feat: Model accepts a wider range of data

Values which resemble Numpy `ndarray` and pandas `Series` should be
accepted. They will be converted into (JSON-serializable) lists.

Closes #51

stan/model.py

@@ -1,10 +1,14 @@
+import collections.abc
+import json
 import time
 import typing
 
 import requests
 
 import httpstan.models
+import httpstan.schemas
 import httpstan.services.arguments as arguments
+import httpstan.utils
 import stan.common
 import stan.fit
 
@@ -13,6 +17,36 @@
 import numpy as np
 
 
+def _ensure_json_serializable(data: dict) -> dict:
+    """Convert `data` with numpy.ndarray-like values to JSON serializable form.
+
+    Arguments:
+        data (dict): A Python dictionary or mapping providing the data for the
+            model. Variable names are the keys and the values are their
+            associated values. Default is an empty dictionary.
+
+    Returns:
+        dict: Data dictionary with JSON-serializable values.
+    """
+    for key, value in data.copy().items():
+        # first, see if the value is already JSON-serializable
+        try:
+            json.dumps(value)
+        except TypeError:
+            pass
+        else:
+            continue
+        # numpy scalar
+        if isinstance(value, np.ndarray) and value.ndim == 0:
+            data[key] = np.asarray(value).tolist()
+        # numpy.ndarray, pandas.Series, and anything similar
+        elif isinstance(value, collections.abc.Collection):
+            data[key] = np.asarray(value).tolist()
+        else:
+            raise TypeError(f"Value associated with variable `{key}` is not JSON serializable.")
+    return data
+
+
 class Model:
     """Stores data associated with and proxies calls to a Stan model.
 
@@ -164,6 +198,7 @@ def build(program_code, data=None, random_seed=None):
     """
     if data is None:
         data = {}
+    data = _ensure_json_serializable(data)
     with stan.common.httpstan_server() as server:
         host, port = server.host, server.port
 

tests/test_data_validation.py

@@ -0,0 +1,35 @@
+import json
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import stan.model
+
+
+def test_ensure_json():
+    data = {"K": 3}
+    assert stan.model._ensure_json_serializable(data) == data
+    data = {"K": 3, "x": [3, 4, 5]}
+    assert stan.model._ensure_json_serializable(data) == data
+
+    class DummyClass:
+        pass
+
+    data = {"K": DummyClass(), "x": [3, 4, 5]}
+    with pytest.raises(TypeError, match=r"Value associated with variable `K`"):
+        stan.model._ensure_json_serializable(data)
+
+
+def test_ensure_json_numpy():
+    data = {"K": 3, "x": np.array([3, 4, 5])}
+    expected = {"K": 3, "x": [3, 4, 5]}
+    assert stan.model._ensure_json_serializable(data) == expected
+    assert json.dumps(stan.model._ensure_json_serializable(data))
+
+
+def test_ensure_json_pandas():
+    data = {"K": 3, "x": pd.Series([3, 4, 5])}
+    expected = {"K": 3, "x": [3, 4, 5]}
+    assert stan.model._ensure_json_serializable(data) == expected
+    assert json.dumps(stan.model._ensure_json_serializable(data))

tests/test_eight_schools.py

@@ -1,3 +1,5 @@
+import numpy as np
+import pandas as pd
 import pytest
 
 import stan
@@ -41,6 +43,17 @@ def test_eight_schools_build(posterior):
     assert posterior is not None
 
 
+def test_eight_schools_build_numpy(posterior):
+    """Verify eight schools compiles."""
+    schools_data_alt = {
+        "J": 8,
+        "y": np.array([28, 8, -3, 7, -1, 1, 18, 12]),
+        "sigma": pd.Series([15, 10, 16, 11, 9, 11, 10, 18], name="sigma"),
+    }
+    posterior_alt = stan.build(program_code, data=schools_data_alt)
+    assert posterior_alt is not None
+
+
 def test_eight_schools_sample(posterior):
     """Sample from a simple model."""
     num_chains, num_samples = 2, 200