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test: stop checking ml large tests exact numbers #690

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Merged
merged 4 commits into from
May 16, 2024
Merged

test: stop checking ml large tests exact numbers #690

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fc0a642
test: stop checking ml large tests exact numbers
GarrettWu May 15, 2024
c8846f1
clean up
GarrettWu May 15, 2024
d13eec4
Merge remote-tracking branch 'github/main' into garrettwu-test
GarrettWu May 15, 2024
bbf4169
Merge remote-tracking branch 'github/main' into garrettwu-test
GarrettWu May 15, 2024
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26 changes: 8 additions & 18 deletions 26 tests/system/large/ml/test_cluster.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,13 +13,11 @@
# limitations under the License.

import pandas as pd
import pytest

from bigframes.ml import cluster
from tests.system.utils import assert_pandas_df_equal
from tests.system import utils


@pytest.mark.flaky(retries=2)
def test_cluster_configure_fit_score_predict(
session, penguins_df_default_index, dataset_id
):
Expand Down Expand Up @@ -88,26 +86,18 @@ def test_cluster_configure_fit_score_predict(

# Check score to ensure the model was fitted
score_result = model.score(new_penguins).to_pandas()
score_expected = pd.DataFrame(
{"davies_bouldin_index": [1.502182], "mean_squared_distance": [1.953408]},
dtype="Float64",
)
score_expected = score_expected.reindex(index=score_expected.index.astype("Int64"))

pd.testing.assert_frame_equal(
score_result, score_expected, check_exact=False, rtol=0.1
)
eval_metrics = ["davies_bouldin_index", "mean_squared_distance"]
utils.check_pandas_df_schema_and_index(score_result, columns=eval_metrics, index=1)

predictions = model.predict(new_penguins).to_pandas()
assert predictions.shape == (4, 9)
result = predictions[["CENTROID_ID"]]
expected = pd.DataFrame(
{"CENTROID_ID": [2, 3, 1, 2]},
dtype="Int64",
index=pd.Index(["test1", "test2", "test3", "test4"], dtype="string[pyarrow]"),
utils.check_pandas_df_schema_and_index(
predictions,
columns=["CENTROID_ID"],
index=["test1", "test2", "test3", "test4"],
col_exact=False,
)
expected.index.name = "observation"
assert_pandas_df_equal(result, expected, ignore_order=True)

# save, load, check n_clusters to ensure configuration was kept
reloaded_model = model.to_gbq(
Expand Down
85 changes: 29 additions & 56 deletions 85 tests/system/large/ml/test_compose.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,9 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.

import pandas

from bigframes.ml import compose, preprocessing
from tests.system import utils


def test_columntransformer_standalone_fit_and_transform(
Expand Down Expand Up @@ -45,26 +44,18 @@ def test_columntransformer_standalone_fit_and_transform(
)
result = transformer.transform(new_penguins_df).to_pandas()

expected = pandas.DataFrame(
{
"onehotencoded_species": [
[{"index": 1, "value": 1.0}],
[{"index": 1, "value": 1.0}],
[{"index": 2, "value": 1.0}],
],
"standard_scaled_culmen_length_mm": [
-0.811119671289163,
-0.9945520581113803,
-1.104611490204711,
],
"min_max_scaled_culmen_length_mm": [0.269, 0.232, 0.210],
"standard_scaled_flipper_length_mm": [-0.350044, -1.418336, -0.9198],
},
index=pandas.Index([1633, 1672, 1690], dtype="Int64", name="tag_number"),
utils.check_pandas_df_schema_and_index(
result,
columns=[
"onehotencoded_species",
"standard_scaled_culmen_length_mm",
"min_max_scaled_culmen_length_mm",
"standard_scaled_flipper_length_mm",
],
index=[1633, 1672, 1690],
col_exact=False,
)

pandas.testing.assert_frame_equal(result, expected, rtol=0.1, check_dtype=False)


def test_columntransformer_standalone_fit_transform(new_penguins_df):
transformer = compose.ColumnTransformer(
Expand All @@ -86,25 +77,17 @@ def test_columntransformer_standalone_fit_transform(new_penguins_df):
new_penguins_df[["species", "culmen_length_mm", "flipper_length_mm"]]
).to_pandas()

expected = pandas.DataFrame(
{
"onehotencoded_species": [
[{"index": 1, "value": 1.0}],
[{"index": 1, "value": 1.0}],
[{"index": 2, "value": 1.0}],
],
"standard_scaled_culmen_length_mm": [
1.313249,
-0.20198,
-1.111118,
],
"standard_scaled_flipper_length_mm": [1.251098, -1.196588, -0.054338],
},
index=pandas.Index([1633, 1672, 1690], dtype="Int64", name="tag_number"),
utils.check_pandas_df_schema_and_index(
result,
columns=[
"onehotencoded_species",
"standard_scaled_culmen_length_mm",
"standard_scaled_flipper_length_mm",
],
index=[1633, 1672, 1690],
col_exact=False,
)

pandas.testing.assert_frame_equal(result, expected, rtol=0.1, check_dtype=False)


def test_columntransformer_save_load(new_penguins_df, dataset_id):
transformer = compose.ColumnTransformer(
Expand Down Expand Up @@ -147,23 +130,13 @@ def test_columntransformer_save_load(new_penguins_df, dataset_id):
new_penguins_df[["species", "culmen_length_mm", "flipper_length_mm"]]
).to_pandas()

# TODO(b/340888429): fix type error
expected = pandas.DataFrame( # type: ignore
{
"onehotencoded_species": [
[{"index": 1, "value": 1.0}],
[{"index": 1, "value": 1.0}],
[{"index": 2, "value": 1.0}],
],
"standard_scaled_culmen_length_mm": [
1.313249,
-0.20198,
-1.111118,
],
"standard_scaled_flipper_length_mm": [1.251098, -1.196588, -0.054338],
},
index=pandas.Index([1633, 1672, 1690], dtype="Int64", name="tag_number"),
utils.check_pandas_df_schema_and_index(
result,
columns=[
"onehotencoded_species",
"standard_scaled_culmen_length_mm",
"standard_scaled_flipper_length_mm",
],
index=[1633, 1672, 1690],
col_exact=False,
)

# TODO(b/340888429): fix type error
pandas.testing.assert_frame_equal(result, expected, rtol=0.1, check_dtype=False) # type: ignore
117 changes: 41 additions & 76 deletions 117 tests/system/large/ml/test_core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,14 +12,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.

import pandas
import pytest

from bigframes.ml import globals
from tests.system import utils


# TODO(garrettwu): Re-enable or not check exact numbers.
@pytest.mark.skip(reason="bqml regression")
def test_bqml_e2e(session, dataset_id, penguins_df_default_index, new_penguins_df):
df = penguins_df_default_index.dropna()
X_train = df[
Expand All @@ -38,41 +34,33 @@ def test_bqml_e2e(session, dataset_id, penguins_df_default_index, new_penguins_d
X_train, y_train, options={"model_type": "linear_reg"}
)

eval_metrics = [
"mean_absolute_error",
"mean_squared_error",
"mean_squared_log_error",
"median_absolute_error",
"r2_score",
"explained_variance",
]
# no data - report evaluation from the automatic data split
evaluate_result = model.evaluate().to_pandas()
evaluate_expected = pandas.DataFrame(
{
"mean_absolute_error": [225.817334],
"mean_squared_error": [80540.705944],
"mean_squared_log_error": [0.004972],
"median_absolute_error": [173.080816],
"r2_score": [0.87529],
"explained_variance": [0.87529],
},
dtype="Float64",
)
evaluate_expected = evaluate_expected.reindex(
index=evaluate_expected.index.astype("Int64")
)
pandas.testing.assert_frame_equal(
evaluate_result, evaluate_expected, check_exact=False, rtol=0.1
utils.check_pandas_df_schema_and_index(
evaluate_result, columns=eval_metrics, index=1
)

# evaluate on all training data
evaluate_result = model.evaluate(df).to_pandas()
pandas.testing.assert_frame_equal(
evaluate_result, evaluate_expected, check_exact=False, rtol=0.1
utils.check_pandas_df_schema_and_index(
evaluate_result, columns=eval_metrics, index=1
)

# predict new labels
predictions = model.predict(new_penguins_df).to_pandas()
expected = pandas.DataFrame(
{"predicted_body_mass_g": [4030.1, 3280.8, 3177.9]},
dtype="Float64",
index=pandas.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
)
pandas.testing.assert_frame_equal(
predictions[["predicted_body_mass_g"]], expected, check_exact=False, rtol=0.1
utils.check_pandas_df_schema_and_index(
predictions,
columns=["predicted_body_mass_g"],
index=[1633, 1672, 1690],
col_exact=False,
)

new_name = f"{dataset_id}.my_model"
Expand Down Expand Up @@ -108,42 +96,34 @@ def test_bqml_manual_preprocessing_e2e(
X_train, y_train, transforms=transforms, options=options
)

eval_metrics = [
"mean_absolute_error",
"mean_squared_error",
"mean_squared_log_error",
"median_absolute_error",
"r2_score",
"explained_variance",
]

# no data - report evaluation from the automatic data split
evaluate_result = model.evaluate().to_pandas()
evaluate_expected = pandas.DataFrame(
{
"mean_absolute_error": [309.477334],
"mean_squared_error": [152184.227218],
"mean_squared_log_error": [0.009524],
"median_absolute_error": [257.727777],
"r2_score": [0.764356],
"explained_variance": [0.764356],
},
dtype="Float64",
)
evaluate_expected = evaluate_expected.reindex(
index=evaluate_expected.index.astype("Int64")
)

pandas.testing.assert_frame_equal(
evaluate_result, evaluate_expected, check_exact=False, rtol=0.1
utils.check_pandas_df_schema_and_index(
evaluate_result, columns=eval_metrics, index=1
)

# evaluate on all training data
evaluate_result = model.evaluate(df).to_pandas()
pandas.testing.assert_frame_equal(
evaluate_result, evaluate_expected, check_exact=False, rtol=0.1
utils.check_pandas_df_schema_and_index(
evaluate_result, columns=eval_metrics, index=1
)

# predict new labels
predictions = model.predict(new_penguins_df).to_pandas()
expected = pandas.DataFrame(
{"predicted_body_mass_g": [3968.8, 3176.3, 3545.2]},
dtype="Float64",
index=pandas.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
)
pandas.testing.assert_frame_equal(
predictions[["predicted_body_mass_g"]], expected, check_exact=False, rtol=0.1
utils.check_pandas_df_schema_and_index(
predictions,
columns=["predicted_body_mass_g"],
index=[1633, 1672, 1690],
col_exact=False,
)

new_name = f"{dataset_id}.my_model"
Expand All @@ -168,24 +148,9 @@ def test_bqml_standalone_transform(penguins_df_default_index, new_penguins_df):
)

transformed = model.transform(new_penguins_df).to_pandas()
expected = pandas.DataFrame(
{
"scaled_culmen_length_mm": [-0.8099, -0.9931, -1.103],
"onehotencoded_species": [
[{"index": 1, "value": 1.0}],
[{"index": 1, "value": 1.0}],
[{"index": 2, "value": 1.0}],
],
},
index=pandas.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
)
expected["scaled_culmen_length_mm"] = expected["scaled_culmen_length_mm"].astype(
"Float64"
)
pandas.testing.assert_frame_equal(
transformed[["scaled_culmen_length_mm", "onehotencoded_species"]],
expected,
check_exact=False,
rtol=0.1,
check_dtype=False,
utils.check_pandas_df_schema_and_index(
transformed,
columns=["scaled_culmen_length_mm", "onehotencoded_species"],
index=[1633, 1672, 1690],
col_exact=False,
)
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