def iterative_fit(self, X, y, n_iter=2, refit=False, sample_weight=None):
from sklearn.linear_model.passive_aggressive import \
PassiveAggressiveRegressor
# Need to fit at least two iterations, otherwise early stopping will not
# work because we cannot determine whether the algorithm actually
# converged. The only way of finding this out is if the sgd spends less
# iterations than max_iter. If max_iter == 1, it has to spend at least
# one iteration and will always spend at least one iteration, so we
# cannot know about convergence.
if refit:
self.estimator = None
if self.estimator is None:
self.fully_fit_ = False
self.average = check_for_bool(self.average)
self.fit_intercept = check_for_bool(self.fit_intercept)
self.tol = float(self.tol)
self.C = float(self.C)
call_fit = True
self.estimator = PassiveAggressiveRegressor(
C=self.C,
fit_intercept=self.fit_intercept,
max_iter=n_iter,
tol=self.tol,
loss=self.loss,
shuffle=True,
random_state=self.random_state,
warm_start=True,
average=self.average,
)
else:
call_fit = False
if call_fit:
self.estimator.fit(X, y)
else:
self.estimator.max_iter += n_iter
self.estimator.max_iter = min(self.estimator.max_iter,
1000)
self.estimator._validate_params()
lr = "pa1" if self.estimator.loss == "epsilon_insensitive" else "pa2"
self.estimator._partial_fit(
X, y,
alpha=1.0,
C=self.estimator.C,
loss="epsilon_insensitive",
learning_rate=lr,
max_iter=n_iter,
sample_weight=sample_weight,
coef_init=None,
intercept_init=None
)
if self.estimator.max_iter >= 1000 or n_iter > self.estimator.n_iter_:
self.fully_fit_ = True
return self
def fit(self, X, Y):
from sklearn.svm import LinearSVR
# In case of nested loss
if isinstance(self.loss, dict):
combination = self.loss
self.loss = combination['loss']
self.dual = combination['dual']
self.C = float(self.C)
self.tol = float(self.tol)
self.dual = check_for_bool(self.dual)
self.fit_intercept = check_for_bool(self.fit_intercept)
self.intercept_scaling = float(self.intercept_scaling)
self.estimator = LinearSVR(loss=self.loss,
dual=self.dual,
tol=self.tol,
C=self.C,
fit_intercept=self.fit_intercept,
intercept_scaling=self.intercept_scaling,
random_state=self.random_state)
self.estimator.fit(X, Y)
return self
def fit(self, X, Y):
from sklearn.svm import SVR
self.C = float(self.C)
if self.degree is None:
self.degree = 3
else:
self.degree = int(self.degree)
if self.gamma is None:
self.gamma = 0.0
else:
self.gamma = float(self.gamma)
if self.coef0 is None:
self.coef0 = 0.0
else:
self.coef0 = float(self.coef0)
self.tol = float(self.tol)
self.max_iter = float(self.max_iter)
self.shrinking = check_for_bool(self.shrinking)
self.estimator = SVR(C=self.C,
kernel=self.kernel,
degree=self.degree,
gamma=self.gamma,
coef0=self.coef0,
shrinking=self.shrinking,
tol=self.tol,
max_iter=self.max_iter)
self.estimator.fit(X, Y)
return self
def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False):
from sklearn.ensemble import RandomForestClassifier
if refit:
self.estimator = None
if self.estimator is None:
self.n_estimators = int(self.n_estimators)
if check_none(self.max_depth):
self.max_depth = None
else:
self.max_depth = int(self.max_depth)
self.min_samples_split = int(self.min_samples_split)
self.min_samples_leaf = int(self.min_samples_leaf)
self.min_weight_fraction_leaf = float(
self.min_weight_fraction_leaf)
if self.max_features not in ("sqrt", "log2", "auto"):
max_features = int(X.shape[1]**float(self.max_features))
else:
max_features = self.max_features
self.bootstrap = check_for_bool(self.bootstrap)
if check_none(self.max_leaf_nodes):
self.max_leaf_nodes = None
else:
self.max_leaf_nodes = int(self.max_leaf_nodes)
self.min_impurity_decrease = float(self.min_impurity_decrease)
# initial fit of only increment trees
self.estimator = RandomForestClassifier(
n_estimators=n_iter,
criterion=self.criterion,
max_features=max_features,
max_depth=self.max_depth,
min_samples_split=self.min_samples_split,
min_samples_leaf=self.min_samples_leaf,
min_weight_fraction_leaf=self.min_weight_fraction_leaf,
bootstrap=self.bootstrap,
max_leaf_nodes=self.max_leaf_nodes,
min_impurity_decrease=self.min_impurity_decrease,
random_state=self.random_state,
n_jobs=self.n_jobs,
class_weight=self.class_weight,
warm_start=True)
else:
self.estimator.n_estimators += n_iter
self.estimator.n_estimators = min(self.estimator.n_estimators,
self.n_estimators)
self.estimator.fit(X, y, sample_weight=sample_weight)
return self
def fit(self, X, Y):
import sklearn.svm
import sklearn.multiclass
# In case of nested penalty
if isinstance(self.penalty, dict):
combination = self.penalty
self.penalty = combination['penalty']
self.loss = combination['loss']
self.dual = combination['dual']
self.C = float(self.C)
self.tol = float(self.tol)
self.dual = check_for_bool(self.dual)
self.fit_intercept = check_for_bool(self.fit_intercept)
self.intercept_scaling = float(self.intercept_scaling)
if check_none(self.class_weight):
self.class_weight = None
estimator = sklearn.svm.LinearSVC(
penalty=self.penalty,
loss=self.loss,
dual=self.dual,
tol=self.tol,
C=self.C,
class_weight=self.class_weight,
fit_intercept=self.fit_intercept,
intercept_scaling=self.intercept_scaling,
multi_class=self.multi_class,
random_state=self.random_state)
if len(Y.shape) == 2 and Y.shape[1] > 1:
self.estimator = sklearn.multiclass.OneVsRestClassifier(estimator,
n_jobs=1)
else:
self.estimator = estimator
self.estimator.fit(X, Y)
return self
def fit(self, X, Y):
from sklearn.svm import LinearSVR
self.C = float(self.C)
self.tol = float(self.tol)
self.dual = check_for_bool(self.dual)
self.fit_intercept = check_for_bool(self.fit_intercept)
self.intercept_scaling = float(self.intercept_scaling)
self.estimator = LinearSVR(loss=self.loss,
dual=self.dual,
tol=self.tol,
C=self.C,
fit_intercept=self.fit_intercept,
intercept_scaling=self.intercept_scaling,
random_state=self.random_state)
self.estimator.fit(X, Y)
return self
def fit(self, X, y, sample_weight=None):
from sklearn.ensemble import ExtraTreesClassifier
self.bootstrap = check_for_bool(self.bootstrap)
self.estimator = ExtraTreesClassifier(n_estimators=self.n_estimators,
max_leaf_nodes=None,
criterion=self.criterion,
max_features=self.max_features,
min_samples_split=self.min_samples_split,
min_samples_leaf=self.min_samples_leaf,
max_depth=None,
bootstrap=self.bootstrap,
random_state=self.random_state,
n_jobs=self.n_jobs)
self.estimator.fit(X, y, sample_weight=sample_weight)
return self
def fit(self, X, Y):
import sklearn.svm
# Nested kernel
if isinstance(self.kernel, tuple):
nested_kernel = self.kernel
self.kernel = nested_kernel[0]
if self.kernel == 'poly':
self.degree = nested_kernel[1]['degree']
self.coef0 = nested_kernel[1]['coef0']
elif self.kernel == 'sigmoid':
self.coef0 = nested_kernel[1]['coef0']
self.C = float(self.C)
if self.degree is None:
self.degree = 3
else:
self.degree = int(self.degree)
if self.gamma is None:
self.gamma = 0.0
else:
self.gamma = float(self.gamma)
if self.coef0 is None:
self.coef0 = 0.0
else:
self.coef0 = float(self.coef0)
self.tol = float(self.tol)
self.max_iter = float(self.max_iter)
self.shrinking = check_for_bool(self.shrinking)
if check_none(self.class_weight):
self.class_weight = None
self.estimator = sklearn.svm.SVC(C=self.C,
kernel=self.kernel,
degree=self.degree,
gamma=self.gamma,
coef0=self.coef0,
shrinking=self.shrinking,
tol=self.tol,
class_weight=self.class_weight,
max_iter=self.max_iter,
random_state=self.random_state,
decision_function_shape='ovr')
self.estimator.fit(X, Y)
return self
def fit(self, X, Y):
from sklearn.svm import SVR
# Nested kernel
if isinstance(self.kernel, tuple):
nested_kernel = self.kernel
self.kernel = nested_kernel[0]
if self.kernel == 'poly':
self.degree = nested_kernel[1]['degree']
self.coef0 = nested_kernel[1]['coef0']
elif self.kernel == 'sigmoid':
self.coef0 = nested_kernel[1]['coef0']
self.C = float(self.C)
if self.degree is None:
self.degree = 3
else:
self.degree = int(self.degree)
if self.gamma is None:
self.gamma = 0.0
else:
self.gamma = float(self.gamma)
if self.coef0 is None:
self.coef0 = 0.0
else:
self.coef0 = float(self.coef0)
self.tol = float(self.tol)
self.max_iter = float(self.max_iter)
self.shrinking = check_for_bool(self.shrinking)
self.estimator = SVR(C=self.C,
kernel=self.kernel,
degree=self.degree,
gamma=self.gamma,
coef0=self.coef0,
shrinking=self.shrinking,
tol=self.tol,
max_iter=self.max_iter)
self.estimator.fit(X, Y)
return self
def iterative_fit(self, X, y, n_iter=2, refit=False, sample_weight=None):
from sklearn.linear_model.stochastic_gradient import SGDClassifier
# Need to fit at least two iterations, otherwise early stopping will not
# work because we cannot determine whether the algorithm actually
# converged. The only way of finding this out is if the sgd spends less
# iterations than max_iter. If max_iter == 1, it has to spend at least
# one iteration and will always spend at least one iteration, so we
# cannot know about convergence.
if refit:
self.estimator = None
if self.estimator is None:
if isinstance(self.loss, tuple):
nested_loss = self.loss
self.loss = nested_loss[0]
if self.loss == 'modified_huber':
self.epsilon = nested_loss[1]['epsilon']
if isinstance(self.penalty, tuple):
nested_penalty = self.penalty
self.penalty = nested_penalty[0]
if self.penalty == "elasticnet":
self.l1_ratio = nested_penalty[1]['l1_ratio']
if isinstance(self.learning_rate, tuple):
nested_learning_rate = self.learning_rate
self.learning_rate = nested_learning_rate[0]
if self.learning_rate == 'invscaling':
self.eta0 = nested_learning_rate[1]['eta0']
self.power_t = nested_learning_rate[1]['power_t']
elif self.learning_rate == 'constant':
self.eta0 = nested_learning_rate[1]['eta0']
self.fully_fit_ = False
self.alpha = float(self.alpha)
self.l1_ratio = float(self.l1_ratio) if self.l1_ratio is not None \
else 0.15
self.epsilon = float(self.epsilon) if self.epsilon is not None \
else 0.1
self.eta0 = float(self.eta0) if self.eta0 is not None else 0.01
self.power_t = float(self.power_t) if self.power_t is not None \
else 0.5
self.average = check_for_bool(self.average)
self.fit_intercept = check_for_bool(self.fit_intercept)
self.tol = float(self.tol)
self.estimator = SGDClassifier(loss=self.loss,
penalty=self.penalty,
alpha=self.alpha,
fit_intercept=self.fit_intercept,
max_iter=n_iter,
tol=self.tol,
learning_rate=self.learning_rate,
l1_ratio=self.l1_ratio,
epsilon=self.epsilon,
eta0=self.eta0,
power_t=self.power_t,
shuffle=True,
average=self.average,
random_state=self.random_state,
warm_start=True)
self.estimator.fit(X, y, sample_weight=sample_weight)
else:
self.estimator.max_iter += n_iter
self.estimator.max_iter = min(self.estimator.max_iter, 512)
self.estimator._validate_params()
self.estimator._partial_fit(
X, y,
alpha=self.estimator.alpha,
C=1.0,
loss=self.estimator.loss,
learning_rate=self.estimator.learning_rate,
max_iter=n_iter,
sample_weight=sample_weight,
classes=None,
coef_init=None,
intercept_init=None
)
if self.estimator.max_iter >= 512 or n_iter > self.estimator.n_iter_:
self.fully_fit_ = True
return self
def iterative_fit(self, X, y, n_iter=2, refit=False, sample_weight=None):
from sklearn.linear_model.passive_aggressive import \
PassiveAggressiveClassifier
# Need to fit at least two iterations, otherwise early stopping will not
# work because we cannot determine whether the algorithm actually
# converged. The only way of finding this out is if the sgd spends less
# iterations than max_iter. If max_iter == 1, it has to spend at least
# one iteration and will always spend at least one iteration, so we
# cannot know about convergence.
if refit:
self.estimator = None
if self.estimator is None:
self.fully_fit_ = False
self.average = check_for_bool(self.average)
self.fit_intercept = check_for_bool(self.fit_intercept)
self.tol = float(self.tol)
self.C = float(self.C)
call_fit = True
self.estimator = PassiveAggressiveClassifier(
C=self.C,
fit_intercept=self.fit_intercept,
max_iter=n_iter,
tol=self.tol,
loss=self.loss,
shuffle=True,
random_state=self.random_state,
warm_start=True,
average=self.average,
)
self.classes_ = np.unique(y.astype(int))
else:
call_fit = False
# Fallback for multilabel classification
if len(y.shape) > 1 and y.shape[1] > 1:
import sklearn.multiclass
self.estimator.max_iter = 50
self.estimator = sklearn.multiclass.OneVsRestClassifier(
self.estimator, n_jobs=1)
self.estimator.fit(X, y)
self.fully_fit_ = True
else:
if call_fit:
self.estimator.fit(X, y)
else:
self.estimator.max_iter += n_iter
self.estimator.max_iter = min(self.estimator.max_iter, 1000)
self.estimator._validate_params()
lr = "pa1" if self.estimator.loss == "hinge" else "pa2"
self.estimator._partial_fit(X,
y,
alpha=1.0,
C=self.estimator.C,
loss="hinge",
learning_rate=lr,
max_iter=n_iter,
classes=None,
sample_weight=sample_weight,
coef_init=None,
intercept_init=None)
if (self.estimator.max_iter >= 1000
or n_iter > self.estimator.n_iter_):
self.fully_fit_ = True
return self
def iterative_fit(self, X, y, n_iter=2, refit=False, sample_weight=None):
from sklearn.linear_model.stochastic_gradient import SGDRegressor
# Need to fit at least two iterations, otherwise early stopping will not
# work because we cannot determine whether the algorithm actually
# converged. The only way of finding this out is if the sgd spends less
# iterations than max_iter. If max_iter == 1, it has to spend at least
# one iteration and will always spend at least one iteration, so we
# cannot know about convergence.
if refit:
self.estimator = None
if self.estimator is None:
self.fully_fit_ = False
self.alpha = float(self.alpha)
if not check_none(self.epsilon_insensitive):
self.epsilon_insensitive = float(self.epsilon_insensitive)
self.l1_ratio = float(self.l1_ratio) if self.l1_ratio is not None \
else 0.15
self.epsilon_huber = float(self.epsilon_huber) if self.epsilon_huber is not None \
else 0.1
self.eta0 = float(self.eta0) if self.eta0 is not None else 0.01
self.power_t = float(self.power_t) if self.power_t is not None \
else 0.5
self.average = check_for_bool(self.average)
self.fit_intercept = check_for_bool(self.fit_intercept)
self.tol = float(self.tol)
if self.loss == "huber":
epsilon = self.epsilon_huber
elif self.loss in [
"epsilon_insensitive", "squared_epsilon_insensitive"
]:
epsilon = self.epsilon_insensitive
else:
epsilon = None
self.estimator = SGDRegressor(loss=self.loss,
penalty=self.penalty,
alpha=self.alpha,
fit_intercept=self.fit_intercept,
max_iter=n_iter,
tol=self.tol,
learning_rate=self.learning_rate,
l1_ratio=self.l1_ratio,
epsilon=epsilon,
eta0=self.eta0,
power_t=self.power_t,
shuffle=True,
average=self.average,
random_state=self.random_state,
warm_start=True)
self.estimator.fit(X, y, sample_weight=sample_weight)
else:
self.estimator.max_iter += n_iter
self.estimator.max_iter = min(self.estimator.max_iter, 512)
self.estimator._validate_params()
self.estimator._partial_fit(
X,
y,
alpha=self.estimator.alpha,
C=1.0,
loss=self.estimator.loss,
learning_rate=self.estimator.learning_rate,
max_iter=n_iter,
sample_weight=sample_weight,
coef_init=None,
intercept_init=None)
if self.estimator.max_iter >= 512 or n_iter > self.estimator.n_iter_:
self.fully_fit_ = True
return self
