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Remove deprecated selection in skill, score, etc. #442

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Remove deprecated selection in skill, score, etc. #442

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7cdf02e
Remove deprecated selection in skill, score, etc.
ecomodeller Jun 27, 2024
a9f1330
Remove obsolete tests
ecomodeller Jun 27, 2024
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180 changes: 18 additions & 162 deletions modelskill/comparison/_collection.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,7 @@
from ..skill import SkillTable
from ..skill_grid import SkillGrid

from ..utils import _get_idx, _get_name
from ..utils import _get_name
from ._comparison import Comparer, Scoreable
from ..metrics import _parse_metric
from ._utils import (
Expand All @@ -40,31 +40,6 @@
IdxOrNameTypes,
TimeTypes,
)
from ._comparison import _get_deprecated_args # TODO remove in v 1.1


def _get_deprecated_obs_var_args(kwargs): # type: ignore
observation, variable = None, None

# Don't bother refactoring this, it will be removed in v1.1
if "observation" in kwargs:
observation = kwargs.pop("observation")
if observation is not None:
warnings.warn(
f"The 'observation' argument is deprecated, use 'sel(observation='{observation}') instead",
FutureWarning,
)

if "variable" in kwargs:
variable = kwargs.pop("variable")

if variable is not None:
warnings.warn(
f"The 'variable' argument is deprecated, use 'sel(quantity='{variable}') instead",
FutureWarning,
)

return observation, variable


class ComparerCollection(Mapping, Scoreable):
Expand Down Expand Up @@ -446,7 +421,6 @@ def skill(
by: str | Iterable[str] | None = None,
metrics: Iterable[str] | Iterable[Callable] | str | Callable | None = None,
observed: bool = False,
**kwargs: Any,
) -> SkillTable:
"""Aggregated skill assessment of model(s)

Expand Down Expand Up @@ -505,36 +479,18 @@ def skill(
2017-10-29 163 -0.21 0.52 0.47 0.42 0.79 0.11 0.99
"""

# TODO remove in v1.1 ----------
model, start, end, area = _get_deprecated_args(kwargs) # type: ignore
observation, variable = _get_deprecated_obs_var_args(kwargs) # type: ignore
assert kwargs == {}, f"Unknown keyword arguments: {kwargs}"

cc = self.sel(
model=model,
observation=observation,
quantity=variable,
start=start,
end=end,
area=area,
)
if cc.n_points == 0:
raise ValueError("Dataset is empty, no data to compare.")

## ---- end of deprecated code ----

pmetrics = _parse_metric(metrics)

agg_cols = _parse_groupby(by, n_mod=cc.n_models, n_qnt=cc.n_quantities)
agg_cols = _parse_groupby(by, n_mod=self.n_models, n_qnt=self.n_quantities)
agg_cols, attrs_keys = self._attrs_keys_in_by(agg_cols)

df = cc._to_long_dataframe(attrs_keys=attrs_keys, observed=observed)
df = self._to_long_dataframe(attrs_keys=attrs_keys, observed=observed)

res = _groupby_df(df, by=agg_cols, metrics=pmetrics)
mtr_cols = [m.__name__ for m in pmetrics] # type: ignore
res = res.dropna(subset=mtr_cols, how="all") # TODO: ok to remove empty?
res = self._append_xy_to_res(res, cc)
res = cc._add_as_col_if_not_in_index(df, skilldf=res) # type: ignore
res = self._append_xy_to_res(res, self)
res = self._add_as_col_if_not_in_index(df, skilldf=res) # type: ignore
return SkillTable(res)

def _to_long_dataframe(
Expand Down Expand Up @@ -674,30 +630,12 @@ def gridded_skill(
* y (y) float64 51.5 52.5 53.5 54.5 55.5 56.5
"""

model, start, end, area = _get_deprecated_args(kwargs) # type: ignore
observation, variable = _get_deprecated_obs_var_args(kwargs) # type: ignore
assert kwargs == {}, f"Unknown keyword arguments: {kwargs}"

cmp = self.sel(
model=model,
observation=observation,
quantity=variable,
start=start,
end=end,
area=area,
)

if cmp.n_points == 0:
raise ValueError("Dataset is empty, no data to compare.")

## ---- end of deprecated code ----

metrics = _parse_metric(metrics)

df = cmp._to_long_dataframe()
df = self._to_long_dataframe()
df = _add_spatial_grid_to_df(df=df, bins=bins, binsize=binsize)

agg_cols = _parse_groupby(by, n_mod=cmp.n_models, n_qnt=cmp.n_quantities)
agg_cols = _parse_groupby(by, n_mod=self.n_models, n_qnt=self.n_quantities)
if "x" not in agg_cols:
agg_cols.insert(0, "x")
if "y" not in agg_cols:
Expand Down Expand Up @@ -764,39 +702,21 @@ def mean_skill(
>>> sk = cc.mean_skill(weights={"EPL": 2.0}) # more weight on EPL, others=1.0
"""

# TODO remove in v1.1
model, start, end, area = _get_deprecated_args(kwargs) # type: ignore
observation, variable = _get_deprecated_obs_var_args(kwargs) # type: ignore
assert kwargs == {}, f"Unknown keyword arguments: {kwargs}"

# filter data
cc = self.sel(
model=model, # deprecated
observation=observation, # deprecated
quantity=variable, # deprecated
start=start, # deprecated
end=end, # deprecated
area=area, # deprecated
)
if cc.n_points == 0:
raise ValueError("Dataset is empty, no data to compare.")

## ---- end of deprecated code ----

df = cc._to_long_dataframe() # TODO: remove
mod_names = cc.mod_names
df = self._to_long_dataframe() # TODO: remove
mod_names = self.mod_names
# obs_names = cmp.obs_names # df.observation.unique()
qnt_names = cc.quantity_names
qnt_names = self.quantity_names

# skill assessment
pmetrics = _parse_metric(metrics)
sk = cc.skill(metrics=pmetrics)
sk = self.skill(metrics=pmetrics)
if sk is None:
# TODO don't return None
return None
skilldf = sk.to_dataframe()

# weights
weights = cc._parse_weights(weights, sk.obs_names)
weights = self._parse_weights(weights, sk.obs_names)
skilldf["weights"] = (
skilldf.n if weights is None else np.tile(weights, len(mod_names)) # type: ignore
)
Expand All @@ -805,7 +725,7 @@ def weighted_mean(x: Any) -> Any:
return np.average(x, weights=skilldf.loc[x.index, "weights"])

# group by
by = cc._mean_skill_by(skilldf, mod_names, qnt_names) # type: ignore
by = self._mean_skill_by(skilldf, mod_names, qnt_names) # type: ignore
agg = {"n": "sum"}
for metric in pmetrics: # type: ignore
agg[metric.__name__] = weighted_mean # type: ignore
Expand All @@ -815,7 +735,7 @@ def weighted_mean(x: Any) -> Any:
res.index.name = "model"

# output
res = cc._add_as_col_if_not_in_index(df, res, fields=["model", "quantity"]) # type: ignore
res = self._add_as_col_if_not_in_index(df, res, fields=["model", "quantity"]) # type: ignore
return SkillTable(res.astype({"n": int}))

# def mean_skill_points(
Expand Down Expand Up @@ -1004,32 +924,7 @@ def score(
if not (callable(metric) or isinstance(metric, str)):
raise ValueError("metric must be a string or a function")

model, start, end, area = _get_deprecated_args(kwargs) # type: ignore
observation, variable = _get_deprecated_obs_var_args(kwargs) # type: ignore
assert kwargs == {}, f"Unknown keyword arguments: {kwargs}"

if model is None:
models = self.mod_names
else:
# TODO: these two lines looks familiar, extract to function
models = [model] if np.isscalar(model) else model # type: ignore
models = [_get_name(m, self.mod_names) for m in models] # type: ignore

cmp = self.sel(
model=models, # deprecated
observation=observation, # deprecated
quantity=variable, # deprecated
start=start, # deprecated
end=end, # deprecated
area=area, # deprecated
)

if cmp.n_points == 0:
raise ValueError("Dataset is empty, no data to compare.")

## ---- end of deprecated code ----

sk = cmp.mean_skill(weights=weights, metrics=[metric])
sk = self.mean_skill(weights=weights, metrics=[metric])
df = sk.to_dataframe()

metric_name = metric if isinstance(metric, str) else metric.__name__
Expand Down Expand Up @@ -1153,29 +1048,7 @@ def scatter(
):
warnings.warn("scatter is deprecated, use plot.scatter instead", FutureWarning)

# TODO remove in v1.1
model, start, end, area = _get_deprecated_args(kwargs)
observation, variable = _get_deprecated_obs_var_args(kwargs)

# select model
mod_idx = _get_idx(model, self.mod_names)
mod_name = self.mod_names[mod_idx]

# select variable
qnt_idx = _get_idx(variable, self.quantity_names)
qnt_name = self.quantity_names[qnt_idx]

# filter data
cmp = self.sel(
model=mod_name,
observation=observation,
quantity=qnt_name,
start=start,
end=end,
area=area,
)

return cmp.plot.scatter(
return self.plot.scatter(
bins=bins,
quantiles=quantiles,
fit_to_quantiles=fit_to_quantiles,
Expand Down Expand Up @@ -1206,30 +1079,13 @@ def taylor(
):
warnings.warn("taylor is deprecated, use plot.taylor instead", FutureWarning)

model, start, end, area = _get_deprecated_args(kwargs)
observation, variable = _get_deprecated_obs_var_args(kwargs)
assert kwargs == {}, f"Unknown keyword arguments: {kwargs}"

cmp = self.sel(
model=model,
observation=observation,
quantity=variable,
start=start,
end=end,
area=area,
)

if cmp.n_points == 0:
warnings.warn("No data!")
return

if (not aggregate_observations) and (not normalize_std):
raise ValueError(
"aggregate_observations=False is only possible if normalize_std=True!"
)

metrics = [mtr._std_obs, mtr._std_mod, mtr.cc]
skill_func = cmp.mean_skill if aggregate_observations else cmp.skill
skill_func = self.mean_skill if aggregate_observations else self.skill
sk = skill_func(metrics=metrics)

df = sk.to_dataframe()
Expand Down
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