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Posted to commits@systemds.apache.org by ba...@apache.org on 2021/09/14 12:40:03 UTC
[systemds] 03/03: [MINOR] PythonAPI update builtin algorithms
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 <ba...@tugraz.at>
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)