from ls_dataset.d3m_prediction import D3MPrediction
from ls_problem_desc.ls_problem import ProblemDesc
from ls_problem_desc.d3m_problem import DefaultProblemDesc
from d3m_ta2.ta2_client import TA2Client
from d3m_eval.summer_2018.prob_discovery import ProblemDiscoveryWriter
# from ls_workflow.workflow import Workflow as Solution
from modeling.models import *
from modeling.component_out import *
__version__ = '0.1'
if __name__ == '__main__':
# Parse argumennts
parser = get_default_arg_parser("Model Search")
parser.add_argument('-file0', type=argparse.FileType('r'),
help='the dataset json provided for the search')
parser.add_argument('-file1', type=argparse.FileType('r'),
help='the problem json provided for the search')
args = parser.parse_args()
if args.is_test is not None:
is_test = args.is_test == 1
else:
is_test = False
# Get config file
config = SettingsFactory.get_settings(path.join(args.programDir, 'program', 'settings.cfg'),
program_dir=args.programDir,
working_dir=args.workingDir,
import pandas as pd
# Workflow component specific imports
from ls_utilities.ls_logging import setup_logging
from ls_utilities.cmd_parser import get_default_arg_parser
from ls_utilities.ls_wf_settings import *
from ls_dataset.d3m_dataset import D3MDataset
from modeling.models import *
from modeling.component_out import *
__version__ = '0.1'
if __name__ == '__main__':
# Parse argumennts
parser = get_default_arg_parser("Rerank Model")
parser.add_argument('-model_id',
type=str,
help='the name of the dataset to import')
parser.add_argument('-new_rank',
type=int,
help='the new rank to resort the specified model')
parser.add_argument('-file0',
type=argparse.FileType('r'),
help='the tab-separated list of models to select from')
args = parser.parse_args()
if args.is_test is not None:
is_test = args.is_test == 1
else:
is_test = False
# plt.yticks(tick_marks, classes)
# fmt = '.2f' if normalize else 'd'
# thresh = cm.max() / 2.
# for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
# plt.text(j, i, format(cm[i, j], fmt),
# horizontalalignment="center",
# color="white" if cm[i, j] > thresh else "black")
# plt.tight_layout()
# plt.ylabel('True label')
# plt.xlabel('Predicted label')
if __name__ == '__main__':
# Parse argumennts
parser = get_default_arg_parser("D3M Compare Model Predictions")
parser.add_argument('-file0',
type=argparse.FileType('r'),
help='the dataset json provided for the search')
parser.add_argument('-file1',
type=argparse.FileType('r'),
help='the problem json provided for the search')
parser.add_argument('-file2',
type=argparse.FileType('r'),
help='at tab-delimited list of the fitted models')
parser.add_argument('-file3',
type=argparse.FileType('r'),
help='the csv of a data predictions dataframe')
args = parser.parse_args()
if args.is_test is not None:
import csv
# Workflow component specific imports
from ls_utilities.ls_logging import setup_logging
from ls_utilities.cmd_parser import get_default_arg_parser
from ls_utilities.ls_wf_settings import *
from ls_dataset.d3m_dataset import D3MDataset
# from ls_problem_desc.d3m_problem import *
from ls_problem_desc.ls_problem import *
__version__ = '0.1'
if __name__ == '__main__':
# Parse argumennts
parser = get_default_arg_parser("Initialize a new problem")
parser.add_argument('-probname',
type=str,
help='the name of the new problem given by the user')
parser.add_argument(
'-probdesc',
type=str,
help='the plain text description of the problem supplied by the user')
parser.add_argument('-targetname',
type=str,
help='the name of the column from the dataset to use')
parser.add_argument('-file0',
type=argparse.FileType('r'),
help='the description of the dataset')
args = parser.parse_args()
from ls_utilities.cmd_parser import get_default_arg_parser
from ls_utilities.ls_wf_settings import SettingsFactory
from ls_dataset.d3m_dataset import D3MDataset
# from ls_dataset.d3m_prediction import D3MPrediction
# from ls_problem_desc.ls_problem import ProblemDesc
# from ls_problem_desc.d3m_problem import D3MProblemDesc
# from ls_workflow.workflow import Workflow
from d3m_ta2.ta2_client import TA2Client
from modeling.models import *
from modeling.component_out import *
__version__ = '0.1'
if __name__ == '__main__':
# Parse argumennts
parser = get_default_arg_parser("Model Predict")
parser.add_argument(
'-file0',
type=argparse.FileType('r'),
help='the dataset json provided for making predictions')
parser.add_argument('-file1',
type=argparse.FileType('r'),
help='at tab-delimited list of the fitted models')
args = parser.parse_args()
if args.is_test is not None:
is_test = args.is_test == 1
else:
is_test = False
# Get config file
# from ls_utilities.ls_wf_settings import Settings as stg
from ls_utilities.ls_wf_settings import SettingsFactory
from ls_dataset.d3m_dataset import D3MDataset
from ls_dataset.d3m_prediction import D3MPrediction
from ls_problem_desc.ls_problem import ProblemDesc
from ls_problem_desc.d3m_problem import DefaultProblemDesc
from d3m_ta2.ta2_client import TA2Client
from d3m_eval.summer_2018.prob_discovery import ProblemDiscoveryWriter
from modeling.models import *
from modeling.component_out import *
__version__ = '0.1'
if __name__ == '__main__':
# Parse argumennts
parser = get_default_arg_parser("Fit Models")
parser.add_argument('-file0',
type=argparse.FileType('r'),
help='the dataset json provided for the search')
parser.add_argument('-file1',
type=argparse.FileType('r'),
help='at tab-delimited list of models to fit')
args = parser.parse_args()
if args.is_test is not None:
is_test = args.is_test == 1
else:
is_test = False
# Get config file
config = SettingsFactory.get_settings(path.join(args.programDir, 'program',
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j,
i,
format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
if __name__ == '__main__':
# Parse argumennts
parser = get_default_arg_parser("D3M Visualize Confusion Matrix")
parser.add_argument(
'-file0',
type=argparse.FileType('r'),
help='the dataset json including pipeline search result')
args = parser.parse_args()
if args.is_test is not None:
is_test = args.is_test == 1
else:
is_test = False
# Get config file
config = SettingsFactory.get_settings(path.join(args.programDir, 'program',
'settings.cfg'),
program_dir=args.programDir,
from ls_dataset.d3m_prediction import D3MPrediction
from ls_problem_desc.ls_problem import ProblemDesc
from ls_problem_desc.d3m_problem import DefaultProblemDesc
from d3m_ta2.ta2_client import TA2Client
# from ls_workflow.workflow import Workflow as Solution
from modeling.models import Model
from modeling.component_out import *
from modeling.scores import *
__version__ = '0.1'
if __name__ == '__main__':
# Parse argumennts
parser = get_default_arg_parser("Model Rank")
parser.add_argument('-metric', type=str,
help='the metric to use to compare the models')
parser.add_argument('-ordering', type=str,
help='the sort order use to rank the models')
parser.add_argument('-file0', type=argparse.FileType('r'),
help='the dataset json provided for the search')
parser.add_argument('-file1', type=argparse.FileType('r'),
help='the set of models to score')
args = parser.parse_args()
if args.is_test is not None:
is_test = args.is_test == 1
else:
is_test = False
import csv
# Workflow component specific imports
from ls_utilities.ls_logging import setup_logging
from ls_utilities.cmd_parser import get_default_arg_parser
from ls_utilities.ls_wf_settings import *
from ls_dataset.d3m_dataset import D3MDataset
# from ls_problem_desc.d3m_problem import *
from ls_problem_desc.ls_problem import *
__version__ = '0.1'
if __name__ == '__main__':
# Parse argumennts
parser = get_default_arg_parser("Select Problem Task Select")
parser.add_argument('-task_name',
type=str,
help='the task type the user selected')
parser.add_argument('-file0',
type=argparse.FileType('r'),
help='the description of the dataset')
parser.add_argument('-file1',
type=argparse.FileType('r'),
help='the problem template with target selected')
args = parser.parse_args()
if args.is_test is not None:
is_test = args.is_test == 1
else:
is_test = False
import os.path as path
import os
import csv
# Workflow component specific imports
from ls_utilities.ls_logging import setup_logging
from ls_utilities.cmd_parser import get_default_arg_parser
from ls_utilities.ls_wf_settings import *
from ls_dataset.d3m_dataset import D3MDataset
__version__ = '0.1'
if __name__ == '__main__':
# Parse argumennts
parser = get_default_arg_parser("Import List of available D3M Datasets")
args = parser.parse_args()
if args.is_test is not None:
is_test = args.is_test == 1
else:
is_test = False
# Get config file
config = SettingsFactory.get_settings(path.join(args.programDir, 'program',
'settings.cfg'),
program_dir=args.programDir,
working_dir=args.workingDir,
is_test=is_test)
# Setup Logging
import csv
# Workflow component specific imports
from ls_utilities.ls_logging import setup_logging
from ls_utilities.cmd_parser import get_default_arg_parser
from ls_utilities.ls_wf_settings import *
from ls_dataset.d3m_dataset import D3MDataset
from modeling.models import *
from modeling.component_out import *
__version__ = '0.1'
if __name__ == '__main__':
# Parse argumennts
parser = get_default_arg_parser("Select Model")
parser.add_argument('-model_id',
type=str,
help='the name of the dataset to import')
parser.add_argument('-file0',
type=argparse.FileType('r'),
help='the tab-separated list of models to select from')
args = parser.parse_args()
if args.is_test is not None:
is_test = args.is_test == 1
else:
is_test = False
# Get config file
config = SettingsFactory.get_settings(path.join(args.programDir, 'program',
# Workflow component specific imports
from ls_utilities.ls_logging import setup_logging
from ls_utilities.cmd_parser import get_default_arg_parser
from ls_utilities.ls_wf_settings import *
from ls_dataset.d3m_dataset import D3MDataset
from ls_problem_desc.d3m_problem import DefaultProblemDesc
from ls_problem_desc.ls_problem import ProblemDesc
__version__ = '0.1'
if __name__ == '__main__':
# Parse argumennts
parser = get_default_arg_parser("Generate Default Problem")
parser.add_argument('-file0', type=argparse.FileType('r'),
help='the dataset json provided for the search')
args = parser.parse_args()
if args.is_test is not None:
is_test = args.is_test == 1
else:
is_test = False
# Get config file
config = SettingsFactory.get_settings(path.join(args.programDir, 'program', 'settings.cfg'),
program_dir=args.programDir,
working_dir=args.workingDir,
is_test=is_test
)
class LS_Path_Factory(object):
def __init__(self, workingDir, programDir):
self.workingDir = workingDir
self.programDir = programDir
def get_out_path(self, fpath):
return path.join(self.workingDir, fpath)
def get_hosted_path(self, fpath):
return "LearnSphere?htmlPath=" + self.get_out_path(fpath)
if __name__ == '__main__':
# Parse argumennts
parser = get_default_arg_parser("Compare Model Scores")
parser.add_argument('-file0',
type=argparse.FileType('r'),
help='the scores for the model to render in a boxplot')
args = parser.parse_args()
if args.is_test is not None:
is_test = args.is_test == 1
else:
is_test = False
# Get config file
config = SettingsFactory.get_settings(path.join(args.programDir, 'program',
'settings.cfg'),
program_dir=args.programDir,
working_dir=args.workingDir,
from ls_utilities.ls_logging import setup_logging
from ls_utilities.cmd_parser import get_default_arg_parser
from ls_utilities.ls_wf_settings import *
from ls_dataset.d3m_dataset import D3MDataset
from modeling.models import *
from modeling.component_out import *
from d3m_ta2.ta2_client import TA2Client
from d3m_eval.summer_2018.model_generation import RankedPipelineWriter
__version__ = '0.1'
if __name__ == '__main__':
# Parse argumennts
parser = get_default_arg_parser("Export Models")
parser.add_argument('-file0', type=argparse.FileType('r'),
help='the tab-separated list of ranked models to export')
args = parser.parse_args()
if args.is_test is not None:
is_test = args.is_test == 1
else:
is_test = False
# Get config file
config = SettingsFactory.get_settings(path.join(args.programDir, 'program', 'settings.cfg'),
program_dir=args.programDir,
working_dir=args.workingDir,
is_test=is_test
)
import csv
# Workflow component specific imports
from ls_utilities.ls_logging import setup_logging
from ls_utilities.cmd_parser import get_default_arg_parser
from ls_utilities.ls_wf_settings import *
from ls_dataset.d3m_dataset import D3MDataset
# from ls_problem_desc.d3m_problem import *
from ls_problem_desc.ls_problem import *
__version__ = '0.1'
if __name__ == '__main__':
# Parse argumennts
parser = get_default_arg_parser("Select Problem Metric")
parser.add_argument('-metric',
type=str,
help='the metric the user selected')
parser.add_argument('-file0',
type=argparse.FileType('r'),
help='the description of the dataset')
parser.add_argument('-file1',
type=argparse.FileType('r'),
help='the problem template with target selected')
args = parser.parse_args()
if args.is_test is not None:
is_test = args.is_test == 1
else:
is_test = False
from ls_utilities.ls_wf_settings import *
from ls_dataset.d3m_dataset import D3MDataset
from ls_dataset.d3m_prediction import D3MPrediction
from ls_problem_desc.ls_problem import ProblemDesc
from ls_problem_desc.d3m_problem import DefaultProblemDesc
from d3m_ta2.ta2_client import TA2Client
# from ls_workflow.workflow import Workflow as Solution
from modeling.models import Model
from modeling.component_out import *
from modeling.scores import *
__version__ = '0.1'
if __name__ == '__main__':
# Parse argumennts
parser = get_default_arg_parser("Model Score")
parser.add_argument('-metric',
type=str,
help='the metric to use to compare the models')
parser.add_argument('-file0',
type=argparse.FileType('r'),
help='the dataset json provided for the search')
parser.add_argument('-file1',
type=argparse.FileType('r'),
help='the set of models to score')
args = parser.parse_args()
if args.is_test is not None:
is_test = args.is_test == 1
else:
is_test = False
from ls_utilities.cmd_parser import get_default_arg_parser
from ls_utilities.ls_wf_settings import SettingsFactory
from ls_utilities.ls_wf_settings import *
from ls_dataset.d3m_dataset import D3MDataset
from ls_dataset.d3m_prediction import D3MPrediction
from ls_problem_desc.ls_problem import ProblemDesc
from ls_problem_desc.d3m_problem import DefaultProblemDesc
from modeling.models import *
from modeling.component_out import *
__version__ = '0.1'
if __name__ == '__main__':
# Parse argumennts
parser = get_default_arg_parser("D3M Dataset Augmenter")
parser.add_argument('-file0',
type=argparse.FileType('r'),
help='the dataset json provided for the search')
parser.add_argument('-file1',
type=argparse.FileType('r'),
help='the problem json provided for the search')
args = parser.parse_args()
if args.is_test is not None:
is_test = args.is_test == 1
else:
is_test = False
# Get config file
config = SettingsFactory.get_settings(path.join(args.programDir, 'program',
import pprint
import argparse
import csv
# Workflow component specific imports
from ls_utilities.ls_logging import setup_logging
from ls_utilities.cmd_parser import get_default_arg_parser
from ls_utilities.ls_wf_settings import *
from ls_dataset.d3m_dataset import D3MDataset
__version__ = '0.1'
if __name__ == '__main__':
# Parse argumennts
parser = get_default_arg_parser("Select Dataset")
parser.add_argument('-ds_name', type=str,
help='the name of the dataset to import')
parser.add_argument('-file0', type=argparse.FileType('r'),
help='the file list of all datasets')
args = parser.parse_args()
if args.is_test is not None:
is_test = args.is_test == 1
else:
is_test = False
# Get config file
config = SettingsFactory.get_settings(path.join(args.programDir, 'program', 'settings.cfg'),
program_dir=args.programDir,
working_dir=args.workingDir,