Note
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Datasets Size Comparison#
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from deepchecks.tabular import Dataset
from deepchecks.tabular.checks import DatasetsSizeComparison
df = pd.DataFrame(np.random.randn(1000, 3), columns=['x1', 'x2', 'x3'])
df['label'] = df['x2'] + 0.1 * df['x1']
train, test = train_test_split(df, test_size=0.4)
train = Dataset(train, features=['x1', 'x2', 'x3'], label='label')
test = Dataset(test, features=['x1', 'x2', 'x3'], label='label')
check_instance = (
DatasetsSizeComparison()
.add_condition_train_dataset_not_smaller_than_test()
.add_condition_test_size_not_smaller_than(100)
.add_condition_test_train_size_ratio_not_smaller_than(0.2)
)
Out:
It is recommended to initialize Dataset with categorical features by doing "Dataset(df, cat_features=categorical_list)". No categorical features were passed, therefore heuristically inferring categorical features in the data.
0 categorical features were inferred
check_instance.run(train, test)
Total running time of the script: ( 0 minutes 0.048 seconds)