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Functions675 in github.com/COVIDAnalytics/DELPHI

⨍ Functions 675◇ Types & classes 63
↓ 2 callersMethodget_aggregation_per_continent
Aggregates predictions at the continent level from the predictions dataframe :param df_predictions: DELPHI predictions dataframe
Connecticut/DELPHI_utils_CT_static.py:1265
↓ 2 callersMethodget_aggregation_per_continent
(df: pd.DataFrame)
KIT/DELPHI_utils_KIT.py:387
↓ 2 callersMethodget_aggregation_per_continent
(df: pd.DataFrame)
CDC/DELPHI_utils_CDC.py:387
↓ 2 callersMethodget_aggregation_per_continent
(df: pd.DataFrame)
archive/V1 - No Jump/DELPHI_utils.py:457
↓ 2 callersMethodget_aggregation_per_continent
(df: pd.DataFrame)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_trust.py:414
↓ 2 callersMethodget_aggregation_per_continent
(df: pd.DataFrame)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_annealing.py:610
↓ 2 callersMethodget_aggregation_per_continent
(df: pd.DataFrame)
archive/Test Models for V3/ArcTan Jump/DELPHI_utils_V4.py:414
↓ 2 callersMethodget_aggregation_per_continent
(df: pd.DataFrame)
archive/Test Models for V3/Discrete Jump/DELPHI_utils_V2.py:412
↓ 2 callersMethodget_aggregation_per_continent
Aggregates predictions at the continent level from the predictions dataframe :param df_predictions: DELPHI predictions dataframe
archive/V3 Model/DELPHI_utils_V3_static_serology.py:1168
↓ 2 callersMethodget_aggregation_per_continent
Aggregates predictions at the continent level from the predictions dataframe :param df_predictions: DELPHI predictions dataframe
archive/V3 Model/DELPHI_utils_V3_static.py:1163
↓ 2 callersMethodget_aggregation_per_continent
(df: pd.DataFrame)
archive/Other Analyses/DELPHI_utils_secondwave.py:459
↓ 2 callersMethodget_aggregation_per_continent
(df: pd.DataFrame)
archive/Other Analyses/DELPHI_utils_new.py:409
↓ 2 callersMethodget_aggregation_per_country
Aggregates predictions at the country level from the predictions dataframe :param df_predictions: DELPHI predictions dataframe
DELPHI_utils_V4_static.py:1183
↓ 2 callersMethodget_aggregation_per_country
Aggregates predictions at the country level from the predictions dataframe :param df_predictions: DELPHI predictions dataframe
Connecticut/DELPHI_utils_CT_static.py:1252
↓ 2 callersMethodget_aggregation_per_country
(df: pd.DataFrame)
KIT/DELPHI_utils_KIT.py:376
↓ 2 callersMethodget_aggregation_per_country
(df: pd.DataFrame)
CDC/DELPHI_utils_CDC.py:376
↓ 2 callersMethodget_aggregation_per_country
(df: pd.DataFrame)
archive/V1 - No Jump/DELPHI_utils.py:446
↓ 2 callersMethodget_aggregation_per_country
(df: pd.DataFrame)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_trust.py:403
↓ 2 callersMethodget_aggregation_per_country
(df: pd.DataFrame)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_annealing.py:602
↓ 2 callersMethodget_aggregation_per_country
(df: pd.DataFrame)
archive/Test Models for V3/ArcTan Jump/DELPHI_utils_V4.py:403
↓ 2 callersMethodget_aggregation_per_country
(df: pd.DataFrame)
archive/Test Models for V3/Discrete Jump/DELPHI_utils_V2.py:401
↓ 2 callersMethodget_aggregation_per_country
Aggregates predictions at the country level from the predictions dataframe :param df_predictions: DELPHI predictions dataframe
archive/V3 Model/DELPHI_utils_V3_static_serology.py:1155
↓ 2 callersMethodget_aggregation_per_country
Aggregates predictions at the country level from the predictions dataframe :param df_predictions: DELPHI predictions dataframe
archive/V3 Model/DELPHI_utils_V3_static.py:1150
↓ 2 callersMethodget_aggregation_per_country
(df: pd.DataFrame)
archive/Other Analyses/DELPHI_utils_secondwave.py:448
↓ 2 callersMethodget_aggregation_per_country
(df: pd.DataFrame)
archive/Other Analyses/DELPHI_utils_new.py:398
↓ 2 callersMethodget_aggregation_world
Aggregates predictions at the world level from the predictions dataframe :param df_predictions: DELPHI predictions dataframe
DELPHI_utils_V4_static.py:1209
↓ 2 callersMethodget_aggregation_world
Aggregates predictions at the world level from the predictions dataframe :param df_predictions: DELPHI predictions dataframe
Connecticut/DELPHI_utils_CT_static.py:1278
↓ 2 callersMethodget_aggregation_world
(df: pd.DataFrame)
KIT/DELPHI_utils_KIT.py:398
↓ 2 callersMethodget_aggregation_world
(df: pd.DataFrame)
CDC/DELPHI_utils_CDC.py:398
↓ 2 callersMethodget_aggregation_world
(df: pd.DataFrame)
archive/V1 - No Jump/DELPHI_utils.py:468
↓ 2 callersMethodget_aggregation_world
(df: pd.DataFrame)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_trust.py:425
↓ 2 callersMethodget_aggregation_world
(df: pd.DataFrame)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_annealing.py:618
↓ 2 callersMethodget_aggregation_world
(df: pd.DataFrame)
archive/Test Models for V3/ArcTan Jump/DELPHI_utils_V4.py:425
↓ 2 callersMethodget_aggregation_world
(df: pd.DataFrame)
archive/Test Models for V3/Discrete Jump/DELPHI_utils_V2.py:423
↓ 2 callersMethodget_aggregation_world
Aggregates predictions at the world level from the predictions dataframe :param df_predictions: DELPHI predictions dataframe
archive/V3 Model/DELPHI_utils_V3_static_serology.py:1181
↓ 2 callersMethodget_aggregation_world
Aggregates predictions at the world level from the predictions dataframe :param df_predictions: DELPHI predictions dataframe
archive/V3 Model/DELPHI_utils_V3_static.py:1176
↓ 2 callersMethodget_aggregation_world
(df: pd.DataFrame)
archive/Other Analyses/DELPHI_utils_secondwave.py:470
↓ 2 callersMethodget_aggregation_world
(df: pd.DataFrame)
archive/Other Analyses/DELPHI_utils_new.py:420
↓ 2 callersFunctionget_bounds_params_from_pastparams
Generates the lower and upper bounds of the past parameters used as warm starts for the optimization process to predict with DELPHI: the outp
archive/V3 Model/DELPHI_utils_V3_dynamic.py:13
↓ 2 callersFunctionget_initial_conditions
Generates the initial conditions for the DELPHI model based on global fixed parameters (mostly populations and some constant rates) and fitte
Connecticut/DELPHI_utils_CT_static.py:1842
↓ 2 callersFunctionget_initial_conditions
(params_fitted, global_params_fixed)
KIT/DELPHI_utils_KIT.py:470
↓ 2 callersFunctionget_initial_conditions
(params_fitted, global_params_fixed)
CDC/DELPHI_utils_CDC.py:470
↓ 2 callersFunctionget_initial_conditions
(params_fitted, global_params_fixed)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_trust.py:497
↓ 2 callersFunctionget_initial_conditions
(params_fitted, global_params_fixed)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_annealing.py:687
↓ 2 callersFunctionget_initial_conditions
(params_fitted, global_params_fixed)
archive/Test Models for V3/ArcTan Jump/DELPHI_utils_V4.py:497
↓ 2 callersFunctionget_initial_conditions
(params_fitted, global_params_fixed)
archive/Test Models for V3/Discrete Jump/DELPHI_utils_V2.py:495
↓ 2 callersFunctionget_initial_conditions
(params_fitted, global_params_fixed)
archive/backtesting_archived/DELPHI_backtest_utils_ventilator.py:574
↓ 2 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_serology.py:1745
↓ 2 callersFunctionget_initial_conditions_with_testing
(params_fitted, global_params_fixed)
archive/Other Analyses/DELPHI_utils_new.py:522
↓ 2 callersFunctionget_list_and_bounds_params
( df_updated: pd.DataFrame, parameter_list_line: list, param_MATHEMATICA: bool )
archive/Adaptive Policy Model - Continuous Retraining/DELPHI_policies_utils_cr.py:243
↓ 2 callersFunctionget_mape_data_fitting
Computes MAPE on cases & deaths (averaged) either on last 15 days of historical data (if there are more than 15) or exactly the number of day
DELPHI_utils_V4_static.py:1917
↓ 2 callersFunctionget_normalized_policy_shifts_and_current_policy_all_countries
( policy_data_countries: pd.DataFrame, pastparameters: pd.DataFrame, )
archive/Other Analyses/DELPHI_utils_new.py:906
↓ 2 callersFunctionget_normalized_policy_shifts_and_current_policy_us_only
( policy_data_us_only: pd.DataFrame, pastparameters: pd.DataFrame, )
archive/Other Analyses/DELPHI_utils_new.py:860
↓ 2 callersFunctionget_params_constant_policies
( df_updated: pd.DataFrame, yesterday: str )
archive/Adaptive Policy Model - Continuous Retraining/DELPHI_policies_utils_cr.py:173
↓ 2 callersFunctionget_policy_names_before_after_constant
:return: a list of tuples like [(policy_str_before_shift, policy_str_after_shift)], here for the constant params only
archive/Adaptive Policy Model - Continuous Retraining/DELPHI_policies_utils_cr.py:220
↓ 2 callersFunctionget_policy_names_before_after_fitted
:return: a list of tuples like [(policy_str_before_shift, policy_str_after_shift)], here for the fitted params only
archive/Adaptive Policy Model - Continuous Retraining/DELPHI_policies_utils_cr.py:206
↓ 2 callersFunctionget_policy_shift_names_tuples
(df_updated: pd.DataFrame)
archive/Adaptive Policy Model - Continuous Retraining/DELPHI_policies_utils_cr.py:234
↓ 2 callersFunctionmape
(y_true, y_pred)
archive/backtesting_archived/DELPHI_backtest_utils_ventilator.py:676
↓ 2 callersFunctionmape_daily_delta_since_last_train
(true_last_train, pred_last_train, y_true, y_pred)
KIT/DELPHI_utils_KIT.py:548
↓ 2 callersFunctionmape_daily_delta_since_last_train
(true_last_train, pred_last_train, y_true, y_pred)
CDC/DELPHI_utils_CDC.py:547
↓ 2 callersFunctionmape_daily_delta_since_last_train
(true_last_train, pred_last_train, y_true, y_pred)
archive/V1 - No Jump/DELPHI_utils.py:618
↓ 2 callersFunctionmape_daily_delta_since_last_train
(true_last_train, pred_last_train, y_true, y_pred)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_trust.py:575
↓ 2 callersFunctionmape_daily_delta_since_last_train
(true_last_train, pred_last_train, y_true, y_pred)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_annealing.py:765
↓ 2 callersFunctionmape_daily_delta_since_last_train
(true_last_train, pred_last_train, y_true, y_pred)
archive/Test Models for V3/ArcTan Jump/DELPHI_utils_V4.py:575
↓ 2 callersFunctionmape_daily_delta_since_last_train
(true_last_train, pred_last_train, y_true, y_pred)
archive/Test Models for V3/Discrete Jump/DELPHI_utils_V2.py:573
↓ 2 callersFunctionmape_daily_delta_since_last_train
(true_last_train, pred_last_train, y_true, y_pred)
archive/backtesting_archived/DELPHI_backtest_utils_ventilator.py:654
↓ 2 callersFunctionmape_daily_delta_since_last_train
(true_last_train, pred_last_train, y_true, y_pred)
archive/Other Analyses/DELPHI_utils_secondwave.py:620
↓ 2 callersFunctionmape_daily_delta_since_last_train
(true_last_train, pred_last_train, y_true, y_pred)
archive/Other Analyses/DELPHI_utils_new.py:570
↓ 2 callersFunctionread_measures_oxford_data
(yesterday: str)
archive/Other Analyses/DELPHI_utils_new.py:751
↓ 2 callersFunctionread_policy_data_us_only
(filepath_data_sandbox: str)
archive/Other Analyses/DELPHI_utils_new.py:708
↓ 2 callersMethodsave_all_datasets
(self, save_since_100_cases=False, website=False)
KIT/DELPHI_utils_KIT.py:27
↓ 2 callersMethodsave_all_datasets
(self, save_since_100_cases=False, website=False)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_trust.py:27
↓ 2 callersMethodsave_all_datasets
Saves the parameters and predictions datasets (since 100 cases and since the day of running) based on the different flags and the inp
archive/V3 Model/DELPHI_utils_V3_static.py:35
↓ 2 callersFunctionupdate_tracking_when_policy_changed
( df_updated: pd.DataFrame, df_previous: pd.DataFrame, yesterday: str, current_policy_in_track
archive/Adaptive Policy Model - Continuous Retraining/DELPHI_policies_utils_cr.py:21
↓ 2 callersFunctionupdate_tracking_without_policy_change
( df_updated: pd.DataFrame, yesterday: str, )
archive/Adaptive Policy Model - Continuous Retraining/DELPHI_policies_utils_cr.py:8
↓ 1 callersFunctionadd_aggregations_backtest
(df_backtest_performance: pd.DataFrame)
archive/V1 - No Jump/DELPHI_utils.py:1019
↓ 1 callersFunctionadd_policy_shift_initial_param
This corresponds to the new value of k_0' (normalized difference of values in the tree for going from one policy to another)
archive/Adaptive Policy Model - Continuous Retraining/DELPHI_policies_utils_cr.py:69
↓ 1 callersMethodappend_all_aggregations_cf
Creates and appends all the predictions' aggregations & Confidnece Intervals at the country, continent and world levels :para
DELPHI_utils_V4_static.py:1491
↓ 1 callersFunctioncheck_us_policy_data_consistency
Checks consistency of the policy data in the US retrieved e.g. from IHME by verifying that if there is an end date there must also be a start
DELPHI_utils_V4_dynamic.py:188
↓ 1 callersFunctioncheck_us_policy_data_consistency
Checks consistency of the policy data in the US retrieved e.g. from IHME by verifying that if there is an end date there must also be a start
Connecticut/DELPHI_utils_CT_dynamic.py:197
↓ 1 callersFunctioncheck_us_policy_data_consistency
(policies: list, df_policy_raw_us: pd.DataFrame)
KIT/DELPHI_utils_KIT.py:584
↓ 1 callersFunctioncheck_us_policy_data_consistency
(policies: list, df_policy_raw_us: pd.DataFrame)
CDC/DELPHI_utils_CDC.py:583
↓ 1 callersFunctioncheck_us_policy_data_consistency
(policies: list, df_policy_raw_us: pd.DataFrame)
archive/V1 - No Jump/DELPHI_utils.py:654
↓ 1 callersFunctioncheck_us_policy_data_consistency
(policies: list, df_policy_raw_us: pd.DataFrame)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_trust.py:611
↓ 1 callersFunctioncheck_us_policy_data_consistency
(policies: list, df_policy_raw_us: pd.DataFrame)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_annealing.py:801
↓ 1 callersFunctioncheck_us_policy_data_consistency
(policies: list, df_policy_raw_us: pd.DataFrame)
archive/Test Models for V3/ArcTan Jump/DELPHI_utils_V4.py:611
↓ 1 callersFunctioncheck_us_policy_data_consistency
(policies: list, df_policy_raw_us: pd.DataFrame)
archive/Test Models for V3/Discrete Jump/DELPHI_utils_V2.py:609
↓ 1 callersFunctioncheck_us_policy_data_consistency
Checks consistency of the policy data in the US retrieved e.g. from IHME by verifying that if there is an end date there must also be a start
archive/V3 Model/DELPHI_utils_V3_dynamic.py:179
↓ 1 callersFunctioncheck_us_policy_data_consistency
(policies: list, df_policy_raw_us: pd.DataFrame)
archive/Other Analyses/DELPHI_utils_secondwave.py:656
↓ 1 callersFunctioncheck_us_policy_data_consistency
(policies: list, df_policy_raw_us: pd.DataFrame)
archive/Other Analyses/DELPHI_utils_new.py:606
↓ 1 callersMethodcompare_metric
Computes the given metric for predictions with annealing and tnc and the MAPE for annealing. Returns the metrics along with a flag sh
DELPHI_utils_V4_dynamic.py:902
↓ 1 callersMethodcompare_metric
Computes the given metric for predictions with annealing and tnc and the MAPE for annealing. Returns the metrics along with a flag sh
archive/V3 Model/DELPHI_utils_V3_dynamic.py:892
↓ 1 callersFunctionconvert_dates_us_policies
(x)
archive/backtesting_archived/DELPHI_backtest_utils_ventilator.py:682
↓ 1 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
Connecticut/DELPHI_utils_CT_static.py:274
↓ 1 callersMethodcreate_dataset_parameters
(self, mape)
CDC/DELPHI_utils_CDC.py:139
↓ 1 callersMethodcreate_dataset_parameters
(self, mape)
archive/Test Models for V3/ArcTan Jump/DELPHI_utils_V4.py:166
↓ 1 callersMethodcreate_dataset_parameters
(self, mape)
archive/Test Models for V3/Discrete Jump/DELPHI_utils_V2.py:166
↓ 1 callersMethodcreate_dataset_parameters
(self, mape)
archive/backtesting_archived/DELPHI_backtest_utils_ventilator.py:174
↓ 1 callersMethodcreate_dataset_parameters
(self, mape)
archive/Other Analyses/DELPHI_utils_new.py:162
↓ 1 callersMethodcreate_datasets_predictions
(self)
CDC/DELPHI_utils_CDC.py:230
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