def run(file, meta, features, response, family, outpath, predict):
"""
Fit a generalized linear regression model.
"""
import pathlib
from pybda.util.string import drop_suffix
from pybda.logger import set_logger
from pybda.spark_session import SparkSession
from pybda.io.as_filename import as_logfile
from pybda.io.io import read_and_transmute, read_column_info
outpath = drop_suffix(outpath, "/")
set_logger(as_logfile(outpath))
with SparkSession() as spark:
try:
meta, features = read_column_info(meta, features)
data = read_and_transmute(spark, file, features, response)
fl = GBM(spark, response, features, family)
fl = fl.fit(data)
fl.write(outpath)
if pathlib.Path(predict).exists():
pre_data = read_and_transmute(spark,
predict,
features,
drop=False)
pre_data = fl.predict(pre_data)
pre_data.write(outpath)
except Exception as e:
logger.error("Some error: %s", str(e))
def run(factors, file, features, outpath):
"""
Fit a factor analysis to a data set
"""
from pybda.util.string import drop_suffix
from pybda.logger import set_logger
from pybda.spark_session import SparkSession
from pybda.io.io import read_info, read_and_transmute
from pybda.io.as_filename import as_logfile
outpath = drop_suffix(outpath, "/")
set_logger(as_logfile(outpath))
with SparkSession() as spark:
try:
features = read_info(features)
data = read_and_transmute(spark,
file,
features,
assemble_features=False)
fl = FactorAnalysis(spark, factors, features)
trans = fl.fit_transform(data)
trans.write(outpath)
except Exception as e:
logger.error("Some error: {}".format(str(e)))
def run(clusters, file, features, outpath):
"""
Fit a kmeans-clustering to a data set.
"""
from pybda.io.as_filename import as_logfile
from pybda.logger import set_logger
from pybda.spark_session import SparkSession
from pybda.util.string import drop_suffix
from pybda.io.io import read_info, read_and_transmute
outfolder = drop_suffix(outpath, "/")
set_logger(as_logfile(outpath))
with SparkSession() as spark:
try:
features = read_info(features)
data = read_and_transmute(spark, file, features)
fit = KMeans(spark, clusters, features)
fit = fit.fit(data, outfolder)
fit.write(data, outfolder)
except Exception as e:
logger.error("Some error: {}".format(e))