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feature_sets.py
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feature_sets.py
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# Highly inspired by https://github.com/krassowski/gsea-api
import logging
from collections import Counter
from collections.abc import Collection
from collections.abc import Iterable
from pathlib import Path
from typing import Optional
import numpy as np
import pandas as pd
from sklearn.metrics import pairwise_distances
logger = logging.getLogger(__name__)
class FeatureSet:
def __init__(
self,
features: Collection[str],
name: str,
description: str = "NO_DESC",
):
self.name = name
self.features = frozenset(features)
self.description = description
if self.empty:
logger.warning(f"FeatureSet {name!r} is empty.")
redundant_features = None
if len(features) != len(self.features):
redundant_features = {
feature: count
for feature, count in Counter(features).items()
if count > 1
}
logger.warning(
f"FeatureSet {name!r} received a non-unique "
f"collection of features; redundant features: {redundant_features}"
)
self.redundant_features = redundant_features
@property
def empty(self):
return len(self) == 0
def __len__(self):
return len(self.features)
def __repr__(self) -> str:
features = (
": " + ", ".join(sorted(self.features)) if len(self.features) < 5 else ""
)
return f"<FeatureSet {self.name!r} with {len(self)} features{features}>"
def __iter__(self) -> Iterable[str]:
return iter(self.features)
def __eq__(self, other: "FeatureSet") -> bool:
return self.features == other.features
def __hash__(self) -> int:
return hash(self.features)
def __and__(self, other: "FeatureSet") -> "FeatureSet":
return FeatureSet(
self.features & other.features,
name=f"{self.name}&{other.name}",
)
def __or__(self, other: "FeatureSet") -> "FeatureSet":
return FeatureSet(
self.features | other.features,
name=f"{self.name}|{other.name}",
)
def __add__(self, other: "FeatureSet") -> "FeatureSet":
return self.__or__(other)
def subset(self, features: Iterable[str]) -> "FeatureSet":
"""Subset features from a feature set.
Parameters
----------
features : Iterable[str]
Features to subset.
Returns
-------
FeatureSet
A new feature set with the subset of features.
"""
return FeatureSet(
self.features & features,
name=self.name,
)
class FeatureSets:
def __init__(
self,
feature_sets: Collection[FeatureSet],
name: str = "UNL",
remove_empty: bool = True,
):
self.name = name
if remove_empty:
feature_sets = {
feature_set for feature_set in feature_sets if not feature_set.empty
}
redundant_feature_sets = None
if len(set(feature_sets)) != len(feature_sets):
redundant_feature_sets = {
feature_set: count
for feature_set, count in Counter(feature_sets).items()
if count > 1
}
logger.warning(
f"FeatureSets {name!r} received a non-unique "
"collection of feature sets; redundant feature sets: "
f"{redundant_feature_sets}"
)
self.feature_sets = frozenset(feature_sets)
self.redundant_feature_sets = redundant_feature_sets
@property
def empty(self):
return len(self) == 0
@property
def median(self) -> int:
return int(np.median([len(fs) for fs in self.feature_sets]))
@property
def features(self) -> frozenset:
return frozenset.union(*[fs.features for fs in self.feature_sets])
@property
def feature_set_by_name(self) -> dict:
return {feature_set.name: feature_set for feature_set in self.feature_sets}
def __getitem__(self, name: str) -> FeatureSet:
return self.feature_set_by_name[name]
def __len__(self):
return len(self.feature_sets)
def __iter__(self) -> Iterable[FeatureSet]:
return iter(self.feature_sets)
def __repr__(self) -> str:
feature_sets = (
": " + ", ".join(sorted({fs.name for fs in self.feature_sets}))
if len(self.feature_sets) < 5
else ""
)
return (
f"<FeatureSets {self.name!r} with {len(self)} "
+ f"feature sets{feature_sets}>"
)
def __eq__(self, other: "FeatureSets") -> bool:
return self.feature_sets == other.feature_sets
def __hash__(self) -> int:
return hash(self.feature_sets)
def __and__(self, other: "FeatureSets") -> "FeatureSets":
return FeatureSets(
name=f"{self.name}&{other.name}",
feature_sets=self.feature_sets & other.feature_sets,
)
def __or__(self, other: "FeatureSets") -> "FeatureSets":
return FeatureSets(
name=f"{self.name}|{other.name}",
feature_sets=self.feature_sets | other.feature_sets,
)
def __add__(self, other: "FeatureSets") -> "FeatureSets":
return self.__or__(other)
def find(self, partial_name: str):
"""Perform a simple search given a (partial) feature set name.
Parameters
----------
partial_name : str
Feature set (partial) name to search for.
Returns
-------
FeatureSets
Search results.
"""
return FeatureSets(
{
feature_set
for feature_set in self.feature_sets
if partial_name in feature_set.name
},
name=f"{self.name}:{partial_name}",
)
def remove(self, names: Iterable[str]):
"""Remove feature sets by name.
Parameters
----------
names : Iterable[str]
Collection of feature set names.
"""
return FeatureSets(
{
feature_set
for feature_set in self.feature_sets
if feature_set.name not in names
},
name=self.name,
)
def keep(self, names: Iterable[str]):
"""Keep feature sets by name.
Parameters
----------
names : Iterable[str]
Collection of feature set names.
"""
return FeatureSets(
{
feature_set
for feature_set in self.feature_sets
if feature_set.name in names
},
name=self.name,
)
def trim(self, min_count: int = 1, max_count: Optional[int] = None):
"""Trim feature sets by min/max size.
Parameters
----------
min_count : int, optional
Minimum number of features, by default 1.
max_count : int, optional
Maximum number of features, by default None.
"""
return FeatureSets(
{
feature_set
for feature_set in self.feature_sets
if min_count
<= len(feature_set.features)
<= (max_count or len(feature_set.features))
},
name=self.name,
)
def subset(self, features: Iterable[str]):
"""Subset feature sets by features.
Parameters
----------
features : Iterable[str]
Collection of features.
"""
return FeatureSets(
{feature_set.subset(set(features)) for feature_set in self.feature_sets},
name=self.name,
)
def filter(
self,
features: Iterable[str],
min_fraction: float = 0.5,
min_count: int = 5,
max_count: Optional[int] = None,
keep: Optional[Iterable[str]] = None,
subset: bool = True,
):
"""Filter feature sets.
Parameters
----------
features : Iterable[str]
Features to filter.
min_fraction : float, optional
Mininimum portion of the feature set to be present in `features`,
by default 0.5
min_count : int, optional
Minimum size of the intersection set
between a feature set and the set of `features`,
by default 5
max_count : int, optional
Maximum size of the intersection set
between a feature set and the set of `features`,
by default None
keep : Iterable[str], optional
Feature sets to keep regardless of the filter conditions,
by default None
subset : bool, optional
Whether to subset the resulting feature sets based on `features`,
by default True
Returns
-------
FeatureSets
Filtered feature sets.
"""
features = set(features)
if keep is None:
keep = set()
feature_set_subset = set()
for feature_set in self.feature_sets:
if feature_set.name in keep:
feature_set_subset.add(feature_set)
continue
intersection = features & feature_set.features
count = len(intersection)
fraction = count / len(feature_set)
if (
count >= min_count
and fraction >= min_fraction
and (max_count is None or count <= max_count)
):
feature_set_subset.add(feature_set)
filtered_feature_sets = FeatureSets(feature_set_subset, name=self.name)
if subset:
filtered_feature_sets = filtered_feature_sets.subset(features)
return filtered_feature_sets
def to_mask(
self, features: Optional[Iterable[str]] = None, sort: bool = True
) -> pd.DataFrame:
"""Convert feature sets to a mask.
Parameters
----------
features : Iterable[str], optional
Collection of features, by default None.
sort : bool, optional
Sort feature sets alphabetically, by default True.
Returns
-------
pd.DataFrame
Mask of features.
"""
features = features or self.features
features_list = list(features)
feature_sets_list = list(self.feature_sets)
if sort:
feature_sets_list = sorted(feature_sets_list, key=lambda fs: fs.name)
return pd.DataFrame(
[
[feature in feature_set.features for feature in features_list]
for feature_set in feature_sets_list
],
index=[feature_set.name for feature_set in feature_sets_list],
columns=features_list,
)
def similarity_to_feature_sets(
self, other: "FeatureSets" = None, metric: str = "jaccard"
) -> pd.DataFrame:
"""Compute similarity matrix between feature sets.
Parameters
----------
other : FeatureSets, optional
Other feature set collection, by default None.
metric : str, optional
Similarity metric, by default "jaccard".
Returns
-------
pd.DataFrame
Similarity matrix as 1 minus distance matrix,
may lead to negative values for some distance metrics.
"""
if metric not in ["jaccard", "cosine"]:
logger.warning(
f"Similarity matrix for `{metric}` might be negative. "
"Recommended metrics are `jaccard` or `cosine`."
)
self_mask = self.to_mask()
other_mask = other.to_mask() if other else self_mask
return 1 - pd.DataFrame(
pairwise_distances(
self_mask.to_numpy(), other_mask.to_numpy(), metric=metric
),
index=self_mask.index,
columns=other_mask.index,
)
def similarity_to_observations(
self,
observations: pd.DataFrame,
) -> pd.DataFrame:
"""Compute similarity matrix between feature sets.
Parameters
----------
observations : pd.DataFrame
Dataframe of observations.
Returns
-------
pd.DataFrame
Similarity matrix as correlation matrix.
"""
obs_mean = observations.mean(axis=1)
dist_to_mean_dict = {}
for feature_set in self.feature_sets:
col_subset = [
col for col in observations.columns if col in feature_set.features
]
if len(col_subset) == 0:
dist_to_mean_dict[feature_set.name] = pd.Series(
np.nan, index=observations.index
)
continue
dist_to_mean_dict[feature_set.name] = (
observations.loc[:, col_subset].mean(axis=1) - obs_mean
)
return pd.DataFrame(dist_to_mean_dict).corr()
def _find_similar_pairs(
self, sim_matrix: pd.DataFrame, similarity_threshold: float
) -> set[tuple[str, str]]:
"""Find similar pairs of feature sets.
Parameters
----------
sim_matrix : pd.DataFrame
Similarity matrix.
similarity_threshold : float
Similarity threshold to consider similar pairs.
Returns
-------
set[tuple[str, str]]
Similar pairs of feature sets.
"""
pairs = set()
visited = set()
row_offset = 0
for current_fs, row in sim_matrix.iterrows():
row_offset += 1
if row_offset >= len(row):
break
if current_fs in visited:
continue
visited.add(current_fs)
closest_fs = row.iloc[row_offset:].idxmax()
similarity = row[closest_fs]
if similarity >= similarity_threshold and closest_fs not in visited:
pairs.add((current_fs, closest_fs, similarity))
visited.add(closest_fs)
return pairs
def find_similar_pairs(
self,
observations: pd.DataFrame = None,
metric: Optional[str] = None,
similarity_threshold: float = 0.8,
) -> set[tuple[str, str]]:
"""Find similar pairs of feature sets.
Parameters
----------
observations : pd.DataFrame, optional
Dataframe of observations, if provided, the similarity between feature sets
is computed based on the correlation of the similarity from the mean
of the observations in the feature set, by default None.
metric : str, optional
Similarity metric, by default "jaccard" if observations not provided.
similarity_threshold : float, optional
Similarity threshold to consider similar pairs,
by default 0.8.
Returns
-------
set[tuple[str, str]]
Similar pairs of feature sets.
"""
if observations is None and metric is None:
logger.warning(
"Neither observations nor metric is provided,"
" using `metric=jaccard` as default."
)
metric = "jaccard"
sim_matrix = []
if observations is not None:
sim_matrix.append(self.similarity_to_observations(observations))
if metric is not None:
sim_matrix.append(self.similarity_to_feature_sets(metric=metric))
if observations is not None and metric is not None:
sim_matrix[0][sim_matrix[0] < 0] = 0.0
sim_matrix[1][sim_matrix[1] < 0] = 0.0
sim_matrix = (2 * sim_matrix[0] * sim_matrix[1]) / (
sim_matrix[0] + sim_matrix[1]
)
else:
sim_matrix = sim_matrix[0]
return self._find_similar_pairs(sim_matrix.fillna(0.0), similarity_threshold)
def merge_pairs(self, pairs: Iterable[tuple[str, str]]):
"""Merge pairs of feature sets.
Parameters
----------
pairs : Iterable[tuple[str, str]]
Pairs of feature sets.
Returns
-------
FeatureSets
Merged feature sets.
"""
names_to_remove = set()
merged_feature_sets = set()
for pair in pairs:
merged_feature_sets.add(self[pair[0]] | self[pair[1]])
names_to_remove |= {pair[0], pair[1]}
# remove merged feature sets
feature_sets = self.remove(names_to_remove)
# then add merged feature sets
feature_sets |= FeatureSets(merged_feature_sets)
feature_sets.name = self.name
return feature_sets
def merge_similar(
self,
observations: pd.DataFrame = None,
metric: Optional[str] = None,
similarity_threshold: float = 0.8,
iteratively: bool = True,
):
"""Merge similar feature sets.
Parameters
----------
observations : pd.DataFrame, optional
Dataframe of observations, if provided, the similarity between feature sets
is computed based on the correlation of the similarity from the mean
of the observations in the feature set, by default None.
metric : str, optional
Similarity metric, by default "jaccard" if observations not provided.
similarity_threshold : float, optional
Similarity threshold to consider similar pairs,
by default 0.8.
iteratively : bool, optional
Whether to merge iteratively, by default True
Returns
-------
FeatureSets
Merged feature sets.
"""
feature_sets = self
while True:
pairs = {
(name1, name2)
for name1, name2, _ in feature_sets.find_similar_pairs(
observations=observations,
metric=metric,
similarity_threshold=similarity_threshold,
)
}
stopping = ""
if len(pairs) == 0 and iteratively:
stopping = " Stopping..."
logger.info(f"Found {len(pairs)} pairs to merge.{stopping}")
feature_sets = feature_sets.merge_pairs(pairs)
if len(pairs) == 0 or not iteratively:
break
return feature_sets
def to_gmt(self, path: Path):
"""Write this feature set collection to a GMT file.
Parameters
----------
path : Path
Path to the output file.
"""
with open(path, "w") as f:
for feature_set in self.feature_sets:
f.write(
feature_set.name
+ "\t"
+ feature_set.description
+ "\t"
+ "\t".join(feature_set.features)
+ "\n"
)
def to_dict(self) -> dict[str, Iterable[str]]:
"""Convert this feature set collection to a dictionary.
Returns
-------
dict[str, Iterable[str]]
Dictionary of feature sets.
"""
return {fs.name: fs.features for fs in self.feature_sets}
def from_gmt(path: Path, name: Optional[str] = None, **kwargs) -> FeatureSets:
"""Create a FeatureSets object from a GMT file.
Parameters
----------
path : Path
Path to the GMT file.
name : str, optional
Name of the collection, by default None.
Returns
-------
FeatureSets
"""
feature_sets = set()
with open(path) as f:
for line in f:
fs_name, description, *features = line.strip().split("\t")
feature_sets.add(
FeatureSet(
features,
name=fs_name,
description=description,
)
)
return FeatureSets(feature_sets, name=name or Path(path).name, **kwargs)
def from_dict(
d: dict[str, Iterable[str]],
name: Optional[str] = None,
**kwargs,
) -> FeatureSets:
"""Create a FeatureSets object from a dictionary.
Parameters
----------
d : dict[str, Iterable[str]]
Dictionary of feature sets.
name : str, optional
Name of the collection, by default None.
Returns
-------
FeatureSets
"""
feature_sets = set()
for fs_name, features in d.items():
feature_sets.add(FeatureSet(features, name=fs_name))
return FeatureSets(feature_sets, name=name, **kwargs)
def from_dataframe(
df: pd.DataFrame,
name: Optional[str] = None,
name_col: str = "name",
features_col: str = "features",
desc_col: Optional[str] = None,
**kwargs,
) -> FeatureSets:
"""Create a FeatureSets object from a DataFrame.
Parameters
----------
df : pd.DataFrame
DataFrame of feature sets.
name : str, optional
Name of the collection, by default None.
name_col : str, optional
Name of the column containing feature set names, by default "name".
features_col : str, optional
Name of the column containing feature set features, by default "features".
desc_col : str, optional
Name of the column containing feature set descriptions, by default None.
Returns
-------
FeatureSets
"""
feature_sets = set()
for _, row in df.iterrows():
description = "NO_DESC"
if desc_col is not None and not pd.isna(row[desc_col]):
description = row[desc_col]
feature_sets.add(
FeatureSet(
row[features_col],
name=row[name_col],
description=description,
)
)
return FeatureSets(feature_sets, name=name, **kwargs)