predictionarg.py

class PredictionArg(object):

Container class for passing the prediction model arguments to prediction library

Attributes:

• variables
• parameters
• result_vars
• error_check_mode
• allowed_risk_level
• rect_factor
• model_name
• model_language
• warnings
• errors
• error_objects
• output_type
• num_of_res: integer
• mem: array
• result_level: integer
• target_index: numpy array from data Handler’s get_tind
• num_of_targets: integer
• num_of_res_vars: integer
• num_of_res_objs: integer
• remove_targets: set
• warning_flag: boolean
• error_flag: boolean

def __init__(self, name, lang):

Initialize a prediction model argument container:

>>> from simo.simulation.model.predictionarg import PredictionArg
>>> import numpy
>>> arg = PredictionArg('modelname', 'C')
>>> arg.model_name
'modelname'
>>> arg.model_language
'C'

def set_targets(self, tind, torem, nresobjs):

Store target object indices in the argument container:

>>> tind = numpy.array([[0,0,0,0,0],
...                     [0,0,1,0,0],
...                     [0,0,2,0,0],
...                     [0,0,3,0,0],
...                     [0,0,4,0,0],
...                     [0,0,5,0,0]], dtype=int)
>>> arg.set_targets(tind, set([3]), 1)
>>> arg.num_of_targets
6
>>> arg.target_index
array([[0, 0, 0, 0, 0],
       [0, 0, 1, 0, 0],
       [0, 0, 2, 0, 0],
       [0, 0, 3, 0, 0],
       [0, 0, 4, 0, 0],
       [0, 0, 5, 0, 0]])
>>> arg.remove_targets
set([3])

def set_result_level(self, result_level, cur_level):

Set model result level:

>>> arg.set_result_level(0, 'self')
>>> arg.result_level
0

def set_input_variables(self, nvar, values, loc, rem_targets):

Create container for model input variable values if None and store input variable values into the container:

>>> values = numpy.ones(tind.shape[0], dtype=float)
>>> values[0] = numpy.NaN
>>> loc = 2
>>> arg.set_input_variables(3, values, loc, set([0]))
>>> arg.variables  
array([[  0.,   0.,   0.,   0.,   0.,   0.],
       [  0.,   0.,   0.,   0.,   0.,   0.],
       [ ...,   1.,   1.,   1.,   1.,   1.]])
>>> arg.remove_targets
set([0, 3])

def set_result_variables(self, result_variables):

Store result variable indices:

>>> arg.set_result_variables([0,1,2])
>>> arg.result_vars
[0, 1, 2]
>>> arg.num_of_res_vars
3

def add_parameter(self, param):

Add input parameter value to model arguments:

>>> arg.add_parameter(1.0)
>>> arg.add_parameter(2.0)
>>> arg.parameters
deque([1.0, 2.0])

def set_containers(self):

Construct containers for passing results back from the model:

>>> arg.set_containers()
>>> arg.num_of_res_objs
array([0, 0, 0, 0, 0, 0])
>>> arg.mem
array([[ 0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.]])
>>> arg.errors
['', '', '', '', '', '']

def remove_missing(self):

Remove objects with missing input variable values:

>>> arg.remove_missing()
>>> arg.variables
array([[ 0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.],
       [ 1.,  1.,  1.,  1.]])
>>> arg.num_of_res_objs
array([0, 0, 0, 0])
>>> arg.mem
array([[ 0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.]])
>>> arg.errors
['', '', '', '']
>>> arg.num_of_targets
4
>>> arg.target_index
array([[0, 0, 1, 0, 0],
       [0, 0, 2, 0, 0],
       [0, 0, 4, 0, 0],
       [0, 0, 5, 0, 0]])

def python2ctypes(self):

Change argument types from python data types to c data types:

>>> arg.python2ctypes()
>>> arg.mem  
[<simo.simulation.model.predictionarg.c_double_Array_3 object at ...>,
 <simo.simulation.model.predictionarg.c_double_Array_3 object at ...>,
 <simo.simulation.model.predictionarg.c_double_Array_3 object at ...>,
 <simo.simulation.model.predictionarg.c_double_Array_3 object at ...>]
>>> arg.mem[0][0] = 0.0
>>> arg.mem[0][1] = 1.0
>>> arg.mem[0][2] = 2.0
>>> arg.mem[1][0] = 0.1
>>> arg.mem[2][0] = 0.2
>>> arg.mem[3][0] = 0.3

def ctypes2python(self):

Change argument types from ctypes to python and remove objects with evaluation errors from target objects:

>>> arg.ctypes2python()
>>> arg.mem  
array([[ 0. ,  0.1,  0.2,  0.3],
       [ 1. ,  0. ,  0. ,  0. ],
       [ 2. ,  0. ,  0. ,  0. ]])

def __construct_ctypes_array(self,valuearray):

structure. These arrays are used for passing data to the DLL model libraries. Uses ctypes package for the variable types

def add_error(self, i):

Add error to target object i:

>>> arg.add_error(2)
>>> arg.errors
array(['', '', '', ''],
      dtype='|S1')

def drop_error_objects(self):

Trim the prediction model result structures to only contain valid results; i.e., no error in model call. Also remove empty error strings.:

>>> arg.drop_error_objects()
>>> arg.target_index
array([[0, 0, 1, 0, 0],
       [0, 0, 2, 0, 0],
       [0, 0, 5, 0, 0]])
>>> arg.num_of_res_objs
array([0, 0, 0])
>>> arg.mem
array([[ 0. ,  0.1,  0.3],
       [ 1. ,  0. ,  0. ],
       [ 2. ,  0. ,  0. ]])