This is an automated email from the ASF dual-hosted git repository. baunsgaard pushed a commit to branch master in repository https://gitbox.apache.org/repos/asf/systemds.git
commit a264691c9c98e48c59469740c599d08fa65ce034 Author: baunsgaard <[email protected]> AuthorDate: Tue Sep 14 14:39:38 2021 +0200 [MINOR] PythonAPI update builtin algorithms --- .../python/systemds/operator/algorithm/__init__.py | 20 ++++++- .../systemds/operator/algorithm/builtin/bandit.py | 27 ++++----- .../builtin/{winsorize.py => deepWalk.py} | 28 ++++++--- .../algorithm/builtin/{winsorize.py => ffTrain.py} | 29 +++++++-- .../systemds/operator/algorithm/builtin/garch.py | 70 ++++++++++++++++++++++ .../builtin/{winsorize.py => lenetTrain.py} | 17 ++++-- .../builtin/{tomeklink.py => matrixProfile.py} | 29 +++++---- .../builtin/{tomeklink.py => selectByVarThresh.py} | 21 +++---- .../algorithm/builtin/{winsorize.py => tSNE.py} | 24 ++++++-- .../operator/algorithm/builtin/tomeklink.py | 2 +- .../operator/algorithm/builtin/winsorize.py | 4 +- ...insorize.py => xgboostPredictClassification.py} | 18 ++++-- .../{winsorize.py => xgboostPredictRegression.py} | 19 ++++-- 13 files changed, 226 insertions(+), 82 deletions(-) diff --git a/src/main/python/systemds/operator/algorithm/__init__.py b/src/main/python/systemds/operator/algorithm/__init__.py index 377c248..ffe59b3 100644 --- a/src/main/python/systemds/operator/algorithm/__init__.py +++ b/src/main/python/systemds/operator/algorithm/__init__.py @@ -39,9 +39,12 @@ from .builtin.csplineDS import csplineDS from .builtin.cvlm import cvlm from .builtin.dbscan import dbscan from .builtin.decisionTree import decisionTree +from .builtin.deepWalk import deepWalk from .builtin.discoverFD import discoverFD from .builtin.dist import dist from .builtin.executePipeline import executePipeline +from .builtin.ffTrain import ffTrain +from .builtin.garch import garch from .builtin.gaussianClassifier import gaussianClassifier from .builtin.getAccuracy import getAccuracy from .builtin.glm import glm @@ -73,11 +76,13 @@ from .builtin.knnbf import knnbf from .builtin.l2svm import l2svm from .builtin.l2svmPredict import l2svmPredict from .builtin.lasso import lasso +from .builtin.lenetTrain import lenetTrain from .builtin.lm import lm from .builtin.lmCG import lmCG from .builtin.lmDS import lmDS from .builtin.lmPredict import lmPredict from .builtin.logSumExp import logSumExp +from .builtin.matrixProfile import matrixProfile from .builtin.msvm import msvm from .builtin.msvmPredict import msvmPredict from .builtin.multiLogReg import multiLogReg @@ -96,6 +101,7 @@ from .builtin.ppca import ppca from .builtin.randomForest import randomForest from .builtin.scale import scale from .builtin.scaleApply import scaleApply +from .builtin.selectByVarThresh import selectByVarThresh from .builtin.sherlock import sherlock from .builtin.sherlockPredict import sherlockPredict from .builtin.shortestPath import shortestPath @@ -108,6 +114,7 @@ from .builtin.splitBalanced import splitBalanced from .builtin.stableMarriage import stableMarriage from .builtin.statsNA import statsNA from .builtin.steplm import steplm +from .builtin.tSNE import tSNE from .builtin.toOneHot import toOneHot from .builtin.tomeklink import tomeklink from .builtin.univar import univar @@ -115,6 +122,8 @@ from .builtin.vectorToCsv import vectorToCsv from .builtin.winsorize import winsorize from .builtin.xdummy1 import xdummy1 from .builtin.xdummy2 import xdummy2 +from .builtin.xgboostPredictClassification import xgboostPredictClassification +from .builtin.xgboostPredictRegression import xgboostPredictRegression __all__ = ['abstain', 'als', @@ -134,9 +143,12 @@ __all__ = ['abstain', 'cvlm', 'dbscan', 'decisionTree', + 'deepWalk', 'discoverFD', 'dist', 'executePipeline', + 'ffTrain', + 'garch', 'gaussianClassifier', 'getAccuracy', 'glm', @@ -168,11 +180,13 @@ __all__ = ['abstain', 'l2svm', 'l2svmPredict', 'lasso', + 'lenetTrain', 'lm', 'lmCG', 'lmDS', 'lmPredict', 'logSumExp', + 'matrixProfile', 'msvm', 'msvmPredict', 'multiLogReg', @@ -191,6 +205,7 @@ __all__ = ['abstain', 'randomForest', 'scale', 'scaleApply', + 'selectByVarThresh', 'sherlock', 'sherlockPredict', 'shortestPath', @@ -203,10 +218,13 @@ __all__ = ['abstain', 'stableMarriage', 'statsNA', 'steplm', + 'tSNE', 'toOneHot', 'tomeklink', 'univar', 'vectorToCsv', 'winsorize', 'xdummy1', - 'xdummy2'] + 'xdummy2', + 'xgboostPredictClassification', + 'xgboostPredictRegression'] diff --git a/src/main/python/systemds/operator/algorithm/builtin/bandit.py b/src/main/python/systemds/operator/algorithm/builtin/bandit.py index ff6b1c0..5cb87b5 100644 --- a/src/main/python/systemds/operator/algorithm/builtin/bandit.py +++ b/src/main/python/systemds/operator/algorithm/builtin/bandit.py @@ -31,27 +31,20 @@ from systemds.utils.consts import VALID_INPUT_TYPES def bandit(X_train: Matrix, Y_train: Matrix, + X_test: Matrix, + Y_test: Matrix, metaList: Iterable, - targetList: Iterable, + evaluationFunc: str, + evalFunHp: Matrix, lp: Frame, primitives: Frame, param: Frame, + baseLineScore: float, + cv: bool, **kwargs: Dict[str, VALID_INPUT_TYPES]): - params_dict = {'X_train': X_train, 'Y_train': Y_train, 'metaList': metaList, 'targetList': targetList, 'lp': lp, 'primitives': primitives, 'param': param} + params_dict = {'X_train': X_train, 'Y_train': Y_train, 'X_test': X_test, 'Y_test': Y_test, 'metaList': metaList, 'evaluationFunc': evaluationFunc, 'evalFunHp': evalFunHp, 'lp': lp, 'primitives': primitives, 'param': param, 'baseLineScore': baseLineScore, 'cv': cv} params_dict.update(kwargs) - - vX_0 = Frame(X_train.sds_context, '') - vX_1 = Matrix(X_train.sds_context, '') - vX_2 = Matrix(X_train.sds_context, '') - vX_3 = Frame(X_train.sds_context, '') - output_nodes = [vX_0, vX_1, vX_2, vX_3, ] - - op = MultiReturn(X_train.sds_context, 'bandit', output_nodes, named_input_nodes=params_dict) - - vX_0._unnamed_input_nodes = [op] - vX_1._unnamed_input_nodes = [op] - vX_2._unnamed_input_nodes = [op] - vX_3._unnamed_input_nodes = [op] - - return op + return Matrix(X_train.sds_context, + 'bandit', + named_input_nodes=params_dict) diff --git a/src/main/python/systemds/operator/algorithm/builtin/winsorize.py b/src/main/python/systemds/operator/algorithm/builtin/deepWalk.py similarity index 62% copy from src/main/python/systemds/operator/algorithm/builtin/winsorize.py copy to src/main/python/systemds/operator/algorithm/builtin/deepWalk.py index 335d01b..59fdc63 100644 --- a/src/main/python/systemds/operator/algorithm/builtin/winsorize.py +++ b/src/main/python/systemds/operator/algorithm/builtin/deepWalk.py @@ -20,7 +20,7 @@ # ------------------------------------------------------------- # Autogenerated By : src/main/python/generator/generator.py -# Autogenerated From : scripts/builtin/winsorize.dml +# Autogenerated From : scripts/builtin/deepWalk.dml from typing import Dict, Iterable @@ -29,10 +29,24 @@ from systemds.script_building.dag import OutputType from systemds.utils.consts import VALID_INPUT_TYPES -def winsorize(X: Matrix, - verbose: bool): - - params_dict = {'X': X, 'verbose': verbose} - return Matrix(X.sds_context, - 'winsorize', +def deepWalk(Graph: Matrix, + w: int, + d: int, + gamma: int, + t: int, + **kwargs: Dict[str, VALID_INPUT_TYPES]): + """ + :param Graph: adjacency matrix of a graph (n x n) + :param w: window size + :param d: embedding size + :param gamma: walks per vertex + :param t: walk length + :param alpha: learning rate + :param beta: factor for decreasing learning rate + :return: 'OperationNode' containing matrix of vertex/word representation (n x d) + """ + params_dict = {'Graph': Graph, 'w': w, 'd': d, 'gamma': gamma, 't': t} + params_dict.update(kwargs) + return Matrix(Graph.sds_context, + 'deepWalk', named_input_nodes=params_dict) diff --git a/src/main/python/systemds/operator/algorithm/builtin/winsorize.py b/src/main/python/systemds/operator/algorithm/builtin/ffTrain.py similarity index 55% copy from src/main/python/systemds/operator/algorithm/builtin/winsorize.py copy to src/main/python/systemds/operator/algorithm/builtin/ffTrain.py index 335d01b..06a3176 100644 --- a/src/main/python/systemds/operator/algorithm/builtin/winsorize.py +++ b/src/main/python/systemds/operator/algorithm/builtin/ffTrain.py @@ -20,7 +20,7 @@ # ------------------------------------------------------------- # Autogenerated By : src/main/python/generator/generator.py -# Autogenerated From : scripts/builtin/winsorize.dml +# Autogenerated From : scripts/builtin/ffTrain.dml from typing import Dict, Iterable @@ -29,10 +29,27 @@ from systemds.script_building.dag import OutputType from systemds.utils.consts import VALID_INPUT_TYPES -def winsorize(X: Matrix, - verbose: bool): - - params_dict = {'X': X, 'verbose': verbose} +def ffTrain(X: Matrix, + Y: Matrix, + out_activation: str, + loss_fcn: str, + **kwargs: Dict[str, VALID_INPUT_TYPES]): + """ + :param batch_size: Batch size + :param epochs: Number of epochs + :param learning_rate: Learning rate + :param out_activation: User specified ouptut activation function. Possible values: + :param loss_fcn: User specified loss function. Possible values: + :param shuffle: Flag which indicates if dataset should be shuffled or not + :param validation_split: Fraction of training set used as validation set + :param seed: Seed for model initialization + :param verbose: Flag which indicates if function should print to stdout + :param Supported: by the model + :param Supported: by the model + :return: 'OperationNode' containing + """ + params_dict = {'X': X, 'Y': Y, 'out_activation': out_activation, 'loss_fcn': loss_fcn} + params_dict.update(kwargs) return Matrix(X.sds_context, - 'winsorize', + 'ffTrain', named_input_nodes=params_dict) diff --git a/src/main/python/systemds/operator/algorithm/builtin/garch.py b/src/main/python/systemds/operator/algorithm/builtin/garch.py new file mode 100644 index 0000000..b0a8e7e --- /dev/null +++ b/src/main/python/systemds/operator/algorithm/builtin/garch.py @@ -0,0 +1,70 @@ +# ------------------------------------------------------------- +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# +# ------------------------------------------------------------- + +# Autogenerated By : src/main/python/generator/generator.py +# Autogenerated From : scripts/builtin/garch.dml + +from typing import Dict, Iterable + +from systemds.operator import OperationNode, Matrix, Frame, List, MultiReturn, Scalar +from systemds.script_building.dag import OutputType +from systemds.utils.consts import VALID_INPUT_TYPES + + +def garch(X: Matrix, + kmax: int, + momentum: float, + start_stepsize: float, + end_stepsize: float, + start_vicinity: float, + end_vicinity: float, + sim_seed: int, + verbose: bool): + """ + :param X: The input Matrix to apply Arima on. + :param kmax: Number of iterations + :param momentum: Momentum for momentum-gradient descent (set to 0 to deactivate) + :param start_stepsize: Initial gradient-descent stepsize + :param end_stepsize: gradient-descent stepsize at end (linear descent) + :param start_vicinity: proportion of randomness of restart-location for gradient descent at beginning + :param end_vicinity: same at end (linear decay) + :param sim_seed: seed for simulation of process on fitted coefficients + :param verbose: verbosity, comments during fitting + :return: 'OperationNode' containing simulated garch(1,1) process on fitted coefficients & variances of simulated fitted process & constant term of fitted process & 1-st arch-coefficient of fitted process & 1-st garch-coefficient of fitted process & drawbacks: slow convergence of optimization (sort of simulated annealing/gradient descent) + """ + params_dict = {'X': X, 'kmax': kmax, 'momentum': momentum, 'start_stepsize': start_stepsize, 'end_stepsize': end_stepsize, 'start_vicinity': start_vicinity, 'end_vicinity': end_vicinity, 'sim_seed': sim_seed, 'verbose': verbose} + + vX_0 = Matrix(X.sds_context, '') + vX_1 = Matrix(X.sds_context, '') + vX_2 = Scalar(X.sds_context, '') + vX_3 = Scalar(X.sds_context, '') + vX_4 = Scalar(X.sds_context, '') + output_nodes = [vX_0, vX_1, vX_2, vX_3, vX_4, ] + + op = MultiReturn(X.sds_context, 'garch', output_nodes, named_input_nodes=params_dict) + + vX_0._unnamed_input_nodes = [op] + vX_1._unnamed_input_nodes = [op] + vX_2._unnamed_input_nodes = [op] + vX_3._unnamed_input_nodes = [op] + vX_4._unnamed_input_nodes = [op] + + return op diff --git a/src/main/python/systemds/operator/algorithm/builtin/winsorize.py b/src/main/python/systemds/operator/algorithm/builtin/lenetTrain.py similarity index 74% copy from src/main/python/systemds/operator/algorithm/builtin/winsorize.py copy to src/main/python/systemds/operator/algorithm/builtin/lenetTrain.py index 335d01b..87a28da 100644 --- a/src/main/python/systemds/operator/algorithm/builtin/winsorize.py +++ b/src/main/python/systemds/operator/algorithm/builtin/lenetTrain.py @@ -20,7 +20,7 @@ # ------------------------------------------------------------- # Autogenerated By : src/main/python/generator/generator.py -# Autogenerated From : scripts/builtin/winsorize.dml +# Autogenerated From : scripts/builtin/lenetTrain.dml from typing import Dict, Iterable @@ -29,10 +29,17 @@ from systemds.script_building.dag import OutputType from systemds.utils.consts import VALID_INPUT_TYPES -def winsorize(X: Matrix, - verbose: bool): +def lenetTrain(X: Matrix, + Y: Matrix, + X_val: Matrix, + Y_val: Matrix, + C: int, + Hin: int, + Win: int, + **kwargs: Dict[str, VALID_INPUT_TYPES]): - params_dict = {'X': X, 'verbose': verbose} + params_dict = {'X': X, 'Y': Y, 'X_val': X_val, 'Y_val': Y_val, 'C': C, 'Hin': Hin, 'Win': Win} + params_dict.update(kwargs) return Matrix(X.sds_context, - 'winsorize', + 'lenetTrain', named_input_nodes=params_dict) diff --git a/src/main/python/systemds/operator/algorithm/builtin/tomeklink.py b/src/main/python/systemds/operator/algorithm/builtin/matrixProfile.py similarity index 63% copy from src/main/python/systemds/operator/algorithm/builtin/tomeklink.py copy to src/main/python/systemds/operator/algorithm/builtin/matrixProfile.py index e2e020c..3472c40 100644 --- a/src/main/python/systemds/operator/algorithm/builtin/tomeklink.py +++ b/src/main/python/systemds/operator/algorithm/builtin/matrixProfile.py @@ -20,7 +20,7 @@ # ------------------------------------------------------------- # Autogenerated By : src/main/python/generator/generator.py -# Autogenerated From : scripts/builtin/tomeklink.dml +# Autogenerated From : scripts/builtin/matrixProfile.dml from typing import Dict, Iterable @@ -29,24 +29,27 @@ from systemds.script_building.dag import OutputType from systemds.utils.consts import VALID_INPUT_TYPES -def tomeklink(X: Matrix, - y: Matrix): +def matrixProfile(ts: Matrix, + **kwargs: Dict[str, VALID_INPUT_TYPES]): """ - :param X: Data Matrix (nxm) - :param y: Label Matrix (nx1) - :return: 'OperationNode' containing + :param ts: Time series to profile + :param window_size: Sliding window size + :param sample_percent: Degree of approximation + :param between: one (1 + :param computes: solution) + :param is_verbose: Print debug information + :return: 'OperationNode' containing the computed matrix profile & indices of least distances """ - params_dict = {'X': X, 'y': y} + params_dict = {'ts': ts} + params_dict.update(kwargs) - vX_0 = Matrix(X.sds_context, '') - vX_1 = Matrix(X.sds_context, '') - vX_2 = Matrix(X.sds_context, '') - output_nodes = [vX_0, vX_1, vX_2, ] + vX_0 = Matrix(ts.sds_context, '') + vX_1 = Matrix(ts.sds_context, '') + output_nodes = [vX_0, vX_1, ] - op = MultiReturn(X.sds_context, 'tomeklink', output_nodes, named_input_nodes=params_dict) + op = MultiReturn(ts.sds_context, 'matrixProfile', output_nodes, named_input_nodes=params_dict) vX_0._unnamed_input_nodes = [op] vX_1._unnamed_input_nodes = [op] - vX_2._unnamed_input_nodes = [op] return op diff --git a/src/main/python/systemds/operator/algorithm/builtin/tomeklink.py b/src/main/python/systemds/operator/algorithm/builtin/selectByVarThresh.py similarity index 74% copy from src/main/python/systemds/operator/algorithm/builtin/tomeklink.py copy to src/main/python/systemds/operator/algorithm/builtin/selectByVarThresh.py index e2e020c..7069e40 100644 --- a/src/main/python/systemds/operator/algorithm/builtin/tomeklink.py +++ b/src/main/python/systemds/operator/algorithm/builtin/selectByVarThresh.py @@ -20,7 +20,7 @@ # ------------------------------------------------------------- # Autogenerated By : src/main/python/generator/generator.py -# Autogenerated From : scripts/builtin/tomeklink.dml +# Autogenerated From : scripts/builtin/selectByVarThresh.dml from typing import Dict, Iterable @@ -29,24 +29,19 @@ from systemds.script_building.dag import OutputType from systemds.utils.consts import VALID_INPUT_TYPES -def tomeklink(X: Matrix, - y: Matrix): - """ - :param X: Data Matrix (nxm) - :param y: Label Matrix (nx1) - :return: 'OperationNode' containing - """ - params_dict = {'X': X, 'y': y} +def selectByVarThresh(X: Matrix, + **kwargs: Dict[str, VALID_INPUT_TYPES]): + + params_dict = {'X': X} + params_dict.update(kwargs) vX_0 = Matrix(X.sds_context, '') vX_1 = Matrix(X.sds_context, '') - vX_2 = Matrix(X.sds_context, '') - output_nodes = [vX_0, vX_1, vX_2, ] + output_nodes = [vX_0, vX_1, ] - op = MultiReturn(X.sds_context, 'tomeklink', output_nodes, named_input_nodes=params_dict) + op = MultiReturn(X.sds_context, 'selectByVarThresh', output_nodes, named_input_nodes=params_dict) vX_0._unnamed_input_nodes = [op] vX_1._unnamed_input_nodes = [op] - vX_2._unnamed_input_nodes = [op] return op diff --git a/src/main/python/systemds/operator/algorithm/builtin/winsorize.py b/src/main/python/systemds/operator/algorithm/builtin/tSNE.py similarity index 65% copy from src/main/python/systemds/operator/algorithm/builtin/winsorize.py copy to src/main/python/systemds/operator/algorithm/builtin/tSNE.py index 335d01b..ef5556d 100644 --- a/src/main/python/systemds/operator/algorithm/builtin/winsorize.py +++ b/src/main/python/systemds/operator/algorithm/builtin/tSNE.py @@ -20,7 +20,7 @@ # ------------------------------------------------------------- # Autogenerated By : src/main/python/generator/generator.py -# Autogenerated From : scripts/builtin/winsorize.dml +# Autogenerated From : scripts/builtin/tSNE.dml from typing import Dict, Iterable @@ -29,10 +29,22 @@ from systemds.script_building.dag import OutputType from systemds.utils.consts import VALID_INPUT_TYPES -def winsorize(X: Matrix, - verbose: bool): - - params_dict = {'X': X, 'verbose': verbose} +def tSNE(X: Matrix, + **kwargs: Dict[str, VALID_INPUT_TYPES]): + """ + :param X: Data Matrix of shape + :param reduced_dims: Output dimensionality + :param perplexity: Perplexity Parameter + :param lr: Learning rate + :param momentum: Momentum Parameter + :param max_iter: Number of iterations + :param seed: The seed used for initial values. + :param If: -1 random seeds are selected. + :param is_verbose: Print debug information + :return: 'OperationNode' containing data matrix of shape (number of data points, reduced_dims) + """ + params_dict = {'X': X} + params_dict.update(kwargs) return Matrix(X.sds_context, - 'winsorize', + 'tSNE', named_input_nodes=params_dict) diff --git a/src/main/python/systemds/operator/algorithm/builtin/tomeklink.py b/src/main/python/systemds/operator/algorithm/builtin/tomeklink.py index e2e020c..dc80c67 100644 --- a/src/main/python/systemds/operator/algorithm/builtin/tomeklink.py +++ b/src/main/python/systemds/operator/algorithm/builtin/tomeklink.py @@ -33,7 +33,7 @@ def tomeklink(X: Matrix, y: Matrix): """ :param X: Data Matrix (nxm) - :param y: Label Matrix (nx1) + :param y: Label Matrix (nx1), greater than zero :return: 'OperationNode' containing """ params_dict = {'X': X, 'y': y} diff --git a/src/main/python/systemds/operator/algorithm/builtin/winsorize.py b/src/main/python/systemds/operator/algorithm/builtin/winsorize.py index 335d01b..1ce9192 100644 --- a/src/main/python/systemds/operator/algorithm/builtin/winsorize.py +++ b/src/main/python/systemds/operator/algorithm/builtin/winsorize.py @@ -30,9 +30,11 @@ from systemds.utils.consts import VALID_INPUT_TYPES def winsorize(X: Matrix, - verbose: bool): + verbose: bool, + **kwargs: Dict[str, VALID_INPUT_TYPES]): params_dict = {'X': X, 'verbose': verbose} + params_dict.update(kwargs) return Matrix(X.sds_context, 'winsorize', named_input_nodes=params_dict) diff --git a/src/main/python/systemds/operator/algorithm/builtin/winsorize.py b/src/main/python/systemds/operator/algorithm/builtin/xgboostPredictClassification.py similarity index 67% copy from src/main/python/systemds/operator/algorithm/builtin/winsorize.py copy to src/main/python/systemds/operator/algorithm/builtin/xgboostPredictClassification.py index 335d01b..4da8b77 100644 --- a/src/main/python/systemds/operator/algorithm/builtin/winsorize.py +++ b/src/main/python/systemds/operator/algorithm/builtin/xgboostPredictClassification.py @@ -20,7 +20,7 @@ # ------------------------------------------------------------- # Autogenerated By : src/main/python/generator/generator.py -# Autogenerated From : scripts/builtin/winsorize.dml +# Autogenerated From : scripts/builtin/xgboostPredictClassification.dml from typing import Dict, Iterable @@ -29,10 +29,16 @@ from systemds.script_building.dag import OutputType from systemds.utils.consts import VALID_INPUT_TYPES -def winsorize(X: Matrix, - verbose: bool): - - params_dict = {'X': X, 'verbose': verbose} +def xgboostPredictClassification(X: Matrix, + M: Matrix, + learning_rate: float): + """ + :param X: Matrix of feature vectors we want to predict (X_test) + :param M: The model created at xgboost + :param learning_rate: the learning rate used in the model + :return: 'OperationNode' containing the predictions of the samples using the given xgboost model. (y_prediction) + """ + params_dict = {'X': X, 'M': M, 'learning_rate': learning_rate} return Matrix(X.sds_context, - 'winsorize', + 'xgboostPredictClassification', named_input_nodes=params_dict) diff --git a/src/main/python/systemds/operator/algorithm/builtin/winsorize.py b/src/main/python/systemds/operator/algorithm/builtin/xgboostPredictRegression.py similarity index 67% copy from src/main/python/systemds/operator/algorithm/builtin/winsorize.py copy to src/main/python/systemds/operator/algorithm/builtin/xgboostPredictRegression.py index 335d01b..1b9e217 100644 --- a/src/main/python/systemds/operator/algorithm/builtin/winsorize.py +++ b/src/main/python/systemds/operator/algorithm/builtin/xgboostPredictRegression.py @@ -20,7 +20,7 @@ # ------------------------------------------------------------- # Autogenerated By : src/main/python/generator/generator.py -# Autogenerated From : scripts/builtin/winsorize.dml +# Autogenerated From : scripts/builtin/xgboostPredictRegression.dml from typing import Dict, Iterable @@ -29,10 +29,17 @@ from systemds.script_building.dag import OutputType from systemds.utils.consts import VALID_INPUT_TYPES -def winsorize(X: Matrix, - verbose: bool): - - params_dict = {'X': X, 'verbose': verbose} +def xgboostPredictRegression(X: Matrix, + M: Matrix, + **kwargs: Dict[str, VALID_INPUT_TYPES]): + """ + :param X: Matrix of feature vectors we want to predict (X_test) + :param M: The model created at xgboost + :param learning_rate: the learning rate used in the model + :return: 'OperationNode' containing the predictions of the samples using the given xgboost model. (y_prediction) + """ + params_dict = {'X': X, 'M': M} + params_dict.update(kwargs) return Matrix(X.sds_context, - 'winsorize', + 'xgboostPredictRegression', named_input_nodes=params_dict)
