@dc.param(
String(
key="odType",
default="IncToDec",
help="The type of the overfitting detector to use.IncToDec,Iter",
)
)
@dc.param(Int(key="randomSeed", default=0, help="The random seed used for training."))
@dc.param(
Int(
key="metricPeriod",
default=1,
help="The frequency of iterations to calculate the values of objectives and metrics. ",
)
)
@dc.param(Bool(key="useBestModel", default=True, help="Use Best Model."))
@dc.param(
Float(
key="baggingTemperature",
default=1,
help="Defines the settings of the Bayesian bootstrap. ",
)
)
@dc.param(
String(
key="evalMetric",
default="MAE",
help="""The metric used for overfitting detection and best model selection.
MAE,MAPE,RMSE,Poisson,SMAPE,R2,MSLE,MedianAbsoluteError""",
)
)
# 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,
help="Subsample ratio of columns when constructing each tree.",
))
@dc.param(
Float(key="regAlpha",
default=0.0,
help="L1 regularization term on weights."))
@dc.param(
Float(key="regLambda",
default=0.0,
help="L2 regularization term on weights."))
@dc.param(Int(key="randomState", default=0, help="Random number seed."))
@dc.param(Int(key="nJobs", default=-1, help="Number of parallel threads."))
@dc.param(
Bool(
key="silent",
default=True,
help="Whether to print messages while running boosting.",
))
@dc.param(Bool(key="needTrain", default=True))
@dc.output(SklearnModel(key="outputModel"))
def SPLightGBMClassifier(context):
# 从 Context 中获取相关数据
args = context.args
# 查看上一节点发送的 args.inputData 数据
df = args.inputData
featureColumns = args.featureColumns
labelColumn = args.labelColumn
features = df[featureColumns].values
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,
ARMAResultsWrapper,
ARIMAResultsWrapper,
SARIMAXResultsWrapper,
),
):
print("Time series model loaded")
default=[0.75, -0.25],
help=
"coefficient for autoregressive lag polynomial, including zero lag",
))
@dc.param(
ListOfFloat(
key="ma",
default=[0.65, 0.35],
help=
"coefficient for moving-average lag polynomial, including zero lag",
))
@dc.param(
Int(key="nsample", default=250, help="length of simulated time series"))
@dc.param(Float(key="sigma", default=1.0, help="standard deviation of noise"))
@dc.param(Int(key="randomSeed", default=12345, help="random seed"))
@dc.param(Bool(key="dateCol", default=True, help="date in dataset"))
@dc.param(
String(
key="startDate",
default="19800131",
help="The first abbreviated date, for instance, '1965q1' or '1965m1'",
))
@dc.param(String(key="freq", default="M", help="DateOffset"))
@dc.output(Csv(key="outputData"))
def SPARMASample(context):
# 从 Context 中获取相关数据
args = context.args
# 查看上一节点发送的 args.inputData 数据
np.random.seed(args.randomSeed)
arparams = np.array(args.ar)
maparams = np.array(args.ma)
# coding=utf-8
from __future__ import absolute_import, print_function
from suanpan.docker import DockerComponent as dc
from suanpan.docker.arguments import Int, Csv, ListOfInt, 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(
ListOfInt(
key="order",
default=[1, 0, 0],
help="The (p,d,q) order of the model for the number of AR parameters, "
"differences, and MA parameters.",
)
)
@dc.param(
ListOfInt(
key="seasonalOrder",
default=[0, 0, 0, 0],
help="The (P,D,Q,s) order of the seasonal component of the model for the"
" AR parameters, differences, MA parameters, and periodicity.",
)
)
@dc.param(