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# # Computes embeddings for the dataset and prints their dimensions
from chemCPA.paths import DATA_DIR, PROJECT_DIR, ROOT, EMBEDDING_DIR
import sys
import os
from tqdm.auto import tqdm
import pandas as pd
# Add the parent directory of embeddings to Python path
sys.path.append(str(ROOT))
import embeddings.rdkit.embedding_rdkit as embedding_rdkit
# Define the datasets to process with their corresponding SMILES keys
datasets = [
('lincs_smiles.h5ad', 'SMILES'),
('lincs_full_smiles.h5ad', 'canonical_smiles'), # Changed SMILES key to lowercase
#('sciplex_complete.h5ad', 'SMILES'),
#('adata_MCF7.h5ad', 'SMILES'),
#('adata_MCF7_lincs_genes.h5ad', 'SMILES'),
#('adata_K562.h5ad', 'SMILES'),
#('adata_K562_lincs_genes.h5ad', 'SMILES'),
#('adata_A549.h5ad', 'SMILES'),
#('adata_A549_lincs_genes.h5ad', 'SMILES'),
#('sciplex_complete_subset_lincs_genes_v2.h5ad', 'SMILES'),
#('sciplex_complete_middle_subset_v2.h5ad', 'SMILES'),
#('sciplex_complete_middle_subset_lincs_genes_v2.h5ad', 'SMILES'),
('sciplex_complete_v2.h5ad', 'SMILES'),
('sciplex_complete_lincs_genes_v2.h5ad', 'SMILES')
#('combo_sciplex_prep_hvg_filtered.h5ad', 'smiles_rdkit')
]
# Define desired embedding dimension
FIXED_EMBEDDING_DIM = 200 # or whatever dimension you want
# Define whether to skip variance filtering to keep dimensions consistent
SKIP_VARIANCE_FILTER = False # Set this to True to keep all dimensions
print("\nComputing and analyzing embeddings:")
print(f"Using fixed embedding dimension: {FIXED_EMBEDDING_DIM}")
print(f"Skip variance filtering: {SKIP_VARIANCE_FILTER}")
print("-" * 50)
# Process each dataset
for dataset, smiles_key in tqdm(datasets, desc="Computing RDKit embeddings"):
h5ad_path = os.path.join(DATA_DIR, dataset)
base_name = os.path.splitext(dataset)[0]
output_filename = f"{base_name}_rdkit2D_embedding.parquet"
output_path = os.path.join(EMBEDDING_DIR, 'rdkit', output_filename)
# Create the output directory if it doesn't exist
os.makedirs(os.path.dirname(output_path), exist_ok=True)
try:
# Compute embeddings without variance filtering
embedding_rdkit.compute_rdkit_embeddings(
h5ad_path,
output_path=output_path,
smiles_key=smiles_key,
skip_variance_filter=SKIP_VARIANCE_FILTER
)
# Read and analyze the generated embeddings
embeddings_df = pd.read_parquet(output_path)
print(f"\nEmbedding analysis for {dataset}:")
print(f"Shape: {embeddings_df.shape}")
print(f"Number of features: {embeddings_df.shape[1]}")
print(f"Memory usage: {embeddings_df.memory_usage().sum() / 1024**2:.2f} MB")
print(f"File location: {output_path}")
print("-" * 50)
except Exception as e:
tqdm.write(f"Error processing {dataset}: {str(e)}")
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