def exp_train():
"""Exports trained model."""
team_stats = get_team_stats()
model = net.OneLayer(30, 15, 1)
net.train_model(10000, 0.0005, model, team_stats, no_graph=True)
return model
sys.path.append('../../')
import nba_net.core.networks as net
#
# DB_URL = \
# "mongodb+srv://cs4701:[email protected]/" + \
# "attempt1?retryWrites=true"
DB_URL = "mongodb://localhost:27017/"
def get_team_stats():
"""Returns a list of all the team-level ML stats for each game, as well as
the outcome. Each list has the form [0..., 0..., 0..., 0..., ..., 1/0]"""
# client = pymongo.MongoClient(DB_URL, ssl=True, ssl_cert_reqs=ssl.CERT_NONE)
client = pymongo.MongoClient(DB_URL)
db = client.attempt1
ml_stats = db.learningStats
parsed_stats = []
for game_stats in ml_stats.find():
del game_stats["_id"]
del game_stats["game_id"]
parsed_stats.append(list(game_stats.values()))
return parsed_stats
if __name__ == "__main__":
team_stats = get_team_stats()
model = net.OneLayer(8, 5, 1)
net.train_model(15000, 0.06, model, team_stats)
# DB_URL = \ # "mongodb+srv://cs4701:[email protected]/" + \ # "attempt2?retryWrites=true" DB_URL = "mongodb://localhost:27017/" def get_stats(): """Returns a list of all the team-level ML stats for each game, as well as the outcome. Each list has the form [0..., 0..., 0..., 0..., ..., 1/0]""" # client = pymongo.MongoClient(DB_URL, ssl=True, ssl_cert_reqs=ssl.CERT_NONE) client = pymongo.MongoClient(DB_URL) db = client.attempt2 ml_stats = db.learningStats parsed_stats = [] for game_stats in ml_stats.find(): del game_stats["_id"] del game_stats["game_id"] winner = game_stats["winner"] del game_stats["winner"] stats = list(game_stats.values()) stats.append(winner) parsed_stats.append(stats) return parsed_stats if __name__ == "__main__": team_stats = get_stats() model = net.OneLayer(8, 7, 1) net.train_model(1750, 0.01, model, team_stats)
away_data.append(pd["plus_minus"])
return home_data, away_data
def get_team_stats():
"""Returns a list of all the team-level ML stats for each game, as well as
the outcome. Each list has the form [0..., 0..., 0..., 0..., ..., 1/0]"""
client = pymongo.MongoClient(DB_URL)
db = client.attempt4
ml_stats = db.learningStats
parsed_stats = []
for game_stats in ml_stats.find():
temp_list = []
del game_stats["_id"]
del game_stats["game_id"]
winner = game_stats["winner"]
del game_stats["winner"]
home_player_data, away_player_data = filter_data(game_stats)
temp_list = temp_list + home_player_data
temp_list = temp_list + away_player_data
temp_list.append(winner)
parsed_stats.append(temp_list)
return parsed_stats
if __name__ == "__main__":
team_stats = get_team_stats()
model = net.OneLayer(30, 15, 1)
net.train_model(10000, 0.0005, model, team_stats)
away_data.append(pd["usg_pct"])
return home_data, away_data
def get_team_stats():
"""Returns a list of all the team-level ML stats for each game, as well as
the outcome. Each list has the form [0..., 0..., 0..., 0..., ..., 1/0]"""
client = pymongo.MongoClient(DB_URL)
db = client.attempt4
ml_stats = db.learningStats
parsed_stats = []
for game_stats in ml_stats.find():
temp_list = []
del game_stats["_id"]
del game_stats["game_id"]
winner = game_stats["winner"]
del game_stats["winner"]
home_player_data, away_player_data = filter_data(game_stats)
temp_list = temp_list + home_player_data
temp_list = temp_list + away_player_data
temp_list.append(winner)
parsed_stats.append(temp_list)
return parsed_stats
if __name__ == "__main__":
team_stats = get_team_stats()
model = net.OneLayer(120, 60, 1)
net.train_model(10000, 0.00019, model, team_stats)
def exp_train():
"""Exports trained model."""
team_stats = get_team_stats()
model = net.OneLayer(1, 5, 1)
net.train_model(2000, 0.01, model, team_stats)
return model
def exp_train():
"""Exports trained model."""
team_stats = get_stats()
model = net.OneLayer(8, 7, 1)
net.train_model(1750, 0.01, model, team_stats, no_graph=True)
return model