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[MNT] Remove mutable objects from defaults (#1699)
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### Description

This PR is a fix for #1668. Removes mutable default arguments and
replaces them with internal newly initialized mutable defaults.
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@eugenio-mercuriali
eugenio-mercuriali authored Nov 9, 2024
1 parent a884c4d commit 193f539
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Showing 17 changed files with 511 additions and 303 deletions.
52 changes: 26 additions & 26 deletions docs/source/tutorials/building.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -875,13 +875,13 @@
" assert dataset.max_prediction_length == dataset.min_prediction_length, \"Decoder only supports a fixed length\"\n",
" assert dataset.min_encoder_length == dataset.max_encoder_length, \"Encoder only supports a fixed length\"\n",
" assert (\n",
" len(dataset.time_varying_known_categoricals) == 0\n",
" and len(dataset.time_varying_known_reals) == 0\n",
" and len(dataset.time_varying_unknown_categoricals) == 0\n",
" and len(dataset.static_categoricals) == 0\n",
" and len(dataset.static_reals) == 0\n",
" and len(dataset.time_varying_unknown_reals) == 1\n",
" and dataset.time_varying_unknown_reals[0] == dataset.target\n",
" len(dataset._time_varying_known_categoricals) == 0\n",
" and len(dataset._time_varying_known_reals) == 0\n",
" and len(dataset._time_varying_unknown_categoricals) == 0\n",
" and len(dataset._static_categoricals) == 0\n",
" and len(dataset._static_reals) == 0\n",
" and len(dataset._time_varying_unknown_reals) == 1\n",
" and dataset._time_varying_unknown_reals[0] == dataset.target\n",
" ), \"Only covariate should be the target in 'time_varying_unknown_reals'\"\n",
"\n",
" return super().from_dataset(dataset, **new_kwargs)"
Expand Down Expand Up @@ -1587,12 +1587,12 @@
" assert dataset.max_prediction_length == dataset.min_prediction_length, \"Decoder only supports a fixed length\"\n",
" assert dataset.min_encoder_length == dataset.max_encoder_length, \"Encoder only supports a fixed length\"\n",
" assert (\n",
" len(dataset.time_varying_known_categoricals) == 0\n",
" and len(dataset.time_varying_known_reals) == 0\n",
" and len(dataset.time_varying_unknown_categoricals) == 0\n",
" and len(dataset.static_categoricals) == 0\n",
" and len(dataset.static_reals) == 0\n",
" and len(dataset.time_varying_unknown_reals) == 1\n",
" len(dataset._time_varying_known_categoricals) == 0\n",
" and len(dataset._time_varying_known_reals) == 0\n",
" and len(dataset._time_varying_unknown_categoricals) == 0\n",
" and len(dataset._static_categoricals) == 0\n",
" and len(dataset._static_reals) == 0\n",
" and len(dataset._time_varying_unknown_reals) == 1\n",
" ), \"Only covariate should be in 'time_varying_unknown_reals'\"\n",
"\n",
" return super().from_dataset(dataset, **new_kwargs)\n",
Expand Down Expand Up @@ -2136,12 +2136,12 @@
" assert dataset.max_prediction_length == dataset.min_prediction_length, \"Decoder only supports a fixed length\"\n",
" assert dataset.min_encoder_length == dataset.max_encoder_length, \"Encoder only supports a fixed length\"\n",
" assert (\n",
" len(dataset.time_varying_known_categoricals) == 0\n",
" and len(dataset.time_varying_known_reals) == 0\n",
" and len(dataset.time_varying_unknown_categoricals) == 0\n",
" and len(dataset.static_categoricals) == 0\n",
" and len(dataset.static_reals) == 0\n",
" and len(dataset.time_varying_unknown_reals)\n",
" len(dataset._time_varying_known_categoricals) == 0\n",
" and len(dataset._time_varying_known_reals) == 0\n",
" and len(dataset._time_varying_unknown_categoricals) == 0\n",
" and len(dataset._static_categoricals) == 0\n",
" and len(dataset._static_reals) == 0\n",
" and len(dataset._time_varying_unknown_reals)\n",
" == len(dataset.target_names) # Expect as as many unknown reals as targets\n",
" ), \"Only covariate should be in 'time_varying_unknown_reals'\"\n",
"\n",
Expand Down Expand Up @@ -3414,13 +3414,13 @@
" assert dataset.max_prediction_length == dataset.min_prediction_length, \"Decoder only supports a fixed length\"\n",
" assert dataset.min_encoder_length == dataset.max_encoder_length, \"Encoder only supports a fixed length\"\n",
" assert (\n",
" len(dataset.time_varying_known_categoricals) == 0\n",
" and len(dataset.time_varying_known_reals) == 0\n",
" and len(dataset.time_varying_unknown_categoricals) == 0\n",
" and len(dataset.static_categoricals) == 0\n",
" and len(dataset.static_reals) == 0\n",
" and len(dataset.time_varying_unknown_reals) == 1\n",
" and dataset.time_varying_unknown_reals[0] == dataset.target\n",
" len(dataset._time_varying_known_categoricals) == 0\n",
" and len(dataset._time_varying_known_reals) == 0\n",
" and len(dataset._time_varying_unknown_categoricals) == 0\n",
" and len(dataset._static_categoricals) == 0\n",
" and len(dataset._static_reals) == 0\n",
" and len(dataset._time_varying_unknown_reals) == 1\n",
" and dataset._time_varying_unknown_reals[0] == dataset.target\n",
" ), \"Only covariate should be the target in 'time_varying_unknown_reals'\"\n",
"\n",
" return super().from_dataset(dataset, **new_kwargs)\n",
Expand Down
95 changes: 50 additions & 45 deletions pytorch_forecasting/data/encoders.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -2,11 +2,12 @@
Encoders for encoding categorical variables and scaling continuous data.
"""

from typing import Any, Callable, Dict, Iterable, List, Tuple, Union
from typing import Any, Callable, Dict, Iterable, List, Tuple, Union, Optional
import warnings

import numpy as np
import pandas as pd
from copy import deepcopy
from sklearn.base import BaseEstimator, TransformerMixin
import torch
from torch.distributions import constraints
Expand Down Expand Up @@ -396,7 +397,7 @@ def __init__(
method: str = "standard",
center: bool = True,
transformation: Union[str, Tuple[Callable, Callable]] = None,
method_kwargs: Dict[str, Any] = {},
method_kwargs: Optional[Dict[str, Any]] = None,
):
"""
Args:
Expand Down Expand Up @@ -428,6 +429,7 @@ def __init__(
self.center = center
self.transformation = transformation
self.method_kwargs = method_kwargs
self._method_kwargs = deepcopy(method_kwargs) if method_kwargs is not None else {}

def get_parameters(self, *args, **kwargs) -> torch.Tensor:
"""
Expand Down Expand Up @@ -496,17 +498,17 @@ def _set_parameters(

elif self.method == "robust":
if isinstance(y_center, torch.Tensor):
self.center_ = y_center.quantile(self.method_kwargs.get("center", 0.5), dim=-1)
q_75 = y_scale.quantile(self.method_kwargs.get("upper", 0.75), dim=-1)
q_25 = y_scale.quantile(self.method_kwargs.get("lower", 0.25), dim=-1)
self.center_ = y_center.quantile(self._method_kwargs.get("center", 0.5), dim=-1)
q_75 = y_scale.quantile(self._method_kwargs.get("upper", 0.75), dim=-1)
q_25 = y_scale.quantile(self._method_kwargs.get("lower", 0.25), dim=-1)
elif isinstance(y_center, np.ndarray):
self.center_ = np.percentile(y_center, self.method_kwargs.get("center", 0.5) * 100, axis=-1)
q_75 = np.percentile(y_scale, self.method_kwargs.get("upper", 0.75) * 100, axis=-1)
q_25 = np.percentile(y_scale, self.method_kwargs.get("lower", 0.25) * 100, axis=-1)
self.center_ = np.percentile(y_center, self._method_kwargs.get("center", 0.5) * 100, axis=-1)
q_75 = np.percentile(y_scale, self._method_kwargs.get("upper", 0.75) * 100, axis=-1)
q_25 = np.percentile(y_scale, self._method_kwargs.get("lower", 0.25) * 100, axis=-1)
else:
self.center_ = np.percentile(y_center, self.method_kwargs.get("center", 0.5) * 100, axis=-1)
q_75 = np.percentile(y_scale, self.method_kwargs.get("upper", 0.75) * 100)
q_25 = np.percentile(y_scale, self.method_kwargs.get("lower", 0.25) * 100)
self.center_ = np.percentile(y_center, self._method_kwargs.get("center", 0.5) * 100, axis=-1)
q_75 = np.percentile(y_scale, self._method_kwargs.get("upper", 0.75) * 100)
q_25 = np.percentile(y_scale, self._method_kwargs.get("lower", 0.25) * 100)
self.scale_ = (q_75 - q_25) / 2.0 + eps
if not self.center and self.method != "identity":
self.scale_ = self.center_
Expand Down Expand Up @@ -623,7 +625,7 @@ def __init__(
center: bool = True,
max_length: Union[int, List[int]] = None,
transformation: Union[str, Tuple[Callable, Callable]] = None,
method_kwargs: Dict[str, Any] = {},
method_kwargs: Dict[str, Any] = None,
):
"""
Initialize
Expand Down Expand Up @@ -655,6 +657,7 @@ def __init__(
should be defined if ``reverse`` is not the inverse of the forward transformation. ``inverse_torch``
can be defined to provide a torch distribution transform for inverse transformations.
"""
method_kwargs = deepcopy(method_kwargs) if method_kwargs is not None else {}
super().__init__(method=method, center=center, transformation=transformation, method_kwargs=method_kwargs)
self.max_length = max_length

Expand Down Expand Up @@ -726,11 +729,11 @@ class GroupNormalizer(TorchNormalizer):
def __init__(
self,
method: str = "standard",
groups: List[str] = [],
groups: Optional[List[str]] = None,
center: bool = True,
scale_by_group: bool = False,
transformation: Union[str, Tuple[Callable, Callable]] = None,
method_kwargs: Dict[str, Any] = {},
transformation: Optional[Union[str, Tuple[Callable, Callable]]] = None,
method_kwargs: Optional[Dict[str, Any]] = None,
):
"""
Group normalizer to normalize a given entry by groups. Can be used as target normalizer.
Expand Down Expand Up @@ -765,7 +768,9 @@ def __init__(
"""
self.groups = groups
self._groups = list(groups) if groups is not None else []
self.scale_by_group = scale_by_group
method_kwargs = deepcopy(method_kwargs) if method_kwargs is not None else {}
super().__init__(method=method, center=center, transformation=transformation, method_kwargs=method_kwargs)

def fit(self, y: pd.Series, X: pd.DataFrame):
Expand All @@ -781,17 +786,17 @@ def fit(self, y: pd.Series, X: pd.DataFrame):
"""
y = self.preprocess(y)
eps = np.finfo(np.float16).eps
if len(self.groups) == 0:
if len(self._groups) == 0:
assert not self.scale_by_group, "No groups are defined, i.e. `scale_by_group=[]`"
if self.method == "standard":
self.norm_ = {"center": np.mean(y), "scale": np.std(y) + eps} # center and scale
else:
quantiles = np.quantile(
y,
[
self.method_kwargs.get("lower", 0.25),
self.method_kwargs.get("center", 0.5),
self.method_kwargs.get("upper", 0.75),
self._method_kwargs.get("lower", 0.25),
self._method_kwargs.get("center", 0.5),
self._method_kwargs.get("upper", 0.75),
],
)
self.norm_ = {
Expand All @@ -810,7 +815,7 @@ def fit(self, y: pd.Series, X: pd.DataFrame):
.groupby(g, observed=True)
.agg(center=("y", "mean"), scale=("y", "std"))
.assign(center=lambda x: x["center"], scale=lambda x: x.scale + eps)
for g in self.groups
for g in self._groups
}
else:
self.norm_ = {
Expand All @@ -819,21 +824,21 @@ def fit(self, y: pd.Series, X: pd.DataFrame):
.groupby(g, observed=True)
.y.quantile(
[
self.method_kwargs.get("lower", 0.25),
self.method_kwargs.get("center", 0.5),
self.method_kwargs.get("upper", 0.75),
self._method_kwargs.get("lower", 0.25),
self._method_kwargs.get("center", 0.5),
self._method_kwargs.get("upper", 0.75),
]
)
.unstack(-1)
.assign(
center=lambda x: x[self.method_kwargs.get("center", 0.5)],
center=lambda x: x[self._method_kwargs.get("center", 0.5)],
scale=lambda x: (
x[self.method_kwargs.get("upper", 0.75)] - x[self.method_kwargs.get("lower", 0.25)]
x[self._method_kwargs.get("upper", 0.75)] - x[self._method_kwargs.get("lower", 0.25)]
)
/ 2.0
+ eps,
)[["center", "scale"]]
for g in self.groups
for g in self._groups
}
# calculate missings
if not self.center: # swap center and scale
Expand All @@ -849,29 +854,29 @@ def swap_parameters(norm):
else:
if self.method == "standard":
self.norm_ = (
X[self.groups]
X[self._groups]
.assign(y=y)
.groupby(self.groups, observed=True)
.groupby(self._groups, observed=True)
.agg(center=("y", "mean"), scale=("y", "std"))
.assign(center=lambda x: x["center"], scale=lambda x: x.scale + eps)
)
else:
self.norm_ = (
X[self.groups]
X[self._groups]
.assign(y=y)
.groupby(self.groups, observed=True)
.groupby(self._groups, observed=True)
.y.quantile(
[
self.method_kwargs.get("lower", 0.25),
self.method_kwargs.get("center", 0.5),
self.method_kwargs.get("upper", 0.75),
self._method_kwargs.get("lower", 0.25),
self._method_kwargs.get("center", 0.5),
self._method_kwargs.get("upper", 0.75),
]
)
.unstack(-1)
.assign(
center=lambda x: x[self.method_kwargs.get("center", 0.5)],
center=lambda x: x[self._method_kwargs.get("center", 0.5)],
scale=lambda x: (
x[self.method_kwargs.get("upper", 0.75)] - x[self.method_kwargs.get("lower", 0.25)]
x[self._method_kwargs.get("upper", 0.75)] - x[self._method_kwargs.get("lower", 0.25)]
)
/ 2.0
+ eps,
Expand All @@ -883,7 +888,7 @@ def swap_parameters(norm):
self.missing_ = self.norm_.median().to_dict()

if (
(self.scale_by_group and any((self.norm_[group]["scale"] < 1e-7).any() for group in self.groups))
(self.scale_by_group and any((self.norm_[group]["scale"] < 1e-7).any() for group in self._groups))
or (not self.scale_by_group and isinstance(self.norm_["scale"], float) and self.norm_["scale"] < 1e-7)
or (
not self.scale_by_group
Expand Down Expand Up @@ -973,13 +978,13 @@ def get_parameters(self, groups: Union[torch.Tensor, list, tuple], group_names:
if isinstance(groups, list):
groups = tuple(groups)
if group_names is None:
group_names = self.groups
group_names = self._groups
else:
# filter group names
group_names = [name for name in group_names if name in self.groups]
assert len(group_names) == len(self.groups), "Passed groups and fitted do not match"
group_names = [name for name in group_names if name in self._groups]
assert len(group_names) == len(self._groups), "Passed groups and fitted do not match"

if len(self.groups) == 0:
if len(self._groups) == 0:
params = np.array([self.norm_["center"], self.norm_["scale"]])
elif self.scale_by_group:
norm = np.array([1.0, 1.0])
Expand All @@ -988,7 +993,7 @@ def get_parameters(self, groups: Union[torch.Tensor, list, tuple], group_names:
norm = norm * self.norm_[group_name].loc[group].to_numpy()
except KeyError:
norm = norm * np.asarray([self.missing_[group_name][name] for name in self.names])
norm = np.power(norm, 1.0 / len(self.groups))
norm = np.power(norm, 1.0 / len(self._groups))
params = norm
else:
try:
Expand All @@ -1007,7 +1012,7 @@ def get_norm(self, X: pd.DataFrame) -> pd.DataFrame:
Returns:
pd.DataFrame: dataframe with scaling parameterswhere each row corresponds to the input dataframe
"""
if len(self.groups) == 0:
if len(self._groups) == 0:
norm = np.asarray([self.norm_["center"], self.norm_["scale"]]).reshape(1, -1)
elif self.scale_by_group:
norm = [
Expand All @@ -1017,15 +1022,15 @@ def get_norm(self, X: pd.DataFrame) -> pd.DataFrame:
.map(self.norm_[group_name][name])
.fillna(self.missing_[group_name][name])
.to_numpy()
for group_name in self.groups
for group_name in self._groups
],
axis=0,
)
for name in self.names
]
norm = np.power(np.stack(norm, axis=1), 1.0 / len(self.groups))
norm = np.power(np.stack(norm, axis=1), 1.0 / len(self._groups))
else:
norm = X[self.groups].set_index(self.groups).join(self.norm_).fillna(self.missing_).to_numpy()
norm = X[self._groups].set_index(self._groups).join(self.norm_).fillna(self.missing_).to_numpy()
return norm


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