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>")