def _func_fit(sessionId, objs, datatype, has_weights, **kwargs): from cuml.cluster.kmeans_mg import KMeansMG as cumlKMeans handle = get_raft_comm_state(sessionId)["handle"] if not has_weights: inp_data = concatenate(objs) inp_weights = None else: inp_data = concatenate([X for X, weights in objs]) inp_weights = concatenate([weights for X, weights in objs]) return cumlKMeans(handle=handle, output_type=datatype, **kwargs).fit(inp_data, sample_weight=inp_weights)
def _func_fit(sessionId, objs, datatype, **kwargs): from cuml.cluster.kmeans_mg import KMeansMG as cumlKMeans handle = worker_state(sessionId)["handle"] inp_data = concatenate(objs) return cumlKMeans(handle=handle, output_type=datatype, **kwargs).fit(inp_data)
def _func_predict_proba_partial(model, input_data, **kwargs): """ Whole dataset inference with part of the model (trees at disposal locally). Transfer dataset instead of model. Interesting when model is larger than dataset. """ X = concatenate(input_data) with using_output_type('cupy'): prediction = model.predict_proba(X, **kwargs) return cp.expand_dims(prediction, axis=1)
def _func_fit(model, input_data, convert_dtype): X = concatenate([item[0] for item in input_data]) y = concatenate([item[1] for item in input_data]) return model.fit(X, y, convert_dtype)