Hugging Face's logo

Spaces:
Multichem
/
MLB_Contest_Sims
Runtime error

App Files Community
MLB_Contest_Sims
File size: 12,863 Bytes 
666100e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
991f67f
666100e
 
 
 
 
 
 
 
70a1dd5
666100e
0402531
 
 
 
70a1dd5
bc57db2
 
 
 
 
 
 
 
 
 
 
 
 
 
0402531
bc57db2
0402531
12c5542
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
 
import streamlit as st
st.set_page_config(layout="wide")

for name in dir():
    if not name.startswith('_'):
        del globals()[name]
        
import numpy as np
import pandas as pd
import streamlit as st
import gspread
import gc
import plotly.express as px
import plotly.io as pio
import pymongo
import certifi
ca = certifi.where()

@st.cache_resource
def init_conn():
        scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']

        credentials = {
          "type": "service_account",
          "project_id": "model-sheets-connect",
          "private_key_id": "0e0bc2fdef04e771172fe5807392b9d6639d945e",
          "private_key": "-----BEGIN PRIVATE KEY-----\nMIIEvgIBADANBgkqhkiG9w0BAQEFAASCBKgwggSkAgEAAoIBAQDiu1v/e6KBKOcK\ncx0KQ23nZK3ZVvADYy8u/RUn/EDI82QKxTd/DizRLIV81JiNQxDJXSzgkbwKYEDm\n48E8zGvupU8+Nk76xNPakrQKy2Y8+VJlq5psBtGchJTuUSHcXU5Mg2JhQsB376PJ\nsCw552K6Pw8fpeMDJDZuxpKSkaJR6k9G5Dhf5q8HDXnC5Rh/PRFuKJ2GGRpX7n+2\nhT/sCax0J8jfdTy/MDGiDfJqfQrOPrMKELtsGHR9Iv6F4vKiDqXpKfqH+02E9ptz\nBk+MNcbZ3m90M8ShfRu28ebebsASfarNMzc3dk7tb3utHOGXKCf4tF8yYKo7x8BZ\noO9X4gSfAgMBAAECggEAU8ByyMpSKlTCF32TJhXnVJi/kS+IhC/Qn5JUDMuk4LXr\naAEWsWO6kV/ZRVXArjmuSzuUVrXumISapM9Ps5Ytbl95CJmGDiLDwRL815nvv6k3\nUyAS8EGKjz74RpoIoH6E7EWCAzxlnUgTn+5oP9Flije97epYk3H+e2f1f5e1Nn1d\nYNe8U+1HqJgILcxA1TAUsARBfoD7+K3z/8DVPHI8IpzAh6kTHqhqC23Rram4XoQ6\nzj/ZdVBjvnKuazETfsD+Vl3jGLQA8cKQVV70xdz3xwLcNeHsbPbpGBpZUoF73c65\nkAXOrjYl0JD5yAk+hmYhXr6H9c6z5AieuZGDrhmlFQKBgQDzV6LRXmjn4854DP/J\nI82oX2GcI4eioDZPRukhiQLzYerMQBmyqZIRC+/LTCAhYQSjNgMa+ZKyvLqv48M0\n/x398op/+n3xTs+8L49SPI48/iV+mnH7k0WI/ycd4OOKh8rrmhl/0EWb9iitwJYe\nMjTV/QxNEpPBEXfR1/mvrN/lVQKBgQDuhomOxUhWVRVH6x03slmyRBn0Oiw4MW+r\nrt1hlNgtVmTc5Mu+4G0USMZwYuOB7F8xG4Foc7rIlwS7Ic83jMJxemtqAelwOLdV\nXRLrLWJfX8+O1z/UE15l2q3SUEnQ4esPHbQnZowHLm0mdL14qSVMl1mu1XfsoZ3z\nJZTQb48CIwKBgEWbzQRtKD8lKDupJEYqSrseRbK/ax43DDITS77/DWwHl33D3FYC\nMblUm8ygwxQpR4VUfwDpYXBlklWcJovzamXpSnsfcYVkkQH47NuOXPXPkXQsw+w+\nDYcJzeu7F/vZqk9I7oBkWHUrrik9zPNoUzrfPvSRGtkAoTDSwibhoc5dAoGBAMHE\nK0T/ANeZQLNuzQps6S7G4eqjwz5W8qeeYxsdZkvWThOgDd/ewt3ijMnJm5X05hOn\ni4XF1euTuvUl7wbqYx76Wv3/1ZojiNNgy7ie4rYlyB/6vlBS97F4ZxJdxMlabbCW\n6b3EMWa4EVVXKoA1sCY7IVDE+yoQ1JYsZmq45YzPAoGBANWWHuVueFGZRDZlkNlK\nh5OmySmA0NdNug3G1upaTthyaTZ+CxGliwBqMHAwpkIRPwxUJpUwBTSEGztGTAxs\nWsUOVWlD2/1JaKSmHE8JbNg6sxLilcG6WEDzxjC5dLL1OrGOXj9WhC9KX3sq6qb6\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n",
          "client_email": "[email protected]",
          "client_id": "100369174533302798535",
          "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%40model-sheets-connect.iam.gserviceaccount.com"
        }
        uri = "mongodb+srv://multichem:[email protected]/?retryWrites=true&w=majority&appName=TestCluster"
        client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=100000)
        db = client["testing_db"]

        gc_con = gspread.service_account_from_dict(credentials, scope)
      
        return gc_con, client, db

gcservice_account, client, db = init_conn()

MLB_Data = 'https://docs.google.com/spreadsheets/d/1f42Ergav8K1VsOLOK9MUn7DM_MLMvv4GR2Fy7EfnZTc/edit#gid=340831852'

percentages_format = {'Exposure': '{:.2%}'}

@st.cache_resource(ttl = 599)
def init_baselines():
    sh = gcservice_account.open_by_url(MLB_Data)
    collection = db["DK_MLB_seed_frame"] 
    cursor = collection.find()
    
    raw_display = pd.DataFrame(list(cursor))
    DK_seed = raw_display[['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'Team', 'Team_count', 'salary', 'proj']]

    collection = db["FD_MLB_seed_frame"] 
    cursor = collection.find()
    
    raw_display = pd.DataFrame(list(cursor))
    FD_seed = raw_display[['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'Team', 'Team_count', 'salary', 'proj']]
    
    worksheet = sh.worksheet('DK_Projections')
    load_display = pd.DataFrame(worksheet.get_all_records())
    load_display.replace('', np.nan, inplace=True)
    
    dk_raw = load_display.dropna(subset=['Median'])
    
    worksheet = sh.worksheet('FD_Projections')
    load_display = pd.DataFrame(worksheet.get_all_records())
    load_display.replace('', np.nan, inplace=True)
    
    fd_raw = load_display.dropna(subset=['Median'])

    return DK_seed, FD_seed, dk_raw, fd_raw

DK_seed, FD_seed, dk_raw, fd_raw = init_baselines()

tab1, tab2 = ['Data Export', 'Contest Sims']
with tab1:
    col1, col2 = st.columns([1, 7])
    with col1:
        if st.button("Load/Reset Data", key='reset1'):
              st.cache_data.clear()
              for key in st.session_state.keys():
                  del st.session_state[key]
              DK_seed, FD_seed, dk_raw, fd_raw = init_baselines()
              
        slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Other Main Slate'))
        site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
        if site_var1 == 'Draftkings':
            raw_baselines = dk_raw
        elif site_var1 == 'Fanduel':
            raw_baselines = fd_raw
        team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
        if team_var1 == 'Specific Teams':
                team_var2 = st.multiselect('Which teams do you want?', options = DK_seed['Team'].unique())
        elif team_var1 == 'Full Players':
                team_var2 = DK_seed.Team.values.tolist()
    
    with col2:
        if site_var1 == 'Draftkings':
            DK_seed_parse = DK_seed[DK_seed['Team'].isin[team_var2]]
            st.session_state.data_export_display = DK_seed_parse.head(1000)
            st.session_state.data_export = DK_seed_parse
            st.session_state.data_export_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.data_export.iloc[:,0:9].values, return_counts=True)),
                                        columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            st.session_state.data_export_freq['Freq'] = st.session_state.data_export_freq['Freq'].astype(int)
            st.session_state.data_export_freq['Exposure'] = st.session_state.data_export_freq['Freq']/(500000)
            
            if 'data_export' in st.session_state:
                    st.download_button(
                        label="Export optimals set",
                        data=st.session_state.data_export.to_csv().encode('utf-8'),
                        file_name='MLB_optimals_export.csv',
                        mime='text/csv',
                    )
            st.dataframe(st.session_state.data_export_display.style.format(precision=2), height=500, use_container_width=True)
            st.dataframe(st.session_state.data_export_freq.style.format(percentages_format, precision=2), height=500, use_container_width=True)
        elif site_var1 == 'Fanduel':
            FD_seed_parse = FD_seed[FD_seed['Team'].isin[team_var2]]
            st.session_state.data_export_display = FD_seed_parse.head(1000)
            st.session_state.data_export = FD_seed_parse
            st.session_state.data_export_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.data_export.iloc[:,0:8].values, return_counts=True)),
                                        columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            st.session_state.data_export_freq['Freq'] = st.session_state.data_export_freq['Freq'].astype(int)
            st.session_state.data_export_freq['Exposure'] = st.session_state.data_export_freq['Freq']/(500000)
            
            if 'data_export' in st.session_state:
                    st.download_button(
                        label="Export optimals set",
                        data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
                        file_name='MLB_optimals_export.csv',
                        mime='text/csv',
                    )
            st.dataframe(st.session_state.data_export_display.style.format(precision=2), height=500, use_container_width=True)
            st.dataframe(st.session_state.data_export_freq.style.format(percentages_format, precision=2), height=500, use_container_width=True)
            
with tab2:
    col1, col2 = st.columns([1, 7])
    with col1:
        if st.button("Load/Reset Data", key='reset2'):
              st.cache_data.clear()
              for key in st.session_state.keys():
                  del st.session_state[key]
              DK_seed, FD_seed, dk_raw, fd_raw = init_baselines()
              
    #     slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Other Main Slate'))
    #     site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
    #     if site_var1 == 'Draftkings':
    #         raw_baselines = dk_raw
    #     elif site_var1 == 'Fanduel':
    #         raw_baselines = fd_raw
            
    #     contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large'))
    #     if contest_var1 == 'Small':
    #         Contest_Size = 1000
    #     elif contest_var1 == 'Medium':
    #         Contest_Size = 5000
    #     elif contest_var1 == 'Large':
    #         Contest_Size = 10000
    #     elif contest_var1 == 'Massive':
    #         Contest_Size = 100000
    #     strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Not Very', 'Average', 'Very'))
    #     if strength_var1 == 'Not Very':
    #         sharp_split = [400000,100000] 
    #     elif strength_var1 == 'Average':
    #         sharp_split = [500000,200000]
    #     elif strength_var1 == 'Very':
    #         sharp_split = [500000,300000]
    
    with col2:
    #     if site_var1 == 'Draftkings':
    #         st.session_state.Sim_Winner_Frame = DK_seed.head(Contest_Size)
    #         st.session_state.Sim_Winner_Display = DK_seed.head(Contest_Size)
    #         st.session_state.Sim_Winner_Export = DK_seed
    #         st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,0:9].values, return_counts=True)),
    #                                     columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
    #         st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'].astype(int)
    #         st.session_state.player_freq['Exposure'] = st.session_state.player_freq['Freq']/(Contest_Size)
            
    #         if 'Sim_Winner_Export' in st.session_state:
    #                 st.download_button(
    #                     label="Export 500k optimals",
    #                     data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
    #                     file_name='MLB_consim_export.csv',
    #                     mime='text/csv',
    #                 )
    #         st.dataframe(st.session_state.Sim_Winner_Display.style.format(precision=2), height=500, use_container_width=True)
    #         st.dataframe(st.session_state.player_freq.style.format(percentages_format, precision=2), height=500, use_container_width=True)
    #     elif site_var1 == 'Fanduel':
    #         st.session_state.Sim_Winner_Frame = FD_seed.head(Contest_Size)
    #         st.session_state.Sim_Winner_Display = FD_seed.head(Contest_Size)
    #         st.session_state.Sim_Winner_Export = FD_seed
    #         st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,0:8].values, return_counts=True)),
    #                                     columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
    #         st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'].astype(int)
    #         st.session_state.player_freq['Exposure'] = st.session_state.player_freq['Freq']/(Contest_Size)
            
    #         if 'Sim_Winner_Export' in st.session_state:
    #                 st.download_button(
    #                     label="Export 500k optimals",
    #                     data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
    #                     file_name='MLB_consim_export.csv',
    #                     mime='text/csv',
    #                 )
    #         st.dataframe(st.session_state.Sim_Winner_Display.style.format(precision=2), height=500, use_container_width=True)
    #         st.dataframe(st.session_state.player_freq.style.format(percentages_format, precision=2), height=500, use_container_width=True)