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[Fix] Post Modern UI deploy Bugfixes (#240)

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Co-authored-by: Tim Lorsbach <tim@lorsba.ch>
Reviewed-on: enviPath/enviPy#240
This commit is contained in:
jebus
2025-11-27 10:28:04 +13:00
parent 1a2c9bb543
commit fd2e2c2534
9 changed files with 127 additions and 53 deletions
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2
envipath/settings.py
View File

@ -366,7 +366,7 @@ LOGIN_EXEMPT_URLS = [
"/cookie-policy",
"/about",
"/contact",
"/jobs",
"/careers",
"/cite",
"/legal",
]

28
epdb/models.py
View File

@ -2282,6 +2282,13 @@ class PackageBasedModel(EPModel):
return Dataset.load(ds_path)
def retrain(self):
# Reset eval fields
self.eval_results = {}
self.eval_packages.clear()
self.model_status = False
self.save()
# Do actual retrain
self.build_dataset()
self.build_model()
@ -2319,7 +2326,7 @@ class PackageBasedModel(EPModel):
self.save()
def evaluate_model(self, multigen: bool, eval_packages: List["Package"] = None, **kwargs):
if self.model_status != self.BUILT_NOT_EVALUATED:
if self.model_status not in [self.BUILT_NOT_EVALUATED, self.FINISHED]:
raise ValueError(f"Can't evaluate a model in state {self.model_status}!")
if multigen:
@ -2327,9 +2334,12 @@ class PackageBasedModel(EPModel):
self.save()
if eval_packages is not None:
self.eval_packages.clear()
for p in eval_packages:
self.eval_packages.add(p)
self.eval_results = {}
self.model_status = self.EVALUATING
self.save()
@ -2383,9 +2393,14 @@ class PackageBasedModel(EPModel):
recall = {f"{t:.2f}": [] for t in thresholds}
# Note: only one root compound supported at this time
root_compounds = [
[p.default_node_label.smiles for p in p.root_nodes][0] for p in pathways
]
root_compounds = []
for pw in pathways:
if pw.root_nodes:
root_compounds.append(pw.root_nodes[0].default_node_label)
else:
logger.info(
f"Skipping MG Eval of Pathway {pw.name} ({pw.uuid}) as it has no root compounds!"
)
# As we need a Model Instance in our setting, get a fresh copy from db, overwrite the serialized mode and
# pass it to the setting used in prediction
@ -3192,7 +3207,7 @@ class EnviFormer(PackageBasedModel):
return args
def evaluate_model(self, multigen: bool, eval_packages: List["Package"] = None, **kwargs):
if self.model_status != self.BUILT_NOT_EVALUATED:
if self.model_status not in [self.BUILT_NOT_EVALUATED, self.FINISHED]:
raise ValueError(f"Can't evaluate a model in state {self.model_status}!")
if multigen:
@ -3200,9 +3215,12 @@ class EnviFormer(PackageBasedModel):
self.save()
if eval_packages is not None:
self.eval_packages.clear()
for p in eval_packages:
self.eval_packages.add(p)
self.eval_results = {}
self.model_status = self.EVALUATING
self.save()

21
epdb/views.py
View File

@ -5,6 +5,7 @@ from typing import Any, Dict, List
import nh3
from django.conf import settings as s
from django.contrib.auth import get_user_model
from django.core.exceptions import BadRequest
from django.http import HttpResponse, HttpResponseBadRequest, HttpResponseNotAllowed, JsonResponse
from django.shortcuts import redirect, render
from django.urls import reverse
@ -319,7 +320,7 @@ def get_base_context(request, for_user=None) -> Dict[str, Any]:
def _anonymous_or_real(request):
if request.user.is_authenticated and not request.user.is_anonymous:
if request.user and (request.user.is_authenticated and not request.user.is_anonymous):
return request.user
return get_user_model().objects.get(username="anonymous")
@ -1261,8 +1262,12 @@ def package_compounds(request, package_uuid):
compound_name = request.POST.get("compound-name")
compound_smiles = request.POST.get("compound-smiles")
compound_description = request.POST.get("compound-description")
c = Compound.create(current_package, compound_smiles, compound_name, compound_description)
try:
c = Compound.create(
current_package, compound_smiles, compound_name, compound_description
)
except ValueError as e:
raise BadRequest(str(e))
return redirect(c.url)
@ -2819,14 +2824,18 @@ def settings(request):
context = get_base_context(request)
if request.method == "GET":
context = get_base_context(request)
context["title"] = "enviPath - Settings"
context["object_type"] = "setting"
# Even if settings are aready in "meta", for consistency add it on root level
context["settings"] = SettingManager.get_all_settings(current_user)
context["breadcrumbs"] = [
{"Home": s.SERVER_URL},
{"Group": s.SERVER_URL + "/setting"},
]
return
context["objects"] = SettingManager.get_all_settings(current_user)
return render(request, "collections/objects_list.html", context)
elif request.method == "POST":
if s.DEBUG:
for k, v in request.POST.items():

36
templates/actions/objects/model.html
View File

@ -7,22 +7,26 @@
<i class="glyphicon glyphicon-edit"></i> Edit Model</a
>
</li>
<li>
<a
role="button"
onclick="document.getElementById('evaluate_model_modal').showModal(); return false;"
>
<i class="glyphicon glyphicon-ok"></i> Evaluate Model</a
>
</li>
<li>
<a
role="button"
onclick="document.getElementById('retrain_model_modal').showModal(); return false;"
>
<i class="glyphicon glyphicon-repeat"></i> Retrain Model</a
>
</li>
{% if model.model_status == 'BUILT_NOT_EVALUATED' or model.model_status == 'FINISHED' %}
<li>
<a
role="button"
onclick="document.getElementById('evaluate_model_modal').showModal(); return false;"
>
<i class="glyphicon glyphicon-ok"></i> Evaluate Model</a
>
</li>
{% endif %}
{% if model.model_status == 'BUILT_NOT_EVALUATED' or model.model_status == 'FINISHED' %}
<li>
<a
role="button"
onclick="document.getElementById('retrain_model_modal').showModal(); return false;"
>
<i class="glyphicon glyphicon-repeat"></i> Retrain Model</a
>
</li>
{% endif %}
<li>
<a
class="button"

2
templates/collections/objects_list.html
View File

@ -471,7 +471,7 @@
<!-- Unreviewable objects such as User / Group / Setting -->
<div class="card bg-base-100">
<div class="card-body">
<ul class="menu bg-base-200 rounded-box">
<ul class="menu bg-base-200 rounded-box w-full">
{% for obj in objects %}
{% if object_type == 'user' %}
<li>

1
templates/modals/objects/evaluate_model_modal.html
View File

@ -45,7 +45,6 @@
name="model-evaluation-packages"
class="select select-bordered w-full h-48"
multiple
required
>
<optgroup label="Reviewed Packages">
{% for obj in meta.readable_packages %}

1
templates/modals/objects/generic_set_aliases_modal.html
View File

@ -65,6 +65,7 @@
method: 'POST',
headers: {
'Content-Type': 'application/x-www-form-urlencoded',
'X-CSRFToken': document.querySelector('[name=csrfmiddlewaretoken]').value
},
body: formData
});

2
templates/modals/objects/set_license_modal.html
View File

@ -52,7 +52,7 @@
}"
@close="reset()"
>
<div class="modal-box">
<div class="modal-box max-w-2xl">
<!-- Header -->
<h3 class="text-lg font-bold">Set License</h3>

87
utilities/ml.py
View File

@ -26,26 +26,38 @@ from utilities.chem import FormatConverter, PredictionResult
logger = logging.getLogger(__name__)
if TYPE_CHECKING:
from epdb.models import Rule, CompoundStructure, Reaction
from epdb.models import Rule, CompoundStructure
class Dataset(ABC):
def __init__(self, columns: List[str] = None, data: List[List[str | int | float]] | pl.DataFrame = None):
if isinstance(data, pl.DataFrame): # Allows for re-creation of self in cases like indexing with __getitem__
def __init__(
self, columns: List[str] = None, data: List[List[str | int | float]] | pl.DataFrame = None
):
if isinstance(
data, pl.DataFrame
): # Allows for re-creation of self in cases like indexing with __getitem__
self.df = data
else:
# Build either an empty dataframe with columns or fill it with list of list data
if data is not None and len(columns) != len(data[0]):
raise ValueError(f"Header and Data are not aligned {len(columns)} columns vs. {len(data[0])} columns")
raise ValueError(
f"Header and Data are not aligned {len(columns)} columns vs. {len(data[0])} columns"
)
if columns is None:
raise ValueError("Columns can't be None if data is not already a DataFrame")
self.df = pl.DataFrame(data=data, schema=columns, orient="row", infer_schema_length=None)
self.df = pl.DataFrame(
data=data, schema=columns, orient="row", infer_schema_length=None
)
def add_rows(self, rows: List[List[str | int | float]]):
"""Add rows to the dataset. Extends the polars dataframe stored in self"""
if len(self.columns) != len(rows[0]):
raise ValueError(f"Header and Data are not aligned {len(self.columns)} columns vs. {len(rows[0])} columns")
new_rows = pl.DataFrame(data=rows, schema=self.columns, orient="row", infer_schema_length=None)
raise ValueError(
f"Header and Data are not aligned {len(self.columns)} columns vs. {len(rows[0])} columns"
)
new_rows = pl.DataFrame(
data=rows, schema=self.columns, orient="row", infer_schema_length=None
)
self.df.extend(new_rows)
def add_row(self, row: List[str | int | float]):
@ -90,7 +102,9 @@ class Dataset(ABC):
"""Item is passed to polars allowing for advanced indexing.
See https://docs.pola.rs/api/python/stable/reference/dataframe/api/polars.DataFrame.__getitem__.html#polars.DataFrame.__getitem__"""
res = self.df[item]
if isinstance(res, pl.DataFrame): # If we get a dataframe back from indexing make new self with res dataframe
if isinstance(
res, pl.DataFrame
): # If we get a dataframe back from indexing make new self with res dataframe
return self.__class__(data=res)
else: # If we don't get a dataframe back (likely base type, int, str, float etc.) return the item
return res
@ -111,9 +125,7 @@ class Dataset(ABC):
return self.df.to_numpy()
def __repr__(self):
return (
f"<{self.__class__.__name__} #rows={len(self.df)} #cols={len(self.columns)}>"
)
return f"<{self.__class__.__name__} #rows={len(self.df)} #cols={len(self.columns)}>"
def __len__(self):
return len(self.df)
@ -130,9 +142,25 @@ class Dataset(ABC):
def with_columns(self, *exprs, **name_exprs):
return self.__class__(data=self.df.with_columns(*exprs, **name_exprs))
def sort(self, by, *more_by, descending=False, nulls_last=False, multithreaded=True, maintain_order=False):
return self.__class__(data=self.df.sort(by, *more_by, descending=descending, nulls_last=nulls_last,
multithreaded=multithreaded, maintain_order=maintain_order))
def sort(
self,
by,
*more_by,
descending=False,
nulls_last=False,
multithreaded=True,
maintain_order=False,
):
return self.__class__(
data=self.df.sort(
by,
*more_by,
descending=descending,
nulls_last=nulls_last,
multithreaded=multithreaded,
maintain_order=maintain_order,
)
)
def item(self, row=None, column=None):
return self.df.item(row, column)
@ -149,7 +177,9 @@ class RuleBasedDataset(Dataset):
def __init__(self, num_labels=None, columns=None, data=None):
super().__init__(columns, data)
# Calculating num_labels allows functions like getitem to be in the base Dataset as it unifies the init.
self.num_labels: int = num_labels if num_labels else sum([1 for c in self.columns if "obs_" in c])
self.num_labels: int = (
num_labels if num_labels else sum([1 for c in self.columns if "obs_" in c])
)
# Pre-calculate the ids of columns for features/labels, useful later in X and y
self._struct_features: List[int] = self.block_indices("feature_")
self._triggered: List[int] = self.block_indices("trig_")
@ -200,7 +230,12 @@ class RuleBasedDataset(Dataset):
return res
@staticmethod
def generate_dataset(reactions, applicable_rules, educts_only=True, feat_funcs: List["Callable | Descriptor"]=None):
def generate_dataset(
reactions,
applicable_rules,
educts_only=True,
feat_funcs: List["Callable | Descriptor"] = None,
):
if feat_funcs is None:
feat_funcs = [FormatConverter.maccs]
_structures = set() # Get all the structures
@ -253,7 +288,7 @@ class RuleBasedDataset(Dataset):
smi = cs.smiles
try:
smi = FormatConverter.standardize(smi, remove_stereo=True)
except Exception as e:
except Exception:
logger.debug(f"Standardizing SMILES failed for {smi}")
standardized_products.append(smi)
if len(set(standardized_products).difference(triggered[key])) == 0:
@ -266,10 +301,12 @@ class RuleBasedDataset(Dataset):
feats = feat_func(compounds[0].smiles)
start_i = len(feat_columns)
feat_columns.extend([f"feature_{start_i + i}" for i, _ in enumerate(feats)])
ds_columns = (["structure_id"] +
feat_columns +
[f"trig_{r.uuid}" for r in applicable_rules] +
[f"obs_{r.uuid}" for r in applicable_rules])
ds_columns = (
["structure_id"]
+ feat_columns
+ [f"trig_{r.uuid}" for r in applicable_rules]
+ [f"obs_{r.uuid}" for r in applicable_rules]
)
rows = []
for i, comp in enumerate(compounds):
@ -337,7 +374,9 @@ class RuleBasedDataset(Dataset):
def add_probs(self, probs):
col_names = [f"prob_{self.columns[r_id].split('_')[-1]}" for r_id in self._observed]
self.df = self.df.with_columns(*[pl.Series(name, probs[:, j]) for j, name in enumerate(col_names)])
self.df = self.df.with_columns(
*[pl.Series(name, probs[:, j]) for j, name in enumerate(col_names)]
)
self.has_probs = True
def to_arff(self, path: "Path"):
@ -910,6 +949,10 @@ def prune_graph(graph, threshold):
"""
Removes edges with probability below the threshold, then keep the subgraph reachable from the root node.
"""
if graph.number_of_nodes() == 0:
return
while True:
try:
cycle = nx.find_cycle(graph)