Merge pull request #9 from S-FM/feat/limix

update readme

Author: ChernovAndrey

README.md

@@ -17,8 +17,6 @@ Production-ready Python SDK for FAIM (Foundation AI Models) - a unified platform
 - **🔄 Async Support**: Built-in async/await support for concurrent requests
 - **📊 Rich Error Handling**: Machine-readable error codes with detailed diagnostics
 - **🧪 Battle-Tested**: Production-ready with comprehensive error handling
-- **📈 Evaluation Tools**: Built-in metrics (MSE, MASE, CRPS) and visualization utilities
-- **🔎 Retrieval-Augmented Inference**: Optional RAI for improved accuracy on small datasets
 
 ## Installation
 
@@ -138,24 +136,6 @@ response.probabilities  # Shape: (n_samples, n_classes) - classification only
 - **Chronos2**: ✅ Supports multivariate forecasting (multiple features)
 - **FlowState**: ⚠️ Univariate only - automatically transforms multivariate input
 - **TiRex**: ⚠️ Univariate only - automatically transforms multivariate input
-- **LimiX**: ✅ Supports multivariate tabular features (standard in tabular inference)
-
-When you provide multivariate input (features > 1) to FlowState or TiRex, the SDK automatically:
-1. Issues a warning
-2. Forecasts each feature independently
-3. Reshapes the output back to your original structure
-
-```python
-# Multivariate input to FlowState
-data = np.random.randn(2, 100, 3)  # 2 series, 3 features
-request = FlowStateForecastRequest(x=data, horizon=24, prediction_type="mean")
-
-# Warning: "FlowState model only supports univariate forecasting..."
-response = client.forecast(request)
-
-# Output is automatically reshaped
-print(response.point.shape)  # (2, 24, 3) - original structure preserved
-```
 
 ## Available Models
 
@@ -355,177 +335,9 @@ print(response.metadata)
 # {'model_name': 'chronos2', 'model_version': '1.0', 'inference_time_ms': 123}
 ```
 
-## Evaluation & Metrics (Time-Series Forecasting)
-
-The SDK includes a comprehensive evaluation toolkit (`faim_sdk.eval`) for measuring time-series forecast quality with standard metrics and visualizations.
-
-**Note**: These metrics are designed for time-series forecasting evaluation. For tabular model evaluation (classification/regression), use standard scikit-learn metrics like `accuracy_score`, `mean_squared_error`, etc. (see tabular examples above).
-
-### Installation
-
-For visualization support, install with the viz extra:
-
-```bash
-pip install faim-sdk[viz]
-```
-
-### Available Metrics for Time-Series
-
-#### Mean Squared Error (MSE)
-
-Measures average squared difference between predictions and ground truth.
-
-```python
-from faim_sdk.eval import mse
-
-# Evaluate point forecast
-mse_score = mse(test_data, response.point, reduction='mean')
-print(f"MSE: {mse_score:.4f}")
-
-# Per-sample MSE
-mse_per_sample = mse(test_data, response.point, reduction='none')
-print(f"MSE per sample shape: {mse_per_sample.shape}")  # (batch_size,)
-```
-
-#### Mean Absolute Scaled Error (MASE)
-
-Scale-independent metric comparing forecast to naive baseline (better than MAPE for series with zeros).
-
-```python
-from faim_sdk.eval import mase
-
-# MASE requires training data for baseline
-mase_score = mase(test_data, response.point, train_data, reduction='mean')
-print(f"MASE: {mase_score:.4f}")
-
-# Interpretation:
-# MASE < 1: Better than naive baseline
-# MASE = 1: Equivalent to naive baseline
-# MASE > 1: Worse than naive baseline
-```
-
-#### Continuous Ranked Probability Score (CRPS)
-
-Proper scoring rule for probabilistic forecasts - generalizes MAE to distributions.
-
-```python
-from faim_sdk.eval import crps_from_quantiles
-
-# Evaluate probabilistic forecast with quantiles
-crps_score = crps_from_quantiles(
-    test_data,
-    response.quantiles,
-    quantile_levels=[0.1, 0.5, 0.9],
-    reduction='mean'
-)
-print(f"CRPS: {crps_score:.4f}")
-```
-
-### Visualization (Time-Series Only)
-
-Plot time-series forecasts with training context and ground truth:
-
-```python
-from faim_sdk.eval import plot_forecast
-
-# Plot single sample (remember to index batch dimension!)
-fig, ax = plot_forecast(
-    train_data=train_data[0],  # (seq_len, features) - 2D array
-    forecast=response.point[0],  # (horizon, features) - 2D array
-    test_data=test_data[0],  # (horizon, features) - optional
-    title="Time Series Forecast"
-)
-
-# Save to file
-fig.savefig("forecast.png", dpi=300, bbox_inches="tight")
-```
-
-#### Multi-Feature Visualization
-
-```python
-# Option 1: All features on same plot
-fig, ax = plot_forecast(
-    train_data[0],
-    response.point[0],
-    test_data[0],
-    features_on_same_plot=True,
-    feature_names=["Temperature", "Humidity", "Pressure"]
-)
-
-# Option 2: Separate subplots per feature
-fig, axes = plot_forecast(
-    train_data[0],
-    response.point[0],
-    test_data[0],
-    features_on_same_plot=False,
-    feature_names=["Temperature", "Humidity", "Pressure"]
-)
-```
-
-### Complete Evaluation Example
-
-```python
-import numpy as np
-from faim_sdk import ForecastClient, Chronos2ForecastRequest
-from faim_sdk.eval import mse, mase, crps_from_quantiles, plot_forecast
-
-# Initialize client
-client = ForecastClient()
-
-# Prepare data splits
-train_data = np.random.randn(32, 100, 1)
-test_data = np.random.randn(32, 24, 1)
-
-# Generate forecast
-request = Chronos2ForecastRequest(
-    x=train_data,
-    horizon=24,
-    output_type="quantiles",
-    quantiles=[0.1, 0.5, 0.9]
-)
-response = client.forecast(request)
-
-# Evaluate point forecast (use median)
-point_pred = response.quantiles[:, :, 1:2]  # Extract median, keep 3D shape
-mse_score = mse(test_data, point_pred)
-mase_score = mase(test_data, point_pred, train_data)
-
-# Evaluate probabilistic forecast
-crps_score = crps_from_quantiles(
-    test_data,
-    response.quantiles,
-    quantile_levels=[0.1, 0.5, 0.9]
-)
-
-print(f"MSE: {mse_score:.4f}")
-print(f"MASE: {mase_score:.4f}")
-print(f"CRPS: {crps_score:.4f}")
-
-# Visualize best and worst predictions
-mse_per_sample = mse(test_data, point_pred, reduction='none')
-best_idx = np.argmin(mse_per_sample)
-worst_idx = np.argmax(mse_per_sample)
-
-fig1, ax1 = plot_forecast(
-    train_data[best_idx],
-    point_pred[best_idx],
-    test_data[best_idx],
-    title=f"Best Forecast (MSE: {mse_per_sample[best_idx]:.4f})"
-)
-fig1.savefig("best_forecast.png")
-
-fig2, ax2 = plot_forecast(
-    train_data[worst_idx],
-    point_pred[worst_idx],
-    test_data[worst_idx],
-    title=f"Worst Forecast (MSE: {mse_per_sample[worst_idx]:.4f})"
-)
-fig2.savefig("worst_forecast.png")
-```
-
 ## Error Handling
 
-The SDK provides **machine-readable error codes** for robust error handling:
+The SDK provides **error codes** for robust error handling:
 
 ```python
 from faim_sdk import (
@@ -629,16 +441,9 @@ See the `examples/` directory for complete Jupyter notebook examples:
 
 ### Tabular Inference with LimiX
 - **`limix_classification_example.ipynb`** - Binary classification on breast cancer dataset
-  - Standard approach with LimiX
-  - Retrieval-Augmented Inference (RAI) comparison
-  - Side-by-side metrics comparison (Accuracy, Precision, Recall, F1-Score)
-
+  
 - **`limix_regression_example.ipynb`** - Regression on California housing dataset
-  - Standard approach with LimiX
-  - Retrieval-Augmented Inference (RAI) comparison
-  - Comprehensive metrics comparison (MSE, RMSE, MAE, R²)
-  - Residual statistics analysis
-
+  
 ## Requirements
 
 - Python >= 3.10