Bosk usage examples#
On this page you can find various examples of the bosk usage with the comments and illustrations.
All listed below files and even more can be found in our GitHub
repository in the examples folder.
The quick self-sufficient example of the bosk usage is available below. You can copy it to the local machine and run it after the bosk installation.
from bosk.pipeline.builder.functional import FunctionalPipelineBuilder
from bosk.executor.topological import TopologicalExecutor
from bosk.stages import Stage
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
n_estimators = 20
random_state = 42
# firstly we must obtain the functional builder object
b = FunctionalPipelineBuilder()
# we get blocks wrappers and connect with each other
X, y = b.Input()(), b.TargetInput()()
rf_1 = b.RFC(n_estimators=n_estimators)(X=X, y=y)
et_1 = b.ETC(n_estimators=n_estimators)(X=X, y=y)
concat_1 = b.Concat(['X', 'rf_1', 'et_1'])(X=X, rf_1=rf_1, et_1=et_1)
rf_2 = b.RFC(n_estimators=n_estimators)(X=concat_1, y=y)
et_2 = b.ETC(n_estimators=n_estimators)(X=concat_1, y=y)
stack = b.Stack(['rf_2', 'et_2'], axis=1)(rf_2=rf_2, et_2=et_2)
average = b.Average(axis=1)(X=stack)
argmax = b.Argmax(axis=1)(X=average)
rf_1_roc_auc = b.RocAuc()(gt_y=y, pred_probas=rf_1)
roc_auc = b.RocAuc()(gt_y=y, pred_probas=average)
# after defining the graph structure we obtain
# the pipeline object from the builder
pipeline = b.build(
{'X': X, 'y': y},
{'labels': argmax, 'probas': average, 'rf_1_roc-auc': rf_1_roc_auc, 'roc-auc': roc_auc}
)
# we can set a random state for the pipeline
pipeline.set_random_state(random_state)
# let's get some data
# than train and test our pipeline
all_X, all_y = make_moons(noise=1, random_state=random_state)
train_X, test_X, train_y, test_y = train_test_split(
all_X, all_y, test_size=0.2, random_state=random_state)
# creating executors
fit_executor = TopologicalExecutor(
pipeline,
stage=Stage.FIT,
inputs=['X', 'y'],
outputs=['probas', 'rf_1_roc-auc', 'roc-auc']
)
transform_executor = TopologicalExecutor(
pipeline,
stage=Stage.TRANSFORM,
inputs=['X'],
outputs=['probas', 'labels']
)
# executing our pipeline and obtaining metrics
fit_result = fit_executor({'X': train_X, 'y': train_y}).numpy()
print(
"Train ROC-AUC calculated by fit executor:",
fit_result['roc-auc']
)
test_result = transform_executor({'X': test_X}).numpy()
print("Test ROC-AUC:", roc_auc_score(test_y, test_result['probas'][:, 1]))