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"""BioNeMo related operations
The intention is to showcase how BioNeMo can be integrated with LynxKite. This should be
considered as a reference implementation and not a production ready code.
The operations are quite specific for this example notebook:
https://github.com/NVIDIA/bionemo-framework/blob/main/docs/docs/user-guide/examples/bionemo-geneformer/geneformer-celltype-classification.ipynb
"""
from lynxkite.core import ops
import requests
import tarfile
import os
from collections import Counter
from . import core
import joblib
import numpy as np
import torch
from pathlib import Path
import random
from contextlib import contextmanager
import cellxgene_census # TODO: This needs numpy < 2
import tempfile
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline
from sklearn.model_selection import StratifiedKFold, cross_validate
from sklearn.metrics import (
make_scorer,
accuracy_score,
precision_score,
recall_score,
f1_score,
roc_auc_score,
confusion_matrix,
)
from sklearn.decomposition import PCA
from sklearn.model_selection import cross_val_predict
from sklearn.preprocessing import LabelEncoder
from bionemo.scdl.io.single_cell_collection import SingleCellCollection
import scanpy
mem = joblib.Memory(".joblib-cache")
op = ops.op_registration(core.ENV)
DATA_PATH = Path("/workspace")
@contextmanager
def random_seed(seed: int):
state = random.getstate()
random.seed(seed)
try:
yield
finally:
# Go back to previous state
random.setstate(state)
@op("BioNeMo > Download CELLxGENE dataset")
@mem.cache()
def download_cellxgene_dataset(
*,
save_path: str,
census_version: str = "2023-12-15",
organism: str = "Homo sapiens",
value_filter='dataset_id=="8e47ed12-c658-4252-b126-381df8d52a3d"',
max_workers: int = 1,
use_mp: bool = False,
) -> None:
"""Downloads a CELLxGENE dataset"""
with cellxgene_census.open_soma(census_version=census_version) as census:
adata = cellxgene_census.get_anndata(
census,
organism,
obs_value_filter=value_filter,
)
with random_seed(32):
indices = list(range(len(adata)))
random.shuffle(indices)
micro_batch_size: int = 32
num_steps: int = 256
selection = sorted(indices[: micro_batch_size * num_steps])
# NOTE: there's a current constraint that predict_step needs to be a function of micro-batch-size.
# this is something we are working on fixing. A quick hack is to set micro-batch-size=1, but this is
# slow. In this notebook we are going to use mbs=32 and subsample the anndata.
adata = adata[selection].copy() # so it's not a view
h5ad_outfile = DATA_PATH / Path("hs-celltype-bench.h5ad")
adata.write_h5ad(h5ad_outfile)
with tempfile.TemporaryDirectory() as temp_dir:
coll = SingleCellCollection(temp_dir)
coll.load_h5ad_multi(
h5ad_outfile.parent, max_workers=max_workers, use_processes=use_mp
)
coll.flatten(DATA_PATH / save_path, destroy_on_copy=True)
return DATA_PATH / save_path
@op("BioNeMo > Import H5AD file")
def import_h5ad(*, file_path: str):
return scanpy.read_h5ad(DATA_PATH / Path(file_path))
@op("BioNeMo > Download model")
@mem.cache(verbose=1)
def download_model(*, model_name: str) -> str:
"""Downloads a model."""
model_download_parameters = {
"geneformer_100m": {
"name": "geneformer_100m",
"version": "2.0",
"path": "geneformer_106M_240530_nemo2",
},
"geneformer_10m": {
"name": "geneformer_10m",
"version": "2.0",
"path": "geneformer_10M_240530_nemo2",
},
"geneformer_10m2": {
"name": "geneformer_10m",
"version": "2.1",
"path": "geneformer_10M_241113_nemo2",
},
}
# Define the URL and output file
url_template = "https://api.ngc.nvidia.com/v2/models/org/nvidia/team/clara/{name}/{version}/files?redirect=true&path={path}.tar.gz"
url = url_template.format(**model_download_parameters[model_name])
model_filename = f"{DATA_PATH}/{model_download_parameters[model_name]['path']}"
output_file = f"{model_filename}.tar.gz"
# Send the request
response = requests.get(url, allow_redirects=True, stream=True)
response.raise_for_status() # Raise an error for bad responses (4xx and 5xx)
# Save the file to disk
with open(f"{output_file}", "wb") as file:
for chunk in response.iter_content(chunk_size=8192):
file.write(chunk)
# Extract the tar.gz file
os.makedirs(model_filename, exist_ok=True)
with tarfile.open(output_file, "r:gz") as tar:
tar.extractall(path=model_filename)
return model_filename
@op("BioNeMo > Infer")
@mem.cache(verbose=1)
def infer(
dataset_path: str, model_path: str | None = None, *, results_path: str
) -> str:
"""Infer on a dataset."""
# This import is slow, so we only import it when we need it.
from bionemo.geneformer.scripts.infer_geneformer import infer_model
infer_model(
data_path=dataset_path,
checkpoint_path=model_path,
results_path=DATA_PATH / results_path,
include_hiddens=False,
micro_batch_size=32,
include_embeddings=True,
include_logits=False,
seq_length=2048,
precision="bf16-mixed",
devices=1,
num_nodes=1,
num_dataset_workers=10,
)
return DATA_PATH / results_path
@op("BioNeMo > Load results")
def load_results(results_path: str):
embeddings = (
torch.load(f"{results_path}/predictions__rank_0.pt")["embeddings"]
.float()
.cpu()
.numpy()
)
return embeddings
@op("BioNeMo > Get labels")
def get_labels(adata):
infer_metadata = adata.obs
labels = infer_metadata["cell_type"].values
label_encoder = LabelEncoder()
integer_labels = label_encoder.fit_transform(labels)
label_encoder.integer_labels = integer_labels
return label_encoder
@op("BioNeMo > Plot labels", view="visualization")
def plot_labels(adata):
infer_metadata = adata.obs
labels = infer_metadata["cell_type"].values
label_counts = Counter(labels)
labels = list(label_counts.keys())
values = list(label_counts.values())
options = {
"title": {
"text": "Cell type counts for classification dataset",
"left": "center",
},
"tooltip": {"trigger": "axis", "axisPointer": {"type": "shadow"}},
"xAxis": {
"type": "category",
"data": labels,
"axisLabel": {"rotate": 45, "align": "right"},
},
"yAxis": {"type": "value"},
"series": [
{
"name": "Count",
"type": "bar",
"data": values,
"itemStyle": {"color": "#4285F4"},
}
],
}
return options
@op("BioNeMo > Run benchmark")
@mem.cache(verbose=1)
def run_benchmark(data, labels, *, use_pca: bool = False):
"""
data - contains the single cell expression (or whatever feature) in each row.
labels - contains the string label for each cell
data_shape (R, C)
labels_shape (R,)
"""
np.random.seed(1337)
# Define the target dimension 'n_components'
n_components = 10 # for example, adjust based on your specific needs
# Create a pipeline that includes Gaussian random projection and RandomForestClassifier
if use_pca:
pipeline = Pipeline(
[
("projection", PCA(n_components=n_components)),
("classifier", RandomForestClassifier(class_weight="balanced")),
]
)
else:
pipeline = Pipeline(
[("classifier", RandomForestClassifier(class_weight="balanced"))]
)
# Set up StratifiedKFold to ensure each fold reflects the overall distribution of labels
cv = StratifiedKFold(n_splits=5)
# Define the scoring functions
scoring = {
"accuracy": make_scorer(accuracy_score),
"precision": make_scorer(
precision_score, average="macro"
), # 'macro' averages over classes
"recall": make_scorer(recall_score, average="macro"),
"f1_score": make_scorer(f1_score, average="macro"),
# 'roc_auc' requires probability or decision function; hence use multi_class if applicable
"roc_auc": make_scorer(roc_auc_score, multi_class="ovr"),
}
labels = labels.integer_labels
# Perform stratified cross-validation with multiple metrics using the pipeline
results = cross_validate(
pipeline, data, labels, cv=cv, scoring=scoring, return_train_score=False
)
# Print the cross-validation results
print("Cross-validation metrics:")
results_out = {}
for metric, scores in results.items():
if metric.startswith("test_"):
results_out[metric] = (scores.mean(), scores.std())
print(f"{metric[5:]}: {scores.mean():.3f} (+/- {scores.std():.3f})")
predictions = cross_val_predict(pipeline, data, labels, cv=cv)
# v Return confusion matrix and metrics.
conf_matrix = confusion_matrix(labels, predictions)
return results_out, conf_matrix
@op("BioNeMo > Plot confusion matrix", view="visualization")
@mem.cache(verbose=1)
def plot_confusion_matrix(benchmark_output, labels):
cm = benchmark_output[1]
labels = labels.classes_
str_labels = [str(label) for label in labels]
norm_cm = [[float(val / sum(row)) if sum(row) else 0 for val in row] for row in cm]
# heatmap has the 0,0 at the bottom left corner
num_rows = len(str_labels)
heatmap_data = [
[j, num_rows - i - 1, norm_cm[i][j]]
for i in range(len(labels))
for j in range(len(labels))
]
options = {
"title": {"text": "Confusion Matrix", "left": "center"},
"tooltip": {"position": "top"},
"xAxis": {
"type": "category",
"data": str_labels,
"splitArea": {"show": True},
"axisLabel": {"rotate": 70, "align": "right"},
},
"yAxis": {
"type": "category",
"data": list(reversed(str_labels)),
"splitArea": {"show": True},
},
"grid": {
"height": "70%",
"width": "70%",
"left": "20%",
"right": "10%",
"bottom": "10%",
"top": "10%",
},
"visualMap": {
"min": 0,
"max": 1,
"calculable": True,
"orient": "vertical",
"right": 10,
"top": "center",
"inRange": {
"color": ["#E0F7FA", "#81D4FA", "#29B6F6", "#0288D1", "#01579B"]
},
},
"series": [
{
"name": "Confusion matrix",
"type": "heatmap",
"data": heatmap_data,
"emphasis": {"itemStyle": {"borderColor": "#333", "borderWidth": 1}},
"itemStyle": {"borderColor": "#D3D3D3", "borderWidth": 2},
}
],
}
return options
@op("BioNeMo > Plot accuracy comparison", view="visualization")
def accuracy_comparison(benchmark_output10m, benchmark_output100m):
results_10m = benchmark_output10m[0]
results_106M = benchmark_output100m[0]
data = {
"model": ["10M parameters", "106M parameters"],
"accuracy_mean": [
results_10m["test_accuracy"][0],
results_106M["test_accuracy"][0],
],
"accuracy_std": [
results_10m["test_accuracy"][1],
results_106M["test_accuracy"][1],
],
}
labels = data["model"] # X-axis labels
values = data["accuracy_mean"] # Y-axis values
error_bars = data["accuracy_std"] # Standard deviation for error bars
options = {
"title": {
"text": "Accuracy Comparison",
"left": "center",
"textStyle": {
"fontSize": 20, # Bigger font for title
"fontWeight": "bold", # Make title bold
},
},
"grid": {
"height": "70%",
"width": "70%",
"left": "20%",
"right": "10%",
"bottom": "10%",
"top": "10%",
},
"tooltip": {"trigger": "axis", "axisPointer": {"type": "shadow"}},
"xAxis": {
"type": "category",
"data": labels,
"axisLabel": {
"rotate": 45, # Rotate labels for better readability
"align": "right",
"textStyle": {
"fontSize": 14, # Bigger font for X-axis labels
"fontWeight": "bold",
},
},
},
"yAxis": {
"type": "value",
"name": "Accuracy",
"min": 0,
"max": 1,
"interval": 0.1, # Matches np.arange(0, 1.05, 0.05)
"axisLabel": {
"textStyle": {
"fontSize": 14, # Bigger font for X-axis labels
"fontWeight": "bold",
}
},
},
"series": [
{
"name": "Accuracy",
"type": "bar",
"data": values,
"itemStyle": {
"color": "#440154" # Viridis color palette (dark purple)
},
},
{
"name": "Error Bars",
"type": "errorbar",
"data": [
[val - err, val + err] for val, err in zip(values, error_bars)
],
"itemStyle": {"color": "#1f77b4"},
},
],
}
return options
@op("BioNeMo > Plot f1 comparison", view="visualization")
def f1_comparison(benchmark_output10m, benchmark_output100m):
results_10m = benchmark_output10m[0]
results_106M = benchmark_output100m[0]
data = {
"model": ["10M parameters", "106M parameters"],
"f1_score_mean": [
results_10m["test_f1_score"][0],
results_106M["test_f1_score"][0],
],
"f1_score_std": [
results_10m["test_f1_score"][1],
results_106M["test_f1_score"][1],
],
}
labels = data["model"] # X-axis labels
values = data["f1_score_mean"] # Y-axis values
error_bars = data["f1_score_std"] # Standard deviation for error bars
options = {
"title": {
"text": "F1 Score Comparison",
"left": "center",
"textStyle": {
"fontSize": 20, # Bigger font for title
"fontWeight": "bold", # Make title bold
},
},
"grid": {
"height": "70%",
"width": "70%",
"left": "20%",
"right": "10%",
"bottom": "10%",
"top": "10%",
},
"tooltip": {"trigger": "axis", "axisPointer": {"type": "shadow"}},
"xAxis": {
"type": "category",
"data": labels,
"axisLabel": {
"rotate": 45, # Rotate labels for better readability
"align": "right",
"textStyle": {
"fontSize": 14, # Bigger font for X-axis labels
"fontWeight": "bold",
},
},
},
"yAxis": {
"type": "value",
"name": "F1 Score",
"min": 0,
"max": 1,
"interval": 0.1, # Matches np.arange(0, 1.05, 0.05),
"axisLabel": {
"textStyle": {
"fontSize": 14, # Bigger font for X-axis labels
"fontWeight": "bold",
}
},
},
"series": [
{
"name": "F1 Score",
"type": "bar",
"data": values,
"itemStyle": {
"color": "#440154" # Viridis color palette (dark purple)
},
},
{
"name": "Error Bars",
"type": "errorbar",
"data": [
[val - err, val + err] for val, err in zip(values, error_bars)
],
"itemStyle": {"color": "#1f77b4"},
},
],
}
return options
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