def dump_predictions(league_id):
manager = bot.ManagerBot(league_id=league_id)
predictions = pd.read_csv(prediction_csv,
converters={"eligible_positions": lambda x: x.strip("[]").replace("'", "").split(", ")})
predictions['league_id'] = league_id
predictions.rename(columns={"id": "player_id"}, inplace=True)
predictions.set_index('player_id', inplace=True)
scored_categories = manager.lg.scoring_categories() # ["G", "A", "+/-", "PIM", "SOG", "FW", "HIT"]
d_scored_categories = scored_categories.copy()
# if FW are in cats, let's ignore them for D. Keeps D fantasy score close relative to F
try:
d_scored_categories.remove('FW')
except ValueError:
pass
# d_scored_categories = ["G", "A", "+/-", "PIM", "SOG", "HIT"]
es_colums = ["name", "position","team","preseason_rank","current_rank","fantasy_score","GP", "csh_rank", "league_id", "player_id"]
def produce_csh_ranking(predictions, scoring_categories, selector):
"""Create ranking by summing standard deviation of each stat, summing, then dividing by num stats."""
f_mean = predictions.loc[selector,scoring_categories].mean()
f_std =predictions.loc[selector,scoring_categories].std()
f_std_performance = (predictions.loc[selector,scoring_categories] - f_mean)/f_std
for stat in scoring_categories:
predictions.loc[selector, stat + '_std'] = (predictions[stat] - f_mean[stat])/f_std[stat]
# predictions = predictions.join(f_std_performance, how='left', rsuffix='_std')
predictions.loc[selector, 'fantasy_score'] = f_std_performance.sum(axis=1)/len(scoring_categories)
# predictions.sort_values('fantasy_score', inplace=True,ascending=False)
# sorted_predictions = predictions.reset_index().sort_values('fantasy_score', ascending=False, ignore_index=True)
# sorted_predictions['csh_rank'] = sorted_predictions.index + 1
return predictions
# the bulk importer for ES can't handled NAN, so let's initialize to 0
for stat in scored_categories:
predictions.loc[:, stat + '_std'] = 0
produce_csh_ranking(predictions, scored_categories, predictions['position'] != 'D')
produce_csh_ranking(predictions, d_scored_categories, predictions['position'] == 'D')
# predictions.sort_values('fantasy_score', inplace=True,ascending=False)
sorted_predictions = predictions.reset_index().sort_values('fantasy_score', ascending=False, ignore_index=True)
sorted_predictions['csh_rank'] = sorted_predictions.index + 1
print(sorted_predictions.head(20))
def filter_keys(document, columns):
"""Return dict as specified by colums list."""
return {key: document[key] for key in columns}
def doc_generator_linescores(player_predictions, categories):
df_iter = player_predictions.iterrows()
for index, player_prediction in df_iter:
# game['timestamp'] = game['gameDate']
# document['player_id'] = index
yield {
"_index": 'fantasy-nhl-preseason-predictions-2021',
"_type": "_doc",
"_id": f"{player_prediction['league_id']}-{player_prediction['player_id']}",
"_source": filter_keys(player_prediction, categories +
[cat + "_std" for cat in categories] +
es_colums),
}
helpers.bulk(es, doc_generator_linescores(sorted_predictions, scored_categories))
# helpers.bulk(es, doc_generator_linescores(ordinal_d_predictions, d_scored_categories))
pass
import logging
logging.basicConfig(level=logging.INFO)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
from elasticsearch import Elasticsearch
from elasticsearch import helpers
es = Elasticsearch(hosts='http://localhost:9200', http_compress=True)
stats = ['G','A','SOG','+/-','HIT','PIM','FW']
weights_series = pd.Series([1, .75, 1, .5, 1, .1, 1], index=stats)
week_number = 1
manager = bot.ManagerBot(week_number)
projections = manager.all_players[manager.all_players.position_type == 'P']
projections.reset_index(inplace=True)
projections = manager.pred_bldr.predict(projections)
projection_date = date(2020,3,8)
projections.loc[:, 'projection_date'] = projection_date
projections['timestamp'] = projection_date
player_projection_columns =['name','eligible_positions','team_id','team_name','projection_date','player_id','timestamp'] + stats
def filter_keys(document, columns):
return {key: document[key] for key in columns}
def __init__(self, week):
self.bot = bot.ManagerBot(week, league_id="403.l.18782")
import pandas as pd
import numpy as np
from datetime import datetime, timezone
from yahoo_fantasy_api import League
from csh_fantasy_bot import bot, nhl, roster_change_optimizer
import logging
logging.basicConfig(level=logging.INFO)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
from elasticsearch import Elasticsearch
from elasticsearch import helpers
week_number = 19
es = Elasticsearch(hosts='http://192.168.1.20:9200', http_compress=True)
manager: bot.ManagerBot = bot.ManagerBot(league_id="403.l.18782") #league_id=266295
league :League = manager.lg
pass
}
helpers.bulk(es, doc_generator_team_results(data))
week_number = 5
league_id = '403.l.41177'
# league_id = "403.l.18782"
simulation_mode = False
manager: bot.ManagerBot = None
if 'YAHOO_OAUTH_FILE' in os.environ:
auth_file = os.environ['YAHOO_OAUTH_FILE']
manager = bot.ManagerBot(week_number,
oauth_file=auth_file,
league_id=league_id,
simulation_mode=simulation_mode)
else:
manager = bot.ManagerBot(week_number,
league_id=league_id,
simulation_mode=simulation_mode)
my_team_id = manager.tm.team_key.split('.')[-1]
my_opponent_id = manager.opp_team_key
my_scores = manager.my_team.scores()
# print("My team has {} roster changes available.".format(manager.roster_changes_allowed))
# print("Scoring no roster changes:")
# print(my_scores.sum())
# Date: 2021-02-17, in: Adam Pelech(5756), out: Nazem Kadri(4687)
import datetime
import logging
import pandas as pd
from csh_fantasy_bot import bot
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
logger = logging.getLogger(__name__)
game_week = 17
mgr = bot.ManagerBot(week=game_week)
# what are days for this week matchup
(week_start, week_end) = mgr.lg.week_date_range(game_week)
week = pd.date_range(week_start, week_end)
# get opponent
opp_team_key = mgr.tm.matchup(game_week)
opp_team = mgr.lg.to_team(opp_team_key)
opp_team_2 = mgr.game_week().opponent
opp_scores = opp_team_2.scores()
# for days in week that have passed, lets load roster used and find stats
today = datetime.datetime.now()
def get_team_results(team):
opp_daily_rosters = pd.DataFrame()
for game_day in week:
if today > game_day:
import logging
logging.basicConfig(level=logging.INFO)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
from elasticsearch import Elasticsearch
from elasticsearch import helpers
es = Elasticsearch(hosts='http://192.168.1.20:9200', http_compress=True)
# league_id = "403.l.18782"
league_id = "411.l.85094"
manager: bot.ManagerBot = bot.ManagerBot(league_id=league_id)
class JsonLoggingYHandler(yhandler.YHandler):
def __init__(self, sc):
super().__init__(sc)
self.num_players_picked = -1
def get_draft_results(self, league_id):
raw = super().get_draft_results(league_id)
t = objectpath.Tree(raw)
elems = t.execute('$..draft_result')
num_drafted = 0
for ele in elems:
if 'player_key' in ele.keys():
num_drafted += 1
from csh_fantasy_bot.config import ELASTIC_URL
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
if 'YAHOO_OAUTH_FILE' in os.environ:
oauth_file = os.environ['YAHOO_OAUTH_FILE']
else:
oauth_file = 'oauth2.json'
es = Elasticsearch(hosts=ELASTIC_URL, http_compress=True)
league_id = '396.l.53432'
league_id = "403.l.18782"
manager = bot.ManagerBot(1,league_id=league_id)
lg = manager.lg
sc = lg.sc
teams = lg.teams()
roster_positions = lg.positions()
all_players = manager.all_players
# player_stats = manager.stat_categories
# weights_series = pd.Series([1, 1, .5, .5, 1, .1, .7], index=player_stats)
projected_player_stats = ['proj_{}'.format(stat) for stat in manager.stat_categories]
# player_projection_columns =['name','eligible_positions','team_id','team_name','game_date','player_id'] + player_stats
def filter_keys(document, columns):
return {key: document[key] for key in columns}
import pandas as pd
import numpy as np
from csh_fantasy_bot import bot
simulation_mode = False
week_number = 9
l_id = '411.l.85094'
# league_id = "403.l.18782"
manager = bot.ManagerBot(league_id=l_id,
week=week_number,
simulation_mode=simulation_mode)
league_scorer = manager.game_week().score_comparer
predictions = manager.game_week().all_player_predictions
print(predictions.head())
def display_sort_ftps(df, ascending=False):
return df.sort_values('fpts', ascending=ascending)[[
'name', 'eligible_positions', 'status', 'fantasy_status', 'fpts'
]]
rest_season_predictions = manager.game_week().all_player_predictions
my_team = rest_season_predictions[rest_season_predictions.fantasy_status == 8]
print(display_sort_ftps(my_team))
print(my_team[[
'name', 'eligible_positions', 'G', 'A', 'HIT', 'SOG', 'fantasy_status',