def genDoe(nDV, doeMethod):
if doeMethod == 'lhs':
doeTable = DOE.lhs(nDV, samples=nDV * 10)
elif doeMethod == 'pbdesign':
doeTable = DOE.pbdesign(nDV)
elif doeMethod == 'bbdesign':
doeTable = DOE.bbdesign(nDV)
else:
print("Error, You have to check a parameter")
return doeTable
def genDoe(self):
if self.doeMethod == 'lhs':
doeTable = DOE.lhs(self.nDV, samples=self.nDV * 10)
elif self.doeMethod == 'pbdesign':
doeTable = DOE.pbdesign(self.nDV)
temp = len(doeTable) - 1
for j in range(self.nDV):
doeTable[temp, j] = 0
elif self.doeMethod == 'bbdesign':
doeTable = DOE.bbdesign(self.nDV, center=1)
else:
print("Error, You have to check a parameter")
return doeTable
def _generate_design(self, size):
"""
Generate a Box-Behnken DOE design.
Parameters
----------
size : int
The number of factors for the design.
Returns
-------
ndarray
The design matrix as a size x levels array of indices.
"""
if size < 3:
raise RuntimeError("Total size of design variables is %d,"
"but must be at least 3 when using %s. " %
(size, self.__class__.__name__))
doe = pyDOE2.bbdesign(size, center=self._center)
return doe + 1 # replace [-1, 0, 1] with [0, 1, 2]
def ge_compute_pls(X, y, n_comp, pts, delta_x, xlimits, extra_points):
"""
Gradient-enhanced PLS-coefficients.
Parameters
----------
X: np.ndarray [n_obs,dim]
- - The input variables.
y: np.ndarray [n_obs,ny]
- The output variable
n_comp: int
- Number of principal components used.
pts: dict()
- The gradient values.
delta_x: real
- The step used in the FOTA.
xlimits: np.ndarray[dim, 2]
- The upper and lower var bounds.
extra_points: int
- The number of extra points per each training point.
Returns
-------
Coeff_pls: np.ndarray[dim, n_comp]
- The PLS-coefficients.
XX: np.ndarray[extra_points*nt, dim]
- Extra points added (when extra_points > 0)
yy: np.ndarray[extra_points*nt, 1]
- Extra points added (when extra_points > 0)
"""
nt, dim = X.shape
XX = np.empty(shape=(0, dim))
yy = np.empty(shape=(0, y.shape[1]))
_pls = pls(n_comp)
coeff_pls = np.zeros((nt, dim, n_comp))
for i in range(nt):
if dim >= 3:
sign = np.roll(bbdesign(int(dim), center=1), 1, axis=0)
_X = np.zeros((sign.shape[0], dim))
_y = np.zeros((sign.shape[0], 1))
sign = sign * delta_x * (xlimits[:, 1] - xlimits[:, 0])
_X = X[i, :] + sign
for j in range(1, dim + 1):
sign[:, j - 1] = sign[:, j - 1] * pts[None][j][1][i, 0]
_y = y[i, :] + np.sum(sign, axis=1).reshape((sign.shape[0], 1))
else:
_X = np.zeros((9, dim))
_y = np.zeros((9, 1))
# center
_X[:, :] = X[i, :].copy()
_y[0, 0] = y[i, 0].copy()
# right
_X[1, 0] += delta_x * (xlimits[0, 1] - xlimits[0, 0])
_y[1, 0] = _y[0, 0].copy() + pts[None][1][1][i, 0] * delta_x * (
xlimits[0, 1] - xlimits[0, 0])
# up
_X[2, 1] += delta_x * (xlimits[1, 1] - xlimits[1, 0])
_y[2, 0] = _y[0, 0].copy() + pts[None][2][1][i, 0] * delta_x * (
xlimits[1, 1] - xlimits[1, 0])
# left
_X[3, 0] -= delta_x * (xlimits[0, 1] - xlimits[0, 0])
_y[3, 0] = _y[0, 0].copy() - pts[None][1][1][i, 0] * delta_x * (
xlimits[0, 1] - xlimits[0, 0])
# down
_X[4, 1] -= delta_x * (xlimits[1, 1] - xlimits[1, 0])
_y[4, 0] = _y[0, 0].copy() - pts[None][2][1][i, 0] * delta_x * (
xlimits[1, 1] - xlimits[1, 0])
# right up
_X[5, 0] += delta_x * (xlimits[0, 1] - xlimits[0, 0])
_X[5, 1] += delta_x * (xlimits[1, 1] - xlimits[1, 0])
_y[5, 0] = (_y[0, 0].copy() + pts[None][1][1][i, 0] * delta_x *
(xlimits[0, 1] - xlimits[0, 0]) +
pts[None][2][1][i, 0] * delta_x *
(xlimits[1, 1] - xlimits[1, 0]))
# left up
_X[6, 0] -= delta_x * (xlimits[0, 1] - xlimits[0, 0])
_X[6, 1] += delta_x * (xlimits[1, 1] - xlimits[1, 0])
_y[6, 0] = (_y[0, 0].copy() - pts[None][1][1][i, 0] * delta_x *
(xlimits[0, 1] - xlimits[0, 0]) +
pts[None][2][1][i, 0] * delta_x *
(xlimits[1, 1] - xlimits[1, 0]))
# left down
_X[7, 0] -= delta_x * (xlimits[0, 1] - xlimits[0, 0])
_X[7, 1] -= delta_x * (xlimits[1, 1] - xlimits[1, 0])
_y[7, 0] = (_y[0, 0].copy() - pts[None][1][1][i, 0] * delta_x *
(xlimits[0, 1] - xlimits[0, 0]) -
pts[None][2][1][i, 0] * delta_x *
(xlimits[1, 1] - xlimits[1, 0]))
# right down
_X[8, 0] += delta_x * (xlimits[0, 1] - xlimits[0, 0])
_X[8, 1] -= delta_x * (xlimits[1, 1] - xlimits[1, 0])
_y[8, 0] = (_y[0, 0].copy() + pts[None][1][1][i, 0] * delta_x *
(xlimits[0, 1] - xlimits[0, 0]) -
pts[None][2][1][i, 0] * delta_x *
(xlimits[1, 1] - xlimits[1, 0]))
# As of sklearn 0.24.1 a zeroed _y raises an exception while sklearn 0.23 returns zeroed x_rotations
# For now the try/except below is a workaround to restore the 0.23 behaviour
try:
_pls.fit(_X.copy(), _y.copy())
coeff_pls[i, :, :] = _pls.x_rotations_
except StopIteration:
coeff_pls[i, :, :] = 0
# Add additional points
if extra_points != 0:
max_coeff = np.argsort(np.abs(coeff_pls[i, :, 0]))[-extra_points:]
for ii in max_coeff:
XX = np.vstack((XX, X[i, :]))
XX[-1, ii] += delta_x * (xlimits[ii, 1] - xlimits[ii, 0])
yy = np.vstack((yy, y[i]))
yy[-1] += (pts[None][1 + ii][1][i] * delta_x *
(xlimits[ii, 1] - xlimits[ii, 0]))
return np.abs(coeff_pls).mean(axis=0), XX, yy
class ExperimentDesigner:
_matrix_designers = {
'fullfactorial2levels': pyDOE2.ff2n,
'fullfactorial3levels': lambda n: pyDOE2.fullfact([3] * n),
'placketburman': pyDOE2.pbdesign,
'boxbehnken': lambda n: pyDOE2.bbdesign(n, 1),
'ccc': lambda n: pyDOE2.ccdesign(n, (0, 3), face='ccc'),
'ccf': lambda n: pyDOE2.ccdesign(n, (0, 3), face='ccf'),
'cci': lambda n: pyDOE2.ccdesign(n, (0, 3), face='cci'),
}
def __init__(self,
factors,
design_type,
responses,
skip_screening=True,
at_edges='distort',
relative_step=.25,
gsd_reduction='auto',
model_selection='brute',
n_folds='loo',
manual_formula=None,
shrinkage=1.0,
q2_limit=0.5,
gsd_span_ratio=0.5):
try:
assert at_edges in ('distort', 'shrink'),\
'unknown action at_edges: {0}'.format(at_edges)
assert relative_step is None or 0 < relative_step < 1,\
'relative_step must be float between 0 and 1 not {}'.format(relative_step)
assert model_selection in ('brute', 'greedy', 'manual'), \
'model_selection must be "brute", "greedy", "manual".'
assert n_folds == 'loo' or (isinstance(n_folds, int) and n_folds > 0), \
'n_folds must be "loo" or positive integer'
assert 0.9 <= shrinkage <= 1, 'shrinkage must be float between 0.9 and 1.0, not {}'.format(
shrinkage)
assert 0 <= q2_limit <= 1, 'q2_limit must be float between 0 and 1, not {}'.format(
q2_limit)
if model_selection == 'manual':
assert isinstance(manual_formula, str), \
'If model_selection is "manual" formula must be provided.'
except AssertionError as e:
raise ValueError(str(e))
self.factors = OrderedDict()
factor_types = list()
for factor_name, f_spec in factors.items():
factor = factor_from_spec(f_spec)
if isinstance(factor, CategoricalFactor) and skip_screening:
raise DesignerError(
'Can\'t perform optimization with categorical '
'variables without prior screening.')
self.factors[factor_name] = factor
logging.debug('Sets factor {}: {}'.format(factor_name, factor))
factor_types.append(f_spec.get('type', 'continuous'))
self.skip_screening = skip_screening
self.step_length = relative_step
self.design_type = design_type
self.responses = responses
self.response_values = None
self.gsd_reduction = gsd_reduction
self.model_selection = model_selection
self.n_folds = n_folds
self.shrinkage = shrinkage
self.q2_limit = q2_limit
self._formula = manual_formula
self._edge_action = at_edges
self._allowed_phases = ['optimization', 'screening']
self._phase = 'optimization' if self.skip_screening else 'screening'
self._n_screening_evaluations = 0
self._factor_types = factor_types
self._gsd_span_ratio = gsd_span_ratio
self._stored_transform = lambda x: x
self._best_experiment = {
'optimal_x': pd.Series([]),
'optimal_y': None,
'weighted_y': None
}
n = len(self.factors)
try:
self._matrix_designers[self.design_type.lower()]
except KeyError:
raise UnsupportedDesign(self.design_type)
if len(self.responses) > 1:
self._desirabilites = {
name: make_desirability_function(factor)
for name, factor in self.responses.items()
}
else:
self._desirabilites = None
def new_design(self):
"""
:return: Experimental design-sheet.
:rtype: pandas.DataFrame
"""
if self._phase == 'screening':
return self._new_screening_design(reduction=self.gsd_reduction)
else:
return self._new_optimization_design()
def write_factor_csv(self, out_file):
factors = list()
idx = pd.Index(['fixed_value', 'current_low', 'current_high'])
for name, factor in self.factors.items():
current_min = None
current_high = None
fixed_value = None
if issubclass(type(factor), NumericFactor):
current_min = factor.current_low
current_high = factor.current_high
elif isinstance(factor, CategoricalFactor):
fixed_value = factor.fixed_value
else:
raise NotImplementedError
data = [fixed_value, current_min, current_high]
factors.append(pd.Series(data, index=idx, name=name))
factors_df = pd.DataFrame(factors)
logging.info('Saving factor settings to {}'.format(out_file))
factors_df.to_csv(out_file)
def update_factors_from_csv(self, csv_file):
factors_df = pd.DataFrame.from_csv(csv_file)
logging.info('Reading factor settings from {}'.format(csv_file))
for name, factor in self.factors.items():
logging.info('Updating factor {}'.format(name))
if issubclass(type(factor), NumericFactor):
current_low = factors_df.loc[name]['current_low']
current_high = factors_df.loc[name]['current_high']
logging.info('Factor: {}. Setting current_low to {}'.format(
name, current_low))
logging.info('Factor: {}. Setting current_high to {}'.format(
name, current_high))
factor.current_low = current_low
factor.current_high = current_high
elif isinstance(factor, CategoricalFactor):
if pd.isnull(factors_df.loc[name]['fixed_value']):
fixed_value = None
logging.info(
'Factor: {}. Had no fixed_value.'.format(name))
else:
fixed_value = factors_df.loc[name]['fixed_value']
logging.info(
'Factor: {}. Setting fixed_value to {}.'.format(
name, fixed_value))
factor.fixed_value = fixed_value
def get_optimal_settings(self, response):
"""
Calculate optimal factor settings given response. Returns calculated
optimum.
If the current phase is 'screening': returns the factor settings of
the best run and updates the current factor settings.
If the current phase is 'optimization': returns the factor settings of
the predicted optimum, but doesn't update current factor settings in
case a validation step is to be run first
:param pandas.DataFrame response: Response sheet.
:returns: Calculated optimum.
:rtype: OptimizationResult
"""
self._response_values = response.copy()
response = response.copy()
# Perform any transformations or weigh together multiple responses:
treated_response, criterion = self.treat_response(response)
if self._phase == 'screening':
# Find the best screening result and update factors accordingly
self._screening_response = treated_response
self._screening_criterion = criterion
return self._evaluate_screening(treated_response, criterion,
self._gsd_span_ratio)
else:
# Predict optimal parameter settings, but don't update factors
return self._predict_optimum_settings(treated_response, criterion)
def _update_best_experiment(self, result):
update = False
if self._best_experiment['optimal_x'].empty:
update = True
elif result['criterion'] == 'maximize':
if result['weighted_response'] > self._best_experiment[
'weighted_y']:
update = True
elif result['criterion'] == 'minimize':
if result['weighted_response'] < self._best_experiment[
'weighted_y']:
update = True
if update:
self._best_experiment['optimal_x'] = result['factor_settings']
self._best_experiment['optimal_y'] = result['response']
self._best_experiment['weighted_y'] = result['weighted_response']
return update
def get_best_experiment(self,
experimental_sheet,
response_sheet,
use_index=1):
"""
Accepts an experimental design and the corresponding response values.
Finds the best experiment and updates self._best_experiment.
Returns the best experiment, to be used in fnc update_factors_from_optimum
"""
assert isinstance(experimental_sheet, pd.core.frame.DataFrame), \
'The input experimental sheet must be a pandas DataFrame'
assert isinstance(response_sheet, pd.core.frame.DataFrame), \
'The input response sheet must be a pandas DataFrame'
assert sorted(experimental_sheet.columns) == sorted(self.factors), \
'The factors of the experimental sheet must match those in the \
pipeline. You input:\n{}\nThey should be:\n{}' .format(
list(experimental_sheet.columns),
list(self.factors.keys()))
assert sorted(response_sheet.columns) == sorted(self.responses), \
'The responses of the response sheet must match those in the \
pipeline. You input:\n{}\nThey should be:\n{}' .format(
list(response_sheet.columns),
list(self.responses.keys()))
response = response_sheet.copy()
treated_response, criterion = self.treat_response(
response, perform_transform=False)
treated_response = treated_response.iloc[:, 0]
if criterion == 'maximize':
optimum_i = treated_response.argsort().iloc[-use_index]
elif criterion == 'minimize':
optimum_i = treated_response.argsort().iloc[use_index - 1]
else:
raise NotImplementedError
optimum_settings = experimental_sheet.iloc[optimum_i]
results = OrderedDict()
optimal_weighted_response = np.array(treated_response.iloc[optimum_i])
optimal_response = response_sheet.iloc[optimum_i]
results['factor_settings'] = optimum_settings
results['weighted_response'] = optimal_weighted_response
results['response'] = optimal_response
results['criterion'] = criterion
results['new_best'] = False
results['old_best'] = self._best_experiment
has_multiple_responses = response_sheet.shape[1] > 1
logging.debug('The best response was found in experiment:\n{}'.format(
optimum_settings.name))
logging.debug('The response values were:\n{}'.format(
response_sheet.iloc[optimum_i]))
if has_multiple_responses:
logging.debug('The weighed response was:\n{}'.format(
treated_response.iloc[optimum_i]))
logging.debug('Will return optimum settings:\n{}'.format(
results['factor_settings']))
logging.debug('And best response:\n{}'.format(results['response']))
if self._update_best_experiment(results):
results['new_best'] = True
return results
def update_factors_from_optimum(self,
optimal_experiment,
tol=0.25,
recovery=False):
"""
Updates the factor settings based on how far the current settings are
from those supplied in optimal_experiment['factor_settings'].
:param OrderedDict optimal_experiment: Output from get_best_experiment
:param float tol: Accepted relative distance to design space edge.
:returns: Calculated optimum.
:rtype: OptimizationResult
"""
are_numeric = np.array(self._factor_types) != 'categorical'
numeric_names = np.array(list(self.factors.keys()))[are_numeric]
numeric_factors = np.array(list(self.factors.values()))[are_numeric]
optimal_x = optimal_experiment['factor_settings']
optimal_y = optimal_experiment['weighted_response']
criterion = optimal_experiment['criterion']
# Get only numeric factors
if recovery:
optimal_x = optimal_x.iloc[optimal_x.index.isin(numeric_names)]
centers = np.array([f.center for f in numeric_factors])
spans = np.array([f.span for f in numeric_factors])
ratios = (optimal_x - centers) / spans
if not recovery:
logging.debug(
'The distance of the factor optimas from the factor centers, '
'expressed as the ratio of the step length:\n{}'.format(
ratios))
if (abs(ratios) < tol).all():
converged = True
if not recovery:
logging.info('Convergence reached.')
else:
converged = False
if not recovery:
logging.info('Convergence not reached. Moves design.')
for ratio, name, factor in zip(ratios, numeric_names,
numeric_factors):
if abs(ratio) < tol:
if not recovery:
logging.debug(
('Factor {} not updated - within tolerance '
'limits.').format(name))
continue
if not recovery:
self._update_numeric_factor(factor, name, ratio)
converged, reached_limits = self._check_convergence(centers,
converged,
criterion,
optimal_y,
numeric_factors,
recovery=recovery)
optimization_results = pd.Series(index=self._design_sheet.columns,
dtype=object)
for name, factor in self.factors.items():
if isinstance(factor, CategoricalFactor):
optimization_results[name] = factor.fixed_value
else:
optimization_results[name] = optimal_x[name]
results = OptimizationResult(optimization_results,
converged,
tol,
reached_limits,
empirically_found=True)
return results
def _predict_optimum_settings(self, response, criterion):
"""
Calculate a model from the response and find the optimum.
:returns: Calculated optimum.
:rtype: OptimizationResult
"""
logging.info('Predicting optimum')
are_numeric = np.array(self._factor_types) != 'categorical'
numeric_names = np.array(list(self.factors.keys()))[are_numeric]
optimal_x, model, prediction = predict_optimum(
self._design_sheet.loc[:, are_numeric],
response.iloc[:, 0].values,
numeric_names,
criterion=criterion,
n_folds=self.n_folds,
model_selection=self.model_selection,
manual_formula=self._formula,
q2_limit=self.q2_limit)
optimization_results = pd.Series(index=self._design_sheet.columns,
dtype=object)
if not optimal_x.empty:
# If Q2 of model was above the limit and if an optimum was found
for name, factor in self.factors.items():
if isinstance(factor, CategoricalFactor):
optimization_results[name] = factor.fixed_value
elif isinstance(factor, OrdinalFactor):
optimization_results[name] = int(np.round(optimal_x[name]))
else:
optimization_results[name] = optimal_x[name]
result = OptimizationResult(optimization_results,
converged=False,
tol=0,
reached_limits=False,
empirically_found=False)
return result
def treat_response(self, response, perform_transform=True):
"""
Perform any specified transformations on the response.
If several responses are defined, combine them into one. The geometric
mean of Derringer and Suich's desirability functions will be used for
optimization, see:
Derringer, G., and Suich, R., (1980), "Simultaneous Optimization
of Several Response Variables," Journal of Quality Technology, 12,
4, 214-219.
Returns a single response variable and the associated maximize/minimize
criterion.
"""
has_multiple_responses = response.shape[1] > 1
for name, spec in self.responses.items():
transform = spec.get('transform', None)
response_values = response[name]
if perform_transform:
if transform == 'log':
logging.debug('Log-transforming response {}'.format(name))
response_values = np.log(response_values)
self._stored_transform = np.log
elif transform == 'box-cox':
response_values, lambda_ = scipy.stats.boxcox(
response_values)
logging.debug('Box-cox transforming response {} '
'(lambda={:.4f})'.format(name, lambda_))
self._stored_transform = _make_stored_boxcox(lambda_)
else:
self._stored_transform = lambda x: x
if has_multiple_responses:
desirability_function = self._desirabilites[name]
response_values = [
desirability_function(value) for value in response_values
]
response[name] = response_values
if has_multiple_responses:
response = np.power(response.product(axis=1),
(1 / response.shape[1]))
response = response.to_frame('combined_response')
criterion = 'maximize'
else:
criterion = list(self.responses.values())[0]['criterion']
return response, criterion
def reevaluate_screening(self):
if self._screening_response is None:
raise DesignerError('screening must be run before re-evaluation')
return self._evaluate_screening(self._screening_response,
self._screening_criterion,
self._gsd_span_ratio,
self._n_screening_evaluations + 1)
def _validate_new_factor_limits(self, factor, factor_name, low_limit,
high_limit):
# If the proposed step change takes us below or above min and max:
logging.debug('Factor {}: Proposed new factor low is {}.'.format(
factor_name, low_limit))
logging.debug('Factor {}: Proposed new factor high is {}.'.format(
factor_name, high_limit))
adjusted_settings = False
if low_limit < factor.min:
nudge = abs(low_limit - factor.min)
logging.debug(
'Factor {}: Minimum allowed setting ({}) would be exceeded by '
'the proposed new factor low.'.format(factor_name, factor.min))
low_limit += nudge
high_limit += nudge
adjusted_settings = True
elif high_limit > factor.max:
nudge = abs(high_limit - factor.max)
logging.debug(
'Factor {}: Maximum allowed setting ({}) would be exceeded by '
'the proposed new factor high.'.format(factor_name,
factor.max))
low_limit -= nudge
high_limit -= nudge
adjusted_settings = True
if adjusted_settings:
logging.debug('Factor {}: Adjusted the proposed new factor '
'settings by {}.'.format(factor_name, nudge))
logging.debug('Factor {}: New factor low is {}.'.format(
factor_name, low_limit))
logging.debug('Factor {}: New factor high is {}.'.format(
factor_name, high_limit))
return (low_limit, high_limit)
def _evaluate_screening(self,
response,
criterion,
span_ratio,
use_index=1):
"""
:param float span_ratio: The ratio of the span between gsd points that will be used in the following optimization design.
"""
self._n_screening_evaluations += 1
logging.info('Evaluating screening results.')
response_series = response.iloc[:, 0]
factor_items = sorted(self.factors.items())
if criterion == 'maximize':
optimum_i = response_series.argsort().iloc[-use_index]
elif criterion == 'minimize':
optimum_i = response_series.argsort().iloc[use_index - 1]
else:
raise NotImplementedError
optimum_design_row = self._design_matrix[optimum_i]
optimum_settings = OrderedDict()
# Update all factors according to current results. For each factor,
# the current_high and current_low will be set to factors level above
# and below the point in the screening design with the best response.
for factor_level, (name, factor) in zip(optimum_design_row,
factor_items):
if isinstance(factor, CategoricalFactor):
factor_levels = np.array(factor.values)
factor.fixed_value = factor_levels[factor_level]
else:
factor_levels = sorted(self._design_sheet[name].unique())
min_ = factor_levels[max([0, factor_level - 1])]
max_ = factor_levels[min(
[factor_level + 1,
len(factor_levels) - 1])]
span = max_ - min_
# Shrink the span a bit
logging.debug('Factor {} span: {}'.format(name, span))
logging.debug('Factor {}: adjusting span with '
'gsd_span_ratio {}'.format(name, span_ratio))
span = span * span_ratio
if isinstance(factor, OrdinalFactor) and span < 2.0:
# Make sure ordinal factors' spans don't shrink to the
# point where there's no spread in the exp. design
logging.debug('Factor {}: span ({}) too small, adjusting '
'to minimal span for ordinal factor.'.format(
name, span))
span = 2.0
logging.debug('Factor {} span: {}'.format(name, span))
# center around best point
best_point = factor_levels[factor_level]
new_low = best_point - span / 2
new_high = best_point + span / 2
if isinstance(factor, OrdinalFactor):
new_low = int(np.round(new_low))
new_high = int(np.round(new_high))
# nudge new high and low so we don't exceed the limits
new_low, new_high = self._validate_new_factor_limits(
factor, name, new_low, new_high)
# update factors
factor.current_low = new_low
factor.current_high = new_high
optimum_settings[name] = factor_levels[factor_level]
logging.info('New settings for factor {}:\n{}'.format(
name, factor))
results = OptimizationResult(pd.Series(optimum_settings),
converged=False,
tol=0,
reached_limits=False,
empirically_found=True)
logging.info('Best screening result was exp no {}'.format(optimum_i))
logging.info('The corresponding response was:\n{}'.format(
self._response_values.iloc[optimum_i]))
if len(self._response_values.columns) > 1:
logging.info('The combined response was:\n{}'.format(
response.iloc[optimum_i]))
logging.info('The factor settings were:\n{}'.format(
results.predicted_optimum))
# update current best experiment
self.get_best_experiment(
self._design_sheet, self._response_values
if len(self._response_values.columns) > 1 else response)
self._phase = 'optimization'
return results
def set_phase(self, phase):
assert phase in self._allowed_phases, 'phase must be one of {}'.format(
self._allowed_phases)
self._phase = phase
def _update_numeric_factor(self, factor, name, ratio):
logging.info('Factor {}: Updating settings.'.format(name))
logging.info('Factor {}: Current settings: {}'.format(name, factor))
step_length = self.step_length if self.step_length is not None \
else abs(ratio)
step = factor.span * step_length * np.sign(ratio)
logging.debug(
'Factor {}: Step by which settings are adjusted is {}.'.format(
name, step))
logging.debug(
'Factor {}: Current span between high and low is {}.'.format(
name, factor.span))
logging.debug('Factor {}: Will shrink the span by {}.'.format(
name, self.shrinkage))
new_span = factor.span * self.shrinkage
logging.debug('Factor {}: New span is {}.'.format(name, new_span))
if isinstance(factor, QuantitativeFactor):
current_low_new = factor.center + step - new_span / 2
current_high_new = factor.center + step + new_span / 2
elif isinstance(factor, OrdinalFactor):
current_low_new = np.round(factor.center + step - new_span / 2)
current_high_new = np.round(factor.center + step + new_span / 2)
else:
raise NotImplementedError
# If the proposed step change takes us below or above min and max:
new_low, new_high = self._validate_new_factor_limits(
factor, name, current_low_new, current_high_new)
factor.current_low = new_low
factor.current_high = new_high
logging.info('Factor {}: New settings: {}'.format(name, factor))
logging.info('Factor {}: Done updating.'.format(name))
def _new_screening_design(self, reduction='auto'):
factor_items = sorted(self.factors.items())
levels = list()
names = list()
dtypes = list()
for name, factor in factor_items:
names.append(name)
if isinstance(factor, CategoricalFactor):
levels.append(factor.values)
dtypes.append(object)
continue
num_levels = factor.screening_levels
spacing = getattr(factor, 'screening_spacing', 'linear')
min_ = factor.min
max_ = factor.max
if not np.isfinite([min_, max_]).all():
raise ValueError(
'Can\'t perform screening with unbounded factors')
space = np.linspace if spacing == 'linear' else np.logspace
values = space(min_, max_, num_levels)
if isinstance(factor, OrdinalFactor):
values = sorted(np.unique(np.round(values)))
dtypes.append(int)
else:
dtypes.append(float)
levels.append(values)
design_matrix = pyDOE2.gsd(
[len(values) for values in levels],
reduction if reduction is not 'auto' else len(levels))
factor_matrix = list()
for i, (values, dtype) in enumerate(zip(levels, dtypes)):
values = np.array(values)[design_matrix[:, i]]
series = pd.Series(values, dtype=dtype)
factor_matrix.append(series)
self._design_matrix = design_matrix
self._design_sheet = pd.concat(factor_matrix, axis=1, keys=names)
return self._design_sheet
def _new_optimization_design(self):
matrix_designer = self._matrix_designers[self.design_type.lower()]
numeric_factors = [(name, factor)
for name, factor in self.factors.items()
if isinstance(factor, NumericFactor)]
numeric_factor_names = [name for name, factor in numeric_factors]
design_matrix = matrix_designer(len(numeric_factors))
mins = np.array([f.min for _, f in numeric_factors])
maxes = np.array([f.max for _, f in numeric_factors])
span = np.array([f.span for _, f in numeric_factors])
centers = np.array([f.center for _, f in numeric_factors])
factor_matrix = design_matrix * (span / 2.0) + centers
# Check if current settings are outside allowed design space.
# Also, for factors that are specified as ordinal, adjust their values
# in the design matrix to be rounded floats
for i, (factor_name, factor) in enumerate(numeric_factors):
if isinstance(factor, OrdinalFactor):
factor_matrix[:, i] = np.round(factor_matrix[:, i])
logging.debug('Current setting {}: {}'.format(factor_name, factor))
if (factor_matrix < mins).any() or (factor_matrix > maxes).any():
logging.warning(('Out of design space factors. Adjusts factors'
'by {}.'.format(self._edge_action + 'ing')))
if self._edge_action == 'distort':
# Simply cap out-of-boundary values at mins and maxes.
capped_mins = np.maximum(factor_matrix, mins)
capped_mins_and_maxes = np.minimum(capped_mins, maxes)
factor_matrix = capped_mins_and_maxes
elif self._edge_action == 'shrink':
raise NotImplementedError
factors = list()
for name, factor in self.factors.items():
if isinstance(factor, CategoricalFactor):
values = np.repeat(factor.fixed_value, len(design_matrix))
factors.append(pd.Series(values))
else:
i = numeric_factor_names.index(name)
dtype = int if isinstance(factor, OrdinalFactor) else float
factors.append(pd.Series(factor_matrix[:, i].astype(dtype)))
self._design_sheet = pd.concat(factors,
axis=1,
keys=self.factors.keys())
return self._design_sheet
def _check_convergence(self,
centers,
converged,
criterion,
prediction,
numeric_factors,
recovery=False):
# It's possible that the optimum is predicted to be at the edge of the allowed
# min or max factor setting. This will produce a high 'ratio' and the algorithm
# is not considered to have converged (above). However, in this situation we
# can't move the space any further and we should stop iterating.
new_centers = np.array([f.center for f in numeric_factors])
if (centers == new_centers).all():
if not recovery:
logging.info(
'The design has not moved since last iteration. Converged.'
)
converged = True
reached_limits = True
if len(self.responses) > 1 and prediction < 1:
reached_limits = False
elif len(self.responses) == 1:
r_spec = list(self.responses.values())[0]
low_limit = self._stored_transform(r_spec.get('low_limit', 1))
high_limit = self._stored_transform(r_spec.get(
'high_limit', 1))
if criterion == 'maximize' and 'low_limit' in r_spec:
reached_limits = prediction >= low_limit
elif criterion == 'minimize' and 'high_limit' in r_spec:
reached_limits = prediction <= high_limit
elif criterion == 'target' and 'low_limit' in r_spec and 'high_limit' in r_spec:
reached_limits = low_limit <= prediction <= high_limit
else:
reached_limits = False
return converged, reached_limits
def opmsens_write_cases(basefile, header, factors, scenario):
""" Main Function for Writing out Scenario Cases
This is the main function that controls the writing out of the various requested scenario cases (jobs). The
function first calls the opmsens_checkerr routine to check for errors and then the opmsens_clean routine to
remove previously created scenario files. After which the opmsens_write_param and opmsens_write_data functions are
called to create the scenario PARAM and DATA files
Parameters
----------
basefile : str
The basefile used to generate all the cases
header : list
A list of of header names
factors : table
A table of design factors
scenario : str
The type of scenario to be generated
Returns
------
None
"""
# Check for Errors and Return if Errors Found
checkerr = opmsens_check(basefile, header, factors, scenario)
if checkerr:
return ()
#
# Cleanup Existing Files
#
opmsens_clean(basefile)
#
# Define Factor and Job Data Frame
#
df = pd.DataFrame(factors, columns=header)
df = df[df != ''].dropna()
jobdf = pd.DataFrame()
jobdf[header[1]] = df[header[1]]
for slevel in ['Low', 'Best', 'High']:
if slevel in scenario:
jobdf[slevel] = df[slevel]
nfactor = jobdf.shape[0]
nlevel = jobdf.shape[1]
#
# Write PARAM and DATA Files
#
jobs = []
jobstart = 1
joberr = False
jobdata = Path(basefile)
jobparam = Path(basefile).with_suffix('.param')
jobque = Path(basefile).with_suffix('.que')
print('Scenario: ' + scenario + ' Start')
#
# Low, Best and High Scenario
#
if 'Scenario' in scenario:
jobnum = 0
for joblevel in range(1, nlevel):
(joberr, jobs) = opmsens_write_param(jobstart, jobnum, jobparam,
jobdata, jobs)
if joberr:
break
joberr = opmsens_write_data(scenario, joblevel, nfactor, jobdf,
jobstart, jobnum, jobdata)
if joberr:
break
jobstart = jobstart + joblevel
#
# One Job per Factor
#
elif 'One Job per Factor' in scenario:
for joblevel in range(1, nlevel):
for jobnum in range(0, nfactor):
(joberr, jobs) = opmsens_write_param(jobstart, jobnum,
jobparam, jobdata, jobs)
if joberr:
break
joberr = opmsens_write_data(scenario, joblevel, nfactor, jobdf,
jobstart, jobnum, jobdata)
if joberr:
break
jobstart = jobstart + nfactor
#
# Factorial Low and High Full
#
elif 'Factorial' in scenario:
#
# Obtain DOE Matrix and Convert to Data Frame
#
doedata = pd.DataFrame()
if 'Factorial Low and High Full' in scenario:
doedata = pyDOE2.ff2n(nfactor) + 2
if 'Factorial Low and High Plackett-Burman' in scenario:
doedata = pyDOE2.pbdesign(nfactor) + 2
if 'Factorial Low, Best and High Full' in scenario:
doedata = (pyDOE2.fullfact([nlevel - 1] * nfactor)) - 1
if 'Factorial Low, Best and High Box-Behnken' in scenario:
doedata = pyDOE2.bbdesign(nfactor)
doedf = pd.DataFrame(data=doedata).transpose()
doedf = doedf.rename(columns=lambda x: 'RUN' + str(x + 1).zfill(3),
inplace=False)
#
# Set Factor Values
#
for n in range(0, nfactor):
doedf.iloc[n, :] = doedf.iloc[n, :].replace(
[1.0, 2.0, 3.0], [df.iloc[n, 2], df.iloc[n, 3], df.iloc[n, 4]])
#
# Merge Data Frames and Write Out Files
#
jobdf = pd.DataFrame()
jobdf[header[1]] = df[header[1]]
jobdf = pd.concat([jobdf, doedf], axis=1)
nfactor = jobdf.shape[0]
nlevel = jobdf.shape[1]
jobstart = 0
for joblevel in range(1, nlevel):
jobnum = joblevel
(joberr, jobs) = opmsens_write_param(jobstart, jobnum, jobparam,
jobdata, jobs)
if joberr:
break
joberr = opmsens_write_data(scenario, joblevel, nfactor, jobdf,
jobstart, jobnum, jobdata)
if joberr:
break
print('Scenario: ' + scenario + ' End')
if not joberr:
print('WriteQueu: Start')
opmsens_write_queue(jobs)
print('WriteQueu: End')
return ()
