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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| from sklearn.metrics import roc_auc_score, precision_score, recall_score | |
| from pandas.tseries.offsets import BDay | |
| st.set_page_config( | |
| page_title="Gameday Model for $SPX", | |
| page_icon="🎮" | |
| ) | |
| st.title('🎮 Gameday Model for $SPX') | |
| st.markdown('**PLEASE NOTE:** Model should be run at or after market open. Documentation and information about model coming soon.') | |
| if st.button("🧹 Clear All"): | |
| st.cache_data.clear() | |
| col1, col2 = st.columns(2) | |
| option = st.selectbox( | |
| 'Select a model, then run.', | |
| ('', '🌞 At Open', '⌚ 30 Mins', '⏳ 60 Mins')) | |
| if option == '': | |
| st.write('Gotta pick one.') | |
| elif option == '🌞 At Open': | |
| if st.button('🏃🏽♂️ Run'): | |
| from model_day import * | |
| with st.spinner('Loading data...'): | |
| data, df_final, final_row = get_data() | |
| # st.success("✅ Historical data") | |
| with st.spinner("Training models..."): | |
| def train_models(): | |
| res1, xgbr, seq2 = walk_forward_validation_seq(df_final.dropna(), 'Target_clf', 'Target', 100, 1) | |
| return res1, xgbr, seq2 | |
| res1, xgbr, seq2 = train_models() | |
| # st.success("✅ Models trained") | |
| with st.spinner("Getting new prediction..."): | |
| # Get last row | |
| new_pred = data.loc[final_row, ['BigNewsDay', | |
| 'Quarter', | |
| 'Perf5Day', | |
| 'Perf5Day_n1', | |
| 'DaysGreen', | |
| 'DaysRed', | |
| 'CurrentGap', | |
| 'RangePct', | |
| 'RangePct_n1', | |
| 'RangePct_n2', | |
| 'OHLC4_VIX', | |
| 'OHLC4_VIX_n1', | |
| 'OHLC4_VIX_n2']] | |
| new_pred = pd.DataFrame(new_pred).T | |
| # new_pred_show = pd.DataFrame(index=[new_pred.columns], columns=[new_pred.index], data=[[v] for v in new_pred.values]) | |
| # last_date = datetime.datetime.strptime(data.loc[final_row], '%Y-%m-%d') | |
| curr_date = final_row + BDay(1) | |
| curr_date = curr_date.strftime('%Y-%m-%d') | |
| new_pred['BigNewsDay'] = new_pred['BigNewsDay'].astype(float) | |
| new_pred['Quarter'] = new_pred['Quarter'].astype(int) | |
| new_pred['Perf5Day'] = new_pred['Perf5Day'].astype(bool) | |
| new_pred['Perf5Day_n1'] = new_pred['Perf5Day_n1'].astype(bool) | |
| new_pred['DaysGreen'] = new_pred['DaysGreen'].astype(float) | |
| new_pred['DaysRed'] = new_pred['DaysRed'].astype(float) | |
| new_pred['CurrentGap'] = new_pred['CurrentGap'].astype(float) | |
| new_pred['RangePct'] = new_pred['RangePct'].astype(float) | |
| new_pred['RangePct_n1'] = new_pred['RangePct_n1'].astype(float) | |
| new_pred['RangePct_n2'] = new_pred['RangePct_n2'].astype(float) | |
| new_pred['OHLC4_VIX'] = new_pred['OHLC4_VIX'].astype(float) | |
| new_pred['OHLC4_VIX_n1'] = new_pred['OHLC4_VIX_n1'].astype(float) | |
| new_pred['OHLC4_VIX_n2'] = new_pred['OHLC4_VIX_n2'].astype(float) | |
| st.success("✅ All done!") | |
| tab1, tab2, tab3, tab4 = st.tabs(["🔮 Prediction", "✨ New Data", "🗄 Historical", "📊 Performance"]) | |
| seq_proba = seq_predict_proba(new_pred, xgbr, seq2) | |
| green_proba = seq_proba[0] | |
| red_proba = 1 - green_proba | |
| do_not_play = (seq_proba[0] > 0.4) and (seq_proba[0] <= 0.6) | |
| stdev = 0.01 | |
| score = None | |
| num_obs = None | |
| cond = None | |
| historical_proba = None | |
| text_cond = None | |
| operator = None | |
| if do_not_play: | |
| text_cond = '🟨' | |
| operator = '' | |
| score = seq_proba[0] | |
| cond = (res1['Predicted'] > 0.4) & (res1['Predicted'] <= 0.6) | |
| num_obs = len(res1.loc[cond]) | |
| historical_proba = res1.loc[cond, 'True'].mean() | |
| elif green_proba > red_proba: | |
| # If the day is predicted to be green, say so | |
| text_cond = '🟩' | |
| operator = '>=' | |
| score = green_proba | |
| # How many with this score? | |
| cond = (res1['Predicted'] >= green_proba) | |
| num_obs = len(res1.loc[cond]) | |
| # How often green? | |
| historical_proba = res1.loc[cond, 'True'].mean() | |
| # print(cond) | |
| elif green_proba <= red_proba: | |
| # If the day is predicted to be green, say so | |
| text_cond = '🟥' | |
| operator = '<=' | |
| score = red_proba | |
| # How many with this score? | |
| cond = (res1['Predicted'] <= red_proba) | |
| num_obs = len(res1.loc[cond]) | |
| # How often green? | |
| historical_proba = 1 - res1.loc[cond, 'True'].mean() | |
| # print(cond) | |
| score_fmt = f'{score:.1%}' | |
| results = pd.DataFrame(index=[ | |
| 'PrevClose', | |
| 'Confidence Score', | |
| 'Success Rate', | |
| f'NumObs {operator} {"" if do_not_play else score_fmt}', | |
| ], data = [ | |
| f"{data.loc[final_row,'Close']:.2f}", | |
| f'{text_cond} {score:.1%}', | |
| f'{historical_proba:.1%}', | |
| num_obs, | |
| ]) | |
| results.columns = ['Outputs'] | |
| # st.subheader('New Prediction') | |
| int_labels = ['(-∞, .20]', '(.20, .40]', '(.40, .60]', '(.60, .80]', '(.80, ∞]'] | |
| # df_probas = res1.groupby(pd.qcut(res1['Predicted'],5)).agg({'True':[np.mean,len,np.sum]}) | |
| df_probas = res1.groupby(pd.cut(res1['Predicted'], bins = [-np.inf, 0.2, 0.4, 0.6, 0.8, np.inf], labels = int_labels)).agg({'True':[np.mean,len,np.sum]}) | |
| df_probas.columns = ['PctGreen','NumObs','NumGreen'] | |
| roc_auc_score_all = roc_auc_score(res1['True'].astype(int), res1['Predicted'].values) | |
| precision_score_all = precision_score(res1['True'].astype(int), res1['Predicted'] > 0.5) | |
| recall_score_all = recall_score(res1['True'].astype(int), res1['Predicted'] > 0.5) | |
| len_all = len(res1) | |
| res2_filtered = res1.loc[(res1['Predicted'] > 0.6) | (res1['Predicted'] <= 0.4)] | |
| roc_auc_score_hi = roc_auc_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'].values) | |
| precision_score_hi = precision_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'] > 0.5) | |
| recall_score_hi = recall_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'] > 0.5) | |
| len_hi = len(res2_filtered) | |
| df_performance = pd.DataFrame( | |
| index=[ | |
| 'N', | |
| 'ROC AUC', | |
| 'Precision', | |
| 'Recall' | |
| ], | |
| columns = [ | |
| 'All', | |
| 'High Confidence' | |
| ], | |
| data = [ | |
| [len_all, len_hi], | |
| [roc_auc_score_all, roc_auc_score_hi], | |
| [precision_score_all, precision_score_hi], | |
| [recall_score_all, recall_score_hi] | |
| ] | |
| ).round(2) | |
| def get_acc(t, p): | |
| if t == False and p <= 0.4: | |
| return '✅' | |
| elif t == True and p > 0.6: | |
| return '✅' | |
| elif t == False and p > 0.6: | |
| return '❌' | |
| elif t == True and p <= 0.4: | |
| return '❌' | |
| else: | |
| return '🟨' | |
| perf_daily = res1.copy() | |
| perf_daily['Accuracy'] = [get_acc(t, p) for t, p in zip(perf_daily['True'], perf_daily['Predicted'])] | |
| tab1.subheader(f'Pred for {curr_date} as of 6:30AM PST') | |
| tab1.write(results) | |
| tab1.write(df_probas) | |
| tab2.subheader('Latest Data for Pred') | |
| tab2.write(new_pred) | |
| tab3.subheader('Historical Data') | |
| tab3.write(df_final) | |
| tab4.subheader('Performance') | |
| tab4.write(df_performance) | |
| tab4.write(perf_daily) | |
| elif option == '⌚ 30 Mins': | |
| if st.button('🏃🏽♂️ Run'): | |
| from model_30m import * | |
| with st.spinner('Loading data...'): | |
| data, df_final, final_row = get_data() | |
| # st.success("✅ Historical data") | |
| with st.spinner("Training models..."): | |
| def train_models(): | |
| res1, xgbr, seq2 = walk_forward_validation_seq(df_final.dropna(), 'Target_clf', 'Target', 100, 1) | |
| return res1, xgbr, seq2 | |
| res1, xgbr, seq2 = train_models() | |
| # st.success("✅ Models trained") | |
| with st.spinner("Getting new prediction..."): | |
| # Get last row | |
| new_pred = data.loc[final_row, ['BigNewsDay', | |
| 'Quarter', | |
| 'Perf5Day', | |
| 'Perf5Day_n1', | |
| 'DaysGreen', | |
| 'DaysRed', | |
| 'CurrentHigh30toClose', | |
| 'CurrentLow30toClose', | |
| 'CurrentClose30toClose', | |
| 'CurrentRange30', | |
| 'GapFill30', | |
| 'CurrentGap', | |
| 'RangePct', | |
| 'RangePct_n1', | |
| 'RangePct_n2', | |
| 'OHLC4_VIX', | |
| 'OHLC4_VIX_n1', | |
| 'OHLC4_VIX_n2']] | |
| new_pred = pd.DataFrame(new_pred).T | |
| # new_pred_show = pd.DataFrame(index=[new_pred.columns], columns=[new_pred.index], data=[[v] for v in new_pred.values]) | |
| # last_date = datetime.datetime.strptime(data.loc[final_row], '%Y-%m-%d') | |
| curr_date = final_row + BDay(1) | |
| curr_date = curr_date.strftime('%Y-%m-%d') | |
| new_pred['BigNewsDay'] = new_pred['BigNewsDay'].astype(float) | |
| new_pred['Quarter'] = new_pred['Quarter'].astype(int) | |
| new_pred['Perf5Day'] = new_pred['Perf5Day'].astype(bool) | |
| new_pred['Perf5Day_n1'] = new_pred['Perf5Day_n1'].astype(bool) | |
| new_pred['DaysGreen'] = new_pred['DaysGreen'].astype(float) | |
| new_pred['DaysRed'] = new_pred['DaysRed'].astype(float) | |
| new_pred['CurrentHigh30toClose'] = new_pred['CurrentHigh30toClose'].astype(float) | |
| new_pred['CurrentLow30toClose'] = new_pred['CurrentLow30toClose'].astype(float) | |
| new_pred['CurrentClose30toClose'] = new_pred['CurrentClose30toClose'].astype(float) | |
| new_pred['CurrentRange30'] = new_pred['CurrentRange30'].astype(float) | |
| new_pred['GapFill30'] = new_pred['GapFill30'].astype(float) | |
| new_pred['CurrentGap'] = new_pred['CurrentGap'].astype(float) | |
| new_pred['RangePct'] = new_pred['RangePct'].astype(float) | |
| new_pred['RangePct_n1'] = new_pred['RangePct_n1'].astype(float) | |
| new_pred['RangePct_n2'] = new_pred['RangePct_n2'].astype(float) | |
| new_pred['OHLC4_VIX'] = new_pred['OHLC4_VIX'].astype(float) | |
| new_pred['OHLC4_VIX_n1'] = new_pred['OHLC4_VIX_n1'].astype(float) | |
| new_pred['OHLC4_VIX_n2'] = new_pred['OHLC4_VIX_n2'].astype(float) | |
| st.success("✅ All done!") | |
| tab1, tab2, tab3, tab4 = st.tabs(["🔮 Prediction", "✨ New Data", "🗄 Historical", "📊 Performance"]) | |
| seq_proba = seq_predict_proba(new_pred, xgbr, seq2) | |
| green_proba = seq_proba[0] | |
| red_proba = 1 - green_proba | |
| do_not_play = (seq_proba[0] > 0.4) and (seq_proba[0] <= 0.6) | |
| stdev = 0.01 | |
| score = None | |
| num_obs = None | |
| cond = None | |
| historical_proba = None | |
| text_cond = None | |
| operator = None | |
| if do_not_play: | |
| text_cond = '🟨' | |
| operator = '' | |
| score = seq_proba[0] | |
| cond = (res1['Predicted'] > 0.4) & (res1['Predicted'] <= 0.6) | |
| num_obs = len(res1.loc[cond]) | |
| historical_proba = res1.loc[cond, 'True'].mean() | |
| elif green_proba > red_proba: | |
| # If the day is predicted to be green, say so | |
| text_cond = '🟩' | |
| operator = '>=' | |
| score = green_proba | |
| # How many with this score? | |
| cond = (res1['Predicted'] >= green_proba) | |
| num_obs = len(res1.loc[cond]) | |
| # How often green? | |
| historical_proba = res1.loc[cond, 'True'].mean() | |
| # print(cond) | |
| elif green_proba <= red_proba: | |
| # If the day is predicted to be green, say so | |
| text_cond = '🟥' | |
| operator = '<=' | |
| score = red_proba | |
| # How many with this score? | |
| cond = (res1['Predicted'] <= red_proba) | |
| num_obs = len(res1.loc[cond]) | |
| # How often green? | |
| historical_proba = 1 - res1.loc[cond, 'True'].mean() | |
| # print(cond) | |
| score_fmt = f'{score:.1%}' | |
| results = pd.DataFrame(index=[ | |
| 'PrevClose', | |
| 'Confidence Score', | |
| 'Success Rate', | |
| f'NumObs {operator} {"" if do_not_play else score_fmt}', | |
| ], data = [ | |
| f"{data.loc[final_row,'Close']:.2f}", | |
| f'{text_cond} {score:.1%}', | |
| f'{historical_proba:.1%}', | |
| num_obs, | |
| ]) | |
| results.columns = ['Outputs'] | |
| # st.subheader('New Prediction') | |
| int_labels = ['(-∞, .20]', '(.20, .40]', '(.40, .60]', '(.60, .80]', '(.80, ∞]'] | |
| # df_probas = res1.groupby(pd.qcut(res1['Predicted'],5)).agg({'True':[np.mean,len,np.sum]}) | |
| df_probas = res1.groupby(pd.cut(res1['Predicted'], bins = [-np.inf, 0.2, 0.4, 0.6, 0.8, np.inf], labels = int_labels)).agg({'True':[np.mean,len,np.sum]}) | |
| df_probas.columns = ['PctGreen','NumObs','NumGreen'] | |
| roc_auc_score_all = roc_auc_score(res1['True'].astype(int), res1['Predicted'].values) | |
| precision_score_all = precision_score(res1['True'].astype(int), res1['Predicted'] > 0.5) | |
| recall_score_all = recall_score(res1['True'].astype(int), res1['Predicted'] > 0.5) | |
| len_all = len(res1) | |
| res2_filtered = res1.loc[(res1['Predicted'] > 0.6) | (res1['Predicted'] <= 0.4)] | |
| roc_auc_score_hi = roc_auc_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'].values) | |
| precision_score_hi = precision_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'] > 0.5) | |
| recall_score_hi = recall_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'] > 0.5) | |
| len_hi = len(res2_filtered) | |
| df_performance = pd.DataFrame( | |
| index=[ | |
| 'N', | |
| 'ROC AUC', | |
| 'Precision', | |
| 'Recall' | |
| ], | |
| columns = [ | |
| 'All', | |
| 'High Confidence' | |
| ], | |
| data = [ | |
| [len_all, len_hi], | |
| [roc_auc_score_all, roc_auc_score_hi], | |
| [precision_score_all, precision_score_hi], | |
| [recall_score_all, recall_score_hi] | |
| ] | |
| ).round(2) | |
| def get_acc(t, p): | |
| if t == False and p <= 0.4: | |
| return '✅' | |
| elif t == True and p > 0.6: | |
| return '✅' | |
| elif t == False and p > 0.6: | |
| return '❌' | |
| elif t == True and p <= 0.4: | |
| return '❌' | |
| else: | |
| return '🟨' | |
| perf_daily = res1.copy() | |
| perf_daily['Accuracy'] = [get_acc(t, p) for t, p in zip(perf_daily['True'], perf_daily['Predicted'])] | |
| tab1.subheader(f'Pred for {curr_date} as of 7AM PST') | |
| tab1.write(results) | |
| tab1.write(df_probas) | |
| tab2.subheader('Latest Data for Pred') | |
| tab2.write(new_pred) | |
| tab3.subheader('Historical Data') | |
| tab3.write(df_final) | |
| tab4.subheader('Performance') | |
| tab4.write(df_performance) | |
| tab4.write(perf_daily.sort_index(ascending=False)) | |
| elif option == '⏳ 60 Mins': | |
| if st.button('🏃🏽♂️ Run'): | |
| from model_1h import * | |
| with st.spinner('Loading data...'): | |
| data, df_final, final_row = get_data() | |
| # st.success("✅ Historical data") | |
| with st.spinner("Training models..."): | |
| def train_models(): | |
| res1, xgbr, seq2 = walk_forward_validation_seq(df_final.dropna(), 'Target_clf', 'Target', 100, 1) | |
| return res1, xgbr, seq2 | |
| res1, xgbr, seq2 = train_models() | |
| # st.success("✅ Models trained") | |
| with st.spinner("Getting new prediction..."): | |
| # Get last row | |
| new_pred = data.loc[final_row, ['BigNewsDay', | |
| 'Quarter', | |
| 'Perf5Day', | |
| 'Perf5Day_n1', | |
| 'DaysGreen', | |
| 'DaysRed', | |
| 'CurrentHigh30toClose', | |
| 'CurrentLow30toClose', | |
| 'CurrentClose30toClose', | |
| 'CurrentRange30', | |
| 'GapFill30', | |
| 'CurrentGap', | |
| 'RangePct', | |
| 'RangePct_n1', | |
| 'RangePct_n2', | |
| 'OHLC4_VIX', | |
| 'OHLC4_VIX_n1', | |
| 'OHLC4_VIX_n2']] | |
| new_pred = pd.DataFrame(new_pred).T | |
| # new_pred_show = pd.DataFrame(index=[new_pred.columns], columns=[new_pred.index], data=[[v] for v in new_pred.values]) | |
| # last_date = datetime.datetime.strptime(data.loc[final_row], '%Y-%m-%d') | |
| curr_date = final_row + BDay(1) | |
| curr_date = curr_date.strftime('%Y-%m-%d') | |
| new_pred['BigNewsDay'] = new_pred['BigNewsDay'].astype(float) | |
| new_pred['Quarter'] = new_pred['Quarter'].astype(int) | |
| new_pred['Perf5Day'] = new_pred['Perf5Day'].astype(bool) | |
| new_pred['Perf5Day_n1'] = new_pred['Perf5Day_n1'].astype(bool) | |
| new_pred['DaysGreen'] = new_pred['DaysGreen'].astype(float) | |
| new_pred['DaysRed'] = new_pred['DaysRed'].astype(float) | |
| new_pred['CurrentHigh30toClose'] = new_pred['CurrentHigh30toClose'].astype(float) | |
| new_pred['CurrentLow30toClose'] = new_pred['CurrentLow30toClose'].astype(float) | |
| new_pred['CurrentClose30toClose'] = new_pred['CurrentClose30toClose'].astype(float) | |
| new_pred['CurrentRange30'] = new_pred['CurrentRange30'].astype(float) | |
| new_pred['GapFill30'] = new_pred['GapFill30'].astype(float) | |
| new_pred['CurrentGap'] = new_pred['CurrentGap'].astype(float) | |
| new_pred['RangePct'] = new_pred['RangePct'].astype(float) | |
| new_pred['RangePct_n1'] = new_pred['RangePct_n1'].astype(float) | |
| new_pred['RangePct_n2'] = new_pred['RangePct_n2'].astype(float) | |
| new_pred['OHLC4_VIX'] = new_pred['OHLC4_VIX'].astype(float) | |
| new_pred['OHLC4_VIX_n1'] = new_pred['OHLC4_VIX_n1'].astype(float) | |
| new_pred['OHLC4_VIX_n2'] = new_pred['OHLC4_VIX_n2'].astype(float) | |
| st.success("✅ All done!") | |
| tab1, tab2, tab3, tab4 = st.tabs(["🔮 Prediction", "✨ New Data", "🗄 Historical", "📊 Performance"]) | |
| seq_proba = seq_predict_proba(new_pred, xgbr, seq2) | |
| green_proba = seq_proba[0] | |
| red_proba = 1 - green_proba | |
| do_not_play = (seq_proba[0] > 0.4) and (seq_proba[0] <= 0.6) | |
| stdev = 0.01 | |
| score = None | |
| num_obs = None | |
| cond = None | |
| historical_proba = None | |
| text_cond = None | |
| operator = None | |
| if do_not_play: | |
| text_cond = '🟨' | |
| operator = '' | |
| score = seq_proba[0] | |
| cond = (res1['Predicted'] > 0.4) & (res1['Predicted'] <= 0.6) | |
| num_obs = len(res1.loc[cond]) | |
| historical_proba = res1.loc[cond, 'True'].mean() | |
| elif green_proba > red_proba: | |
| # If the day is predicted to be green, say so | |
| text_cond = '🟩' | |
| operator = '>=' | |
| score = green_proba | |
| # How many with this score? | |
| cond = (res1['Predicted'] >= green_proba) | |
| num_obs = len(res1.loc[cond]) | |
| # How often green? | |
| historical_proba = res1.loc[cond, 'True'].mean() | |
| # print(cond) | |
| elif green_proba <= red_proba: | |
| # If the day is predicted to be green, say so | |
| text_cond = '🟥' | |
| operator = '<=' | |
| score = red_proba | |
| # How many with this score? | |
| cond = (res1['Predicted'] <= red_proba) | |
| num_obs = len(res1.loc[cond]) | |
| # How often green? | |
| historical_proba = 1 - res1.loc[cond, 'True'].mean() | |
| # print(cond) | |
| score_fmt = f'{score:.1%}' | |
| results = pd.DataFrame(index=[ | |
| 'PrevClose', | |
| 'Confidence Score', | |
| 'Success Rate', | |
| f'NumObs {operator} {"" if do_not_play else score_fmt}', | |
| ], data = [ | |
| f"{data.loc[final_row,'Close']:.2f}", | |
| f'{text_cond} {score:.1%}', | |
| f'{historical_proba:.1%}', | |
| num_obs, | |
| ]) | |
| results.columns = ['Outputs'] | |
| # st.subheader('New Prediction') | |
| int_labels = ['(-∞, .20]', '(.20, .40]', '(.40, .60]', '(.60, .80]', '(.80, ∞]'] | |
| # df_probas = res1.groupby(pd.qcut(res1['Predicted'],5)).agg({'True':[np.mean,len,np.sum]}) | |
| df_probas = res1.groupby(pd.cut(res1['Predicted'], bins = [-np.inf, 0.2, 0.4, 0.6, 0.8, np.inf], labels = int_labels)).agg({'True':[np.mean,len,np.sum]}) | |
| df_probas.columns = ['PctGreen','NumObs','NumGreen'] | |
| roc_auc_score_all = roc_auc_score(res1['True'].astype(int), res1['Predicted'].values) | |
| precision_score_all = precision_score(res1['True'].astype(int), res1['Predicted'] > 0.5) | |
| recall_score_all = recall_score(res1['True'].astype(int), res1['Predicted'] > 0.5) | |
| len_all = len(res1) | |
| res2_filtered = res1.loc[(res1['Predicted'] > 0.6) | (res1['Predicted'] <= 0.4)] | |
| roc_auc_score_hi = roc_auc_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'].values) | |
| precision_score_hi = precision_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'] > 0.5) | |
| recall_score_hi = recall_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'] > 0.5) | |
| len_hi = len(res2_filtered) | |
| df_performance = pd.DataFrame( | |
| index=[ | |
| 'N', | |
| 'ROC AUC', | |
| 'Precision', | |
| 'Recall' | |
| ], | |
| columns = [ | |
| 'All', | |
| 'High Confidence' | |
| ], | |
| data = [ | |
| [len_all, len_hi], | |
| [roc_auc_score_all, roc_auc_score_hi], | |
| [precision_score_all, precision_score_hi], | |
| [recall_score_all, recall_score_hi] | |
| ] | |
| ).round(2) | |
| def get_acc(t, p): | |
| if t == False and p <= 0.4: | |
| return '✅' | |
| elif t == True and p > 0.6: | |
| return '✅' | |
| elif t == False and p > 0.6: | |
| return '❌' | |
| elif t == True and p <= 0.4: | |
| return '❌' | |
| else: | |
| return '🟨' | |
| perf_daily = res1.copy() | |
| perf_daily['Accuracy'] = [get_acc(t, p) for t, p in zip(perf_daily['True'], perf_daily['Predicted'])] | |
| tab1.subheader(f'Pred for {curr_date} as of 7:30AM PST') | |
| tab1.write(results) | |
| tab1.write(df_probas) | |
| tab2.subheader('Latest Data for Pred') | |
| tab2.write(new_pred) | |
| tab3.subheader('Historical Data') | |
| tab3.write(df_final) | |
| tab4.subheader('Performance') | |
| tab4.write(df_performance) | |
| tab4.write(perf_daily) |