fito.model package

Submodules

fito.model.model module

class fito.model.model.BaseModelField(pos=None, default=<object object>, base_type=None, serialize=True, grid=None, *args, **kwargs)[source]

Bases: fito.specs.fields.BaseSpecField

class fito.model.model.Model(*args, **kwargs)[source]

Bases: fito.operations.operation.Operation

Model fields:
out_data_store = BaseSpecField(default=None, serialize=False)
classmethod get_hyper_parameters_grid()[source]
classmethod get_primitive_param_grid()[source]
fito.model.model.ModelField(pos=None, default=<object object>, base_type=None, serialize=True, grid=None)[source]
class fito.model.model.ModelParameter(pos=None, default=<object object>, serialize=True, grid=None, *args, **kwargs)[source]

Bases: fito.specs.fields.PrimitiveField

fito.model.scikit_learn module

class fito.model.scikit_learn.GradientBoostingClassifier(*args, **kwargs)[source]

Bases: fito.model.scikit_learn.SKLearnModel

GradientBoostingClassifier fields:
init = PrimitiveField(default=None) learning_rate = ModelParameter(default=0.1) loss = ModelParameter(default=deviance) max_depth = ModelParameter(default=3) max_features = ModelParameter(default=None) max_leaf_nodes = ModelParameter(default=None) min_samples_leaf = ModelParameter(default=1) min_samples_split = ModelParameter(default=2) min_weight_fraction_leaf = ModelParameter(default=0.0) n_estimators = ModelParameter(default=100) out_data_store = BaseSpecField(default=None, serialize=False) presort = PrimitiveField(default=auto) random_state = PrimitiveField(default=None) subsample = ModelParameter(default=1.0) verbose = PrimitiveField(default=0) warm_start = PrimitiveField(default=False)
apply(runner)[source]
init = PrimitiveField(default=None)
learning_rate = ModelParameter(default=0.1)
loss = ModelParameter(default=deviance)
max_depth = ModelParameter(default=3)
max_features = ModelParameter(default=None)
max_leaf_nodes = ModelParameter(default=None)
min_samples_leaf = ModelParameter(default=1)
min_samples_split = ModelParameter(default=2)
min_weight_fraction_leaf = ModelParameter(default=0.0)
n_estimators = ModelParameter(default=100)
presort = PrimitiveField(default=auto)
random_state = PrimitiveField(default=None)
subsample = ModelParameter(default=1.0)
verbose = PrimitiveField(default=0)
warm_start = PrimitiveField(default=False)
class fito.model.scikit_learn.LinearRegression(*args, **kwargs)[source]

Bases: fito.model.scikit_learn.SKLearnModel

LinearRegression fields:
copy_X = PrimitiveField(default=True) fit_intercept = ModelParameter(default=True) n_jobs = PrimitiveField(default=1) normalize = ModelParameter(default=False) out_data_store = BaseSpecField(default=None, serialize=False)
apply(runner)[source]
copy_X = PrimitiveField(default=True)
fit_intercept = ModelParameter(default=True)
n_jobs = PrimitiveField(default=1)
normalize = ModelParameter(default=False)
class fito.model.scikit_learn.LogisticRegression(*args, **kwargs)[source]

Bases: fito.model.scikit_learn.SKLearnModel

LogisticRegression fields:
C = ModelParameter(default=1.0) class_weight = ModelParameter(default=None) dual = ModelParameter(default=False) fit_intercept = ModelParameter(default=True) intercept_scaling = ModelParameter(default=1) max_iter = PrimitiveField(default=100) multi_class = ModelParameter(default=ovr) n_jobs = PrimitiveField(default=1) out_data_store = BaseSpecField(default=None, serialize=False) penalty = ModelParameter(default=l2) random_state = PrimitiveField(default=None) solver = PrimitiveField(default=liblinear) tol = PrimitiveField(default=0.0001) verbose = PrimitiveField(default=0) warm_start = PrimitiveField(default=False)
C = ModelParameter(default=1.0)
apply(runner)[source]
class_weight = ModelParameter(default=None)
dual = ModelParameter(default=False)
fit_intercept = ModelParameter(default=True)
intercept_scaling = ModelParameter(default=1)
max_iter = PrimitiveField(default=100)
multi_class = ModelParameter(default=ovr)
n_jobs = PrimitiveField(default=1)
penalty = ModelParameter(default=l2)
random_state = PrimitiveField(default=None)
solver = PrimitiveField(default=liblinear)
tol = PrimitiveField(default=0.0001)
verbose = PrimitiveField(default=0)
warm_start = PrimitiveField(default=False)
class fito.model.scikit_learn.SKLearnModel(*args, **kwargs)[source]

Bases: fito.model.model.Model

SKLearnModel fields:
out_data_store = BaseSpecField(default=None, serialize=False)
instance_model(constructor)[source]

fito.model.word2vec module

class fito.model.word2vec.Word2Vec(*args, **kwargs)[source]

Bases: fito.model.model.Model

Word2Vec fields:
out_data_store = BaseSpecField(default=None, serialize=False) sentences = CollectionField(0, serialize=False) train_iterator = PrimitiveField(default=None, serialize=False) size = ModelParameter(1, default=100) alpha = ModelParameter(2, default=0.025) window = ModelParameter(3, default=5) min_count = ModelParameter(4, default=5) max_vocab_size = ModelParameter(5, default=None) sample = ModelParameter(6, default=0.001) seed = ModelParameter(7, default=1) workers = PrimitiveField(8, default=3, serialize=False)
alpha = ModelParameter(2, default=0.025)
apply(runner)[source]
max_vocab_size = ModelParameter(5, default=None)
min_count = ModelParameter(4, default=5)
sample = ModelParameter(6, default=0.001)
seed = ModelParameter(7, default=1)
sentences = CollectionField(0, serialize=False)
size = ModelParameter(1, default=100)
train_iterator = PrimitiveField(default=None, serialize=False)
window = ModelParameter(3, default=5)
workers = PrimitiveField(8, default=3, serialize=False)

Module contents