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FROM neurips23 | ||
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RUN apt-get update | ||
RUN apt-get install -y git cmake liblapack-dev bc | ||
RUN pip3 install wheel pybind11 | ||
RUN git clone https://github.com/masajiro/NGT-neurips23.git NGT | ||
RUN cd NGT && git log -n 1 | ||
RUN cd NGT && mkdir build && cd build && cmake .. | ||
RUN cd NGT/build && make -j 8 && make install | ||
RUN ldconfig | ||
RUN cd NGT/python && python3 setup.py bdist_wheel | ||
RUN pip3 install NGT/python/dist/ngt-*-linux_x86_64.whl |
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random-xs: | ||
ngt: | ||
docker-tag: neurips23-ood-ngt | ||
module: neurips23.ood.ngt.module | ||
constructor: NGT | ||
base-args: ["@metric"] | ||
run-groups: | ||
base: | ||
args: | | ||
[{"edge": 50, "outdegree": 10, "indegree": 100, | ||
"epsilon": 0.1, "reduction": 0.39}] | ||
# "url": "https://public-rlab.east.edge.storage-yahoo.jp/neurips23/indexes/onng-random-50-10-100-0.10-0.39.tgz"}] | ||
query-args: | | ||
[{"epsilon": 1.1}] | ||
text2image-10M: | ||
ngt: | ||
docker-tag: neurips23-ood-ngt | ||
module: neurips23.ood.ngt.module | ||
constructor: NGT | ||
base-args: ["@metric"] | ||
run-groups: | ||
base: | ||
args: | | ||
[{"edge": 140, "outdegree": 10, "indegree": 180, | ||
"epsilon": 0.1, "reduction": 0.39}] | ||
# "url": "https://public-rlab.east.edge.storage-yahoo.jp/neurips23/indexes/onng-text2image-140-10-180-0.10-0.39.tgz"}] | ||
query-args: | | ||
[{"epsilon": 1.010}, | ||
{"epsilon": 1.014}, | ||
{"epsilon": 1.016}, | ||
{"epsilon": 1.017}, | ||
{"epsilon": 1.018}, | ||
{"epsilon": 1.020}, | ||
{"epsilon": 1.025}] |
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import os | ||
import subprocess | ||
import time | ||
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from neurips23.ood.base import BaseOODANN | ||
from benchmark.datasets import DATASETS, download_accelerated | ||
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import ngtpy | ||
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class NGT(BaseOODANN): | ||
def __init__(self, metric, params): | ||
metrics = {"euclidean": "2", "angular": "E", "ip": "i"} | ||
self._params = params | ||
self._edge_size = int(params["edge"]) | ||
self._outdegree = int(params["outdegree"]) | ||
self._indegree = int(params["indegree"]) | ||
self._metric = metrics[metric] | ||
self._edge_size_for_search = int(params["search_edge"]) if "search_edge" in params.keys() else 0 | ||
self._build_time_limit = float(params["timeout"]) if "timeout" in params.keys() else 12 | ||
self._epsilon = float(params["epsilon"]) if "epsilon" in params.keys() else 0.1 | ||
self._reduction_range = float(params["reduction"]) if "reduction" in params.keys() else 1.8 | ||
print("ONNG: edge_size:", self._edge_size) | ||
print("ONNG: outdegree:", self._outdegree) | ||
print("ONNG: indegree=:", self._indegree) | ||
print("ONNG: edge_size_for_search:", self._edge_size_for_search) | ||
print("ONNG: epsilon:", self._epsilon) | ||
print("ONNG: reduction range:", self._reduction_range) | ||
print("ONNG: metric:", metric) | ||
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def get_title(self): | ||
return "index-%s-%s-%s-%.2f-%.2f" % ( | ||
self._edge_size, | ||
self._outdegree, | ||
self._indegree, | ||
self._epsilon, | ||
self._reduction_range, | ||
) | ||
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def set_index_path(self, dataset): | ||
self._index_dir = os.path.join("data", "indices", "ood", "ngt", self.get_title()) | ||
self._index_path = os.path.join(self._index_dir, "onng") | ||
self._sanng_path = os.path.join(self._index_dir, "sanng") | ||
self._anng_path = os.path.join(self._index_dir, "anng-" + str(self._edge_size)) | ||
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def fit(self, dataset): | ||
print("ONNG: start indexing...") | ||
ds = DATASETS[dataset]() | ||
print("ONNG: dataset:", dataset) | ||
print("ONNG: dataset str:", ds.__str__()) | ||
print("ONNG: distance:", ds.distance()) | ||
print("ONNG: dimension:", ds.d) | ||
print("ONNG: type:", ds.dtype) | ||
print("ONNG: nb:", ds.nb) | ||
print("ONNG: dataset file name:", ds.get_dataset_fn()) | ||
print("ONNG: index path:", self._index_path) | ||
self.set_index_path(dataset) | ||
if not os.path.exists(self._index_dir): | ||
os.makedirs(self._index_dir) | ||
print("ONNG: index:", self._index_path) | ||
dim = ds.d | ||
if (not os.path.exists(self._index_path)) and (not os.path.exists(self._sanng_path)): | ||
print("ONNG: create a sparse ANNG to optimize the graph.") | ||
t = time.time() | ||
args = [ | ||
"ngt", | ||
"create", | ||
"-v", | ||
"-it", | ||
"-p8", | ||
"-b500", | ||
"-ga", | ||
"-of", | ||
"-D" + self._metric, | ||
"-d" + str(dim), | ||
"-E5", | ||
"-S-2", | ||
"-e0.0", | ||
"-P0", | ||
"-B30", | ||
"-T0", | ||
self._sanng_path, | ||
] | ||
print("ONNG: '{}'".format(" ".join(args))) | ||
subprocess.run(args, check=True) | ||
print("ONNG: append for SANNG") | ||
args = ["ngt", "append", "-mb", | ||
"-n" + str(ds.nb), | ||
self._sanng_path, | ||
ds.get_dataset_fn()] | ||
print("ONNG: '{}'".format(" ".join(args))) | ||
subprocess.run(args, check=True) | ||
print("ONNG: SANNG appending time(sec)=" + str(time.time() - t)) | ||
print("ONNG: build a sparse ANNG index.") | ||
t = time.time() | ||
args = ["ngt", "construct-graph", "-v", "-G-", "-E0.0", "-S100", | ||
self._sanng_path] | ||
print("ONNG: '{}'".format(" ".join(args))) | ||
subprocess.run(args, check=True) | ||
print("ONNG: SANNG index build time(sec)=", str(time.time() - t)) | ||
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if (not os.path.exists(self._index_path)) and (not os.path.exists(self._anng_path)): | ||
print("ONNG: build ANNG") | ||
t = time.time() | ||
args = [ | ||
"ngt", | ||
"create", | ||
"-v", | ||
"-it", | ||
"-p8", | ||
"-b20", | ||
"-ga", | ||
"-of", | ||
"-D" + self._metric, | ||
"-d" + str(dim), | ||
"-E18", | ||
"-S" + str(self._edge_size_for_search), | ||
"-e" + str(self._epsilon), | ||
"-P0", | ||
"-B30", | ||
"-T" + str(self._build_time_limit), | ||
self._anng_path, | ||
] | ||
print("ONNG: '{}'".format(" ".join(args))) | ||
subprocess.run(args, check=True) | ||
print("ONNG: degree adjustment") | ||
t = time.time() | ||
args = [ | ||
"ngt", | ||
"construct-graph", | ||
"-v", | ||
"-Go", | ||
"-T0", | ||
"-P0", | ||
"-N" + str(self._edge_size), | ||
"-O" + str(self._outdegree), | ||
"-I" + str(self._indegree), | ||
self._anng_path, | ||
self._sanng_path, | ||
] | ||
print("ONNG: '{}'".format(" ".join(args))) | ||
subprocess.run(args, check=True) | ||
print("ONNG: degree ajustment time(sec)=" + str(time.time() - t)) | ||
if not os.path.exists(self._index_path): | ||
print("ONNG: shortcut reduction") | ||
t = time.time() | ||
args = [ | ||
"ngt", | ||
"reconstruct-graph", | ||
"-v", | ||
"-R" + str(self._reduction_range), | ||
"-mS", | ||
"-Ps", | ||
"-sp", | ||
"-o0", | ||
"-i0", | ||
self._anng_path, | ||
self._index_path, | ||
] | ||
print("ONNG: '{}'".format(" ".join(args))) | ||
subprocess.run(args, check=True) | ||
print("ONNG: shortcut reduction time(sec)=" + str(time.time() - t)) | ||
if os.path.exists(self._index_path): | ||
print("ONNG: index already exists!", self._index_path) | ||
t = time.time() | ||
self.index = ngtpy.Index(self._index_path, read_only=True, tree_disabled=False) | ||
self.indexName = self._index_path | ||
print("ONNG: open time(sec)=" + str(time.time() - t)) | ||
else: | ||
print("ONNG: something wrong...") | ||
print("ONNG: end of fit") | ||
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def load_index(self, dataset): | ||
self.set_index_path(dataset) | ||
if not os.path.exists(self._index_path + "/grp"): | ||
if "url" not in self._params: | ||
return False | ||
if not os.path.exists(self._index_dir): | ||
os.makedirs(self._index_dir) | ||
tar_file = self._index_path + ".tgz"; | ||
if not os.path.exists(tar_file): | ||
print("ONNG: downloading the index... index={} => {}".format(self._params["url"], self._index_path)) | ||
download_accelerated(self._params["url"], tar_file, quiet=True) | ||
args = ["tar", "zxf", tar_file, "-C", self._index_dir] | ||
print("ONNG: '{}'".format(" ".join(args))) | ||
subprocess.run(args, check=True) | ||
args = ["rm", "-r", tar_file] | ||
print("ONNG: '{}'".format(" ".join(args))) | ||
subprocess.run(args, check=True) | ||
os.makedirs(self._sanng_path) | ||
os.makedirs(self._anng_path) | ||
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def set_query_arguments(self, query_args): | ||
epsilon = query_args.get("epsilon", 1.0) | ||
edge_size = query_args.get("edge", 0) | ||
print("ONNG: edge_size:", edge_size) | ||
print("ONNG: epsilon:", epsilon) | ||
self.name = "ngt-onng(%s, %s, %s, %s, %s)" % ( | ||
self._edge_size, | ||
self._outdegree, | ||
self._indegree, | ||
self._reduction_range, | ||
epsilon, | ||
) | ||
epsilon = epsilon - 1.0 | ||
self.index.set(epsilon=epsilon, edge_size=edge_size) | ||
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def query(self, X, n): | ||
self._results = ngtpy.BatchResults() | ||
return self.index.batch_search(X, self._results, n, with_distance=False) | ||
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def get_results(self): | ||
return self._results.get_ids() | ||
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