aggregation: Allow date, datetime and decimal dtypes in min/max aggre…

…gation

fennel/CHANGELOG.md

@@ -1,5 +1,8 @@
 # Changelog
 
+## [1.5.58] - 2024-11-24
+- Allow min/max aggregation on date, datetime and decimal dtypes
+
 ## [1.5.57] - 2024-12-02
 - Fix signature method for filter class
 

fennel/client_tests/test_complex_aggregation.py

@@ -0,0 +1,204 @@
+from datetime import datetime, timezone, date
+from decimal import Decimal as PythonDecimal
+
+import pandas as pd
+import pytest
+
+from fennel._vendor import requests
+from fennel.connectors import Webhook, source
+from fennel.datasets import (
+    dataset,
+    field,
+    pipeline,
+    Dataset,
+    Max,
+    Min,
+)
+from fennel.dtypes import Decimal, Continuous
+from fennel.featuresets import featureset, feature as F
+from fennel.lib import inputs
+from fennel.testing import mock
+
+webhook = Webhook(name="fennel_webhook")
+__owner__ = "[email protected]"
+
+
+@pytest.mark.integration
+@mock
+def test_complex_min_max(client):
+    @source(webhook.endpoint("UserInfoDataset"), disorder="14d", cdc="append")
+    @dataset
+    class UserInfoDataset:
+        user_id: int
+        birthdate: date
+        birthtime: datetime
+        country: str
+        income: Decimal[2]
+        timestamp: datetime = field(timestamp=True)
+
+    @dataset(index=True)
+    class CountryDS:
+        country: str = field(key=True)
+        min_income: Decimal[2]
+        max_income: Decimal[2]
+        min_birthdate: date
+        max_birthdate: date
+        min_birthtime: datetime
+        max_birthtime: datetime
+        timestamp: datetime = field(timestamp=True)
+
+        @pipeline
+        @inputs(UserInfoDataset)
+        def avg_income_pipeline(cls, event: Dataset):
+            return event.groupby("country").aggregate(
+                min_income=Min(
+                    of="income",
+                    window=Continuous("forever"),
+                    default=PythonDecimal("1.20"),
+                ),
+                max_income=Max(
+                    of="income",
+                    window=Continuous("forever"),
+                    default=PythonDecimal("2.20"),
+                ),
+                min_birthdate=Min(
+                    of="birthdate",
+                    window=Continuous("forever"),
+                    default=date(1970, 1, 1),
+                ),
+                max_birthdate=Max(
+                    of="birthdate",
+                    window=Continuous("forever"),
+                    default=date(1970, 1, 2),
+                ),
+                min_birthtime=Min(
+                    of="birthtime",
+                    window=Continuous("forever"),
+                    default=datetime(1970, 1, 1, tzinfo=timezone.utc),
+                ),
+                max_birthtime=Max(
+                    of="birthtime",
+                    window=Continuous("forever"),
+                    default=datetime(1970, 1, 2, tzinfo=timezone.utc),
+                ),
+            )
+
+    @featureset
+    class CountryFeatures:
+        country: str
+        min_income: Decimal[2] = F(
+            CountryDS.min_income, default=PythonDecimal("1.20")
+        )
+        max_income: Decimal[2] = F(
+            CountryDS.max_income, default=PythonDecimal("2.20")
+        )
+        min_birthdate: date = F(
+            CountryDS.min_birthdate, default=date(1970, 1, 1)
+        )
+        max_birthdate: date = F(
+            CountryDS.max_birthdate, default=date(1970, 1, 2)
+        )
+        min_birthtime: datetime = F(
+            CountryDS.min_birthtime,
+            default=datetime(1970, 1, 1, tzinfo=timezone.utc),
+        )
+        max_birthtime: datetime = F(
+            CountryDS.max_birthtime,
+            default=datetime(1970, 1, 2, tzinfo=timezone.utc),
+        )
+
+    # Sync the dataset
+    response = client.commit(
+        message="msg",
+        datasets=[UserInfoDataset, CountryDS],
+        featuresets=[CountryFeatures],
+    )
+    assert response.status_code == requests.codes.OK, response.json()
+
+    client.sleep(30)
+
+    now = datetime.now(timezone.utc)
+    df = pd.DataFrame(
+        {
+            "user_id": [1, 2, 3, 4, 5],
+            "country": ["India", "USA", "India", "USA", "UK"],
+            "birthdate": [
+                date(1980, 1, 1),
+                date(1990, 2, 11),
+                date(2000, 3, 15),
+                date(1997, 5, 22),
+                date(1987, 6, 6),
+            ],
+            "birthtime": [
+                datetime(1980, 1, 1, tzinfo=timezone.utc),
+                datetime(1990, 2, 11, tzinfo=timezone.utc),
+                datetime(2000, 3, 15, tzinfo=timezone.utc),
+                datetime(1997, 5, 22, tzinfo=timezone.utc),
+                datetime(1987, 6, 6, tzinfo=timezone.utc),
+            ],
+            "income": [
+                PythonDecimal("1200.10"),
+                PythonDecimal("1000.10"),
+                PythonDecimal("1400.10"),
+                PythonDecimal("90.10"),
+                PythonDecimal("1100.10"),
+            ],
+            "timestamp": [now, now, now, now, now],
+        }
+    )
+    response = client.log("fennel_webhook", "UserInfoDataset", df)
+    assert response.status_code == requests.codes.OK, response.json()
+
+    client.sleep()
+
+    # Querying UserInfoFeatures
+    df = client.query(
+        inputs=[CountryFeatures.country],
+        outputs=[CountryFeatures],
+        input_dataframe=pd.DataFrame(
+            {"CountryFeatures.country": ["India", "USA", "UK", "China"]}
+        ),
+    )
+    assert df.shape == (4, 7)
+    assert df["CountryFeatures.country"].tolist() == [
+        "India",
+        "USA",
+        "UK",
+        "China",
+    ]
+    assert df["CountryFeatures.min_income"].tolist() == [
+        PythonDecimal("1200.10"),
+        PythonDecimal("90.10"),
+        PythonDecimal("1100.10"),
+        PythonDecimal("1.20"),
+    ]
+    assert df["CountryFeatures.max_income"].tolist() == [
+        PythonDecimal("1400.10"),
+        PythonDecimal("1000.10"),
+        PythonDecimal("1100.10"),
+        PythonDecimal("2.20"),
+    ]
+    assert df["CountryFeatures.min_birthdate"].tolist() == [
+        date(1980, 1, 1),
+        date(1990, 2, 11),
+        date(1987, 6, 6),
+        date(1970, 1, 1),
+    ]
+    assert df["CountryFeatures.max_birthdate"].tolist() == [
+        date(2000, 3, 15),
+        date(1997, 5, 22),
+        date(1987, 6, 6),
+        date(1970, 1, 2),
+    ]
+    assert df["CountryFeatures.min_birthtime"].tolist() == [
+        datetime(1980, 1, 1, tzinfo=timezone.utc),
+        datetime(1990, 2, 11, tzinfo=timezone.utc),
+        datetime(1987, 6, 6, tzinfo=timezone.utc),
+        datetime(1970, 1, 1, tzinfo=timezone.utc),
+    ]
+    assert df["CountryFeatures.max_birthtime"].tolist() == [
+        datetime(2000, 3, 15, tzinfo=timezone.utc),
+        datetime(1997, 5, 22, tzinfo=timezone.utc),
+        datetime(1987, 6, 6, tzinfo=timezone.utc),
+        datetime(1970, 1, 2, tzinfo=timezone.utc),
+    ]

fennel/datasets/aggregate.py

@@ -1,3 +1,5 @@
+from datetime import date, datetime
+from decimal import Decimal
 from typing import List, Union, Optional
 
 import fennel.gen.spec_pb2 as spec_proto
@@ -212,17 +214,25 @@ def validate(self):
 
 class Max(AggregateType):
     of: str
-    default: float
+    default: Union[float, int, date, datetime, Decimal]
 
     def to_proto(self):
         if self.window is None:
             raise ValueError("Window must be specified for Distinct")
+        if isinstance(self.default, datetime):
+            default = float(self.default.timestamp() * 1000000.0)
+        elif isinstance(self.default, date):
+            default = float((self.default - date(1970, 1, 1)).days)
+        elif isinstance(self.default, Decimal):
+            default = float(self.default)
+        else:
+            default = float(self.default)
         return spec_proto.PreSpec(
             max=spec_proto.Max(
                 window=self.window.to_proto(),
                 name=self.into_field,
                 of=self.of,
-                default=self.default,
+                default=default,
             )
         )
 
@@ -235,17 +245,25 @@ def agg_type(self):
 
 class Min(AggregateType):
     of: str
-    default: float
+    default: Union[float, int, date, datetime, Decimal]
 
     def to_proto(self):
         if self.window is None:
             raise ValueError("Window must be specified for Distinct")
+        if isinstance(self.default, datetime):
+            default = float(self.default.timestamp() * 1000000.0)
+        elif isinstance(self.default, date):
+            default = float((self.default - date(1970, 1, 1)).days)
+        elif isinstance(self.default, Decimal):
+            default = float(self.default)
+        else:
+            default = float(self.default)
         return spec_proto.PreSpec(
             min=spec_proto.Min(
                 window=self.window.to_proto(),
                 name=self.into_field,
                 of=self.of,
-                default=self.default,
+                default=default,
             )
         )
 

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "fennel-ai"
-version = "1.5.57"
+version = "1.5.58"
 description = "The modern realtime feature engineering platform"
 authors = ["Fennel AI <[email protected]>"]
 packages = [{ include = "fennel" }]