Metadata-Version: 2.4
Name: safefeat
Version: 0.1.2
Summary: Leakage-safe, point-in-time feature engineering for event logs.
Project-URL: Homepage, https://github.com/AlishaAng/safefeat
Project-URL: Documentation, https://alishaang.github.io/safefeat/
Project-URL: Issues, https://github.com/AlishaAng/safefeat/issues
Author: Alisha Angdembe
License: MIT License
        
        Copyright (c) 2026 Alisha Angdembe
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
License-File: LICENSE
Keywords: event-log,feature-engineering,leakage,machine-learning,time-series
Requires-Python: >=3.9
Requires-Dist: numpy
Requires-Dist: pandas
Provides-Extra: dev
Requires-Dist: mkdocs-material>=9.0; extra == 'dev'
Requires-Dist: mkdocs>=1.5; extra == 'dev'
Requires-Dist: mkdocstrings[python]>=0.24; extra == 'dev'
Requires-Dist: pytest>=7; extra == 'dev'
Requires-Dist: ruff>=0.5; extra == 'dev'
Description-Content-Type: text/markdown

# safefeat
[![PyPI version](https://img.shields.io/pypi/v/safefeat.svg)](https://pypi.org/project/safefeat/)
[![Documentation](https://img.shields.io/badge/docs-online-blue.svg)](https://alishaang.github.io/safefeat/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

**Leakage-safe, point-in-time feature engineering for event logs.**

`safefeat` builds ML features from event data using only information available *at prediction time* — no future data, no silent leakage, no surprises in production.

---

## The Problem

When you compute features like "total purchases in the last 30 days" without anchoring to a cutoff time, you accidentally include future events. Your model looks great in training — then falls apart in production.

```python
# ❌ Leaky — uses ALL events, including future ones
features = events.groupby("user_id")["amount"].sum()
df = spine.merge(features, on="user_id")

# ✅ Safe — only uses events before each cutoff_time
X = build_features(spine, tables, spec, event_time_cols={"events": "event_time"})
```

---

## Install

```bash
pip install safefeat
```

---

## How It Works

safefeat works with three components:

| Component | Description |
| --------- | ----------- |
| **Spine** | When to make predictions — one row per `(entity_id, cutoff_time)` |
| **Events** | Historical time-series data tied to each entity |
| **Spec** | Declarative definition of what features to compute |

For each row in the spine, safefeat joins only events where `event_time <= cutoff_time`, then computes your features. Future events are excluded.

---

## Quick Start

### Window aggregations

```python
import pandas as pd
from safefeat import build_features, WindowAgg

spine = pd.DataFrame({
    "entity_id":   ["u1", "u2"],
    "cutoff_time": ["2024-01-10", "2024-01-31"],
})

events = pd.DataFrame({
    "entity_id":  ["u1", "u1", "u2", "u2"],
    "event_time": ["2024-01-05", "2024-01-06", "2024-01-10", "2024-01-30"],
    "amount":     [10.0, 20.0, 5.0, 25.0],
    "event_type": ["click", "purchase", "purchase", "click"],
})

spec = [
    WindowAgg(
        table="events",
        windows=["7D", "30D"],
        metrics={
            "*":          ["count"],        # total events
            "amount":     ["sum", "mean"],  # numeric aggregations
            "event_type": ["nunique"],      # distinct event types
        },
    )
]

X = build_features(
    spine=spine,
    tables={"events": events},
    spec=spec,
    event_time_cols={"events": "event_time"},
    allowed_lag="0s",
)
```

Output columns follow the pattern `{table}__{column}__{agg}__{window}`:

```
events__n_events__7d
events__amount__sum__7d
events__amount__mean__30d
events__event_type__nunique__30d
```

### Recency features

Time since the most recent event before each cutoff — useful for churn, fraud, and behavioural modelling:

```python
from safefeat import RecencyBlock

spec = [RecencyBlock(table="events")]

X = build_features(
    spine=spine,
    tables={"events": events},
    spec=spec,
    event_time_cols={"events": "event_time"},
)
# Adds: events__recency (days since last event before cutoff_time)
```

Filter by event type:

```python
spec = [
    RecencyBlock(
        table="events",
        filter_col="event_type",
        filter_value="purchase",
    )
]
# Adds: events__recency__event_type_purchase
```

### Audit report

Verify exactly which events were included and dropped for each prediction point:

```python
X, audit = build_features(
    spine=spine,
    tables={"events": events},
    spec=spec,
    event_time_cols={"events": "event_time"},
    return_report=True,
)

events_audit = audit.tables.get("events")
print(events_audit.total_joined_pairs)    # total event-cutoff pairs considered
print(events_audit.kept_pairs)            # events before cutoff (used)
print(events_audit.dropped_future_pairs)  # events after cutoff (excluded)
```

---

## Development

```bash
pip install -e ".[dev]"
pytest -q
ruff check .
```

---

## Documentation

Full documentation, concepts, and API reference:
👉 **https://alishaang.github.io/safefeat/**