def __init__(self, binner=None, estimator=None, n_jobs=None, verbose=False):
"""
@param binner transformer or predictor which creates the buckets
@param estimator predictor trained on every bucket
@param n_jobs number of parallel jobs (for training and predicting)
@param verbose boolean or use ``'tqdm'`` to use :epkg:`tqdm`
to fit the estimators
*binner* allows the following values:
- ``tree``: the model is :epkg:`sklearn:tree:DecisionTreeRegressor`
- ``'bins'``: the model :epkg:`sklearn:preprocessing:KBinsDiscretizer`
- any instanciated model
*estimator* allows the following values:
- ``None``: the model is :epkg:`sklearn:linear_model:LinearRegression`
- any instanciated model
"""
if estimator is None:
estimator = LinearRegression()
if binner in ('tree', None):
binner = DecisionTreeRegressor(min_samples_leaf=2)
RegressorMixin.__init__(self)
PiecewiseEstimator.__init__(self, binner=binner, estimator=estimator,
n_jobs=n_jobs, verbose=verbose)
def __init__(self, num_inputs, mxseed=0, epochs=5000, net_type=1):
BaseEstimator.__init__(self)
RegressorMixin.__init__(self)
self.net = None
self.num_inputs = num_inputs
self.mxseed = mxseed
self.epochs = epochs
self.net_type = net_type
return
def __init__(self, model='SIR', t=0, max_iter=100,
learning_rate_init=0.1, lr_schedule='constant',
momentum=0.9, power_t=0.5, early_th=None,
min_threshold='auto', max_threshold='auto',
verbose=False, init=None):
if init is not None:
if isinstance(init, EpidemicRegressor):
if hasattr(init, 'coef_'):
init = init.coef_.copy()
else:
init = None # pragma: no cover
elif not isinstance(init, dict):
raise TypeError(
f"init must be a dictionary not {type(init)}.")
BaseEstimator.__init__(self)
RegressorMixin.__init__(self)
self.t = t
self.model = model
self.max_iter = max_iter
self.learning_rate_init = learning_rate_init
self.lr_schedule = lr_schedule
self.momentum = momentum
self.power_t = power_t
self.early_th = early_th
self.verbose = verbose
if min_threshold == 'auto':
if model.upper() in ('SIR', 'SIRD'):
min_threshold = 0.0001
elif model.upper() in ('SIRC', ):
pmin = dict(beta=0.001, nu=0.0001, mu=0.0001,
a=-1., b=0., c=0.)
min_threshold = numpy.array(
[pmin[k[0]] for k in CovidSIRDc.P0])
elif model.upper() in ('SIRDC'):
pmin = dict(beta=0.001, nu=0.001, mu=0.001,
a=-1., b=0., c=0.)
min_threshold = numpy.array(
[pmin[k[0]] for k in CovidSIRDc.P0])
if max_threshold == 'auto':
if model.upper() in ('SIR', 'SIRD'):
max_threshold = 1.
elif model.upper() in ('SIRC', 'SIRDC'):
pmax = dict(beta=1., nu=0.5, mu=0.5,
a=0., b=4., c=2.)
max_threshold = numpy.array(
[pmax[k[0]] for k in CovidSIRDc.P0])
self.min_threshold = min_threshold
self.max_threshold = max_threshold
self._get_model()
self.init = init
if init is not None:
self.coef_ = init
def __init__(self, force_positive=False, **kwargs):
"""
*kwargs* should contains parameters
for :epkg:`sklearn:decomposition:NMF`.
The parameter *force_positive* removes all
negative predictions and replaces by zero.
"""
BaseEstimator.__init__(self)
RegressorMixin.__init__(self)
MultiOutputMixin.__init__(self)
for k, v in kwargs.items():
setattr(self, k, v)
self.force_positive = force_positive
def __init__(self, rf_estimator=None, lasso_estimator=None):
"""
@param rf_estimator random forest estimator,
:epkg:`sklearn:ensemble:RandomForestRegressor`
by default
@param lass_estimator Lasso estimator,
:epkg:`sklearn:linear_model:LassoRegression`
by default
"""
BaseEstimator.__init__(self)
RegressorMixin.__init__(self)
if rf_estimator is None:
rf_estimator = RandomForestRegressor()
if lasso_estimator is None:
lasso_estimator = Lasso()
self.rf_estimator = rf_estimator
self.lasso_estimator = lasso_estimator
def __init__(self, estimator=None, n_estimators=10, n_jobs=None,
alpha=1., verbose=False):
"""
@param estimator predictor trained on every bucket
@param n_estimators number of estimators to train
@param n_jobs number of parallel jobs (for training and predicting)
@param alpha proportion of samples resampled for each training
@param verbose boolean or use ``'tqdm'`` to use :epkg:`tqdm`
to fit the estimators
"""
BaseEstimator.__init__(self)
RegressorMixin.__init__(self)
if estimator is None:
raise ValueError("estimator cannot be null.")
self.estimator = estimator
self.n_jobs = n_jobs
self.alpha = alpha
self.verbose = verbose
self.n_estimators = n_estimators
def __init__(self):
RegressorMixin.__init__(self)
BaseEstimator.__init__(self)
def __init__(self, base_estimator):
RegressorMixin.__init__(self)
BaseEstimator.__init__(self)
self.base_estimator = base_estimator
