ludwig-ai--ludwig
wehub-resource-sync
593b94c120
pytest / Unit Tests (push) Has been cancelled
pytest / Integration (integration_tests_a) (push) Has been cancelled
pytest / Integration (integration_tests_b) (push) Has been cancelled
pytest / Integration (integration_tests_c) (push) Has been cancelled
pytest / Integration (integration_tests_d) (push) Has been cancelled
pytest / Integration (integration_tests_e) (push) Has been cancelled
pytest / Integration (integration_tests_f) (push) Has been cancelled
pytest / Integration (integration_tests_g) (push) Has been cancelled
pytest / Integration (integration_tests_h) (push) Has been cancelled
pytest / Integration (integration_tests_i) (push) Has been cancelled
pytest / Integration (integration_tests_j) (push) Has been cancelled
pytest / Distributed (distributed_a) (push) Has been cancelled
pytest / Distributed (distributed_b) (push) Has been cancelled
pytest / Distributed (distributed_c) (push) Has been cancelled
pytest / Distributed (distributed_d) (push) Has been cancelled
pytest / Distributed (distributed_e) (push) Has been cancelled
pytest / Distributed (distributed_f) (push) Has been cancelled
pytest / Minimal Install (push) Has been cancelled
pytest / Event File (push) Has been cancelled
pytest (slow) / py-slow (push) Has been cancelled
Publish JSON Schema / publish-schema (push) Has been cancelled
86 行
2.5 KiB
Python
86 行
2.5 KiB
Python
import numpy as np
|
|
import pandas as pd
|
|
import pytest
|
|
|
|
from ludwig.backend import create_backend, LOCAL_BACKEND
|
|
from ludwig.utils.dataframe_utils import to_numpy_dataset, to_scalar_df
|
|
|
|
try:
|
|
import dask.dataframe as dd
|
|
except ImportError:
|
|
pass
|
|
|
|
|
|
@pytest.mark.distributed
|
|
@pytest.mark.distributed_f
|
|
def test_to_numpy_dataset_with_dask(ray_cluster_2cpu):
|
|
dd_df = dd.from_pandas(pd.DataFrame([[1, 2, 3]], columns=["col1", "col2", "col3"]), npartitions=1)
|
|
ray_backend = create_backend("ray")
|
|
|
|
np_df = to_numpy_dataset(dd_df, backend=ray_backend)
|
|
|
|
assert np_df == {"col1": np.array([1]), "col2": np.array([2]), "col3": np.array([3])}
|
|
|
|
|
|
@pytest.mark.distributed
|
|
@pytest.mark.distributed_f
|
|
def test_to_numpy_dataset_with_dask_backend_mismatch():
|
|
dd_df = dd.from_pandas(pd.DataFrame([[1, 2, 3]], columns=["col1", "col2", "col3"]), npartitions=1)
|
|
|
|
with pytest.raises(AttributeError):
|
|
to_numpy_dataset(dd_df, backend=LOCAL_BACKEND)
|
|
|
|
|
|
def test_to_numpy_dataset_with_pandas():
|
|
pd_df = pd.DataFrame([[1, 2, 3]], columns=["col1", "col2", "col3"])
|
|
|
|
np_df = to_numpy_dataset(pd_df, backend=LOCAL_BACKEND)
|
|
|
|
assert np_df == {"col1": np.array([1]), "col2": np.array([2]), "col3": np.array([3])}
|
|
|
|
|
|
def test_to_numpy_dataset_empty_with_columns():
|
|
pd_df = pd.DataFrame(columns=["col1", "col2", "col3"])
|
|
|
|
np_df = to_numpy_dataset(pd_df, backend=LOCAL_BACKEND)
|
|
|
|
assert np_df == {"col1": [], "col2": [], "col3": []}
|
|
|
|
|
|
def test_to_numpy_dataset_empty():
|
|
pd_df = pd.DataFrame()
|
|
|
|
np_df = to_numpy_dataset(pd_df, backend=LOCAL_BACKEND)
|
|
|
|
assert np_df == {}
|
|
|
|
|
|
@pytest.mark.distributed
|
|
@pytest.mark.distributed_f
|
|
def test_to_numpy_dataset_with_pandas_backend_mismatch(ray_cluster_2cpu):
|
|
pd_df = pd.DataFrame([[1, 2, 3]], columns=["col1", "col2", "col3"])
|
|
ray_backend = create_backend("ray")
|
|
|
|
with pytest.raises(AttributeError):
|
|
to_numpy_dataset(pd_df, backend=ray_backend)
|
|
|
|
|
|
def test_to_scalar_df():
|
|
data = [
|
|
[True, np.array([1, 2, 3]), 42],
|
|
[False, np.array([4, 5, 6]), 28],
|
|
[True, np.array([7, 8, 9]), 99],
|
|
]
|
|
df = pd.DataFrame(data, columns=["bin", "cat_encoded", "num"])
|
|
|
|
scalar_data = [
|
|
[True, 1, 2, 3, 42],
|
|
[False, 4, 5, 6, 28],
|
|
[True, 7, 8, 9, 99],
|
|
]
|
|
expected_df = pd.DataFrame(scalar_data, columns=["bin", "cat_encoded_0", "cat_encoded_1", "cat_encoded_2", "num"])
|
|
|
|
scalar_df = to_scalar_df(df)
|
|
assert scalar_df.columns.tolist() == expected_df.columns.tolist()
|
|
assert scalar_df.equals(expected_df)
|