roclark

''' Script for downloading all GLUE data.

Note: for legal reasons, we are unable to host MRPC.
You can either use the version hosted by the SentEval team, which is already tokenized, 
or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually.
For Windows users, you can run the .msi file. For Mac and Linux users, consider an external library such as 'cabextract' (see below for an example).
You should then rename and place specific files in a folder (see below for an example).

mkdir MRPC
cabextract MSRParaphraseCorpus.msi -d MRPC

roclark

def main():
    def env_creator_lambda(env_config):
        return env_creator(args.environment,
                           config,
                           args.dimension,
                           args.framestack)

    args = parse_args()
    config = {
        'env': 'super_mario_bros',

roclark

def print_results(result, iteration):
    table = [['IMPALA',
              iteration,
              result['timesteps_total'],
              round(result['episode_reward_max'], 3),
              round(result['episode_reward_min'], 3),
              round(result['episode_reward_mean'], 3)]]
    print(tabulate(table,
                   headers=['Agent',
                            'Iteration',

roclark

def env_creator(env_name, config, dim, framestack):
    env = gym_super_mario_bros.make(env_name)
    env = CustomReward(env)
    env = JoypadSpace(env, SIMPLE_MOVEMENT)
    env = MonitorEnv(env)
    env = NoopResetEnv(env, noop_max=30)
    env = EpisodicLifeEnv(env)
    env = WarpFrame(env, dim)
    if framestack:
        env = FrameStack(env, framestack)

roclark

def parse_args():
    parser = ArgumentParser(description='Train an agent to beat Super Mario '
                            'Bros. levels.')
    parser.add_argument('--checkpoint', help='Specify an existing checkpoint '
                        'which can be used to restore progress from a previous'
                        ' training run.')
    parser.add_argument('--dimension', help='The image dimensions to resize to'
                        ' while preprocessing the game states.', type=int,
                        default=84)
    parser.add_argument('--environment', help='The Super Mario Bros level to '

roclark

# An updated version of the EpisodicLifeEnv wrapper from RLLib which is
# compatible with the SuperMarioBros environments.
class EpisodicLifeEnv(gym.Wrapper):
    def __init__(self, env):
        """Make end-of-life == end-of-episode, but only reset on true game
        over. Done by DeepMind for the DQN and co. since it helps value
        estimation.
        """
        gym.Wrapper.__init__(self, env)
        self.lives = 0

roclark

import gym
import gym_super_mario_bros
import ray
from argparse import ArgumentParser
from gym_super_mario_bros.actions import SIMPLE_MOVEMENT
from nes_py.wrappers import JoypadSpace
from ray import tune
from ray.tune.registry import register_env
from ray.rllib.agents.impala import ImpalaTrainer
from ray.rllib.env.atari_wrappers import (MonitorEnv,

roclark

import gym
import gym_super_mario_bros
import ray
from argparse import ArgumentParser
from gym_super_mario_bros.actions import SIMPLE_MOVEMENT
from nes_py.wrappers import JoypadSpace
from ray import tune
from ray.tune.registry import register_env
from ray.rllib.agents.impala import ImpalaTrainer
from ray.rllib.env.atari_wrappers import (MonitorEnv,

roclark

import pandas as pd
from argparse import ArgumentParser
from os.path import isfile
from sportsreference.ncaab.teams import Teams
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split

DATASET_NAME = 'dataset.pkl'
FIELDS_TO_DROP = ['away_points', 'home_points', 'date', 'location',
                  'losing_abbr', 'losing_name', 'winner', 'winning_abbr',

roclark

model = RandomForestRegressor(bootstrap=False,
                              min_samples_leaf=3,
                              ...
                              max_depth=6)