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"""Example class to represent endpoint examples."""
from abc import abstractmethod
from datetime import date, datetime, timedelta
from typing import Any, Dict, List, Literal, Optional, Union, _GenericAlias
from pydantic import (
BaseModel,
ConfigDict,
Field,
computed_field,
model_validator,
)
QUOTE_TYPES = {str, date}
class Example(BaseModel):
"""Example model."""
scope: str
model_config = ConfigDict(validate_assignment=True)
@abstractmethod
def to_python(self, **kwargs) -> str:
"""Return a Python code representation of the example."""
class APIEx(Example):
"""API Example model."""
scope: Literal["api"] = "api"
description: Optional[str] = Field(
default=None, description="Optional description unless more than 3 parameters"
)
parameters: Dict[str, Union[str, int, float, bool, List[str], List[Dict[str, Any]]]]
@computed_field # type: ignore[misc]
@property
def provider(self) -> Optional[str]:
"""Return the provider from the parameters."""
return self.parameters.get("provider") # type: ignore
@model_validator(mode="before")
@classmethod
def validate_model(cls, values: dict) -> dict:
"""Validate model."""
parameters = values.get("parameters", {})
if "provider" not in parameters and "data" not in parameters:
raise ValueError("API example must specify a provider.")
provider = parameters.get("provider")
if provider and not isinstance(provider, str):
raise ValueError("Provider must be a string.")
return values
@staticmethod
def _unpack_type(type_: type) -> set:
"""Unpack types from types, example Union[List[str], int] -> {str, int}."""
if (
hasattr(type_, "__args__")
and type(type_) # pylint: disable=unidiomatic-typecheck
is not _GenericAlias
):
return set().union(*map(APIEx._unpack_type, type_.__args__))
return {type_} if isinstance(type_, type) else {type(type_)}
@staticmethod
def _shift(i: int) -> float:
"""Return a transformation of the integer."""
return 2 * (i + 1) / (2 * i) % 1 + 1
@staticmethod
def mock_data(
dataset: Literal["timeseries", "panel"],
size: int = 5,
sample: Optional[Dict[str, Any]] = None,
multiindex: Optional[Dict[str, Any]] = None,
) -> List[Dict]:
"""Generate mock data from a sample.
Parameters
----------
dataset : str
The type of data to return:
- 'timeseries': Time series data
- 'panel': Panel data (multiindex)
size : int
The size of the data to return, default is 5.
sample : Optional[Dict[str, Any]], optional
A sample of the data to return, by default None.
multiindex_names : Optional[List[str]], optional
The names of the multiindex, by default None.
Timeseries default sample:
{
"date": "2023-01-01",
"open": 110.0,
"high": 120.0,
"low": 100.0,
"close": 115.0,
"volume": 10000,
}
Panel default sample:
{
"portfolio_value": 100000,
"risk_free_rate": 0.02,
}
multiindex: {"asset_manager": "AM", "time": 0}
Returns
-------
List[Dict]
A list of dictionaries with the mock data.
"""
if dataset == "timeseries":
sample = sample or {
"date": "2023-01-01",
"open": 110.0,
"high": 120.0,
"low": 100.0,
"close": 115.0,
"volume": 10000,
}
result = []
for i in range(1, size + 1):
s = APIEx._shift(i)
obs = {}
for k, v in sample.items():
if k == "date":
obs[k] = (
datetime.strptime(v, "%Y-%m-%d") + timedelta(days=i)
).strftime("%Y-%m-%d")
else:
obs[k] = round(v * s, 2)
result.append(obs)
return result
if dataset == "panel":
sample = sample or {
"portfolio_value": 100000.0,
"risk_free_rate": 0.02,
}
multiindex = multiindex or {"asset_manager": "AM", "time": 0}
multiindex_names = list(multiindex.keys())
idx_1 = multiindex_names[0]
idx_2 = multiindex_names[1]
items_per_idx = 2
item: Dict[str, Any] = {
"is_multiindex": True,
"multiindex_names": str(multiindex_names),
}
# Iterate over the number of items to create and add them to the result
result = []
for i in range(1, size + 1):
item[idx_1] = f"{idx_1}_{i}"
for j in range(items_per_idx):
item[idx_2] = j
for k, v in sample.items():
if isinstance(v, str):
item[k] = f"{v}_{j}"
else:
item[k] = round(v * APIEx._shift(i + j), 2)
result.append(item.copy())
return result
raise ValueError(f"Dataset '{dataset}' not found.")
def to_python(self, **kwargs) -> str:
"""Return a Python code representation of the example."""
indentation = kwargs.get("indentation", "")
func_path = kwargs.get("func_path", ".func_router.func_name")
param_types: Dict[str, type] = kwargs.get("param_types", {})
prompt = kwargs.get("prompt", "")
eg = ""
if self.description:
eg += f"{indentation}{prompt}# {self.description}\n"
eg += f"{indentation}{prompt}obb{func_path}("
for k, v in self.parameters.items():
if k in param_types and (type_ := param_types.get(k)):
if QUOTE_TYPES.intersection(self._unpack_type(type_)):
eg += f"{k}='{v}', "
else:
eg += f"{k}={v}, "
else:
eg += f"{k}={v}, "
eg = indentation + eg.strip(", ") + ")\n"
return eg
class PythonEx(Example):
"""Python Example model."""
scope: Literal["python"] = "python"
description: str
code: List[str]
def to_python(self, **kwargs) -> str:
"""Return a Python code representation of the example."""
indentation = kwargs.get("indentation", "")
prompt = kwargs.get("prompt", "")
eg = ""
if self.description:
eg += f"{indentation}{prompt}# {self.description}\n"
for line in self.code:
eg += f"{indentation}{prompt}{line}\n"
return eg
def filter_list(
examples: List[Example],
providers: List[str],
) -> List[Example]:
"""Filter list of examples."""
return [
e
for e in examples
if (isinstance(e, APIEx) and (not e.provider or e.provider in providers))
or e.scope != "api"
]
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