[python-package] [dask] Add DaskLGBMRanker

python-package/lightgbm/dask.py

@@ -21,7 +21,7 @@
 from dask.distributed import Client, default_client, get_worker, wait
 
 from .basic import _ConfigAliases, _LIB, _safe_call
-from .sklearn import LGBMClassifier, LGBMRegressor
+from .sklearn import LGBMClassifier, LGBMRegressor, LGBMRanker
 
 logger = logging.getLogger(__name__)
 
@@ -133,15 +133,24 @@ def _train_part(params, model_factory, list_of_parts, worker_address_to_port, re
     }
     params.update(network_params)
 
+    is_ranker = model_factory.__qualname__ == 'LGBMRanker'
+
     # Concatenate many parts into one
     parts = tuple(zip(*list_of_parts))
     data = _concat(parts[0])
     label = _concat(parts[1])
-    weight = _concat(parts[2]) if len(parts) == 3 else None
 
     try:
         model = model_factory(**params)
-        model.fit(data, label, sample_weight=weight, **kwargs)
+
+        if is_ranker:
+            group = _concat(parts[-1])
+            weight = _concat(parts[2]) if len(parts) == 4 else None
+            model.fit(data, y=label, sample_weight=weight, group=group, **kwargs)
+        else:
+            weight = _concat(parts[2]) if len(parts) == 3 else None
+            model.fit(data, y=label, sample_weight=weight, **kwargs)
+
     finally:
         _safe_call(_LIB.LGBM_NetworkFree())
 
@@ -156,7 +165,7 @@ def _split_to_parts(data, is_matrix):
     return parts
 
 
-def _train(client, data, label, params, model_factory, weight=None, **kwargs):
+def _train(client, data, label, params, model_factory, sample_weight=None, group=None, **kwargs):
     """Inner train routine.
 
     Parameters
@@ -167,22 +176,35 @@ def _train(client, data, label, params, model_factory, weight=None, **kwargs):
     y : dask array of shape = [n_samples]
         The target values (class labels in classification, real numbers in regression).
     params : dict
-    model_factory : lightgbm.LGBMClassifier or lightgbm.LGBMRegressor class
+    model_factory : lightgbm.LGBMClassifier, lightgbm.LGBMRegressor, or lightgbm.LGBMRanker class
     sample_weight : array-like of shape = [n_samples] or None, optional (default=None)
-            Weights of training data.
+        Weights of training data.
+    group : array-like or None, optional (default=None)
+        Group/query data.
+        Only used in the learning-to-rank task.
+        sum(group) = n_samples.
+        For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, etc.
     """
     params = deepcopy(params)
 
     # Split arrays/dataframes into parts. Arrange parts into tuples to enforce co-locality
     data_parts = _split_to_parts(data, is_matrix=True)
     label_parts = _split_to_parts(label, is_matrix=False)
-    if weight is None:
-        parts = list(map(delayed, zip(data_parts, label_parts)))
+    weight_parts = _split_to_parts(sample_weight, is_matrix=False) if sample_weight is not None else None
+    group_parts = _split_to_parts(group, is_matrix=False) if group is not None else None
+
+    # choose between four options of (sample_weight, group) being (un)specified
+    if weight_parts is None and group_parts is None:
+        parts = zip(data_parts, label_parts)
+    elif weight_parts is not None and group_parts is None:
+        parts = zip(data_parts, label_parts, weight_parts)
+    elif weight_parts is None and group_parts is not None:
+        parts = zip(data_parts, label_parts, group_parts)
     else:
-        weight_parts = _split_to_parts(weight, is_matrix=False)
-        parts = list(map(delayed, zip(data_parts, label_parts, weight_parts)))
+        parts = zip(data_parts, label_parts, weight_parts, group_parts)
 
     # Start computation in the background
+    parts = list(map(delayed, parts))
     parts = client.compute(parts)
     wait(parts)
 
@@ -281,13 +303,13 @@ def _predict(model, data, proba=False, dtype=np.float32, **kwargs):
 
     Parameters
     ----------
-    model :
+    model : lightgbm.LGBMClassifier, lightgbm.LGBMRegressor, or lightgbm.LGBMRanker class
     data : dask array of shape = [n_samples, n_features]
         Input feature matrix.
     proba : bool
-        Should method return results of predict_proba (proba == True) or predict (proba == False)
+        Should method return results of predict_proba (proba == True) or predict (proba == False).
     dtype : np.dtype
-        Dtype of the output
+        Dtype of the output.
     kwargs : other parameters passed to predict or predict_proba method
     """
     if isinstance(data, dd._Frame):
@@ -304,13 +326,14 @@ def _predict(model, data, proba=False, dtype=np.float32, **kwargs):
 
 class _LGBMModel:
 
-    def _fit(self, model_factory, X, y=None, sample_weight=None, client=None, **kwargs):
+    def _fit(self, model_factory, X, y=None, sample_weight=None, group=None, client=None, **kwargs):
         """Docstring is inherited from the LGBMModel."""
         if client is None:
             client = default_client()
 
         params = self.get_params(True)
-        model = _train(client, X, y, params, model_factory, sample_weight, **kwargs)
+        model = _train(client, data=X, label=y, params=params, model_factory=model_factory,
+                       sample_weight=sample_weight, group=group, **kwargs)
 
         self.set_params(**model.get_params())
         self._copy_extra_params(model, self)
@@ -335,8 +358,8 @@ class DaskLGBMClassifier(_LGBMModel, LGBMClassifier):
     """Distributed version of lightgbm.LGBMClassifier."""
 
     def fit(self, X, y=None, sample_weight=None, client=None, **kwargs):
-        """Docstring is inherited from the LGBMModel."""
-        return self._fit(LGBMClassifier, X, y, sample_weight, client, **kwargs)
+        """Docstring is inherited from the lightgbm.LGBMClassifier.fit."""
+        return self._fit(LGBMClassifier, X=X, y=y, sample_weight=sample_weight, client=client, **kwargs)
     fit.__doc__ = LGBMClassifier.fit.__doc__
 
     def predict(self, X, **kwargs):
@@ -364,7 +387,7 @@ class DaskLGBMRegressor(_LGBMModel, LGBMRegressor):
 
     def fit(self, X, y=None, sample_weight=None, client=None, **kwargs):
         """Docstring is inherited from the lightgbm.LGBMRegressor.fit."""
-        return self._fit(LGBMRegressor, X, y, sample_weight, client, **kwargs)
+        return self._fit(LGBMRegressor, X=X, y=y, sample_weight=sample_weight, client=client, **kwargs)
     fit.__doc__ = LGBMRegressor.fit.__doc__
 
     def predict(self, X, **kwargs):
@@ -380,3 +403,29 @@ def to_local(self):
         model : lightgbm.LGBMRegressor
         """
         return self._to_local(LGBMRegressor)
+
+
+class DaskLGBMRanker(_LGBMModel, LGBMRanker):
+    """Docstring is inherited from the lightgbm.LGBMRanker."""
+
+    def fit(self, X, y=None, sample_weight=None, init_score=None, group=None, client=None, **kwargs):
+        """Docstring is inherited from the lightgbm.LGBMRanker.fit."""
+        if init_score is not None:
+            raise RuntimeError('init_score is not currently supported in lightgbm.dask')
+
+        return self._fit(LGBMRanker, X=X, y=y, sample_weight=sample_weight, group=group, client=client, **kwargs)
+    fit.__doc__ = LGBMRanker.fit.__doc__
+
+    def predict(self, X, **kwargs):
+        """Docstring is inherited from the lightgbm.LGBMRanker.predict."""
+        return _predict(self.to_local(), X, **kwargs)
+    predict.__doc__ = LGBMRanker.predict.__doc__
+
+    def to_local(self):
+        """Create regular version of lightgbm.LGBMRanker from the distributed version.
+
+        Returns
+        -------
+        model : lightgbm.LGBMRanker
+        """
+        return self._to_local(LGBMRanker)