# coding=utf-8 from __future__ import absolute_import, print_function from suanpan.docker import DockerComponent as dc from suanpan.docker.arguments import Csv import statsmodels.api as sm @dc.output(Csv(key="outputData")) def SPMacroData(context): dta = sm.datasets.macrodata.load_pandas().data return dta if __name__ == "__main__": SPMacroData()
# coding=utf-8 from __future__ import absolute_import, print_function from suanpan.docker import DockerComponent as dc from suanpan.docker.arguments import Int, String, Csv, Bool, Float, ListOfString from arguments import SklearnModel from catboost import CatBoostRegressor @dc.input(Csv(key="inputData", required=True)) @dc.column(ListOfString(key="featureColumns", default=[])) @dc.column(String(key="labelColumn", default="MEDV")) @dc.param( Int( key="iterations", default=1000, help= "The maximum number of trees that can be built when solving machine learning problems.", )) @dc.param(Float(key="learningRate", default=0.03, help="The learning rate.")) @dc.param(Int(key="depth", default=6, help="Depth of the tree.")) @dc.param( Float( key="l2LeafReg", default=3.0, help="Coefficient at the L2 regularization term of the cost function.", )) @dc.param( Float( key="rsm", default=1,
# coding=utf-8 from __future__ import absolute_import, print_function from suanpan.docker import DockerComponent as dc from suanpan.docker.arguments import Int, Csv, String, Bool import statsmodels.api as sm import pandas as pd from arguments import SklearnModel @dc.input(Csv(key="inputData")) @dc.column(Bool(key="timestampIndex", default=False)) @dc.column(String(key="timestampColumn", default="date")) @dc.column(String(key="labelColumn", default="y")) @dc.param( String( key="missing", default="none", help="Available options are ‘none’, ‘drop’, and ‘raise’.", )) @dc.param( String( key="trend", default="c", help= "Whether to include a constant or not. ‘c’ includes constant, ‘nc’ no constant.", )) @dc.param(String(key="method", default="cmle", help="‘cmle’, ‘mle’")) @dc.param( Int(key="maxiter", default=35,
from suanpan.docker import DockerComponent as dc from suanpan.docker.arguments import Csv, String, Bool, ListOfString import pandas as pd import numpy as np from statsmodels.tsa.ar_model import ARResultsWrapper from statsmodels.tsa.statespace.sarimax import SARIMAXResultsWrapper from statsmodels.tsa.arima_model import ARMAResultsWrapper, ARIMAResultsWrapper from statsmodels.regression.linear_model import RegressionResultsWrapper from statsmodels.discrete.discrete_model import ( BinaryResultsWrapper, MultinomialResultsWrapper, ) from arguments import SklearnModel @dc.input(Csv(key="inputData")) @dc.input(SklearnModel(key="inputModel")) @dc.column(ListOfString(key="featureColumns", default=["a", "b", "c", "d"])) @dc.column(String(key="predictColumn", default="prediction")) @dc.param(String(key="start", default="2000-11-30")) @dc.param(String(key="end", default="2001-05-31")) @dc.param(Bool(key="dynamic", default=True)) @dc.output(Csv(key="outputData")) def SPStatsPredict(context): args = context.args model = args.inputModel if isinstance( model, ( ARResultsWrapper,