def test_resolve_metrics_with_incomplete_metric_input():
engine = PandasExecutionEngine()
mean = MetricConfiguration(
metric_name="column.mean",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
)
stdev = MetricConfiguration(
metric_name="column.standard_deviation",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
)
desired_metric = MetricConfiguration(
metric_name="column_values.z_score.map",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
metric_dependencies={
"column.standard_deviation": stdev,
"column.mean": mean,
},
)
# Ensuring that incomplete metrics given raises a GreatExpectationsError
with pytest.raises(GreatExpectationsError) as error:
engine.resolve_metrics(metrics_to_resolve=(desired_metric,), metrics={})
def test_resolve_metrics_with_aggregates_and_column_map():
# Testing resolve metric function for a variety of cases - test from test_core used
df = pd.DataFrame({"a": [1, 2, 3, None]})
engine = PandasExecutionEngine(batch_data_dict={"my_id": df})
mean = MetricConfiguration(
metric_name="column.mean",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
)
stdev = MetricConfiguration(
metric_name="column.standard_deviation",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
)
desired_metrics = (mean, stdev)
metrics = engine.resolve_metrics(metrics_to_resolve=desired_metrics)
desired_metric = MetricConfiguration(
metric_name="column_values.z_score.map",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
metric_dependencies={
"column.standard_deviation": stdev,
"column.mean": mean,
},
)
results = engine.resolve_metrics(metrics_to_resolve=(desired_metric, ),
metrics=metrics)
metrics.update(results)
desired_metric = MetricConfiguration(
metric_name="column_values.z_score.under_threshold.condition",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs={
"double_sided": True,
"threshold": 2
},
metric_dependencies={"column_values.z_score.map": desired_metric},
)
results = engine.resolve_metrics(metrics_to_resolve=(desired_metric, ),
metrics=metrics)
assert list(results[desired_metric.id][0]) == [False, False, False]
metrics.update(results)
desired_metric = MetricConfiguration(
metric_name="column_values.z_score.under_threshold.unexpected_count",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs={
"double_sided": True,
"threshold": 2
},
metric_dependencies={"unexpected_condition": desired_metric},
)
results = engine.resolve_metrics(metrics_to_resolve=(desired_metric, ),
metrics=metrics)
assert results[desired_metric.id] == 0
def test_basic_metric():
df = pd.DataFrame({"a": [1, 2, 3, 3, None]})
batch = Batch(data=df)
engine = PandasExecutionEngine(batch_data_dict={batch.id: batch.data})
metrics: dict = {}
table_columns_metric: MetricConfiguration
results: dict
table_columns_metric, results = get_table_columns_metric(engine=engine)
metrics.update(results)
desired_metric = MetricConfiguration(
metric_name="column.max",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
metric_dependencies={
"table.columns": table_columns_metric,
},
)
results = engine.resolve_metrics(
metrics_to_resolve=(desired_metric,), metrics=metrics
)
metrics.update(results)
assert results == {desired_metric.id: 3}
def test_max_metric_pd_column_does_not_exist():
df = pd.DataFrame({"a": [1, 2, 3, 3, None]})
batch = Batch(data=df)
engine = PandasExecutionEngine(batch_data_dict={batch.id: batch.data})
metrics: dict = {}
table_columns_metric: MetricConfiguration
results: dict
table_columns_metric, results = get_table_columns_metric(engine=engine)
metrics.update(results)
desired_metric = MetricConfiguration(
metric_name="column.max",
metric_domain_kwargs={"column": "non_existent_column"},
metric_value_kwargs=dict(),
metric_dependencies={
"table.columns": table_columns_metric,
},
)
with pytest.raises(ge_exceptions.ExecutionEngineError) as eee:
# noinspection PyUnusedLocal
results = engine.resolve_metrics(
metrics_to_resolve=(desired_metric,), metrics=metrics
)
metrics.update(results)
assert (
str(eee.value)
== 'Error: The column "non_existent_column" in BatchData does not exist.'
)
def test_table_metric_pd():
df = pd.DataFrame({"a": [1, 2, 3, 3, None], "b": [1, 2, 3, 3, None]})
engine = PandasExecutionEngine(batch_data_dict={"my_id": df})
desired_metric = MetricConfiguration(
metric_name="table.row_count",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
)
results = engine.resolve_metrics(metrics_to_resolve=(desired_metric, ))
assert results == {desired_metric.id: 5}
def test_column_pairs_equal_metric_pd():
df = pd.DataFrame({"a": [1, 2, 3, 3], "b": [1, 2, 3, 3]})
engine = PandasExecutionEngine(batch_data_dict={"my_id": df})
desired_metric = MetricConfiguration(
metric_name="column_pair_values.equal.condition",
metric_domain_kwargs={"column_A": "a", "column_B": "b"},
metric_value_kwargs=dict(),
)
results = engine.resolve_metrics(metrics_to_resolve=(desired_metric,))
assert results[desired_metric.id][0].equals(pd.Series([True, True, True, True]))
def test_column_max():
df = pd.DataFrame({"a": [1, 2, 3, 3, None]})
batch = Batch(data=df)
engine = PandasExecutionEngine(batch_data_dict={batch.id: batch.data})
desired_metric = MetricConfiguration(
metric_name="column.max",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
)
results = engine.resolve_metrics(metrics_to_resolve=(desired_metric,))
assert results == {desired_metric.id: 3}
def test_column_pairs_in_set_metric_pd():
df = pd.DataFrame({"a": [10, 3, 4, None, 3, None], "b": [1, 2, 3, None, 3, 5]})
engine = PandasExecutionEngine(batch_data_dict={"my_id": df})
desired_metric = MetricConfiguration(
metric_name="column_pair_values.in_set.condition",
metric_domain_kwargs={"column_A": "a", "column_B": "b"},
metric_value_kwargs={
"value_pairs_set": [(2, 1), (3, 2), (4, 3), (3, 3)],
"ignore_row_if": "either_value_is_missing",
},
)
results = engine.resolve_metrics(metrics_to_resolve=(desired_metric,))
assert (
results[desired_metric.id][0]
.reset_index(drop=True)
.equals(pd.Series([False, True, True, True]))
)
def test_resolve_metrics_with_extraneous_value_key():
df = pd.DataFrame({"a": [1, 2, 3, None]})
engine = PandasExecutionEngine(batch_data_dict={"my_id": df})
metrics: dict = {}
table_columns_metric: MetricConfiguration
results: dict
table_columns_metric, results = get_table_columns_metric(engine=engine)
metrics.update(results)
mean = MetricConfiguration(
metric_name="column.mean",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
metric_dependencies={
"table.columns": table_columns_metric,
},
)
# Ensuring that an unused value key will not mess up computation
stdev = MetricConfiguration(
metric_name="column.standard_deviation",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs={"value_set": [1, 2, 3, 4, 5]},
metric_dependencies={
"table.columns": table_columns_metric,
},
)
desired_metrics = (mean, stdev)
results = engine.resolve_metrics(
metrics_to_resolve=desired_metrics, metrics=metrics
)
metrics.update(results)
# Ensuring extraneous value key did not change computation
assert (
metrics[("column.standard_deviation", "column=a", "value_set=[1, 2, 3, 4, 5]")]
== 1.0
)
def test_z_score_under_threshold_pd():
df = pd.DataFrame({"a": [1, 2, 3, None]})
engine = PandasExecutionEngine(batch_data_dict={"my_id": df})
metrics: dict = {}
table_columns_metric: MetricConfiguration
results: dict
table_columns_metric, results = get_table_columns_metric(engine=engine)
metrics.update(results)
mean = MetricConfiguration(
metric_name="column.mean",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
metric_dependencies={
"table.columns": table_columns_metric,
},
)
stdev = MetricConfiguration(
metric_name="column.standard_deviation",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
metric_dependencies={
"table.columns": table_columns_metric,
},
)
desired_metrics = (mean, stdev)
results = engine.resolve_metrics(
metrics_to_resolve=desired_metrics, metrics=metrics
)
metrics.update(results)
desired_metric = MetricConfiguration(
metric_name="column_values.z_score.map",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
metric_dependencies={
"column.standard_deviation": stdev,
"column.mean": mean,
"table.columns": table_columns_metric,
},
)
results = engine.resolve_metrics(
metrics_to_resolve=(desired_metric,), metrics=metrics
)
metrics.update(results)
desired_metric = MetricConfiguration(
metric_name="column_values.z_score.under_threshold.condition",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs={"double_sided": True, "threshold": 2},
metric_dependencies={
"column_values.z_score.map": desired_metric,
"table.columns": table_columns_metric,
},
)
results = engine.resolve_metrics(
metrics_to_resolve=(desired_metric,), metrics=metrics
)
assert list(results[desired_metric.id][0]) == [False, False, False]
metrics.update(results)
desired_metric = MetricConfiguration(
metric_name="column_values.z_score.under_threshold.unexpected_count",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs={"double_sided": True, "threshold": 2},
metric_dependencies={"unexpected_condition": desired_metric},
)
results = engine.resolve_metrics(
metrics_to_resolve=(desired_metric,), metrics=metrics
)
assert results[desired_metric.id] == 0
