James McCool
commited on
Commit
·
72880a2
1
Parent(s):
560df46
Refactor seed frame initialization in app.py to accept a 'sharp_split' parameter, allowing for dynamic data retrieval limits. Update related functions and session state handling to improve user input flexibility for DraftKings and FanDuel simulations.
Browse files
app.py
CHANGED
@@ -55,52 +55,56 @@ dk_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'sal
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fd_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
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@st.cache_data(ttl = 600)
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-
def init_DK_seed_frames():
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collection = db["DK_NFL_seed_frame"]
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-
cursor = collection.find()
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raw_display = pd.DataFrame(list(cursor))
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raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
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DK_seed = raw_display.to_numpy()
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return
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@st.cache_data(ttl = 600)
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def init_DK_Secondary_seed_frames():
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collection = db["DK_NFL_Secondary_seed_frame"]
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cursor = collection.find()
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raw_display = pd.DataFrame(list(cursor))
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raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
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DK_seed = raw_display.to_numpy()
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return
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@st.cache_data(ttl = 599)
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def init_FD_seed_frames():
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collection = db["FD_NFL_seed_frame"]
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cursor = collection.find()
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raw_display = pd.DataFrame(list(cursor))
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raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
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FD_seed = raw_display.to_numpy()
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return
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@st.cache_data(ttl = 599)
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def init_FD_Secondary_seed_frames():
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collection = db["FD_NFL_Secondary_seed_frame"]
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cursor = collection.find()
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raw_display = pd.DataFrame(list(cursor))
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raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
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FD_seed = raw_display.to_numpy()
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return
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@st.cache_data(ttl = 599)
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def init_baselines():
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@@ -146,11 +150,10 @@ def calculate_FD_value_frequencies(np_array):
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return combined_array
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@st.cache_data
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def sim_contest(Sim_size, seed_frame, maps_dict,
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SimVar = 1
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Sim_Winners = []
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fp_array = seed_frame
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-
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# Pre-vectorize functions
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vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__)
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vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__)
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@@ -189,22 +192,17 @@ with tab2:
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st.cache_data.clear()
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for key in st.session_state.keys():
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del st.session_state[key]
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DK_seed = init_DK_seed_frames()
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FD_seed = init_FD_seed_frames()
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dk_raw, fd_raw = init_baselines()
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dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
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fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
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slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate'))
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site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
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if site_var1 == 'Draftkings':
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if slate_var1 == 'Main Slate':
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DK_seed = init_DK_seed_frames()
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elif slate_var1 == 'Secondary Slate':
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DK_seed = init_DK_Secondary_seed_frames()
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raw_baselines = dk_raw
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column_names = dk_columns
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team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
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if team_var1 == 'Specific Teams':
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@@ -219,13 +217,6 @@ with tab2:
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stack_var2 = [5, 4, 3, 2, 1, 0]
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elif site_var1 == 'Fanduel':
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if slate_var1 == 'Main Slate':
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FD_seed = init_FD_seed_frames()
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elif slate_var1 == 'Secondary Slate':
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FD_seed = init_FD_Secondary_seed_frames()
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raw_baselines = fd_raw
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column_names = fd_columns
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team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
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if team_var1 == 'Specific Teams':
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@@ -245,7 +236,27 @@ with tab2:
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
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elif 'working_seed' not in st.session_state:
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-
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
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data_export = st.session_state.working_seed.copy()
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@@ -266,7 +277,16 @@ with tab2:
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
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elif 'working_seed' not in st.session_state:
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-
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
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@@ -277,7 +297,15 @@ with tab2:
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
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elif 'working_seed' not in st.session_state:
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-
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
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@@ -293,30 +321,14 @@ with tab1:
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st.cache_data.clear()
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for key in st.session_state.keys():
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del st.session_state[key]
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DK_seed = init_DK_seed_frames()
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FD_seed = init_FD_seed_frames()
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dk_raw, fd_raw = init_baselines()
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dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
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fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
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sim_slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate'), key='sim_slate_var1')
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sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1')
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if sim_site_var1 == 'Draftkings':
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if sim_slate_var1 == 'Main Slate':
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DK_seed = init_DK_seed_frames()
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elif sim_slate_var1 == 'Secondary Slate':
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DK_seed = init_DK_Secondary_seed_frames()
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raw_baselines = dk_raw
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column_names = dk_columns
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elif sim_site_var1 == 'Fanduel':
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if sim_slate_var1 == 'Main Slate':
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FD_seed = init_FD_seed_frames()
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elif sim_slate_var1 == 'Secondary Slate':
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FD_seed = init_FD_Secondary_seed_frames()
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raw_baselines = fd_raw
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column_names = fd_columns
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contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Custom'))
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if contest_var1 == 'Small':
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@@ -351,7 +363,7 @@ with tab1:
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'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
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'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
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}
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Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict,
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Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
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#st.table(Sim_Winner_Frame)
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@@ -378,9 +390,21 @@ with tab1:
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else:
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if sim_site_var1 == 'Draftkings':
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elif sim_site_var1 == 'Fanduel':
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-
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st.session_state.maps_dict = {
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'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
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'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
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'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
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'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
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}
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Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict,
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Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
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#st.table(Sim_Winner_Frame)
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fd_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
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@st.cache_data(ttl = 600)
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def init_DK_seed_frames(sharp_split):
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collection = db["DK_NFL_seed_frame"]
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cursor = collection.find().limit(sharp_split)
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raw_display = pd.DataFrame(list(cursor))
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raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
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DK_seed = raw_display.to_numpy()
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fp_array = DK_seed[:sharp_split, :]
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return fp_array
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@st.cache_data(ttl = 600)
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def init_DK_Secondary_seed_frames(sharp_split):
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collection = db["DK_NFL_Secondary_seed_frame"]
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cursor = collection.find().limit(sharp_split)
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raw_display = pd.DataFrame(list(cursor))
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raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
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DK_seed = raw_display.to_numpy()
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fp_array = DK_seed[:sharp_split, :]
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return fp_array
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@st.cache_data(ttl = 599)
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def init_FD_seed_frames(sharp_split):
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collection = db["FD_NFL_seed_frame"]
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cursor = collection.find().limit(sharp_split)
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raw_display = pd.DataFrame(list(cursor))
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raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
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FD_seed = raw_display.to_numpy()
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fp_array = FD_seed[:sharp_split, :]
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return fp_array
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@st.cache_data(ttl = 599)
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def init_FD_Secondary_seed_frames(sharp_split):
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collection = db["FD_NFL_Secondary_seed_frame"]
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cursor = collection.find().limit(sharp_split)
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raw_display = pd.DataFrame(list(cursor))
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raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
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FD_seed = raw_display.to_numpy()
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fp_array = FD_seed[:sharp_split, :]
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return fp_array
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@st.cache_data(ttl = 599)
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def init_baselines():
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return combined_array
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@st.cache_data
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def sim_contest(Sim_size, seed_frame, maps_dict, Contest_Size):
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SimVar = 1
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Sim_Winners = []
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fp_array = seed_frame.copy()
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# Pre-vectorize functions
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vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__)
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vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__)
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st.cache_data.clear()
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for key in st.session_state.keys():
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del st.session_state[key]
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DK_seed = init_DK_seed_frames(10000)
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FD_seed = init_FD_seed_frames(10000)
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dk_raw, fd_raw = init_baselines()
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dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
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fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
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slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate'))
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site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
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sharp_split_var = st.number_input("How many lineups do you want?", value=10000, max_value=500000, min_value=10000, step=10000)
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if site_var1 == 'Draftkings':
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team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
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if team_var1 == 'Specific Teams':
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stack_var2 = [5, 4, 3, 2, 1, 0]
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elif site_var1 == 'Fanduel':
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team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
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if team_var1 == 'Specific Teams':
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
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elif 'working_seed' not in st.session_state:
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if site_var1 == 'Draftkings':
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if slate_var1 == 'Main Slate':
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st.session_state.working_seed = init_DK_seed_frames(sharp_split_var)
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dk_id_dict = dict(zip(st.session_state.working_seed.Player, st.session_state.working_seed.player_id))
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elif slate_var1 == 'Secondary Slate':
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st.session_state.working_seed = init_DK_Secondary_seed_frames(sharp_split_var)
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dk_id_dict = dict(zip(st.session_state.working_seed.Player, st.session_state.working_seed.player_id))
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raw_baselines = dk_raw
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column_names = dk_columns
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elif site_var1 == 'Fanduel':
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if slate_var1 == 'Main Slate':
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st.session_state.working_seed = init_FD_seed_frames(sharp_split_var)
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fd_id_dict = dict(zip(st.session_state.working_seed.Player, st.session_state.working_seed.player_id))
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elif slate_var1 == 'Secondary Slate':
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st.session_state.working_seed = init_FD_Secondary_seed_frames(sharp_split_var)
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fd_id_dict = dict(zip(st.session_state.working_seed.Player, st.session_state.working_seed.player_id))
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raw_baselines = fd_raw
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column_names = fd_columns
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
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data_export = st.session_state.working_seed.copy()
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
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elif 'working_seed' not in st.session_state:
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if slate_var1 == 'Main Slate':
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st.session_state.working_seed = init_DK_seed_frames(sharp_split_var)
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dk_id_dict = dict(zip(st.session_state.working_seed.Player, st.session_state.working_seed.player_id))
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elif slate_var1 == 'Secondary Slate':
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st.session_state.working_seed = init_DK_Secondary_seed_frames(sharp_split_var)
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285 |
+
dk_id_dict = dict(zip(st.session_state.working_seed.Player, st.session_state.working_seed.player_id))
|
286 |
+
|
287 |
+
raw_baselines = dk_raw
|
288 |
+
column_names = dk_columns
|
289 |
+
|
290 |
st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
|
291 |
st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
|
292 |
st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
|
|
|
297 |
st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
|
298 |
st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
|
299 |
elif 'working_seed' not in st.session_state:
|
300 |
+
if slate_var1 == 'Main Slate':
|
301 |
+
st.session_state.working_seed = init_FD_seed_frames(sharp_split_var)
|
302 |
+
fd_id_dict = dict(zip(st.session_state.working_seed.Player, st.session_state.working_seed.player_id))
|
303 |
+
elif slate_var1 == 'Secondary Slate':
|
304 |
+
st.session_state.working_seed = init_FD_Secondary_seed_frames(sharp_split_var)
|
305 |
+
fd_id_dict = dict(zip(st.session_state.working_seed.Player, st.session_state.working_seed.player_id))
|
306 |
+
|
307 |
+
raw_baselines = fd_raw
|
308 |
+
column_names = fd_columns
|
309 |
st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
|
310 |
st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
|
311 |
st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
|
|
|
321 |
st.cache_data.clear()
|
322 |
for key in st.session_state.keys():
|
323 |
del st.session_state[key]
|
324 |
+
DK_seed = init_DK_seed_frames(10000)
|
325 |
+
FD_seed = init_FD_seed_frames(10000)
|
326 |
dk_raw, fd_raw = init_baselines()
|
327 |
dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
|
328 |
fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
|
329 |
|
330 |
sim_slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate'), key='sim_slate_var1')
|
331 |
sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1')
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
332 |
|
333 |
contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Custom'))
|
334 |
if contest_var1 == 'Small':
|
|
|
363 |
'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
|
364 |
'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
|
365 |
}
|
366 |
+
Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict, Contest_Size)
|
367 |
Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
|
368 |
|
369 |
#st.table(Sim_Winner_Frame)
|
|
|
390 |
|
391 |
else:
|
392 |
if sim_site_var1 == 'Draftkings':
|
393 |
+
if sim_slate_var1 == 'Main Slate':
|
394 |
+
st.session_state.working_seed = init_DK_seed_frames(sharp_split)
|
395 |
+
elif sim_slate_var1 == 'Secondary Slate':
|
396 |
+
st.session_state.working_seed = init_DK_Secondary_seed_frames(sharp_split)
|
397 |
+
|
398 |
+
raw_baselines = dk_raw
|
399 |
+
column_names = dk_columns
|
400 |
elif sim_site_var1 == 'Fanduel':
|
401 |
+
if sim_slate_var1 == 'Main Slate':
|
402 |
+
st.session_state.working_seed = init_FD_seed_frames(sharp_split)
|
403 |
+
elif sim_slate_var1 == 'Secondary Slate':
|
404 |
+
st.session_state.working_seed = init_FD_Secondary_seed_frames(sharp_split)
|
405 |
+
|
406 |
+
raw_baselines = fd_raw
|
407 |
+
column_names = fd_columns
|
408 |
st.session_state.maps_dict = {
|
409 |
'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
|
410 |
'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
|
|
|
413 |
'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
|
414 |
'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
|
415 |
}
|
416 |
+
Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict, Contest_Size)
|
417 |
Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
|
418 |
|
419 |
#st.table(Sim_Winner_Frame)
|