automatically download nonzero datatype metadata

This commit is contained in:
Carl Pearson
2021-12-01 14:27:44 -08:00
parent 4a09bc2d33
commit 9800c3b5f9
7 changed files with 182 additions and 14 deletions

View File

@@ -39,6 +39,12 @@ This makes use of a [fork of the `ssgetpy`](github.com/cwpearson/ssgetpy) packag
ssgetpy does not discriminate "real" datatype from "integer" datatype, as shown on the suitesparse collection website.
Therefore, `lists.py` maintains a manually-curated list of `integer` datatype matrices to facilitate discrimination.
## Transfer data to a different filesystem
```
rsync -rzvh --links pearson@cori.nersc.gov:$SS_DIR/ .
```
## how this was done
```
@@ -48,4 +54,4 @@ poetry add ssgetpy
```
poetry install
```
```

View File

@@ -2,8 +2,15 @@ import os
import sys
from pathlib import Path
from lib import matrix
try:
DIR = Path(os.environ["SS_DIR"])
except KeyError as e:
print("ERROR: $SS_DIR not set")
sys.exit(1)
SS_ROOT_URL = "https://sparse.tamu.edu"

View File

@@ -3,7 +3,7 @@ import sys
import ssgetpy
from lib import lists
from lib import dtypes
Dataset = collections.namedtuple("Dataset", ["name", "mats"])
@@ -15,18 +15,19 @@ def safe_dir_name(s):
t = t.lower()
return t
def mat_is_integer(mat):
return mat.name in lists.INTEGER_MATS
def mat_is_real(mat):
val = dtypes.DTYPES[(mat.group, mat.name)] == "real"
return val
def filter_reject_integer(mats):
return [mat for mat in mats if not mat_is_integer(mat)]
def filter_keep_real(mats):
return [mat for mat in mats if mat_is_real(mat)]
def mat_is_small(mat):
return (mat.rows < 1_000 and mat.cols < 1_000) \
or mat.nnz < 20_000
def mat_is_large(mat):
return (mat.rows > 1_000_000 and mat.cols < 1_000_000) \
return (mat.rows > 1_000_000 and mat.cols > 1_000_000) \
or mat.nnz > 20_000_000
def filter_reject_large(mats):
@@ -38,7 +39,7 @@ def filter_reject_small(mats):
## all real-valued matrices
REAL_MATS = Dataset(
name = "reals",
mats = filter_reject_integer(ssgetpy.search(
mats = filter_keep_real(ssgetpy.search(
dtype='real',
limit=1_000_000
))
@@ -66,7 +67,7 @@ for kind in kinds:
)
REGULAR_REAL_MATS = Dataset(
name="regular_reals",
mats = filter_reject_integer(mats)
mats = filter_keep_real(mats)
)
## keep "small" matrices
@@ -91,7 +92,7 @@ REAL_MED_MATS = Dataset (
## export all datasets
DATASETS = [
REAL_MATS,
# REAL_MATS,
REAL_SMALL_MATS,
REAL_MED_MATS,
REGULAR_REAL_MATS,
@@ -114,7 +115,7 @@ for kind in get_kinds():
name = "kind_"+safe_dir_name(kind),
mats = filter_reject_large( \
filter_reject_small( \
filter_reject_integer(ssgetpy.search(
filter_keep_real(ssgetpy.search(
kind=kind,
dtype='real',
limit=1_000_000

54
lib/dtypes.py Normal file
View File

@@ -0,0 +1,54 @@
"""export a map that is (group, name) -> dtype for all mats"""
import requests
import datetime
import os
import scipy.io
from lib import config
def download_ss_index(path):
with open(path, "wb") as f:
req = requests.get(config.SS_ROOT_URL + "/files/ss_index.mat")
f.write(req.content)
def ensure_ss_index(path):
if not os.path.exists(path):
download_ss_index(path)
mtime = datetime.datetime.utcfromtimestamp(os.path.getmtime(config.DIR / ".ss_index.mat"))
if datetime.datetime.utcnow() - mtime > datetime.timedelta(days=90):
download_ss_index(path)
# download metadata file if missing
local = config.DIR / ".ss_index.mat"
ensure_ss_index(local)
# load metadata and convert to a database
mat = scipy.io.loadmat(config.DIR / ".ss_index.mat", squeeze_me=True)
s = mat["ss_index"].item()
for i,x in enumerate(s):
print(i, x)
groups = s[1]
names = s[2]
# 3 letters, first letter:
# r=real, p=binary, c=complex, i=integer
rbtype = s[19]
def dtype_from_rbtype(rbtype):
if rbtype[0] == "r":
return "real"
elif rbtype[0] == "p":
return "binary"
elif rbtype[0] == "c":
return "complex"
elif rbtype[0] == "i":
return "integer"
else:
raise LookupError
DTYPES = {}
for i in range(len(names)):
DTYPES[(groups[i], names[i])] = dtype_from_rbtype(rbtype[i])

17
lib/matrix.py Normal file
View File

@@ -0,0 +1,17 @@
class Matrix:
def __init__(self, group, name, dtype, nrows, ncols, nnz):
self.group = group
self.name = name
self.dtype = dtype
self.nrows = int(nrows)
self.ncols = int(ncols)
self.nnz = int(nnz)
def to_tuple(self):
return (self.group, self.name, self.dtype, self.nrows, self.ncols, self.nnz)
def __repr__(self):
return repr(self.to_tuple())
def url(self):
return "/".join(("https://sparse.tamu.edu", "MM", self.group, self.name + ".tar.gz"))

86
poetry.lock generated
View File

@@ -33,6 +33,14 @@ category = "main"
optional = false
python-versions = ">=3.5"
[[package]]
name = "numpy"
version = "1.21.4"
description = "NumPy is the fundamental package for array computing with Python."
category = "main"
optional = false
python-versions = ">=3.7,<3.11"
[[package]]
name = "requests"
version = "2.26.0"
@@ -51,6 +59,17 @@ urllib3 = ">=1.21.1,<1.27"
socks = ["PySocks (>=1.5.6,!=1.5.7)", "win-inet-pton"]
use_chardet_on_py3 = ["chardet (>=3.0.2,<5)"]
[[package]]
name = "scipy"
version = "1.7.3"
description = "SciPy: Scientific Library for Python"
category = "main"
optional = false
python-versions = ">=3.7,<3.11"
[package.dependencies]
numpy = ">=1.16.5,<1.23.0"
[[package]]
name = "ssgetpy"
version = "1.0-pre2"
@@ -101,8 +120,8 @@ socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
[metadata]
lock-version = "1.1"
python-versions = "^3.7"
content-hash = "4a624c76d5d28333a13081a3fe5fba3eadcdfc09ac0963d1f1ecd89eb03451aa"
python-versions = ">=3.7,<3.11"
content-hash = "5a1bf7fe65d1fe23f7c34d44076cc157e3343699790a742492686d6198fb88eb"
[metadata.files]
certifi = [
@@ -121,10 +140,73 @@ idna = [
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]
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tqdm = [
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View File

@@ -5,8 +5,9 @@ description = ""
authors = ["Carl Pearson <cwpears@sandia.gov>"]
[tool.poetry.dependencies]
python = "^3.7"
python = ">=3.7,<3.11"
ssgetpy = {git = "https://github.com/cwpearson/ssgetpy.git", rev = "be00d2a"}
scipy = "^1.7.3"
[tool.poetry.dev-dependencies]