Removed GPP sepcific options, set optimizer to be bare bones

app.py

@@ -15,34 +15,51 @@ from itertools import combinations
 
 @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 = gspread.service_account_from_dict(credentials)
-          return gc
-
-gcservice_account = 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": st.secrets['model_sheets_connect_pk'],
+          "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": "gspread-connection@model-sheets-connect.iam.gserviceaccount.com",
+          "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"
+        }
+        
+        credentials2 = {
+          "type": "service_account",
+          "project_id": "sheets-api-connect-378620",
+          "private_key_id": st.secrets['sheets_api_connect_pk'],
+          "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"
+        }
+     
+        NHL_Data = st.secrets['NHL_Data']
+
+        gc = gspread.service_account_from_dict(credentials)
+        gc2 = gspread.service_account_from_dict(credentials2)
+
+        return gc, gc2, NHL_Data
+    
+gcservice_account, gcservice_account2, NHL_Data = init_conn()
 
 expose_format = {'Proj Own': '{:.2%}','Exposure': '{:.2%}'}
 
-all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1NmKa-b-2D3w7rRxwMPSchh31GKfJ1XcDI2GU8rXWnHI/edit#gid=943304327'
-
 @st.cache_resource(ttl = 599)
 def grab_baseline_stuff():
-    sh = gcservice_account.open_by_url(all_dk_player_projections)
+    try:
+        sh = gcservice_account.open_by_url(NHL_Data)
+    except:
+        sh = gcservice_account2.open_by_url(NHL_Data)
     worksheet = sh.worksheet('Player_Data_Master')
     raw_display = pd.DataFrame(worksheet.get_all_records())
     raw_display.replace('  -   ', 0, inplace=True)
@@ -53,15 +70,6 @@ def grab_baseline_stuff():
     dk_raw_proj = dk_raw_proj.dropna(subset='Salary')
     fd_raw_proj = raw_display[[' Clean Name ', ' Team ', ' Opp ', ' Line ', ' PP Unit ', ' FD Position ', ' FD Salary ', ' Final FD Projection ', ' FD uploadID ', 'FD_Own', ' MainSlateFD ']]
     fd_raw_proj = fd_raw_proj.set_axis(['Player', 'Team', 'Opp', 'Line', 'PP Unit', 'Position', 'Salary', 'Median', 'player_id', 'Own', 'MainSlateFD'], axis=1)
-    # fd_raw_proj = fd_raw_proj.dropna(subset='Salary')
-    # dk_raw_proj['Salary'] = dk_raw_proj['Salary'].str.replace(',', '')
-    # dk_raw_proj['Salary'] = dk_raw_proj['Salary'].str.replace('.', '')
-    # dk_raw_proj['Median'] = dk_raw_proj['Median'].astype(float)
-    # dk_raw_proj['Salary'] = dk_raw_proj['Salary'].str[:-2].astype(int)
-    # fd_raw_proj['Salary'] = fd_raw_proj['Salary'].str.replace(',', '')
-    # fd_raw_proj['Salary'] = fd_raw_proj['Salary'].str.replace('.', '')
-    # fd_raw_proj['Median'] = fd_raw_proj['Median'].astype(float)
-    # fd_raw_proj['Salary'] = fd_raw_proj['Salary'].str[:-2].astype(int)
     dk_raw_proj['Own'] = dk_raw_proj['Own'].astype(float)
     fd_raw_proj['Own'] = fd_raw_proj['Own'].astype(float)
     dk_raw_proj['player_id'] = dk_raw_proj['player_id'].astype(str)
@@ -91,25 +99,9 @@ dk_raw_proj, fd_raw_proj, dkid_dict, fdid_dict, timestamp, line_frame = grab_bas
 t_stamp = f"Last Update: " + str(timestamp) + f" CST"
 opp_dict = dict(zip(dk_raw_proj.Team, dk_raw_proj.Opp))
 
-tab1, tab2 = st.tabs(['Uploads and Info', 'Optimizer'])
-
-with tab1:
-    st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Line', 'PP Unit', 'Own', and 'player_id'. The player_id is the draftkings or fanduel ID associated with the player for upload.")
-    col1, col2 = st.columns([1, 5])
-
-    with col1:
-        proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader')
-    
-        if proj_file is not None:
-                  try:
-                            proj_dataframe = pd.read_csv(proj_file)
-                  except:
-                            proj_dataframe = pd.read_excel(proj_file)
-    with col2:
-        if proj_file is not None:  
-                  st.dataframe(proj_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+tab1, tab2 = st.tabs(['Optimizer', 'Uploads and Info'])
         
-with tab2:
+with tab1:
     col1, col2 = st.columns([1, 5])
     with col1:
         st.info(t_stamp)
@@ -143,15 +135,7 @@ with tab2:
                       init_baselines = init_baselines.loc[init_baselines['MainSlateFD'] == ' Main ']
                   if mainvar1 != 'Main Slate':
                       init_baselines = init_baselines.loc[init_baselines['MainSlateFD'] != ' Main ']
-        contest_var1 = st.selectbox("What contest type are you optimizing for?", ('Cash', 'Small Field GPP', 'Large Field GPP', 'Round Robin'), key='contest_var1')
-        if contest_var1 != 'Cash':
-            stack_var1 = st.selectbox('Which team are you stacking?', options = init_baselines['Team'].unique(), key='stack_var1')
-            stack_size_var1 = st.selectbox('What size of stack?', options = [3, 4], key='stack_size_var1')
-            line_choice_var1 = st.selectbox('Which line for main?', options = [1, 2, 3, 4], key='line_choice_var1')
-            ministack_var1 = st.selectbox('Who should be the secondary stack?', options = init_baselines['Team'].unique(), key='ministack_var1')
-            ministack_size_var1 = st.selectbox('What size of secondary stack?', options = [2, 3, 4], key='ministack_size_var1')
-            miniline_choice_var1 = st.selectbox('Which line for secondary?', options = [1, 2, 3, 4], key='miniline_choice_var1')
-            opp_var1 = opp_dict[stack_var1]
+        contest_var1 = st.selectbox("What contest type are you optimizing for?", ('Cash', 'GPP'), key='contest_var1')
         split_var1 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var1')
         if split_var1 == 'Specific Games':
             team_var1 = st.multiselect('Which teams would you like to include in the optimization?', options = init_baselines['Team'].unique(), key='team_var1')
@@ -161,30 +145,15 @@ with tab2:
         avoid_var1 = st.multiselect("Are there any players you want to remove from the pool (Drop Button)?", options = init_baselines['Player'].unique(), key='avoid_var1')
         linenum_var1 = st.number_input("How many lineups would you like to produce?", min_value = 1, max_value = 300, value = 20, step = 1, key='linenum_var1')
         if site_var1 == 'Draftkings':
-            min_sal1 = st.number_input('Min Salary', min_value = 35000, max_value = 49900, value = 47000, step = 100, key='min_sal1')
+            min_sal1 = st.number_input('Min Salary', min_value = 35000, max_value = 49900, value = 49000, step = 100, key='min_sal1')
             max_sal1 = st.number_input('Max Salary', min_value = 35000, max_value = 50000, value = 50000, step = 100, key='max_sal1')
         elif site_var1 == 'Fanduel':
-            min_sal1 = st.number_input('Min Salary', min_value = 45000, max_value = 54900, value = 52000, step = 100, key='min_sal1')
+            min_sal1 = st.number_input('Min Salary', min_value = 45000, max_value = 54900, value = 54000, step = 100, key='min_sal1')
             max_sal1 = st.number_input('Max Salary', min_value = 45000, max_value = 55000, value = 55000, step = 100, key='max_sal1')
     with col2:
         init_baselines = init_baselines[init_baselines['Team'].isin(team_var1)]
         init_baselines = init_baselines[~init_baselines['Player'].isin(avoid_var1)]
         ownframe = init_baselines.copy()
-        if contest_var1 == 'Cash':
-                ownframe['Own%'] = np.where((ownframe['Position'] == 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean() >= 0), ownframe['Own'] * (10 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean(), ownframe['Own'])
-                ownframe['Own%'] = np.where((ownframe['Position'] != 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean() >= 0), ownframe['Own'] * (5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean(), ownframe['Own%'])
-                ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%'])
-                ownframe['Own'] = ownframe['Own%'] * (900 / ownframe['Own%'].sum())
-        if contest_var1 == 'Small Field GPP':
-                ownframe['Own%'] = np.where((ownframe['Position'] == 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean() >= 0), ownframe['Own'] * (6 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean(), ownframe['Own'])
-                ownframe['Own%'] = np.where((ownframe['Position'] != 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean() >= 0), ownframe['Own'] * (3 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean(), ownframe['Own%'])
-                ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%'])
-                ownframe['Own'] = ownframe['Own%'] * (900 / ownframe['Own%'].sum())
-        if contest_var1 == 'Large Field GPP':
-                ownframe['Own%'] = np.where((ownframe['Position'] == 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean() >= 0), ownframe['Own'] * (3 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean(), ownframe['Own'])
-                ownframe['Own%'] = np.where((ownframe['Position'] != 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean() >= 0), ownframe['Own'] * (1.5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean(), ownframe['Own%'])
-                ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%'])
-                ownframe['Own'] = ownframe['Own%'] * (900 / ownframe['Own%'].sum())
         raw_baselines = ownframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Line', 'PP Unit', 'Median', 'Own']]
         raw_baselines = raw_baselines.sort_values(by='Median', ascending=False)
         raw_baselines['lock'] = np.where(raw_baselines['Player'].isin(lock_var1), 1, 0)
@@ -252,19 +221,9 @@ with tab2:
 
                         if site_var1 == 'Draftkings':
                             
-                            if contest_var1 == 'Cash':
-                                for flex in flex_file['Team'].unique():
-                                    sub_idx = flex_file[(flex_file['Team'] == flex) & (flex_file['Position'] != 'G')].index
-                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4
-                            elif contest_var1 != 'Cash':
-                                for flex in flex_file['Team'].unique():
-                                    sub_idx = flex_file[(flex_file['Team'] == stack_var1) & (flex_file['Position'] != 'G') & (flex_file['Position'] != 'D')
-                                                        & (flex_file['Line'] == line_choice_var1)].index
-                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == stack_size_var1
-                                for flex in flex_file['Team'].unique():
-                                    sub_idx = flex_file[(flex_file['Team'] == ministack_var1) & (flex_file['Position'] != 'G') & (flex_file['Position'] != 'D')
-                                                        & (flex_file['Line'] == miniline_choice_var1)].index
-                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == ministack_size_var1
+                            for flex in flex_file['Team'].unique():
+                                sub_idx = flex_file[(flex_file['Team'] == flex) & (flex_file['Position'] != 'G')].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4
                             
                             for flex in flex_file['lock'].unique():
                                 sub_idx = flex_file[flex_file['lock'] == 1].index
@@ -304,19 +263,9 @@ with tab2:
 
                         elif site_var1 == 'Fanduel':
 
-                            if contest_var1 == 'Cash':
-                                for flex in flex_file['Team'].unique():
-                                    sub_idx = flex_file[(flex_file['Team'] == flex) & (flex_file['Position'] != 'G')].index
-                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4
-                            elif contest_var1 != 'Cash':
-                                for flex in flex_file['Team'].unique():
-                                    sub_idx = flex_file[(flex_file['Team'] == stack_var1) & (flex_file['Position'] != 'G') & (flex_file['Position'] != 'D')
-                                                        & (flex_file['Line'] == line_choice_var1)].index
-                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == stack_size_var1
-                                for flex in flex_file['Team'].unique():
-                                    sub_idx = flex_file[(flex_file['Team'] == ministack_var1) & (flex_file['Position'] != 'G') & (flex_file['Position'] != 'D')
-                                                        & (flex_file['Line'] == miniline_choice_var1)].index
-                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == ministack_size_var1
+                            for flex in flex_file['Team'].unique():
+                                sub_idx = flex_file[(flex_file['Team'] == flex) & (flex_file['Position'] != 'G')].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4
                             
                             for flex in flex_file['lock'].unique():
                                 sub_idx = flex_file[flex_file['lock'] == 1].index
@@ -651,4 +600,20 @@ with tab2:
         with freq_container:
             freq_container = st.empty()
             if 'player_freq' in st.session_state:
-                st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(expose_format, precision=2), use_container_width = True)
+                st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(expose_format, precision=2), use_container_width = True)
+
+with tab2:
+    st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Line', 'PP Unit', 'Own', and 'player_id'. The player_id is the draftkings or fanduel ID associated with the player for upload.")
+    col1, col2 = st.columns([1, 5])
+
+    with col1:
+        proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader')
+    
+        if proj_file is not None:
+                  try:
+                            proj_dataframe = pd.read_csv(proj_file)
+                  except:
+                            proj_dataframe = pd.read_excel(proj_file)
+    with col2:
+        if proj_file is not None:  
+                  st.dataframe(proj_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)