import streamlit as st st.set_page_config(layout="wide") for name in dir(): if not name.startswith('_'): del globals()[name] import pulp import numpy as np import pandas as pd import streamlit as st import gspread import gc @st.cache_resource def init_conn(): scope = ['https://www.googleapis.com/auth/spreadsheets', "https://www.googleapis.com/auth/drive"] credentials = { "type": "service_account", "project_id": "sheets-api-connect-378620", "private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9", "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n", "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com", "client_id": "106625872877651920064", "auth_uri": "https://accounts.google.com/o/oauth2/auth", "token_uri": "https://oauth2.googleapis.com/token", "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com" } gc_con = gspread.service_account_from_dict(credentials) return gc_con gcservice_account = init_conn() dk_player_url = 'https://docs.google.com/spreadsheets/d/1lMLxWdvCnOFBtG9dhM0zv2USuxZbkogI_2jnxFfQVVs/edit#gid=1828092624' CSV_URL = 'https://docs.google.com/spreadsheets/d/1lMLxWdvCnOFBtG9dhM0zv2USuxZbkogI_2jnxFfQVVs/edit#gid=1828092624' player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '100+%': '{:.2%}', '10x%': '{:.2%}', '11x%': '{:.2%}', '12x%': '{:.2%}','LevX': '{:.2%}'} @st.cache_resource(ttl = 600) def init_baselines(): sh = gcservice_account.open_by_url(dk_player_url) worksheet = sh.worksheet('ROO') raw_display = pd.DataFrame(worksheet.get_all_records()) roo_data = raw_display worksheet = sh.worksheet('DK_CSV') draftkings_data = pd.DataFrame(worksheet.get_all_records()) draftkings_data.rename(columns={"Name": "Player"}, inplace = True) return roo_data, draftkings_data def convert_df_to_csv(df): return df.to_csv().encode('utf-8') roo_data, draftkings_data = init_baselines() hold_display = roo_data csv_data = draftkings_data csv_merge = pd.merge(csv_data, hold_display, how='left', left_on=['Player'], right_on = ['Player']) id_dict = dict(zip(csv_merge['Player'], csv_merge['Name + ID'])) lineup_display = [] check_list = [] rand_player = 0 boost_player = 0 salaryCut = 0 tab1, tab2 = st.tabs(["Player Overall Projections", "Optimizer"]) with tab1: if st.button("Reset Data", key='reset1'): # Clear values from *all* all in-memory and on-disk data caches: # i.e. clear values from both square and cube st.cache_data.clear() roo_data, draftkings_data = init_baselines() hold_display = roo_data csv_data = draftkings_data csv_merge = pd.merge(csv_data, hold_display, how='left', left_on=['Player'], right_on = ['Player']) id_dict = dict(zip(csv_merge['Player'], csv_merge['Name + ID'])) lineup_display = [] check_list = [] rand_player = 0 boost_player = 0 salaryCut = 0 hold_container = st.empty() display = hold_display.set_index('Player') st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True) st.download_button( label="Export Projections", data=convert_df_to_csv(display), file_name='PGA_DFS_export.csv', mime='text/csv', ) with tab2: col1, col2 = st.columns([1, 4]) with col1: max_sal = st.number_input('Max Salary', min_value = 35000, max_value = 50000, value = 50000, step = 100) min_sal = st.number_input('Min Salary', min_value = 35000, max_value = 49900, value = 49000, step = 100) proj_cut = st.number_input('Lowest median allowed', min_value = 0, max_value = 100, value = 25, step = 1) slack_var = st.number_input('Median randomness', min_value = 0, max_value = 5, value = 0, step = 1) totalRuns_raw = st.number_input('How many Lineups', min_value = 1, max_value = 1000, value = 5, step = 1) totalRuns = totalRuns_raw cut_group_1 = [] cut_group_2 = [] force_group_1 = [] force_group_2 = [] avoid_players = [] lock_player = [] lineups = [] player_pool_raw = [] player_pool = [] player_count = [] player_trim_pool = [] portfolio = pd.DataFrame() x = 1 if st.button('Optimize'): max_proj = 1000 max_own = 1000 total_proj = 0 total_own = 0 with col2: with st.spinner('Wait for it...'): with hold_container.container(): while x <= totalRuns: raw_proj_file = hold_display raw_flex_file = raw_proj_file.dropna(how='all') raw_flex_file = raw_flex_file.loc[raw_flex_file['Median'] > 0] raw_flex_file = raw_flex_file.loc[raw_flex_file['Median'] > proj_cut] flex_file = raw_flex_file flex_file = flex_file[['Player', 'Salary', 'Median', 'Own', 'LevX']] flex_file.rename(columns={"Own": "Proj DK Own%"}, inplace = True) flex_file['name_var'] = flex_file['Player'] flex_file['lock'] = flex_file['Player'].isin(lock_player)*1 flex_file['Pos'] = 'G' flex_file = flex_file[['Player', 'name_var', 'Pos', 'Salary', 'Median', 'Proj DK Own%', 'lock', 'LevX']] if x > 1: if slack_var > 0: flex_file['randNumCol'] = np.random.randint(-int(slack_var),int(slack_var), flex_file.shape[0]) elif slack_var ==0: flex_file['randNumCol'] = 0 elif x == 1: flex_file['randNumCol'] = 0 flex_file['Median'] = flex_file['Median'] + flex_file['randNumCol'] flex_file_check = flex_file check_list.append(flex_file['Median'][4]) player_ids = flex_file.index overall_players = flex_file[['Player']] overall_players['player_var_add'] = flex_file.index overall_players['player_var'] = 'player_vars_' + overall_players['player_var_add'].astype(str) player_vars = pulp.LpVariable.dicts("player_vars", flex_file.index, 0, 1, pulp.LpInteger) total_score = pulp.LpProblem("Fantasy_Points_Problem", pulp.LpMaximize) player_match = dict(zip(overall_players['player_var'], overall_players['Player'])) player_index_match = dict(zip(overall_players['player_var'], overall_players['player_var_add'])) player_own = dict(zip(flex_file['Player'], flex_file['Proj DK Own%'])) player_sal = dict(zip(flex_file['Player'], flex_file['Salary'])) player_lev = dict(zip(flex_file['Player'], flex_file['LevX'])) player_proj = dict(zip(flex_file['Player'], flex_file['Median'])) obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index} total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) obj_points_max = {idx: (flex_file['Median'][idx]) for idx in flex_file.index} obj_own_max = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index} obj_salary = {idx: (flex_file['Salary'][idx]) for idx in flex_file.index} total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) <= max_sal total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) >= min_sal for flex in flex_file['Pos'].unique(): sub_idx = flex_file[flex_file['Pos'] != "Var"].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 6 player_count = [] player_trim = [] lineup_list = [] total_score += pulp.lpSum([player_vars[idx]*obj_points_max[idx] for idx in flex_file.index]) <= max_proj - .01 total_score.solve() for v in total_score.variables(): if v.varValue > 0: lineup_list.append(v.name) df = pd.DataFrame(lineup_list) df['Names'] = df[0].map(player_match) df['Cost'] = df['Names'].map(player_sal) df['Proj'] = df['Names'].map(player_proj) df['Own'] = df['Names'].map(player_own) total_cost = sum(df['Cost']) total_own = sum(df['Own']) total_proj = sum(df['Proj']) lineup_raw = pd.DataFrame(lineup_list) lineup_raw['Names'] = lineup_raw[0].map(player_match) lineup_raw['value'] = lineup_raw[0].map(player_index_match) lineup_final = lineup_raw.sort_values(by=['value']) del lineup_final[lineup_final.columns[0]] del lineup_final[lineup_final.columns[1]] lineup_final = lineup_final.reset_index(drop=True) lineup_test = lineup_final lineup_final = lineup_final.T lineup_final['Cost'] = total_cost lineup_final['Proj'] = total_proj lineup_final['Own'] = total_own if total_cost < 50001: lineups.append(lineup_final) lineup_test['Salary'] = lineup_test['Names'].map(player_sal) lineup_test['Proj'] = lineup_test['Names'].map(player_proj) lineup_test['Own'] = lineup_test['Names'].map(player_own) lineup_test['LevX'] = lineup_test['Names'].map(player_lev) lineup_test.loc['Column_Total'] = lineup_test.sum(numeric_only=True, axis=0) lineup_display.append(lineup_test) with col2: with st.container(): st.table(lineup_test) max_proj = total_proj max_own = total_own check_list.append(total_proj) portfolio = pd.concat([portfolio, lineup_final], ignore_index=True) x += 1 portfolio.rename(columns={0: "Player_1", 1: "Player_2", 2: "Player_3", 3: "Player_4", 4: "Player_5", 5: "Player_6"}, inplace = True) portfolio = portfolio.dropna() final_outcomes = portfolio final_outcomes['p1 id'] = final_outcomes['Player_1'].map(id_dict) final_outcomes['p2 id'] = final_outcomes['Player_2'].map(id_dict) final_outcomes['p3 id'] = final_outcomes['Player_3'].map(id_dict) final_outcomes['p4 id'] = final_outcomes['Player_4'].map(id_dict) final_outcomes['p5 id'] = final_outcomes['Player_5'].map(id_dict) final_outcomes['p6 id'] = final_outcomes['Player_6'].map(id_dict) final_outcomes = final_outcomes[['p1 id', 'p2 id', 'p3 id', 'p4 id', 'p5 id', 'p6 id']] with col1: st.download_button( label="Export Lineups", data=convert_df_to_csv(final_outcomes), file_name='PGA_DFS_export.csv', mime='text/csv', ) with hold_container: hold_container = st.empty()