🚀 Featrix Sphere API Client

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| ___| ___| _ |_ _| __ \_ _| | |
| ___| ___| | | | | <_| |_|- -|
|___| |_______|___|___| |___| |___|__|_______|___|___|

✨ What Makes This Special?

🎯 Real Results

# Actual results from fuel card fraud detection:
prediction = {
 'True': 0.9999743700027466, # 99.997% confidence - IS fraud
 'False': 0.000024269439, # 0.002% - not fraud 
 '<UNKNOWN>': 0.000001335 # 0.0001% - uncertain
}
# Perfect classification with extreme confidence!

🚀 Quick Start

1. Install & Import

from test_api_client import FeatrixSphereClient
# Initialize client
client = FeatrixSphereClient("http://your-sphere-server.com")

2. Upload Data & Train Model

# Upload your CSV and automatically start training
session = client.upload_file_and_create_session("your_data.csv")
session_id = session.session_id
# Wait for the magic to happen (embedding space + vector DB + projections)
final_session = client.wait_for_session_completion(session_id)
# Add a predictor for your target column
client.train_single_predictor(
 session_id=session_id,
 target_column="is_fraud",
 target_column_type="set", # "set" for classification, "scalar" for regression
 epochs=50
)
# Wait for predictor training
client.wait_for_session_completion(session_id)

3. Make Predictions

# Single prediction
result = client.make_prediction(session_id, {
 "transaction_amount": 1500.00,
 "merchant_category": "gas_station", 
 "location": "highway_exit"
})
print(result['prediction'])
# {'fraud': 0.95, 'legitimate': 0.05} # 95% fraud probability!
# Batch predictions on 1000s of records
csv_results = client.test_csv_predictions(
 session_id=session_id,
 csv_file="test_data.csv",
 target_column="is_fraud",
 sample_size=1000
)
print(f"Accuracy: {csv_results['accuracy_metrics']['accuracy']*100:.2f}%")
# Accuracy: 99.87% 🎯

🎨 Beautiful Examples

🏦 Fraud Detection

# Train on transaction data
client.train_single_predictor(
 session_id=session_id,
 target_column="is_fraudulent",
 target_column_type="set"
)
# Detect fraud in real-time
fraud_check = client.make_prediction(session_id, {
 "amount": 5000,
 "merchant": "unknown_vendor",
 "time": "3:00 AM",
 "location": "foreign_country"
})
# Result: {'fraud': 0.98, 'legitimate': 0.02} ⚠️

🎯 Customer Segmentation

# Predict customer lifetime value
client.train_single_predictor(
 session_id=session_id,
 target_column="customer_value_segment", 
 target_column_type="set" # high/medium/low
)
# Classify new customers
segment = client.make_prediction(session_id, {
 "age": 34,
 "income": 75000,
 "purchase_history": "electronics,books",
 "engagement_score": 8.5
})
# Result: {'high_value': 0.87, 'medium_value': 0.12, 'low_value': 0.01}

🏠 Real Estate Pricing

# Predict house prices (regression)
client.train_single_predictor(
 session_id=session_id,
 target_column="sale_price",
 target_column_type="scalar" # continuous values
)
# Get price estimates
price = client.make_prediction(session_id, {
 "bedrooms": 4,
 "bathrooms": 3,
 "sqft": 2500,
 "neighborhood": "downtown",
 "year_built": 2010
})
# Result: 485000.0 (predicted price: 485,000ドル)

🧪 Comprehensive Testing

Full Model Validation

# Run complete test suite
results = client.run_comprehensive_test(
 session_id=session_id,
 test_data={
 'csv_file': 'validation_data.csv',
 'target_column': 'target',
 'sample_size': 500
 }
)
# Results include:
# ✅ Individual prediction tests
# ✅ Batch accuracy metrics 
# ✅ Training performance data
# ✅ Model confidence analysis

CSV Batch Testing

# Test your model on any CSV file
results = client.test_csv_predictions(
 session_id=session_id,
 csv_file="holdout_test.csv", 
 target_column="actual_outcome",
 sample_size=1000
)
print(f"""
🎯 Model Performance:
 Accuracy: {results['accuracy_metrics']['accuracy']*100:.2f}%
 Avg Confidence: {results['accuracy_metrics']['average_confidence']*100:.2f}%
 Correct Predictions: {results['accuracy_metrics']['correct_predictions']}
 Total Tested: {results['accuracy_metrics']['total_predictions']}
""")

🔍 Advanced Features

Similarity Search

# Find similar records using neural embeddings
similar = client.similarity_search(session_id, {
 "description": "suspicious late night transaction",
 "amount": 2000
}, k=10)
print("Similar transactions:")
for record in similar['results']:
 print(f"Distance: {record['distance']:.3f} - {record['record']}")

Vector Embeddings

# Get neural embeddings for any record
embedding = client.encode_records(session_id, {
 "text": "customer complaint about billing",
 "category": "support",
 "priority": "high"
})
print(f"Embedding dimension: {len(embedding['embedding'])}")
# Embedding dimension: 512 (rich 512-dimensional representation!)

Training Metrics & Monitoring

# Get detailed training metrics
metrics = client.get_training_metrics(session_id)
training_info = metrics['training_metrics']['training_info']
print(f"Training epochs: {len(training_info)}")
# Each epoch contains:
# - Training loss
# - Validation loss 
# - Accuracy metrics
# - Learning rate
# - Timestamps

Model Inventory

# See what models are available
models = client.get_session_models(session_id)
print(f"""
📦 Available Models:
 Embedding Space: {'✅' if models['summary']['training_complete'] else '❌'}
 Single Predictor: {'✅' if models['summary']['prediction_ready'] else '❌'}
 Similarity Search: {'✅' if models['summary']['similarity_search_ready'] else '❌'}
 Visualizations: {'✅' if models['summary']['visualization_ready'] else '❌'}
""")

📊 API Reference

Core Methods

Monitoring & Analysis

🎯 Pro Tips

🚀 Performance Optimization

# Use batch predictions for better throughput
batch_results = client.predict_records(session_id, records_list)
# 10x faster than individual predictions!
# Adjust training parameters for your data size
client.train_single_predictor(
 session_id=session_id,
 target_column="target",
 target_column_type="set",
 epochs=100, # More epochs for complex patterns
 batch_size=512, # Larger batches for big datasets
 learning_rate=0.001 # Lower LR for stable training
)

🎨 Data Preparation

# Your CSV just needs:
# ✅ Clean column names (no spaces/special chars work best)
# ✅ Target column for prediction
# ✅ Mix of categorical and numerical features
# ✅ At least 100+ rows (more = better accuracy)
# The system handles:
# ✅ Missing values
# ✅ Mixed data types
# ✅ Categorical encoding
# ✅ Feature scaling
# ✅ Train/validation splits

🔍 Debugging & Monitoring

# Check session status anytime
status = client.get_session_status(session_id)
print(f"Status: {status.status}")
for job_id, job in status.jobs.items():
 print(f"Job {job_id}: {job['status']} ({job.get('progress', 0)*100:.1f}%)")
# Monitor training in real-time
import time
while True:
 status = client.get_session_status(session_id)
 if status.status == 'done':
 break
 print(f"Training... {status.status}")
 time.sleep(10)

🏆 Success Stories

🎯 Ready to Get Started?

# It's literally this simple:
client = FeatrixSphereClient("http://your-server.com")
session = client.upload_file_and_create_session("your_data.csv")
client.train_single_predictor(session.session_id, "target_column", "set")
result = client.make_prediction(session.session_id, your_record)
print(f"Prediction: {result['prediction']}")

Questions?