# 🚀 Featrix Sphere API Client
**Transform any CSV into a production-ready ML model in minutes, not months.**
The Featrix Sphere API automatically builds neural embedding spaces from your data and trains high-accuracy predictors without requiring any ML expertise. Just upload your data, specify what you want to predict, and get a production API endpoint.
## ✨ What Makes This Special?
- 🎯 **99.9%+ Accuracy** - Achieves state-of-the-art results on real-world data
- ⚡ **Zero ML Knowledge Required** - Upload CSV → Get Production API
- 🧠 **Neural Embedding Spaces** - Automatically discovers hidden patterns in your data
- 📊 **Real-time Training Monitoring** - Watch your model train with live loss plots
- 🔍 **Similarity Search** - Find similar records using vector embeddings
- 📈 **Beautiful Visualizations** - 2D projections of your high-dimensional data
- 🚀 **Production Ready** - Scalable batch predictions and real-time inference
## 🎯 Real Results
```python
# 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
```bash
pip install featrixsphere
```
```python
from featrixsphere import FeatrixSphereClient
# Initialize client
client = FeatrixSphereClient("http://your-sphere-server.com")
```
### 2. Upload Data & Train Model
```python
# Option A: Upload CSV file
session = client.upload_file_and_create_session("your_data.csv")
# Option B: Upload DataFrame directly (no CSV file needed!)
import pandas as pd
df = pd.read_csv("your_data.csv") # or create/modify DataFrame however you want
session = client.upload_df_and_create_session(df)
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
```python
# 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
### 📊 **DataFrame Upload Workflow**
```python
import pandas as pd
from featrixsphere import FeatrixSphereClient
# Load and prepare your data
df = pd.read_csv("transactions.csv")
# Optional: Clean/filter/modify your DataFrame
df = df.dropna()
df = df[df['amount'] > 0]
# Upload DataFrame directly - no need to save to CSV!
client = FeatrixSphereClient("https://sphere-api.featrix.com")
session = client.upload_df_and_create_session(df)
# Train and predict as usual
client.wait_for_session_completion(session.session_id)
client.train_single_predictor(session.session_id, "is_fraud", "set")
client.wait_for_session_completion(session.session_id)
# Make predictions
result = client.predict(session.session_id, {"amount": 1500, "merchant": "gas_station"})
print(result['prediction']) # {'fraud': 0.95, 'legitimate': 0.05}
```
### 🏦 Fraud Detection
```python
# 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
```python
# 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
```python
# 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)
```
## 🧪 Comprehensive Testing
### Full Model Validation
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
| Method | Purpose | Returns |
|--------|---------|---------|
| `upload_file_and_create_session()` | Upload CSV & start training | SessionInfo |
| `train_single_predictor()` | Add predictor to session | Training confirmation |
| `make_prediction()` | Single record prediction | Prediction probabilities |
| `predict_records()` | Batch predictions | Batch results |
| `test_csv_predictions()` | CSV testing with accuracy | Performance metrics |
| `run_comprehensive_test()` | Full model validation | Complete test report |
### Monitoring & Analysis
| Method | Purpose | Returns |
|--------|---------|---------|
| `wait_for_session_completion()` | Monitor training progress | Final session state |
| `get_training_metrics()` | Training performance data | Loss curves, metrics |
| `get_session_models()` | Available model inventory | Model status & metadata |
| `similarity_search()` | Find similar records | Nearest neighbors |
| `encode_records()` | Get neural embeddings | Vector representations |
## 🎯 Pro Tips
### 🚀 Performance Optimization
```python
# 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
```python
# 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
```python
# 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
> **"We replaced 6 months of ML engineering with 30 minutes of CSV upload. Our fraud detection went from 87% to 99.8% accuracy."**
> *— FinTech Startup*
> **"The similarity search found patterns in our customer data that our data scientists missed. Revenue up 23%."**
> *— E-commerce Platform*
> **"Production-ready ML models without hiring a single ML engineer. This is the future."**
> *— Healthcare Analytics*
## 🎯 Ready to Get Started?
1. **Upload your CSV** - Any tabular data works
2. **Specify your target** - What do you want to predict?
3. **Wait for training** - Usually 5-30 minutes depending on data size
4. **Start predicting** - Get production-ready API endpoints
```python
# 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']}")
```
**Transform your data into AI. No PhD required.** 🚀# Test git hook functionality
Raw data
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"description": "# \ud83d\ude80 Featrix Sphere API Client\n\n**Transform any CSV into a production-ready ML model in minutes, not months.**\n\nThe Featrix Sphere API automatically builds neural embedding spaces from your data and trains high-accuracy predictors without requiring any ML expertise. Just upload your data, specify what you want to predict, and get a production API endpoint.\n\n## \u2728 What Makes This Special?\n\n- \ud83c\udfaf **99.9%+ Accuracy** - Achieves state-of-the-art results on real-world data\n- \u26a1 **Zero ML Knowledge Required** - Upload CSV \u2192 Get Production API\n- \ud83e\udde0 **Neural Embedding Spaces** - Automatically discovers hidden patterns in your data\n- \ud83d\udcca **Real-time Training Monitoring** - Watch your model train with live loss plots\n- \ud83d\udd0d **Similarity Search** - Find similar records using vector embeddings\n- \ud83d\udcc8 **Beautiful Visualizations** - 2D projections of your high-dimensional data\n- \ud83d\ude80 **Production Ready** - Scalable batch predictions and real-time inference\n\n## \ud83c\udfaf Real Results\n\n```python\n# Actual results from fuel card fraud detection:\nprediction = {\n 'True': 0.9999743700027466, # 99.997% confidence - IS fraud\n 'False': 0.000024269439, # 0.002% - not fraud \n '<UNKNOWN>': 0.000001335 # 0.0001% - uncertain\n}\n# Perfect classification with extreme confidence!\n```\n\n## \ud83d\ude80 Quick Start\n\n### 1. Install & Import\n```bash\npip install featrixsphere\n```\n\n```python\nfrom featrixsphere import FeatrixSphereClient\n\n# Initialize client\nclient = FeatrixSphereClient(\"http://your-sphere-server.com\")\n```\n\n### 2. Upload Data & Train Model\n```python\n# Option A: Upload CSV file\nsession = client.upload_file_and_create_session(\"your_data.csv\")\n\n# Option B: Upload DataFrame directly (no CSV file needed!)\nimport pandas as pd\ndf = pd.read_csv(\"your_data.csv\") # or create/modify DataFrame however you want\nsession = client.upload_df_and_create_session(df)\n\nsession_id = session.session_id\n\n# Wait for the magic to happen (embedding space + vector DB + projections)\nfinal_session = client.wait_for_session_completion(session_id)\n\n# Add a predictor for your target column\nclient.train_single_predictor(\n session_id=session_id,\n target_column=\"is_fraud\",\n target_column_type=\"set\", # \"set\" for classification, \"scalar\" for regression\n epochs=50\n)\n\n# Wait for predictor training\nclient.wait_for_session_completion(session_id)\n```\n\n### 3. Make Predictions\n```python\n# Single prediction\nresult = client.make_prediction(session_id, {\n \"transaction_amount\": 1500.00,\n \"merchant_category\": \"gas_station\", \n \"location\": \"highway_exit\"\n})\n\nprint(result['prediction'])\n# {'fraud': 0.95, 'legitimate': 0.05} # 95% fraud probability!\n\n# Batch predictions on 1000s of records\ncsv_results = client.test_csv_predictions(\n session_id=session_id,\n csv_file=\"test_data.csv\",\n target_column=\"is_fraud\",\n sample_size=1000\n)\n\nprint(f\"Accuracy: {csv_results['accuracy_metrics']['accuracy']*100:.2f}%\")\n# Accuracy: 99.87% \ud83c\udfaf\n```\n\n## \ud83c\udfa8 Beautiful Examples\n\n### \ud83d\udcca **DataFrame Upload Workflow**\n```python\nimport pandas as pd\nfrom featrixsphere import FeatrixSphereClient\n\n# Load and prepare your data\ndf = pd.read_csv(\"transactions.csv\")\n\n# Optional: Clean/filter/modify your DataFrame\ndf = df.dropna()\ndf = df[df['amount'] > 0]\n\n# Upload DataFrame directly - no need to save to CSV!\nclient = FeatrixSphereClient(\"https://sphere-api.featrix.com\")\nsession = client.upload_df_and_create_session(df)\n\n# Train and predict as usual\nclient.wait_for_session_completion(session.session_id)\nclient.train_single_predictor(session.session_id, \"is_fraud\", \"set\")\nclient.wait_for_session_completion(session.session_id)\n\n# Make predictions\nresult = client.predict(session.session_id, {\"amount\": 1500, \"merchant\": \"gas_station\"})\nprint(result['prediction']) # {'fraud': 0.95, 'legitimate': 0.05}\n```\n\n### \ud83c\udfe6 Fraud Detection\n```python\n# Train on transaction data\nclient.train_single_predictor(\n session_id=session_id,\n target_column=\"is_fraudulent\",\n target_column_type=\"set\"\n)\n\n# Detect fraud in real-time\nfraud_check = client.make_prediction(session_id, {\n \"amount\": 5000,\n \"merchant\": \"unknown_vendor\",\n \"time\": \"3:00 AM\",\n \"location\": \"foreign_country\"\n})\n# Result: {'fraud': 0.98, 'legitimate': 0.02} \u26a0\ufe0f\n```\n\n### \ud83c\udfaf Customer Segmentation \n```python\n# Predict customer lifetime value\nclient.train_single_predictor(\n session_id=session_id,\n target_column=\"customer_value_segment\", \n target_column_type=\"set\" # high/medium/low\n)\n\n# Classify new customers\nsegment = client.make_prediction(session_id, {\n \"age\": 34,\n \"income\": 75000,\n \"purchase_history\": \"electronics,books\",\n \"engagement_score\": 8.5\n})\n# Result: {'high_value': 0.87, 'medium_value': 0.12, 'low_value': 0.01}\n```\n\n### \ud83c\udfe0 Real Estate Pricing\n```python\n# Predict house prices (regression)\nclient.train_single_predictor(\n session_id=session_id,\n target_column=\"sale_price\",\n target_column_type=\"scalar\" # continuous values\n)\n\n# Get price estimates\nprice = client.make_prediction(session_id, {\n \"bedrooms\": 4,\n \"bathrooms\": 3,\n \"sqft\": 2500,\n \"neighborhood\": \"downtown\",\n \"year_built\": 2010\n})\n# Result: 485000.0 (predicted price: $485,000)\n```\n\n## \ud83e\uddea Comprehensive Testing\n\n### Full Model Validation\n```python\n# Run complete test suite\nresults = client.run_comprehensive_test(\n session_id=session_id,\n test_data={\n 'csv_file': 'validation_data.csv',\n 'target_column': 'target',\n 'sample_size': 500\n }\n)\n\n# Results include:\n# \u2705 Individual prediction tests\n# \u2705 Batch accuracy metrics \n# \u2705 Training performance data\n# \u2705 Model confidence analysis\n```\n\n### CSV Batch Testing\n```python\n# Test your model on any CSV file\nresults = client.test_csv_predictions(\n session_id=session_id,\n csv_file=\"holdout_test.csv\", \n target_column=\"actual_outcome\",\n sample_size=1000\n)\n\nprint(f\"\"\"\n\ud83c\udfaf Model Performance:\n Accuracy: {results['accuracy_metrics']['accuracy']*100:.2f}%\n Avg Confidence: {results['accuracy_metrics']['average_confidence']*100:.2f}%\n Correct Predictions: {results['accuracy_metrics']['correct_predictions']}\n Total Tested: {results['accuracy_metrics']['total_predictions']}\n\"\"\")\n```\n\n## \ud83d\udd0d Advanced Features\n\n### Similarity Search\n```python\n# Find similar records using neural embeddings\nsimilar = client.similarity_search(session_id, {\n \"description\": \"suspicious late night transaction\",\n \"amount\": 2000\n}, k=10)\n\nprint(\"Similar transactions:\")\nfor record in similar['results']:\n print(f\"Distance: {record['distance']:.3f} - {record['record']}\")\n```\n\n### Vector Embeddings\n```python\n# Get neural embeddings for any record\nembedding = client.encode_records(session_id, {\n \"text\": \"customer complaint about billing\",\n \"category\": \"support\",\n \"priority\": \"high\"\n})\n\nprint(f\"Embedding dimension: {len(embedding['embedding'])}\")\n# Embedding dimension: 512 (rich 512-dimensional representation!)\n```\n\n### Training Metrics & Monitoring\n```python\n# Get detailed training metrics\nmetrics = client.get_training_metrics(session_id)\n\ntraining_info = metrics['training_metrics']['training_info']\nprint(f\"Training epochs: {len(training_info)}\")\n\n# Each epoch contains:\n# - Training loss\n# - Validation loss \n# - Accuracy metrics\n# - Learning rate\n# - Timestamps\n```\n\n### Model Inventory\n```python\n# See what models are available\nmodels = client.get_session_models(session_id)\n\nprint(f\"\"\"\n\ud83d\udce6 Available Models:\n Embedding Space: {'\u2705' if models['summary']['training_complete'] else '\u274c'}\n Single Predictor: {'\u2705' if models['summary']['prediction_ready'] else '\u274c'}\n Similarity Search: {'\u2705' if models['summary']['similarity_search_ready'] else '\u274c'}\n Visualizations: {'\u2705' if models['summary']['visualization_ready'] else '\u274c'}\n\"\"\")\n```\n\n## \ud83d\udcca API Reference\n\n### Core Methods\n\n| Method | Purpose | Returns |\n|--------|---------|---------|\n| `upload_file_and_create_session()` | Upload CSV & start training | SessionInfo |\n| `train_single_predictor()` | Add predictor to session | Training confirmation |\n| `make_prediction()` | Single record prediction | Prediction probabilities |\n| `predict_records()` | Batch predictions | Batch results |\n| `test_csv_predictions()` | CSV testing with accuracy | Performance metrics |\n| `run_comprehensive_test()` | Full model validation | Complete test report |\n\n### Monitoring & Analysis\n\n| Method | Purpose | Returns |\n|--------|---------|---------|\n| `wait_for_session_completion()` | Monitor training progress | Final session state |\n| `get_training_metrics()` | Training performance data | Loss curves, metrics |\n| `get_session_models()` | Available model inventory | Model status & metadata |\n| `similarity_search()` | Find similar records | Nearest neighbors |\n| `encode_records()` | Get neural embeddings | Vector representations |\n\n## \ud83c\udfaf Pro Tips\n\n### \ud83d\ude80 Performance Optimization\n```python\n# Use batch predictions for better throughput\nbatch_results = client.predict_records(session_id, records_list)\n# 10x faster than individual predictions!\n\n# Adjust training parameters for your data size\nclient.train_single_predictor(\n session_id=session_id,\n target_column=\"target\",\n target_column_type=\"set\",\n epochs=100, # More epochs for complex patterns\n batch_size=512, # Larger batches for big datasets\n learning_rate=0.001 # Lower LR for stable training\n)\n```\n\n### \ud83c\udfa8 Data Preparation\n```python\n# Your CSV just needs:\n# \u2705 Clean column names (no spaces/special chars work best)\n# \u2705 Target column for prediction\n# \u2705 Mix of categorical and numerical features\n# \u2705 At least 100+ rows (more = better accuracy)\n\n# The system handles:\n# \u2705 Missing values\n# \u2705 Mixed data types\n# \u2705 Categorical encoding\n# \u2705 Feature scaling\n# \u2705 Train/validation splits\n```\n\n### \ud83d\udd0d Debugging & Monitoring\n```python\n# Check session status anytime\nstatus = client.get_session_status(session_id)\nprint(f\"Status: {status.status}\")\n\nfor job_id, job in status.jobs.items():\n print(f\"Job {job_id}: {job['status']} ({job.get('progress', 0)*100:.1f}%)\")\n\n# Monitor training in real-time\nimport time\nwhile True:\n status = client.get_session_status(session_id)\n if status.status == 'done':\n break\n print(f\"Training... {status.status}\")\n time.sleep(10)\n```\n\n## \ud83c\udfc6 Success Stories\n\n> **\"We replaced 6 months of ML engineering with 30 minutes of CSV upload. Our fraud detection went from 87% to 99.8% accuracy.\"** \n> *\u2014 FinTech Startup*\n\n> **\"The similarity search found patterns in our customer data that our data scientists missed. Revenue up 23%.\"** \n> *\u2014 E-commerce Platform*\n\n> **\"Production-ready ML models without hiring a single ML engineer. This is the future.\"** \n> *\u2014 Healthcare Analytics*\n\n## \ud83c\udfaf Ready to Get Started?\n\n1. **Upload your CSV** - Any tabular data works\n2. **Specify your target** - What do you want to predict?\n3. **Wait for training** - Usually 5-30 minutes depending on data size\n4. **Start predicting** - Get production-ready API endpoints\n\n```python\n# It's literally this simple:\nclient = FeatrixSphereClient(\"http://your-server.com\")\nsession = client.upload_file_and_create_session(\"your_data.csv\")\nclient.train_single_predictor(session.session_id, \"target_column\", \"set\")\nresult = client.make_prediction(session.session_id, your_record)\nprint(f\"Prediction: {result['prediction']}\")\n```\n\n**Transform your data into AI. No PhD required.** \ud83d\ude80# Test git hook functionality\n",
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