Multi-grained scanning example

Multi-grained scanning is the algorithm that can be used for image processing. Description is given in this article. It is said that deep forest with MGS can be compared with the CNN.

from bosk.painter.graphviz import GraphvizPainter
from bosk.executor.recursive import RecursiveExecutor
from bosk.stages import Stage
from bosk.block.zoo.multi_grained_scanning import MultiGrainedScanning2DBlock
from bosk.pipeline.builder.functional import FunctionalPipelineBuilder
from sklearn.datasets import load_digits
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
import numpy as np
from IPython.display import Image

random_state = 42
n_estimators = 7
EXEC_CLS = RecursiveExecutor

def get_image_dataset():
    digits = load_digits()
    all_X = digits.data
    all_y = digits.target
    train_X, test_X, train_y, test_y = train_test_split(
        all_X, all_y, test_size=0.2, random_state=random_state)
    return train_X, test_X, train_y, test_y

b = FunctionalPipelineBuilder()
X, y = b.Input()(), b.TargetInput()()
ms = b.new(MultiGrainedScanning2DBlock, models=(
    RandomForestClassifier(n_estimators=n_estimators),
    ExtraTreesClassifier(n_estimators=n_estimators)),
    window_size=5, stride=1, shape_sample=[8, 8])(X=X, y=y)
rf_1 = b.RFC()(X=ms, y=y)
et_1 = b.ETC()(X=ms, y=y)
concat_1 = b.Concat(['ms', 'rf_1', 'et_1'])(ms=ms, rf_1=rf_1, et_1=et_1)
rf_2 = b.RFC()(X=concat_1, y=y)
et_2 = b.ETC()(X=concat_1, y=y)
concat_2 = b.Concat(['ms', 'rf_2', 'et_2'])(ms=ms, rf_2=rf_2, et_2=et_2)
rf_3 = b.RFC()(X=concat_2, y=y)
et_3 = b.ETC()(X=concat_2, y=y)
stack_3 = b.Stack(['rf_3', 'et_3'], axis=1)(rf_3=rf_3, et_3=et_3)
average_3 = b.Average(axis=1)(X=stack_3)
argmax_3 = b.Argmax(axis=1)(X=average_3)
#
rf_1_roc_auc = b.RocAucMultiLabel()(gt_y=y, pred_probas=rf_1)
roc_auc = b.RocAucMultiLabel()(gt_y=y, pred_probas=average_3)
pipeline = b.build({'X': X, 'y': y},
                   {'probas': average_3, 'rf_1_roc-auc': rf_1_roc_auc,
                    'roc-auc': roc_auc, 'labels': argmax_3}
                   )
pipeline.set_random_state(random_state)
# drawing our pipeline
GraphvizPainter(figure_dpi=100).from_pipeline(pipeline).render('deep_forest.jpeg')
display(Image(filename='deep_forest.jpeg'))

# let's run our pipeline
fit_executor = EXEC_CLS(
    pipeline,
    stage=Stage.FIT,
    inputs=['X', 'y'],
    outputs=['probas', 'rf_1_roc-auc', 'roc-auc'],
)
transform_executor = EXEC_CLS(
    pipeline,
    stage=Stage.TRANSFORM,
    inputs=['X'],
    outputs=['probas', 'labels']
)
train_X, test_X, train_y, test_y = get_image_dataset()
fit_result = fit_executor({'X': train_X, 'y': train_y}).numpy()
train_result = transform_executor({'X': train_X}).numpy()
print("Fit probas == probas on train:", np.allclose(fit_result['probas'], train_result['probas']))
test_result = transform_executor({'X': test_X}).numpy()
print("Train ROC-AUC:", roc_auc_score(train_y, train_result['probas'], multi_class="ovr"))
print(
    "Train ROC-AUC calculated by fit_executor:",
    fit_result['roc-auc']
)
print(
    "Train ROC-AUC for RF_1:",
    fit_result['rf_1_roc-auc']
)
print("Test ROC-AUC:", roc_auc_score(test_y, test_result['probas'], multi_class="ovr"))

Fit probas == probas on train: True
Train ROC-AUC: 1.0
Train ROC-AUC calculated by fit_executor: 1.0
Train ROC-AUC for RF_1: 1.0
Test ROC-AUC: 0.998348204661033