def __init__(self, arg_str):
ProbabilisticInterleave.__init__(self, arg_str)
# parse arguments
parser = argparse.ArgumentParser(
description="Initialize probabilistic"
" interleave with history.",
prog="ProbabilisticInterleaveWithHistory")
parser.add_argument(
"-l",
"--history_length",
type=int,
required=True,
help="Number of historical data points to keep in memory and use "
"to infer feedback.")
parser.add_argument(
"-b",
"--biased",
default=False,
help="Set to true if comparison should be biased (i.e., not use"
"importance sampling).")
if not arg_str:
raise (Exception("Comparison arguments missing. " +
parser.format_usage()))
args = vars(parser.parse_args(split_arg_str(arg_str)))
self.history_length = args["history_length"]
self.biased = string_to_boolean(args["biased"])
logging.info("Initialized historical data usage to: %r" % self.biased)
# initialize history
self.history = []
def __init__(self, feature_count, arg_str):
"""
@param featur_count: the number of features
@param arg_str: "-h HISTORY_LENGTH -e NUM_CANDIDATES \
-s SELECT_CANDIDATE".
"""
ListwiseLearningSystem.__init__(self, feature_count, arg_str)
parser = argparse.ArgumentParser(prog=self.__class__.__name__)
parser.add_argument("-e", "--num_candidates", required=True, type=int,
help="Number of candidate rankers to explore in each round.")
parser.add_argument("-l", "--history_length", required=True, type=int,
help="Number of historic data points to take into account when "
"pre-selecting candidates.")
parser.add_argument("-s", "--select_candidate", required=True,
help="Method for selecting a candidate ranker from a ranker pool."
" Options: select_candidate_random, select_candidate_simple,"
" select_candidate_repeated, or own implementation.")
parser.add_argument("-b", "--biased", default="False",
help="Set to true if comparison should be biased (i.e., not use"
"importance sampling).")
parser.add_argument("-r", "--num_repetitions", type=int, default=1,
help="The number of repetitions for each ranker pair evaluation"
"(when the selection method is select_candidate_repeated).")
args = vars(parser.parse_known_args(split_arg_str(arg_str))[0])
self.num_candidates = args["num_candidates"]
self.select_candidate = getattr(self, args["select_candidate"])
self.history_length = args["history_length"]
self.biased = string_to_boolean(args["biased"])
logging.info("Initialized historical data usage to: %r" % self.biased)
self.num_repetitions = args["num_repetitions"]
self.history = []
def __init__(self, arg_str):
ProbabilisticInterleave.__init__(self, arg_str)
# parse arguments
parser = argparse.ArgumentParser(
description="Initialize probabilistic" " interleave with history.",
prog="ProbabilisticInterleaveWithHistory",
)
parser.add_argument(
"-l",
"--history_length",
type=int,
required=True,
help="Number of historical data points to keep in memory and use " "to infer feedback.",
)
parser.add_argument(
"-b",
"--biased",
default=False,
help="Set to true if comparison should be biased (i.e., not use" "importance sampling).",
)
if not arg_str:
raise (Exception("Comparison arguments missing. " + parser.format_usage()))
args = vars(parser.parse_args(split_arg_str(arg_str)))
self.history_length = args["history_length"]
self.biased = string_to_boolean(args["biased"])
logging.info("Initialized historical data usage to: %r" % self.biased)
# initialize history
self.history = []
def __init__(self, arg_str=None):
self.pi = ProbabilisticInterleave(arg_str)
if arg_str:
parser = argparse.ArgumentParser(description="Parse arguments for "
"interleaving method.",
prog=self.__class__.__name__)
parser.add_argument("-a",
"--aggregate",
choices=[
"expectation", "log-likelihood-ratio",
"likelihood-ratio", "log-ratio", "binary"
],
default="expectation")
parser.add_argument("-e",
"--exploration_rate",
type=float,
required=True,
help="Exploration rate, 0.5 = perfect "
"exploration, 0.0 = perfect exploitation.")
parser.add_argument("-b", "--biased", default="False")
args = vars(parser.parse_known_args(split_arg_str(arg_str))[0])
self.exploration_rate = args["exploration_rate"]
self.aggregate = args["aggregate"]
self.biased = string_to_boolean(args["biased"])
else:
raise ValueError("Configuration arguments required. Please provide"
" at least a value for the exploration rate.")
def __init__(self, feature_count, arg_str):
"""
@param featur_count: the number of features
@param arg_str: "-h HISTORY_LENGTH -e NUM_CANDIDATES \
-s SELECT_CANDIDATE".
"""
ListwiseLearningSystem.__init__(self, feature_count, arg_str)
parser = argparse.ArgumentParser(prog=self.__class__.__name__)
parser.add_argument(
"-e",
"--num_candidates",
required=True,
type=int,
help="Number of candidate rankers to explore in each round.")
parser.add_argument(
"-l",
"--history_length",
required=True,
type=int,
help="Number of historic data points to take into account when "
"pre-selecting candidates.")
parser.add_argument(
"-s",
"--select_candidate",
required=True,
help="Method for selecting a candidate ranker from a ranker pool."
" Options: select_candidate_random, select_candidate_simple,"
" select_candidate_repeated, or own implementation.")
parser.add_argument(
"-b",
"--biased",
default="False",
help="Set to true if comparison should be biased (i.e., not use"
"importance sampling).")
parser.add_argument(
"-r",
"--num_repetitions",
type=int,
default=1,
help="The number of repetitions for each ranker pair evaluation"
"(when the selection method is select_candidate_repeated).")
args = vars(parser.parse_known_args(split_arg_str(arg_str))[0])
self.num_candidates = args["num_candidates"]
self.select_candidate = getattr(self, args["select_candidate"])
self.history_length = args["history_length"]
self.biased = string_to_boolean(args["biased"])
logging.info("Initialized historical data usage to: %r" % self.biased)
self.num_repetitions = args["num_repetitions"]
self.history = []
def __init__(self, arg_str=None):
self.pi = ProbabilisticInterleave(arg_str)
if arg_str:
parser = argparse.ArgumentParser(description="Parse arguments for "
"interleaving method.", prog=self.__class__.__name__)
parser.add_argument("-a", "--aggregate", choices=["expectation",
"log-likelihood-ratio", "likelihood-ratio", "log-ratio",
"binary"], default="expectation")
parser.add_argument("-e", "--exploration_rate", type=float,
required=True, help="Exploration rate, 0.5 = perfect "
"exploration, 0.0 = perfect exploitation.")
parser.add_argument("-b", "--biased", default="False")
args = vars(parser.parse_known_args(split_arg_str(arg_str))[0])
self.exploration_rate = args["exploration_rate"]
self.aggregate = args["aggregate"]
self.biased = string_to_boolean(args["biased"])
else:
raise ValueError("Configuration arguments required. Please provide"
" at least a value for the exploration rate.")
import os
from flask import Flask, send_from_directory
from flask_restful import Api
from flask_cors import CORS
from db import redis_client
from utils import string_to_boolean
from resources.test_resource import TestResource
from resources.tags_resource import TagsResource
app = Flask(__name__, static_folder="front/build/")
if string_to_boolean(os.environ['DEV']):
CORS(app)
api = Api(app)
# Environment variables
debug_mode = string_to_boolean(os.environ['DEV'])
@app.route("/", defaults={"path": ""})
@app.route("/<path:path>")
def serve_react(path):
if path != "" and os.path.exists(app.static_folder + "/" + path):
return send_from_directory(app.static_folder, path)
else:
return send_from_directory(app.static_folder, "index.html")
api.add_resource(TestResource, "/api/test")
api.add_resource(TagsResource, "/api/tags/<server_id>")
