def gen_factorizations(stats, wk):
'''
Input: List - stats to factorize, Int - week to factorize stats until
Generates DataFrames with factorized stats from input list for each player that week,
includes their position, team, the week number, their factorized skill, the skill of
the defense they played that week for that position, and the fantasy points they
actually produced that week.
'''
for_nmf_df = get_yr_until_wk(year, wk, nfl_frames)
for stat in stats:
stat_cols = ['{}_factorized_{}'.format(ball_side, stat) for ball_side in ['off', 'def']]
cols_to_keep = cols + stat_cols
decomp_off_stat, decomp_def_stat = nmf_all_positions(stat, for_nmf_df)
week_df = nfl_frames.get_year_week_frame(year, wk)
stat_df = week_df.merge(decomp_off_stat, on='player_id')
stat_opp_df = stat_df.merge(decomp_def_stat, how='left', on=['opponent', 'position'])
yield stat_opp_df[cols_to_keep]
def check_nmf_model(nfl_frames, year, week, position='All'):
'''
Input: NFLFrames, Int, Int, Str
Output: None
Prints comparison of standard deviation in fanduel points versus RMSE in predictions.
'''
fp_stat = 'fanduel_points'
historical_data = get_yr_until_wk(year, week, nfl_frames)
offense, defense = nmf_all_positions(fp_stat, historical_data)
all_week_actuals_df = nfl_frames.get_year_week_frame(year, week)
preds_to_make = get_preds_to_make(year, week + 1, nfl_frames)
for_preds_df = merge_factorizations_to_main_df(preds_to_make, offense, defense)
if position is not 'All':
week_actuals = all_week_actuals_df.query('position == @position')
for_preds = for_preds_df.query('position == @position')
else:
week_actuals = all_week_actuals_df
for_preds = for_preds_df
preds = preds_from_factorized_skills(for_preds)
my_rmse = year_week_rmse(week_actuals, preds)
check_model_string = 'STD in fanduel points:\t{}\nRMSE in predictions:\t{}'
print(check_model_string.format(week_actuals.fanduel_points.std(), my_rmse))
