This code is intended to work closely with Ocean protocol. Algorithms from this code
should run on ocean provider. Training local models and aggregating them into global
model.
Entry commands:
```bash
felt-train
felt-aggregate
felt-export
```
## Common Usage
After installing this library you can load models trained using FELT as:
```python
from feltlabs.model import load_model
# Load scikit-learn model
model = load_model("final-model.json")
# Data shape must be: (number_of_samples, number_of_features)
data = [
[1980, 2, 2, 2, 0, 0],
[1700, 3, 2, 3, 1, 1],
[2100, 3, 2, 3, 1, 0],
]
result = model.predict(data)
print(result)
# Use following line for analytics as mean, std...
# result = model.predict(None)
```
### Command: felt-export
You can use `felt-export` for converting trained models into pickle object:
Resulting file will then contain a pickled object of scikit-learn model.
```bash
felt-export --input "final-model-House Prices.json" --output "model.pkl"
```
Then you can use the created file as follows:
```python
import pickle
with open('model.pkl', 'rb') as f:
model = pickle.load(object, f)
# See the above code example for data definition
model.predict(data)
```
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