89 lines
3.9 KiB
Python
89 lines
3.9 KiB
Python
from sympy.strategies.branch import condition
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from . import LabelTensor
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from .utils import check_consistency, merge_tensors
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class Collector:
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def __init__(self, problem):
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self.problem = problem # hook Collector <-> Problem
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self.data_collections = {name : {} for name in self.problem.conditions} # collection of data
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self.is_conditions_ready = {
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name : False for name in self.problem.conditions} # names of the conditions that need to be sampled
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self.full = False # collector full, all points for all conditions are given and the data are ready to be used in trainig
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@property
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def full(self):
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return all(self.is_conditions_ready.values())
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@full.setter
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def full(self, value):
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check_consistency(value, bool)
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self._full = value
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@property
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def problem(self):
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return self._problem
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@problem.setter
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def problem(self, value):
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self._problem = value
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def store_fixed_data(self):
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# loop over all conditions
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for condition_name, condition in self.problem.conditions.items():
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# if the condition is not ready and domain is not attribute
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# of condition, we get and store the data
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if (not self.is_conditions_ready[condition_name]) and (not hasattr(condition, "domain")):
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# get data
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keys = condition.__slots__
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values = [getattr(condition, name) for name in keys]
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self.data_collections[condition_name] = dict(zip(keys, values))
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# condition now is ready
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self.is_conditions_ready[condition_name] = True
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def store_sample_domains(self, n, mode, variables, sample_locations):
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# loop over all locations
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for loc in sample_locations:
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# get condition
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condition = self.problem.conditions[loc]
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keys = ["input_points", "equation"]
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# if the condition is not ready, we get and store the data
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if (not self.is_conditions_ready[loc]):
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# if it is the first time we sample
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if not self.data_collections[loc]:
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already_sampled = []
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# if we have sampled the condition but not all variables
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else:
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already_sampled = [self.data_collections[loc]['input_points']]
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# if the condition is ready but we want to sample again
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else:
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self.is_conditions_ready[loc] = False
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already_sampled = []
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# get the samples
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samples = [
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condition.domain.sample(n=n, mode=mode, variables=variables)
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] + already_sampled
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pts = merge_tensors(samples)
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if (
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set(pts.labels).issubset(sorted(self.problem.input_variables))
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):
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pts = pts.sort_labels()
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if sorted(pts.labels)==sorted(self.problem.input_variables):
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self.is_conditions_ready[loc] = True
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values = [pts, condition.equation]
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self.data_collections[loc] = dict(zip(keys, values))
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else:
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raise RuntimeError('Try to sample variables which are not in problem defined in the problem')
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def add_points(self, new_points_dict):
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"""
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Add input points to a sampled condition
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:param new_points_dict: Dictonary of input points (condition_name: LabelTensor)
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:raises RuntimeError: if at least one condition is not already sampled
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"""
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for k,v in new_points_dict.items():
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if not self.is_conditions_ready[k]:
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raise RuntimeError('Cannot add points on a non sampled condition')
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self.data_collections[k]['input_points'] = self.data_collections[k]['input_points'].vstack(v) |