[FSTORE-1424] Feature logging (logicalclocks#396)

* feature logging

* transformed feature

* fix

* add to index

* update transformed features

* address comments

* update

* update code

Author: kennethmhc

docs/user_guides/fs/feature_view/feature_logging.md

@@ -0,0 +1,209 @@
+# User Guide: Feature and Prediction Logging with a Feature View
+
+Feature logging is essential for debugging, monitoring, and auditing the data your models use. This guide explains how to log features and predictions, and retrieve and manage these logs with feature view in Hopsworks.
+
+## Logging Features and Predictions
+
+After you have trained a model, you can log the features it uses and the predictions with the feature view used to create the training data for the model. You can log either transformed or/and untransformed features values.
+
+### Enabling Feature Logging
+
+To enable logging, set `logging_enabled=True` when creating the feature view. Two feature groups will be created for storing transformed and untransformed features, but they are not visible in the UI. The logged features will be written to the offline feature store every hour by scheduled materialization jobs which are created automatically.
+
+```python
+feature_view = fs.create_feature_view("name", query, logging_enabled=True)
+```
+
+Alternatively, you can enable logging on an existing feature view by calling `feature_view.enable_logging()`. Also, calling `feature_view.log()` will implicitly enable logging if it has not already been enabled.
+
+### Logging Features and Predictions
+
+You can log features and predictions by calling `feature_view.log`. The logged features are written periodically to the offline store. If you need it to be available immediately, call `feature_view.materialize_log`.
+
+You can log either transformed or/and untransformed features. To get untransformed features, you can specify `transform=False` in `feature_view.get_batch_data` or `feature_view.get_feature_vector(s)`. Inference helper columns are returned along with the untransformed features. If you have On-Demand features as well, call `feature_view.compute_on_demand_features` to get the on demand features before calling `feature_view.log`.To get the transformed features, you can call `feature_view.transform` and pass the untransformed feature with the on-demand feature.
+
+Predictions can be optionally provided as one or more columns in the DataFrame containing the features or separately in the `predictions` argument. There must be the same number of prediction columns as there are labels in the feature view. It is required to provide predictions in the `predictions` argument if you provide the features as `list` instead of pandas `dataframe`. The training dataset version will also be logged if you have called either `feature_view.init_serving(...)` or `feature_view.init_batch_scoring(...)` or if the provided model has a training dataset version.
+
+The wallclock time of calling `feature_view.log` is automatically logged, enabling filtering by logging time when retrieving logs.
+
+#### Example 1: Log Features Only
+
+You have a DataFrame of features you want to log.
+
+```python
+import pandas as pd
+
+features = pd.DataFrame({
+    "feature1": [1.1, 2.2, 3.3],
+    "feature2": [4.4, 5.5, 6.6]
+})
+
+# Log features
+feature_view.log(features)
+```
+
+#### Example 2: Log Features, Predictions, and Model
+
+You can also log predictions, and optionally the training dataset and the model used for prediction.
+
+```python
+predictions = pd.DataFrame({
+    "prediction": [0, 1, 0]
+})
+
+# Log features and predictions
+feature_view.log(features, 
+                 predictions=predictions, 
+                 training_dataset_version=1, 
+                 model=Model(1, "model", version=1)
+)
+```
+
+#### Example 3: Log Both Transformed and Untransformed Features
+
+**Batch Features**
+```python
+untransformed_df = fv.get_batch_data(transformed=False)
+# then apply the transformations after:
+transformed_df = fv.transform(untransformed_df)
+# Log untransformed features
+feature_view.log(untransformed_df)
+# Log transformed features
+feature_view.log(transformed_df, transformed=True)
+```
+
+**Real-time Features**
+```python
+untransformed_vector = fv.get_feature_vector({"id": 1}, transform=False)
+# then apply the transformations after:
+transformed_vector = fv.transform(untransformed_vector)
+# Log untransformed features
+feature_view.log(untransformed_vector)
+# Log transformed features
+feature_view.log(transformed_vector, transformed=True)
+```
+
+## Retrieving the Log Timeline
+
+To audit and review the feature/prediction logs, you might want to retrieve the timeline of log entries. This helps understand when data was logged and monitor the logs.
+
+### Retrieve Log Timeline
+
+A log timeline is the hudi commit timeline of the logging feature group.
+
+```python
+# Retrieve the latest 10 log entries
+log_timeline = feature_view.get_log_timeline(limit=10)
+print(log_timeline)
+```
+
+## Reading Log Entries
+
+You may need to read specific log entries for analysis, such as entries within a particular time range or for a specific model version and training dataset version.
+
+### Read all Log Entries
+
+Read all log entries for comprehensive analysis. The output will return all values of the same primary keys instead of just the latest value.
+
+```python
+# Read all log entries
+log_entries = feature_view.read_log()
+print(log_entries)
+```
+
+### Read Log Entries within a Time Range
+
+Focus on logs within a specific time range. You can specify `start_time` and `end_time` for filtering, but the time columns will not be returned in the DataFrame. You can provide the `start/end_time` as `datetime`, `date`, `int`, or `str` type. Accepted date format are: `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`, or `%Y-%m-%d %H:%M:%S.%f`
+
+```python
+# Read log entries from January 2022
+log_entries = feature_view.read_log(start_time="2022-01-01", end_time="2022-01-31")
+print(log_entries)
+```
+
+### Read Log Entries by Training Dataset Version
+
+Analyze logs from a particular version of the training dataset. The training dataset version column will be returned in the DataFrame.
+
+```python
+# Read log entries of training dataset version 1
+log_entries = feature_view.read_log(training_dataset_version=1)
+print(log_entries)
+```
+
+### Read Log Entries by Model in Hopsworks
+
+Analyze logs from a particular name and version of the HSML model. The HSML model column will be returned in the DataFrame.
+
+```python
+# Read log entries of a specific HSML model
+log_entries = feature_view.read_log(model=Model(1, "model", version=1))
+print(log_entries)
+```
+
+### Read Log Entries using a Custom Filter
+
+Provide filters which work similarly to the filter method in the `Query` class. The filter should be part of the query in the feature view.
+
+```python
+# Read log entries where feature1 is greater than 0
+log_entries = feature_view.read_log(filter=fg.feature1 > 0)
+print(log_entries)
+```
+
+## Pausing and Resuming Logging
+
+During maintenance or updates, you might need to pause logging to save computation resources.
+
+### Pause Logging
+
+Pause the schedule of the materialization job for writing logs to the offline store.
+
+```python
+# Pause logging
+feature_view.pause_logging()
+```
+
+### Resume Logging
+
+Resume the schedule of the materialization job for writing logs to the offline store.
+
+```python
+# Resume logging
+feature_view.resume_logging()
+```
+
+## Materializing Logs
+
+Besides the scheduled materialization job, you can materialize logs from Kafka to the offline store on demand. This does not pause the scheduled job. By default, it materializes both transformed and untransformed logs, optionally specifying whether to materialize transformed (transformed=True) or untransformed (transformed=False) logs.
+
+### Materialize Logs
+
+Materialize logs and optionally wait for the process to complete.
+
+```python
+# Materialize logs and wait for completion
+materialization_result = feature_view.materialize_log(wait=True)
+# Materialize only transformed log entries
+feature_view.materialize_log(wait=True, transformed=True)
+```
+
+## Deleting Logs
+
+When log data is no longer needed, you might want to delete it to free up space and maintain data hygiene. This operation deletes the feature groups and recreates new ones. Scheduled materialization job and log timeline are reset as well.
+
+### Delete Logs
+
+Remove all log entries (both transformed and untransformed logs), optionally specifying whether to delete transformed (transformed=True) or untransformed (transformed=False) logs. 
+
+```python
+# Delete all log entries
+feature_view.delete_log()
+
+# Delete only transformed log entries
+feature_view.delete_log(transformed=True)
+```
+
+## Summary
+
+Feature logging is a crucial part of maintaining and monitoring your machine learning workflows. By following these examples, you can effectively log, retrieve, and delete logs, as well as manage the lifecycle of log materialization jobs, adding observability for your AI system and making it auditable.

mkdocs.yml

@@ -99,6 +99,7 @@ nav:
               - Feature Monitoring:
                   - Getting started: user_guides/fs/feature_view/feature_monitoring.md
                   - Advanced guide: user_guides/fs/feature_monitoring/feature_monitoring_advanced.md
+              - Feature Logging: user_guides/fs/feature_view/feature_logging.md
           - Vector Similarity Search: user_guides/fs/vector_similarity_search.md
           - Compute Engines: user_guides/fs/compute_engines.md
           - Client Integrations: