"""
Library for general purpose functions related to data processing in RL model
"""
import os
import pandas as pd
import numpy as np
###############
### GLOBALS ###
###############
DEFAULT_FILE = 'rps_v2_clean.csv'
TRANSITIONS = ["up", "down", "stay"]
OUTCOMES = ["win", "tie", "loss"]
N_ROUNDS = 300
#######################
### Data processing ###
#######################
def read_rps_data(fp = DEFAULT_FILE):
"""
Read in the data! Yay!
"""
return pd.read_csv(fp)
def add_col(df, col_names, value = 0):
"""
Assign a new column with values initialized as `value`
col_names = list of new col names
"""
for name in col_names:
df[name] = value
return df
def drop_bot_rows(df):
"""
Filter out human rows from data frame
"""
df = df[df['is_bot'] != 1]
return df
def separate_df(df):
"""
Create a list of sub-datafames with unque game ids
"""
experiments = []
for game in df['game_id'].unique():
e = df.loc[df['game_id'] == game]
e = e.reset_index(drop=True)
experiments.append(e)
return experiments
############################################
### Evaluate transitions, outcomes, etc. ###
############################################
def evaluate_outcome(player_move, opponent_move):
"""
Determine outcome based on player move and opponent move
TODO check that both moves are in OUTCOME_LOOKUP
"""
OUTCOME_LOOKUP = np.transpose(
pd.DataFrame.from_dict({
'rock': ['tie', 'lose', 'win'],
'paper': ['win', 'tie', 'lose'],
'scissors': ['lose', 'win', 'tie']
}, orient='index', columns=['rock', 'paper', 'scissors'])
)
return OUTCOME_LOOKUP[player_move][opponent_move]
