idk

api/py/test/sample/group_bys/affirm/paybright/repayment_data_v1_0.py

@@ -0,0 +1,66 @@
+# affirm/group_bys/paybright/repayment_data_v1_0.py
+
+from ai.chronon.api.ttypes import Source, EntitySource, MetaData
+from ai.chronon.query import Query, select
+from ai.chronon.group_by import GroupBy
+
+# This source uses a StagingQuery to automatically handle S3 YYYY/MM/DD partitioning.
+# No manual view creation needed - Chronon handles everything automatically!
+
+from ai.chronon.staging_query import StagingQuery
+from ai.chronon.utils import get_staging_query_output_table_name
+
+# StagingQuery automatically converts your S3 partitioning to Chronon's expected format
+paybright_staging_query = StagingQuery(
+    query="""
+        SELECT *, 
+               CONCAT(year, '-', LPAD(month, 2, '0'), '-', LPAD(day, 2, '0')) as ds
+        FROM (
+          SELECT *, 
+                 INPUT_FILE_NAME() as file_path,
+                 REGEXP_EXTRACT(INPUT_FILE_NAME(), '.*/([0-9]{4})/([0-9]{1,2})/([0-9]{1,2})/.*', 1) as year,
+                 REGEXP_EXTRACT(INPUT_FILE_NAME(), '.*/([0-9]{4})/([0-9]{1,2})/([0-9]{1,2})/.*', 2) as month,
+                 REGEXP_EXTRACT(INPUT_FILE_NAME(), '.*/([0-9]{4})/([0-9]{1,2})/([0-9]{1,2})/.*', 3) as day
+          FROM parquet.`s3://affirm-risk-sherlock-ca/feature-store/paybright_repayment_data/v1`
+          WHERE ds BETWEEN '{{ start_date }}' AND '{{ end_date }}'
+        )
+    """,
+    startPartition="2025-01-01",  # Adjust to your data start date
+    metaData=MetaData(
+        name="paybright_repayment_data_v1",
+        team="affirm",
+        outputNamespace="affirm"
+    )
+)
+
+paybright_snapshot_src = Source(
+    entities=EntitySource(
+        snapshotTable=get_staging_query_output_table_name(paybright_staging_query.v1),   # ← Auto-generated table
+        query=Query(
+            selects=select(
+                # entity key
+                "user_phone_number",
+                # timestamps
+                "snapshot_time",
+                # features
+                "galactus__paybright__user__history_average_lateness_days__v1",
+                "galactus__paybright__user__history_average_zeroed_lateness_days__v1",
+                "galactus__paybright__user__history_days_since_last_payment__v1",
+                "galactus__paybright__user__history_max_lateness_days__v1",
+                "galactus__paybright__user__history_num_outstanding_loans__v1",
+                "galactus__paybright__user__history_num_payments_last_60d__v1",
+                "galactus__paybright__user__history_prop_fully_paid_off_loans__il__v1",
+                "galactus__paybright__user__history_prop_fully_paid_off_loans__sp__v1",
+                "galactus__paybright__user__history_total_payment_amount_cents_60d__v1",
+                "galactus__paybright__user__history_total_payment_amount_cents__v1",
+            )
+        ),
+    )
+)
+
+# Passthrough snapshot → no aggregations
+paybright_repayment_data_v1_0 = GroupBy(
+    sources=[paybright_snapshot_src],
+    keys=["user_phone_number"],
+    aggregations=None,
+)

api/py/test/sample/teams.json

@@ -65,5 +65,10 @@
   "cs_ds": {
         "description": "Used for unit testing purposes",
         "namespace": "default"
+    },
+    "affirm": {
+        "description": "Affirm team configurations",
+        "namespace": "default"
     }
 }
+

group_bys/affirm/instrument_risk/instrument_risk_data_v1_0.py

@@ -0,0 +1,74 @@
+ # affirm/group_bys/instrument_risk/instrument_risk_data_v1_0.py
+
+from ai.chronon.api.ttypes import Source, EntitySource, MetaData, Aggregation, Operation, Window, TimeUnit
+from ai.chronon.query import Query, select
+from ai.chronon.group_by import GroupBy
+
+# This source uses a StagingQuery to automatically handle S3 YYYY/MM/DD partitioning.
+# No manual view creation needed - Chronon handles everything automatically!
+
+from ai.chronon.utils import get_staging_query_output_table_name
+from staging_queries.affirm.instrument_risk_staging_query import v1 as instrument_risk_staging_query
+
+instrument_risk_snapshot_src = Source(
+    entities=EntitySource(
+        snapshotTable=get_staging_query_output_table_name(instrument_risk_staging_query),   # ← Auto-generated table
+        query=Query(
+            selects=select(
+                # entity key
+                "user_ari",
+                # timestamps
+                "snapshot_time",
+                "processed_time",
+                # features
+                "model_name",
+                "model_version",
+                "galactus__predict_pay__instrument_aris__v1",
+                "galactus__predict_pay__instrument_risk_scores__v1",
+            ),
+            time_column="snapshot_time"  # Required for windowed aggregations
+        ),
+    )
+)
+
+# Add meaningful aggregations for instrument risk data analysis
+instrument_risk_data_v1_0 = GroupBy(
+    sources=[instrument_risk_snapshot_src],
+    keys=["user_ari"],
+    aggregations=[
+        # Model version tracking
+        Aggregation(
+            inputColumn="model_version",
+            operation=Operation.LAST,
+            windows=[Window(7, TimeUnit.DAYS), Window(30, TimeUnit.DAYS)]
+        ),
+
+        # Risk score aggregations (for arrays, we'll use LAST for now)
+        Aggregation(
+            inputColumn="galactus__predict_pay__instrument_risk_scores__v1",
+            operation=Operation.LAST,
+            windows=[Window(7, TimeUnit.DAYS), Window(30, TimeUnit.DAYS), Window(90, TimeUnit.DAYS)]
+        ),
+
+        # Instrument ARIs tracking
+        Aggregation(
+            inputColumn="galactus__predict_pay__instrument_aris__v1",
+            operation=Operation.LAST,
+            windows=[Window(7, TimeUnit.DAYS), Window(30, TimeUnit.DAYS), Window(90, TimeUnit.DAYS)]
+        ),
+
+        # Model name tracking
+        Aggregation(
+            inputColumn="model_name",
+            operation=Operation.LAST,
+            windows=[Window(7, TimeUnit.DAYS), Window(30, TimeUnit.DAYS)]
+        ),
+
+        # Count of predictions over time
+        Aggregation(
+            inputColumn="snapshot_time",
+            operation=Operation.COUNT,
+            windows=[Window(7, TimeUnit.DAYS), Window(30, TimeUnit.DAYS), Window(90, TimeUnit.DAYS)]
+        ),
+    ],
+)

group_bys/affirm/paybright/repayment_data_v1_0.py

@@ -0,0 +1,108 @@
+# affirm/group_bys/paybright/repayment_data_v1_0.py
+
+from ai.chronon.api.ttypes import Source, EntitySource, MetaData, Aggregation, Operation, Window, TimeUnit
+from ai.chronon.query import Query, select
+from ai.chronon.group_by import GroupBy
+
+# This source uses a StagingQuery to automatically handle S3 YYYY/MM/DD partitioning.
+# No manual view creation needed - Chronon handles everything automatically!
+
+from ai.chronon.utils import get_staging_query_output_table_name
+from staging_queries.affirm.paybright_staging_query import v1 as paybright_staging_query
+
+paybright_snapshot_src = Source(
+    entities=EntitySource(
+        snapshotTable=get_staging_query_output_table_name(paybright_staging_query),   # ← Auto-generated table
+        query=Query(
+            selects=select(
+                # entity key
+                "user_phone_number",
+                # timestamps
+                "snapshot_time",
+                # features
+                "galactus__paybright__user__history_average_lateness_days__v1",
+                "galactus__paybright__user__history_average_zeroed_lateness_days__v1",
+                "galactus__paybright__user__history_days_since_last_payment__v1",
+                "galactus__paybright__user__history_max_lateness_days__v1",
+                "galactus__paybright__user__history_num_outstanding_loans__v1",
+                "galactus__paybright__user__history_num_payments_last_60d__v1",
+                "galactus__paybright__user__history_prop_fully_paid_off_loans__il__v1",
+                "galactus__paybright__user__history_prop_fully_paid_off_loans__sp__v1",
+                "galactus__paybright__user__history_total_payment_amount_cents_60d__v1",
+                "galactus__paybright__user__history_total_payment_amount_cents__v1",
+            ),
+            time_column="snapshot_time"  # Required for windowed aggregations
+        ),
+    )
+)
+
+# Add meaningful aggregations for repayment data analysis
+paybright_repayment_data_v1_0 = GroupBy(
+    sources=[paybright_snapshot_src],
+    keys=["user_phone_number"],
+    aggregations=[
+        # Average lateness metrics
+        Aggregation(
+            inputColumn="galactus__paybright__user__history_average_lateness_days__v1",
+            operation=Operation.AVERAGE,
+            windows=[Window(7, TimeUnit.DAYS), Window(30, TimeUnit.DAYS), Window(90, TimeUnit.DAYS)]
+        ),
+        Aggregation(
+            inputColumn="galactus__paybright__user__history_average_zeroed_lateness_days__v1",
+            operation=Operation.AVERAGE,
+            windows=[Window(7, TimeUnit.DAYS), Window(30, TimeUnit.DAYS), Window(90, TimeUnit.DAYS)]
+        ),
+
+        # Max lateness
+        Aggregation(
+            inputColumn="galactus__paybright__user__history_max_lateness_days__v1",
+            operation=Operation.MAX,
+            windows=[Window(7, TimeUnit.DAYS), Window(30, TimeUnit.DAYS), Window(90, TimeUnit.DAYS)]
+        ),
+
+        # Days since last payment
+        Aggregation(
+            inputColumn="galactus__paybright__user__history_days_since_last_payment__v1",
+            operation=Operation.MIN,
+            windows=[Window(7, TimeUnit.DAYS), Window(30, TimeUnit.DAYS)]
+        ),
+
+        # Outstanding loans count
+        Aggregation(
+            inputColumn="galactus__paybright__user__history_num_outstanding_loans__v1",
+            operation=Operation.COUNT,
+            windows=[Window(7, TimeUnit.DAYS), Window(30, TimeUnit.DAYS), Window(90, TimeUnit.DAYS)]
+        ),
+
+        # Payment frequency
+        Aggregation(
+            inputColumn="galactus__paybright__user__history_num_payments_last_60d__v1",
+            operation=Operation.SUM,
+            windows=[Window(7, TimeUnit.DAYS), Window(30, TimeUnit.DAYS)]
+        ),
+
+        # Payment success rates
+        Aggregation(
+            inputColumn="galactus__paybright__user__history_prop_fully_paid_off_loans__il__v1",
+            operation=Operation.AVERAGE,
+            windows=[Window(30, TimeUnit.DAYS), Window(90, TimeUnit.DAYS)]
+        ),
+        Aggregation(
+            inputColumn="galactus__paybright__user__history_prop_fully_paid_off_loans__sp__v1",
+            operation=Operation.AVERAGE,
+            windows=[Window(30, TimeUnit.DAYS), Window(90, TimeUnit.DAYS)]
+        ),
+
+        # Payment amounts
+        Aggregation(
+            inputColumn="galactus__paybright__user__history_total_payment_amount_cents_60d__v1",
+            operation=Operation.SUM,
+            windows=[Window(7, TimeUnit.DAYS), Window(30, TimeUnit.DAYS)]
+        ),
+        Aggregation(
+            inputColumn="galactus__paybright__user__history_total_payment_amount_cents__v1",
+            operation=Operation.SUM,
+            windows=[Window(7, TimeUnit.DAYS), Window(30, TimeUnit.DAYS), Window(90, TimeUnit.DAYS)]
+        ),
+    ],
+)

production/group_bys/affirm/instrument_risk.instrument_risk_data_v1_0.instrument_risk_data_v1_0

@@ -0,0 +1,122 @@
+{
+  "metaData": {
+    "name": "affirm.instrument_risk.instrument_risk_data_v1_0.instrument_risk_data_v1_0",
+    "customJson": "{\"lag\": 0, \"groupby_tags\": null, \"column_tags\": {}}",
+    "dependencies": [
+      "{\"name\": \"wait_for_affirm_instrument_risk_staging_query_v1_ds\", \"spec\": \"affirm_instrument_risk_staging_query_v1/ds={{ ds }}\", \"start\": null, \"end\": null}"
+    ],
+    "tableProperties": {
+      "source": "chronon"
+    },
+    "outputNamespace": "default",
+    "team": "affirm",
+    "offlineSchedule": "@daily"
+  },
+  "sources": [
+    {
+      "entities": {
+        "snapshotTable": "affirm_instrument_risk_staging_query_v1",
+        "query": {
+          "selects": {
+            "user_ari": "user_ari",
+            "snapshot_time": "snapshot_time",
+            "processed_time": "processed_time",
+            "model_name": "model_name",
+            "model_version": "model_version",
+            "galactus__predict_pay__instrument_aris__v1": "galactus__predict_pay__instrument_aris__v1",
+            "galactus__predict_pay__instrument_risk_scores__v1": "galactus__predict_pay__instrument_risk_scores__v1"
+          },
+          "timeColumn": "snapshot_time",
+          "setups": []
+        }
+      }
+    }
+  ],
+  "keyColumns": [
+    "user_ari"
+  ],
+  "aggregations": [
+    {
+      "inputColumn": "model_version",
+      "operation": 3,
+      "windows": [
+        {
+          "length": 7,
+          "timeUnit": 1
+        },
+        {
+          "length": 30,
+          "timeUnit": 1
+        }
+      ]
+    },
+    {
+      "inputColumn": "galactus__predict_pay__instrument_risk_scores__v1",
+      "operation": 3,
+      "windows": [
+        {
+          "length": 7,
+          "timeUnit": 1
+        },
+        {
+          "length": 30,
+          "timeUnit": 1
+        },
+        {
+          "length": 90,
+          "timeUnit": 1
+        }
+      ]
+    },
+    {
+      "inputColumn": "galactus__predict_pay__instrument_aris__v1",
+      "operation": 3,
+      "windows": [
+        {
+          "length": 7,
+          "timeUnit": 1
+        },
+        {
+          "length": 30,
+          "timeUnit": 1
+        },
+        {
+          "length": 90,
+          "timeUnit": 1
+        }
+      ]
+    },
+    {
+      "inputColumn": "model_name",
+      "operation": 3,
+      "windows": [
+        {
+          "length": 7,
+          "timeUnit": 1
+        },
+        {
+          "length": 30,
+          "timeUnit": 1
+        }
+      ]
+    },
+    {
+      "inputColumn": "snapshot_time",
+      "operation": 6,
+      "windows": [
+        {
+          "length": 7,
+          "timeUnit": 1
+        },
+        {
+          "length": 30,
+          "timeUnit": 1
+        },
+        {
+          "length": 90,
+          "timeUnit": 1
+        }
+      ]
+    }
+  ]
+}