def __init__(self, name, minimum, maximum):
"""
Constructor.
:param name: The name of the column in the data
view to which this constraint should be applied
:type name: str
:param minimum: The minimum allowed value
:type minimum: float
:param maximum: The maximum allowed value
:type maximum: float
"""
try:
minimum_f = float(minimum)
maximum_f = float(maximum)
except TypeError:
raise CitrinationClientError("RealRangeConstraint requires that minimum and maximum must be able to be cast to float")
if minimum_f > maximum_f:
raise CitrinationClientError("RealRangeConstraint requires that minimum be less than maximum")
self._min = minimum_f
self._max = maximum_f
self._type = "real"
self._name = name
def validate(self):
categories = self.options['descriptor_values']
if type(categories) is not list:
raise CitrinationClientError(
"CategoricalColumn requires that the categories value is a list of strings"
)
if not all(isinstance(item, string_types) for item in categories):
raise CitrinationClientError(
"CategoricalColumn requires that the categories value is a list of strings"
)
def length(self, value):
try:
self._length = int(value)
except ValueError:
raise CitrinationClientError(
"When constructing a VectorColumn, parameter length must be castable as an int"
)
def _cast_and_validate_float(self, value, attr_name):
try:
return float(value)
except ValueError:
raise CitrinationClientError(
"For a RealColumn, {} must be castable as a float".format(
attr_name))
def _validate_search_query(self, returning_query):
"""
Checks to see that the query will not exceed the max query depth
:param returning_query: The PIF system or Dataset query to execute.
:type returning_query: :class:`PifSystemReturningQuery` or :class: `DatasetReturningQuery`
"""
start_index = returning_query.from_index or 0
size = returning_query.size or 0
if start_index < 0:
raise CitrinationClientError(
"start_index cannot be negative. Please enter a value greater than or equal to zero")
if size < 0:
raise CitrinationClientError("Size cannot be negative. Please enter a value greater than or equal to zero")
if start_index + size > MAX_QUERY_DEPTH:
raise CitrinationClientError(
"Citrination does not support pagination past the {0}th result. Please reduce either the from_index and/or size such that their sum is below {0}".format(
MAX_QUERY_DEPTH))
def __init__(self, name, elements, minimum, maximum):
"""
Constructor.
:param name: The name of the column in the data
view to which this constraint should be applied
:type name: str
:param elements: An array of element abbreviations as
strings, e.g. ["Mg", "C"]
:type elements: list of str
:param minimum: The minimum value (<= 100) as a percentage
at which the specified elements should appear in
candidate compositions
:type minimum: float
:param maximum: The maximum value (<= 100) as a percentage
at which the specified elements should appear in
candidate compositions
:type maximum: float
"""
if not 0 <= minimum <= 100:
raise CitrinationClientError(
"ElementalCompositionConstraint requires that minimum be between 0 and 100"
)
if not 0 <= maximum <= 100:
raise CitrinationClientError(
"ElementalCompositionConstraint requires that maximum be between 0 and 100"
)
if not maximum >= minimum:
raise CitrinationClientError(
"ElementalCompositionConstraint requires that maximum be greater than minimum"
)
self._type = "elementalCompositionConstraint"
self._elements = elements
self._name = name
self._min = minimum
self._max = maximum
def submit_design_run(self,
data_view_id,
num_candidates,
effort,
target=None,
constraints=[],
sampler="Default"):
"""
Submits a new experimental design run
:param data_view_id: The ID number of the data view to which the
run belongs, as a string
:type data_view_id: str
:param num_candidates: The number of candidates to return
:type num_candidates: int
:param target: An :class:``Target`` instance representing
the design run optimization target
:type target: :class:``Target``
:param constraints: An array of design constraints (instances of
objects which extend :class:``BaseConstraint``)
:type constraints: list of :class:``BaseConstraint``
:param sampler: The name of the sampler to use during the design run:
either "Default" or "This view"
:type sampler: str
:return: A :class:`DesignRun` instance containing the UID of the
new run
"""
if effort > 30:
raise CitrinationClientError(
"Parameter effort must be less than 30 to trigger a design run"
)
if target is not None:
target = target.to_dict()
constraint_dicts = [c.to_dict() for c in constraints]
body = {
"num_candidates": num_candidates,
"target": target,
"effort": effort,
"constraints": constraint_dicts,
"sampler": sampler
}
url = routes.submit_data_view_design(data_view_id)
response = self._post_json(url, body).json()
return DesignRun(response["data"]["design_run"]["uid"])
def _validate_role(self, role):
"""
Validates that the role parameter has a valid value.
:param role: Name of the column's role
:type role: str
"""
valid_roles = [
"Input", "Output", "Latent Variable", "Ignore", "input", "output",
"latentVariable", "ignore"
]
if role not in valid_roles:
raise CitrinationClientError(
"Column role must be one of: {}".format(valid_roles))
def __init__(self, name, objective):
"""
Constructor.
:param name: The name of the target output column
:type name: str
:param objective: The optimization objective; either "Min"
or "Max"
:type objective: str
"""
if objective not in ["Max", "Min"]:
raise CitrinationClientError(
"Target objective must either be \"Min\" or \"Max\"")
self._name = name
self._objective = objective
def __init__(self, name, objective):
"""
Constructor.
:param name: The name of the target output column
:type name: str
:param objective: The optimization objective; "Min", "Max", or a scalar value (such as "5.0")
:type objective: str
"""
try:
self._objective = float(objective)
except ValueError:
if objective.lower() not in ["max", "min"]:
raise CitrinationClientError(
"Target objective must either be \"min\" or \"max\"")
self._objective = objective
self._name = name
def __init__(self, name, elements, logic):
"""
Constructor.
:param name: The name of the column in the data
view to which this constraint should be applied
:type name: str
:param elements: An array of element abbreviations as
strings, e.g. ["Mg", "C"]
:type elements: list of str
:param logic: The logic to apply to the constraint; either
"must", "should", or "exclude"
:type logic: str
"""
bad_logic_msg = "ElementalInclusionConstraint must be initialized with the logic parameter equal to \"must\", \"should\", or \"exclude\""
if logic not in ["must", "should", "exclude"]:
raise CitrinationClientError(bad_logic_msg)
self._name = name
self._type = "elementalInclusionConstraint"
self._elements = elements
self._logic = logic
def _cast_and_validate_int(self, value, attr_name):
try:
return int(value)
except ValueError:
raise CitrinationClientError("For a IntColumn, {} must be castable as an integer".format(attr_name))
def _validate_bounds(self):
if self._both_bounds_present() and self._lower_bound > self._upper_bound:
raise CitrinationClientError("When constructing a IntColumn, lower_bound must be less than upper_bound")