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deprecate adaptive_k and matrix theta, fix tests
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@scottgigante
scottgigante committed Feb 6, 2019
1 parent c2ad629 commit a54cd4b
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Showing 3 changed files with 79 additions and 144 deletions.
28 changes: 28 additions & 0 deletions graphtools/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -65,6 +65,9 @@ def _get_param_names(cls):
return parameters

def set_params(self, **kwargs):
# for k in kwargs:
# raise TypeError("set_params() got an unexpected "
# "keyword argument '{}'".format(k))
return self


Expand Down Expand Up @@ -866,3 +869,28 @@ def interpolate(self, transform, transitions=None, Y=None):
transitions = self.extend_to_data(Y)
Y_transform = transitions.dot(transform)
return Y_transform

def set_params(self, **params):
"""Set parameters on this object
Safe setter method - attributes should not be modified directly as some
changes are not valid.
Valid parameters:
- n_jobs
- verbose
Parameters
----------
params : key-value pairs of parameter name and new values
Returns
-------
self
"""
if 'n_jobs' in params:
self.n_jobs = params['n_jobs']
if 'verbose' in params:
self.verbose = params['verbose']
tasklogger.set_level(self.verbose)
super().set_params(**params)
return self
112 changes: 12 additions & 100 deletions graphtools/graphs.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -2,12 +2,12 @@
from builtins import super
import numpy as np
from sklearn.neighbors import NearestNeighbors
from scipy.spatial.distance import pdist, cdist
from scipy.spatial.distance import squareform
from sklearn.utils.extmath import randomized_svd
from sklearn.preprocessing import normalize
from sklearn.cluster import MiniBatchKMeans
from sklearn.utils.graph import graph_shortest_path
from scipy.spatial.distance import pdist, cdist
from scipy.spatial.distance import squareform
from scipy import sparse
import numbers
import warnings
Expand Down Expand Up @@ -1038,8 +1038,8 @@ class MNNGraph(DataGraph):

def __init__(self, data, sample_idx,
knn=5, beta=1, n_pca=None,
adaptive_k=None,
decay=None,
adaptive_k=None,
bandwidth=None,
distance='euclidean',
thresh=1e-4,
Expand All @@ -1049,14 +1049,12 @@ def __init__(self, data, sample_idx,
self.sample_idx = sample_idx
self.samples, self.n_cells = np.unique(
self.sample_idx, return_counts=True)
self.adaptive_k = adaptive_k
self.knn = knn
self.decay = decay
self.distance = distance
self.bandwidth = bandwidth
self.thresh = thresh
self.n_jobs = n_jobs
self.weighted_knn = self._weight_knn()

if sample_idx is None:
raise ValueError("sample_idx must be given. For a graph without"
Expand All @@ -1068,78 +1066,25 @@ def __init__(self, data, sample_idx,
elif len(self.samples) == 1:
raise ValueError(
"sample_idx must contain more than one unique value")
if adaptive_k is not None:
warnings.warn("`adaptive_k` has been deprecated. Using fixed knn.",
DeprecationWarning)

super().__init__(data, n_pca=n_pca, **kwargs)

def _check_symmetrization(self, kernel_symm, theta):
if kernel_symm == 'theta' and theta is not None and \
not isinstance(theta, numbers.Number):
# matrix theta
try:
theta.shape
except AttributeError:
raise ValueError("theta {} not recognized. "
"Expected a float between 0 and 1 "
"or a [n_batch,n_batch] matrix of "
"floats between 0 and 1".format(theta))
if not np.shape(theta) == (len(self.samples),
len(self.samples)):
raise ValueError(
"Matrix theta must be of shape "
"({}), got ({})".format(
(len(self.samples),
len(self.samples)), theta.shape))
elif np.max(theta) > 1 or np.min(theta) < 0:
raise ValueError(
"Values in matrix theta must be between"
" 0 and 1, got values between {} and {}".format(
np.max(theta), np.min(theta)))
elif np.any(theta != theta.T):
raise ValueError("theta must be a symmetric matrix")
raise TypeError("Expected `theta` as a float. "
"Got {}.".format(type(theta)))
else:
super()._check_symmetrization(kernel_symm, theta)

def _weight_knn(self, sample_size=None):
"""Select adaptive values of knn
Parameters
----------
sample_size : `int` or `None`
Number of cells in the sample in question. Used only for
out-of-sample extension. If `None`, calculates within-sample
knn values.
Returns
-------
knn : array-like or `int`, weighted knn values
"""
if sample_size is None:
# calculate within sample knn values
sample_size = self.n_cells
if self.adaptive_k == 'min':
# the smallest sample has k
knn_weight = self.n_cells / np.min(self.n_cells)
elif self.adaptive_k == 'mean':
# the average sample has k
knn_weight = self.n_cells / np.mean(self.n_cells)
elif self.adaptive_k == 'sqrt':
# the samples are sqrt'd first, then smallest has k
knn_weight = np.sqrt(self.n_cells / np.min(self.n_cells))
elif self.adaptive_k is None:
knn_weight = np.repeat(1, len(self.n_cells))
weighted_knn = np.round(self.knn * knn_weight).astype(np.int32)
if len(weighted_knn) == 1:
weighted_knn = weighted_knn[0]
return weighted_knn

def get_params(self):
"""Get parameters from this object
"""
params = super().get_params()
params.update({'beta': self.beta,
'adaptive_k': self.adaptive_k,
'knn': self.knn,
'decay': self.decay,
'bandwidth': self.bandwidth,
Expand Down Expand Up @@ -1176,9 +1121,6 @@ def set_params(self, **params):
# mnn specific arguments
if 'beta' in params and params['beta'] != self.beta:
raise ValueError("Cannot update beta. Please create a new graph")
if 'adaptive_k' in params and params['adaptive_k'] != self.adaptive_k:
raise ValueError(
"Cannot update adaptive_k. Please create a new graph")

# knn arguments
knn_kernel_args = ['knn', 'decay', 'distance', 'thresh', 'bandwidth']
Expand Down Expand Up @@ -1216,19 +1158,20 @@ def build_kernel(self):
tasklogger.log_debug("subgraph {}: sample {}, "
"n = {}, knn = {}".format(
i, idx, np.sum(self.sample_idx == idx),
self.weighted_knn[i]))
self.knn))
# select data for sample
data = self.data_nu[self.sample_idx == idx]
# build a kNN graph for cells within sample
graph = Graph(data, n_pca=None,
knn=self.weighted_knn[i],
knn=self.knn,
decay=self.decay,
bandwidth=self.bandwidth,
distance=self.distance,
thresh=self.thresh,
verbose=self.verbose,
random_state=self.random_state,
n_jobs=self.n_jobs,
kernel_symm='+',
initialize=True)
self.subgraphs.append(graph) # append to list of subgraphs
tasklogger.log_complete("subgraphs")
Expand All @@ -1251,7 +1194,7 @@ def build_kernel(self):
self.samples[j]))
Kij = Y.build_kernel_to_data(
X.data_nu,
knn=self.weighted_knn[i])
knn=self.knn)
between_batch_norm = np.array(np.sum(Kij, 1)).flatten()
scale = np.minimum(1, within_batch_norm /
between_batch_norm) * self.beta
Expand All @@ -1267,37 +1210,6 @@ def build_kernel(self):
tasklogger.log_complete("MNN kernel")
return K

def symmetrize_kernel(self, K):
if self.kernel_symm == 'theta' and self.theta is not None and \
not isinstance(self.theta, numbers.Number):
# matrix theta
# Theta can be a matrix with specific values transitions for
# each batch. This allows for technical replicates and
# experimental samples to be corrected simultaneously
tasklogger.log_debug("Using theta symmetrization. "
"Theta:\n{}".format(self.theta))
for i, sample_i in enumerate(self.samples):
for j, sample_j in enumerate(self.samples):
if j < i:
continue
Kij = K[np.ix_(self.sample_idx == sample_i,
self.sample_idx == sample_j)]
Kji = K[np.ix_(self.sample_idx == sample_j,
self.sample_idx == sample_i)]
Kij_symm = self.theta[i, j] * \
elementwise_minimum(Kij, Kji.T) + \
(1 - self.theta[i, j]) * \
elementwise_maximum(Kij, Kji.T)
K = set_submatrix(K, self.sample_idx == sample_i,
self.sample_idx == sample_j, Kij_symm)
if not i == j:
K = set_submatrix(K, self.sample_idx == sample_j,
self.sample_idx == sample_i,
Kij_symm.T)
else:
K = super().symmetrize_kernel(K)
return K

def build_kernel_to_data(self, Y, theta=None):
"""Build transition matrix from new data to the graph
Expand Down
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