1  import numpy as np
 2  import pandas as pd
 3  
 4  from evidently import BinaryClassification
 5  from evidently import DataDefinition
 6  from evidently import Dataset
 7  from evidently import Report
 8  from evidently.core.metric_types import DataframeValue
 9  from evidently.metrics import ColumnCorrelationMatrix
10  from evidently.metrics import ColumnCorrelations
11  from evidently.metrics.data_quality import CorrelationMatrix
12  
13  
14  def test_column_correlations():
15      df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
16      ds = Dataset.from_pandas(df)
17  
18      metric = ColumnCorrelations(column_name="a")
19      report = Report(metrics=[metric])
20  
21      run = report.run(ds)
22  
23      result = run.context.get_metric_result(ColumnCorrelationMatrix(column_name="a", kind="cramer_v"))
24      assert isinstance(result, DataframeValue)
25      pd.testing.assert_frame_equal(result.value, pd.DataFrame([{"kind": "cramer_v", "column_name": "b", "value": 1.0}]))
26  
27  
28  def test_dataset_correlations():
29      df = pd.DataFrame(
30          {
31              "my_target": [1, np.nan, 3] * 1000,
32              "my_prediction": [1, 2, np.nan] * 1000,
33              "feature_1": [1, 2, 3] * 1000,
34              "feature_2": ["a", np.nan, "a"] * 1000,
35          }
36      )
37      ds = Dataset.from_pandas(
38          df,
39          data_definition=DataDefinition(
40              classification=[BinaryClassification(target="my_target", prediction_labels="my_prediction")]
41          ),
42      )
43  
44      metric = CorrelationMatrix()
45      report = Report(metrics=[metric])
46  
47      run = report.run(ds)
48  
49      result = run.context.get_metric_result(metric)
50      assert isinstance(result, DataframeValue)
51      pd.testing.assert_frame_equal(
52          result.value,
53          pd.DataFrame(
54              [{"my_target": 1, "my_prediction": np.nan}, {"my_target": np.nan, "my_prediction": 1}],
55              index=["my_target", "my_prediction"],
56          ),
57      )