# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# pyre-strict
from __future__ import annotations
import json
from collections.abc import Iterable
from enum import IntEnum
from logging import Logger
from typing import Any, Protocol
import pandas as pd
from ax.core.experiment import Experiment
from ax.core.generation_strategy_interface import GenerationStrategyInterface
from ax.utils.common.base import Base
from ax.utils.common.logger import get_logger
from ax.utils.common.result import Err, ExceptionE, Ok, Result
from IPython.display import display, Markdown
logger: Logger = get_logger(__name__)
[docs]
class AnalysisCardLevel(IntEnum):
DEBUG = 0
LOW = 10
MID = 20
HIGH = 30
CRITICAL = 40
[docs]
class AnalysisCard(Base):
# Name of the analysis computed, usually the class name of the Analysis which
# produced the card. Useful for grouping by when querying a large collection of
# cards.
name: str
# Arguments passed to the Analysis which produced the card, or their eventual
# values if they were inferred.
attributes: dict[str, Any]
title: str
subtitle: str
level: int
df: pd.DataFrame # Raw data produced by the Analysis
# Blob is the data processed for end-user consumption, encoded as a string,
# typically JSON. Subclasses of Analysis can define their own methods for consuming
# the blob and presenting it to the user (ex. PlotlyAnalysisCard.get_figure()
# decodes the blob into a go.Figure object).
blob: str
# How to interpret the blob (ex. "dataframe", "plotly", "markdown")
blob_annotation = "dataframe"
def __init__(
self,
name: str,
title: str,
subtitle: str,
level: int,
df: pd.DataFrame,
blob: str,
attributes: dict[str, Any] | None = None,
) -> None:
self.name = name
self.title = title
self.subtitle = subtitle
self.level = level
self.df = df
self.blob = blob
self.attributes = {} if attributes is None else attributes
def _ipython_display_(self) -> None:
"""
IPython display hook. This is called when the AnalysisCard is printed in an
IPython environment (ex. Jupyter). This method should be implemented by
subclasses of Analysis to display the AnalysisCard in a useful way.
By default, this method displays the raw data in a pandas DataFrame.
"""
display(Markdown(f"## {self.title}\n\n### {self.subtitle}"))
display(self.df)
[docs]
def display_cards(
cards: Iterable[AnalysisCard], minimum_level: int = AnalysisCardLevel.LOW
) -> None:
"""
Display a collection of AnalysisCards in IPython environments (ex. Jupyter).
Args:
cards: Collection of AnalysisCards to display.
minimum_level: Minimum level of cards to display.
"""
for card in sorted(cards, key=lambda x: x.level, reverse=True):
if card.level >= minimum_level:
display(card)
[docs]
class Analysis(Protocol):
"""
An Analysis is a class that given either and Experiment, a GenerationStrategy, or
both can compute some data intended for end-user consumption. The data is returned
to the user in the form of an AnalysisCard which contains the raw data, a blob (the
data processed for end-user consumption), and miscellaneous metadata that can be
useful for rendering the card or a collection of cards.
The AnalysisCard is a thin wrapper around the raw data and the processed blob;
Analyses impose structure on their blob should subclass Analysis. See
PlotlyAnalysis for an example which produces cards where the blob is always a
Plotly Figure object.
A good pattern to follow when implementing your own Analyses is to configure
"settings" (like which parameter or metrics to operate on, or whether to use
observed or modeled effects) in your Analyses' __init__ methods, then to consume
these settings in the compute method.
"""
[docs]
def compute(
self,
experiment: Experiment | None = None,
generation_strategy: GenerationStrategyInterface | None = None,
) -> AnalysisCard:
# Note: when implementing compute always prefer experiment.lookup_data() to
# experiment.fetch_data() to avoid unintential data fetching within the report
# generation.
...
[docs]
def compute_result(
self,
experiment: Experiment | None = None,
generation_strategy: GenerationStrategyInterface | None = None,
) -> Result[AnalysisCard, AnalysisE]:
"""
Utility method to compute an AnalysisCard as a Result. This can be useful for
computing many Analyses at once and handling Exceptions later.
"""
try:
card = self.compute(
experiment=experiment, generation_strategy=generation_strategy
)
return Ok(value=card)
except Exception as e:
logger.error(f"Failed to compute {self.__class__.__name__}: {e}")
return Err(
value=AnalysisE(
message=f"Failed to compute {self.__class__.__name__}",
exception=e,
analysis=self,
)
)
def _create_analysis_card(
self,
title: str,
subtitle: str,
level: int,
df: pd.DataFrame,
) -> AnalysisCard:
"""
Make an AnalysisCard from this Analysis using provided fields and
details about the Analysis class.
"""
return AnalysisCard(
name=self.name,
attributes=self.attributes,
title=title,
subtitle=subtitle,
level=level,
df=df,
blob=df.to_json(),
)
@property
def name(self) -> str:
"""The name the AnalysisCard will be given in compute."""
return self.__class__.__name__
@property
def attributes(self) -> dict[str, Any]:
"""The attributes the AnalysisCard will be given in compute."""
return self.__dict__
def __repr__(self) -> str:
try:
return (
f"{self.__class__.__name__}(name={self.name}, "
f"attributes={json.dumps(self.attributes)})"
)
# in case there is logic in name or attributes that throws a json error
except Exception:
return self.__class__.__name__
[docs]
class AnalysisE(ExceptionE):
analysis: Analysis
def __init__(
self,
message: str,
exception: Exception,
analysis: Analysis,
) -> None:
super().__init__(message, exception)
self.analysis = analysis
