outhad_edge 0.1.1
Outhad_Edge is a powerful Python library for user retention analysis. It provides a simple and intuitive interface for tracking user behavior, analyzing data, and gaining valuable insights into your users.
License: MIT
Requires Python: <3.13,>=3.8
Keywords: ANALYTICS, CLICKSTREAM, RETENTION, GRAPHS, TRAJECTORIES, CJM, CUSTOMER-SEGMENTATION

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  <img src="outhad_logo.png" alt="Outhad_Edge" width="250"/>

# Outhad_Edge

**Next-Generation Behavioral Analytics for User Journey Intelligence**

[Get Started](#installation--setup) · [Documentation](#technical-architecture) · [Examples](#live-examples) · [Use Cases](#industry-applications)

---



### Transform Raw Events Into Actionable Behavioral Insights

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---

## 🎯 Powerful Features That Drive Insights

### 🔧 Event Data Management
- **Automated Schema Verification**: Built-in validation ensures your user_id, event, and timestamp columns are properly formatted
- **Visual Workflow Designer**: Drag-and-drop interface for building complex data transformation pipelines
- **Smart Session Detection**: Automatically segments user activity into meaningful interaction sessions
- **Flexible Event Filtering**: Powerful filtering and aggregation tools for precise data manipulation

### ⚙️ Transformation Workflow Engine
- **DAG-Based Processing**: Build sophisticated preprocessing chains using directed acyclic graph architecture
- **Comprehensive Processor Library**: 14+ pre-built operators including session segmentation, event categorization, user lifecycle tracking, and journey truncation
- **Pipeline Persistence**: Export and import workflow configurations for consistency across team projects
- **Collaborative Analytics**: Share standardized preprocessing templates across multiple analysts

### 📊 Behavioral Intelligence Suite
- **Flow Network Analysis**: Dynamic visualizations revealing user navigation patterns and transition probabilities
- **Sequential Behavior Tracking**: Step-by-step progression analysis showing conversion at each journey stage
- **Retention Cohort Engine**: Time-series tracking of user engagement and return behavior
- **ML-Driven Segmentation**: Unsupervised clustering algorithms for automatic user group discovery
- **Conversion Path Optimization**: Traditional and multi-step funnel analysis with drop-off diagnostics
- **Experiment Validation Tools**: Statistical hypothesis testing for A/B experiments and significance analysis
- **Multi-Path Flow Diagrams**: Sankey visualizations comparing parallel user journey streams

### 🎨 Visualization & Platform Integration
- **Native Jupyter Support**: First-class integration with Jupyter Notebook and JupyterLab environments
- **Rich Interactive Components**: Dynamic widgets enabling real-time data exploration
- **Multi-Format Export**: Generate outputs in various formats suitable for presentations and reporting
- **Plotly-Powered Charts**: Industry-standard interactive visualizations with professional aesthetics

---

## The Problem We Solve

Traditional analytics tells you **what users do**. Outhad_Edge reveals **why they do it**.

| Traditional Analytics | Outhad_Edge |
|----------------------|-------------|
| Conversion rate: 3.2% | Identifies 5 user segments with conversion rates from 1.1% to 12.4% |
| Users dropped at checkout | Maps 47 unique paths to purchase, surfaces friction points |
| 30-day retention: 18% | Cohort analysis reveals retention peaks at 7 days, suggests onboarding optimization |
| Funnel: 100 → 45 → 12 → 3 | Transition graphs show alternative high-value paths outside your funnel |

---

## Installation & Setup

**Standard Installation**
```bash
pip install outhad_edge
```

**With AI Capabilities** (Natural Language Queries)
```bash
pip install outhad_edge[ai]

# Configure API access
export OPENAI_API_KEY="sk-..."
# OR
export ANTHROPIC_API_KEY="sk-ant-..."
```

**Development Environment**
```bash
git clone https://github.com/Outhad-Lab/outhad_edge.git
cd outhad_edge-tools
poetry install --with dev,docs,ai
```

---

## Live Examples

### Example 1: Talk to Your Data (AI-Powered)

No code. No SQL. Just ask.

```python
from outhad_edge import Eventstream
import pandas as pd

# Your event data
events = pd.read_csv('user_events.csv')
stream = Eventstream(events)

# Initialize AI interface
nlq = stream.nlq(model="gpt-4")

# Natural language queries
nlq.ask("What's driving our conversion rate drop in the mobile segment?")
# → Answer: "Mobile users experience 3.2x higher cart abandonment.
#    Top friction: payment method selection (avg 47s vs 12s desktop)"

nlq.ask("Compare retention across user acquisition channels")
# → Auto-generates cohort analysis + visualization

nlq.ask("Find behavioral patterns that predict churn")
# → Runs clustering + statistical analysis, returns actionable segments
```

**How It Works:** RAG-powered code generation → Sandboxed execution → Self-correction → Semantic caching

---

### Example 2: Session-Based Journey Analysis

```python
import outhad_edge as oe

# Load clickstream data
df = pd.read_csv('web_analytics.csv')  # user_id, event, timestamp
stream = oe.Eventstream(df)

# Define user sessions (30-minute timeout)
stream = stream.split_sessions(timeout=(30, 'm'))

# Filter to core conversion events
stream = stream.filter_events([
    'homepage', 'search', 'product_view',
    'add_to_cart', 'checkout', 'purchase'
])

# Visualize user flow
stream.transition_graph()  # Interactive network diagram
```

---

### Example 3: Advanced Preprocessing Pipeline

```python
# Build reproducible preprocessing workflow
pipeline = oe.PreprocessingGraph(stream)

# Step 1: Split into sessions
pipeline.add_node(
    processor=oe.data_processors_lib.SplitSessions,
    timeout=(20, 'm'),
    session_col='session_id'
)

# Step 2: Label new vs returning users
pipeline.add_node(
    processor=oe.data_processors_lib.LabelNewUsers,
    new_users_list=['first_visit', 'signup']
)

# Step 3: Group granular events
pipeline.add_node(
    processor=oe.data_processors_lib.GroupEvents,
    event_groups={
        'engagement': ['like', 'share', 'comment'],
        'commerce': ['add_to_cart', 'purchase', 'wishlist']
    }
)

# Execute pipeline
processed = pipeline.combine()

# Share with team (save graph configuration)
pipeline.export('preprocessing_config.json')
```

---

### Example 4: ML-Powered Behavioral Segmentation

```python
# Extract behavioral features
clusters = stream.clusters()

# TF-IDF feature extraction from event sequences
features = clusters.extract_features(
    method='tfidf',
    ngram_range=(1, 3)  # Single events + 2-3 event sequences
)

# K-means clustering
clusters.fit(method='kmeans', n_clusters=5, X=features)

# Analyze segments
segments = clusters.cluster_mapping
print(segments.groupby('cluster_id').agg({
    'user_id': 'count',
    'conversion': 'mean',
    'ltv': 'mean'
}))

# Visualize
clusters.plot()  # Interactive cluster visualization
```

---

## What Makes Us Different

<table>
<tr>
<td width="50%">

**Traditional Product Analytics**
- Pre-built dashboards
- Fixed metrics
- Funnel-centric view
- Report what happened
- Requires analysts for insights
- Static visualizations

</td>
<td width="50%">

**Outhad_Edge**
- AI-driven exploration
- Custom behavioral analysis
- Journey-centric view
- Explain why it happened
- Natural language interface
- Interactive, programmable viz

</td>
</tr>
</table>

---

## Core Capabilities

### 1. AI Query Engine (NEW)

**Technology Stack:** LangChain · ChromaDB · OpenAI/Anthropic · Redis

| Feature | Description | Benefit |
|---------|-------------|---------|
| **Natural Language Interface** | Ask questions in plain English | Non-technical users get insights instantly |
| **RAG Architecture** | Vector embeddings + semantic search | 95%+ query accuracy with domain context |
| **Self-Correction** | Automatic error fixing (3 retry limit) | Handles edge cases without manual debugging |
| **Semantic Caching** | Redis-backed similarity matching | 90%+ cache hit rate = 10x faster responses |
| **Code Transparency** | Shows generated Python code | Trust + learning for technical users |

**Architecture:**
```
User Query → Semantic Retrieval (ChromaDB) → LLM Code Gen (GPT-4/Claude)
           → Safety Validation → Sandboxed Execution → Result + Visualization
```

---

### 2. Behavioral Analysis Toolkit

| Tool | Purpose | Output |
|------|---------|--------|
| **Transition Graph** | User flow network analysis | Interactive D3.js graph with event transitions |
| **Step Matrix** | Sequential step-by-step analysis | Conversion rates between each event pair |
| **Cohort Analysis** | Time-based retention tracking | Heatmap showing retention by cohort |
| **Funnel Analysis** | Traditional conversion funnels | Stage-by-stage drop-off with statistics |
| **Clustering** | Behavioral segmentation (K-means, DBSCAN) | User segments with defining characteristics |
| **Statistical Tests** | A/B testing, Chi-square, T-tests | Significance testing for experiments |
| **Sankey Diagrams** | Multi-path flow visualization | Parallel path comparison |

---

### 3. Data Preprocessing Engine

**14 Built-in Processors:**

```python
# Session management
SplitSessions          # Time-based session splitting
CollapseLoops          # Remove repetitive event cycles

# User lifecycle
LabelNewUsers          # Identify user acquisition events
LabelLostUsers         # Churn event detection
LabelCroppedPaths      # Incomplete journey handling

# Event manipulation
FilterEvents           # Include/exclude specific events
GroupEvents            # Categorize events into groups
AddStartEndEvents      # Synthetic boundary events
TruncatePaths          # Limit path length

# Advanced
AddPositiveEvents      # Inject success indicators
AddNegativeEvents      # Inject failure indicators
DropPaths              # Remove specific user journeys
```

**Visual Pipeline Builder:** Drag-and-drop GUI in Jupyter for non-coders

---

## Technical Architecture

```
outhad_edge/
│
├─ ai/                          # AI Query System
│  ├─ nlq_engine.py             # Main NLQ orchestrator
│  ├─ semantic_layer.py         # Business glossary + schema metadata
│  ├─ vector_store.py           # ChromaDB embeddings manager
│  ├─ llm_agent.py              # LangChain LLM integration
│  ├─ code_executor.py          # Sandboxed Python execution
│  ├─ code_validator.py         # Security validation layer
│  └─ cache_manager.py          # Redis semantic cache
│
├─ eventstream/                 # Core Data Structure
│  ├─ eventstream.py            # Main Eventstream class
│  ├─ schema.py                 # RawDataSchema validation
│  └─ helpers.py                # Utility functions
│
├─ preprocessing_graph/         # Pipeline Engine
│  ├─ preprocessing_graph.py    # DAG-based workflow
│  └─ graph_widgets.py          # Jupyter GUI components
│
├─ data_processors_lib/         # Transformation Operators
│  ├─ split_sessions.py
│  ├─ filter_events.py
│  ├─ [12 more processors...]
│  └─ base.py                   # Abstract processor class
│
├─ tooling/                     # Analysis Tools
│  ├─ transition_graph/         # Network flow viz
│  ├─ cohorts/                  # Retention analysis
│  ├─ funnel/                   # Conversion funnels
│  ├─ clusters/                 # ML segmentation
│  ├─ step_matrix/              # Sequential analysis
│  └─ stattests/                # Statistical testing
│
├─ backend/                     # Infrastructure
│  ├─ tracker.py                # Usage analytics
│  └─ server.py                 # Jupyter widget server
│
└─ datasets/                    # Sample Data
   └─ data/
      └─ simple-onlineshop.csv  # Demo e-commerce data
```

---

## Data Requirements

**Input Schema:**

| Column | Type | Required | Description |
|--------|------|----------|-------------|
| `user_id` | string/int | Yes | Unique user identifier |
| `event` | string | Yes | Event name (e.g., "page_view", "purchase") |
| `timestamp` | datetime | Yes | Event timestamp (any pandas-compatible format) |
| `*` | any | No | Additional custom columns |

**Example:**

```python
import pandas as pd

data = pd.DataFrame({
    'user_id': ['U001', 'U001', 'U002', 'U002', 'U001'],
    'event': ['login', 'view_product', 'signup', 'view_product', 'purchase'],
    'timestamp': ['2024-01-15 09:00:00', '2024-01-15 09:05:00',
                  '2024-01-15 09:02:00', '2024-01-15 09:08:00',
                  '2024-01-15 09:15:00'],
    'device': ['mobile', 'mobile', 'desktop', 'desktop', 'mobile'],  # optional
    'revenue': [0, 0, 0, 0, 49.99]  # optional
})

stream = oe.Eventstream(data)
```

**Supported Data Sources:**
- Google Analytics BigQuery exports
- Segment, Amplitude, Mixpanel exports
- Custom event tracking (Snowplow, RudderStack)
- Database event logs (PostgreSQL, MongoDB)
- Web server logs (Apache, Nginx)

---

## Industry Applications

### SaaS & B2B Software

**Challenge:** 60% of trial users never activate a key feature

```python
nlq.ask("Which feature combinations predict trial-to-paid conversion?")
# → "Users who complete profile setup + invite team member convert at 8.3x rate.
#    Only 12% of trials do both. Suggest onboarding flow A/B test."
```

**Use Cases:**
- Product-led growth optimization
- Feature adoption tracking
- Onboarding funnel analysis
- Expansion revenue triggers

---

### E-Commerce & Retail

**Challenge:** Cart abandonment without knowing which step fails

```python
# Analyze checkout micro-steps
checkout_stream = stream.filter_events(lambda df:
    df['event'].str.contains('checkout_')
)
checkout_stream.transition_graph(threshold=0.05)
# → Reveals 23% drop at "payment_method_selection"
```

**Use Cases:**
- Cart abandonment analysis
- Product recommendation optimization
- Cross-sell/upsell pattern detection
- Customer journey mapping

---

### Media & Content Platforms

**Challenge:** Understand binge behavior vs churn patterns

```python
# Segment by engagement patterns
clusters = stream.clusters()
features = clusters.extract_features(method='tfidf', ngram_range=(1,4))
clusters.fit(method='kmeans', n_clusters=6, X=features)

# Label clusters
for cluster_id in range(6):
    cluster_users = clusters.cluster_mapping[
        clusters.cluster_mapping['cluster_id'] == cluster_id
    ]
    print(f"Cluster {cluster_id}: {len(cluster_users)} users")
    nlq.ask(f"Describe behavior patterns of cluster {cluster_id}")
```

**Use Cases:**
- Content consumption patterns
- Churn prediction
- Personalization strategies
- Engagement scoring

---

### Financial Services

**Challenge:** Identify fraud patterns in transaction sequences

```python
# Anomaly detection using sequence analysis
suspicious = stream.filter_events(lambda df:
    df.groupby('user_id')['event'].transform('count') > 50  # High velocity
)

nlq.ask("Find unusual transaction sequences in the last 7 days")
# → Flags accounts with rare event combinations
```

**Use Cases:**
- Fraud detection
- Customer lifecycle analysis
- Cross-product adoption
- Compliance monitoring

---

## Performance & Scale

| Metric | Specification |
|--------|---------------|
| **Event Processing** | 10M+ events in <30s (single machine) |
| **Memory Efficiency** | Lazy loading, chunked processing |
| **Parallelization** | Multi-core support via pandas/numpy |
| **AI Query Latency** | <5s average (with caching: <500ms) |
| **Supported Python** | 3.8, 3.9, 3.10, 3.11 |
| **Dependencies** | pandas, networkx, scikit-learn, plotly |

---

## Development Workflow

**Testing:**
```bash
pytest tests/                    # Full test suite
pytest tests/eventstream/        # Component tests
tox -e py38,py39,py310,py311    # Multi-version testing
```

**Code Quality:**
```bash
black outhad_edge/ tests/ --line-length=120
mypy outhad_edge/
pre-commit run --all-files
```

**Build Documentation:**
```bash
cd docs/
make html  # Generates HTML docs
```

---

## Why Teams Choose Outhad_Edge

**For Data Scientists:**
- Built on pandas/numpy/scikit-learn (familiar stack)
- Fully programmable, not a black box
- Export to any format (CSV, Parquet, SQL)
- Jupyter-native with interactive widgets

**For Product Managers:**
- Natural language queries (no SQL/Python required)
- Visual pipeline builder (drag-and-drop)
- Share insights as interactive reports
- Faster iteration vs BI tools

**For Analysts:**
- Pre-built behavioral analytics methods
- Reproducible workflows (save/load pipelines)
- Statistical rigor built-in
- Production-ready code

**For Engineering:**
- Comprehensive test coverage (>85%)
- Type hints throughout
- Well-documented codebase
- Apache 2.0 license

---

## Comparison Matrix

| Feature | Outhad_Edge | Amplitude | Mixpanel | Google Analytics |
|---------|-------------|-----------|----------|------------------|
| **AI Natural Language Queries** | ✅ Built-in | ❌ No | ❌ No | ❌ No |
| **Custom Behavioral Analysis** | ✅ Unlimited | ⚠️ Limited | ⚠️ Limited | ❌ No |
| **Open Source** | ✅ Yes | ❌ No | ❌ No | ❌ No |
| **Self-Hosted** | ✅ Yes | ❌ Cloud only | ❌ Cloud only | ❌ Cloud only |
| **Python Integration** | ✅ Native | ⚠️ API only | ⚠️ API only | ⚠️ API only |
| **ML Segmentation** | ✅ scikit-learn | ⚠️ Basic | ⚠️ Basic | ❌ No |
| **Visual Pipeline Builder** | ✅ Jupyter GUI | ❌ No | ❌ No | ❌ No |
| **Cost (1M events/mo)** | Free | ~$2,000 | ~$1,500 | Free (limited) |

---

## Sample Datasets

**Quick Start with Built-in Data:**

```python
from outhad_edge.datasets import load_simple_shop

# Load e-commerce sample data
df = load_simple_shop(as_dataframe=True)
print(df.shape)  # (6,332 events, 10,283 users)

stream = oe.Eventstream(df)
stream.describe()  # Summary statistics

# Try AI queries
nlq = stream.nlq()
nlq.ask("What's the most common path to purchase?")
```

**Public Datasets Compatible:**
- Kaggle: E-Commerce Clickstream 2024 (285M events)
- UCI: Online Retail Dataset
- Coveo: Shoppers Intent Prediction
- TheLook: E-commerce Analytics (BigQuery)

---

## Roadmap

**Current Version:** v3.3.0

**In Development:**
- ✅ AI-powered natural language queries (Completed)
- 🔄 Real-time streaming integration (Bytewax)
- 🔄 Session replay + heatmaps
- 📋 Cross-device identity resolution
- 📋 Predictive analytics (churn, LTV)
- 📋 A/B test orchestration

**See:** [FEATURE_ROADMAP_2025.md](FEATURE_ROADMAP_2025.md) for details

---

## Contributing

We welcome contributions! See development setup above.

**Priority Areas:**
- New data processors
- Additional analysis tools
- Performance optimizations
- Documentation improvements

**Community:**
- GitHub Issues: Bug reports & feature requests
- Discussions: Q&A and ideas
- Pull Requests: Code contributions

---

## License

Apache 2.0 - Free for commercial and private use

---

<div align="center">

**Built for teams who move fast and break things (but want to know exactly what broke)**

[Get Started Now](#installation--setup) · [Read the Docs](#technical-architecture) · [See Examples](#live-examples)

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