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.

Outhad_Edge

Next-Generation Behavioral Analytics for User Journey Intelligence

Get Started · Documentation · Examples · Use Cases

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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

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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

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Installation & Setup

Standard Installation

pip install outhad_edge

With AI Capabilities (Natural Language Queries)

pip install outhad_edge[ai]

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

Development Environment

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

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Live Examples

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

No code. No SQL. Just ask.

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

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Example 2: Session-Based Journey Analysis

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

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Example 3: Advanced Preprocessing Pipeline

# 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')

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Example 4: ML-Powered Behavioral Segmentation

# 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

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What Makes Us Different

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

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

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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

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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

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3. Data Preprocessing Engine

14 Built-in Processors:

# 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

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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

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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:

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)

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Industry Applications

SaaS & B2B Software

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

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

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E-Commerce & Retail

Challenge: Cart abandonment without knowing which step fails

# 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

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Media & Content Platforms

Challenge: Understand binge behavior vs churn patterns

# 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

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Financial Services

Challenge: Identify fraud patterns in transaction sequences

# 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

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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

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Development Workflow

Testing:

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

Code Quality:

black outhad_edge/ tests/ --line-length=120
mypy outhad_edge/
pre-commit run --all-files

Build Documentation:

cd docs/
make html  # Generates HTML docs

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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

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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)

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Sample Datasets

Quick Start with Built-in Data:

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)

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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 for details

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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

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License

Apache 2.0 - Free for commercial and private use

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