from sklearn.ensemble import RandomForestClassifier as randomforest
from sklearn.svm import SVC
from sklearn.base import ClassifierMixin
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.feature_selection import VarianceThreshold
from sklearn.neural_network import MLPClassifier
__all__ = ['randomforest', 'svm', 'neuralnetwork']
[docs]class neuralnetwork(ClassifierMixin):
def __init__(self, *args, **kwargs):
""" Assemble Neural network using sklearn pipeline """
# Cherrypick arguments for model. Exclude 'steps', which is pipeline argument
local_kwargs = {key: kwargs.pop(key) for key in list(kwargs.keys())
if key != 'steps' and len(key.split('__', 1)) == 1}
self.pipeline = Pipeline([('empty_dims_remover', VarianceThreshold()),
('scaler', StandardScaler()),
('neural_network', MLPClassifier(*args, **local_kwargs))
]).set_params(**kwargs)
[docs] def get_params(self, deep=True):
return self.pipeline.get_params(deep=deep)
[docs] def set_params(self, **kwargs):
return self.pipeline.set_params(**kwargs)
[docs] def fit(self, descs, target_values, **kwargs):
self.pipeline.fit(descs, target_values, **kwargs)
return self
[docs] def predict(self, descs):
return self.pipeline.predict(descs)
[docs] def predict_proba(self, descs):
return self.pipeline.predict_proba(descs)
[docs] def predict_log_proba(self, descs):
return self.pipeline.predict_log_proba(descs)
[docs] def score(self, descs, target_values):
return self.pipeline.score(descs, target_values)
[docs]class svm(ClassifierMixin):
def __init__(self, *args, **kwargs):
""" Assemble a proper SVM classifier"""
# Cherrypick arguments for model. Exclude 'steps', which is pipeline argument
local_kwargs = {key: kwargs.pop(key) for key in list(kwargs.keys())
if key != 'steps' and len(key.split('__', 1)) == 1}
self.pipeline = Pipeline([('empty_dims_remover', VarianceThreshold()),
('scaler', StandardScaler()),
('svm', SVC(*args, **local_kwargs))
]).set_params(**kwargs)
[docs] def get_params(self, deep=True):
return self.pipeline.get_params(deep=deep)
[docs] def set_params(self, **kwargs):
return self.pipeline.set_params(**kwargs)
[docs] def fit(self, descs, target_values, **kwargs):
self.pipeline.fit(descs, target_values, **kwargs)
return self
[docs] def predict(self, descs):
return self.pipeline.predict(descs)
[docs] def predict_proba(self, descs):
return self.pipeline.predict_proba(descs)
[docs] def predict_log_proba(self, descs):
return self.pipeline.predict_log_proba(descs)
[docs] def score(self, descs, target_values):
return self.pipeline.score(descs, target_values)