import logging
import math
import os
import shutil
import time
import warnings
from datetime import datetime
import cloudpickle
import numpy as np
import pandas as pd
from woodwork.logical_types import (
Age,
AgeNullable,
Boolean,
BooleanNullable,
Integer,
IntegerNullable,
)
from featuretools.computational_backends.feature_set import FeatureSet
from featuretools.computational_backends.feature_set_calculator import (
FeatureSetCalculator,
)
from featuretools.computational_backends.utils import (
_check_cutoff_time_type,
_validate_cutoff_time,
bin_cutoff_times,
create_client_and_cluster,
gather_approximate_features,
gen_empty_approx_features_df,
get_ww_types_from_features,
save_csv_decorator,
)
from featuretools.entityset.relationship import RelationshipPath
from featuretools.feature_base import AggregationFeature, FeatureBase
from featuretools.utils import Trie
from featuretools.utils.gen_utils import (
import_or_raise,
make_tqdm_iterator,
)
logger = logging.getLogger("featuretools.computational_backend")
PBAR_FORMAT = "Elapsed: {elapsed} | Progress: {l_bar}{bar}"
FEATURE_CALCULATION_PERCENTAGE = (
0.95 # make total 5% higher to allot time for wrapping up at end
)
[docs]def calculate_feature_matrix(
features,
entityset=None,
cutoff_time=None,
instance_ids=None,
dataframes=None,
relationships=None,
cutoff_time_in_index=False,
training_window=None,
approximate=None,
save_progress=None,
verbose=False,
chunk_size=None,
n_jobs=1,
dask_kwargs=None,
progress_callback=None,
include_cutoff_time=True,
):
""" 计算给定实例ID和计算时间的矩阵.
Args:
features (list[:class:`.FeatureBase`]): 要计算的特征定义.
entityset (EntitySet): 已初始化的EntitySet.如果未提供`dataframes`和`relationships`,则需要此参数.
cutoff_time (pd.DataFrame 或 Datetime): 指定为每个实例计算特征的时间.生成的特征矩阵将使用截止时间之前的数据.可以是DataFrame或单个值.如果传递的是DataFrame,则实例ID必须在目标DataFrame索引同名的列中,或者在名为`instance_id`的列中.DataFrame中的截止时间值必须在目标DataFrame时间索引同名的列中,或者在名为`time`的列中.如果DataFrame包含两个以上的列,任何额外的列都将添加到生成的特征矩阵中.如果传递的是单个值,则该值将用于所有实例.
instance_ids (list): 要计算特征的实例列表.仅在`cutoff_time`为单个datetime时使用.
dataframes (dict[str -> tuple(DataFrame, str, str, dict[str -> str/Woodwork.LogicalType], dict[str->str/set], boolean)]):
DataFrame的字典.条目格式为{DataFrame名称 -> (DataFrame, 索引列, 时间索引, 逻辑类型, 语义标签, 创建索引)}.注意,只有DataFrame是必需的.如果提供了Woodwork DataFrame,则其他参数将被忽略.
relationships (list[(str, str, str, str)]): DataFrame之间关系的列表.列表项是一个元组,格式为(父DataFrame名称, 父列, 子DataFrame名称, 子列).
cutoff_time_in_index (bool): 如果为True,返回一个具有MultiIndex的DataFrame,其中第二个索引是截止时间（第一个是实例ID）.DataFrame将按(时间, 实例ID)排序.
training_window (Timedelta 或 str, 可选):
定义在计算特征时截止时间之前可以使用多少时间数据的窗口.如果为``None``,则使用截止时间之前的所有数据.默认为``None``.
approximate (Timedelta 或 str): 用于将具有相似截止时间的实例分组以计算昂贵特征的频率.例如,如果桶为24小时,则同一天内具有截止时间的所有实例将使用相同的计算方法来计算昂贵的特征.
verbose (bool, 可选): 打印进度信息.时间粒度为每个块.
chunk_size (int 或 float 或 None): 每次计算的输出特征矩阵的最大行数.如果传递一个大于0的整数,将尝试每块使用那么多行.如果传递一个介于0和1之间的浮点数,则将块大小设置为所有行的该百分比.如果为None,并且n_jobs > 1,则将其设置为1/n_jobs.
n_jobs (int, 可选): 计算特征矩阵时要使用的并行进程数.如果不等于1,则需要Dask.
dask_kwargs (dict, 可选): 创建dask客户端和调度程序时要传递的关键字参数字典.即使未设置n_jobs,使用`dask_kwargs`也会启用多进程.主要参数:
cluster (str 或 dask.distributed.LocalCluster):
要发送任务的集群或集群地址.如果未指定,将创建一个集群.
diagnostics port (int):
用于Web仪表板的端口号.如果未指定,则不会启用Web界面.
还将接受LocalCluster的有效关键字参数.
save_progress (str, 可选): 保存中间计算结果的路径.
progress_callback (callable): 随着进度更新调用的函数.具有以下参数:
update: 自上次调用以来的进度变化百分比（介于0和100之间的浮点数）
progress_percent: 已完成计算的百分比（介于0和100之间的浮点数）
time_elapsed: 自调用开始以来经过的总时间（以秒为单位）
include_cutoff_time (bool): 在特征计算中包含截止时间的数据.默认为``True``.
Returns:
pd.DataFrame: 特征矩阵.
"""
assert (
isinstance(features, list)
and features != []
and all([isinstance(feature, FeatureBase) for feature in features])
), "features must be a non-empty list of features"
# handle loading entityset
from featuretools.entityset.entityset import EntitySet
if not isinstance(entityset, EntitySet):
if dataframes is not None:
entityset = EntitySet("entityset", dataframes, relationships)
else:
raise TypeError("No dataframes or valid EntitySet provided")
target_dataframe = entityset[features[0].dataframe_name]
cutoff_time = _validate_cutoff_time(cutoff_time, target_dataframe)
entityset._check_time_indexes()
if isinstance(cutoff_time, pd.DataFrame):
if instance_ids:
msg = "Passing 'instance_ids' is valid only if 'cutoff_time' is a single value or None - ignoring"
warnings.warn(msg)
pass_columns = [
col for col in cutoff_time.columns if col not in ["instance_id", "time"]
]
# make sure dtype of instance_id in cutoff time
# is same as column it references
target_dataframe = features[0].dataframe
ltype = target_dataframe.ww.logical_types[target_dataframe.ww.index]
cutoff_time.ww.init(logical_types={"instance_id": ltype})
else:
pass_columns = []
if cutoff_time is None:
if entityset.time_type == "numeric":
cutoff_time = np.inf
else:
cutoff_time = datetime.now()
if instance_ids is None:
index_col = target_dataframe.ww.index
df = entityset._handle_time(
dataframe_name=target_dataframe.ww.name,
df=target_dataframe,
time_last=cutoff_time,
training_window=training_window,
include_cutoff_time=include_cutoff_time,
)
instance_ids = df[index_col]
# convert list or range object into series
if not isinstance(instance_ids, pd.Series):
instance_ids = pd.Series(instance_ids)
cutoff_time = (cutoff_time, instance_ids)
_check_cutoff_time_type(cutoff_time, entityset.time_type)
# Approximate provides no benefit with a single cutoff time, so ignore it
if isinstance(cutoff_time, tuple) and approximate is not None:
msg = (
"Using approximate with a single cutoff_time value or no cutoff_time "
"provides no computational efficiency benefit"
)
warnings.warn(msg)
cutoff_time = pd.DataFrame(
{
"instance_id": cutoff_time[1],
"time": [cutoff_time[0]] * len(cutoff_time[1]),
},
)
target_dataframe = features[0].dataframe
ltype = target_dataframe.ww.logical_types[target_dataframe.ww.index]
cutoff_time.ww.init(logical_types={"instance_id": ltype})
feature_set = FeatureSet(features)
# Get features to approximate
if approximate is not None:
approximate_feature_trie = gather_approximate_features(feature_set)
# Make a new FeatureSet that ignores approximated features
feature_set = FeatureSet(
features,
approximate_feature_trie=approximate_feature_trie,
)
# Check if there are any non-approximated aggregation features
no_unapproximated_aggs = True
for feature in features:
if isinstance(feature, AggregationFeature):
# do not need to check if feature is in to_approximate since
# only base features of direct features can be in to_approximate
no_unapproximated_aggs = False
break
if approximate is not None:
all_approx_features = {
f for _, feats in feature_set.approximate_feature_trie for f in feats
}
else:
all_approx_features = set()
deps = feature.get_dependencies(deep=True, ignored=all_approx_features)
for dependency in deps:
if isinstance(dependency, AggregationFeature):
no_unapproximated_aggs = False
break
cutoff_df_time_col = "time"
target_time = "_original_time"
if approximate is not None:
# If there are approximated aggs, bin times
binned_cutoff_time = bin_cutoff_times(cutoff_time, approximate)
# Think about collisions: what if original time is a feature
binned_cutoff_time.ww[target_time] = cutoff_time[cutoff_df_time_col]
cutoff_time_to_pass = binned_cutoff_time
else:
cutoff_time_to_pass = cutoff_time
if isinstance(cutoff_time, pd.DataFrame):
cutoff_time_len = cutoff_time.shape[0]
else:
cutoff_time_len = len(cutoff_time[1])
chunk_size = _handle_chunk_size(chunk_size, cutoff_time_len)
tqdm_options = {
"total": (cutoff_time_len / FEATURE_CALCULATION_PERCENTAGE),
"bar_format": PBAR_FORMAT,
"disable": True,
}
if verbose:
tqdm_options.update({"disable": False})
elif progress_callback is not None:
# allows us to utilize progress_bar updates without printing to anywhere
tqdm_options.update({"file": open(os.devnull, "w"), "disable": False})
with make_tqdm_iterator(**tqdm_options) as progress_bar:
if n_jobs != 1 or dask_kwargs is not None:
feature_matrix = parallel_calculate_chunks(
cutoff_time=cutoff_time_to_pass,
chunk_size=chunk_size,
feature_set=feature_set,
approximate=approximate,
training_window=training_window,
save_progress=save_progress,
entityset=entityset,
n_jobs=n_jobs,
no_unapproximated_aggs=no_unapproximated_aggs,
cutoff_df_time_col=cutoff_df_time_col,
target_time=target_time,
pass_columns=pass_columns,
progress_bar=progress_bar,
dask_kwargs=dask_kwargs or {},
progress_callback=progress_callback,
include_cutoff_time=include_cutoff_time,
)
else:
feature_matrix = calculate_chunk(
cutoff_time=cutoff_time_to_pass,
chunk_size=chunk_size,
feature_set=feature_set,
approximate=approximate,
training_window=training_window,
save_progress=save_progress,
entityset=entityset,
no_unapproximated_aggs=no_unapproximated_aggs,
cutoff_df_time_col=cutoff_df_time_col,
target_time=target_time,
pass_columns=pass_columns,
progress_bar=progress_bar,
progress_callback=progress_callback,
include_cutoff_time=include_cutoff_time,
)
# ensure rows are sorted by input order
if isinstance(cutoff_time, pd.DataFrame):
feature_matrix = feature_matrix.ww.reindex(
pd.MultiIndex.from_frame(
cutoff_time[["instance_id", "time"]],
names=feature_matrix.index.names,
),
)
else:
# Maintain index dtype
index_dtype = feature_matrix.index.get_level_values(0).dtype
feature_matrix = feature_matrix.ww.reindex(
cutoff_time[1].astype(index_dtype),
level=0,
)
if not cutoff_time_in_index:
feature_matrix.ww.reset_index(level="time", drop=True, inplace=True)
if save_progress and os.path.exists(os.path.join(save_progress, "temp")):
shutil.rmtree(os.path.join(save_progress, "temp"))
# force to 100% since we saved last 5 percent
previous_progress = progress_bar.n
progress_bar.update(progress_bar.total - progress_bar.n)
if progress_callback is not None:
(
update,
progress_percent,
time_elapsed,
) = update_progress_callback_parameters(progress_bar, previous_progress)
progress_callback(update, progress_percent, time_elapsed)
progress_bar.refresh()
return feature_matrix
def calculate_chunk(
cutoff_time,
chunk_size,
feature_set,
entityset,
approximate,
training_window,
save_progress,
no_unapproximated_aggs,
cutoff_df_time_col,
target_time,
pass_columns,
progress_bar=None,
progress_callback=None,
include_cutoff_time=True,
schema=None,
):
if not isinstance(feature_set, FeatureSet):
feature_set = cloudpickle.loads(feature_set) # pragma: no cover
feature_matrix = []
if no_unapproximated_aggs and approximate is not None:
if entityset.time_type == "numeric":
group_time = np.inf
else:
group_time = datetime.now()
if isinstance(cutoff_time, tuple):
update_progress_callback = None
if progress_bar is not None:
def update_progress_callback(done):
previous_progress = progress_bar.n
progress_bar.update(done * len(cutoff_time[1]))
if progress_callback is not None:
(
update,
progress_percent,
time_elapsed,
) = update_progress_callback_parameters(
progress_bar,
previous_progress,
)
progress_callback(update, progress_percent, time_elapsed)
time_last = cutoff_time[0]
ids = cutoff_time[1]
calculator = FeatureSetCalculator(
entityset,
feature_set,
time_last,
training_window=training_window,
)
_feature_matrix = calculator.run(
ids,
progress_callback=update_progress_callback,
include_cutoff_time=include_cutoff_time,
)
time_index = pd.Index([time_last] * len(ids), name="time")
_feature_matrix = _feature_matrix.set_index(time_index, append=True)
feature_matrix.append(_feature_matrix)
else:
if schema:
cutoff_time.ww.init_with_full_schema(schema=schema) # pragma: no cover
for _, group in cutoff_time.groupby(cutoff_df_time_col):
# if approximating, calculate the approximate features
if approximate is not None:
group.ww.init(schema=cutoff_time.ww.schema, validate=False)
precalculated_features_trie = approximate_features(
feature_set,
group,
window=approximate,
entityset=entityset,
training_window=training_window,
include_cutoff_time=include_cutoff_time,
)
else:
precalculated_features_trie = None
@save_csv_decorator(save_progress)
def calc_results(
time_last,
ids,
precalculated_features=None,
training_window=None,
include_cutoff_time=True,
):
update_progress_callback = None
if progress_bar is not None:
def update_progress_callback(done):
previous_progress = progress_bar.n
progress_bar.update(done * group.shape[0])
if progress_callback is not None:
(
update,
progress_percent,
time_elapsed,
) = update_progress_callback_parameters(
progress_bar,
previous_progress,
)
progress_callback(update, progress_percent, time_elapsed)
calculator = FeatureSetCalculator(
entityset,
feature_set,
time_last,
training_window=training_window,
precalculated_features=precalculated_features,
)
matrix = calculator.run(
ids,
progress_callback=update_progress_callback,
include_cutoff_time=include_cutoff_time,
)
return matrix
# if all aggregations have been approximated, can calculate all together
if no_unapproximated_aggs and approximate is not None:
inner_grouped = [[group_time, group]]
else:
# if approximated features, set cutoff_time to unbinned time
if precalculated_features_trie is not None:
group[cutoff_df_time_col] = group[target_time]
inner_grouped = group.groupby(cutoff_df_time_col, sort=True)
if chunk_size is not None:
inner_grouped = _chunk_dataframe_groups(inner_grouped, chunk_size)
for time_last, group in inner_grouped:
# sort group by instance id
ids = group["instance_id"].sort_values().values
if no_unapproximated_aggs and approximate is not None:
window = None
else:
window = training_window
# calculate values for those instances at time time_last
_feature_matrix = calc_results(
time_last,
ids,
precalculated_features=precalculated_features_trie,
training_window=window,
include_cutoff_time=include_cutoff_time,
)
id_name = _feature_matrix.index.name
# if approximate, merge feature matrix with group frame to get original
# cutoff times and passed columns
if approximate:
cols = [c for c in _feature_matrix.columns if c not in pass_columns]
indexer = group[["instance_id", target_time] + pass_columns]
_feature_matrix = _feature_matrix[cols].merge(
indexer,
right_on=["instance_id"],
left_index=True,
how="right",
)
_feature_matrix.set_index(
["instance_id", target_time],
inplace=True,
)
_feature_matrix.index.set_names([id_name, "time"], inplace=True)
_feature_matrix.sort_index(level=1, kind="mergesort", inplace=True)
else:
# all rows have same cutoff time. set time and add passed columns
num_rows = len(ids)
if len(pass_columns) > 0:
pass_through = group[
["instance_id", cutoff_df_time_col] + pass_columns
]
pass_through.rename(
columns={
"instance_id": id_name,
cutoff_df_time_col: "time",
},
inplace=True,
)
time_index = pd.Index([time_last] * num_rows, name="time")
_feature_matrix = _feature_matrix.set_index(
time_index,
append=True,
)
if len(pass_columns) > 0:
pass_through.set_index([id_name, "time"], inplace=True)
for col in pass_columns:
_feature_matrix[col] = pass_through[col]
feature_matrix.append(_feature_matrix)
ww_init_kwargs = get_ww_types_from_features(
feature_set.target_features,
entityset,
pass_columns,
cutoff_time,
)
feature_matrix = init_ww_and_concat_fm(feature_matrix, ww_init_kwargs)
return feature_matrix
def approximate_features(
feature_set,
cutoff_time,
window,
entityset,
training_window=None,
include_cutoff_time=True,
):
"""给定一组特征和要传递给calculate_feature_matrix的cutoff_times,计算某些特征的近似值以加快计算速度.cutoff_times被排序到窗口大小的桶中,并且每个桶仅在一个cutoff时间点计算近似特征值.
..note:: 这仅近似于目标数据帧上的AggregationFeatures的DirectFeatures.在未来的版本中,可能还可以近似这些特征在其他顶级数据帧上的值.
Args:
cutoff_time (pd.DataFrame): 指定为每个实例计算特征的时间.生成的特征矩阵将使用截止时间之前（包括截止时间）的数据.一个包含'instance_id'和'time'列的DataFrame.
window (Timedelta 或 str): 用于将具有相似截止时间的实例分组以计算成本高昂特征的频率.例如,如果桶为24小时,则同一天内具有截止时间的所有实例将使用相同的计算来计算昂贵的特征.
entityset (:class:`.EntitySet`): 一个已经初始化的实体集.
feature_set (:class:`.FeatureSet`): 要计算的特征.
training_window (`Timedelta`, 可选): 定义在截止时间之前可以包含多少旧数据以计算特征的窗口.如果为None,则使用所有旧数据.
include_cutoff_time (bool): 如果为True,则在特征计算中包含截止时间的数据.
"""
approx_fms_trie = Trie(path_constructor=RelationshipPath)
target_time_colname = "target_time"
cutoff_time.ww[target_time_colname] = cutoff_time["time"]
approx_cutoffs = bin_cutoff_times(cutoff_time, window)
cutoff_df_time_col = "time"
cutoff_df_instance_col = "instance_id"
# should this order be by dependencies so that calculate_feature_matrix
# doesn't skip approximating something?
for relationship_path, approx_feature_names in feature_set.approximate_feature_trie:
if not approx_feature_names:
continue
(
cutoffs_with_approx_e_ids,
new_approx_dataframe_index_col,
) = _add_approx_dataframe_index_col(
entityset,
feature_set.target_df_name,
approx_cutoffs.copy(),
relationship_path,
)
# Select only columns we care about
columns_we_want = [
new_approx_dataframe_index_col,
cutoff_df_time_col,
target_time_colname,
]
cutoffs_with_approx_e_ids = cutoffs_with_approx_e_ids[columns_we_want]
cutoffs_with_approx_e_ids = cutoffs_with_approx_e_ids.drop_duplicates()
cutoffs_with_approx_e_ids.dropna(
subset=[new_approx_dataframe_index_col],
inplace=True,
)
approx_features = [
feature_set.features_by_name[name] for name in approx_feature_names
]
if cutoffs_with_approx_e_ids.empty:
approx_fm = gen_empty_approx_features_df(approx_features)
else:
cutoffs_with_approx_e_ids.sort_values(
[cutoff_df_time_col, new_approx_dataframe_index_col],
inplace=True,
)
# CFM assumes specific column names for cutoff_time argument
rename = {new_approx_dataframe_index_col: cutoff_df_instance_col}
cutoff_time_to_pass = cutoffs_with_approx_e_ids.rename(columns=rename)
cutoff_time_to_pass = cutoff_time_to_pass[
[cutoff_df_instance_col, cutoff_df_time_col]
]
cutoff_time_to_pass.drop_duplicates(inplace=True)
approx_fm = calculate_feature_matrix(
approx_features,
entityset,
cutoff_time=cutoff_time_to_pass,
training_window=training_window,
approximate=None,
cutoff_time_in_index=False,
chunk_size=cutoff_time_to_pass.shape[0],
include_cutoff_time=include_cutoff_time,
)
approx_fms_trie.get_node(relationship_path).value = approx_fm
return approx_fms_trie
def scatter_warning(num_scattered_workers, num_workers):
if num_scattered_workers != num_workers:
scatter_warning = "EntitySet was only scattered to {} out of {} workers"
logger.warning(scatter_warning.format(num_scattered_workers, num_workers))
def parallel_calculate_chunks(
cutoff_time,
chunk_size,
feature_set,
approximate,
training_window,
save_progress,
entityset,
n_jobs,
no_unapproximated_aggs,
cutoff_df_time_col,
target_time,
pass_columns,
progress_bar,
dask_kwargs=None,
progress_callback=None,
include_cutoff_time=True,
):
import_or_raise(
"distributed",
"Dask must be installed to calculate feature matrix with n_jobs set to anything but 1",
)
from dask.base import tokenize
from distributed import Future, as_completed
client = None
cluster = None
try:
client, cluster = create_client_and_cluster(
n_jobs=n_jobs,
dask_kwargs=dask_kwargs,
entityset_size=entityset.__sizeof__(),
)
# scatter the entityset
# denote future with leading underscore
start = time.time()
es_token = "EntitySet-{}".format(tokenize(entityset))
if es_token in client.list_datasets():
msg = "Using EntitySet persisted on the cluster as dataset {}"
progress_bar.write(msg.format(es_token))
_es = client.get_dataset(es_token)
else:
_es = client.scatter([entityset])[0]
client.publish_dataset(**{_es.key: _es})
# save features to a tempfile and scatter it
pickled_feats = cloudpickle.dumps(feature_set)
_saved_features = client.scatter(pickled_feats)
client.replicate([_es, _saved_features])
num_scattered_workers = len(
client.who_has([Future(es_token)]).get(es_token, []),
)
num_workers = len(client.scheduler_info()["workers"].values())
schema = None
if isinstance(cutoff_time, pd.DataFrame):
schema = cutoff_time.ww.schema
chunks = cutoff_time.groupby(cutoff_df_time_col)
cutoff_time_len = cutoff_time.shape[0]
else:
chunks = cutoff_time
cutoff_time_len = len(cutoff_time[1])
if not chunk_size:
chunk_size = _handle_chunk_size(1.0 / num_workers, cutoff_time_len)
chunks = _chunk_dataframe_groups(chunks, chunk_size)
chunks = [df for _, df in chunks]
if len(chunks) < num_workers: # pragma: no cover
chunk_warning = (
"Fewer chunks ({}), than workers ({}) consider reducing the chunk size"
)
warning_string = chunk_warning.format(len(chunks), num_workers)
progress_bar.write(warning_string)
scatter_warning(num_scattered_workers, num_workers)
end = time.time()
scatter_time = round(end - start)
# if enabled, reset timer after scatter for better time remaining estimates
if not progress_bar.disable:
progress_bar.reset()
scatter_string = "EntitySet scattered to {} workers in {} seconds"
progress_bar.write(scatter_string.format(num_scattered_workers, scatter_time))
# map chunks
# TODO: consider handling task submission dask kwargs
_chunks = client.map(
calculate_chunk,
chunks,
feature_set=_saved_features,
chunk_size=None,
entityset=_es,
approximate=approximate,
training_window=training_window,
save_progress=save_progress,
no_unapproximated_aggs=no_unapproximated_aggs,
cutoff_df_time_col=cutoff_df_time_col,
target_time=target_time,
pass_columns=pass_columns,
progress_bar=None,
progress_callback=progress_callback,
include_cutoff_time=include_cutoff_time,
schema=schema,
)
feature_matrix = []
iterator = as_completed(_chunks).batches()
for batch in iterator:
results = client.gather(batch)
for result in results:
feature_matrix.append(result)
previous_progress = progress_bar.n
progress_bar.update(result.shape[0])
if progress_callback is not None:
(
update,
progress_percent,
time_elapsed,
) = update_progress_callback_parameters(
progress_bar,
previous_progress,
)
progress_callback(update, progress_percent, time_elapsed)
except Exception:
raise
finally:
if client is not None:
client.close()
if "cluster" not in dask_kwargs and cluster is not None:
cluster.close() # pragma: no cover
ww_init_kwargs = get_ww_types_from_features(
feature_set.target_features,
entityset,
pass_columns,
cutoff_time,
)
feature_matrix = init_ww_and_concat_fm(feature_matrix, ww_init_kwargs)
return feature_matrix
def _add_approx_dataframe_index_col(es, target_dataframe_name, cutoffs, path):
""" 向截止日期数据框添加一列,将其链接到路径末端的数据框.
返回更新后的截止日期数据框及该列的名称.名称将由连接的列组成.
"""
last_child_col = "instance_id"
last_parent_col = es[target_dataframe_name].ww.index
for _, relationship in path:
child_cols = [last_parent_col, relationship._child_column_name]
child_df = es[relationship.child_name][child_cols]
# Rename relationship.child_column to include the columns we have
# joined through.
new_col_name = "%s.%s" % (last_child_col, relationship._child_column_name)
to_rename = {relationship._child_column_name: new_col_name}
child_df = child_df.rename(columns=to_rename)
cutoffs = cutoffs.merge(
child_df,
left_on=last_child_col,
right_on=last_parent_col,
)
# These will be used in the next iteration.
last_child_col = new_col_name
last_parent_col = relationship._parent_column_name
return cutoffs, new_col_name
def _chunk_dataframe_groups(grouped, chunk_size):
"""将一个分组的数据框分成不大于 chunk_size 的组."""
if isinstance(grouped, tuple):
for i in range(0, len(grouped[1]), chunk_size):
yield None, (grouped[0], grouped[1].iloc[i : i + chunk_size])
else:
for group_key, group_df in grouped:
for i in range(0, len(group_df), chunk_size):
yield group_key, group_df.iloc[i : i + chunk_size]
def _handle_chunk_size(chunk_size, total_size):
if chunk_size is not None:
assert chunk_size > 0, "Chunk size must be greater than 0"
if chunk_size < 1:
chunk_size = math.ceil(chunk_size * total_size)
chunk_size = int(chunk_size)
return chunk_size
def update_progress_callback_parameters(progress_bar, previous_progress):
update = (progress_bar.n - previous_progress) / progress_bar.total * 100
progress_percent = (progress_bar.n / progress_bar.total) * 100
time_elapsed = progress_bar.format_dict["elapsed"]
return (update, progress_percent, time_elapsed)
def init_ww_and_concat_fm(feature_matrix, ww_init_kwargs):
cols_to_check = {
col
for col, ltype in ww_init_kwargs["logical_types"].items()
if isinstance(ltype, (Age, Boolean, Integer))
}
replacement_type = {
"age": AgeNullable(),
"boolean": BooleanNullable(),
"integer": IntegerNullable(),
}
for fm in feature_matrix:
updated_cols = set()
for col in cols_to_check:
# Only convert types if null values are present
if fm[col].isnull().any():
current_type = ww_init_kwargs["logical_types"][col].type_string
ww_init_kwargs["logical_types"][col] = replacement_type[current_type]
updated_cols.add(col)
cols_to_check = cols_to_check - updated_cols
fm.ww.init(**ww_init_kwargs)
feature_matrix = pd.concat(feature_matrix)
feature_matrix.ww.init(**ww_init_kwargs)
return feature_matrix