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,
):
"""Calculates a matrix for a given set of instance ids and calculation times.
Args:
features (list[:class:`.FeatureBase`]): Feature definitions to be calculated.
entityset (EntitySet): An already initialized entityset. Required if `dataframes` and `relationships`
not provided
cutoff_time (pd.DataFrame or Datetime): Specifies times at which to calculate
the features for each instance. The resulting feature matrix will use data
up to and including the cutoff_time. Can either be a DataFrame or a single
value. If a DataFrame is passed the instance ids for which to calculate features
must be in a column with the same name as the target dataframe index or a column
named `instance_id`. The cutoff time values in the DataFrame must be in a column with
the same name as the target dataframe time index or a column named `time`. If the
DataFrame has more than two columns, any additional columns will be added to the
resulting feature matrix. If a single value is passed, this value will be used for
all instances.
instance_ids (list): List of instances to calculate features on. Only
used if cutoff_time is a single datetime.
dataframes (dict[str -> tuple(DataFrame, str, str, dict[str -> str/Woodwork.LogicalType], dict[str->str/set], boolean)]):
Dictionary of DataFrames. Entries take the format
{dataframe name -> (dataframe, index column, time_index, logical_types, semantic_tags, make_index)}.
Note that only the dataframe is required. If a Woodwork DataFrame is supplied, any other parameters
will be ignored.
relationships (list[(str, str, str, str)]): list of relationships
between dataframes. List items are a tuple with the format
(parent dataframe name, parent column, child dataframe name, child column).
cutoff_time_in_index (bool): If True, return a DataFrame with a MultiIndex
where the second index is the cutoff time (first is instance id).
DataFrame will be sorted by (time, instance_id).
training_window (Timedelta or str, optional):
Window defining how much time before the cutoff time data
can be used when calculating features. If ``None``, all data before cutoff time is used.
Defaults to ``None``.
approximate (Timedelta or str): Frequency to group instances with similar
cutoff times by for features with costly calculations. For example,
if bucket is 24 hours, all instances with cutoff times on the same
day will use the same calculation for expensive features.
verbose (bool, optional): Print progress info. The time granularity is
per chunk.
chunk_size (int or float or None): maximum number of rows of
output feature matrix to calculate at time. If passed an integer
greater than 0, will try to use that many rows per chunk. If passed
a float value between 0 and 1 sets the chunk size to that
percentage of all rows. if None, and n_jobs > 1 it will be set to 1/n_jobs
n_jobs (int, optional): number of parallel processes to use when
calculating feature matrix. Requires Dask if not equal to 1.
dask_kwargs (dict, optional): Dictionary of keyword arguments to be
passed when creating the dask client and scheduler. Even if n_jobs
is not set, using `dask_kwargs` will enable multiprocessing.
Main parameters:
cluster (str or dask.distributed.LocalCluster):
cluster or address of cluster to send tasks to. If unspecified,
a cluster will be created.
diagnostics port (int):
port number to use for web dashboard. If left unspecified, web
interface will not be enabled.
Valid keyword arguments for LocalCluster will also be accepted.
save_progress (str, optional): path to save intermediate computational results.
progress_callback (callable): function to be called with incremental progress updates.
Has the following parameters:
update: percentage change (float between 0 and 100) in progress since last call
progress_percent: percentage (float between 0 and 100) of total computation completed
time_elapsed: total time in seconds that has elapsed since start of call
include_cutoff_time (bool): Include data at cutoff times in feature calculations. Defaults to ``True``.
Returns:
pd.DataFrame: The feature matrix.
"""
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,
):
"""Given a set of features and cutoff_times to be passed to
calculate_feature_matrix, calculates approximate values of some features
to speed up calculations. Cutoff times are sorted into
window-sized buckets and the approximate feature values are only calculated
at one cutoff time for each bucket.
..note:: this only approximates DirectFeatures of AggregationFeatures, on
the target dataframe. In future versions, it may also be possible to
approximate these features on other top-level dataframes
Args:
cutoff_time (pd.DataFrame): specifies what time to calculate
the features for each instance at. The resulting feature matrix will use data
up to and including the cutoff_time. A DataFrame with
'instance_id' and 'time' columns.
window (Timedelta or str): frequency to group instances with similar
cutoff times by for features with costly calculations. For example,
if bucket is 24 hours, all instances with cutoff times on the same
day will use the same calculation for expensive features.
entityset (:class:`.EntitySet`): An already initialized entityset.
feature_set (:class:`.FeatureSet`): The features to be calculated.
training_window (`Timedelta`, optional):
Window defining how much older than the cutoff time data
can be to be included when calculating the feature. If None, all older data is used.
include_cutoff_time (bool):
If True, data at cutoff times are included in feature calculations.
"""
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):
"""
Add a column to the cutoff df linking it to the dataframe at the end of the
path.
Return the updated cutoff df and the name of this column. The name will
consist of the columns which were joined through.
"""
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):
"""chunks a grouped dataframe into groups no larger than chunk_size"""
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