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refactoring

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This commit is contained in:
Andy Eschbacher
2017-03-30 15:11:12 -04:00
parent cc555cb322
commit 78f72d25e6
2 changed files with 60 additions and 22 deletions
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10
src/pg/sql/25_optimization.sql
View File

@@ -6,14 +6,16 @@ CDB_OptimAssignments(drain text,
marginal_cost text,
waste_per_person numeric DEFAULT 0.01,
recycle_rate numeric DEFAULT 0.0,
dist_rate numeric DEFAULT 0.15)
dist_rate numeric DEFAULT 0.15,
dist_threshold numeric DEFAULT null)
RETURNS table(drain_id bigint, source_id int, cost numeric) AS $$
from crankshaft.optimization import Optim
params = {'waste_per_person': float(waste_per_person),
'recycle_rate': float(recycle_rate),
'dist_rate': float(dist_rate)}
params = {'waste_per_person': waste_per_person,
'recycle_rate': recycle_rate,
'dist_rate': dist_rate,
'dist_threshold': dist_threshold}
optim = Optim(drain, source, drain_capacity, source_production, marginal_cost,
**params)

72
src/py/crankshaft/crankshaft/optimization/optim.py
View File

@@ -24,7 +24,9 @@ class Optim(object):
self.model_params = {
'amount_per_unit': kwargs.get('amount_per_unit', 0.01),
'dist_cost': kwargs.get('dist_cost', 0.15),
'recycle_rate': kwargs.get('recycle_rate', 0.0)}
'recycle_rate': kwargs.get('recycle_rate', 0.0),
'dist_threshold': kwargs.get('dist_threshold', None)}
self._check_model_params()
# model data
self.model_data = {
@@ -35,7 +37,11 @@ class Optim(object):
self.data_provider.get_column(source_table,
production_column)),
'marginal_cost': self.data_provider.get_column(drain_table,
marginal_column)}
marginal_column),
'distance': self.data_provider.get_pairwise_distances(source_table,
drain_table),
'cost': self.calc_cost()
}
# database ids
self.ids = {
@@ -45,20 +51,46 @@ class Optim(object):
'source': self.data_provider.get_column(source_table,
'cartodb_id',
dtype=int)}
# derivative data
self.n_sources = len(self.ids['source'])
self.n_drains = len(self.ids['drain'])
self.cost = self.calc_cost(source_table,
drain_table)
def _check_constraints(self):
"""Check if inputs are within constraints"""
if self.model_data['source_amount'].sum() > self.model_data['drain_capacity'].sum():
if (self.model_data['source_amount'].sum() >
self.model_data['drain_capacity'].sum()):
plpy.error("Solution not possible. Drain capacity is smaller "
"than total source production.")
return None
def _check_model_params(self):
"""Ensure model parameters are well formed"""
if (self.model_params['recycle_rate'] is None or
self.model_params['recycle_rate'] < 0 or
self.model_params['recycle_rate'] > 1):
raise ValueError("`recycle_rate` must be between 0 and 1.")
if (self.model_params['amount_per_unit'] is None or
self.model_params['amount_per_unit'] < 0):
raise ValueError("`amount_per_unit` must be greater than zero.")
if (self.model_params['dist_threshold'] is None or
self.model_params['dist_threshold'] < 0):
raise ValueError("`dist_threshold` must be greater than zero")
if (self.model_params['dist_cost'] is None or
self.model_params['dist_cost'] < 0):
raise ValueError("`dist_cost must be greater than zero")
return None
def output(self):
"""..."""
"""Output the calculated 'optimal' assignments if solution is not infeasible.
:returns: List of source id/drain id pairs and the associated cost of
transport from source to drain
:rtype: List of tuples
"""
# n_drains x n_sources matrix (row, column)
assignments = self.optim()
@@ -80,8 +112,8 @@ class Optim(object):
#
assigned_costs = [(drain_id_crosswalk[drain_index[i]],
source_id_crosswalk[source_index[i]],
self.cost[drain_index[i],
source_index[i]])
self.model_data['cost'][drain_index[i],
source_index[i]])
for i in range(len(source_index))]
return assigned_costs
@@ -103,26 +135,24 @@ class Optim(object):
"""
return waste * (marginal + self.model_params['dist_cost'] * distance)
def calc_cost(self, source_table, drain_table):
def calc_cost(self):
"""
Populate an d x s matrix according to the cost equation
:returns: d x s matrix of costs from area i to plant j
:rtype: NumPy matrix
"""
distances = self.data_provider.get_pairwise_distances(source_table,
drain_table)
costs = np.array([self.cost_func(distance,
self.model_data['source_amount'][pair[1]],
self.model_data['marginal_cost'][pair[0]])
for pair, distance in np.ndenumerate(distances)])
return costs.reshape(distances.shape)
for pair, distance in np.ndenumerate(self.model_data['distance'])])
return costs.reshape(self.model_data['distance'].shape)
def optim(self):
"""solve linear optimization problem
Equations of the form:
minimize c'*x
minimize c'*x by assigning x values
subject to G*x <= h
A*x = b
x[k] is binary
@@ -132,7 +162,7 @@ class Optim(object):
# ---
# costs
# elements chosen to minimize sum
cost = cvxopt.matrix(self.cost.ravel('F'))
cost = cvxopt.matrix(self.model_data['cost'].ravel('F'))
# ---
# equality constraint variables
@@ -143,6 +173,12 @@ class Optim(object):
range(self.n_drains * self.n_sources))
b = cvxopt.matrix(np.ones((self.n_sources, 1)), tc='d')
# knock out values above distance threshold
if self.model_params['dist_threshold']:
j_locs, i_locs = np.where(self.model_data['distance'] > 100)
for idx, ival in enumerate(i_locs):
A[int(ival), int(ival * 10 + j_locs[idx])] = 0
# ---
# inequality constraint variables
# each plant never goes over capacity
@@ -160,8 +196,8 @@ class Optim(object):
A=A, b=b, B=binary_entries)
if sol != 'optimal':
raise Exception("No solution possible: {}".format(sol))
assign_shape = (self.cost.shape[1], self.cost.shape[0])
assign_shape = (self.model_data['cost'].shape[1], self.model_data['cost'].shape[0])
# Note: assignments needs to be shaped like self.cost.T
# Note: assignments needs to be shaped like self.model_data['cost'].T
return np.array(assignments,
dtype=int).flatten().reshape(assign_shape)