Overload series.rolling.kurt()

examples/series/rolling/series_rolling_kurt.py

@@ -0,0 +1,39 @@
+# *****************************************************************************
+# Copyright (c) 2019, Intel Corporation All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+#     Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#     Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
+# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
+# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
+# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
+# EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+# *****************************************************************************
+
+import pandas as pd
+from numba import njit
+
+
+@njit
+def series_rolling_kurt():
+    series = pd.Series([4, 3, 5, 2, 6])  # Series of 4, 3, 5, 2, 6
+    out_series = series.rolling(4).kurt()
+
+    return out_series  # Expect series of NaN, NaN, NaN, -1.2, -3.3
+
+
+print(series_rolling_kurt())

sdc/datatypes/hpat_pandas_series_rolling_functions.py

@@ -114,6 +114,31 @@ def arr_cov(x, y, ddof):
     return numpy.cov(x, y, ddof=ddof)[0, 1]
 
 
+@register_jitable
+def _moment(arr, moment):
+    mn = numpy.mean(arr)
+    s = numpy.power((arr - mn), moment)
+
+    return numpy.mean(s)
+
+
+@register_jitable
+def arr_kurt(arr):
+    """Calculate unbiased kurtosis of values"""
+    n = len(arr)
+    if n < 4:
+        return numpy.nan
+
+    m2 = _moment(arr, 2)
+    m4 = _moment(arr, 4)
+    val = 0 if m2 == 0 else m4 / m2 ** 2.0
+
+    if (n > 2) & (m2 > 0):
+        val = 1.0/(n-2)/(n-3) * ((n**2-1.0)*m4/m2**2.0 - 3*(n-1)**2.0)
+
+    return val
+
+
 @register_jitable
 def arr_max(arr):
     """Calculate maximum of values"""
@@ -270,6 +295,8 @@ def impl(self):
 
 hpat_pandas_rolling_series_count_impl = register_jitable(
     gen_hpat_pandas_series_rolling_zerominp_impl(arr_nonnan_count, float64))
+hpat_pandas_rolling_series_kurt_impl = register_jitable(
+    gen_hpat_pandas_series_rolling_impl(arr_kurt, float64))
 hpat_pandas_rolling_series_max_impl = register_jitable(
     gen_hpat_pandas_series_rolling_impl(arr_max, float64))
 hpat_pandas_rolling_series_mean_impl = register_jitable(
@@ -504,6 +531,15 @@ def calc_cov(main, other, ddof, minp):
     return hpat_pandas_rolling_series_cov_impl
 
 
+@sdc_overload_method(SeriesRollingType, 'kurt')
+def hpat_pandas_series_rolling_kurt(self):
+
+    ty_checker = TypeChecker('Method rolling.kurt().')
+    ty_checker.check(self, SeriesRollingType)
+
+    return hpat_pandas_rolling_series_kurt_impl
+
+
 @sdc_overload_method(SeriesRollingType, 'max')
 def hpat_pandas_series_rolling_max(self):
     """
@@ -883,6 +919,13 @@ def culc_var(arr, ddof, minp):
     """
 })
 
+hpat_pandas_series_rolling_kurt.__doc__ = hpat_pandas_series_rolling_docstring_tmpl.format(**{
+    'method_name': 'kurt',
+    'example_caption': 'Calculate unbiased rolling kurtosis.',
+    'limitations_block': '',
+    'extra_params': ''
+})
+
 hpat_pandas_series_rolling_mean.__doc__ = hpat_pandas_series_rolling_docstring_tmpl.format(**{
     'method_name': 'mean',
     'example_caption': 'Calculate the rolling mean of the values.',

sdc/tests/test_rolling.py

@@ -730,6 +730,25 @@ def test_impl(pairwise, ddof):
         msg = msg_tmpl.format('ddof', 'unicode_type', 'int')
         self.assertIn(msg, str(raises.exception))
 
+    @skip_sdc_jit('Series.rolling.kurt() unsupported Series index')
+    def test_series_rolling_kurt(self):
+        def test_impl(series, window, min_periods):
+            return series.rolling(window, min_periods).kurt()
+
+        hpat_func = self.jit(test_impl)
+
+        all_data = test_global_input_data_float64
+        indices = [list(range(len(data)))[::-1] for data in all_data]
+        for data, index in zip(all_data, indices):
+            series = pd.Series(data, index, name='A')
+            for window in range(4, len(series) + 1):
+                for min_periods in range(window + 1):
+                    with self.subTest(series=series, window=window,
+                                      min_periods=min_periods):
+                        ref_result = test_impl(series, window, min_periods)
+                        jit_result = hpat_func(series, window, min_periods)
+                        pd.testing.assert_series_equal(jit_result, ref_result)
+
     @skip_sdc_jit('Series.rolling.max() unsupported Series index')
     def test_series_rolling_max(self):
         def test_impl(series, window, min_periods):