Add complete docstrings and typehinting to aepsych python client (#664)

Summary:
Pull Request resolved: #664

Updated the aepsych python client to match the rest of the aepsych documentation/type hinting.

Differential Revision: D70426277

clients/python/aepsych_client/client.py

@@ -7,9 +7,10 @@
 import json
 import socket
 import warnings
-from typing import Any, Dict, List, Optional, TYPE_CHECKING, Union
+from typing import Any, Dict, List, Literal, Optional, TYPE_CHECKING, Union
 
 if TYPE_CHECKING:
+    import torch
     from aepsych.server import AEPsychServer
 
 
@@ -56,7 +57,7 @@ def __init__(
             self.socket = None
 
     def load_config_index(self) -> None:
-        """Loads the config index when server is not None"""
+        """Loads the config index when there is an in-memory server."""
         if self.server is None:
             raise AttributeError("there is no in-memory server")
 
@@ -78,7 +79,15 @@ def connect(self, ip: str, port: int) -> None:
         self.socket.connect(addr)
 
     def finalize(self) -> Dict[str, Any]:
-        """Let the server know experiment is complete."""
+        """Let the server know experiment is complete and stop the server.
+
+        Returns:
+            Dict[str, Any]: A dictionary with two entries:
+            - "config": dictionary with config (keys are strings, values are floats).
+                Currently always "Terminate" if this function succeeds.
+            - "is_finished": boolean, true if the strat is finished. Currently always
+                true if this function succeeds.
+        """
         request = {"message": "", "type": "exit"}
         return self._send_recv(request)
 
@@ -110,11 +119,11 @@ def ask(
             num_points[int]: Number of points to return.
 
         Returns:
-            Dict[int, Dict[str, Any]]: Next configuration(s) to evaluate.
-            If using the legacy backend, this is formatted as a dictionary where keys are parameter names and values
-            are lists of parameter values.
-            If using the Ax backend, this is formatted as a dictionary of dictionaries where the outer keys are trial indices,
-            the inner keys are parameter names, and the values are parameter values.
+            Dict[str, Any]: A dictionary with three entries
+                - "config": dictionary with config (keys are strings, values are floats), None
+                    if skipping computations during replay.
+                - "is_finished": boolean, true if the strat is finished
+                - "num_points": integer, number of points returned.
         """
         request = {"message": {"num_points": num_points}, "type": "ask"}
         response = self._send_recv(request)
@@ -143,9 +152,6 @@ def tell(
                 - "trials_recorded": integer, the number of trials recorded in the
                     database.
                 - "model_data_added": integer, the number of datapoints added to the model.
-
-        Raises:
-            AssertionError if server failed to acknowledge the tell.
         """
         message = {
             "config": config,
@@ -177,9 +183,6 @@ def configure(
         Returns:
             Dict[str, Any]: A dictionary with one entry
                 - "strat_id": integer, the stategy ID for what was just set up.
-
-        Raises:
-            AssertionError if neither config path nor config_str is passed.
         """
 
         if config_path is not None:
@@ -214,9 +217,6 @@ def resume(
         Returns:
             Dict[str, Any]: A dictionary with one entry
                 - "strat_id": integer, the stategy ID that was resumed.
-
-        Raises:
-            AssertionError if name or ID does not exist, or if both name and ID are passed.
         """
         if config_id is not None:
             assert config_name is None, "if config_id is passed, don't pass config_name"
@@ -238,11 +238,11 @@ def resume(
 
     def query(
         self,
-        query_type="max",
-        probability_space=False,
-        x=None,
-        y=None,
-        constraints=None,
+        query_type: Literal["max", "min", "prediction", "inverse"] = "max",
+        probability_space: bool = False,
+        x: Optional[Dict[str, Any]] = None,
+        y: Optional[Union[float, "torch.Tensor"]] = None,
+        constraints: Optional[Dict[int, float]] = None,
         **kwargs,
     ) -> Dict[str, Any]:
         """Queries the underlying model for a specific query.
@@ -284,5 +284,5 @@ def query(
 
         return self._send_recv(request)
 
-    def __del___(self):
+    def __del__(self):
         self.finalize()