Upload upload.py

upload.py

@@ -0,0 +1,273 @@
+import os
+from Bio import SeqIO
+from tqdm.auto import tqdm
+import pandas as pd
+from huggingface_hub import HfApi
+
+
+aav_files = [
+    "des_mut",
+    "low_vs_high",
+    "mut_des",
+    "one_vs_many",
+    "sampled",
+    "seven_vs_many",
+    "two_vs_many",
+]
+
+
+meltome_files = [
+    "human",
+    "human_cell",
+    "mixed_split",
+]
+
+
+sav_files = [
+    "human",
+    "only_savs",
+    "mixed",
+]
+
+
+scl_files = [
+    "balanced",
+    "human_hard",
+    "human_soft",
+    "mixed_hard",
+    "mixed_soft",
+]
+
+
+gb1_files = [
+    "low_vs_high",
+    "one_vs_rest",
+    "sampled",
+    "three_vs_rest",
+    "two_vs_rest",
+]
+
+
+def download_wget(filename, repo):
+    if os.path.exists(f"{filename}.fasta"):
+        return
+    url = f"http://data.bioembeddings.com/public/FLIP/fasta/{repo}/{filename}.fasta"
+    os.system(f"wget {url}")
+
+
+def upload_aav():
+    repo = "aav"
+    for filename in aav_files:
+        download_wget(filename, repo)
+        fasta_file = f"{filename}.fasta"
+        csv_file = f"{filename}.csv"
+        db = SeqIO.index(fasta_file, "fasta")
+        output = []
+
+        for _, record in tqdm(db.items()):
+            description = record.description
+            description = description.split()
+            sequence = str(record.seq)
+            seqid = description[0]
+            label = float(description[1].split("=")[1])
+            split = description[2].split("=")[1]
+            validation = description[3].split("=")[1]
+
+            output.append({
+                "seqid": seqid,
+                "label": label,
+                "sequence": sequence,
+                "split": split.lower(),
+                "validation": validation.lower(),
+            })
+        pd.DataFrame(output).to_csv(csv_file, index=False)
+
+        api = HfApi()
+        api.create_repo(
+            repo_id=f"hazemessam/{repo}",
+            repo_type="dataset",
+            exist_ok=True,
+        )
+        api.upload_file(
+            path_or_fileobj=csv_file,
+            path_in_repo=csv_file,
+            repo_id=f"hazemessam/{repo}",
+            repo_type="dataset",
+        )
+        os.system(f"rm -rf {fasta_file}")
+        os.system(f"rm -rf {csv_file}")
+
+
+def upload_meltome():
+    repo = "meltome"
+    for filename in meltome_files:
+        download_wget(filename, repo)
+        fasta_file = f"{filename}.fasta"
+        csv_file = f"{filename}.csv"
+        db = SeqIO.index(fasta_file, "fasta")
+        output = []
+        for _, record in tqdm(db.items()):
+            description = record.description
+            description = description.split()
+            sequence = str(record.seq)
+            seqid = description[0]
+            label = float(description[1].split("=")[1])
+            split = description[2].split("=")[1]
+            validation = description[3].split("=")[1]
+
+            output.append({
+                "seqid": seqid,
+                "label": label,
+                "sequence": sequence,
+                "split": split.lower(),
+                "validation": validation.lower(),
+            })
+        pd.DataFrame(output).to_csv(csv_file, index=False)
+
+        api = HfApi()
+        api.create_repo(
+            repo_id=f"hazemessam/{repo}",
+            repo_type="dataset",
+            exist_ok=True,
+        )
+        api.upload_file(
+            path_or_fileobj=csv_file,
+            path_in_repo=csv_file,
+            repo_id=f"hazemessam/{repo}",
+            repo_type="dataset",
+        )
+        os.system(f"rm -rf {fasta_file}")
+        os.system(f"rm -rf {csv_file}")
+
+
+def upload_sav():
+    repo = "sav"
+    for filename in sav_files:
+        download_wget(filename, repo)
+        fasta_file = f"{filename}.fasta"
+        csv_file = f"{filename}.csv"
+        db = SeqIO.index(fasta_file, "fasta")
+        output = []
+        for _, record in tqdm(db.items()):
+            description = record.description
+            description = description.split()
+            sequence = str(record.seq)
+            seqid = description[0]
+            # the label is a string in sav and scl datasets
+            label = description[1].split("=")[1]
+            split = description[2].split("=")[1]
+            validation = description[3].split("=")[1]
+
+            output.append({
+                "seqid": seqid,
+                "label": label,
+                "sequence": sequence,
+                "split": split.lower(),
+                "validation": validation.lower(),
+            })
+        pd.DataFrame(output).to_csv(csv_file, index=False)
+        api = HfApi()
+        api.create_repo(
+            repo_id=f"hazemessam/{repo}",
+            repo_type="dataset",
+            exist_ok=True,
+        )
+        api.upload_file(
+            path_or_fileobj=csv_file,
+            path_in_repo=csv_file,
+            repo_id=f"hazemessam/{repo}",
+            repo_type="dataset",
+        )
+        os.system(f"rm -rf {fasta_file}")
+        os.system(f"rm -rf {csv_file}")
+
+
+def upload_scl():
+    repo = "scl"
+    for filename in scl_files:
+        download_wget(filename, repo)
+        fasta_file = f"{filename}.fasta"
+        csv_file = f"{filename}.csv"
+        db = SeqIO.index(fasta_file, "fasta")
+        output = []
+        for _, record in tqdm(db.items()):
+            description = record.description
+            description = description.split()
+            sequence = str(record.seq)
+            seqid = description[0]
+            # the label is a string in sav and scl datasets
+            label = description[1].split("=")[1]
+            split = description[2].split("=")[1]
+            validation = description[3].split("=")[1]
+
+            output.append({
+                "seqid": seqid,
+                "label": label,
+                "sequence": sequence,
+                "split": split.lower(),
+                "validation": validation.lower(),
+            })
+        pd.DataFrame(output).to_csv(csv_file, index=False)
+        api = HfApi()
+        api.create_repo(
+            repo_id=f"hazemessam/{repo}",
+            repo_type="dataset",
+            exist_ok=True,
+        )
+        api.upload_file(
+            path_or_fileobj=csv_file,
+            path_in_repo=csv_file,
+            repo_id=f"hazemessam/{repo}",
+            repo_type="dataset",
+        )
+        os.system(f"rm -rf {fasta_file}")
+        os.system(f"rm -rf {csv_file}")
+
+
+def upload_gb1():
+    repo = "gb1"
+    for filename in gb1_files:
+        download_wget(filename, repo)
+        fasta_file = f"{filename}.fasta"
+        csv_file = f"{filename}.csv"
+        db = SeqIO.index(fasta_file, "fasta")
+        output = []
+        for _, record in tqdm(db.items()):
+            description = record.description
+            description = description.split()
+            sequence = str(record.seq)
+            seqid = description[0]
+            label = float(description[1].split("=")[1])
+            split = description[2].split("=")[1]
+            validation = description[3].split("=")[1]
+
+            output.append({
+                "seqid": seqid,
+                "label": label,
+                "sequence": sequence,
+                "split": split.lower(),
+                "validation": validation.lower(),
+            })
+        pd.DataFrame(output).to_csv(csv_file, index=False)
+        api = HfApi()
+        api.create_repo(
+            repo_id=f"hazemessam/{repo}",
+            repo_type="dataset",
+            exist_ok=True,
+        )
+        api.upload_file(
+            path_or_fileobj=csv_file,
+            path_in_repo=csv_file,
+            repo_id=f"hazemessam/{repo}",
+            repo_type="dataset",
+        )
+        os.system(f"rm -rf {fasta_file}")
+        os.system(f"rm -rf {csv_file}")
+
+
+if __name__ == "__main__":
+    upload_gb1()
+    upload_meltome()
+    upload_sav()
+    upload_scl()
+    upload_aav()