def fit_and_select(input,
save=True,
verbose=True,
read_existing=True,
hall=None):
"""
Arguments
---------
input: dict
The input settings as a dict
save: boolean, optional, default=True
Save a pickle file containing the training data and scoring metric. The file
name, which can be specified by input["fit_data_filename"], defaults to "fit_data.pkl".
verbose: boolean, optional, default=True
Print information to stdout.
read_existing: boolean, optional, default=True
If it exists, read the pickle file containing the training data and scoring
metric. The file name, which can be specified by input["fit_data_filename"],
defaults to "fit_data.pkl".
hall: deap.tools.HallOfFame, optional, default=None
A Hall Of Fame to add resulting individuals to
Returns
-------
(fdata, estimator, selector)
fdata: casm.learn.FittingData
A FittingData instance containing the problem data.
estimator: estimator object implementing 'fit'
The estimator specified by the input settings.
selector: selector object implementing 'fit' and having either a
'get_support()' or 'get_halloffame()' member
The feature selector specified by the input settings.
"""
# construct FittingData
fdata = make_fitting_data(input,
save=True,
verbose=verbose,
read_existing=True)
# construct model used for fitting
estimator = make_estimator(input, verbose=verbose)
# feature selection
selector = make_selector(input,
estimator,
scoring=fdata.scoring,
cv=fdata.cv,
penalty=fdata.penalty,
verbose=verbose)
if not hasattr(selector, "get_halloffame") and not hasattr(
selector, "get_support"):
raise Exception(
"Selector has neither 'get_halloffame' nor 'get_support'")
# fit
if verbose:
print("# Fit and select...")
t = time.clock()
selector.fit(fdata.weighted_X, fdata.weighted_y)
# print calculation properties that may be of interest
if verbose:
# custom for SelectFromModel
if hasattr(selector, "estimator_") and hasattr(selector.estimator_,
"n_iter_"):
print("# Iterations:", selector.estimator_.n_iter_)
if hasattr(selector, "estimator_") and hasattr(selector.estimator_,
"alpha_"):
print("# Alpha:", selector.estimator_.alpha_)
if hasattr(selector, "threshold"):
print("# Feature selection threshold:", selector.threshold)
print("# DONE Runtime:", time.clock() - t, "(s)\n")
if hall is not None:
# store results: Assume selector either has a 'get_halloffame()' attribute, or 'get_support()' member
if hasattr(selector, "get_halloffame"):
if verbose:
print("Adding statistics...")
selector_hall = selector.get_halloffame()
for i in range(len(selector_hall)):
add_individual_detail(selector_hall[i],
estimator,
fdata,
input,
selector=selector)
if verbose:
print(" DONE\n")
if verbose:
print("Result:")
print_halloffame(selector_hall)
hall.update(selector_hall)
elif hasattr(selector, "get_support"):
indiv = casm.learn.creator.Individual(selector.get_support())
if verbose:
print("Adding statistics...")
indiv.fitness.values = casm.learn.model_selection.cross_val_score(
estimator,
fdata.weighted_X,
indiv,
y=fdata.weighted_y,
scoring=fdata.scoring,
cv=fdata.cv,
penalty=fdata.penalty)
add_individual_detail(indiv,
estimator,
fdata,
input,
selector=selector)
if verbose:
print(" DONE\n")
if verbose:
print("Result:")
print_halloffame([indiv])
hall.update([indiv])
return (fdata, estimator, selector)
def direct_fit(input, save=True, verbose=True, read_existing=True, hall=None):
"""
Fit ECI and add details for a set of individuals specified for feature_selection
method 'DirectSelection' via the 'population' kwarg.
Arguments
---------
input: dict
The input settings as a dict
save: boolean, optional, default=True
Save a pickle file containing the training data and scoring metric. The file
name, which can be specified by input["fit_data_filename"], defaults to "fit_data.pkl".
verbose: boolean, optional, default=True
Print information to stdout.
read_existing: boolean, optional, default=True
If it exists, read the pickle file containing the training data and scoring
metric. The file name, which can be specified by input["fit_data_filename"],
defaults to "fit_data.pkl".
hall: deap.tools.HallOfFame, optional, default=None
A Hall Of Fame to add resulting individuals to
Returns
-------
population: List[individuals]
A list of the individuals that were fit
"""
# construct FittingData
fdata = make_fitting_data(input,
save=True,
verbose=verbose,
read_existing=True)
kwargs = input["feature_selection"]["kwargs"]
population = make_population(fdata.n_features, kwargs["population"])
for indiv_i, indiv in enumerate(population):
if verbose:
print "Begin fitting individual", indiv_i, "of", len(population)
_input = copy.deepcopy(input)
use_saved_estimator = kwargs.get("use_saved_estimator", False)
# check for 'use_saved_estimator'
if use_saved_estimator and getattr(indiv, "input", None) is not None:
_input["estimator"] = indiv.input["estimator"]
estimator = make_estimator(_input)
indiv.fitness.values = cross_val_score(estimator,
fdata.weighted_X,
indiv,
y=fdata.weighted_y,
scoring=fdata.scoring,
cv=fdata.cv,
penalty=fdata.penalty)
add_individual_detail(indiv, estimator, fdata, _input, selector=None)
if verbose:
print " DONE\n"
if verbose:
print "Result:"
print_halloffame(population)
if hall is not None:
hall.update(population)
return population
def direct_fit(input, save=True, verbose=True, read_existing=True, hall=None):
"""
Fit ECI and add details for a set of individuals specified for feature_selection
method 'DirectSelection' via the 'population' kwarg.
Arguments
---------
input: dict
The input settings as a dict
save: boolean, optional, default=True
Save a pickle file containing the training data and scoring metric. The file
name, which can be specified by input["fit_data_filename"], defaults to "fit_data.pkl".
verbose: boolean, optional, default=True
Print information to stdout.
read_existing: boolean, optional, default=True
If it exists, read the pickle file containing the training data and scoring
metric. The file name, which can be specified by input["fit_data_filename"],
defaults to "fit_data.pkl".
hall: deap.tools.HallOfFame, optional, default=None
A Hall Of Fame to add resulting individuals to
Returns
-------
population: List[individuals]
A list of the individuals that were fit
"""
# construct FittingData
fdata = make_fitting_data(input, save=True, verbose=verbose, read_existing=True)
kwargs = input["feature_selection"]["kwargs"]
population = make_population(fdata.n_features, kwargs["population"])
for indiv_i, indiv in enumerate(population):
if verbose:
print "Begin fitting individual", indiv_i, "of", len(population)
_input = copy.deepcopy(input)
use_saved_estimator = kwargs.get("use_saved_estimator", False)
# check for 'use_saved_estimator'
if use_saved_estimator and getattr(indiv, "input", None) is not None:
_input["estimator"] = indiv.input["estimator"]
estimator = make_estimator(_input)
indiv.fitness.values = cross_val_score(estimator, fdata.weighted_X, indiv,
y=fdata.weighted_y, scoring=fdata.scoring, cv=fdata.cv, penalty=fdata.penalty)
add_individual_detail(indiv, estimator, fdata, _input, selector=None)
if verbose:
print " DONE\n"
if verbose:
print "Result:"
print_halloffame(population);
if hall is not None:
hall.update(population)
return population