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Optimize cinema-ot #713

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144 changes: 39 additions & 105 deletions pertpy/tools/_cinemaot.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,9 @@
from __future__ import annotations

from functools import cached_property
from typing import TYPE_CHECKING

import anndata as ad
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
Expand All @@ -12,7 +14,6 @@
from ott.geometry import pointcloud
from ott.problems.linear import linear_problem
from ott.solvers.linear import sinkhorn, sinkhorn_lr
from scanpy.plotting import _utils
from scipy.sparse import issparse
from sklearn.decomposition import FastICA
from sklearn.linear_model import LinearRegression
Expand All @@ -30,9 +31,6 @@
class Cinemaot:
"""CINEMA-OT is a causal framework for perturbation effect analysis to identify individual treatment effects and synergy."""

def __init__(self):
pass

def causaleffect(
self,
adata: AnnData,
Expand Down Expand Up @@ -103,12 +101,8 @@ def causaleffect(
cf = np.array(X_transformed[:, xi < thres], np.float32)
cf1 = np.array(cf[adata.obs[pert_key] == control, :], np.float32)
cf2 = np.array(cf[adata.obs[pert_key] != control, :], np.float32)
if sum(xi < thres) == 1:
sklearn.metrics.pairwise_distances(cf1.reshape(-1, 1), cf2.reshape(-1, 1))
elif sum(xi < thres) == 0:
if sum(xi < thres) == 0:
raise ValueError("No confounder components identified. Please try a higher threshold.")
else:
sklearn.metrics.pairwise_distances(cf1, cf2)

e = smoothness * sum(xi < thres)
geom = pointcloud.PointCloud(cf1, cf2, epsilon=e, batch_size=batch_size)
Expand Down Expand Up @@ -189,7 +183,7 @@ def causaleffect(
ot_matrix / np.sum(ot_matrix, axis=1)[:, None], adata.obsm[cf_rep][adata.obs[pert_key] == control, :]
)

TE = sc.AnnData(np.array(te2), obs=adata[adata.obs[pert_key] != control, :].obs.copy(), var=adata.var.copy())
TE = ad.AnnData(np.array(te2), obs=adata[adata.obs[pert_key] != control, :].obs.copy(), var=adata.var.copy())
TE.obsm["X_embedding"] = embedding

if return_matching:
Expand Down Expand Up @@ -346,7 +340,7 @@ def generate_pseudobulk(
df = df.groupby(label_list).sum()
new_index = df.index.map(lambda x: "_".join(map(str, x)))
df_ = df.set_index(new_index)
adata_pb = sc.AnnData(df_)
adata_pb = ad.AnnData(df_)
adata_pb.obs = pd.DataFrame(
df.index.to_frame().values,
index=adata_pb.obs_names,
Expand Down Expand Up @@ -579,7 +573,7 @@ def synergy(
**kwargs,
)
ot0 = de0.obsm["ot"]
syn = sc.AnnData(
syn = ad.AnnData(
np.array(-((ot0 / np.sum(ot0, axis=1)[:, None]) @ de2.X - de1.X)), obs=de1.obs.copy(), var=de1.var.copy()
)
syn.obsm["X_embedding"] = (ot0 / np.sum(ot0, axis=1)[:, None]) @ de2.obsm["X_embedding"] - de1.obsm[
Expand Down Expand Up @@ -723,139 +717,79 @@ def plot_vis_matching(


class Xi:
"""
A fast implementation of cross-rank dependence metric used in CINEMA-OT.

"""

def __init__(self, x, y):
self.x = x
self.y = y
self.x = np.asarray(x)
self.y = np.asarray(y)
self._sample_size = len(x)

@property
def sample_size(self):
return len(self.x)
return self._sample_size

@property
@cached_property
def x_ordered_rank(self):
# PI is the rank vector for x, with ties broken at random
# Not mine: source (https://stackoverflow.com/a/47430384/1628971)
# random shuffling of the data - reason to use random.choice is that
# pd.sample(frac=1) uses the same randomizing algorithm
len_x = len(self.x)
rng = np.random.default_rng()
randomized_indices = rng.choice(np.arange(len_x), len_x, replace=False)
randomized = [self.x[idx] for idx in randomized_indices]
# same as pandas rank method 'first'
randomized_indices = rng.permutation(self._sample_size)
randomized = self.x[randomized_indices]
rankdata = ss.rankdata(randomized, method="ordinal")
# Reindexing based on pairs of indices before and after
unrandomized = [rankdata[j] for i, j in sorted(zip(randomized_indices, range(len_x), strict=False))]
return unrandomized
return rankdata[np.argsort(randomized_indices)]

@property
@cached_property
def y_rank_max(self):
# f[i] is number of j s.t. y[j] <= y[i], divided by n.
return ss.rankdata(self.y, method="max") / self.sample_size
return ss.rankdata(self.y, method="max") / self._sample_size

@property
@cached_property
def g(self):
# g[i] is number of j s.t. y[j] >= y[i], divided by n.
return ss.rankdata([-i for i in self.y], method="max") / self.sample_size
return ss.rankdata(-self.y, method="max") / self._sample_size

@property
@cached_property
def x_ordered(self):
# order of the x's, ties broken at random.
return np.argsort(self.x_ordered_rank)

@property
@cached_property
def x_rank_max_ordered(self):
x_ordered_result = self.x_ordered
y_rank_max_result = self.y_rank_max
# Rearrange f according to ord.
return [y_rank_max_result[i] for i in x_ordered_result]
return self.y_rank_max[self.x_ordered]

@property
@cached_property
def mean_absolute(self):
x1 = self.x_rank_max_ordered[0 : (self.sample_size - 1)]
x2 = self.x_rank_max_ordered[1 : self.sample_size]

return (
np.mean(
np.abs(
[
x - y
for x, y in zip(
x1,
x2,
strict=False,
)
]
)
)
* (self.sample_size - 1)
/ (2 * self.sample_size)
)
x1 = self.x_rank_max_ordered[:-1]
x2 = self.x_rank_max_ordered[1:]
return np.mean(np.abs(x1 - x2)) * (self._sample_size - 1) / (2 * self._sample_size)

@property
@cached_property
def inverse_g_mean(self):
gvalue = self.g
return np.mean(gvalue * (1 - gvalue))
return np.mean(self.g * (1 - self.g))

@property
@cached_property
def correlation(self):
"""xi correlation"""
return 1 - self.mean_absolute / self.inverse_g_mean

@classmethod
def xi(cls, x, y):
return cls(x, y)

def pval_asymptotic(self, ties: bool = False):
"""Returns p values of the correlation.

Args:
ties: boolean
If ties is true, the algorithm assumes that the data has ties
and employs the more elaborated theory for calculating
the P-value. Otherwise, it uses the simpler theory. There is
no harm in setting tiles True, even if there are no ties.

Returns:
p value
"""
# If there are no ties, return xi and theoretical P-value:
def pval_asymptotic(self, ties=False):
if ties:
return 1 - ss.norm.cdf(np.sqrt(self.sample_size) * self.correlation / np.sqrt(2 / 5))

# If there are ties, and the theoretical method is to be used for calculation P-values:
# The following steps calculate the theoretical variance in the presence of ties:
sorted_ordered_x_rank = sorted(self.x_rank_max_ordered)

ind = [i + 1 for i in range(self.sample_size)]
ind2 = [2 * self.sample_size - 2 * ind[i - 1] + 1 for i in ind]
return 1 - ss.norm.cdf(np.sqrt(self._sample_size) * self.correlation / np.sqrt(0.4))

a = np.mean([i * j * j for i, j in zip(ind2, sorted_ordered_x_rank, strict=False)]) / self.sample_size
sorted_ordered_x_rank = np.sort(self.x_rank_max_ordered)
ind = np.arange(1, self._sample_size + 1)
ind2 = 2 * self._sample_size - 2 * ind + 1

c = np.mean([i * j for i, j in zip(ind2, sorted_ordered_x_rank, strict=False)]) / self.sample_size
a = np.mean(ind2 * sorted_ordered_x_rank * sorted_ordered_x_rank) / self._sample_size
c = np.mean(ind2 * sorted_ordered_x_rank) / self._sample_size

cq = np.cumsum(sorted_ordered_x_rank)
m = (cq + (self._sample_size - ind) * sorted_ordered_x_rank) / self._sample_size

m = [
(i + (self.sample_size - j) * k) / self.sample_size
for i, j, k in zip(cq, ind, sorted_ordered_x_rank, strict=False)
]

b = np.mean([np.square(i) for i in m])
b = np.mean(np.square(m))
v = (a - 2 * b + np.square(c)) / np.square(self.inverse_g_mean)

return 1 - ss.norm.cdf(np.sqrt(self.sample_size) * self.correlation / np.sqrt(v))
return 1 - ss.norm.cdf(np.sqrt(self._sample_size) * self.correlation / np.sqrt(v))


class SinkhornKnopp:
"""
An simple implementation of Sinkhorn iteration used in the biwhitening approach.

"""
"""An simple implementation of Sinkhorn iteration used in the biwhitening approach."""

def __init__(self, max_iter: float = 1000, setr: int = 0, setc: float = 0, epsilon: float = 1e-3):
if max_iter < 0:
Expand Down
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