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Functions675 in github.com/COVIDAnalytics/DELPHI

⨍ Functions 675◇ Types & classes 63
↓ 20 callersFunctionmake_increasing
Used to force the Confidence Intervals generated for DELPHI to be always increasing :param sequence: list, sequence of values :return: li
archive/V3 Model/DELPHI_utils_V3_dynamic.py:619
↓ 14 callersFunctionmape
(y_true, y_pred)
archive/V1 - No Jump/DELPHI_utils.py:640
↓ 10 callersFunctionmake_increasing
Used to force the Confidence Intervals generated for DELPHI to be always increasing :param sequence: list, sequence of values :return: li
DELPHI_utils_V4_dynamic.py:628
↓ 10 callersFunctionmake_increasing
Used to force the Confidence Intervals generated for DELPHI to be always increasing :param sequence: list, sequence of values :return: li
Connecticut/DELPHI_utils_CT_dynamic.py:637
↓ 9 callersFunctionget_initial_conditions
(params_fitted, global_params_fixed)
archive/V1 - No Jump/DELPHI_utils.py:540
↓ 8 callersFunctioncompute_mape
Compute the Mean Absolute Percentage Error (MAPE) between two lists of values :param y_true: list of true historical values :param y_pred
DELPHI_utils_V4_static.py:2009
↓ 8 callersFunctioncompute_mape
Compute the Mean Absolute Percentage Error (MAPE) between two lists of values :param y_true: list of true historical values :param y_pred
archive/V3 Model/DELPHI_utils_V3_static.py:1969
↓ 8 callersFunctionmape
(y_true, y_pred)
archive/Other Analyses/DELPHI_utils_new.py:592
↓ 8 callersMethodsave_dataframe
(df, path, logger)
DELPHI_utils_V4_static.py:38
↓ 8 callersMethodsave_dataframe
(df, path, logger)
Connecticut/DELPHI_utils_CT_static.py:38
↓ 6 callersFunctionmae_and_mape
(y_true, y_pred)
archive/V1 - No Jump/DELPHI_utils.py:633
↓ 5 callersFunctionget_normalized_policy_shifts_and_current_policy_all_countries
( policy_data_countries: pd.DataFrame, pastparameters: pd.DataFrame, )
archive/V1 - No Jump/DELPHI_utils.py:954
↓ 5 callersFunctionget_normalized_policy_shifts_and_current_policy_us_only
( policy_data_us_only: pd.DataFrame, pastparameters: pd.DataFrame, )
archive/V1 - No Jump/DELPHI_utils.py:908
↓ 5 callersFunctionread_measures_oxford_data
(yesterday: str)
archive/V1 - No Jump/DELPHI_utils.py:799
↓ 5 callersFunctionread_policy_data_us_only
(filepath_data_sandbox: str)
archive/V1 - No Jump/DELPHI_utils.py:756
↓ 4 callersMethodappend_all_aggregations
(df: pd.DataFrame)
KIT/DELPHI_utils_KIT.py:410
↓ 4 callersMethodappend_all_aggregations
(df: pd.DataFrame)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_trust.py:437
↓ 4 callersMethodappend_all_aggregations
Creates and appends all the predictions' aggregations at the country, continent and world levels :param df_predictions: dataframe wit
archive/V3 Model/DELPHI_utils_V3_static.py:1190
↓ 4 callersFunctioncompute_mape
Compute the Mean Absolute Percentage Error (MAPE) between two lists of values :param y_true: list of true historical values :param y_pred
Connecticut/DELPHI_utils_CT_static.py:2088
↓ 4 callersFunctioncompute_mape
Compute the Mean Absolute Percentage Error (MAPE) between two lists of values :param y_true: list of true historical values :param y_pred
archive/V3 Model/DELPHI_utils_V3_static_serology.py:1973
↓ 4 callersFunctionget_initial_conditions
Generates the initial conditions for the DELPHI model based on global fixed parameters (mostly populations and some constant rates) and fitte
DELPHI_utils_V4_static.py:1773
↓ 4 callersFunctionget_initial_conditions
Generates the initial conditions for the DELPHI model based on global fixed parameters (mostly populations and some constant rates) and fitte
archive/V3 Model/DELPHI_utils_V3_static.py:1741
↓ 4 callersFunctionmae_and_mape
(y_true, y_pred)
KIT/DELPHI_utils_KIT.py:563
↓ 4 callersFunctionmae_and_mape
(y_true, y_pred)
CDC/DELPHI_utils_CDC.py:562
↓ 4 callersFunctionmae_and_mape
(y_true, y_pred)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_trust.py:590
↓ 4 callersFunctionmae_and_mape
(y_true, y_pred)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_annealing.py:780
↓ 4 callersFunctionmae_and_mape
(y_true, y_pred)
archive/Test Models for V3/ArcTan Jump/DELPHI_utils_V4.py:590
↓ 4 callersFunctionmae_and_mape
(y_true, y_pred)
archive/Test Models for V3/Discrete Jump/DELPHI_utils_V2.py:588
↓ 4 callersFunctionmae_and_mape
(y_true, y_pred)
archive/backtesting_archived/DELPHI_backtest_utils_ventilator.py:669
↓ 4 callersFunctionmae_and_mape
(y_true, y_pred)
archive/Other Analyses/DELPHI_utils_secondwave.py:635
↓ 4 callersFunctionmae_and_mape
(y_true, y_pred)
archive/Other Analyses/DELPHI_utils_new.py:585
↓ 4 callersFunctionmape
(y_true, y_pred)
KIT/DELPHI_utils_KIT.py:570
↓ 4 callersFunctionmape
(y_true, y_pred)
CDC/DELPHI_utils_CDC.py:569
↓ 4 callersFunctionmape
(y_true, y_pred)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_trust.py:597
↓ 4 callersFunctionmape
(y_true, y_pred)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_annealing.py:787
↓ 4 callersFunctionmape
(y_true, y_pred)
archive/Test Models for V3/ArcTan Jump/DELPHI_utils_V4.py:597
↓ 4 callersFunctionmape
(y_true, y_pred)
archive/Test Models for V3/Discrete Jump/DELPHI_utils_V2.py:595
↓ 4 callersFunctionmse
(y_true, y_pred)
KIT/DELPHI_utils_KIT.py:557
↓ 4 callersFunctionmse
(y_true, y_pred)
CDC/DELPHI_utils_CDC.py:556
↓ 4 callersFunctionmse
(y_true, y_pred)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_trust.py:584
↓ 4 callersFunctionmse
(y_true, y_pred)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_annealing.py:774
↓ 4 callersFunctionmse
(y_true, y_pred)
archive/Test Models for V3/ArcTan Jump/DELPHI_utils_V4.py:584
↓ 4 callersFunctionmse
(y_true, y_pred)
archive/Test Models for V3/Discrete Jump/DELPHI_utils_V2.py:582
↓ 4 callersFunctionmse
(y_true, y_pred)
archive/backtesting_archived/DELPHI_backtest_utils_ventilator.py:663
↓ 4 callersFunctionmse
(y_true, y_pred)
archive/Other Analyses/DELPHI_utils_new.py:579
↓ 4 callersFunctionsign_mape
(y_true, y_pred)
KIT/DELPHI_utils_KIT.py:540
↓ 4 callersFunctionsign_mape
(y_true, y_pred)
CDC/DELPHI_utils_CDC.py:539
↓ 4 callersFunctionsign_mape
(y_true, y_pred)
archive/V1 - No Jump/DELPHI_utils.py:610
↓ 4 callersFunctionsign_mape
(y_true, y_pred)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_trust.py:567
↓ 4 callersFunctionsign_mape
(y_true, y_pred)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_annealing.py:757
↓ 4 callersFunctionsign_mape
(y_true, y_pred)
archive/Test Models for V3/ArcTan Jump/DELPHI_utils_V4.py:567
↓ 4 callersFunctionsign_mape
(y_true, y_pred)
archive/Test Models for V3/Discrete Jump/DELPHI_utils_V2.py:565
↓ 4 callersFunctionsign_mape
(y_true, y_pred)
archive/backtesting_archived/DELPHI_backtest_utils_ventilator.py:646
↓ 4 callersFunctionsign_mape
(y_true, y_pred)
archive/Other Analyses/DELPHI_utils_secondwave.py:612
↓ 4 callersFunctionsign_mape
(y_true, y_pred)
archive/Other Analyses/DELPHI_utils_new.py:562
↓ 4 callersFunctionupdate_gamma_t_with_constant_params
( t, gamma_t, N_POLICIES_CONSTANT_TUPLE, t_start_end_constant_policy, date_yesterday_int, poli
archive/Adaptive Policy Model - Continuous Retraining/DELPHI_policies_utils_cr.py:318
↓ 3 callersMethodappend_all_aggregations
(df: pd.DataFrame)
archive/V1 - No Jump/DELPHI_utils.py:480
↓ 3 callersMethodcreate_dataset_parameters
(self, mape)
KIT/DELPHI_utils_KIT.py:139
↓ 3 callersFunctioncreate_fitting_data_from_validcases
Creates the balancing coefficient (regularization coefficient between cases & deaths in cost function) as well as the cases and deaths data o
DELPHI_utils_V4_static.py:1855
↓ 3 callersMethodget_aggregation_per_continent
(df: pd.DataFrame)
archive/backtesting_archived/DELPHI_backtest_utils_ventilator.py:438
↓ 3 callersMethodget_aggregation_per_country
(df: pd.DataFrame)
archive/backtesting_archived/DELPHI_backtest_utils_ventilator.py:426
↓ 3 callersMethodget_aggregation_world
(df: pd.DataFrame)
archive/backtesting_archived/DELPHI_backtest_utils_ventilator.py:450
↓ 2 callersFunctionadd_policy_tracking_row_country
continent, country, province, date, n_policies_enacted, n_policy_changes, last_policy, \ start_date_last_policy, end_date_last_policy, n_days_
archive/Adaptive Policy Model - Continuous Retraining/DELPHI_policies_utils_cr.py:264
↓ 2 callersMethodappend_all_aggregations
(df: pd.DataFrame)
CDC/DELPHI_utils_CDC.py:410
↓ 2 callersMethodappend_all_aggregations
(df: pd.DataFrame)
archive/Test Models for V3/ArcTan Jump/DELPHI_utils_V4.py:437
↓ 2 callersMethodappend_all_aggregations
(df: pd.DataFrame)
archive/Test Models for V3/Discrete Jump/DELPHI_utils_V2.py:435
↓ 2 callersMethodappend_all_aggregations
(df: pd.DataFrame)
archive/backtesting_archived/DELPHI_backtest_utils_ventilator.py:463
↓ 2 callersMethodappend_all_aggregations
(df: pd.DataFrame)
archive/Other Analyses/DELPHI_utils_new.py:432
↓ 2 callersMethodappend_all_aggregations_cf
Creates and appends all the predictions' aggregations & Confidnece Intervals at the country, continent and world levels :para
archive/V3 Model/DELPHI_utils_V3_static.py:1458
↓ 2 callersFunctioncompute_mae_and_mape
Compute the Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) between two lists of values :param y_true: list of true histo
DELPHI_utils_V4_static.py:1996
↓ 2 callersFunctioncompute_mae_and_mape
Compute the Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) between two lists of values :param y_true: list of true histo
Connecticut/DELPHI_utils_CT_static.py:2075
↓ 2 callersFunctioncompute_mae_and_mape
Compute the Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) between two lists of values :param y_true: list of true histo
archive/V3 Model/DELPHI_utils_V3_static_serology.py:1960
↓ 2 callersFunctioncompute_mae_and_mape
Compute the Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) between two lists of values :param y_true: list of true histo
archive/V3 Model/DELPHI_utils_V3_static.py:1956
↓ 2 callersFunctioncompute_mse
Compute the Mean Squared Error between two lists :param y_true: list of true historical values :param y_pred: list of predicted values
DELPHI_utils_V4_static.py:1984
↓ 2 callersFunctioncompute_mse
Compute the Mean Squared Error between two lists :param y_true: list of true historical values :param y_pred: list of predicted values
Connecticut/DELPHI_utils_CT_static.py:2063
↓ 2 callersFunctioncompute_mse
Compute the Mean Squared Error between two lists :param y_true: list of true historical values :param y_pred: list of predicted values
archive/V3 Model/DELPHI_utils_V3_static_serology.py:1948
↓ 2 callersFunctioncompute_mse
Compute the Mean Squared Error between two lists :param y_true: list of true historical values :param y_pred: list of predicted values
archive/V3 Model/DELPHI_utils_V3_static.py:1944
↓ 2 callersMethodcreate_dataset_parameters
(self, mape)
archive/V1 - No Jump/DELPHI_utils.py:194
↓ 2 callersMethodcreate_dataset_parameters
(self, mape)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_trust.py:166
↓ 2 callersMethodcreate_dataset_parameters
Creates the parameters dataset with the results from the optimization and the pre-computed MAPE :param mape: MAPE on the last 15 days
archive/V3 Model/DELPHI_utils_V3_static.py:242
↓ 2 callersMethodcreate_datasets_predictions
(self)
KIT/DELPHI_utils_KIT.py:230
↓ 2 callersMethodcreate_datasets_predictions
(self)
archive/V1 - No Jump/DELPHI_utils.py:300
↓ 2 callersMethodcreate_datasets_predictions
(self)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_trust.py:257
↓ 2 callersMethodcreate_datasets_predictions
Creates two dataframes with the predictions of the DELPHI model, the first one since the day of the prediction, the second since the
archive/V3 Model/DELPHI_utils_V3_static.py:275
↓ 2 callersMethodcreate_datasets_predictions_scenario
( self, policy="Lockdown", time=0, totalcases=None, )
archive/V1 - No Jump/DELPHI_utils.py:355
↓ 2 callersMethodcreate_datasets_with_confidence_intervals
Generates the prediction datasets from the date with 100 cases and from the day of running, including columns containing Confidence I
archive/V3 Model/DELPHI_utils_V3_static.py:442
↓ 2 callersFunctiongamma_t
Computes values of our gamma(t) function that was used before the second wave modeling with the extra normal distribution, but is still being
DELPHI_utils_V4_dynamic.py:611
↓ 2 callersFunctiongamma_t
Computes values of our gamma(t) function that was used before the second wave modeling with the extra normal distribution, but is still being
Connecticut/DELPHI_utils_CT_dynamic.py:620
↓ 2 callersFunctiongamma_t
(day, state, params_dic)
KIT/DELPHI_utils_KIT.py:831
↓ 2 callersFunctiongamma_t
(day, state, params_dic)
CDC/DELPHI_utils_CDC.py:830
↓ 2 callersFunctiongamma_t
(day, state, params_dic)
archive/V1 - No Jump/DELPHI_utils.py:901
↓ 2 callersFunctiongamma_t
(day, state, params_dic)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_trust.py:858
↓ 2 callersFunctiongamma_t
(day, state, params_dic)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_annealing.py:1079
↓ 2 callersFunctiongamma_t
(day, state, params_dic)
archive/Test Models for V3/ArcTan Jump/DELPHI_utils_V4.py:858
↓ 2 callersFunctiongamma_t
(day, state, params_dic)
archive/Test Models for V3/Discrete Jump/DELPHI_utils_V2.py:856
↓ 2 callersFunctiongamma_t
(day, state, params_dic)
archive/backtesting_archived/DELPHI_backtest_utils_ventilator.py:910
↓ 2 callersFunctiongamma_t
Computes values of our gamma(t) function that was used before the second wave modeling with the extra normal distribution, but is still being
archive/V3 Model/DELPHI_utils_V3_dynamic.py:602
↓ 2 callersFunctiongamma_t
(day, state, params_dic)
archive/Other Analyses/DELPHI_utils_secondwave.py:903
↓ 2 callersFunctiongamma_t
(day, state, params_dic)
archive/Other Analyses/DELPHI_utils_new.py:853
↓ 2 callersMethodget_aggregation_per_continent
Aggregates predictions at the continent level from the predictions dataframe :param df_predictions: DELPHI predictions dataframe
DELPHI_utils_V4_static.py:1196
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