#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
import json
from hashlib import md5
from typing import Dict, Iterable, Optional, Set, Type
import numpy as np
import pandas as pd
from ax.core.types import TFidelityTrialEvaluation, TTrialEvaluation
from ax.utils.common.base import Base
# pyre-fixme[16]: Module `pd` has no attribute `Timestamp`.
TPdTimestamp = pd.Timestamp
COLUMN_DATA_TYPES = {
"arm_name": str,
"metric_name": str,
"mean": np.float64,
"sem": np.float64,
"trial_index": np.int64,
"start_time": TPdTimestamp,
"end_time": TPdTimestamp,
"n": np.int64,
"frac_nonnull": np.float64,
"random_split": np.int64,
"fidelities": str, # Dictionary stored as json
}
# Note: Although the SEM (standard error of the mean) is a required column in data,
# downstream models can infer missing SEMs. Simply specify NaN as the SEM value,
# either in your Metric class or in Data explicitly.
REQUIRED_COLUMNS = {"arm_name", "metric_name", "mean", "sem"}
[docs]class Data(Base):
"""Class storing data for an experiment.
The dataframe is retrieved via the `df` property. The data can be stored
to an external store for future use by attaching it to an experiment using
`experiment.attach_data()` (this requires a description to be set.)
Attributes:
df: DataFrame with underlying data, and required columns.
description: Human-readable description of data.
"""
def __init__(
self,
# pyre-fixme[11]: Annotation `DataFrame` is not defined as a type.
df: Optional[pd.DataFrame] = None,
description: Optional[str] = None,
) -> None:
"""Init Data.
Args:
df: DataFrame with underlying data, and required columns.
description: Human-readable description of data.
"""
# Initialize with barebones DF.
if df is None:
# pyre-fixme[16]: Module `pd` has no attribute `DataFrame`.
self._df = pd.DataFrame(columns=self.required_columns())
else:
columns = set(df.columns)
missing_columns = self.required_columns() - columns
if missing_columns:
raise ValueError(
f"Dataframe must contain required columns {list(missing_columns)}."
)
extra_columns = columns - set(self.column_data_types())
if extra_columns:
raise ValueError(f"Columns {list(extra_columns)} are not supported.")
df = df.dropna(axis=0, how="all").reset_index(drop=True)
df = self._safecast_df(df=df)
# Reorder the columns for easier viewing
col_order = [c for c in self.column_data_types() if c in df.columns]
self._df = df[col_order]
self.description = description
@classmethod
def _safecast_df(cls, df: pd.DataFrame) -> pd.DataFrame:
"""Function for safely casting df to standard data types.
Needed because numpy does not support NaNs in integer arrays.
Args:
df: DataFrame to safe-cast.
Returns:
safe_df: DataFrame cast to standard dtypes.
"""
dtype = {
# Pandas timestamp handlng is weird
col: "datetime64[ns]" if coltype is TPdTimestamp else coltype
for col, coltype in cls.column_data_types().items()
if col in df.columns.values
and not (
cls.column_data_types()[col] is np.int64
and df.loc[:, col].isnull().any()
)
}
return df.astype(dtype=dtype)
[docs] @staticmethod
def required_columns() -> Set[str]:
"""Names of required columns."""
return REQUIRED_COLUMNS
[docs] @staticmethod
def column_data_types() -> Dict[str, Type]:
"""Type specification for all supported columns."""
return COLUMN_DATA_TYPES
[docs] @staticmethod
def from_multiple_data(
data: Iterable[Data], subset_metrics: Optional[Iterable[str]] = None
) -> Data:
"""Combines multiple data objects into one (with the concatenated
underlying dataframe).
NOTE: if one or more data objects in the iterable is of a custom
subclass of `Data`, object of that class will be returned. If
the iterable contains multiple types of `Data`, an error will be
raised.
Args:
data: Iterable of Ax `Data` objects to combine.
subset_metrics: If specified, combined `Data` will only contain
metrics, names of which appear in this iterable,
in the underlying dataframe.
"""
dfs = [datum.df for datum in data]
if len(dfs) == 0:
return Data()
if subset_metrics:
dfs = [df.loc[df["metric_name"].isin(subset_metrics)] for df in dfs]
# obtain type of first elt in iterable (we know it's not empty)
data_type = type(data[0])
# check if all types in iterable match the first type
if all((type(datum) is data_type) for datum in data):
# if all types in iterable are subclasses of Data, return the subclass
if issubclass(data_type, Data):
# pyre-fixme[16]: Module `pd` has no attribute `concat`.
return data_type(df=pd.concat(dfs, axis=0, sort=True))
else:
# if not, return the original Data object
# pyre-fixme[16]: Module `pd` has no attribute `concat`.
return Data(df=pd.concat(dfs, axis=0, sort=True))
else:
raise ValueError("More than one custom data type found in data iterable")
[docs] @staticmethod
def from_evaluations(
evaluations: Dict[str, TTrialEvaluation],
trial_index: int,
sample_sizes: Optional[Dict[str, int]] = None,
start_time: Optional[int] = None,
end_time: Optional[int] = None,
) -> Data:
"""
Convert dict of evaluations to Ax data object.
Args:
evaluations: Map from arm name to metric outcomes (itself a mapping
of metric names to tuples of mean and optionally a SEM).
trial_index: Trial index to which this data belongs.
sample_sizes: Number of samples collected for each arm.
start_time: Optional start time of run of the trial that produced this
data, in milliseconds.
end_time: Optional end time of run of the trial that produced this
data, in milliseconds.
Returns:
Ax Data object.
"""
records = [
{
"arm_name": name,
"metric_name": metric_name,
"mean": evaluation[metric_name][0],
"sem": evaluation[metric_name][1],
"trial_index": trial_index,
}
for name, evaluation in evaluations.items()
for metric_name in evaluation.keys()
]
if start_time is not None or end_time is not None:
for record in records:
record.update({"start_time": start_time, "end_time": end_time})
if sample_sizes:
for record in records:
record["n"] = sample_sizes[str(record["arm_name"])]
# pyre-fixme[16]: Module `pd` has no attribute `DataFrame`.
return Data(df=pd.DataFrame(records))
[docs] @staticmethod
def from_fidelity_evaluations(
evaluations: Dict[str, TFidelityTrialEvaluation],
trial_index: int,
sample_sizes: Optional[Dict[str, int]] = None,
start_time: Optional[int] = None,
end_time: Optional[int] = None,
) -> Data:
"""
Convert dict of fidelity evaluations to Ax data object.
Args:
evaluations: Map from arm name to list of (fidelity, metric outcomes)
(where metric outcomes is itself a mapping of metric names to
tuples of mean and SEM).
trial_index: Trial index to which this data belongs.
sample_sizes: Number of samples collected for each arm.
start_time: Optional start time of run of the trial that produced this
data, in milliseconds.
end_time: Optional end time of run of the trial that produced this
data, in milliseconds.
Returns:
Ax Data object.
"""
records = [
{
"arm_name": name,
"metric_name": metric_name,
"mean": evaluation[metric_name][0],
"sem": evaluation[metric_name][1],
"trial_index": trial_index,
"fidelities": json.dumps(fidelity),
}
for name, fidelity_and_metrics_list in evaluations.items()
for fidelity, evaluation in fidelity_and_metrics_list
for metric_name in evaluation.keys()
]
if start_time is not None or end_time is not None:
for record in records:
record.update({"start_time": start_time, "end_time": end_time})
if sample_sizes:
for record in records:
record["n"] = sample_sizes[str(record["arm_name"])]
# pyre-fixme[16]: Module `pd` has no attribute `DataFrame`.
return Data(df=pd.DataFrame(records))
@property
def df(self) -> pd.DataFrame:
return self._df
@property
def df_hash(self) -> str:
"""Compute hash of pandas DataFrame.
This first serializes the DataFrame and computes the md5 hash on the
resulting string. Note that this may cause performance issue for very large
DataFrames.
Args:
df: The DataFrame for which to compute the hash.
Returns
str: The hash of the DataFrame.
"""
return md5(self.df.to_json().encode("utf-8")).hexdigest()
@property
def metric_names(self) -> Set[str]:
"""Set of metric names that appear in the underlying dataframe of
this `Data` object.
"""
return set() if self.df.empty else set(self.df["metric_name"].values)
[docs]def set_single_trial(data: Data) -> Data:
"""Returns a new Data object where we set all rows to have the same
trial index (i.e. 0). This is meant to be used with our IVW transform,
which will combine multiple observations of the same metric.
"""
df = data._df.copy()
if "trial_index" in df:
df["trial_index"] = 0
return Data(df=df)
[docs]def clone_without_metrics(data: Data, excluded_metric_names: Iterable[str]) -> Data:
"""Returns a new data object where rows containing the metrics specified by
`metric_names` are filtered out. Used to sanitize data before using it as
training data for a model that requires data rectangularity.
Args:
data: Original data to clone.
excluded_metric_names: Metrics to avoid copying
Returns:
new version of the original data without specified metrics.
"""
return Data(
df=data.df[
data.df["metric_name"].apply(lambda n: n not in excluded_metric_names)
].copy()
)
[docs]def custom_data_class(
column_data_types: Optional[Dict[str, Type]] = None,
required_columns: Optional[Set[str]] = None,
time_columns: Optional[Set[str]] = None,
) -> Type[Data]:
"""Creates a custom data class with additional columns.
All columns and their designations on the base data class are preserved,
the inputs here are appended to the definitions on the base class.
Args:
column_data_types: Dict from column name to column type.
required_columns: Set of additional columns required for this data object.
time_columns: Set of additional columns to cast to timestamp.
Returns:
New data subclass with amended column definitions.
"""
class CustomData(Data):
@staticmethod
def required_columns() -> Set[str]:
return (required_columns or set()).union(REQUIRED_COLUMNS)
@staticmethod
def column_data_types() -> Dict[str, Type]:
return {**(column_data_types or {}), **COLUMN_DATA_TYPES}
return CustomData
