# coding=utf-8
from __future__ import absolute_import, print_function
from suanpan.docker import DockerComponent as dc
from suanpan.docker.arguments import Int, String, Table, Bool, Float, ListOfString
from arguments import SklearnModel
from catboost import CatBoostRegressor
@dc.input(
Table(
key="inputData", table="inputTable", partition="inputPartition", 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.",
)
# coding=utf-8
from __future__ import absolute_import, print_function
from suanpan.docker import DockerComponent as dc
from suanpan.docker.arguments import Int, String, Bool, Float, ListOfString, Table
import lightgbm as lgb
from arguments import SklearnModel
@dc.input(
Table(key="inputData",
table="inputTable",
partition="inputPartition",
required=True))
@dc.column(ListOfString(key="featureColumns", default=["f1", "f2", "f3",
"f4"]))
@dc.column(String(key="labelColumn", default="label"))
@dc.param(
Int(key="maxDepth",
default=-1,
help="Maximum tree depth for base learners"))
@dc.param(
String(
key="boostingType",
default="gbdt",
help="Specify which booster to use: 'goss', 'rf' or 'dart'",
))
@dc.param(
Int(key="numLeaves",
default=31,
help="Maximum tree leaves for base learners."))
# coding=utf-8
from __future__ import absolute_import, print_function
from suanpan.docker import DockerComponent as dc
from suanpan.docker.arguments import Csv, ListOfString, String, Int
import statsmodels.api as sm
from arguments import SklearnModel
@dc.input(Csv(key="inputData"))
@dc.column(ListOfString(key="featureColumns", default=["a", "b", "c", "d"]))
@dc.column(String(key="labelColumn", default="e"))
@dc.param(
String(
key="missing",
default="none",
help="Available options are ‘none’, ‘drop’, and ‘raise’.",
)
)
@dc.param(
String(
key="method",
default="lbfgs",
help="‘newton’, ‘bfgs’, ‘lbfgs’, ‘powell’, ‘cg’, ‘ncg’, ‘basinhopping’,"
" ‘minimize’",
)
)
@dc.param(
Int(key="maxiter", default=35, help="The maximum number of iterations to perform.")
)
@dc.param(Int(key="disp", default=1, help="Set to True to print convergence messages."))
if not predictColumns:
predictColumns = [
"prediction_{}".format(str(i)) for i in range(actualColumnCount)
]
if len(predictColumns) != actualColumnCount:
raise RecursionError(
"Actual predict column count is: {}, but the length of predict columns given is: {}"
.format(actualColumnCount, len(predictColumns)))
return predictColumns
@dc.input(Csv(key="inputData"))
@dc.input(SklearnModel(key="inputModel"))
@dc.column(ListOfString(key="featureColumns", default=[]))
@dc.column(ListOfString(key="predictColumns", default="prediction"))
@dc.output(Csv(key="outputData"))
def SPPredict(context):
args = context.args
df = args.inputData
model = common_util.encapsulateModel(args.inputModel)
featureColumns = args.featureColumns
predictColumns = args.predictColumns
X = df[featureColumns].values if len(featureColumns) > 0 else df.values
predictions = model.predict(X)
isEstimator = model.isEstimator()