release v0.5.0

Makefile

@@ -1,4 +1,4 @@
-VERSION ?= 0.5.0.dev1
+VERSION ?= 0.5.0
 SHELL := /bin/bash
 
 .PHONY: releasehere

anaconda_build/meta.yaml

@@ -1,6 +1,6 @@
 package:
   name: openprotein-python
-  version: "0.5.0.dev1"
+  version: "0.5.0"
 
 source:
   path: ../

openprotein/api/embedding.py

@@ -6,7 +6,15 @@
 from openprotein.api.align import csv_stream
 from openprotein.base import APISession
 from openprotein.errors import InvalidParameterError
-from openprotein.schemas import AttnJob, EmbeddingsJob, Job, LogitsJob, ModelMetadata
+from openprotein.schemas import (
+    AttnJob,
+    EmbeddingsJob,
+    GenerateJob,
+    LogitsJob,
+    ModelMetadata,
+    ScoreJob,
+    ScoreSingleSiteJob,
+)
 from pydantic import TypeAdapter
 
 PATH_PREFIX = "v1/embeddings"
@@ -256,7 +264,7 @@ def request_score_post(
     model_id: str,
     sequences: list[bytes] | list[str],
     prompt_id: str | None = None,
-) -> Job:
+) -> ScoreJob:
     """
     POST a request for sequence scoring for the given model ID. \
         Returns a Job object referring to this request \
@@ -284,15 +292,15 @@ def request_score_post(
     if prompt_id is not None:
         body["prompt_id"] = prompt_id
     response = session.post(endpoint, json=body)
-    return Job.model_validate(response.json())
+    return ScoreJob.model_validate(response.json())
 
 
 def request_score_single_site_post(
     session: APISession,
     model_id: str,
-    base_sequence: bytes,
+    base_sequence: bytes | str,
     prompt_id: str | None = None,
-) -> Job:
+) -> ScoreSingleSiteJob:
     """
     POST a request for single site mutation scoring for the given model ID. \
         Returns a Job object referring to this request \
@@ -314,12 +322,16 @@ def request_score_single_site_post(
     endpoint = PATH_PREFIX + f"/models/{model_id}/score_single_site"
 
     body: dict = {
-        "base_sequence": base_sequence.decode(),
+        "base_sequence": (
+            base_sequence.decode()
+            if isinstance(base_sequence, bytes)
+            else base_sequence
+        ),
     }
     if prompt_id is not None:
         body["prompt_id"] = prompt_id
     response = session.post(endpoint, json=body)
-    return Job.model_validate(response.json())
+    return ScoreSingleSiteJob.model_validate(response.json())
 
 
 def request_generate_post(
@@ -332,7 +344,7 @@ def request_generate_post(
     max_length: int = 1000,
     random_seed: int | None = None,
     prompt_id: str | None = None,
-) -> Job:
+) -> GenerateJob:
     """
     POST a request for sequence generation for the given model ID. \
         Returns a Job object referring to this request \
@@ -364,7 +376,7 @@ def request_generate_post(
         random_seed = random.randrange(2**32)
 
     body: dict = {
-        "generate_n": num_samples,
+        "n_sequences": num_samples,
         "temperature": temperature,
         "maxlen": max_length,
     }
@@ -377,4 +389,4 @@ def request_generate_post(
     if prompt_id is not None:
         body["prompt_id"] = prompt_id
     response = session.post(endpoint, json=body)
-    return Job.model_validate(response.json())
+    return GenerateJob.model_validate(response.json())

openprotein/api/fold.py

@@ -1,6 +1,6 @@
 from openprotein.api.embedding import ModelMetadata
 from openprotein.base import APISession
-from openprotein.schemas import Job
+from openprotein.schemas import FoldJob
 from pydantic import TypeAdapter
 
 PATH_PREFIX = "v1/fold"
@@ -80,7 +80,7 @@ def fold_models_esmfold_post(
     session: APISession,
     sequences: list[bytes],
     num_recycles: int | None = None,
-) -> Job:
+) -> FoldJob:
     """
     POST a request for structure prediction using ESMFold. Returns a Job object referring to this request
     that can be used to retrieve results later.
@@ -108,7 +108,7 @@ def fold_models_esmfold_post(
         body["num_recycles"] = num_recycles
 
     response = session.post(endpoint, json=body)
-    return Job.model_validate(response.json())
+    return FoldJob.model_validate(response.json())
 
 
 def fold_models_alphafold2_post(
@@ -117,7 +117,7 @@ def fold_models_alphafold2_post(
     num_recycles: int | None = None,
     num_models: int = 1,
     num_relax: int = 0,
-) -> Job:
+) -> FoldJob:
     """
     POST a request for structure prediction using AlphaFold2. Returns a Job object referring to this request
     that can be used to retrieve results later.
@@ -152,4 +152,4 @@ def fold_models_alphafold2_post(
     response = session.post(endpoint, json=body)
     # GET endpoint for AF2 expects the query sequence (first sequence) within the MSA
     # since we don't know what the is, leave the sequence out of the future to be retrieved when calling get()
-    return Job.model_validate(response.json())
+    return FoldJob.model_validate(response.json())

openprotein/api/predictor.py

@@ -3,7 +3,16 @@
 import numpy as np
 import pandas as pd
 from openprotein.base import APISession
-from openprotein.schemas import Job, PredictorMetadata
+from openprotein.schemas import (
+    CVJob,
+    Job,
+    PredictJob,
+    PredictMultiJob,
+    PredictMultiSingleSiteJob,
+    PredictorMetadata,
+    PredictSingleSiteJob,
+    TrainJob,
+)
 from pydantic import TypeAdapter
 
 PATH_PREFIX = "v1/predictor"
@@ -99,13 +108,33 @@ def predictor_fit_gp_post(
         body["description"] = description
 
     response = session.post(endpoint, json=body)
-    return Job.model_validate(response.json())
+    return TrainJob.model_validate(response.json())
 
 
 def predictor_delete(session: APISession, predictor_id: str):
     raise NotImplementedError()
 
 
+def predictor_crossvalidate_post(
+    session: APISession, predictor_id: str, n_splits: int | None = None
+):
+    endpoint = PATH_PREFIX + f"/{predictor_id}/crossvalidate"
+
+    params = {}
+    if n_splits is not None:
+        params["n_splits"] = n_splits
+    response = session.post(endpoint, params=params)
+
+    return CVJob.model_validate(response.json())
+
+
+def predictor_crossvalidate_get(session: APISession, crossvalidate_job_id: str):
+    endpoint = PATH_PREFIX + f"/crossvalidate/{crossvalidate_job_id}"
+
+    response = session.get(endpoint)
+    return response.content
+
+
 def predictor_predict_post(
     session: APISession, predictor_id: str, sequences: list[bytes] | list[str]
 ):
@@ -117,7 +146,25 @@ def predictor_predict_post(
     }
     response = session.post(endpoint, json=body)
 
-    return Job.model_validate(response.json())
+    return PredictJob.model_validate(response.json())
+
+
+def predictor_predict_single_site_post(
+    session: APISession,
+    predictor_id: str,
+    base_sequence: bytes | str,
+):
+    endpoint = PATH_PREFIX + f"/{predictor_id}/predict_single_site"
+
+    base_sequence = (
+        base_sequence.decode() if isinstance(base_sequence, bytes) else base_sequence
+    )
+    body = {
+        "base_sequence": base_sequence,
+    }
+    response = session.post(endpoint, json=body)
+
+    return PredictSingleSiteJob.model_validate(response.json())
 
 
 def predictor_predict_get_sequences(
@@ -179,9 +226,9 @@ def predictor_predict_get_batched_result(
     return response.content
 
 
-def decode_score(data: bytes, batched: bool = False) -> tuple[np.ndarray, np.ndarray]:
+def decode_predict(data: bytes, batched: bool = False) -> tuple[np.ndarray, np.ndarray]:
     """
-    Decode embedding.
+    Decode prediction scores.
 
     Args:
         data (bytes): raw bytes encoding the array received over the API
@@ -203,3 +250,25 @@ def decode_score(data: bytes, batched: bool = False) -> tuple[np.ndarray, np.nda
     mus = scores[:, ::2]
     vars = scores[:, 1::2]
     return mus, vars
+
+
+def decode_crossvalidate(data: bytes) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """
+    Decode crossvalidate scores.
+
+    Args:
+        data (bytes): raw bytes encoding the array received over the API
+
+    Returns:
+        mus (np.ndarray): decoded array of means
+        vars (np.ndarray): decoded array of variances
+    """
+    s = io.BytesIO(data)
+    # should contain header and sequence column
+    df = pd.read_csv(s)
+    scores = df.values
+    # row_num, seq, measurement_name, y, y_mu, y_var
+    y = scores[:, 3]
+    mus = scores[:, 4]
+    vars = scores[:, 5]
+    return y, mus, vars

openprotein/api/svd.py

@@ -1,6 +1,9 @@
+import io
+
+import numpy as np
 from openprotein.base import APISession
 from openprotein.errors import InvalidParameterError
-from openprotein.schemas import FitJob, Job, SVDEmbeddingsJob, SVDMetadata
+from openprotein.schemas import FitJob, SVDEmbeddingsJob, SVDMetadata
 from pydantic import TypeAdapter
 
 PATH_PREFIX = "v1/embeddings/svd"
@@ -40,6 +43,46 @@ def svd_get_sequences(session: APISession, svd_id: str) -> list[bytes]:
     return TypeAdapter(list[bytes]).validate_python(response.json())
 
 
+def embed_get_sequence_result(
+    session: APISession, job_id: str, sequence: str | bytes
+) -> bytes:
+    """
+    Get encoded svd embeddings result for a sequence from the request ID.
+
+    Parameters
+    ----------
+    session : APISession
+        Session object for API communication.
+    job_id : str
+        job ID to retrieve results from
+    sequence : bytes
+        sequence to retrieve results for
+
+    Returns
+    -------
+    result : bytes
+    """
+    if isinstance(sequence, bytes):
+        sequence = sequence.decode()
+    endpoint = PATH_PREFIX + f"/embed/{job_id}/{sequence}"
+    response = session.get(endpoint)
+    return response.content
+
+
+def embed_decode(data: bytes) -> np.ndarray:
+    """
+    Decode embedding.
+
+    Args:
+        data (bytes): raw bytes encoding the array received over the API
+
+    Returns:
+        np.ndarray: decoded array
+    """
+    s = io.BytesIO(data)
+    return np.load(s, allow_pickle=False)
+
+
 def svd_delete(session: APISession, svd_id: str):
     """
     Delete and SVD model.
@@ -121,7 +164,7 @@ def svd_fit_post(
 
 
 def svd_embed_post(
-    session: APISession, svd_id: str, sequences: list[bytes]
+    session: APISession, svd_id: str, sequences: list[bytes] | list[str]
 ) -> SVDEmbeddingsJob:
     """
     POST a request for embeddings from the given SVD model.
@@ -139,7 +182,7 @@ def svd_embed_post(
     -------
     Job
     """
-    endpoint = PATH_PREFIX + f"/svd/{svd_id}/embed"
+    endpoint = PATH_PREFIX + f"/{svd_id}/embed"
 
     sequences_unicode = [(s if isinstance(s, str) else s.decode()) for s in sequences]
     body = {

openprotein/app/models/align/msa.py

@@ -54,14 +54,7 @@ def __init__(
         """
         super().__init__(session, job)
         self.page_size = page_size
-        self._msa_id = None
-        self._prompt_id = None
-
-    @property
-    def msa_id(self) -> str:
-        if self._msa_id is None:
-            self._msa_id = self.job.job_id
-        return self._msa_id
+        self.msa_id = self.job.job_id
 
     # def wait(self, verbose: bool = False):
     #     _ = self.job.wait(

openprotein/app/models/align/prompt.py

@@ -59,12 +59,7 @@ def __init__(
         if msa_id is None:
             msa_id = job_api.job_args_get(self.session, job.job_id).get("root_msa")
         self._msa_id = msa_id
-
-    @property
-    def prompt_id(self) -> str:
-        if self._prompt_id is None:
-            self._prompt_id = self.job.job_id
-        return self._prompt_id
+        self.prompt_id = self.job.job_id
 
     # def wait(self, verbose: bool = False, **kwargs) -> Iterator[list[str]]:
     #     _ = self.job.wait(

openprotein/app/models/design.py

@@ -1,6 +1,6 @@
 from openprotein.api import design
 from openprotein.base import APISession
-from openprotein.schemas import DesignJob, DesignResults
+from openprotein.schemas import DesignJob, DesignResults, DesignStep
 
 from .futures import Future, PagedFuture
 
@@ -20,8 +20,14 @@ def __str__(self) -> str:
     def __repr__(self) -> str:
         return repr(self.job)
 
-    def _fmt_results(self, results: DesignResults) -> list[dict]:
-        return [i.model_dump() for i in results.result]
+    def _fmt_results(
+        self, results: DesignResults
+    ) -> (
+        # list[dict]
+        list[DesignStep]
+    ):
+        # return [i.model_dump() for i in results.result]
+        return results.result
 
     @property
     def id(self):