def main():
if not SKIP_DOWNLOAD:
if os.path.exists('.tmp'):
shutil.rmtree('.tmp')
os.makedirs('.tmp', exist_ok=True)
print("Downloading files...")
download_file(RP_URL, '.tmp/rp.zip')
download_file(BP_URL, '.tmp/bp.zip')
if os.path.exists('.tmp/rp'):
shutil.rmtree('.tmp/rp')
print('Extracting resource pack')
with ZipFile('.tmp/rp.zip') as zipf:
zipf.extractall('.tmp/rp')
if os.path.exists('.tmp/bp'):
shutil.rmtree('.tmp/bp')
print('Extracting behavior pack')
with ZipFile('.tmp/bp.zip') as zipf:
zipf.extractall('.tmp/bp')
stable = DOWNLOAD_MODE == 'stable'
harvest('.tmp/bp', stable)
strip_sounds('.tmp/rp/sounds/sound_definitions.json')
no_console_display = False
# start progress bar for html output files
if no_console_display:
config_number = len(filenames[0])
config_checked = 0
bar = progressbar.ProgressBar(maxval=config_number, widgets=[
progressbar.Bar(left='[', marker='=', right=']'), # Прогресс
progressbar.SimpleProgress(),
]).start()
# Creating dictionary for drawing graph
dict_for_drawing_plot = {}
# creds harvester
if (args.args.dump_creds):
harvester.harvest(filenames[0])
# processing configs one by one
for filename in filenames[0]:
# Define config file name
config_name = filename.partition(config_directory)[2]
# Create output html file full path if needed
if html_directory:
html_file = html_directory + config_name + '.html'
# parsing configs
parsing.parseconfigs(filename, check_disabled)
# getting parse output
def rank(
year,
alpha=0.85,
iters=3500,
print_rankings=False,
plot_rankings=False,
serialize_results=True,
first_year=True,
):
"""
Ranks all Division I NCAA Basketball teams in a given year using PageRank.
PARAMETERS
year --> The year that the NCAA championship game takes place.
The 2019-2020 season would correpond to year=2020.
alpha --> A value between 0 and 1 that is a measure of randomness in
PageRank meant to model the possibility that a user randomly
navigates to a page without using a link. alpha=1 would be
completely deterministic, while alpha=0 would be completely
random. Google is rumored to use alpha=.85.
iters --> The number of iterations of PageRank matrix multiplication to
perform. The default of iters=3500, about 10x the number of
Division I teams, is generally sufficient for ranking.
print_rankings --> Should the function print out its rankings to stdout?
Defaults to False.
plot_rankings --> Should the function plot the rankings in sorted order?
Defaults to False.
serialize_results --> Should the function save its rankings to a .p file
called "./predictions/[YEAR]/rankings.p"?
Defaults to True.
"""
# Try to deserialize .p file summary. Try to create it if it doesn't exist
try:
total_summary = pickle.load(
open("./summaries/" + str(year) + "/total_summary.p", "rb")
)
except FileNotFoundError:
print("--- WARNING: No summary found for", year, "Trying to create summary...")
try:
harvester.harvest(year)
except:
print("--- ERROR: Could not make summary for", year)
return
print("--- SUCCESS: Summary created for", year)
print("--- Trying to rank again with newly created summary")
rank(year, alpha, iters, print_rankings, plot_rankings, serialize_results)
return
# Get an ordered list of all the teams
teams = list(
set(
[
"-".join(game[GameValues.HOME_TEAM.value].split(" "))
for game in total_summary
]
)
)
num_teams = len(teams)
# Create PageRank matrix and initial vector
if first_year:
vec = np.ones((num_teams, 1))
else:
vec = rank(year - 1, alpha - 0.1, serialize_results=False, first_year=True)
num_teams = max(num_teams, len(vec))
mat = np.zeros((num_teams, num_teams))
for game in total_summary:
# We only want to count games where both teams are D1 (in teams list)
# We choose to only look at games where the first team won so we don't double-count games
if (
game[GameValues.HOME_TEAM.value] in teams
and game[GameValues.AWAY_TEAM.value] in teams
and game[GameValues.WIN_LOSS.value]
):
# Game winner
# Since we know the first/home team won, we can already assign the weight for that
home_pr_score, away_pr_score = GameWeights.WEIGHTS.value[0], 0.0
# Effective field goal percentage
if game[GameValues.HOME_eFGp.value] > game[GameValues.AWAY_eFGp.value]:
home_pr_score += GameWeights.WEIGHTS.value[1]
elif game[GameValues.AWAY_eFGp.value] > game[GameValues.HOME_eFGp.value]:
away_pr_score += GameWeights.WEIGHTS.value[1]
# Turnover percentage
if game[GameValues.HOME_TOVp.value] < game[GameValues.AWAY_TOVp.value]:
home_pr_score += GameWeights.WEIGHTS.value[2]
elif game[GameValues.AWAY_TOVp.value] < game[GameValues.HOME_TOVp.value]:
away_pr_score += GameWeights.WEIGHTS.value[2]
# Offensive rebound percentage
if game[GameValues.HOME_ORBp.value] > game[GameValues.AWAY_ORBp.value]:
home_pr_score += GameWeights.WEIGHTS.value[3]
elif game[GameValues.AWAY_ORBp.value] > game[GameValues.HOME_ORBp.value]:
away_pr_score += GameWeights.WEIGHTS.value[3]
# Free throw rate
if game[GameValues.HOME_FTR.value] > game[GameValues.AWAY_FTR.value]:
home_pr_score += GameWeights.WEIGHTS.value[4]
elif game[GameValues.AWAY_FTR.value] > game[GameValues.HOME_FTR.value]:
away_pr_score += GameWeights.WEIGHTS.value[4]
# Add weighted score for this game to matrix for both teams
home_idx = teams.index(game[GameValues.HOME_TEAM.value])
away_idx = teams.index(game[GameValues.AWAY_TEAM.value])
mat[home_idx, away_idx] += home_pr_score
mat[away_idx, home_idx] += away_pr_score
# Alter the matrix to take into account our alpha factor
mat = (alpha * mat) + (1 - alpha) * np.ones((num_teams, num_teams)) / num_teams
# Perform many iterations of matrix multiplication
for i in range(iters):
vec = mat @ vec
vec *= num_teams / sum(vec) # Keep weights summed to set value (numerator)
# Sort the (ranking, team) pair into a list of tuples
sorted_pairs = sorted([(prob[0], team) for team, prob in zip(teams, vec)])
# Print ranking pairs if specificed
if print_rankings:
for i in range(len(sorted_pairs)):
print(num_teams - i, sorted_pairs[i])
# Serialize results if specificed
# TODO: Serialize results as dict?
if serialize_results:
# Make the year folder
outfile1 = f"./predictions/{year}_rankings.p"
outfile2 = f"./predictions/{year}_vector.p"
os.makedirs(os.path.dirname(outfile1), exist_ok=True)
serial = dict()
for team in teams:
serial.setdefault(team, 0)
for item in sorted_pairs:
serial[item[1]] = item[0]
pickle.dump(serial, open(outfile1, "wb"))
pickle.dump(vec, open(outfile2, "wb"))
# Plot graph of rankings if specified
if plot_rankings:
s = sorted(vec)
bins = np.arange(0.0, 3.5, 0.125)
hist, bins = np.histogram(s, bins=bins)
plt.hist(bins[:-1], bins, weights=hist)
plt.show()
return vec