# Author: Leland McInnes <leland.mcinnes@gmail.com>
#
# License: BSD 3 clause
from __future__ import print_function
import locale
from warnings import warn
import time
from scipy.optimize import curve_fit
from sklearn.base import BaseEstimator
from sklearn.utils import check_random_state, check_array
from sklearn.metrics import pairwise_distances
from sklearn.preprocessing import normalize
from sklearn.neighbors import KDTree
try:
import joblib
except ImportError:
# sklearn.externals.joblib is deprecated in 0.21, will be removed in 0.23
from sklearn.externals import joblib
import numpy as np
import scipy.sparse
import scipy.sparse.csgraph
import numba
import umap.distances as dist
import umap.sparse as sparse
import umap.sparse_nndescent as sparse_nn
from umap.utils import (
tau_rand_int,
deheap_sort,
submatrix,
ts,
csr_unique,
fast_knn_indices,
)
from umap.rp_tree import rptree_leaf_array, make_forest
from umap.nndescent import (
# make_nn_descent,
# make_initialisations,
# make_initialized_nnd_search,
nn_descent,
initialized_nnd_search,
initialise_search,
)
from umap.rp_tree import rptree_leaf_array, make_forest
from umap.spectral import spectral_layout
from umap.utils import deheap_sort, submatrix
from umap.layouts import (
optimize_layout_euclidean,
optimize_layout_generic,
optimize_layout_inverse,
)
try:
# Use pynndescent, if installed (python 3 only)
from pynndescent import NNDescent
_HAVE_PYNNDESCENT = True
except ImportError:
_HAVE_PYNNDESCENT = False
locale.setlocale(locale.LC_NUMERIC, "C")
INT32_MIN = np.iinfo(np.int32).min + 1
INT32_MAX = np.iinfo(np.int32).max - 1
SMOOTH_K_TOLERANCE = 1e-5
MIN_K_DIST_SCALE = 1e-3
NPY_INFINITY = np.inf
def breadth_first_search(adjmat, start, min_vertices):
explored = []
queue = [start]
levels = {}
levels[start] = 0
max_level = np.inf
visited = [start]
while queue:
node = queue.pop(0)
explored.append(node)
if max_level == np.inf and len(explored) > min_vertices:
max_level = max(levels.values())
if levels[node] + 1 < max_level:
neighbors = adjmat[node].indices
for neighbour in neighbors:
if neighbour not in visited:
queue.append(neighbour)
visited.append(neighbour)
levels[neighbour] = levels[node] + 1
return np.array(explored)
[docs]@numba.njit(
locals={
"psum": numba.types.float32,
"lo": numba.types.float32,
"mid": numba.types.float32,
"hi": numba.types.float32,
},
fastmath=True,
) # benchmarking `parallel=True` shows it to *decrease* performance
def smooth_knn_dist(distances, k, n_iter=64, local_connectivity=1.0, bandwidth=1.0):
"""Compute a continuous version of the distance to the kth nearest
neighbor. That is, this is similar to knn-distance but allows continuous
k values rather than requiring an integral k. In essence we are simply
computing the distance such that the cardinality of fuzzy set we generate
is k.
Parameters
----------
distances: array of shape (n_samples, n_neighbors)
Distances to nearest neighbors for each samples. Each row should be a
sorted list of distances to a given samples nearest neighbors.
k: float
The number of nearest neighbors to approximate for.
n_iter: int (optional, default 64)
We need to binary search for the correct distance value. This is the
max number of iterations to use in such a search.
local_connectivity: int (optional, default 1)
The local connectivity required -- i.e. the number of nearest
neighbors that should be assumed to be connected at a local level.
The higher this value the more connected the manifold becomes
locally. In practice this should be not more than the local intrinsic
dimension of the manifold.
bandwidth: float (optional, default 1)
The target bandwidth of the kernel, larger values will produce
larger return values.
Returns
-------
knn_dist: array of shape (n_samples,)
The distance to kth nearest neighbor, as suitably approximated.
nn_dist: array of shape (n_samples,)
The distance to the 1st nearest neighbor for each point.
"""
target = np.log2(k) * bandwidth
rho = np.zeros(distances.shape[0], dtype=np.float32)
result = np.zeros(distances.shape[0], dtype=np.float32)
mean_distances = np.mean(distances)
for i in range(distances.shape[0]):
lo = 0.0
hi = NPY_INFINITY
mid = 1.0
# TODO: This is very inefficient, but will do for now. FIXME
ith_distances = distances[i]
non_zero_dists = ith_distances[ith_distances > 0.0]
if non_zero_dists.shape[0] >= local_connectivity:
index = int(np.floor(local_connectivity))
interpolation = local_connectivity - index
if index > 0:
rho[i] = non_zero_dists[index - 1]
if interpolation > SMOOTH_K_TOLERANCE:
rho[i] += interpolation * (
non_zero_dists[index] - non_zero_dists[index - 1]
)
else:
rho[i] = interpolation * non_zero_dists[0]
elif non_zero_dists.shape[0] > 0:
rho[i] = np.max(non_zero_dists)
for n in range(n_iter):
psum = 0.0
for j in range(1, distances.shape[1]):
d = distances[i, j] - rho[i]
if d > 0:
psum += np.exp(-(d / mid))
else:
psum += 1.0
if np.fabs(psum - target) < SMOOTH_K_TOLERANCE:
break
if psum > target:
hi = mid
mid = (lo + hi) / 2.0
else:
lo = mid
if hi == NPY_INFINITY:
mid *= 2
else:
mid = (lo + hi) / 2.0
result[i] = mid
# TODO: This is very inefficient, but will do for now. FIXME
if rho[i] > 0.0:
mean_ith_distances = np.mean(ith_distances)
if result[i] < MIN_K_DIST_SCALE * mean_ith_distances:
result[i] = MIN_K_DIST_SCALE * mean_ith_distances
else:
if result[i] < MIN_K_DIST_SCALE * mean_distances:
result[i] = MIN_K_DIST_SCALE * mean_distances
return result, rho
[docs]def nearest_neighbors(
X,
n_neighbors,
metric,
metric_kwds,
angular,
random_state,
low_memory=False,
use_pynndescent=True,
verbose=False,
):
"""Compute the ``n_neighbors`` nearest points for each data point in ``X``
under ``metric``. This may be exact, but more likely is approximated via
nearest neighbor descent.
Parameters
----------
X: array of shape (n_samples, n_features)
The input data to compute the k-neighbor graph of.
n_neighbors: int
The number of nearest neighbors to compute for each sample in ``X``.
metric: string or callable
The metric to use for the computation.
metric_kwds: dict
Any arguments to pass to the metric computation function.
angular: bool
Whether to use angular rp trees in NN approximation.
random_state: np.random state
The random state to use for approximate NN computations.
low_memory: bool (optional, default False)
Whether to pursue lower memory NNdescent.
verbose: bool (optional, default False)
Whether to print status data during the computation.
Returns
-------
knn_indices: array of shape (n_samples, n_neighbors)
The indices on the ``n_neighbors`` closest points in the dataset.
knn_dists: array of shape (n_samples, n_neighbors)
The distances to the ``n_neighbors`` closest points in the dataset.
rp_forest: list of trees
The random projection forest used for searching (if used, None otherwise)
"""
if verbose:
print(ts(), "Finding Nearest Neighbors")
if metric == "precomputed":
# Note that this does not support sparse distance matrices yet ...
# Compute indices of n nearest neighbors
knn_indices = fast_knn_indices(X, n_neighbors)
# knn_indices = np.argsort(X)[:, :n_neighbors]
# Compute the nearest neighbor distances
# (equivalent to np.sort(X)[:,:n_neighbors])
knn_dists = X[np.arange(X.shape[0])[:, None], knn_indices].copy()
rp_forest = []
else:
# TODO: Hacked values for now
n_trees = 5 + int(round((X.shape[0]) ** 0.5 / 20.0))
n_iters = max(5, int(round(np.log2(X.shape[0]))))
if _HAVE_PYNNDESCENT and use_pynndescent:
nnd = NNDescent(
X,
n_neighbors=n_neighbors,
metric=metric,
metric_kwds=metric_kwds,
random_state=random_state,
n_trees=n_trees,
n_iters=n_iters,
max_candidates=60,
low_memory=low_memory,
verbose=verbose,
)
knn_indices, knn_dists = nnd._neighbor_graph
rp_forest = nnd
else:
# Otherwise fall back to nn descent in umap
if callable(metric):
_distance_func = metric
elif metric in dist.named_distances:
_distance_func = dist.named_distances[metric]
else:
raise ValueError(
"Metric is neither callable, " + "nor a recognised string"
)
if metric in (
"cosine",
"correlation",
"dice",
"jaccard",
"ll_dirichlet",
"hellinger",
):
angular = True
rng_state = random_state.randint(INT32_MIN, INT32_MAX, 3).astype(np.int64)
if scipy.sparse.isspmatrix_csr(X):
if metric in sparse.sparse_named_distances:
_distance_func = sparse.sparse_named_distances[metric]
if metric in sparse.sparse_need_n_features:
metric_kwds["n_features"] = X.shape[1]
elif callable(metric):
_distance_func = metric
else:
raise ValueError(
"Metric {} not supported for sparse " + "data".format(metric)
)
# Create a partial function for distances with arguments
if len(metric_kwds) > 0:
dist_args = tuple(metric_kwds.values())
@numba.njit()
def _partial_dist_func(ind1, data1, ind2, data2):
return _distance_func(ind1, data1, ind2, data2, *dist_args)
distance_func = _partial_dist_func
else:
distance_func = _distance_func
# metric_nn_descent = sparse.make_sparse_nn_descent(
# distance_func, tuple(metric_kwds.values())
# )
if verbose:
print(ts(), "Building RP forest with", str(n_trees), "trees")
rp_forest = make_forest(X, n_neighbors, n_trees, rng_state, angular)
leaf_array = rptree_leaf_array(rp_forest)
if verbose:
print(ts(), "NN descent for", str(n_iters), "iterations")
knn_indices, knn_dists = sparse_nn.sparse_nn_descent(
X.indices,
X.indptr,
X.data,
X.shape[0],
n_neighbors,
rng_state,
max_candidates=60,
sparse_dist=distance_func,
low_memory=low_memory,
rp_tree_init=True,
leaf_array=leaf_array,
n_iters=n_iters,
verbose=verbose,
)
else:
# metric_nn_descent = make_nn_descent(
# distance_func, tuple(metric_kwds.values())
# )
if len(metric_kwds) > 0:
dist_args = tuple(metric_kwds.values())
@numba.njit()
def _partial_dist_func(x, y):
return _distance_func(x, y, *dist_args)
distance_func = _partial_dist_func
else:
distance_func = _distance_func
if verbose:
print(ts(), "Building RP forest with", str(n_trees), "trees")
rp_forest = make_forest(X, n_neighbors, n_trees, rng_state, angular)
leaf_array = rptree_leaf_array(rp_forest)
if verbose:
print(ts(), "NN descent for", str(n_iters), "iterations")
knn_indices, knn_dists = nn_descent(
X,
n_neighbors,
rng_state,
max_candidates=60,
dist=distance_func,
low_memory=low_memory,
rp_tree_init=True,
leaf_array=leaf_array,
n_iters=n_iters,
verbose=verbose,
)
if np.any(knn_indices < 0):
warn(
"Failed to correctly find n_neighbors for some samples."
"Results may be less than ideal. Try re-running with"
"different parameters."
)
if verbose:
print(ts(), "Finished Nearest Neighbor Search")
return knn_indices, knn_dists, rp_forest
[docs]@numba.njit(
locals={
"knn_dists": numba.types.float32[:, ::1],
"sigmas": numba.types.float32[::1],
"rhos": numba.types.float32[::1],
"val": numba.types.float32,
},
parallel=True,
fastmath=True,
)
def compute_membership_strengths(knn_indices, knn_dists, sigmas, rhos):
"""Construct the membership strength data for the 1-skeleton of each local
fuzzy simplicial set -- this is formed as a sparse matrix where each row is
a local fuzzy simplicial set, with a membership strength for the
1-simplex to each other data point.
Parameters
----------
knn_indices: array of shape (n_samples, n_neighbors)
The indices on the ``n_neighbors`` closest points in the dataset.
knn_dists: array of shape (n_samples, n_neighbors)
The distances to the ``n_neighbors`` closest points in the dataset.
sigmas: array of shape(n_samples)
The normalization factor derived from the metric tensor approximation.
rhos: array of shape(n_samples)
The local connectivity adjustment.
Returns
-------
rows: array of shape (n_samples * n_neighbors)
Row data for the resulting sparse matrix (coo format)
cols: array of shape (n_samples * n_neighbors)
Column data for the resulting sparse matrix (coo format)
vals: array of shape (n_samples * n_neighbors)
Entries for the resulting sparse matrix (coo format)
"""
n_samples = knn_indices.shape[0]
n_neighbors = knn_indices.shape[1]
rows = np.zeros(knn_indices.size, dtype=np.int64)
cols = np.zeros(knn_indices.size, dtype=np.int64)
vals = np.zeros(knn_indices.size, dtype=np.float64)
for i in range(n_samples):
for j in range(n_neighbors):
if knn_indices[i, j] == -1:
continue # We didn't get the full knn for i
if knn_indices[i, j] == i:
val = 0.0
elif knn_dists[i, j] - rhos[i] <= 0.0 or sigmas[i] == 0.0:
val = 1.0
else:
val = np.exp(-((knn_dists[i, j] - rhos[i]) / (sigmas[i])))
rows[i * n_neighbors + j] = i
cols[i * n_neighbors + j] = knn_indices[i, j]
vals[i * n_neighbors + j] = val
return rows, cols, vals
[docs]def fuzzy_simplicial_set(
X,
n_neighbors,
random_state,
metric,
metric_kwds={},
knn_indices=None,
knn_dists=None,
angular=False,
set_op_mix_ratio=1.0,
local_connectivity=1.0,
apply_set_operations=True,
verbose=False,
):
"""Given a set of data X, a neighborhood size, and a measure of distance
compute the fuzzy simplicial set (here represented as a fuzzy graph in
the form of a sparse matrix) associated to the data. This is done by
locally approximating geodesic distance at each point, creating a fuzzy
simplicial set for each such point, and then combining all the local
fuzzy simplicial sets into a global one via a fuzzy union.
Parameters
----------
X: array of shape (n_samples, n_features)
The data to be modelled as a fuzzy simplicial set.
n_neighbors: int
The number of neighbors to use to approximate geodesic distance.
Larger numbers induce more global estimates of the manifold that can
miss finer detail, while smaller values will focus on fine manifold
structure to the detriment of the larger picture.
random_state: numpy RandomState or equivalent
A state capable being used as a numpy random state.
metric: string or function (optional, default 'euclidean')
The metric to use to compute distances in high dimensional space.
If a string is passed it must match a valid predefined metric. If
a general metric is required a function that takes two 1d arrays and
returns a float can be provided. For performance purposes it is
required that this be a numba jit'd function. Valid string metrics
include:
* euclidean (or l2)
* manhattan (or l1)
* cityblock
* braycurtis
* canberra
* chebyshev
* correlation
* cosine
* dice
* hamming
* jaccard
* kulsinski
* ll_dirichlet
* mahalanobis
* matching
* minkowski
* rogerstanimoto
* russellrao
* seuclidean
* sokalmichener
* sokalsneath
* sqeuclidean
* yule
* wminkowski
Metrics that take arguments (such as minkowski, mahalanobis etc.)
can have arguments passed via the metric_kwds dictionary. At this
time care must be taken and dictionary elements must be ordered
appropriately; this will hopefully be fixed in the future.
metric_kwds: dict (optional, default {})
Arguments to pass on to the metric, such as the ``p`` value for
Minkowski distance.
knn_indices: array of shape (n_samples, n_neighbors) (optional)
If the k-nearest neighbors of each point has already been calculated
you can pass them in here to save computation time. This should be
an array with the indices of the k-nearest neighbors as a row for
each data point.
knn_dists: array of shape (n_samples, n_neighbors) (optional)
If the k-nearest neighbors of each point has already been calculated
you can pass them in here to save computation time. This should be
an array with the distances of the k-nearest neighbors as a row for
each data point.
angular: bool (optional, default False)
Whether to use angular/cosine distance for the random projection
forest for seeding NN-descent to determine approximate nearest
neighbors.
set_op_mix_ratio: float (optional, default 1.0)
Interpolate between (fuzzy) union and intersection as the set operation
used to combine local fuzzy simplicial sets to obtain a global fuzzy
simplicial sets. Both fuzzy set operations use the product t-norm.
The value of this parameter should be between 0.0 and 1.0; a value of
1.0 will use a pure fuzzy union, while 0.0 will use a pure fuzzy
intersection.
local_connectivity: int (optional, default 1)
The local connectivity required -- i.e. the number of nearest
neighbors that should be assumed to be connected at a local level.
The higher this value the more connected the manifold becomes
locally. In practice this should be not more than the local intrinsic
dimension of the manifold.
verbose: bool (optional, default False)
Whether to report information on the current progress of the algorithm.
Returns
-------
fuzzy_simplicial_set: coo_matrix
A fuzzy simplicial set represented as a sparse matrix. The (i,
j) entry of the matrix represents the membership strength of the
1-simplex between the ith and jth sample points.
"""
if knn_indices is None or knn_dists is None:
knn_indices, knn_dists, _ = nearest_neighbors(
X, n_neighbors, metric, metric_kwds, angular, random_state, verbose=verbose
)
knn_dists = knn_dists.astype(np.float32)
sigmas, rhos = smooth_knn_dist(
knn_dists, float(n_neighbors), local_connectivity=float(local_connectivity),
)
rows, cols, vals = compute_membership_strengths(
knn_indices, knn_dists, sigmas, rhos
)
result = scipy.sparse.coo_matrix(
(vals, (rows, cols)), shape=(X.shape[0], X.shape[0])
)
result.eliminate_zeros()
if apply_set_operations:
transpose = result.transpose()
prod_matrix = result.multiply(transpose)
result = (
set_op_mix_ratio * (result + transpose - prod_matrix)
+ (1.0 - set_op_mix_ratio) * prod_matrix
)
result.eliminate_zeros()
return result, sigmas, rhos
[docs]@numba.njit()
def fast_intersection(rows, cols, values, target, unknown_dist=1.0, far_dist=5.0):
"""Under the assumption of categorical distance for the intersecting
simplicial set perform a fast intersection.
Parameters
----------
rows: array
An array of the row of each non-zero in the sparse matrix
representation.
cols: array
An array of the column of each non-zero in the sparse matrix
representation.
values: array
An array of the value of each non-zero in the sparse matrix
representation.
target: array of shape (n_samples)
The categorical labels to use in the intersection.
unknown_dist: float (optional, default 1.0)
The distance an unknown label (-1) is assumed to be from any point.
far_dist float (optional, default 5.0)
The distance between unmatched labels.
Returns
-------
None
"""
for nz in range(rows.shape[0]):
i = rows[nz]
j = cols[nz]
if (target[i] == -1) or (target[j] == -1):
values[nz] *= np.exp(-unknown_dist)
elif target[i] != target[j]:
values[nz] *= np.exp(-far_dist)
return
[docs]@numba.jit()
def fast_metric_intersection(
rows, cols, values, discrete_space, metric, metric_args, scale
):
"""Under the assumption of categorical distance for the intersecting
simplicial set perform a fast intersection.
Parameters
----------
rows: array
An array of the row of each non-zero in the sparse matrix
representation.
cols: array
An array of the column of each non-zero in the sparse matrix
representation.
values: array of shape
An array of the values of each non-zero in the sparse matrix
representation.
discrete_space: array of shape (n_samples, n_features)
The vectors of categorical labels to use in the intersection.
metric: numba function
The function used to calculate distance over the target array.
scale: float
A scaling to apply to the metric.
Returns
-------
None
"""
for nz in range(rows.shape[0]):
i = rows[nz]
j = cols[nz]
dist = metric(discrete_space[i], discrete_space[j], *metric_args)
values[nz] *= np.exp(-(scale * dist))
return
@numba.njit()
def reprocess_row(probabilities, k=15, n_iters=32):
target = np.log2(k)
lo = 0.0
hi = NPY_INFINITY
mid = 1.0
for n in range(n_iters):
psum = 0.0
for j in range(probabilities.shape[0]):
psum += pow(probabilities[j], mid)
if np.fabs(psum - target) < SMOOTH_K_TOLERANCE:
break
if psum < target:
hi = mid
mid = (lo + hi) / 2.0
else:
lo = mid
if hi == NPY_INFINITY:
mid *= 2
else:
mid = (lo + hi) / 2.0
return np.power(probabilities, mid)
@numba.njit()
def reset_local_metrics(simplicial_set_indptr, simplicial_set_data):
for i in range(simplicial_set_indptr.shape[0] - 1):
simplicial_set_data[
simplicial_set_indptr[i] : simplicial_set_indptr[i + 1]
] = reprocess_row(
simplicial_set_data[simplicial_set_indptr[i] : simplicial_set_indptr[i + 1]]
)
return
[docs]def reset_local_connectivity(simplicial_set, reset_local_metric=False):
"""Reset the local connectivity requirement -- each data sample should
have complete confidence in at least one 1-simplex in the simplicial set.
We can enforce this by locally rescaling confidences, and then remerging the
different local simplicial sets together.
Parameters
----------
simplicial_set: sparse matrix
The simplicial set for which to recalculate with respect to local
connectivity.
Returns
-------
simplicial_set: sparse_matrix
The recalculated simplicial set, now with the local connectivity
assumption restored.
"""
simplicial_set = normalize(simplicial_set, norm="max")
if reset_local_metric:
simplicial_set = simplicial_set.tocsr()
reset_local_metrics(simplicial_set.indptr, simplicial_set.data)
simplicial_set = simplicial_set.tocoo()
transpose = simplicial_set.transpose()
prod_matrix = simplicial_set.multiply(transpose)
simplicial_set = simplicial_set + transpose - prod_matrix
simplicial_set.eliminate_zeros()
return simplicial_set
[docs]def discrete_metric_simplicial_set_intersection(
simplicial_set,
discrete_space,
unknown_dist=1.0,
far_dist=5.0,
metric=None,
metric_kws={},
metric_scale=1.0,
):
"""Combine a fuzzy simplicial set with another fuzzy simplicial set
generated from discrete metric data using discrete distances. The target
data is assumed to be categorical label data (a vector of labels),
and this will update the fuzzy simplicial set to respect that label data.
TODO: optional category cardinality based weighting of distance
Parameters
----------
simplicial_set: sparse matrix
The input fuzzy simplicial set.
discrete_space: array of shape (n_samples)
The categorical labels to use in the intersection.
unknown_dist: float (optional, default 1.0)
The distance an unknown label (-1) is assumed to be from any point.
far_dist: float (optional, default 5.0)
The distance between unmatched labels.
metric: str (optional, default None)
If not None, then use this metric to determine the
distance between values.
metric_scale: float (optional, default 1.0)
If using a custom metric scale the distance values by
this value -- this controls the weighting of the
intersection. Larger values weight more toward target.
Returns
-------
simplicial_set: sparse matrix
The resulting intersected fuzzy simplicial set.
"""
simplicial_set = simplicial_set.tocoo()
if metric is not None:
# We presume target is now a 2d array, with each row being a
# vector of target info
if metric in dist.named_distances:
metric_func = dist.named_distances[metric]
else:
raise ValueError("Discrete intersection metric is not recognized")
fast_metric_intersection(
simplicial_set.row,
simplicial_set.col,
simplicial_set.data,
discrete_space,
metric_func,
tuple(metric_kws.values()),
metric_scale,
)
else:
fast_intersection(
simplicial_set.row,
simplicial_set.col,
simplicial_set.data,
discrete_space,
unknown_dist,
far_dist,
)
simplicial_set.eliminate_zeros()
return reset_local_connectivity(simplicial_set)
def general_simplicial_set_intersection(simplicial_set1, simplicial_set2, weight):
result = (simplicial_set1 + simplicial_set2).tocoo()
left = simplicial_set1.tocsr()
right = simplicial_set2.tocsr()
sparse.general_sset_intersection(
left.indptr,
left.indices,
left.data,
right.indptr,
right.indices,
right.data,
result.row,
result.col,
result.data,
weight,
)
return result
[docs]def make_epochs_per_sample(weights, n_epochs):
"""Given a set of weights and number of epochs generate the number of
epochs per sample for each weight.
Parameters
----------
weights: array of shape (n_1_simplices)
The weights ofhow much we wish to sample each 1-simplex.
n_epochs: int
The total number of epochs we want to train for.
Returns
-------
An array of number of epochs per sample, one for each 1-simplex.
"""
result = -1.0 * np.ones(weights.shape[0], dtype=np.float64)
n_samples = n_epochs * (weights / weights.max())
result[n_samples > 0] = float(n_epochs) / n_samples[n_samples > 0]
return result
[docs]def simplicial_set_embedding(
data,
graph,
n_components,
initial_alpha,
a,
b,
gamma,
negative_sample_rate,
n_epochs,
init,
random_state,
metric,
metric_kwds,
output_metric=dist.named_distances_with_gradients["euclidean"],
output_metric_kwds={},
euclidean_output=True,
parallel=False,
verbose=False,
):
"""Perform a fuzzy simplicial set embedding, using a specified
initialisation method and then minimizing the fuzzy set cross entropy
between the 1-skeletons of the high and low dimensional fuzzy simplicial
sets.
Parameters
----------
data: array of shape (n_samples, n_features)
The source data to be embedded by UMAP.
graph: sparse matrix
The 1-skeleton of the high dimensional fuzzy simplicial set as
represented by a graph for which we require a sparse matrix for the
(weighted) adjacency matrix.
n_components: int
The dimensionality of the euclidean space into which to embed the data.
initial_alpha: float
Initial learning rate for the SGD.
a: float
Parameter of differentiable approximation of right adjoint functor
b: float
Parameter of differentiable approximation of right adjoint functor
gamma: float
Weight to apply to negative samples.
negative_sample_rate: int (optional, default 5)
The number of negative samples to select per positive sample
in the optimization process. Increasing this value will result
in greater repulsive force being applied, greater optimization
cost, but slightly more accuracy.
n_epochs: int (optional, default 0)
The number of training epochs to be used in optimizing the
low dimensional embedding. Larger values result in more accurate
embeddings. If 0 is specified a value will be selected based on
the size of the input dataset (200 for large datasets, 500 for small).
init: string
How to initialize the low dimensional embedding. Options are:
* 'spectral': use a spectral embedding of the fuzzy 1-skeleton
* 'random': assign initial embedding positions at random.
* A numpy array of initial embedding positions.
random_state: numpy RandomState or equivalent
A state capable being used as a numpy random state.
metric: string or callable
The metric used to measure distance in high dimensional space; used if
multiple connected components need to be layed out.
metric_kwds: dict
Key word arguments to be passed to the metric function; used if
multiple connected components need to be layed out.
output_metric: function
Function returning the distance between two points in embedding space and
the gradient of the distance wrt the first argument.
output_metric_kwds: dict
Key word arguments to be passed to the output_metric function.
euclidean_output: bool
Whether to use the faster code specialised for euclidean output metrics
parallel: bool (optional, default False)
Whether to run the computation using numba parallel.
Running in parallel is non-deterministic, and is not used
if a random seed has been set, to ensure reproducibility.
verbose: bool (optional, default False)
Whether to report information on the current progress of the algorithm.
Returns
-------
embedding: array of shape (n_samples, n_components)
The optimized of ``graph`` into an ``n_components`` dimensional
euclidean space.
"""
graph = graph.tocoo()
graph.sum_duplicates()
n_vertices = graph.shape[1]
if n_epochs <= 0:
# For smaller datasets we can use more epochs
if graph.shape[0] <= 10000:
n_epochs = 500
else:
n_epochs = 200
graph.data[graph.data < (graph.data.max() / float(n_epochs))] = 0.0
graph.eliminate_zeros()
if isinstance(init, str) and init == "random":
embedding = random_state.uniform(
low=-10.0, high=10.0, size=(graph.shape[0], n_components)
).astype(np.float32)
elif isinstance(init, str) and init == "spectral":
# We add a little noise to avoid local minima for optimization to come
initialisation = spectral_layout(
data,
graph,
n_components,
random_state,
metric=metric,
metric_kwds=metric_kwds,
)
expansion = 10.0 / np.abs(initialisation).max()
embedding = (initialisation * expansion).astype(
np.float32
) + random_state.normal(
scale=0.0001, size=[graph.shape[0], n_components]
).astype(
np.float32
)
else:
init_data = np.array(init)
if len(init_data.shape) == 2:
if np.unique(init_data, axis=0).shape[0] < init_data.shape[0]:
tree = KDTree(init_data)
dist, ind = tree.query(init_data, k=2)
nndist = np.mean(dist[:, 1])
embedding = init_data + random_state.normal(
scale=0.001 * nndist, size=init_data.shape
).astype(np.float32)
else:
embedding = init_data
epochs_per_sample = make_epochs_per_sample(graph.data, n_epochs)
head = graph.row
tail = graph.col
weight = graph.data
rng_state = random_state.randint(INT32_MIN, INT32_MAX, 3).astype(np.int64)
embedding = (
10.0
* (embedding - np.min(embedding, 0))
/ (np.max(embedding, 0) - np.min(embedding, 0))
).astype(np.float32, order="C")
if euclidean_output:
embedding = optimize_layout_euclidean(
embedding,
embedding,
head,
tail,
n_epochs,
n_vertices,
epochs_per_sample,
a,
b,
rng_state,
gamma,
initial_alpha,
negative_sample_rate,
parallel=parallel,
verbose=verbose,
)
else:
embedding = optimize_layout_generic(
embedding,
embedding,
head,
tail,
n_epochs,
n_vertices,
epochs_per_sample,
a,
b,
rng_state,
gamma,
initial_alpha,
negative_sample_rate,
output_metric,
tuple(output_metric_kwds.values()),
verbose=verbose,
)
return embedding
[docs]def find_ab_params(spread, min_dist):
"""Fit a, b params for the differentiable curve used in lower
dimensional fuzzy simplicial complex construction. We want the
smooth curve (from a pre-defined family with simple gradient) that
best matches an offset exponential decay.
"""
def curve(x, a, b):
return 1.0 / (1.0 + a * x ** (2 * b))
xv = np.linspace(0, spread * 3, 300)
yv = np.zeros(xv.shape)
yv[xv < min_dist] = 1.0
yv[xv >= min_dist] = np.exp(-(xv[xv >= min_dist] - min_dist) / spread)
params, covar = curve_fit(curve, xv, yv)
return params[0], params[1]
[docs]class UMAP(BaseEstimator):
"""Uniform Manifold Approximation and Projection
Finds a low dimensional embedding of the data that approximates
an underlying manifold.
Parameters
----------
n_neighbors: float (optional, default 15)
The size of local neighborhood (in terms of number of neighboring
sample points) used for manifold approximation. Larger values
result in more global views of the manifold, while smaller
values result in more local data being preserved. In general
values should be in the range 2 to 100.
n_components: int (optional, default 2)
The dimension of the space to embed into. This defaults to 2 to
provide easy visualization, but can reasonably be set to any
integer value in the range 2 to 100.
metric: string or function (optional, default 'euclidean')
The metric to use to compute distances in high dimensional space.
If a string is passed it must match a valid predefined metric. If
a general metric is required a function that takes two 1d arrays and
returns a float can be provided. For performance purposes it is
required that this be a numba jit'd function. Valid string metrics
include:
* euclidean
* manhattan
* chebyshev
* minkowski
* canberra
* braycurtis
* mahalanobis
* wminkowski
* seuclidean
* cosine
* correlation
* haversine
* hamming
* jaccard
* dice
* russelrao
* kulsinski
* ll_dirichlet
* hellinger
* rogerstanimoto
* sokalmichener
* sokalsneath
* yule
Metrics that take arguments (such as minkowski, mahalanobis etc.)
can have arguments passed via the metric_kwds dictionary. At this
time care must be taken and dictionary elements must be ordered
appropriately; this will hopefully be fixed in the future.
n_epochs: int (optional, default None)
The number of training epochs to be used in optimizing the
low dimensional embedding. Larger values result in more accurate
embeddings. If None is specified a value will be selected based on
the size of the input dataset (200 for large datasets, 500 for small).
learning_rate: float (optional, default 1.0)
The initial learning rate for the embedding optimization.
init: string (optional, default 'spectral')
How to initialize the low dimensional embedding. Options are:
* 'spectral': use a spectral embedding of the fuzzy 1-skeleton
* 'random': assign initial embedding positions at random.
* A numpy array of initial embedding positions.
min_dist: float (optional, default 0.1)
The effective minimum distance between embedded points. Smaller values
will result in a more clustered/clumped embedding where nearby points
on the manifold are drawn closer together, while larger values will
result on a more even dispersal of points. The value should be set
relative to the ``spread`` value, which determines the scale at which
embedded points will be spread out.
spread: float (optional, default 1.0)
The effective scale of embedded points. In combination with ``min_dist``
this determines how clustered/clumped the embedded points are.
low_memory: bool (optional, default False)
For some datasets the nearest neighbor computation can consume a lot of
memory. If you find that UMAP is failing due to memory constraints
consider setting this option to True. This approach is more
computationally expensive, but avoids excessive memory use.
set_op_mix_ratio: float (optional, default 1.0)
Interpolate between (fuzzy) union and intersection as the set operation
used to combine local fuzzy simplicial sets to obtain a global fuzzy
simplicial sets. Both fuzzy set operations use the product t-norm.
The value of this parameter should be between 0.0 and 1.0; a value of
1.0 will use a pure fuzzy union, while 0.0 will use a pure fuzzy
intersection.
local_connectivity: int (optional, default 1)
The local connectivity required -- i.e. the number of nearest
neighbors that should be assumed to be connected at a local level.
The higher this value the more connected the manifold becomes
locally. In practice this should be not more than the local intrinsic
dimension of the manifold.
repulsion_strength: float (optional, default 1.0)
Weighting applied to negative samples in low dimensional embedding
optimization. Values higher than one will result in greater weight
being given to negative samples.
negative_sample_rate: int (optional, default 5)
The number of negative samples to select per positive sample
in the optimization process. Increasing this value will result
in greater repulsive force being applied, greater optimization
cost, but slightly more accuracy.
transform_queue_size: float (optional, default 4.0)
For transform operations (embedding new points using a trained model_
this will control how aggressively to search for nearest neighbors.
Larger values will result in slower performance but more accurate
nearest neighbor evaluation.
a: float (optional, default None)
More specific parameters controlling the embedding. If None these
values are set automatically as determined by ``min_dist`` and
``spread``.
b: float (optional, default None)
More specific parameters controlling the embedding. If None these
values are set automatically as determined by ``min_dist`` and
``spread``.
random_state: int, RandomState instance or None, optional (default: None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
metric_kwds: dict (optional, default None)
Arguments to pass on to the metric, such as the ``p`` value for
Minkowski distance. If None then no arguments are passed on.
angular_rp_forest: bool (optional, default False)
Whether to use an angular random projection forest to initialise
the approximate nearest neighbor search. This can be faster, but is
mostly on useful for metric that use an angular style distance such
as cosine, correlation etc. In the case of those metrics angular forests
will be chosen automatically.
target_n_neighbors: int (optional, default -1)
The number of nearest neighbors to use to construct the target simplcial
set. If set to -1 use the ``n_neighbors`` value.
target_metric: string or callable (optional, default 'categorical')
The metric used to measure distance for a target array is using supervised
dimension reduction. By default this is 'categorical' which will measure
distance in terms of whether categories match or are different. Furthermore,
if semi-supervised is required target values of -1 will be trated as
unlabelled under the 'categorical' metric. If the target array takes
continuous values (e.g. for a regression problem) then metric of 'l1'
or 'l2' is probably more appropriate.
target_metric_kwds: dict (optional, default None)
Keyword argument to pass to the target metric when performing
supervised dimension reduction. If None then no arguments are passed on.
target_weight: float (optional, default 0.5)
weighting factor between data topology and target topology. A value of
0.0 weights entirely on data, a value of 1.0 weights entirely on target.
The default of 0.5 balances the weighting equally between data and target.
transform_seed: int (optional, default 42)
Random seed used for the stochastic aspects of the transform operation.
This ensures consistency in transform operations.
verbose: bool (optional, default False)
Controls verbosity of logging.
unique: bool (optional, default False)
Controls if the rows of your data should be uniqued before being
embedded. If you have more duplicates than you have n_neighbour
you can have the identical data points lying in different regions of
your space. It also violates the definition of a metric.
"""
def __init__(
self,
n_neighbors=15,
n_components=2,
metric="euclidean",
metric_kwds=None,
output_metric="euclidean",
output_metric_kwds=None,
n_epochs=None,
learning_rate=1.0,
init="spectral",
min_dist=0.1,
spread=1.0,
low_memory=False,
set_op_mix_ratio=1.0,
local_connectivity=1.0,
repulsion_strength=1.0,
negative_sample_rate=5,
transform_queue_size=4.0,
a=None,
b=None,
random_state=None,
angular_rp_forest=False,
target_n_neighbors=-1,
target_metric="categorical",
target_metric_kwds=None,
target_weight=0.5,
transform_seed=42,
force_approximation_algorithm=False,
verbose=False,
unique=False,
):
self.n_neighbors = n_neighbors
self.metric = metric
self.metric_kwds = metric_kwds
self.output_metric = output_metric
if output_metric_kwds is not None:
self._output_metric_kwds = output_metric_kwds
else:
self._output_metric_kwds = {}
self.n_epochs = n_epochs
self.init = init
self.n_components = n_components
self.repulsion_strength = repulsion_strength
self.learning_rate = learning_rate
self.spread = spread
self.min_dist = min_dist
self.low_memory = low_memory
self.set_op_mix_ratio = set_op_mix_ratio
self.local_connectivity = local_connectivity
self.negative_sample_rate = negative_sample_rate
self.random_state = random_state
self.angular_rp_forest = angular_rp_forest
self.transform_queue_size = transform_queue_size
self.target_n_neighbors = target_n_neighbors
self.target_metric = target_metric
self.target_metric_kwds = target_metric_kwds
self.target_weight = target_weight
self.transform_seed = transform_seed
self.force_approximation_algorithm = force_approximation_algorithm
self.verbose = verbose
self.unique = unique
self.a = a
self.b = b
def _validate_parameters(self):
if self.set_op_mix_ratio < 0.0 or self.set_op_mix_ratio > 1.0:
raise ValueError("set_op_mix_ratio must be between 0.0 and 1.0")
if self.repulsion_strength < 0.0:
raise ValueError("repulsion_strength cannot be negative")
if self.min_dist > self.spread:
raise ValueError("min_dist must be less than or equal to spread")
if self.min_dist < 0.0:
raise ValueError("min_dist cannot be negative")
if not isinstance(self.init, str) and not isinstance(self.init, np.ndarray):
raise ValueError("init must be a string or ndarray")
if isinstance(self.init, str) and self.init not in ("spectral", "random"):
raise ValueError('string init values must be "spectral" or "random"')
if (
isinstance(self.init, np.ndarray)
and self.init.shape[1] != self.n_components
):
raise ValueError("init ndarray must match n_components value")
if not isinstance(self.metric, str) and not callable(self.metric):
raise ValueError("metric must be string or callable")
if self.negative_sample_rate < 0:
raise ValueError("negative sample rate must be positive")
if self._initial_alpha < 0.0:
raise ValueError("learning_rate must be positive")
if self.n_neighbors < 2:
raise ValueError("n_neighbors must be greater than 1")
if self.target_n_neighbors < 2 and self.target_n_neighbors != -1:
raise ValueError("target_n_neighbors must be greater than 1")
if not isinstance(self.n_components, int):
if isinstance(self.n_components, str):
raise ValueError("n_components must be an int")
if self.n_components % 1 != 0:
raise ValueError("n_components must be a whole number")
try:
# this will convert other types of int (eg. numpy int64)
# to Python int
self.n_components = int(self.n_components)
except ValueError:
raise ValueError("n_components must be an int")
if self.n_components < 1:
raise ValueError("n_components must be greater than 0")
if self.n_epochs is not None and (
self.n_epochs <= 10 or not isinstance(self.n_epochs, int)
):
raise ValueError("n_epochs must be a positive integer " "larger than 10")
if callable(self.metric):
self._input_distance_func = self.metric
elif self.metric in dist.named_distances:
self._input_distance_func = dist.named_distances[self.metric]
elif self.metric == "precomputed":
warn("Using precomputed metric; transform will be unavailable for new data")
else:
raise ValueError("metric is neither callable, " + "nor a recognised string")
if callable(self.output_metric):
self._output_distance_func = self.output_metric
elif self.output_metric in dist.named_distances_with_gradients:
self._output_distance_func = dist.named_distances_with_gradients[
self.output_metric
]
elif self.output_metric == "precomputed":
raise ValueError("output_metric cannnot be 'precomputed'")
else:
if self.output_metric in dist.named_distances:
raise ValueError(
"gradient function is not yet implemented for "
+ repr(self.output_metric)
+ "."
)
else:
raise ValueError(
"output_metric is neither callable, " + "nor a recognised string"
)
if (self.unique == True) and (self.metric == "precomputed"):
raise ValueError("unique is poorly defined on a precomputed metric")
[docs] def fit(self, X, y=None):
"""Fit X into an embedded space.
Optionally use y for supervised dimension reduction.
Parameters
----------
X : array, shape (n_samples, n_features) or (n_samples, n_samples)
If the metric is 'precomputed' X must be a square distance
matrix. Otherwise it contains a sample per row. If the method
is 'exact', X may be a sparse matrix of type 'csr', 'csc'
or 'coo'.
y : array, shape (n_samples)
A target array for supervised dimension reduction. How this is
handled is determined by parameters UMAP was instantiated with.
The relevant attributes are ``target_metric`` and
``target_metric_kwds``.
"""
X = check_array(X, dtype=np.float32, accept_sparse="csr", order="C")
self._raw_data = X
# Handle all the optional arguments, setting default
if self.a is None or self.b is None:
self._a, self._b = find_ab_params(self.spread, self.min_dist)
else:
self._a = self.a
self._b = self.b
if self.metric_kwds is not None:
self._metric_kwds = self.metric_kwds
else:
self._metric_kwds = {}
if self.target_metric_kwds is not None:
self._target_metric_kwds = self.target_metric_kwds
else:
self._target_metric_kwds = {}
if isinstance(self.init, np.ndarray):
init = check_array(self.init, dtype=np.float32, accept_sparse=False)
else:
init = self.init
self._initial_alpha = self.learning_rate
self._validate_parameters()
if self.metric is "hellinger" and X.min() < 0:
raise ValueError("Metric 'hellinger' does not support negative values")
if self.verbose:
print(str(self))
# NEW CODE
# Check if we should unique the data
# We've already ensured that we aren't in the precomputed case
if self.unique:
# check if the matrix is dense
if scipy.sparse.isspmatrix_csr(X):
# Call a sparse unique function
index, inverse, counts = csr_unique(X)
else:
index, inverse, counts = np.unique(
X,
return_index=True,
return_inverse=True,
return_counts=True,
axis=0,
)[1:4]
if self.verbose:
print(
"Unique=True -> Number of data points reduced from ",
X.shape[0],
" to ",
X[index].shape[0],
)
most_common = np.argmax(counts)
print(
"Most common duplicate is",
index[most_common],
" with a count of ",
counts[most_common],
)
# If we aren't asking for unique use the full index.
# This will save special cases later.
else:
index = list(range(X.shape[0]))
inverse = list(range(X.shape[0]))
# Error check n_neighbors based on data size
if X[index].shape[0] <= self.n_neighbors:
if X[index].shape[0] == 1:
self.embedding_ = np.zeros(
(1, self.n_components)
) # needed to sklearn comparability
return self
warn(
"n_neighbors is larger than the dataset size; truncating to "
"X.shape[0] - 1"
)
self._n_neighbors = X[index].shape[0] - 1
else:
self._n_neighbors = self.n_neighbors
# I could make the unique check a subcall of this...
# probably less readable
if scipy.sparse.isspmatrix_csr(X):
if not X.has_sorted_indices:
X.sort_indices()
self._sparse_data = True
else:
self._sparse_data = False
random_state = check_random_state(self.random_state)
if self.verbose:
print("Construct fuzzy simplicial set")
# Handle small cases efficiently by computing all distances
if X[index].shape[0] < 4096 and not self.force_approximation_algorithm:
self._small_data = True
try:
dmat = pairwise_distances(
X[index], metric=self.metric, **self._metric_kwds
)
except (ValueError, TypeError) as e:
# metric is not supported by sklearn,
# fallback to pairwise special
if self._sparse_data:
dmat = dist.pairwise_special_metric(
X[index].toarray(), metric=self.metric
)
else:
dmat = dist.pairwise_special_metric(X[index], metric=self.metric)
self.graph_, self._sigmas, self._rhos = fuzzy_simplicial_set(
dmat,
self._n_neighbors,
random_state,
"precomputed",
self._metric_kwds,
None,
None,
self.angular_rp_forest,
self.set_op_mix_ratio,
self.local_connectivity,
True,
self.verbose,
)
else:
self._small_data = False
# Standard case
(self._knn_indices, self._knn_dists, self._rp_forest) = nearest_neighbors(
X[index],
self._n_neighbors,
self.metric,
self._metric_kwds,
self.angular_rp_forest,
random_state,
self.low_memory,
use_pynndescent=True,
verbose=self.verbose,
)
self.graph_, self._sigmas, self._rhos = fuzzy_simplicial_set(
X[index],
self.n_neighbors,
random_state,
self.metric,
self._metric_kwds,
self._knn_indices,
self._knn_dists,
self.angular_rp_forest,
self.set_op_mix_ratio,
self.local_connectivity,
True,
self.verbose,
)
self._search_graph = scipy.sparse.lil_matrix(
(X[index].shape[0], X[index].shape[0]), dtype=np.int8
)
self._search_graph.rows = self._knn_indices
self._search_graph.data = (self._knn_dists != 0).astype(np.int8)
self._search_graph = self._search_graph.maximum(
self._search_graph.transpose()
).tocsr()
if callable(self.metric):
_distance_func = self.metric
elif self.metric in dist.named_distances:
# Choose the right metric based on sparsity
if self._sparse_data:
_distance_func = sparse.sparse_named_distances[self.metric]
else:
_distance_func = dist.named_distances[self.metric]
elif self.metric == "precomputed":
warn(
"Using precomputed metric; transform will be unavailable for new data"
)
else:
raise ValueError(
"Metric is neither callable, " + "nor a recognised string"
)
if self.metric != "precomputed":
self._dist_args = tuple(self._metric_kwds.values())
# Create a partial function for distances with arguments
if len(self._dist_args) > 0:
if self._sparse_data:
@numba.njit()
def _partial_dist_func(ind1, data1, ind2, data2):
return _distance_func(
ind1, data1, ind2, data2, *self._dist_args
)
self._distance_func = _partial_dist_func
else:
@numba.njit()
def _partial_dist_func(x, y):
return _distance_func(x, y, *self._dist_args)
self._distance_func = _partial_dist_func
else:
self._distance_func = _distance_func
# self._random_init, self._tree_init = make_initialisations(
# self._distance_func, self._dist_args
# )
# self._search = make_initialized_nnd_search(
# self._distance_func, self._dist_args
# )
# Currently not checking if any duplicate points have differing labels
# Might be worth throwing a warning...
if y is not None:
len_X = len(X) if not scipy.sparse.issparse(X) else X.shape[0]
if len_X != len(y):
raise ValueError(
"Length of x = {len_x}, length of y = {len_y}, while it must be equal.".format(
len_x=len_X, len_y=len(y)
)
)
y_ = check_array(y, ensure_2d=False)[index]
if self.target_metric == "categorical":
if self.target_weight < 1.0:
far_dist = 2.5 * (1.0 / (1.0 - self.target_weight))
else:
far_dist = 1.0e12
self.graph_ = discrete_metric_simplicial_set_intersection(
self.graph_, y_, far_dist=far_dist
)
elif self.target_metric in dist.DISCRETE_METRICS:
if self.target_weight < 1.0:
scale = 2.5 * (1.0 / (1.0 - self.target_weight))
else:
scale = 1.0e12
# self.graph_ = discrete_metric_simplicial_set_intersection(
# self.graph_,
# y_,
# metric=self.target_metric,
# metric_kws=self._target_metric_kwds,
# metric_scale=scale
# )
metric_kws = dist.get_discrete_params(y_, self.target_metric)
self.graph_ = discrete_metric_simplicial_set_intersection(
self.graph_,
y_,
metric=self.target_metric,
metric_kws=metric_kws,
metric_scale=scale,
)
else:
if self.target_n_neighbors == -1:
target_n_neighbors = self._n_neighbors
else:
target_n_neighbors = self.target_n_neighbors
# Handle the small case as precomputed as before
if y.shape[0] < 4096:
ydmat = pairwise_distances(
y_[np.newaxis, :].T,
metric=self.target_metric,
**self._target_metric_kwds
)
target_graph, target_sigmas, target_rhos = fuzzy_simplicial_set(
ydmat,
target_n_neighbors,
random_state,
"precomputed",
self._target_metric_kwds,
None,
None,
False,
1.0,
1.0,
False,
)
else:
# Standard case
target_graph, target_sigmas, target_rhos = fuzzy_simplicial_set(
y_[np.newaxis, :].T,
target_n_neighbors,
random_state,
self.target_metric,
self._target_metric_kwds,
None,
None,
False,
1.0,
1.0,
False,
)
# product = self.graph_.multiply(target_graph)
# # self.graph_ = 0.99 * product + 0.01 * (self.graph_ +
# # target_graph -
# # product)
# self.graph_ = product
self.graph_ = general_simplicial_set_intersection(
self.graph_, target_graph, self.target_weight
)
self.graph_ = reset_local_connectivity(self.graph_)
if self.n_epochs is None:
n_epochs = 0
else:
n_epochs = self.n_epochs
if self.verbose:
print(ts(), "Construct embedding")
self.embedding_ = simplicial_set_embedding(
self._raw_data[index], # JH why raw data?
self.graph_,
self.n_components,
self._initial_alpha,
self._a,
self._b,
self.repulsion_strength,
self.negative_sample_rate,
n_epochs,
init,
random_state,
self.metric,
self._metric_kwds,
self._output_distance_func,
self._output_metric_kwds,
self.output_metric in ("euclidean", "l2"),
self.random_state is None,
self.verbose,
)[inverse]
if self.verbose:
print(ts() + " Finished embedding")
self._input_hash = joblib.hash(self._raw_data)
return self
[docs]class DataFrameUMAP(BaseEstimator):
def __init__(
self,
metrics,
n_neighbors=15,
n_components=2,
output_metric="euclidean",
output_metric_kwds=None,
n_epochs=None,
learning_rate=1.0,
init="spectral",
min_dist=0.1,
spread=1.0,
set_op_mix_ratio=1.0,
local_connectivity=1.0,
repulsion_strength=1.0,
negative_sample_rate=5,
transform_queue_size=4.0,
a=None,
b=None,
random_state=None,
angular_rp_forest=False,
target_n_neighbors=-1,
target_metric="categorical",
target_metric_kwds=None,
target_weight=0.5,
transform_seed=42,
verbose=False,
):
self.metrics = metrics
self.n_neighbors = n_neighbors
self.output_metric = output_metric
if output_metric_kwds is not None:
self._output_metric_kwds = output_metric_kwds
else:
self._output_metric_kwds = {}
if callable(self.output_metric):
self._output_distance_func = self.output_metric
elif (
self.output_metric in dist.named_distances
and self.output_metric in dist.named_distances_with_gradients
):
self._output_distance_func = dist.named_distances_with_gradients[
self.output_metric
]
elif self.output_metric == "precomputed":
raise ValueError("output_metric cannnot be 'precomputed'")
else:
if self.output_metric in dist.named_distances:
raise ValueError(
"gradient function is not yet implemented for "
+ repr(self.output_metric)
+ "."
)
else:
raise ValueError(
"output_metric is neither callable, " + "nor a recognised string"
)
self.n_epochs = n_epochs
self.init = init
self.n_components = n_components
self.repulsion_strength = repulsion_strength
self.learning_rate = learning_rate
self.spread = spread
self.min_dist = min_dist
self.set_op_mix_ratio = set_op_mix_ratio
self.local_connectivity = local_connectivity
self.negative_sample_rate = negative_sample_rate
self.random_state = random_state
self.angular_rp_forest = angular_rp_forest
self.transform_queue_size = transform_queue_size
self.target_n_neighbors = target_n_neighbors
self.target_metric = target_metric
self.target_metric_kwds = target_metric_kwds
self.target_weight = target_weight
self.transform_seed = transform_seed
self.verbose = verbose
self.a = a
self.b = b
def _validate_parameters(self):
if self.set_op_mix_ratio < 0.0 or self.set_op_mix_ratio > 1.0:
raise ValueError("set_op_mix_ratio must be between 0.0 and 1.0")
if self.repulsion_strength < 0.0:
raise ValueError("repulsion_strength cannot be negative")
if self.min_dist > self.spread:
raise ValueError("min_dist must be less than or equal to spread")
if self.min_dist < 0.0:
raise ValueError("min_dist must be greater than 0.0")
if not isinstance(self.init, str) and not isinstance(self.init, np.ndarray):
raise ValueError("init must be a string or ndarray")
if isinstance(self.init, str) and self.init not in ("spectral", "random"):
raise ValueError('string init values must be "spectral" or "random"')
if (
isinstance(self.init, np.ndarray)
and self.init.shape[1] != self.n_components
):
raise ValueError("init ndarray must match n_components value")
if self.negative_sample_rate < 0:
raise ValueError("negative sample rate must be positive")
if self.learning_rate < 0.0:
raise ValueError("learning_rate must be positive")
if self.n_neighbors < 2:
raise ValueError("n_neighbors must be greater than 2")
if self.target_n_neighbors < 2 and self.target_n_neighbors != -1:
raise ValueError("target_n_neighbors must be greater than 2")
if not isinstance(self.n_components, int):
raise ValueError("n_components must be an int")
if self.n_components < 1:
raise ValueError("n_components must be greater than 0")
if self.n_epochs is not None and (
self.n_epochs <= 10 or not isinstance(self.n_epochs, int)
):
raise ValueError("n_epochs must be a positive integer " "larger than 10")
if callable(self.output_metric):
self._output_distance_func = self.output_metric
elif (
self.output_metric in dist.named_distances
and self.output_metric in dist.named_distances_with_gradients
):
self._output_distance_func = dist.named_distances_with_gradients[
self.output_metric
]
elif self.output_metric == "precomputed":
raise ValueError("output_metric cannnot be 'precomputed'")
else:
if self.output_metric in dist.named_distances:
raise ValueError(
"gradient function is not yet implemented for "
+ repr(self.output_metric)
+ "."
)
else:
raise ValueError(
"output_metric is neither callable, " + "nor a recognised string"
)
# validate metrics argument
assert isinstance(self.metrics, list) or self.metrics == "infer"
if self.metrics != "infer":
for item in self.metrics:
assert isinstance(item, tuple) and len(item) == 3
assert isinstance(item[0], str)
assert item[1] in dist.named_distances
assert isinstance(item[2], list) and len(item[2]) >= 1
for col in item[2]:
assert isinstance(col, str) or isinstance(col, int)
def fit(self, X, y=None):
self._validate_parameters()
# X should be a pandas dataframe, or np.array; check
# how column transformer handles this.
self._raw_data = X
# Handle all the optional arguments, setting default
if self.a is None or self.b is None:
self._a, self._b = find_ab_params(self.spread, self.min_dist)
else:
self._a = self.a
self._b = self.b
if self.target_metric_kwds is not None:
self._target_metric_kwds = self.target_metric_kwds
else:
self._target_metric_kwds = {}
if isinstance(self.init, np.ndarray):
init = check_array(self.init, dtype=np.float32, accept_sparse=False)
else:
init = self.init
self._initial_alpha = self.learning_rate
# Error check n_neighbors based on data size
if X.shape[0] <= self.n_neighbors:
if X.shape[0] == 1:
self.embedding_ = np.zeros(
(1, self.n_components)
) # needed to sklearn comparability
return self
warn(
"n_neighbors is larger than the dataset size; truncating to "
"X.shape[0] - 1"
)
self._n_neighbors = X.shape[0] - 1
else:
self._n_neighbors = self.n_neighbors
if self.metrics == "infer":
raise NotImplementedError("Metric inference not implemented yet")
random_state = check_random_state(self.random_state)
self.metric_graphs_ = {}
self._sigmas = {}
self._rhos = {}
self._knn_indices = {}
self._knn_dists = {}
self._rp_forest = {}
self.graph_ = None
def is_discrete_metric(metric_data):
return metric_data[1] in dist.DISCRETE_METRICS
for metric_data in sorted(self.metrics, key=is_discrete_metric):
name, metric, columns = metric_data
print(name, metric, columns)
if metric in dist.DISCRETE_METRICS:
self.metric_graphs_[name] = None
for col in columns:
discrete_space = X[col].values
metric_kws = dist.get_discrete_params(discrete_space, metric)
self.graph_ = discrete_metric_simplicial_set_intersection(
self.graph_,
discrete_space,
metric=metric,
metric_kws=metric_kws,
)
else:
# Sparse not supported yet
sub_data = check_array(
X[columns], dtype=np.float32, accept_sparse=False
)
if X.shape[0] < 4096:
# small case
self._small_data = True
# TODO: metric keywords not supported yet!
if metric in ("ll_dirichlet", "hellinger"):
dmat = dist.pairwise_special_metric(sub_data, metric=metric)
else:
dmat = pairwise_distances(sub_data, metric=metric)
(
self.metric_graphs_[name],
self._sigmas[name],
self._rhos[name],
) = fuzzy_simplicial_set(
dmat,
self._n_neighbors,
random_state,
"precomputed",
{},
None,
None,
self.angular_rp_forest,
self.set_op_mix_ratio,
self.local_connectivity,
False,
self.verbose,
)
else:
self._small_data = False
# Standard case
# TODO: metric keywords not supported yet!
(
self._knn_indices[name],
self._knn_dists[name],
self._rp_forest[name],
) = nearest_neighbors(
sub_data,
self._n_neighbors,
metric,
{},
self.angular_rp_forest,
random_state,
use_pynndescent=True,
verbose=self.verbose,
)
(
self.metric_graphs_[name],
self._sigmas[name],
self._rhos[name],
) = fuzzy_simplicial_set(
sub_data,
self.n_neighbors,
random_state,
metric,
{},
self._knn_indices[name],
self._knn_dists[name],
self.angular_rp_forest,
self.set_op_mix_ratio,
self.local_connectivity,
False,
self.verbose,
)
# TODO: set up transform data
if self.graph_ is None:
self.graph_ = self.metric_graphs_[name]
else:
self.graph_ = general_simplicial_set_intersection(
self.graph_, self.metric_graphs_[name], 0.5
)
print(self.graph_.data)
self.graph_ = reset_local_connectivity(
self.graph_, reset_local_metrics=True
)
if self.n_epochs is None:
n_epochs = 0
else:
n_epochs = self.n_epochs
if self.verbose:
print("Construct embedding")
# TODO: Handle connected component issues properly
# For now we just use manhattan and hope.
self.embedding_ = simplicial_set_embedding(
self._raw_data,
self.graph_,
self.n_components,
self._initial_alpha,
self._a,
self._b,
self.repulsion_strength,
self.negative_sample_rate,
n_epochs,
init,
random_state,
"manhattan",
{},
self._output_distance_func,
self._output_metric_kwds,
self.output_metric in ("euclidean", "l2"),
self.random_state is None,
self.verbose,
)
self._input_hash = joblib.hash(self._raw_data)
return self