Usage

Using heamy in a project:

from heamy.dataset import Dataset
from heamy.estimator import Regressor, Classifier
from heamy.pipeline import ModelsPipeline

Stacking

# load boston dataset from sklearn
from sklearn.datasets import load_boston
data = load_boston()
X, y = data['data'], data['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=111)

# create dataset
dataset = Dataset(X_train,y_train,X_test)

# initialize RandomForest & LinearRegression
model_rf = Regressor(dataset=dataset, estimator=RandomForestRegressor, parameters={'n_estimators': 50},name='rf')
model_lr = Regressor(dataset=dataset, estimator=LinearRegression, parameters={'normalize': True},name='lr')

# Stack two models
# Returns new dataset with out-of-fold predictions
pipeline = ModelsPipeline(model_rf,model_lr)
stack_ds = pipeline.stack(k=10,seed=111)

# Train LinearRegression on stacked data (second stage)
stacker = Regressor(dataset=stack_ds, estimator=LinearRegression)
results = stacker.predict()
# Validate results using 10 fold cross-validation
results = stacker.validate(k=10,scorer=mean_absolute_error)

Blending

# load boston dataset from sklearn
from sklearn.datasets import load_boston
data = load_boston()
X, y = data['data'], data['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=111)

# create dataset
dataset = Dataset(X_train,y_train,X_test)

# initialize RandomForest & LinearRegression
model_rf = Regressor(dataset=dataset, estimator=RandomForestRegressor, parameters={'n_estimators': 50},name='rf')
model_lr = Regressor(dataset=dataset, estimator=LinearRegression, parameters={'normalize': True},name='lr')

# Stack two models
# Returns new dataset with out-of-fold predictions
pipeline = ModelsPipeline(model_rf,model_lr)
stack_ds = pipeline.blend(proportion=0.2,seed=111)

# Train LinearRegression on stacked data (second stage)
stacker = Regressor(dataset=stack_ds, estimator=LinearRegression)
results = stacker.predict()
# Validate results using 10 fold cross-validation
results = stacker.validate(k=10,scorer=mean_absolute_error)

Weighted average

dataset = Dataset(preprocessor=boston_dataset)

model_rf = Regressor(dataset=dataset, estimator=RandomForestRegressor, parameters={'n_estimators': 151},name='rf')
model_lr = Regressor(dataset=dataset, estimator=LinearRegression, parameters={'normalize': True},name='lr')
model_knn = Regressor(dataset=dataset, estimator=KNeighborsRegressor, parameters={'n_neighbors': 15},name='knn')

pipeline = ModelsPipeline(model_rf,model_lr,model_knn)

weights = pipeline.find_weights(mean_absolute_error)
result = pipeline.weight(weights)