Extend test coverage for pandas frequencies (#3179)

*Issue #, if available:* #3178 *Description of changes:* - Add more tests verifying that pandas frequencies are handled correctly (compatible with both pandas 2.1 and pandas 2.2) By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. **Please tag this pr with at least one of these labels to make our release process faster:** BREAKING, new feature, bug fix, other change, dev setup
awslabs · May 24, 2024 · b1e054a · b1e054a
1 parent a132eab
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diff --git a/src/gluonts/time_feature/_base.py b/src/gluonts/time_feature/_base.py
@@ -11,6 +11,7 @@
 # express or implied. See the License for the specific language governing
 # permissions and limitations under the License.
 
+from packaging.version import Version
 from typing import Any, Callable, Dict, List
 
 import numpy as np
@@ -196,7 +197,10 @@ def norm_freq_str(freq_str: str) -> str:
     # Note: Secondly ("S") frequency exists, where we don't want to remove the
     # "S"!
     if len(base_freq) >= 2 and base_freq.endswith("S"):
-        return base_freq[:-1]
+        base_freq = base_freq[:-1]
+        # In pandas >= 2.2, period end frequencies have been renamed, e.g. "M" -> "ME"
+        if Version(pd.__version__) >= Version("2.2.0"):
+            base_freq += "E"
 
     return base_freq
 
@@ -252,17 +256,13 @@ def time_features_from_frequency_str(freq_str: str) -> List[TimeFeature]:
     Unsupported frequency {freq_str}
 
     The following frequencies are supported:
-
-        Y   - yearly
-            alias: A
-        Q   - quarterly
-        M   - monthly
-        W   - weekly
-        D   - daily
-        B   - business days
-        H   - hourly
-        T   - minutely
-            alias: min
-        S   - secondly
+    
     """
+
+    for offset_cls in features_by_offsets:
+        offset = offset_cls()
+        supported_freq_msg += (
+            f"\t{offset.freqstr.split('-')[0]} - {offset_cls.__name__}"
+        )
+
     raise RuntimeError(supported_freq_msg)
diff --git a/src/gluonts/time_feature/seasonality.py b/src/gluonts/time_feature/seasonality.py
@@ -33,6 +33,7 @@
     "ME": 12,
     "B": 5,
     "Q": 4,
+    "QE": 4,
 }
 
 

diff --git a/test/time_feature/__init__.py b/test/time_feature/__init__.py
@@ -0,0 +1,12 @@
+# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License").
+# You may not use this file except in compliance with the License.
+# A copy of the License is located at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# or in the "license" file accompanying this file. This file is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
+# express or implied. See the License for the specific language governing
+# permissions and limitations under the License.
diff --git a/test/time_feature/common.py b/test/time_feature/common.py
@@ -0,0 +1,28 @@
+# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License").
+# You may not use this file except in compliance with the License.
+# A copy of the License is located at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# or in the "license" file accompanying this file. This file is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
+# express or implied. See the License for the specific language governing
+# permissions and limitations under the License.
+
+import pandas as pd
+from packaging.version import Version
+
+if Version(pd.__version__) <= Version("2.2.0"):
+    S = "S"
+    H = "H"
+    M = "M"
+    Q = "Q"
+    Y = "A"
+else:
+    S = "s"
+    H = "h"
+    M = "ME"
+    Q = "QE"
+    Y = "YE"
diff --git a/test/time_feature/test_agg_lags.py b/test/time_feature/test_agg_lags.py
@@ -16,7 +16,6 @@
 import pytest
 
 from gluonts.dataset.common import ListDataset
-
 from gluonts.dataset.field_names import FieldName
 from gluonts.transform import AddAggregateLags
 

diff --git a/test/time_feature/test_base.py b/test/time_feature/test_base.py
@@ -11,21 +11,23 @@
 # express or implied. See the License for the specific language governing
 # permissions and limitations under the License.
 
+import pytest
 from pandas.tseries.frequencies import to_offset
 
 from gluonts.time_feature import norm_freq_str
 
+from .common import M, Q, S, Y
 
-def test_norm_freq_str():
-    assert norm_freq_str(to_offset("Y").name) in ["A", "YE"]
-    assert norm_freq_str(to_offset("YS").name) in ["A", "Y"]
-    assert norm_freq_str(to_offset("A").name) in ["A", "YE"]
-    assert norm_freq_str(to_offset("AS").name) in ["A", "Y"]
 
-    assert norm_freq_str(to_offset("Q").name) in ["Q", "QE"]
-    assert norm_freq_str(to_offset("QS").name) == "Q"
-
-    assert norm_freq_str(to_offset("M").name) in ["M", "ME"]
-    assert norm_freq_str(to_offset("MS").name) in ["M", "ME"]
-
-    assert norm_freq_str(to_offset("S").name) in ["S", "s"]
+@pytest.mark.parametrize(
+    " aliases, normalized_freq_str",
+    [
+        (["Y", "YS", "A", "AS"], Y),
+        (["Q", "QS"], Q),
+        (["M", "MS"], M),
+        (["S"], S),
+    ],
+)
+def test_norm_freq_str(aliases, normalized_freq_str):
+    for alias in aliases:
+        assert norm_freq_str(to_offset(alias).name) == normalized_freq_str
diff --git a/test/time_feature/test_features.py b/test/time_feature/test_features.py
@@ -16,7 +16,6 @@
 import pytest
 
 from gluonts import zebras as zb
-
 from gluonts.time_feature import (
     Constant,
     TimeFeature,

diff --git a/test/time_feature/test_lag.py b/test/time_feature/test_lag.py
@@ -15,12 +15,16 @@
 Test the lags computed for different frequencies.
 """
 
+import pytest
+
 import gluonts.time_feature.lag as date_feature_set
 
+from .common import H, M, Q, Y
+
 # These are the expected lags for common frequencies and corner cases.
 # By default all frequencies have the following lags: [1, 2, 3, 4, 5, 6, 7].
 # Remaining lags correspond to the same `season` (+/- `delta`) in previous `k` cycles.
-expected_lags = {
+EXPECTED_LAGS = {
     # (apart from the default lags) centered around each of the last 3 hours (delta = 2)
     "4S": [
         1,
@@ -179,7 +183,7 @@
     ]
     + [329, 330, 331, 494, 495, 496, 659, 660, 661, 707, 708, 709],
     # centered around each of the last 3 hours (delta = 2) + last 7 days (delta = 1) + last 6 weeks (delta = 1)
-    "H": [1, 2, 3, 4, 5, 6, 7]
+    H: [1, 2, 3, 4, 5, 6, 7]
     + [
         23,
         24,
@@ -206,7 +210,7 @@
     + [335, 336, 337, 503, 504, 505, 671, 672, 673, 719, 720, 721],
     # centered around each of the last 7 days (delta = 1) + last 4 weeks (delta = 1) + last 1 month (delta = 1) +
     #  last 8th and 12th weeks (delta = 0)
-    "6H": [
+    ("6" + H): [
         1,
         2,
         3,
@@ -237,21 +241,21 @@
     + [224, 336],
     # centered around each of the last 7 days (delta = 1) + last 4 weeks (delta = 1) + last 1 month (delta = 1) +
     #  last 8th and 12th weeks (delta = 0) + last year (delta = 1)
-    "12H": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
+    ("12" + H): [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
     + [27, 28, 29, 41, 42, 43, 55, 56, 57]
     + [59, 60, 61]
     + [112, 168]
     + [727, 728, 729],
     # centered around each of the last 7 days (delta = 1) + last 4 weeks (delta = 1) + last 1 month (delta = 1) +
     #  last 8th and 12th weeks (delta = 0) + last 3 years (delta = 1)
-    "23H": [1, 2, 3, 4, 5, 6, 7, 8]
+    ("23" + H): [1, 2, 3, 4, 5, 6, 7, 8]
     + [13, 14, 15, 20, 21, 22, 28, 29]
     + [30, 31, 32]
     + [58, 87]
     + [378, 379, 380, 758, 759, 760, 1138, 1139, 1140],
     # centered around each of the last 7 days (delta = 1) + last 4 weeks (delta = 1) + last 1 month (delta = 1) +
     #  last 8th and 12th weeks (delta = 0) + last 3 years (delta = 1)
-    "25H": [1, 2, 3, 4, 5, 6, 7]
+    ("25" + H): [1, 2, 3, 4, 5, 6, 7]
     + [12, 13, 14, 19, 20, 21, 25, 26, 27]
     + [28, 29]
     + [53, 80]
@@ -285,64 +289,31 @@
     # centered around each of the last 3 years (delta = 1)
     "5W": [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 19, 20, 21, 30, 31, 32],
     # centered around each of the last 3 years (delta = 1)
-    "M": [1, 2, 3, 4, 5, 6, 7, 11, 12, 13, 23, 24, 25, 35, 36, 37],
+    M: [1, 2, 3, 4, 5, 6, 7, 11, 12, 13, 23, 24, 25, 35, 36, 37],
     # default
-    "6M": [1, 2, 3, 4, 5, 6, 7],
+    "6" + M: [1, 2, 3, 4, 5, 6, 7],
     # default
-    "12M": [1, 2, 3, 4, 5, 6, 7],
+    "12" + M: [1, 2, 3, 4, 5, 6, 7],
+    Q: [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13],
+    "QS": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13],
+    Y: [1, 2, 3, 4, 5, 6, 7],
+    "YS": [1, 2, 3, 4, 5, 6, 7],
 }
 
 # For the default multiple (1)
-for freq in ["min", "H", "D", "W", "M"]:
-    expected_lags["1" + freq] = expected_lags[freq]
+for freq in ["min", H, "D", "W", M]:
+    EXPECTED_LAGS["1" + freq] = EXPECTED_LAGS[freq]
 
 # For frequencies that do not have unique form
-expected_lags["60min"] = expected_lags["1H"]
-expected_lags["24H"] = expected_lags["1D"]
-expected_lags["7D"] = expected_lags["1W"]
-
-
-def test_lags():
-    freq_strs = [
-        "4S",
-        "min",
-        "1min",
-        "15min",
-        "30min",
-        "59min",
-        "60min",
-        "61min",
-        "H",
-        "1H",
-        "6H",
-        "12H",
-        "23H",
-        "24H",
-        "25H",
-        "D",
-        "1D",
-        "2D",
-        "6D",
-        "7D",
-        "8D",
-        "W",
-        "1W",
-        "3W",
-        "4W",
-        "5W",
-        "M",
-        "6M",
-        "12M",
-    ]
+EXPECTED_LAGS["60min"] = EXPECTED_LAGS["1" + H]
+EXPECTED_LAGS["24" + H] = EXPECTED_LAGS["1D"]
+EXPECTED_LAGS["7D"] = EXPECTED_LAGS["1W"]
 
-    for freq_str in freq_strs:
-        lags = date_feature_set.get_lags_for_frequency(freq_str)
 
-        assert (
-            lags == expected_lags[freq_str]
-        ), "lags do not match for the frequency '{}':\nexpected: {},\nprovided: {}".format(
-            freq_str, expected_lags[freq_str], lags
-        )
+@pytest.mark.parametrize("freq_str, expected_lags", EXPECTED_LAGS.items())
+def test_lags(freq_str, expected_lags):
+    lags = date_feature_set.get_lags_for_frequency(freq_str)
+    assert lags == expected_lags
 
 
 if __name__ == "__main__":

diff --git a/test/time_feature/test_seasonality.py b/test/time_feature/test_seasonality.py
@@ -15,25 +15,42 @@
 
 from gluonts.time_feature import get_seasonality
 
+from .common import H, M, Q, Y
 
-@pytest.mark.parametrize(
-    "freq, expected_seasonality",
-    [
-        ("30min", 48),
-        ("1H", 24),
-        ("H", 24),
-        ("2H", 12),
-        ("3H", 8),
-        ("4H", 6),
-        ("15H", 1),
-        ("5B", 1),
-        ("1B", 5),
-        ("2W", 1),
-        ("3M", 4),
-        ("1D", 1),
-        ("7D", 1),
-        ("8D", 1),
-    ],
-)
+TEST_CASES = [
+    ("30min", 48),
+    ("5B", 1),
+    ("1B", 5),
+    ("2W", 1),
+    ("1D", 1),
+    ("7D", 1),
+    ("8D", 1),
+    # Monthly
+    ("MS", 12),
+    ("3MS", 4),
+    (M, 12),
+    ("3" + M, 4),
+    # Quarterly
+    ("QS", 4),
+    ("2QS", 2),
+    (Q, 4),
+    ("2" + Q, 2),
+    ("3" + Q, 1),
+    # Hourly
+    ("1" + H, 24),
+    (H, 24),
+    ("2" + H, 12),
+    ("3" + H, 8),
+    ("4" + H, 6),
+    ("15" + H, 1),
+    # Yearly
+    (Y, 1),
+    ("2" + Y, 1),
+    ("YS", 1),
+    ("2YS", 1),
+]
+
+
+@pytest.mark.parametrize("freq, expected_seasonality", TEST_CASES)
 def test_get_seasonality(freq, expected_seasonality):
     assert get_seasonality(freq) == expected_seasonality