nordlys.core.ml.ml module¶
Machine leaning¶
The command-line application for general-purpose machine learning.
Usage¶
python -m nordlys.core.ml.ml <config_file>
Config parameters¶
- training_set: nordlys ML instance file format (MIFF)
- test_set: nordlys ML instance file format (MIFF); if provided then it’s always used for testing. Can be left empty if cross-validation is used, in which case the remaining split is used for testing.
- cross_validation:
- k: number of folds (default: 10); use -1 for leave-one-out
- split_strategy: name of a property (normally query-id for IR problems). If set, the entities with the same value for that property are kept in the same split. if not set, entities are randomly distributed among splits.
- splits_file: JSON file with splits (instance_ids); if the file is provided it is used, otherwise it’s generated
- create_splits: if True, creates the CV splits. Otherwise loads the splits from “split_file” parameter.
- model: ML model, currently supported values: rf, gbrt
- category: [regression | classification], default: “regression”
- parameters: dict with parameters of the given ML model
- If GBRT:
- alpha: learning rate, default: 0.1
- tree: number of trees, default: 1000
- depth: max depth of trees, default: 10% of number of features
- If RF:
- tree: number of trees, default: 1000
- maxfeat: max features of trees, default: 10% of number of features
- model_file: the model is saved to this file
- load_model: if True, loads the model
- feature_imp_file: Feature importance is saved to this file
- output_file: where output is written; default output format: TSV with with instance_id and (estimated) target
Example config¶
{
"model": "gbrt",
"category": "regression",
"parameters":{
"alpha": 0.1,
"tree": 10,
"depth": 5
},
"training_set": "path/to/train.json",
"test_set": "path/to/test.json",
"model_file": "path/to/model.txt",
"output_file": "path/to/output.json",
"cross_validation":{
"create_splits": true,
"splits_file": "path/to/splits.json",
"k": 5,
"split_strategy": "q_id"
}
}
| Authors: | Faegheh Hasibi, Krisztian Balog |
|---|
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class
nordlys.core.ml.ml.ML(config)[source]¶ Bases:
object-
analyse_features(model, feature_names)[source]¶ Ranks features based on their importance. Scikit uses Gini score to get feature importances.
Parameters: - model – trained model
- feature_names – list of feature names
-
apply_model(instances, model)[source]¶ Applies model on a given set of instances.
Parameters: - instances – Instances object
- model – trained model
Returns: Instances
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