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Merge dev into feature/403/emcee_batch
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@TimothyWillard
TimothyWillard committed Dec 4, 2024
2 parents 8a83236 + 0aca24f commit 9ba0c24
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Showing 16 changed files with 109 additions and 85 deletions.
7 changes: 6 additions & 1 deletion flepimop/gempyor_pkg/src/gempyor/inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -438,7 +438,12 @@ def get_all_sim_arguments(self):
self.save,
]

def simulate_proposal(self, proposal):
def get_logloss(self, proposal):
if not self.inferpar.check_in_bound(proposal=proposal):
if not self.silent:
print("`llik` is -inf (out of bound proposal).")
return -np.inf, -np.inf, -np.inf

snpi_df_mod, hnpi_df_mod = self.inferpar.inject_proposal(
proposal=proposal,
snpi_df=self.static_sim_arguments["snpi_df_ref"],
Expand Down
31 changes: 15 additions & 16 deletions flepimop/gempyor_pkg/src/gempyor/model_info.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@ def __init__(self, config: confuse.ConfigView):
self.tf = config["end_date"].as_date()
if self.tf <= self.ti:
raise ValueError(
"tf (time to finish) is less than or equal to ti (time to start)"
f"Final time ('{self.tf}') is less than or equal to initial time ('{self.ti}')."
)
self.n_days = (self.tf - self.ti).days + 1
self.dates = pd.date_range(start=self.ti, end=self.tf, freq="D")
Expand Down Expand Up @@ -77,9 +77,7 @@ def __init__(
# Auto-detect old config
if config["interventions"].exists():
raise ValueError(
"""This config has an intervention section, and has been written for a previous version of flepiMoP/COVIDScenarioPipeline \
Please use flepiMoP Version 1.1 (Commit SHA: 0c30c23937dd496d33c2b9fa7c6edb198ad80dac) to run this config. \
(use git checkout v1.1 inside the flepiMoP directory)"""
"This config has an intervention section, which is only compatible with a previous version (v1.1) of flepiMoP. "
)

# 1. Create a setup name that contains every scenario.
Expand All @@ -105,7 +103,7 @@ def __init__(
subpop_config = config["subpop_setup"]
if "data_path" in config:
raise ValueError(
"The config has a data_path section. This is no longer supported."
"The config has a `data_path` section. This is no longer supported."
)
self.path_prefix = pathlib.Path(path_prefix)

Expand Down Expand Up @@ -166,18 +164,19 @@ def __init__(
self.seir_modifiers_library = config["seir_modifiers"][
"modifiers"
].get()
raise ValueError(
"Not implemented yet"
raise NotImplementedError(
"This feature has not been implemented yet."
) # TODO create a Stacked from all
elif self.seir_modifiers_scenario is not None:
raise ValueError(
"An seir modifiers scenario was provided to ModelInfo but no 'seir_modifiers' sections in config"
"A `seir_modifiers_scenario` argument was provided to `ModelInfo` but there is no `seir_modifiers` section in the config."
)
else:
logging.info("Running ModelInfo with seir but without SEIR Modifiers")
logging.info("Running `ModelInfo` with seir but without SEIR Modifiers")

elif self.seir_modifiers_scenario is not None:
raise ValueError(
"A seir modifiers scenario was provided to ModelInfo but no 'seir:' sections in config"
"A `seir_modifiers_scenario` argument was provided to `ModelInfo` but there is no `seir` section in the config."
)
else:
logging.critical("Running ModelInfo without SEIR")
Expand Down Expand Up @@ -207,28 +206,28 @@ def __init__(
self.outcome_modifiers_library = config["outcome_modifiers"][
"modifiers"
].get()
raise ValueError(
"Not implemented yet"
raise NotImplementedError(
"This feature has not been implemented yet."
) # TODO create a Stacked from all

## NEED TO IMPLEMENT THIS -- CURRENTLY CANNOT USE outcome modifiers
elif self.outcome_modifiers_scenario is not None:
if config["outcome_modifiers"].exists():
raise ValueError(
"An outcome modifiers scenario was provided to ModelInfo but no 'outcome_modifiers' sections in config"
"A `outcome_modifiers_scenario` argument was provided to `ModelInfo` but there is no `outcome_modifiers` section in the config."
)
else:
self.outcome_modifiers_scenario = None
else:
logging.info(
"Running ModelInfo with outcomes but without Outcomes Modifiers"
"Running `ModelInfo` with outcomes but without Outcomes Modifiers"
)
elif self.outcome_modifiers_scenario is not None:
raise ValueError(
"An outcome modifiers scenario was provided to ModelInfo but no 'outcomes:' sections in config"
"A `outcome_modifiers_scenario` argument was provided to `ModelInfo` but there is no `outcomes` section in the config."
)
else:
logging.info("Running ModelInfo without Outcomes")
logging.info("Running `ModelInfo` without outcomes.")

# 6. Inputs and outputs
if in_run_id is None:
Expand Down
21 changes: 11 additions & 10 deletions flepimop/gempyor_pkg/src/gempyor/outcomes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -142,7 +142,7 @@ def read_parameters_from_config(modinf: model_info.ModelInfo):
branching_data = pa.parquet.read_table(branching_file).to_pandas()
if "relative_probability" not in list(branching_data["quantity"]):
raise ValueError(
f"No 'relative_probability' quantity in {branching_file}, therefor making it useless"
f"There is no `relative_probability` quantity in '{branching_file}'."
)

print(
Expand All @@ -164,7 +164,7 @@ def read_parameters_from_config(modinf: model_info.ModelInfo):
modinf.subpop_struct.subpop_names
):
raise ValueError(
f"Places in seir input files does not correspond to subpops in outcome probability file {branching_file}"
f"SEIR input files do not have subpops that match those in outcome probability file '{branching_file}'."
)

parameters = {}
Expand All @@ -180,7 +180,8 @@ def read_parameters_from_config(modinf: model_info.ModelInfo):

else:
raise ValueError(
f"unsure how to read outcome {new_comp}: not a str, nor an incidence or prevalence: {src_name}"
f"Expected a `str` or `dict` containing `incidence` or `prevalence`. "
f"Instead given '{src_name}' for outcome '{new_comp}'."
)

parameters[new_comp]["probability"] = outcomes_config[new_comp][
Expand Down Expand Up @@ -291,7 +292,7 @@ def read_parameters_from_config(modinf: model_info.ModelInfo):
parameters[new_comp] = {}
parameters[new_comp]["sum"] = outcomes_config[new_comp]["sum"].get()
else:
raise ValueError(f"No 'source' or 'sum' specified for comp {new_comp}")
raise ValueError(f"No `source` or `sum` specified for comp '{new_comp}'.")

return parameters

Expand Down Expand Up @@ -395,7 +396,7 @@ def compute_all_multioutcomes(
)
else:
raise ValueError(
f"Unknown type for seir simulation provided, got f{type(seir_sim)}"
f"Unknown type provided for seir simulation, received '{type(seir_sim)}'."
)
# we don't keep source in this cases
else: # already defined outcomes
Expand All @@ -406,7 +407,7 @@ def compute_all_multioutcomes(
continue
else:
raise ValueError(
f"ERROR with outcome {new_comp}: the specified source {source_name} is not a dictionnary (for seir outcome) nor an existing pre-identified outcomes."
f"Issue with outcome '{new_comp}'; the specified source '{source_name}' is neither a dictionnary (for seir outcome) nor an existing pre-identified outcome."
)

if (loaded_values is not None) and (
Expand Down Expand Up @@ -592,7 +593,7 @@ def filter_seir_df(diffI, dates, subpops, filters, outcome_name) -> np.ndarray:
vtype = "prevalence"
else:
raise ValueError(
f"Cannot distinguish the source of outcome {outcome_name}: it is not another previously defined outcome and there is no 'incidence:' or 'prevalence:'."
f"Cannot discern the source of outcome '{outcome_name}'; it is not a previously defined outcome and there is no `incidence` or `prevalence`."
)

diffI = diffI[diffI["mc_value_type"] == vtype]
Expand Down Expand Up @@ -621,7 +622,7 @@ def filter_seir_xr(diffI, dates, subpops, filters, outcome_name) -> np.ndarray:
vtype = "prevalence"
else:
raise ValueError(
f"Cannot distinguish the source of outcome {outcome_name}: it is not another previously defined outcome and there is no 'incidence:' or 'prevalence:'."
f"Cannot discern the source of outcome '{outcome_name}'; it is not a previously defined outcome and there is no `incidence` or `prevalence`."
)
# Filter the data
filters = filters[vtype]
Expand Down Expand Up @@ -671,7 +672,7 @@ def multishiftee(arr, shifts, stoch_delay_flag=True):
result = np.zeros_like(arr)

if stoch_delay_flag:
raise ValueError("NOT SUPPORTED YET")
raise NotImplementedError("`stoch_delay_flag` not supported yet.")
# for i, row in reversed(enumerate(np.rows(arr))):
# for j,elem in reversed(enumerate(row)):
## This function takes in :
Expand All @@ -697,7 +698,7 @@ def multishift(arr, shifts, stoch_delay_flag=True):
result = np.zeros_like(arr)

if stoch_delay_flag:
raise ValueError("NOT SUPPORTED YET")
raise NotImplementedError("`stoch_delay_flag` not supported yet.")
# for i, row in reversed(enumerate(np.rows(arr))):
# for j,elem in reversed(enumerate(row)):
## This function takes in :
Expand Down
10 changes: 3 additions & 7 deletions flepimop/gempyor_pkg/src/gempyor/seeding.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,9 +17,7 @@

def _DataFrame2NumbaDict(df, amounts, modinf) -> nb.typed.Dict:
if not df["date"].is_monotonic_increasing:
raise ValueError(
"_DataFrame2NumbaDict got an unsorted dataframe, exposing itself to non-sense"
)
raise ValueError("The `df` given is not sorted by the 'date' column.")

cmp_grp_names = [
col for col in modinf.compartments.compartments.columns if col != "name"
Expand Down Expand Up @@ -111,7 +109,7 @@ def get_from_config(self, sim_id: int, modinf) -> nb.typed.Dict:
dupes = seeding[seeding.duplicated(["subpop", "date"])].index + 1
if not dupes.empty:
raise ValueError(
f"Repeated subpop-date in rows {dupes.tolist()} of seeding::lambda_file."
f"There are repeating subpop-date in rows '{dupes.tolist()}' of `seeding::lambda_file`."
)
elif method == "FolderDraw":
seeding = pd.read_csv(
Expand All @@ -136,7 +134,7 @@ def get_from_config(self, sim_id: int, modinf) -> nb.typed.Dict:
seeding = pd.DataFrame(columns=["date", "subpop"])
return _DataFrame2NumbaDict(df=seeding, amounts=[], modinf=modinf)
else:
raise NotImplementedError(f"unknown seeding method [got: {method}]")
raise ValueError(f"Unknown seeding method given, '{method}'.")

# Sorting by date is very important here for the seeding format necessary !!!!
# print(seeding.shape)
Expand All @@ -160,8 +158,6 @@ def get_from_config(self, sim_id: int, modinf) -> nb.typed.Dict:
)
elif method == "FolderDraw" or method == "FromFile":
amounts = seeding["amount"]
else:
raise ValueError(f"Unknown seeding method: {method}")

return _DataFrame2NumbaDict(df=seeding, amounts=amounts, modinf=modinf)

Expand Down
12 changes: 6 additions & 6 deletions flepimop/gempyor_pkg/src/gempyor/seir.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,9 @@ def build_step_source_arg(
if integration_method == "rk4":
integration_method = "rk4.jit"
if integration_method not in ["rk4.jit", "legacy"]:
raise ValueError(f"Unknown integration method {integration_method}.")
raise ValueError(
f"Unknown integration method given, '{integration_method}'."
)
if "dt" in modinf.seir_config["integration"].keys():
dt = float(
eval(str(modinf.seir_config["integration"]["dt"].get()))
Expand Down Expand Up @@ -151,8 +153,7 @@ def steps_SEIR(
elif integration_method == "rk4.jit":
if modinf.stoch_traj_flag == True:
raise ValueError(
f"with method {integration_method}, only deterministic "
f"integration is possible (got stoch_straj_flag={modinf.stoch_traj_flag}"
f"'{integration_method}' integration method only supports deterministic integration, but `stoch_straj_flag` is '{modinf.stoch_traj_flag}'."
)
seir_sim = steps_rk4.rk4_integration(**fnct_args, silent=True)
else:
Expand All @@ -167,8 +168,7 @@ def steps_SEIR(
]:
if modinf.stoch_traj_flag == True:
raise ValueError(
f"with method {integration_method}, only deterministic "
f"integration is possible (got stoch_straj_flag={modinf.stoch_traj_flag}"
f"'{integration_method}' integration method only supports deterministic integration, but `stoch_straj_flag` is '{modinf.stoch_traj_flag}'."
)
seir_sim = steps_experimental.ode_integration(
**fnct_args, integration_method=integration_method
Expand All @@ -190,7 +190,7 @@ def steps_SEIR(
elif integration_method == "rk4_aot":
seir_sim = steps_experimental.rk4_aot(**fnct_args)
else:
raise ValueError(f"Unknow integration scheme, got {integration_method}")
raise ValueError(f"Unknown integration method given, '{integration_method}'.")

# We return an xarray instead of a ndarray now
compartment_coords = {}
Expand Down
18 changes: 11 additions & 7 deletions flepimop/gempyor_pkg/src/gempyor/statistics.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -75,7 +75,10 @@ def __init__(self, name: str, statistic_config: confuse.ConfigView) -> None:
reg_name = reg_config["name"].get()
reg_func = getattr(self, f"_{reg_name}_regularize", None)
if reg_func is None:
raise ValueError(f"Unsupported regularization: {reg_name}")
raise ValueError(
f"Unsupported regularization [received: '{reg_name}']. "
f"Currently only `forecast` and `allsubpop` are supported."
)
self.regularizations.append((reg_func, reg_config.get()))

self.resample = False
Expand Down Expand Up @@ -269,7 +272,10 @@ def llik(self, model_data: xr.DataArray, gt_data: xr.DataArray) -> xr.DataArray:
"absolute_error": lambda x, y: -np.log(np.nansum(np.abs(x - y))),
}
if self.dist not in dist_map:
raise ValueError(f"Invalid distribution specified: {self.dist}")
raise ValueError(
f"Invalid distribution specified: '{self.dist}'. "
f"Valid distributions: '{dist_map.keys()}'."
)
if self.dist in ["pois", "nbinom"]:
model_data = model_data.astype(int)
gt_data = gt_data.astype(int)
Expand Down Expand Up @@ -310,11 +316,9 @@ def compute_logloss(

if not model_data.shape == gt_data.shape:
raise ValueError(
(
f"{self.name} Statistic error: data and groundtruth do not have "
f"the same shape: model_data.shape={model_data.shape} != "
f"gt_data.shape={gt_data.shape}"
)
f"`model_data` and `gt_data` do not have "
f"the same shape: `model_data.shape` = '{model_data.shape}' != "
f"`gt_data.shape` = '{gt_data.shape}'."
)

regularization = 0.0
Expand Down
7 changes: 5 additions & 2 deletions flepimop/gempyor_pkg/src/gempyor/steps_source.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -278,8 +278,11 @@ def steps_SEIR_nb(
print(" ", states_current[comp].max())

if (states_current.min() < 0) or (states_current.max() > 10**10):
print((states_current.min() < 0), (states_current.max() > 10**10))
raise ValueError(f"Overflow error. Too small ?. Too large ?")
raise ValueError(
f"State values are outside the valid range. "
f"Minimum value: '{states_current.min()}', Maximum value: '{states_current.max()}'. "
f"Valid range is between 0 and {10**10}."
)

return states, states_daily_incid

Expand Down
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