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import numpy as np
import torch
from pandas.api.types import CategoricalDtype
def minimum(numbers, empty_val=0.):
if isinstance(numbers, torch.Tensor):
if numbers.numel() == 0:
return torch.tensor(empty_val, device=numbers.device)
else:
return numbers[~torch.isnan(numbers)].min()
elif isinstance(numbers, np.ndarray):
if numbers.size == 0:
return np.array(empty_val)
else:
return np.nanmin(numbers)
else:
if len(numbers) == 0:
return empty_val
else:
return min(numbers)
def maximum(numbers, empty_val=0.):
if isinstance(numbers, torch.Tensor):
if numbers.numel() == 0:
return torch.tensor(empty_val, device=numbers.device)
else:
return numbers[~torch.isnan(numbers)].max()
elif isinstance(numbers, np.ndarray):
if numbers.size == 0:
return np.array(empty_val)
else:
return np.nanmax(numbers)
else:
if len(numbers) == 0:
return empty_val
else:
return max(numbers)
def split_into_groups(g):
"""
Args:
- g (Tensor): Vector of groups
Returns:
- groups (Tensor): Unique groups present in g
- group_indices (list): List of Tensors, where the i-th tensor is the indices of the
elements of g that equal groups[i].
Has the same length as len(groups).
- unique_counts (Tensor): Counts of each element in groups.
Has the same length as len(groups).
"""
unique_groups, unique_counts = torch.unique(g, sorted=False, return_counts=True)
group_indices = []
for group in unique_groups:
group_indices.append(
torch.nonzero(g == group, as_tuple=True)[0])
return unique_groups, group_indices, unique_counts
def get_counts(g, n_groups):
"""
This differs from split_into_groups in how it handles missing groups.
get_counts always returns a count Tensor of length n_groups,
whereas split_into_groups returns a unique_counts Tensor
whose length is the number of unique groups present in g.
Args:
- g (Tensor): Vector of groups
Returns:
- counts (Tensor): A list of length n_groups, denoting the count of each group.
"""
unique_groups, unique_counts = torch.unique(g, sorted=False, return_counts=True)
counts = torch.zeros(n_groups, device=g.device)
counts[unique_groups] = unique_counts.float()
return counts
def avg_over_groups(v, g, n_groups):
"""
Args:
v (Tensor): Vector containing the quantity to average over.
g (Tensor): Vector of the same length as v, containing group information.
Returns:
group_avgs (Tensor): Vector of length num_groups
group_counts (Tensor)
"""
import torch_scatter
assert v.device == g.device
assert v.numel() == g.numel()
group_count = get_counts(g, n_groups)
group_avgs = torch_scatter.scatter(src=v, index=g, dim_size=n_groups, reduce='mean')
return group_avgs, group_count
def map_to_id_array(df, ordered_map={}):
maps = {}
array = np.zeros(df.shape)
for i, c in enumerate(df.columns):
if c in ordered_map:
category_type = CategoricalDtype(categories=ordered_map[c], ordered=True)
else:
category_type = 'category'
series = df[c].astype(category_type)
maps[c] = series.cat.categories.values
array[:, i] = series.cat.codes.values
return maps, array
def subsample_idxs(idxs, num=5000, take_rest=False, seed=None):
seed = (seed + 541433) if seed is not None else None
rng = np.random.default_rng(seed)
idxs = idxs.copy()
rng.shuffle(idxs)
if take_rest:
idxs = idxs[num:]
else:
idxs = idxs[:num]
return idxs
def shuffle_arr(arr, seed=None):
seed = (seed + 548207) if seed is not None else None
rng = np.random.default_rng(seed)
arr = arr.copy()
rng.shuffle(arr)
return arr
def threshold_at_recall(y_pred, y_true, global_recall=60):
""" Calculate the model threshold to use to achieve a desired global_recall level. Assumes that
y_true is a vector of the true binary labels."""
return np.percentile(y_pred[y_true == 1], 100 - global_recall)
def numel(obj):
if torch.is_tensor(obj):
return obj.numel()
elif isinstance(obj, list):
return len(obj)
else:
raise TypeError("Invalid type for numel")
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