def rst_table_modules(classifier=False):
"""
produces a table with all modules recommended to do machine learning
@param classifier keep classifiers?
@return string
"""
mod = ensae_fullset()
mod.sort()
df = pandas.DataFrame(_.as_dict(rst_link=True) for _ in mod)
if classifier:
df = df[[
"usage", "rst_link", "kind", "version", "license", "purpose",
"classifier"
]]
df["classifier"] = df.apply(
lambda row: classifiers2string(row["classifier"]), axis=1)
df.columns = [
"usage", "name", "kind", "version", "license", "purpose",
"classifier"
]
else:
df = df[["usage", "rst_link", "kind", "version", "license", "purpose"]]
df.columns = ["usage", "name", "kind", "version", "license", "purpose"]
df["lname"] = df["name"].apply(lambda s: s.lower())
df = df.sort_values("lname").drop("lname", axis=1)
df = df.reset_index(drop=True).reset_index(drop=False)
return df2rst(df)
def rst_table_modules():
"""
produces a table with all modules recommended to do machine learning
@return string
"""
mod = ensae_fullset()
mod.sort()
df = pandas.DataFrame(_.as_dict(rst_link=True) for _ in mod)
df = df[["usage", "rst_link", "kind", "version",
"license", "purpose", "classifier"]]
df.columns = ["usage", "name", "kind", "version",
"license", "purpose", "classifier"]
return df2rst(df)
def rst_table_modules():
"""
produces a table with all modules recommended to do machine learning
@return string
"""
mod = ensae_fullset()
mod.sort()
df = pandas.DataFrame(_.as_dict(rst_link=True) for _ in mod)
df = df[["usage", "rst_link", "kind", "version",
"license", "purpose", "classifier"]]
df["classifier"] = df.apply(
lambda row: classifiers2string(row["classifier"]), axis=1)
df.columns = ["usage", "name", "kind", "version",
"license", "purpose", "classifier"]
return df2rst(df)
def rst_table_modules():
"""
produces a table with all modules recommended to do machine learning
@return string
"""
mod = ensae_fullset()
mod.sort()
df = pandas.DataFrame(_.as_dict(rst_link=True) for _ in mod)
df = df[["usage", "rst_link", "kind", "version",
"license", "purpose", "classifier"]]
df["classifier"] = df.apply(
lambda row: classifiers2string(row["classifier"]), axis=1)
df.columns = ["usage", "name", "kind", "version",
"license", "purpose", "classifier"]
df["lname"] = df["name"].apply(lambda s: s.lower())
df = df.sort_values("lname").drop("lname", axis=1)
return df2rst(df)
try:
import pyquickhelper
except ImportError:
import sys
sys.path.append("../pyquickhelper/src")
import pyquickhelper
if __name__ == "__main__":
import sys
sys.path.append("src")
from pyquickhelper.loghelper import fLOG
fLOG(OutputPrint=True)
from ensae_teaching_cs.automation.win_setup_helper import last_function
from pymyinstall import win_python_setup
from pymyinstall.packaged import ensae_fullset
list_modules = ensae_fullset()
win_python_setup(module_list=list_modules, verbose=True,
download_only=False,
no_setup=False,
last_function=last_function,
# 3.2.5 to be able to use rpy2
selection={"R==3.2.5", "tdm", "jdk"},
documentation=False,
source="2",
fLOG=fLOG)
sys.path.append("../pymyinstall/src")
import pymyinstall
try:
import pyquickhelper
except ImportError:
import sys
sys.path.append("../pyquickhelper/src")
import pyquickhelper
if __name__ == "__main__":
import sys
sys.path.append("src")
from pyquickhelper import fLOG
fLOG(OutputPrint=True)
from ensae_teaching_cs.automation.win_setup_helper import last_function
from pymyinstall import win_python_setup
from pymyinstall.packaged import ensae_fullset
list_modules = ensae_fullset()
win_python_setup(module_list=list_modules,
verbose=True,
download_only=False,
no_setup=False,
last_function=last_function,
selection={"R", "tdm"},
documentation=False,
fLOG=fLOG)
