# Source code for evomap.metrics

```
"""
Useful functions to evaluate maps.
"""
import numpy as np
import pandas as pd
from scipy.spatial.distance import squareform, pdist, cdist, cosine
from scipy.stats import pearsonr
[docs]def misalign_score(Ys, normalize = True):
""" Calculate misalignment of a sequence of maps.
Misaligned is measured as the average Euclidean distance
between objects' subsequent map positions. The final score is averaged across
all objects.
Parameters
----------
Ys : list of ndarrays, each of shape (n_samples, d)
Map coordinates.
normalize : bool, optional
If true, misalignment is normalized by the average interobject distance
on the map. Useful for comparing maps across differently scaled coordinate
systems, by default True.
Returns
-------
float
Misalignment score, bounded within [0, inf).
Lower values indicate better alignment.
"""
n_samples = Ys[0].shape[0]
misalignment = np.zeros(n_samples)
n_periods = len(Ys)
for t in range(1, n_periods):
Y_this = Ys[t]
Y_prev = Ys[t-1]
D = cdist(Y_this, Y_prev)
misalignment_t = np.diag(D)
if normalize:
misalignment_t = misalignment_t / np.mean(cdist(Y_prev, Y_prev))
misalignment += misalignment_t
misalignment /= (n_periods-1)
return misalignment.mean()
[docs]def align_score(Ys):
""" Calculate alignment of a sequence of maps.
Alignment is measured as the mean cosine similarity of objects' subsequent
map positions. The final score is averaged across all objects.
Parameters
----------
Ys : list of ndarrays, each of shape (n_samples, d)
Map coordinates.
Returns
-------
float
Alignment score, bounded between [-1,1].
Higher values indicate better alignment.
"""
mean_alignment = 0
n_samples = Ys[0].shape[0]
n_periods = len(Ys)
for t in range(1, n_periods):
Y_this = Ys[t]
Y_prev = Ys[t-1]
for i in range(n_samples):
mean_alignment += 1 - cosine(Y_this[i, :], Y_prev[i, :])
mean_alignment /= n_samples
mean_alignment /= (n_periods-1)
return mean_alignment
[docs]def hitrate_score(X, Y, n_neighbors = 10, inc = None, input_type = 'similarity'):
""" Calculate Hitrate of nearest neighbor recovery for a single map. The
score is averaged across all objects.
Parameters
----------
X : ndarray
Input data, either a similarity / distance matrix of shape
(n_samples, n_samples), or a matrix of feature vectors of shape (n_samples, d_input).
Y : ndarray of shape (n_samples, d)
Map coordinates.
n_neighbors : int, optional
Number of neighbors considered when calculating the hitrate, by default 10
inc : ndarray of shape (n_samples,), optional
Inclusion array, indicating if an object is present (via 0 and 1s), by default None
input_type : str, optional
One of 'vector', 'similarity', or 'distance', by default 'similarity'
Returns
-------
float
Hitrate of nearest neighbor recovery, bounded within [0,1].
Higher values indicate better recovery.
"""
hit_rate = 0
n_samples = X.shape[0]
if not Y.shape[0] == X.shape[0]:
raise ValueError('Inconsistent array sizes.')
if not input_type in ['similarity', 'distance', 'vector']:
raise ValueError('Input type should be similarity, distance or vector.')
# Need to copy the matrix, else "np.fill_diagonal" will modify the original
# one
X = X.copy()
if input_type == 'vector':
# Turn X into a distance matrix
X = cdist(X,X)
if not inc is None:
if np.any(~np.logical_or(inc == 0, inc == 1)):
raise ValueError('Inclusions should only be 0 or 1.')
if len(inc) != n_samples:
raise ValueError('Incosistent array sizes.')
Y = Y[inc==1, :]
X = X[inc==1, :][:, inc == 1]
Dist_map = squareform(pdist(Y, "sqeuclidean"))
# Make diagonal (self-distances) larger than any other distance
# (thereby, an object never appears as its own nearest neighbor)
np.fill_diagonal(Dist_map, np.max(Dist_map)+1e8)
if input_type == 'distance' or input_type == 'vector':
# X is a distance matrix
# Make diagonal (self-distances) larger than any other distance (thereby, an object never appears as its own nearest neighbor)
np.fill_diagonal(X, np.max(X)+1e8)
for i in range(n_samples):
# Sort i-th row of distance matrix (low-to-high)
nn_data = np.argsort(X[i,:])[:n_neighbors]
nn_map = np.argsort(Dist_map[i, :])[:n_neighbors]
nn_intersec = [id for id in nn_data if id in nn_map]
hit_rate += len(nn_intersec)
hit_rate = hit_rate / (n_neighbors * n_samples)
elif input_type == 'similarity':
# X is a similarity matrix
# For similarities, make diagonal (= self similarities) smaller than any other similarity (see above)
np.fill_diagonal(X, 0)
for i in range(n_samples):
# Find max similarity (rather than min distance)
nn_data = np.argsort(X[i,:])[-n_neighbors:]
nn_map = np.argsort(Dist_map[i, :])[:n_neighbors]
nn_intersec = [id for id in nn_data if id in nn_map]
hit_rate += len(nn_intersec)
hit_rate = hit_rate / (n_neighbors * n_samples)
return hit_rate
[docs]def adjusted_hitrate_score(X, Y, n_neighbors = 10, inc = None, input_type = 'similarity'):
""" Calculate Hitrate of nearest neighbor recovery for a single map, adjusted
for random agreement. The score is averaged across all objects.
Parameters
----------
X : ndarray
Input data, either a similarity / distance matrix of shape
(n_samples, n_samples), or a matrix of feature vectors of shape (n_samples, d_input).
Y : ndarray of shape (n_samples, d)
Map coordinates.
n_neighbors : int, optional
Number of neighbors considered when calculating the hitrate, by default 10
inc : ndarray of shape (n_samples,), optional
Inclusion array, indicating if an object is present (via 0 and 1s), by default None
input_type : str, optional
One of 'vector', 'similarity', or 'distance', by default 'similarity'
Returns
-------
float
Adjusted Hitrate of nearest neighbor recovery, bounded within [0,1].
Higher values indicate better recovery.
"""
hitrate = hitrate_score(X = X, Y = Y, n_neighbors= n_neighbors, inc = inc, input_type=input_type)
n_samples = X.shape[0]
adj_hitrate = hitrate - n_neighbors / (n_samples -1)
return adj_hitrate
[docs]def avg_hitrate_score(Ys, Xs, n_neighbors = 10, Inc_ts = None, input_type = 'distance'):
""" Calculate average Hitrate of nearest neighbor recovery for a sequence of
maps. The score is averaged across all maps within the sequence.
Parameters
----------
Xs : list of ndarrays
Input data, either in the form of distance/similarity matrices, each of
shape (n_samples, n_samples), or or feature vectors of shape (n_samples, d_input).
Ys : list of ndarays, each of shape (n_samples, d)
_description_
n_neighbors : int, optional
Number of neighbors considered when calculating the hitrate, by default 10
Inc_ts : list of ndarays, each of shape (n_samples,), optional
List of inclusion arrays, indicating if an object is present in a
given period (via 0 and 1s), by default None
input_type : str, optional
One of 'vector', 'similarity', or 'distance', by default 'similarity'
Returns
-------
float
Average hitrate, bounded between [0,1]. Higher values indicate better recovery.
"""
avg_hitrate = 0
n_periods = len(Xs)
for t in range(n_periods):
if Inc_ts is None:
inc_t = None
else:
inc_t = Inc_ts[t]
avg_hitrate += hitrate_score(
X = Xs[t],
Y = Ys[t],
n_neighbors = n_neighbors,
inc = inc_t,
input_type = input_type)
avg_hitrate /= n_periods
return avg_hitrate
[docs]def avg_adjusted_hitrate_score(Ys, Xs, n_neighbors = 10, Inc_ts = None, input_type = 'distance'):
""" Calculate average Hitrate of nearest neighbor recovery for a sequence of
maps, adjusted for random agreement. The score is averaged across all
maps within the sequence.
Parameters
----------
Xs : list of ndarrays
Input data, either in the form of distance/similarity matrices, each of
shape (n_samples, n_samples), or or feature vectors of shape (n_samples, d_input).
Ys : list of ndarays, each of shape (n_samples, d)
_description_
n_neighbors : int, optional
Number of neighbors considered when calculating the hitrate, by default 10
Inc_ts : list of ndarays, each of shape (n_samples,), optional
List of inclusion arrays, indicating if an object is present in a
given period (via 0 and 1s), by default None
input_type : str, optional
One of 'vector', 'similarity', or 'distance', by default 'similarity'
Returns
-------
float
Average adjusted hitrate, bounded between [0,1]. Higher values
indicate better recovery.
"""
avg_adj_hitrate = 0
n_periods = len(Xs)
for t in range(n_periods):
if Inc_ts is None:
inc_t = None
else:
inc_t = Inc_ts[t]
avg_adj_hitrate += adjusted_hitrate_score(
X = Xs[t],
Y = Ys[t],
n_neighbors = n_neighbors,
inc = inc_t,
input_type = input_type)
avg_adj_hitrate /= n_periods
return avg_adj_hitrate
[docs]def persistence_score(Ys):
""" Calculate persistence of a sequence of maps as the average Pearson
correlation coefficient between objects' subsequent map movements (i.e., the
first differences of their map positions). The score is averaged across all
objects.
Parameters
----------
Ys : list of ndarrays, each of shape (n_samples, 2)
Map coordinates.
Returns
-------
float
Persistence score, bounded within (-1,1).
Higher positive values indicate higher persistence.
"""
if len(Ys) < 3:
raise ValueError("Persistence can only be computed for a sequence of at least three maps.")
else:
# Define labels for easier data manipulation
labels = [str(i) for i in range(len(Ys[0]))]
df_delta = pd.DataFrame(columns = ['label', 'x', 'y','t'])
def calc_diffs(df):
""" Caluclate first differences in map positions. Leaves NAs
in the first row for each label (at time t = 0).
"""
df[['x_diff', 'y_diff']] = df[['x', 'y']].diff()
df[['x_diff_prev', 'y_diff_prev']] = df[['x_diff', 'y_diff']].shift()
return df
for t in range(len(Ys)):
if Ys[t].shape[1] != 2:
raise ValueError("Persistence metric is only implemented for 2D map coordinates. Will be extended in a future version.")
df_delta_t = pd.DataFrame()
df_delta_t['label'] = labels
df_delta_t['x'] = Ys[t][:,0]
df_delta_t['y'] = Ys[t][:,1]
df_delta_t['t'] = t
df_delta = pd.concat([df_delta, df_delta_t], axis = 0, sort = True)
df_delta.index = range(len(df_delta))
df_delta = df_delta.groupby('label').apply(calc_diffs)
# Drop NAs in first period where no differences can be calculated
df_delta = df_delta.dropna(axis = 0, subset = ['x_diff_prev', 'y_diff_prev'])
if np.sum((df_delta['x_diff_prev'] - df_delta['x_diff'])**2) <= 1e-12:
print("Warning: Map positions completly static, thus Persistence cannot be calculated!")
return np.nan
else:
x_corr = pearsonr(df_delta['x_diff_prev'] , df_delta['x_diff'])[0]
y_corr = pearsonr(df_delta['y_diff_prev'] , df_delta['y_diff'])[0]
return ((x_corr+y_corr)/2)
```