sklearn skeleton

sklearn_template.py

import sklearn

######################
# Estimator
######################

# (Analogue to Keras) Initialization all-in-one
# bind symbol
# define loss function + optimizer
# (optional) metrics for output evaluation
model = sklearn.svm.SVM()

model.fit() # learn

model.predict() # use

model.score # evaluate results


######################
# Ensemble Estimator
######################

assembler = sklearn.ensemble.BaggingClassifier(
  base_estimator = sklearn.neighbors.KNeighborsClassifier() # define an estimator as a weak learner
) # Define the framework

assembler.fit() # learn the structure

assembler.predict() # use

assembler.score() # evaluate model performance

sklearn.ensemble.partial_dependence.partial_dependence(assembler, target_variables) # evaluate importance of target variables


######################
# Transformer
######################

processor = sklearn.preprocessing.OneHotEncoder()

processor.fit() # learn from samples

processor.transform() # use


######################
# Pipeline
######################

pipe = sklearn.pipeline.Pipeline([
  ("trans_1", transformer_1), # call transformer_1.transform()
  ("trans_2", transformer_2), # call transformer_2.transform()
  # ...
  ("estimator", estimator)    # call estimator.predict()
  ])

pipe.fit()

pipe.predict()


######################
# Grid Search
######################

candidate_params = {"key": value}

grid_search = sklearn.model_selection.GridSearchCV(param_grid = candidate_params)

grid_search.fit() # evaluate each combination