implement modified diebold mariano test (#22)

* implement modified diebold mariano test
* added dm test stat tutorial notebook
* Implement  the `acovf` function from `statsmodels`
* updated github actions to run tests on develop branch
* Add copyright from statsmodel
* Improve keyword matching in exclude statements for pyproject.toml
* added warning for dm tests if there are NaNs

---------

Co-authored-by: nicholas loveday <[email protected]>
Co-authored-by: Harrison Cook <[email protected]>
Co-authored-by: Tennessee Leeuwenburg <[email protected]>

.github/workflows/python-app.yml

@@ -5,9 +5,9 @@ name: Unit Tests
 
 on:
   push:
-    branches: [ "main" ]
+    branches: ["develop", "main"]
   pull_request:
-    branches: [ "main" ]
+    branches: ["develop", "main"]
 
 permissions:
   contents: read
@@ -18,7 +18,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.10", "3.11"]    
+        python-version: ["3.9", "3.10", "3.11"]    
 
     steps:
     - uses: actions/checkout@v3

docs/api.md

@@ -10,6 +10,7 @@ This API documentation is automatically generated by mkdocstrings. It can be use
         classification  # Scores which relate to the estimates or categories
         probability     # Scores which relate to estimates of probabilities
         spatial         # Scores which relate to (geo)spatial accuracy
+        stats           # Statistics that support verification such as confidence intervals
 
 ## scores.continuous
 ::: scores.continuous
@@ -19,4 +20,7 @@ This API documentation is automatically generated by mkdocstrings. It can be use
 ## scores.probability.crps
 ::: scores.probability.crps
 
-## scores.spatial
+## scores.spatial
+
+## scores.stats
+::: scores.stats.confidence_intervals

pyproject.toml

@@ -53,9 +53,9 @@ all = ["scores[dev,tutorial]"]
 
 [tool.hatch.build]
 exclude = [
-    "tutorials/",
-    "docs/",
-    "tests/"
+    "/tutorials/",
+    "/docs/",
+    "/tests/"
 ]
 
 [tool.hatch.version]

src/scores/__init__.py

@@ -6,5 +6,6 @@
 import scores.probability
 import scores.sample_data
 import scores.functions
+import scores.stats.tests
 
 __version__ = "v0.0.2"

src/scores/stats/__init__.py

@@ -0,0 +1,4 @@
+"""
+The philosphy is to import the public API during the init phase rather than leaving it to the user
+"""
+import scores.stats.tests

src/scores/stats/tests/__init__.py

@@ -0,0 +1,5 @@
+"""
+Import the functions from the implementations into the public API
+"""
+
+from scores.stats.tests.diebold_mariano_impl import diebold_mariano

src/scores/stats/tests/acovf.py

@@ -0,0 +1,136 @@
+"""
+Estimate autocovariances
+
+Barebones reimplementation of the `acovf` from `statsmodels.api.tsa.acovf`, 
+for use only with `scores.stats.test.diebold_mariano`
+
+Package: https://www.statsmodels.org/devel/
+
+Code reference: https://github.com/statsmodels/statsmodels/blob/main/statsmodels/tsa/stattools.py
+
+Notes:
+    All type checking and other features have been removed, as they aren't needed for the 
+    `diebold_mariano` function.
+
+Why:
+    Reduce dependant packages
+
+## Source License
+
+Copyright (C) 2006, Jonathan E. Taylor
+All rights reserved.
+
+Copyright (c) 2006-2008 Scipy Developers.
+All rights reserved.
+
+Copyright (c) 2009-2018 statsmodels Developers.
+All rights reserved.
+
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+  a. Redistributions of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
+  b. 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.
+  c. Neither the name of statsmodels nor the names of its contributors
+     may be used to endorse or promote products derived from this software
+     without specific prior written permission.
+
+
+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 STATSMODELS 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 numpy as np
+
+__all__ = ["acovf"]
+
+
+def _next_regular(target):
+    """
+    Find the next regular number greater than or equal to target.
+    Regular numbers are composites of the prime factors 2, 3, and 5.
+    Also known as 5-smooth numbers or Hamming numbers, these are the optimal
+    size for inputs to FFTPACK.
+
+    Target must be a positive integer.
+    """
+    if target <= 6:
+        return target
+
+    # Quickly check if it's already a power of 2
+    if not (target & (target - 1)):
+        return target
+
+    match = float("inf")  # Anything found will be smaller
+    p5 = 1
+    while p5 < target:
+        p35 = p5
+        while p35 < target:
+            # Ceiling integer division, avoiding conversion to float
+            # (quotient = ceil(target / p35))
+            quotient = -(-target // p35)
+            # Quickly find next power of 2 >= quotient
+            p2 = 2 ** ((quotient - 1).bit_length())
+
+            N = p2 * p35
+            if N == target:
+                return N
+            elif N < match:
+                match = N
+            p35 *= 3
+            if p35 == target:
+                return p35
+        if p35 < match:
+            match = p35
+        p5 *= 5
+        if p5 == target:
+            return p5
+    if p5 < match:
+        match = p5
+    return match
+
+
+def acovf(x):
+    """
+    Estimate autocovariances.
+
+    Args:
+        x (array_like):
+            Time series data. Must be 1d.
+
+    Returns:
+        (np.ndarray):
+            The estimated autocovariances.
+
+    References:
+        [1] Parzen, E., 1963. On spectral analysis with missing observations
+            and amplitude modulation. Sankhya: The Indian Journal of
+            Statistics, Series A, pp.383-392.
+    """
+
+    xo = x - x.mean()
+
+    n = len(x)
+
+    d = n * np.ones(2 * n - 1)
+
+    nobs = len(xo)
+    n = _next_regular(2 * nobs + 1)
+    Frf = np.fft.fft(xo, n=n)
+    acov = np.fft.ifft(Frf * np.conjugate(Frf))[:nobs] / d[nobs - 1 :]
+    acov = acov.real
+
+    return acov