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Frontend #41

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merged 10 commits into from
Dec 6, 2024
Merged

Frontend #41

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a582de1
start multi page frontend
mishadr Jul 23, 2024
2d5681e
multipage: 1 process per tab
mishadr Aug 9, 2024
5e8b943
+
mishadr Aug 11, 2024
d81b188
little fix for distributions plots
mishadr Sep 21, 2024
9f62607
fix loading models and explanations
mishadr Oct 30, 2024
1fdd771
add value checkers
mishadr Oct 30, 2024
9615a8c
fixed error when too many model weights are sent to frontend
mishadr Oct 31, 2024
e17d372
refactored dataset stats
mishadr Nov 27, 2024
b70c314
Merge branch 'develop' into frontend
mishadr Dec 5, 2024
380a752
style fixed
mishadr Dec 6, 2024
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2 changes: 1 addition & 1 deletion data/multiple-graphs/custom/small/raw/.info
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
{
"count": 8,
"nodes": [5, 4, 4, 8, 6, 7, 7, 9],
"directed": false,
"directed": true,
"node_attributes": {
"names": [
"a", "b"
Expand Down
24 changes: 23 additions & 1 deletion experiments/user_dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -122,9 +122,31 @@ def simgnn():
print("len =", len(gen_dataset))


def nx_to_ptg_converter():
from aux.utils import GRAPHS_DIR
from base.dataset_converter import networkx_to_ptg
from base.datasets_processing import DatasetManager
import networkx as nx

nx_path = GRAPHS_DIR / 'networkx-graphs' / 'input' / 'reply_graph.edgelist'
nx_graph = nx.read_edgelist(nx_path)
nx_graph = nx.to_undirected(nx_graph)
ptg_graph = networkx_to_ptg(nx_graph)
if ptg_graph.x is None:
ptg_graph.x = torch.zeros((ptg_graph.num_nodes, 1))
if ptg_graph.y is None:
ptg_graph.y = torch.zeros(ptg_graph.num_nodes)
ptg_graph.y[0] = 1
ptg_dataset = UserLocalDataset('test_dataset_single', [ptg_graph])
gen_dataset = DatasetManager.register_torch_geometric_local(ptg_dataset)
print(len(gen_dataset))


if __name__ == '__main__':

# local()
converted_local()
# converted_local()
# api()
# simgnn()

nx_to_ptg_converter()
68 changes: 34 additions & 34 deletions metainfo/optimizers_parameters.json
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@@ -1,10 +1,10 @@
{"Adam":
{
"lr": ["learn rate", "float", 0.001, {"min": 0.0001, "step": 0.001}, "learning rate"],
"beta1": ["beta1", "float", 0.9, {}, "coefficient used for computing running averages of gradient and its square"],
"beta2": ["beta2", "float", 0.999, {}, "coefficient used for computing running averages of gradient and its square"],
"eps": ["Epsilon", "float", 0.00000001, {}, "term added to the denominator to improve numerical stability"],
"weight_decay": ["Weight decay (L2)", "float", 5e-4, {}, "weight decay (L2 penalty)"],
"beta1": ["beta1", "float", 0.9, {"min": 0}, "coefficient used for computing running averages of gradient and its square"],
"beta2": ["beta2", "float", 0.999, {"min": 0}, "coefficient used for computing running averages of gradient and its square"],
"eps": ["Epsilon", "float", 0.00000001, {"min": 0}, "term added to the denominator to improve numerical stability"],
"weight_decay": ["Weight decay (L2)", "float", 5e-4, {"min": 0}, "weight decay (L2 penalty)"],
"amsgrad": ["AMSGrad", "bool", false, {}, "whether to use the AMSGrad"],
"_technical_parameter":
{
Expand All @@ -17,22 +17,22 @@
"lr": ["learn rate", "float", 1.0, {"min": 0.0001, "step": 1}, "coefficient that scale delta before it is applied to the parameters"],
"rho": ["rho", "float", 0.9, {}, "coefficient used for computing a running average of squared gradients"],
"eps": ["Epsilon", "float", 1e-6, {}, "term added to the denominator to improve numerical stability"],
"weight_decay": ["Weight decay (L2)", "float", 0, {}, "weight decay (L2 penalty)"]
"weight_decay": ["Weight decay (L2)", "float", 0, {"min": 0}, "weight decay (L2 penalty)"]
},
"Adagrad":
{
"lr": ["learn rate", "float", 0.01, {"min": 0.0001, "step": 0.01}, "learning rate"],
"lr_decay": ["lr decay", "float", 0, {}, "learning rate decay"],
"eps": ["Epsilon", "float", 1e-10, {}, "term added to the denominator to improve numerical stability"],
"weight_decay": ["Weight decay (L2)", "float", 0, {}, "weight decay (L2 penalty)"]
"lr_decay": ["lr decay", "float", 0, {"min": 0}, "learning rate decay"],
"eps": ["Epsilon", "float", 1e-10, {"min": 0}, "term added to the denominator to improve numerical stability"],
"weight_decay": ["Weight decay (L2)", "float", 0, {"min": 0}, "weight decay (L2 penalty)"]
},
"AdamW":
{
"lr": ["learn rate", "float", 0.001, {"min": 0.0001, "step": 0.001}, "learning rate"],
"beta1": ["beta1", "float", 0.9, {}, "coefficient used for computing running averages of gradient and its square"],
"beta2": ["beta2", "float", 0.999, {}, "coefficient used for computing running averages of gradient and its square"],
"eps": ["Epsilon", "float", 1e-8, {}, "term added to the denominator to improve numerical stability"],
"weight_decay": ["Weight decay (L2)", "float", 0.01, {}, "weight decay coefficient"],
"beta1": ["beta1", "float", 0.9, {"min": 0}, "coefficient used for computing running averages of gradient and its square"],
"beta2": ["beta2", "float", 0.999, {"min": 0}, "coefficient used for computing running averages of gradient and its square"],
"eps": ["Epsilon", "float", 1e-8, {"min": 0}, "term added to the denominator to improve numerical stability"],
"weight_decay": ["Weight decay (L2)", "float", 0.01, {"min": 0}, "weight decay coefficient"],
"amsgrad": ["AMSGrad", "bool", false, {}, "whether to use the AMSGrad"],
"maximize": ["maximize", "bool", false, {}, "maximize the params based on the objective, instead of minimizing"],
"_technical_parameter":
Expand All @@ -44,9 +44,9 @@
"SparseAdam":
{
"lr": ["learn rate", "float", 0.001, {"min": 0.0001, "step": 0.001}, "learning rate "],
"beta1": ["beta1", "float", 0.9, {}, "coefficients used for computing running averages of gradient and its square"],
"beta2": ["beta2", "float", 0.999, {}, "coefficients used for computing running averages of gradient and its square"],
"eps": ["Epsilon", "float", 1e-8, {}, "term added to the denominator to improve numerical stability"],
"beta1": ["beta1", "float", 0.9, {"min": 0}, "coefficients used for computing running averages of gradient and its square"],
"beta2": ["beta2", "float", 0.999, {"min": 0}, "coefficients used for computing running averages of gradient and its square"],
"eps": ["Epsilon", "float", 1e-8, {"min": 0}, "term added to the denominator to improve numerical stability"],
"_technical_parameter":
{
"parameters_grouping": [[
Expand All @@ -56,10 +56,10 @@
"Adamax":
{
"lr": ["learn rate", "float", 0.002, {"min": 0.0001, "step": 0.001}, "learning rate"],
"beta1": ["beta1", "float", 0.9, {}, "coefficient used for computing running averages of gradient and its square"],
"beta2": ["beta2", "float", 0.999, {}, "coefficient used for computing running averages of gradient and its square"],
"eps": ["Epsilon", "float", 1e-8, {}, "term added to the denominator to improve numerical stability"],
"weight_decay": ["Weight decay (L2)", "float", 0.01, {}, "weight decay (L2 penalty)"],
"beta1": ["beta1", "float", 0.9, {"min": 0}, "coefficient used for computing running averages of gradient and its square"],
"beta2": ["beta2", "float", 0.999, {"min": 0}, "coefficient used for computing running averages of gradient and its square"],
"eps": ["Epsilon", "float", 1e-8, {"min": 0}, "term added to the denominator to improve numerical stability"],
"weight_decay": ["Weight decay (L2)", "float", 0.01, {"min": 0}, "weight decay (L2 penalty)"],
"maximize": ["maximize", "bool", false, {}, "maximize the params based on the objective, instead of minimizing"],
"_technical_parameter":
{
Expand All @@ -73,7 +73,7 @@
"lambd": ["lambd", "float", 0.0001, {}, "decay term"],
"alpha": ["alpha", "float", 0.75, {}, "power for eta update"],
"t0": ["t0", "float", 1000000.0, {}, "point at which to start averaging"],
"weight_decay": ["Weight decay (L2)", "float", 0, {}, "weight decay (L2 penalty)"]
"weight_decay": ["Weight decay (L2)", "float", 0, {"min": 0}, "weight decay (L2 penalty)"]
},
"LBFGS":
{
Expand All @@ -85,11 +85,11 @@
"NAdam":
{
"lr": ["learn rate", "float", 0.002, {"min": 0.0001, "step": 0.001}, "learning rate"],
"beta1": ["beta1", "float", 0.9, {}, "coefficient used for computing running averages of gradient and its square"],
"beta2": ["beta2", "float", 0.999, {}, "coefficient used for computing running averages of gradient and its square"],
"eps": ["Epsilon", "float", 1e-8, {}, "term added to the denominator to improve numerical stability"],
"weight_decay": ["Weight decay (L2)", "float", 0, {}, "weight decay (L2 penalty)"],
"momentum_decay": ["Momentum decay", "float", 0.004, {}, "momentum momentum_decay"],
"beta1": ["beta1", "float", 0.9, {"min": 0}, "coefficient used for computing running averages of gradient and its square"],
"beta2": ["beta2", "float", 0.999, {"min": 0}, "coefficient used for computing running averages of gradient and its square"],
"eps": ["Epsilon", "float", 1e-8, {"min": 0}, "term added to the denominator to improve numerical stability"],
"weight_decay": ["Weight decay (L2)", "float", 0, {"min": 0}, "weight decay (L2 penalty)"],
"momentum_decay": ["Momentum decay", "float", 0.004, {"min": 0}, "momentum momentum_decay"],
"_technical_parameter":
{
"parameters_grouping": [[
Expand All @@ -99,10 +99,10 @@
"RAdam":
{
"lr": ["learn rate", "float", 0.001, {"min": 0.0001, "step": 0.001}, "learning rate"],
"beta1": ["beta1", "float", 0.9, {}, "coefficient used for computing running averages of gradient and its square"],
"beta2": ["beta2", "float", 0.999, {}, "coefficient used for computing running averages of gradient and its square"],
"eps": ["Epsilon", "float", 1e-8, {}, "term added to the denominator to improve numerical stability"],
"weight_decay": ["Weight decay (L2)", "float", 0, {}, "weight decay (L2 penalty)"],
"beta1": ["beta1", "float", 0.9, {"min": 0}, "coefficient used for computing running averages of gradient and its square"],
"beta2": ["beta2", "float", 0.999, {"min": 0}, "coefficient used for computing running averages of gradient and its square"],
"eps": ["Epsilon", "float", 1e-8, {"min": 0}, "term added to the denominator to improve numerical stability"],
"weight_decay": ["Weight decay (L2)", "float", 0, {"min": 0}, "weight decay (L2 penalty)"],
"_technical_parameter":
{
"parameters_grouping": [[
Expand All @@ -112,9 +112,9 @@
"RMSprop":
{
"lr": ["learn rate", "float", 0.01, {"min": 0.0001, "step": 0.01}, "learning rate"],
"alpha": ["alpha", "float", 0.99, {}, "smoothing constant"],
"eps": ["Epsilon", "float", 1e-8, {}, "term added to the denominator to improve numerical stability"],
"weight_decay": ["Weight decay (L2)", "float", 0, {}, "weight decay (L2 penalty)"],
"alpha": ["alpha", "float", 0.99, {"min": 0}, "smoothing constant"],
"eps": ["Epsilon", "float", 1e-8, {"min": 0}, "term added to the denominator to improve numerical stability"],
"weight_decay": ["Weight decay (L2)", "float", 0, {"min": 0}, "weight decay (L2 penalty)"],
"momentum": ["momentum", "float", 0, {}, "momentum factor"],
"centered": ["centered", "bool", false, {}, "if True, compute the centered RMSProp, the gradient is normalized by an estimation of its variance"]
},
Expand All @@ -136,8 +136,8 @@
"SGD":
{
"lr": ["learn rate", "float", 0.001, {"min": 0.0001, "step": 0.001}, "learning rate"],
"weight_decay": ["Weight decay (L2)", "float", 0, {}, "weight decay (L2 penalty)"],
"momentum": ["momentum", "float", 0, {}, "momentum factor"],
"weight_decay": ["Weight decay (L2)", "float", 0, {"min": 0}, "weight decay (L2 penalty)"],
"momentum": ["momentum", "float", 0, {"min": 0}, "momentum factor"],
"dampening": ["dampening", "float", 0, {}, "dampening for momentum"],
"nesterov": ["nesterov", "bool", false, {}, "enables Nesterov momentum"],
"maximize": ["maximize", "bool", false, {}, "maximize the params based on the objective, instead of minimizing"]
Expand Down
4 changes: 2 additions & 2 deletions requirements2.txt
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
## These are reqs needed for documentation

numpy==1.26.3
multiprocess
multiprocess==0.70.16
pydantic
tqdm
pyparsing
Expand All @@ -23,4 +23,4 @@ opt-einsum
pandas==2.2.0
pylint

torch_geometric==2.3.1
torch_geometric==2.3.1
2 changes: 1 addition & 1 deletion src/aux/configs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -199,7 +199,7 @@ def set_defaults_config_pattern_info(

def to_json(
self
):
) -> dict:
""" Special method which allows to use json.dumps() on Config object """
return self.to_dict()

Expand Down
22 changes: 12 additions & 10 deletions src/base/custom_datasets.py
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Expand Up @@ -36,41 +36,41 @@ def __init__(
@property
def node_attributes_dir(
self
):
) -> Path:
""" Path to dir with node attributes. """
return self.root_dir / 'raw' / (self.name + '.node_attributes')

@property
def edge_attributes_dir(
self
):
) -> Path:
""" Path to dir with edge attributes. """
return self.root_dir / 'raw' / (self.name + '.edge_attributes')

@property
def labels_dir(
self
):
) -> Path:
""" Path to dir with labels. """
return self.root_dir / 'raw' / (self.name + '.labels')

@property
def edges_path(
self
):
) -> Path:
""" Path to file with edge list. """
return self.root_dir / 'raw' / (self.name + '.ij')

@property
def edge_index_path(
self
):
) -> Path:
""" Path to dir with labels. """
return self.root_dir / 'raw' / (self.name + '.edge_index')

def check_validity(
self
):
) -> None:
""" Check that dataset files (graph and attributes) are valid and consistent with .info.
"""
# Assuming info is OK
Expand Down Expand Up @@ -159,6 +159,7 @@ def build(
return

self.dataset_var_data = None
self.stats.update_var_config()
self.dataset_var_config = dataset_var_config
self.dataset = LocalDataset(self.results_dir, process_func=self._create_ptg)

Expand All @@ -178,6 +179,7 @@ def _compute_stat(
with open(self.node_attributes_dir / a, 'r') as f:
attr_node_attrs[a] = json.load(f)

# FIXME misha - for single graph [0]
edges = self.edge_index
node_map = (lambda i: str(self.node_map[i])) if self.node_map else lambda i: str(i)

Expand Down Expand Up @@ -223,8 +225,8 @@ def _compute_stat(
pearson_corr[i][j] = min(1, max(-1, pc))

return {'attributes': attrs, 'correlations': pearson_corr.tolist()}
else:
return super()._compute_stat(stat)

raise NotImplementedError()

def _compute_dataset_data(
self
Expand Down Expand Up @@ -420,7 +422,7 @@ def _iter_nodes(

def _labeling_tensor(
self,
g_ix=None
g_ix: int = None
) -> list:
""" Returns list of labels (not tensors) """
y = []
Expand All @@ -445,7 +447,7 @@ def _labeling_tensor(

def _feature_tensor(
self,
g_ix=None
g_ix: int = None
) -> list:
""" Returns list of features (not tensors) for graph g_ix.
"""
Expand Down
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