Fixing minor typo (#1303)

docs/book/customization/options-for-statistical-tests.md

@@ -92,7 +92,7 @@ To use the following drift detection methods, pass them using the `stattest` par
 | `ed`<br> Energy distance | tabular data<br>only numerical | returns `distance`<br>drift detected when `distance` >= `threshold`<br>default threshold: 0.1 |
 | `es`<br> Epps-Singleton tes | tabular data<br>only numerical | returns `p_value`<br>drift detected when `p_value` < `threshold`<br>default threshold: 0.05 |
 | `t_test`<br> T-Test | tabular data<br>only numerical | returns `p_value`<br>drift detected when `p_value` < `threshold`<br>default threshold: 0.05 |
-| `emperical_mmd`<br> Emperical-MMD | tabular data<br>only numerical | returns `p_value`<br>drift detected when `p_value` < `threshold`<br>default threshold: 0.05 |
+| `empirical_mmd`<br> Empirical-MMD | tabular data<br>only numerical | returns `p_value`<br>drift detected when `p_value` < `threshold`<br>default threshold: 0.05 |
 | `TVD`<br> Total-Variation-Distance | tabular data<br>only categorical | returns `p_value`<br>drift detected when `p_value` < `threshold`<br>default threshold: 0.05 |
 
 # Text drift detection 

examples/how_to_questions/how_to_specify_stattest_for_a_testsuite.ipynb

@@ -123,7 +123,7 @@
  "* 't_test'\n",
  "* 'cramer_von_mises'\n",
  "* 'g_test'\n",
- "* 'emperical_mmd'\n",
+ "* 'empirical_mmd'\n",
  "* 'TVD'\n",
  "\n",
  "You can implement a custom drift test and use it in parameters. Just define a function that takes two pd.Series (reference and current data) and returns a number (e.g. p_value or distance)\n",

src/evidently/calculations/stattests/__init__.py

@@ -14,7 +14,7 @@
 from .kl_div import kl_div_stat_test
 from .ks_stattest import ks_stat_test
 from .mann_whitney_urank_stattest import mann_whitney_u_stat_test
-from .mmd_stattest import emperical_mmd
+from .mmd_stattest import empirical_mmd
 from .psi import psi_stat_test
 from .registry import PossibleStatTestType
 from .registry import StatTest
@@ -41,7 +41,7 @@
  "kl_div_stat_test",
  "ks_stat_test",
  "mann_whitney_u_stat_test",
- "emperical_mmd",
+ "empirical_mmd",
  "psi_stat_test",
  "PossibleStatTestType",
  "StatTest",

src/evidently/calculations/stattests/mmd_stattest.py

@@ -126,7 +126,7 @@ def _mmd_stattest(
  feature_type: ColumnType,
  threshold: float,
 ) -> Tuple[float, bool]:
- """Run the emperical maximum mean discrepancy test.
+ """Run the empirical maximum mean discrepancy test.
  Args:
  reference_data: reference data
  current_data: current data
@@ -144,11 +144,11 @@ def _mmd_stattest(
  return p_value, p_value < threshold
 
 
-emperical_mmd = StatTest(
- name="emperical_mmd",
- display_name="emperical_mmd",
+empirical_mmd = StatTest(
+ name="empirical_mmd",
+ display_name="empirical_mmd",
  allowed_feature_types=[ColumnType.Numerical],
  default_threshold=0.1,
 )
 
-register_stattest(emperical_mmd, _mmd_stattest)
+register_stattest(empirical_mmd, _mmd_stattest)

tests/calculations/stattests/test_get_stattest.py

@@ -22,7 +22,7 @@
  ("num", "kl_div"),
  ("num", "ks"),
  ("num", "mannw"),
- ("num", "emperical_mmd"),
+ ("num", "empirical_mmd"),
  ("cat", "psi"),
  ("num", "psi"),
  ("num", "t_test"),

tests/stattests/test_stattests.py

@@ -14,7 +14,7 @@
 from evidently.calculations.stattests.g_stattest import g_test
 from evidently.calculations.stattests.hellinger_distance import hellinger_stat_test
 from evidently.calculations.stattests.mann_whitney_urank_stattest import mann_whitney_u_stat_test
-from evidently.calculations.stattests.mmd_stattest import emperical_mmd
+from evidently.calculations.stattests.mmd_stattest import empirical_mmd
 from evidently.calculations.stattests.t_test import t_test
 from evidently.calculations.stattests.tvd_stattest import tvd_test
 from evidently.core import ColumnType
@@ -139,9 +139,9 @@ def test_cramer_von_mises() -> None:
  # (pd.Series(np.random.normal(0, 0.5, 100)), pd.Series(np.random.normal(0, 0.9, 100)), 0.1, 0, True),
  ),
 )
-def test_emperical_mmd(reference, current, threshold, expected_pvalue, drift_detected) -> None:
+def test_empirical_mmd(reference, current, threshold, expected_pvalue, drift_detected) -> None:
  np.random.seed(0)
- assert emperical_mmd.func(reference, current, "num", threshold) == (
+ assert empirical_mmd.func(reference, current, "num", threshold) == (
  approx(expected_pvalue, abs=1e-2),
  drift_detected,
  )