacklevel=2)
return self
@property
def design_info(self):
".. deprecated:: 0.4.0"
warnings.warn(DeprecationWarning(
"Starting in patsy v0.4.0, the DesignMatrixBuilder class has "
"been merged into the DesignInfo class. So there's no need to "
"use builder.design_info to access the DesignInfo; 'builder' "
"already *is* a DesignInfo."), stacklevel=2)
return self
def slice(self, columns_specifier):
"""Locate a subset of design matrix columns, specified symbolically.
A patsy design matrix has two levels of structure: the individual
columns (which are named), and the :ref:`terms <formulas>` in
the formula that generated those columns. This is a one-to-many
relationship: a single term may span several columns. This method
provides a user-friendly API for locating those columns.
(While we talk about columns here, this is probably most useful for
indexing into other arrays that are derived from the design matrix,
such as regression coefficients or covariance matrices.)
The `columns_specifier` argument can take a number of forms:
* A term name
* A column name
* A :class:`Term` object
* An integer giving a raw index
* A raw slice object
In all cases, a Python :func:`slice` object is returned, which can be
used directly for indexing.
Example::
y, X = dmatrices("y ~ a", demo_data("y", "a", nlevels=3))
betas = np.linalg.lstsq(X, y)[0]
a_betas = betas[X.design_info.slice("a")]
(If you want to look up a single individual column by name, use
``design_info.column_name_indexes[name]``.)
"""
if isinstance(columns_specifier, slice):
return columns_specifier
if np.issubsctype(type(columns_specifier), np.integer):
return slice(columns_specifier, columns_specifier + 1)
if (self.term_slices is not None
and columns_specifier in self.term_slices):
return self.term_slices[columns_specifier]
if columns_specifier in self.term_name_slices:
return self.term_name_slices[columns_specifier]
if columns_specifier in self.column_name_indexes:
idx = self.column_name_indexes[columns_specifier]
return slice(idx, idx + 1)
raise PatsyError("unknown column specified '%s'"
% (columns_specifier,))
def linear_constraint(self, constraint_likes):
"""Construct a linear constraint in matrix form from a (possibly
symbolic) description.
Possible inputs:
* A dictionary which is taken as a set of equality constraint. Keys
can be either string column names, or integer column indexes.
* A string giving a arithmetic expression referring to the matrix
columns by name.
* A list of such strings which are ANDed together.
* A tuple (A, b) where A and b are array_likes, and the constraint is
Ax = b. If necessary, these will be coerced to the proper
dimensionality by appending dimensions with size 1.
The string-based language has the standard arithmetic operators, / * +
- and parentheses, plus "=" is used for equality and "," is used to
AND together multiple constraint equations within a string. You can
If no = appears in some expression, then that expression is assumed to
be equal to zero. Division is always float-based, even if
``__future__.true_division`` isn't in effect.
Returns a :class:`LinearConstraint` object.
Examples::
di = DesignInfo(["x1", "x2", "x3"])
# Equivalent ways to write x1 == 0:
di.linear_constraint({"x1": 0}) # by name
di.linear_constraint({0: 0}) # by index
di.linear_constraint("x1 = 0") # string based
di.linear_constraint("x1") # can leave out "= 0"
di.linear_constraint("2 * x1 = (x1 + 2 * x1) / 3")
di.linear_constraint(([1, 0, 0], 0)) # constraint matrices
# Equivalent ways to write x1 == 0 and x3 == 10
di.linear_constraint({"x1": 0, "x3": 10})
di.linear_constraint({0: 0, 2: 10})
di.linear_constraint({0: 0, "x3": 10})
di.linear_constraint("x1 = 0, x3 = 10")
di.linear_constraint("x1, x3 = 10")
di.linear_constraint(["x1", "x3 = 0"]) # list of strings
di.linear_constraint("x1 = 0, x3 - 10 = x1")
di.linear_constraint([[1, 0, 0], [0, 0, 1]], [0, 10])
# You can also chain together equalities, just like Python:
di.linear_constraint("x1 = x2 = 3")
"""
return linear_constraint(constraint_likes, self.column_names)
def describe(self):
"""Returns a human-readable string describing this design info.
Example:
.. ipython::
In [1]: y, X = dmatrices("y ~ x1 + x2", demo_data("y", "x1", "x2"))
In [2]: y.design_info.describe()
Out[2]: 'y'
In [3]: X.design_info.describe()
Out[3]: '1 + x1 + x2'
.. warning::
There is no guarantee that the strings returned by this function
can be parsed as formulas, or that if they can be parsed as a
formula that they will produce a model equivalent to the one you
started with. This function produces a best-effort description
intended for humans to read.
"""
names = []
for name in self.term_names:
if name == "Intercept":
names.append("1")
else:
names.append(name)
return " + ".join(names)
def subset(self, which_terms):
"""Create a new :class:`DesignInfo` for design matrices that contain a
subset of the terms that the current :class:`DesignInfo` does.
For example, if ``design_info`` has terms ``x``, ``y``, and ``z``,
then::
design_info2 = design_info.subset(["x", "z"])
will return a new DesignInfo that can be used to construct design
matrices with only the columns corresponding to the terms ``x`` and
``z``. After we do this, then in general these two expressions will
return the same thing (here we assume that ``x``, ``y``, and ``z``
each generate a single column of the output)::
build_design_matrix([design_info], data)[0][:, [0, 2]]
build_design_matrix([design_info2], data)[0]
However, a critical difference is that in the second case, ``data``
need not contain any values for ``y``. This is very useful when doing
prediction using a subset of a model, in which situation R usually
forces you to specify dummy values for ``y``.
If using a formula to specify the terms to include, remember that like
any formula, the intercept term will be included by default, so use
``0`` or ``-1`` in your formula if you want to avoid this.
This method can also be used to reorder the terms in your design
matrix, in case you want to do that for some reason. I can't think of
any.
Note that this method will generally *not* produce the same result as
creating a new model directly. Consider these DesignInfo objects::
design1 = dmatrix("1 + C(a)", data)
design2 = design1.subset("0 + C(a)")
design3 = dmatrix("0 + C(a)", data)
Here ``design2`` and ``design3`` will both produce design matrices
that contain an encoding of ``C(a)`` without any intercept term. But
``design3`` uses a full-rank encoding for the categorical term
``C(a)``, while ``design2`` uses the same reduced-rank encoding as
``design1``.
:arg which_terms: The terms which should be kept in the new
:class:`DesignMatrixBuilder`. If this is a string, then it is parsed
as a formula, and then the names of the resulting terms are taken as
the terms to keep. If it is a list, then it can contain a mixture of
term names (as strings) and :class:`Term` objects.
.. versionadded: 0.2.0
New method on the class DesignMatrixBuilder.
.. versionchanged: 0.4.0
Moved from DesignMatrixBuilder to DesignInfo, as part of the
removal of DesignMatrixBuilder.
"""
if isinstance(which_terms, str):
desc = ModelDesc.from_formula(which_terms)
if desc.lhs_termlist:
raise PatsyError("right-hand-side-only formula required")
which_terms = [term.name() for term in desc.rhs_termlist]
if self.term_codings is None:
# This is a minimal DesignInfo
# If the name is unknown we just let the KeyError escape
new_names = []
for t in which_terms:
new_names += self.column_names[self.term_name_slices[t]]
return DesignInfo(new_names)
else:
term_name_to_term = {}
for term in self.term_codings:
term_name_to_term[term.name()] = term
new_column_names = []
new_factor_infos = {}
new_term_codings = OrderedDict()
for name_or_term in which_terms:
term = term_name_to_term.get(name_or_term, name_or_term)
# If the name is unknown we just let the KeyError escape
s = self.term_slices[term]
new_column_names += self.column_names[s]
for f in term.factors:
new_factor_infos[f] = self.factor_infos[f]
new_term_codings[term] = self.term_codings[term]
return DesignInfo(new_column_names,
factor_infos=new_factor_infos,
term_codings=new_term_codings)
@classmethod
def from_array(cls, array_like, default_column_prefix="column"):
"""Find or construct a DesignInfo appropriate for a given array_like.
If the input `array_like` already has a ``.design_info``
attribute, then it will be returned. Otherwise, a new DesignInfo
object will be constructed, using names either taken from the
`array_like` (e.g., for a pandas DataFrame with named columns), or
constructed using `default_column_prefix`.
This is how :func:`dmatrix` (for example) creates a DesignInfo object
if an arbitrary matrix is passed in.
:arg array_like: An ndarray or pandas container.
:arg default_column_prefix: If it's necessary to invent column names,
then this will be used to construct them.
:returns: a DesignInfo object
"""
if hasattr(array_like, "design_info") and isinstance(array_like.design_info, cls):
return array_like.design_info
arr = atleast_2d_column_default(array_like, preserve_pandas=True)
if arr.ndim > 2:
raise ValueError("design matrix can't have >2 dimensions")
columns = getattr(arr, "columns", range(arr.shape[1]))
if (hasattr(columns, "dtype")
and not safe_issubdtype(columns.dtype, np.integer)):
column_names = [str(obj) for obj in columns]
else:
column_names = ["%s%s" % (default_column_prefix, i)
for i in columns]
return DesignInfo(column_names)
__getstate__ = no_pickling
class DesignInfo(object):
"""A DesignInfo object holds metadata about a design matrix.
This is the main object that Patsy uses to pass metadata about a design
matrix to statistical libraries, in order to allow further downstream
processing like intelligent tests, prediction on new data, etc. Usually
encountered as the `.design_info` attribute on design matrices.
"""
def __init__(self, column_names,
factor_infos=None, term_codings=None):
self.column_name_indexes = OrderedDict(zip(column_names,
range(len(column_names))))
if (factor_infos is None) != (term_codings is None):
raise ValueError("Must specify either both or neither of "
"factor_infos= and term_codings=")
self.factor_infos = factor_infos
self.term_codings = term_codings
# factor_infos is a dict containing one entry for every factor
# mentioned in our terms
# and mapping each to FactorInfo object
if self.factor_infos is not None:
if not isinstance(self.factor_infos, dict):
raise ValueError("factor_infos should be a dict")
if not isinstance(self.term_codings, OrderedDict):
raise ValueError("term_codings must be an OrderedDict")
for term, subterms in six.iteritems(self.term_codings):
if not isinstance(term, Term):
raise ValueError("expected a Term, not %r" % (term,))
if not isinstance(subterms, list):
raise ValueError("term_codings must contain lists")
term_factors = set(term.factors)
for subterm in subterms:
if not isinstance(subterm, SubtermInfo):
raise ValueError("expected SubtermInfo, "
"not %r" % (subterm,))
if not term_factors.issuperset(subterm.factors):
raise ValueError("unexpected factors in subterm")
all_factors = set()
for term in self.term_codings:
all_factors.update(term.factors)
if all_factors != set(self.factor_infos):
raise ValueError("Provided Term objects and factor_infos "
"do not match")
for factor, factor_info in six.iteritems(self.factor_infos):
if not isinstance(factor_info, FactorInfo):
raise ValueError("expected FactorInfo object, not %r"
% (factor_info,))
if factor != factor_info.factor:
raise ValueError("mismatched factor_info.factor")
for term, subterms in six.iteritems(self.term_codings):
for subterm in subterms:
exp_cols = 1
cat_factors = set()
for factor in subterm.factors:
fi = self.factor_infos[factor]
if fi.type == "numerical":
exp_cols *= fi.num_columns
else:
assert fi.type == "categorical"
cm = subterm.contrast_matrices[factor].matrix
if cm.shape[0] != len(fi.categories):
raise ValueError("Mismatched contrast matrix "
"for factor %r" % (factor,))
cat_factors.add(factor)
exp_cols *= cm.shape[1]
if cat_factors != set(subterm.contrast_matrices):
raise ValueError("Mismatch between contrast_matrices "
"and categorical factors")
if exp_cols != subterm.num_columns:
raise ValueError("Unexpected num_columns")
if term_codings is None:
# Need to invent term information
self.term_slices = None
# We invent one term per column, with the same name as the column
term_names = column_names
slices = [slice(i, i + 1) for i in range(len(column_names))]
self.term_name_slices = OrderedDict(zip(term_names, slices))
else:
# Need to derive term information from term_codings
self.term_slices = OrderedDict()
idx = 0
for term, subterm_infos in six.iteritems(self.term_codings):
term_columns = 0
for subterm_info in subterm_infos:
term_columns += subterm_info.num_columns
self.term_slices[term] = slice(idx, idx + term_columns)
idx += term_columns
if idx != len(self.column_names):
raise ValueError("mismatch between column_names and columns "
"coded by given terms")
self.term_name_slices = OrderedDict(
[(term.name(), slice_)
for (term, slice_) in six.iteritems(self.term_slices)])
# Guarantees:
# term_name_slices is never None
# The slices in term_name_slices are in order and exactly cover the
# whole range of columns.
# term_slices may be None
# If term_slices is not None, then its slices match the ones in
# term_name_slices.
assert self.term_name_slices is not None
if self.term_slices is not None:
assert (list(self.term_slices.values())
== list(self.term_name_slices.values()))
# These checks probably aren't necessary anymore now that we always
# generate the slices ourselves, but we'll leave them in just to be
# safe.
covered = 0
for slice_ in six.itervalues(self.term_name_slices):
start, stop, step = slice_.indices(len(column_names))
assert start == covered
assert step == 1
covered = stop
assert covered == len(column_names)
# If there is any name overlap between terms and columns, they refer
# to the same columns.
for column_name, index in six.iteritems(self.column_name_indexes):
if column_name in self.term_name_slices:
slice_ = self.term_name_slices[column_name]
if slice_ != slice(index, index + 1):
raise ValueError("term/column name collision")
__repr__ = repr_pretty_delegate
def _repr_pretty_(self, p, cycle):
assert not cycle
repr_pretty_impl(p, self,
[self.column_names],
[("factor_infos", self.factor_infos),
("term_codings", self.term_codings)])
@property
def column_names(self):
"A list of the column names, in order."
return list(self.column_name_indexes)
@property
def terms(self):
"A list of :class:`Terms`, in order, or else None."
if self.term_slices is None:
return None
return list(self.term_slices)
@property
def term_names(self):
"A list of terms, in order."
return list(self.term_name_slices)
@property
def builder(self):
".. deprecated:: 0.4.0"
warnings.warn(DeprecationWarning(
"The DesignInfo.builder attribute is deprecated starting in "
"patsy v0.4.0; distinct builder objects have been eliminated "
"and design_info.builder is now just a long-winded way of "
"writing 'design_info' (i.e. the .builder attribute just "
"returns self)"), stacklevel=2)
return self
@property
def design_info(self):
".. deprecated:: 0.4.0"
warnings.warn(DeprecationWarning(
"Starting in patsy v0.4.0, the DesignMatrixBuilder class has "
"been merged into the DesignInfo class. So there's no need to "
"use builder.design_info to access the DesignInfo; 'builder' "
"already *is* a DesignInfo."), stacklevel=2)
return self
def slice(self, columns_specifier):
"""Locate a subset of design matrix columns, specified symbolically.
A patsy design matrix has two levels of structure: the individual
columns (which are named), and the :ref:`terms <formulas>` in
the formula that generated those columns. This is a one-to-many
relationship: a single term may span several columns. This method
provides a user-friendly API for locating those columns.
(While we talk about columns here, this is probably most useful for
indexing into other arrays that are derived from the design matrix,
such as regression coefficients or covariance matrices.)
The `columns_specifier` argument can take a number of forms:
* A term name
* A column name
* A :class:`Term` object
* An integer giving a raw index
* A raw slice object
In all cases, a Python :func:`slice` object is returned, which can be
used directly for indexing.
Example::
y, X = dmatrices("y ~ a", demo_data("y", "a", nlevels=3))
betas = np.linalg.lstsq(X, y)[0]
a_betas = betas[X.design_info.slice("a")]
(If you want to look up a single individual column by name, use
``design_info.column_name_indexes[name]``.)
"""
if isinstance(columns_specifier, slice):
return columns_specifier
if np.issubsctype(type(columns_specifier), np.integer):
return slice(columns_specifier, columns_specifier + 1)
if (self.term_slices is not None
and columns_specifier in self.term_slices):
return self.term_slices[columns_specifier]
if columns_specifier in self.term_name_slices:
return self.term_name_slices[columns_specifier]
if columns_specifier in self.column_name_indexes:
idx = self.column_name_indexes[columns_specifier]
return slice(idx, idx + 1)
raise PatsyError("unknown column specified '%s'"
% (columns_specifier,))
def linear_constraint(self, constraint_likes):
"""Construct a linear constraint in matrix form from a (possibly
symbolic) description.
Possible inputs:
* A dictionary which is taken as a set of equality constraint. Keys
can be either string column names, or integer column indexes.
* A string giving a arithmetic expression referring to the matrix
columns by name.
* A list of such strings which are ANDed together.
* A tuple (A, b) where A and b are array_likes, and the constraint is
Ax = b. If necessary, these will be coerced to the proper
dimensionality by appending dimensions with size 1.
The string-based language has the standard arithmetic operators, / * +
- and parentheses, plus "=" is used for equality and "," is used to
AND together multiple constraint equations within a string. You can
If no = appears in some expression, then that expression is assumed to
be equal to zero. Division is always float-based, even if
``__future__.true_division`` isn't in effect.
Returns a :class:`LinearConstraint` object.
Examples::
di = DesignInfo(["x1", "x2", "x3"])
# Equivalent ways to write x1 == 0:
di.linear_constraint({"x1": 0}) # by name
di.linear_constraint({0: 0}) # by index
di.linear_constraint("x1 = 0") # string based
di.linear_constraint("x1") # can leave out "= 0"
di.linear_constraint("2 * x1 = (x1 + 2 * x1) / 3")
di.linear_constraint(([1, 0, 0], 0)) # constraint matrices
# Equivalent ways to write x1 == 0 and x3 == 10
di.linear_constraint({"x1": 0, "x3": 10})
di.linear_constraint({0: 0, 2: 10})
di.linear_constraint({0: 0, "x3": 10})
di.linear_constraint("x1 = 0, x3 = 10")
di.linear_constraint("x1, x3 = 10")
di.linear_constraint(["x1", "x3 = 0"]) # list of strings
di.linear_constraint("x1 = 0, x3 - 10 = x1")
di.linear_constraint([[1, 0, 0], [0, 0, 1]], [0, 10])
# You can also chain together equalities, just like Python:
di.linear_constraint("x1 = x2 = 3")
"""
return linear_constraint(constraint_likes, self.column_names)
def describe(self):
"""Returns a human-readable string describing this design info.
Example:
.. ipython::
In [1]: y, X = dmatrices("y ~ x1 + x2", demo_data("y", "x1", "x2"))
In [2]: y.design_info.describe()
Out[2]: 'y'
In [3]: X.design_info.describe()
Out[3]: '1 + x1 + x2'
.. warning::
There is no guarantee that the strings returned by this function
can be parsed as formulas, or that if they can be parsed as a
formula that they will produce a model equivalent to the one you
started with. This function produces a best-effort description
intended for humans to read.
"""
names = []
for name in self.term_names:
if name == "Intercept":
names.append("1")
else:
names.append(name)
return " + ".join(names)
def subset(self, which_terms):
"""Create a new :class:`DesignInfo` for design matrices that contain a
subset of the terms that the current :class:`DesignInfo` does.
For example, if ``design_info`` has terms ``x``, ``y``, and ``z``,
then::
design_info2 = design_info.subset(["x", "z"])
will return a new DesignInfo that can be used to construct design
matrices with only the columns corresponding to the terms ``x`` and
``z``. After we do this, then in general these two expressions will
return the same thing (here we assume that ``x``, ``y``, and ``z``
each generate a single column of the output)::
build_design_matrix([design_info], data)[0][:, [0, 2]]
build_design_matrix([design_info2], data)[0]
However, a critical difference is that in the second case, ``data``
need not contain any values for ``y``. This is very useful when doing
prediction using a subset of a model, in which situation R usually
forces you to specify dummy values for ``y``.
If using a formula to specify the terms to include, remember that like
any formula, the intercept term will be included by default, so use
``0`` or ``-1`` in your formula if you want to avoid this.
This method can also be used to reorder the terms in your design
matrix, in case you want to do that for some reason. I can't think of
any.
Note that this method will generally *not* produce the same result as
creating a new model directly. Consider these DesignInfo objects::
design1 = dmatrix("1 + C(a)", data)
design2 = design1.subset("0 + C(a)")
design3 = dmatrix("0 + C(a)", data)
Here ``design2`` and ``design3`` will both produce design matrices
that contain an encoding of ``C(a)`` without any intercept term. But
``design3`` uses a full-rank encoding for the categorical term
``C(a)``, while ``design2`` uses the same reduced-rank encoding as
``design1``.
:arg which_terms: The terms which should be kept in the new
:class:`DesignMatrixBuilder`. If this is a string, then it is parsed
as a formula, and then the names of the resulting terms are taken as
the terms to keep. If it is a list, then it can contain a mixture of
term names (as strings) and :class:`Term` objects.
.. versionadded: 0.2.0
New method on the class DesignMatrixBuilder.
.. versionchanged: 0.4.0
Moved from DesignMatrixBuilder to DesignInfo, as part of the
removal of DesignMatrixBuilder.
"""
if isinstance(which_terms, str):
desc = ModelDesc.from_formula(which_terms)
if desc.lhs_termlist:
raise PatsyError("right-hand-side-only formula required")
which_terms = [term.name() for term in desc.rhs_termlist]
if self.term_codings is None:
# This is a minimal DesignInfo
# If the name is unknown we just let the KeyError escape
new_names = []
for t in which_terms:
new_names += self.column_names[self.term_name_slices[t]]
return DesignInfo(new_names)
else:
term_name_to_term = {}
for term in self.term_codings:
term_name_to_term[term.name()] = term
new_column_names = []
new_factor_infos = {}
new_term_codings = OrderedDict()
for name_or_term in which_terms:
term = term_name_to_term.get(name_or_term, name_or_term)
# If the name is unknown we just let the KeyError escape
s = self.term_slices[term]
new_column_names += self.column_names[s]
for f in term.factors:
new_factor_infos[f] = self.factor_infos[f]
new_term_codings[term] = self.term_codings[term]
return DesignInfo(new_column_names,
factor_infos=new_factor_infos,
term_codings=new_term_codings)
@classmethod
def from_array(cls, array_like, default_column_prefix="column"):
"""Find or construct a DesignInfo appropriate for a given array_like.
If the input `array_like` already has a ``.design_info``
attribute, then it will be returned. Otherwise, a new DesignInfo
object will be constructed, using names either taken from the
`array_like` (e.g., for a pandas DataFrame with named columns), or
constructed using `default_column_prefix`.
This is how :func:`dmatrix` (for example) creates a DesignInfo object
if an arbitrary matrix is passed in.
:arg array_like: An ndarray or pandas container.
:arg default_column_prefix: If it's necessary to invent column names,
then this will be used to construct them.
:returns: a DesignInfo object
"""
if hasattr(array_like, "design_info") and isinstance(array_like.design_info, cls):
return array_like.design_info
arr = atleast_2d_column_default(array_like, preserve_pandas=True)
if arr.ndim > 2:
raise ValueError("design matrix can't have >2 dimensions")
columns = getattr(arr, "columns", range(arr.shape[1]))
if (hasattr(columns, "dtype")
and not safe_issubdtype(columns.dtype, np.integer)):
column_names = [str(obj) for obj in columns]
else:
column_names = ["%s%s" % (default_column_prefix, i)
for i in columns]
return DesignInfo(column_names)
__getstate__ = no_pickling
class DesignInfo(object):
"""A DesignInfo object holds metadata about a design matrix.
This is the main object that Patsy uses to pass information to
statistical libraries. Usually encountered as the `.design_info` attribute
on design matrices.
"""
def __init__(self, column_names,
term_slices=None, term_name_slices=None,
builder=None):
self.column_name_indexes = OrderedDict(zip(column_names,
range(len(column_names))))
if term_slices is not None:
#: An OrderedDict mapping :class:`Term` objects to Python
#: func:`slice` objects. May be None, for design matrices which
#: were constructed directly rather than by using the patsy
#: machinery. If it is not None, then it
#: is guaranteed to list the terms in order, and the slices are
#: guaranteed to exactly cover all columns with no overlap or
#: gaps.
self.term_slices = OrderedDict(term_slices)
if term_name_slices is not None:
raise ValueError("specify only one of term_slices and "
"term_name_slices")
term_names = [term.name() for term in self.term_slices]
#: And OrderedDict mapping term names (as strings) to Python
#: :func:`slice` objects. Guaranteed never to be None. Guaranteed
#: to list the terms in order, and the slices are
#: guaranteed to exactly cover all columns with no overlap or
#: gaps. Name overlap is allowed between term names and column
#: names, but it is guaranteed that if it occurs, then they refer
#: to exactly the same column.
self.term_name_slices = OrderedDict(zip(term_names,
self.term_slices.values()))
else: # term_slices is None
self.term_slices = None
if term_name_slices is None:
# Make up one term per column
term_names = column_names
slices = [slice(i, i + 1) for i in xrange(len(column_names))]
term_name_slices = zip(term_names, slices)
self.term_name_slices = OrderedDict(term_name_slices)
self.builder = builder
# Guarantees:
# term_name_slices is never None
# The slices in term_name_slices are in order and exactly cover the
# whole range of columns.
# term_slices may be None
# If term_slices is not None, then its slices match the ones in
# term_name_slices.
# If there is any name overlap between terms and columns, they refer
# to the same columns.
assert self.term_name_slices is not None
if self.term_slices is not None:
assert self.term_slices.values() == self.term_name_slices.values()
covered = 0
for slice_ in self.term_name_slices.itervalues():
start, stop, step = slice_.indices(len(column_names))
if start != covered:
raise ValueError, "bad term slices"
if step != 1:
raise ValueError, "bad term slices"
covered = stop
if covered != len(column_names):
raise ValueError, "bad term indices"
for column_name, index in self.column_name_indexes.iteritems():
if column_name in self.term_name_slices:
slice_ = self.term_name_slices[column_name]
if slice_ != slice(index, index + 1):
raise ValueError, "term/column name collision"
__repr__ = repr_pretty_delegate
def _repr_pretty_(self, p, cycle):
assert not cycle
if self.term_slices is None:
kwargs = [("term_name_slices", self.term_name_slices)]
else:
kwargs = [("term_slices", self.term_slices)]
if self.builder is not None:
kwargs.append(("builder", self.builder))
repr_pretty_impl(p, self, [self.column_names], kwargs)
@property
def column_names(self):
"A list of the column names, in order."
return self.column_name_indexes.keys()
@property
def terms(self):
"A list of :class:`Terms`, in order, or else None."
if self.term_slices is None:
return None
return self.term_slices.keys()
@property
def term_names(self):
"A list of terms, in order."
return self.term_name_slices.keys()
def slice(self, columns_specifier):
"""Locate a subset of design matrix columns, specified symbolically.
A patsy design matrix has two levels of structure: the individual
columns (which are named), and the :ref:`terms <formulas>` in
the formula that generated those columns. This is a one-to-many
relationship: a single term may span several columns. This method
provides a user-friendly API for locating those columns.
(While we talk about columns here, this is probably most useful for
indexing into other arrays that are derived from the design matrix,
such as regression coefficients or covariance matrices.)
The `columns_specifier` argument can take a number of forms:
* A term name
* A column name
* A :class:`Term` object
* An integer giving a raw index
* A raw slice object
In all cases, a Python :func:`slice` object is returned, which can be
used directly for indexing.
Example::
y, X = dmatrices("y ~ a", demo_data("y", "a", nlevels=3))
betas = np.linalg.lstsq(X, y)[0]
a_betas = betas[X.design_info.slice("a")]
(If you want to look up a single individual column by name, use
``design_info.column_name_indexes[name]``.)
"""
if isinstance(columns_specifier, slice):
return columns_specifier
if np.issubsctype(type(columns_specifier), np.integer):
return slice(columns_specifier, columns_specifier + 1)
if (self.term_slices is not None
and columns_specifier in self.term_slices):
return self.term_slices[columns_specifier]
if columns_specifier in self.term_name_slices:
return self.term_name_slices[columns_specifier]
if columns_specifier in self.column_name_indexes:
idx = self.column_name_indexes[columns_specifier]
return slice(idx, idx + 1)
raise PatsyError("unknown column specified '%s'"
% (columns_specifier,))
def linear_constraint(self, constraint_likes):
"""Construct a linear constraint in matrix form from a (possibly
symbolic) description.
Possible inputs:
* A dictionary which is taken as a set of equality constraint. Keys
can be either string column names, or integer column indexes.
* A string giving a arithmetic expression referring to the matrix
columns by name.
* A list of such strings which are ANDed together.
* A tuple (A, b) where A and b are array_likes, and the constraint is
Ax = b. If necessary, these will be coerced to the proper
dimensionality by appending dimensions with size 1.
The string-based language has the standard arithmetic operators, / * +
- and parentheses, plus "=" is used for equality and "," is used to
AND together multiple constraint equations within a string. You can
If no = appears in some expression, then that expression is assumed to
be equal to zero. Division is always float-based, even if
``__future__.true_division`` isn't in effect.
Returns a :class:`LinearConstraint` object.
Examples::
di = DesignInfo(["x1", "x2", "x3"])
# Equivalent ways to write x1 == 0:
di.linear_constraint({"x1": 0}) # by name
di.linear_constraint({0: 0}) # by index
di.linear_constraint("x1 = 0") # string based
di.linear_constraint("x1") # can leave out "= 0"
di.linear_constraint("2 * x1 = (x1 + 2 * x1) / 3")
di.linear_constraint(([1, 0, 0], 0)) # constraint matrices
# Equivalent ways to write x1 == 0 and x3 == 10
di.linear_constraint({"x1": 0, "x3": 10})
di.linear_constraint({0: 0, 2: 10})
di.linear_constraint({0: 0, "x3": 10})
di.linear_constraint("x1 = 0, x3 = 10")
di.linear_constraint("x1, x3 = 10")
di.linear_constraint(["x1", "x3 = 0"]) # list of strings
di.linear_constraint("x1 = 0, x3 - 10 = x1")
di.linear_constraint([[1, 0, 0], [0, 0, 1]], [0, 10])
# You can also chain together equalities, just like Python:
di.linear_constraint("x1 = x2 = 3")
"""
return linear_constraint(constraint_likes, self.column_names)
def describe(self):
"""Returns a human-readable string describing this design info.
Example:
.. ipython::
In [1]: y, X = dmatrices("y ~ x1 + x2", demo_data("y", "x1", "x2"))
In [2]: y.design_info.describe()
Out[2]: 'y'
In [3]: X.design_info.describe()
Out[3]: '1 + x1 + x2'
.. warning::
There is no guarantee that the strings returned by this
function can be parsed as formulas. They are best-effort descriptions
intended for human users.
"""
names = []
for name in self.term_names:
if name == "Intercept":
names.append("1")
else:
names.append(name)
return " + ".join(names)
@classmethod
def from_array(cls, array_like, default_column_prefix="column"):
"""Find or construct a DesignInfo appropriate for a given array_like.
If the input `array_like` already has a ``.design_info``
attribute, then it will be returned. Otherwise, a new DesignInfo
object will be constructed, using names either taken from the
`array_like` (e.g., for a pandas DataFrame with named columns), or
constructed using `default_column_prefix`.
This is how :func:`dmatrix` (for example) creates a DesignInfo object
if an arbitrary matrix is passed in.
:arg array_like: An ndarray or pandas container.
:arg default_column_prefix: If it's necessary to invent column names,
then this will be used to construct them.
:returns: a DesignInfo object
"""
if hasattr(array_like, "design_info") and isinstance(array_like.design_info, cls):
return array_like.design_info
arr = atleast_2d_column_default(array_like, preserve_pandas=True)
if arr.ndim > 2:
raise ValueError, "design matrix can't have >2 dimensions"
columns = getattr(arr, "columns", xrange(arr.shape[1]))
if (isinstance(columns, np.ndarray)
and not np.issubdtype(columns.dtype, np.integer)):
column_names = [str(obj) for obj in columns]
else:
column_names = ["%s%s" % (default_column_prefix, i)
for i in columns]
return DesignInfo(column_names)
