chunk size 1 and stats std results (#363)

* no inf std fix

* add test for chunk size 1

* cause alert when nan result for stats module

* refactor stats std sampling error to better catch issues

nannyml/sampling_error/summary_stats.py

@@ -7,7 +7,9 @@
 import numpy as np
 import pandas as pd
 from scipy.stats import gaussian_kde, moment
+from logging import getLogger
 
+logger = getLogger(__name__)
 
 def summary_stats_std_sampling_error_components(col: pd.Series) -> Tuple:
     """
@@ -38,8 +40,10 @@ def summary_stats_std_sampling_error(sampling_error_components, col) -> float:
 
     Parameters
     ----------
-    sampling_error_components : a set of parameters that were derived from reference data.
-    col : the (analysis) column you want to calculate sampling error for.
+    sampling_error_components:
+        a set of parameters that were derived from reference data.
+    col:
+        the (analysis) column you want to calculate sampling error for.
 
     Returns
     -------
@@ -49,7 +53,15 @@ def summary_stats_std_sampling_error(sampling_error_components, col) -> float:
     _std = sampling_error_components[0]
     _mu4 = sampling_error_components[1]
     _size = col.shape[0]
-    err_var = np.sqrt((1 / _size) * (_mu4 - ((_size - 3) * (_std**4) / (_size - 1))))
+
+    err_var_parenthesis_part = (_mu4 - ((_size - 3) * (_std**4) / (_size - 1)))
+    if not (
+        np.isfinite(err_var_parenthesis_part) and
+        err_var_parenthesis_part >= 0
+    ):
+        logger.debug("Summary Stats sampling error calculation imputed to nan because of non finite positive parenthesis factor.")
+        return np.nan
+    err_var = np.sqrt((1 / _size) * err_var_parenthesis_part)
     return (1 / (2 * _std)) * err_var
 
 

nannyml/stats/base.py

@@ -5,9 +5,13 @@
 
 """Module containing base classes for data quality calculations."""
 
+from numpy import isnan
+
 
 def _add_alert_flag(row_result: dict) -> bool:
     flag = False
+    if isnan(row_result['value']):
+        flag = True
     if row_result['upper_threshold'] is not None:
         if row_result['value'] > row_result['upper_threshold']:
             flag = True

tests/stats/test_std.py

@@ -6,9 +6,13 @@
 """Tests for Drift package."""
 
 import pytest
+import pandas as pd
+import numpy as np
+
 
 from nannyml.datasets import load_synthetic_car_loan_dataset
 from nannyml.stats import SummaryStatsStdCalculator
+from nannyml.chunk import SizeBasedChunker
 
 # @pytest.fixture(scope="module")
 # def status_sum_result() -> Result:
@@ -33,3 +37,36 @@ def test_stats_std_calculator_with_default_params_should_not_fail():  # noqa: D1
         _ = calc.calculate(data=analysis)
     except Exception:
         pytest.fail()
+
+
+def test_stats_std_calculator_with_default_params_chunk_size_one():  # noqa: D103
+    reference, analysis, _ = load_synthetic_car_loan_dataset()
+
+    chunker = SizeBasedChunker(chunk_size=5_000, incomplete='keep')
+    calc = SummaryStatsStdCalculator(
+        column_names=['car_value'],
+        chunker=chunker
+    ).fit(reference)
+    result = calc.calculate(data=analysis.head(5_001))
+    expected = pd.DataFrame(
+        {
+            ('chunk', 'key'): ['[0:4999]', '[5000:5000]'],
+            ('chunk', 'chunk_index'): [0,1],
+            ('chunk', 'start_index'): [0,5000],
+            ('chunk', 'end_index'): [4999,5000],
+            ('chunk', 'start_date'): [None,None],
+            ('chunk', 'end_date'): [None,None],
+            ('chunk', 'period'): ['analysis','analysis'],
+            ('car_value', 'value'): [20614.8926,np.nan],
+            ('car_value', 'sampling_error'): [271.9917,np.nan],
+            ('car_value', 'upper_confidence_boundary'): [21430.8679,np.nan],
+            ('car_value', 'lower_confidence_boundary'): [19798.9174,np.nan],
+            ('car_value', 'upper_threshold'): [20978.5658, 20978.5658],
+            ('car_value', 'lower_threshold'): [19816.9091, 19816.9091],
+            ('car_value', 'alert'): [False, True],
+        }
+    )
+    pd.testing.assert_frame_equal(
+        expected,
+        result.filter(period='analysis').to_df().round(4)
+    )