处理时间#
在处理时间数据时，仔细选择用于任何计算的数据非常重要。通过使用Woodwork的时间索引列对数据框进行注释，并在特征计算过程中提供一个截止时间，Featuretools将在运行任何计算之前自动过滤掉截止时间之后的任何数据。
Note
This guide focuses on performing feature engineering on temporal data, but it is not specific to feature engineering for time series problems, which are their own class of machine learning problems. A guide on using Featuretools for time series feature engineering can be found here.
什么是时间索引？#
时间索引是数据中指定每行数据何时变为已知状态的列。例如，让我们来看一个客户交易表：
[2]:
import featuretools as ft
es = ft.demo.load_mock_customer(return_entityset=True, random_seed=0)
es["transactions"].head()
2024-10-11 14:48:39,763 featuretools - WARNING While loading primitives via "premium_primitives" entry point, ignored primitive "DiversityScore" from "premium_primitives.diversity_score" because a primitive with that name already exists in "nlp_primitives.diversity_score"
2024-10-11 14:48:39,763 featuretools - WARNING While loading primitives via "premium_primitives" entry point, ignored primitive "LSA" from "premium_primitives.lsa" because a primitive with that name already exists in "nlp_primitives.lsa"
2024-10-11 14:48:39,763 featuretools - WARNING While loading primitives via "premium_primitives" entry point, ignored primitive "MeanCharactersPerSentence" from "premium_primitives.mean_characters_per_sentence" because a primitive with that name already exists in "nlp_primitives.mean_characters_per_sentence"
2024-10-11 14:48:39,764 featuretools - WARNING While loading primitives via "premium_primitives" entry point, ignored primitive "NumberOfSentences" from "premium_primitives.number_of_sentences" because a primitive with that name already exists in "nlp_primitives.number_of_sentences"
2024-10-11 14:48:39,764 featuretools - WARNING While loading primitives via "premium_primitives" entry point, ignored primitive "PartOfSpeechCount" from "premium_primitives.part_of_speech_count" because a primitive with that name already exists in "nlp_primitives.part_of_speech_count"
2024-10-11 14:48:39,764 featuretools - WARNING While loading primitives via "premium_primitives" entry point, ignored primitive "PolarityScore" from "premium_primitives.polarity_score" because a primitive with that name already exists in "nlp_primitives.polarity_score"
2024-10-11 14:48:39,764 featuretools - WARNING While loading primitives via "premium_primitives" entry point, ignored primitive "StopwordCount" from "premium_primitives.stopword_count" because a primitive with that name already exists in "nlp_primitives.stopword_count"
2024-10-11 14:48:39,780 featuretools - WARNING Featuretools failed to load plugin tsfresh from library featuretools_tsfresh_primitives.__init__. For a full stack trace, set logging to debug.
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
[2]:
| transaction_id | session_id | transaction_time | product_id | amount | _ft_last_time | |
|---|---|---|---|---|---|---|
| 298 | 298 | 1 | 2014-01-01 00:00:00 | 5 | 127.64 | 2014-01-01 00:00:00 |
| 2 | 2 | 1 | 2014-01-01 00:01:05 | 2 | 109.48 | 2014-01-01 00:01:05 |
| 308 | 308 | 1 | 2014-01-01 00:02:10 | 3 | 95.06 | 2014-01-01 00:02:10 |
| 116 | 116 | 1 | 2014-01-01 00:03:15 | 4 | 78.92 | 2014-01-01 00:03:15 |
| 371 | 371 | 1 | 2014-01-01 00:04:20 | 3 | 31.54 | 2014-01-01 00:04:20 |
在这个表中，每笔交易都有一行，还有一个transaction_time列，指定了交易发生的时间。这意味着transaction_time是时间索引，因为它指示了每行中的信息何时变得可用，可以用于特征计算。暂时忽略_ft_last_time列。这是Featuretools生成的列，稍后会详细讨论。然而，并非每个日期时间列都是时间索引。考虑customers数据框：
[3]:
es["customers"]
[3]:
| customer_id | zip_code | join_date | birthday | _ft_last_time | |
|---|---|---|---|---|---|
| 5 | 5 | 60091 | 2010-07-17 05:27:50 | 1984-07-28 | 2014-01-01 08:09:40 |
| 4 | 4 | 60091 | 2011-04-08 20:08:14 | 2006-08-15 | 2014-01-01 05:31:30 |
| 1 | 1 | 60091 | 2011-04-17 10:48:33 | 1994-07-18 | 2014-01-01 07:26:20 |
| 3 | 3 | 13244 | 2011-08-13 15:42:34 | 2003-11-21 | 2014-01-01 09:00:35 |
| 2 | 2 | 13244 | 2012-04-15 23:31:04 | 1986-08-18 | 2014-01-01 08:23:45 |
这里，我们有两列时间，join_date 和 birthday。虽然这两列都可能对生成特征有用，但 join_date 应该被用作时间索引，因为它表示客户首次在数据集中出现的时间。
Important
The time index is defined as the first time that any information from a row can be used. If a cutoff time is specified when calculating features, rows that have a later value for the time index are automatically ignored.
什么是截止时间？#
截止时间指定了行数据在特征计算中可以使用的最后时间点。在计算特征之前，将过滤掉此时间点之后的任何数据。例如，假设有一个时间戳客户交易数据集，我们想要预测客户“1”、“2”和“3”是否会在1月1日的“04:00”到当天结束之间花费500美元。在构建这个预测问题的特征时，我们需要确保在我们的计算中不使用“04:00”之后的任何数据。
We pass the cutoff time to featuretools.dfs() or featuretools.calculate_feature_matrix() using the cutoff_time argument like this:
[4]:
fm, features = ft.dfs(
entityset=es,
target_dataframe_name="customers",
cutoff_time=pd.Timestamp("2014-1-1 04:00"),
instance_ids=[1, 2, 3],
cutoff_time_in_index=True,
)
fm
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
[4]:
| zip_code | COUNT(sessions) | MODE(sessions.device) | NUM_UNIQUE(sessions.device) | COUNT(transactions) | MAX(transactions.amount) | MEAN(transactions.amount) | MIN(transactions.amount) | MODE(transactions.product_id) | NUM_UNIQUE(transactions.product_id) | SKEW(transactions.amount) | STD(transactions.amount) | SUM(transactions.amount) | DAY(birthday) | DAY(join_date) | MONTH(birthday) | MONTH(join_date) | WEEKDAY(birthday) | WEEKDAY(join_date) | YEAR(birthday) | YEAR(join_date) | MAX(sessions.COUNT(transactions)) | MAX(sessions.MEAN(transactions.amount)) | MAX(sessions.MIN(transactions.amount)) | MAX(sessions.NUM_UNIQUE(transactions.product_id)) | MAX(sessions.SKEW(transactions.amount)) | MAX(sessions.STD(transactions.amount)) | MAX(sessions.SUM(transactions.amount)) | MEAN(sessions.COUNT(transactions)) | MEAN(sessions.MAX(transactions.amount)) | MEAN(sessions.MEAN(transactions.amount)) | MEAN(sessions.MIN(transactions.amount)) | MEAN(sessions.NUM_UNIQUE(transactions.product_id)) | MEAN(sessions.SKEW(transactions.amount)) | MEAN(sessions.STD(transactions.amount)) | MEAN(sessions.SUM(transactions.amount)) | MIN(sessions.COUNT(transactions)) | MIN(sessions.MAX(transactions.amount)) | MIN(sessions.MEAN(transactions.amount)) | MIN(sessions.NUM_UNIQUE(transactions.product_id)) | MIN(sessions.SKEW(transactions.amount)) | MIN(sessions.STD(transactions.amount)) | MIN(sessions.SUM(transactions.amount)) | MODE(sessions.DAY(session_start)) | MODE(sessions.MODE(transactions.product_id)) | MODE(sessions.MONTH(session_start)) | MODE(sessions.WEEKDAY(session_start)) | MODE(sessions.YEAR(session_start)) | NUM_UNIQUE(sessions.DAY(session_start)) | NUM_UNIQUE(sessions.MODE(transactions.product_id)) | NUM_UNIQUE(sessions.MONTH(session_start)) | NUM_UNIQUE(sessions.WEEKDAY(session_start)) | NUM_UNIQUE(sessions.YEAR(session_start)) | SKEW(sessions.COUNT(transactions)) | SKEW(sessions.MAX(transactions.amount)) | SKEW(sessions.MEAN(transactions.amount)) | SKEW(sessions.MIN(transactions.amount)) | SKEW(sessions.NUM_UNIQUE(transactions.product_id)) | SKEW(sessions.STD(transactions.amount)) | SKEW(sessions.SUM(transactions.amount)) | STD(sessions.COUNT(transactions)) | STD(sessions.MAX(transactions.amount)) | STD(sessions.MEAN(transactions.amount)) | STD(sessions.MIN(transactions.amount)) | STD(sessions.NUM_UNIQUE(transactions.product_id)) | STD(sessions.SKEW(transactions.amount)) | STD(sessions.SUM(transactions.amount)) | SUM(sessions.MAX(transactions.amount)) | SUM(sessions.MEAN(transactions.amount)) | SUM(sessions.MIN(transactions.amount)) | SUM(sessions.NUM_UNIQUE(transactions.product_id)) | SUM(sessions.SKEW(transactions.amount)) | SUM(sessions.STD(transactions.amount)) | MODE(transactions.sessions.device) | NUM_UNIQUE(transactions.sessions.device) | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| customer_id | time | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 1 | 2014-01-01 04:00:00 | 60091 | 4 | tablet | 3 | 67 | 139.23 | 74.002836 | 5.81 | 4 | 5 | -0.006928 | 42.309717 | 4958.19 | 18 | 17 | 7 | 4 | 0 | 6 | 1994 | 2011 | 25.0 | 85.469167 | 8.74 | 5.0 | 0.234349 | 46.905665 | 1613.93 | 16.75 | 135.0100 | 76.150425 | 6.905 | 5.0 | -0.126261 | 42.393218 | 1239.5475 | 12.0 | 129.00 | 64.557200 | 5.0 | -0.830975 | 39.825249 | 1025.63 | 1 | 4 | 1 | 2 | 2014 | 1 | 3 | 1 | 1 | 1 | 1.614843 | -0.451371 | -0.233453 | 1.452325 | 0.0 | 1.235445 | 1.197406 | 5.678908 | 5.027226 | 10.426572 | 1.285833 | 0.0 | 0.500353 | 271.917637 | 540.04 | 304.601700 | 27.62 | 20.0 | -0.505043 | 169.572874 | tablet | 3 |
| 2 | 2014-01-01 04:00:00 | 13244 | 4 | desktop | 2 | 49 | 146.81 | 84.700000 | 12.07 | 4 | 5 | -0.134786 | 39.289512 | 4150.30 | 18 | 15 | 8 | 4 | 0 | 6 | 1986 | 2012 | 16.0 | 96.581000 | 56.46 | 5.0 | 0.295458 | 47.935920 | 1320.64 | 12.25 | 142.3225 | 85.197948 | 26.310 | 5.0 | 0.011293 | 39.315685 | 1037.5750 | 8.0 | 138.38 | 76.813125 | 5.0 | -0.455197 | 27.839228 | 634.84 | 1 | 2 | 1 | 2 | 2014 | 1 | 3 | 1 | 1 | 1 | -0.169238 | 0.459305 | 0.651941 | 1.815491 | 0.0 | -0.966834 | -0.823347 | 3.862210 | 3.470527 | 8.983533 | 20.424007 | 0.0 | 0.324809 | 307.743859 | 569.29 | 340.791792 | 105.24 | 20.0 | 0.045171 | 157.262738 | desktop | 2 |
| 3 | 2014-01-01 04:00:00 | 13244 | 1 | tablet | 1 | 15 | 146.31 | 62.791333 | 8.19 | 1 | 5 | 0.618455 | 47.264797 | 941.87 | 21 | 13 | 11 | 8 | 4 | 5 | 2003 | 2011 | 15.0 | 62.791333 | 8.19 | 5.0 | 0.618455 | 47.264797 | 941.87 | 15.00 | 146.3100 | 62.791333 | 8.190 | 5.0 | 0.618455 | 47.264797 | 941.8700 | 15.0 | 146.31 | 62.791333 | 5.0 | 0.618455 | 47.264797 | 941.87 | 1 | 1 | 1 | 2 | 2014 | 1 | 1 | 1 | 1 | 1 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 146.31 | 62.791333 | 8.19 | 5.0 | 0.618455 | 47.264797 | tablet | 1 |
即使实体集包含每个客户的完整交易历史记录，但上面计算特征时仅使用了具有时间索引直到截止时间的数据。##
使用截止时间数据框通常，机器学习的训练样本将来自不同时间点。为了为生成的特征矩阵的每一行指定唯一的截止时间，我们可以传递一个数据框，其中包括一个用于实例ID的列和另一个用于相应截止时间的列。这些列可以以任何顺序排列，但它们必须被正确命名。具有实例ID的列必须命名为instance_id或与目标数据框的index具有相同的名称。具有截止时间值的列必须命名为time或与目标数据框的time_index具有相同的名称。实例ID和截止时间值的列名应该是清晰明了的。传递一个包含既有与目标数据框index同名列又有名为instance_id的列的数据框将导致错误。同样，如果截止时间数据框既包含与目标数据框time_index同名列又包含名为time的列，将会引发错误。
Note
Only the columns corresponding to the instance ids and the cutoff times are used to calculate features. Any additional columns passed through are appended to the resulting feature matrix. This is typically used to pass through machine learning labels to ensure that they stay aligned with the feature matrix.
[5]:
cutoff_times = pd.DataFrame()
cutoff_times["customer_id"] = [1, 2, 3, 1]
cutoff_times["time"] = pd.to_datetime(
["2014-1-1 04:00", "2014-1-1 05:00", "2014-1-1 06:00", "2014-1-1 08:00"]
)
cutoff_times["label"] = [True, True, False, True]
cutoff_times
fm, features = ft.dfs(
entityset=es,
target_dataframe_name="customers",
cutoff_time=cutoff_times,
cutoff_time_in_index=True,
)
fm
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
[5]:
| zip_code | COUNT(sessions) | MODE(sessions.device) | NUM_UNIQUE(sessions.device) | COUNT(transactions) | MAX(transactions.amount) | MEAN(transactions.amount) | MIN(transactions.amount) | MODE(transactions.product_id) | NUM_UNIQUE(transactions.product_id) | SKEW(transactions.amount) | STD(transactions.amount) | SUM(transactions.amount) | DAY(birthday) | DAY(join_date) | MONTH(birthday) | MONTH(join_date) | WEEKDAY(birthday) | WEEKDAY(join_date) | YEAR(birthday) | YEAR(join_date) | MAX(sessions.COUNT(transactions)) | MAX(sessions.MEAN(transactions.amount)) | MAX(sessions.MIN(transactions.amount)) | MAX(sessions.NUM_UNIQUE(transactions.product_id)) | MAX(sessions.SKEW(transactions.amount)) | MAX(sessions.STD(transactions.amount)) | MAX(sessions.SUM(transactions.amount)) | MEAN(sessions.COUNT(transactions)) | MEAN(sessions.MAX(transactions.amount)) | MEAN(sessions.MEAN(transactions.amount)) | MEAN(sessions.MIN(transactions.amount)) | MEAN(sessions.NUM_UNIQUE(transactions.product_id)) | MEAN(sessions.SKEW(transactions.amount)) | MEAN(sessions.STD(transactions.amount)) | MEAN(sessions.SUM(transactions.amount)) | MIN(sessions.COUNT(transactions)) | MIN(sessions.MAX(transactions.amount)) | MIN(sessions.MEAN(transactions.amount)) | MIN(sessions.NUM_UNIQUE(transactions.product_id)) | MIN(sessions.SKEW(transactions.amount)) | MIN(sessions.STD(transactions.amount)) | MIN(sessions.SUM(transactions.amount)) | MODE(sessions.DAY(session_start)) | MODE(sessions.MODE(transactions.product_id)) | MODE(sessions.MONTH(session_start)) | MODE(sessions.WEEKDAY(session_start)) | MODE(sessions.YEAR(session_start)) | NUM_UNIQUE(sessions.DAY(session_start)) | NUM_UNIQUE(sessions.MODE(transactions.product_id)) | NUM_UNIQUE(sessions.MONTH(session_start)) | NUM_UNIQUE(sessions.WEEKDAY(session_start)) | NUM_UNIQUE(sessions.YEAR(session_start)) | SKEW(sessions.COUNT(transactions)) | SKEW(sessions.MAX(transactions.amount)) | SKEW(sessions.MEAN(transactions.amount)) | SKEW(sessions.MIN(transactions.amount)) | SKEW(sessions.NUM_UNIQUE(transactions.product_id)) | SKEW(sessions.STD(transactions.amount)) | SKEW(sessions.SUM(transactions.amount)) | STD(sessions.COUNT(transactions)) | STD(sessions.MAX(transactions.amount)) | STD(sessions.MEAN(transactions.amount)) | STD(sessions.MIN(transactions.amount)) | STD(sessions.NUM_UNIQUE(transactions.product_id)) | STD(sessions.SKEW(transactions.amount)) | STD(sessions.SUM(transactions.amount)) | SUM(sessions.MAX(transactions.amount)) | SUM(sessions.MEAN(transactions.amount)) | SUM(sessions.MIN(transactions.amount)) | SUM(sessions.NUM_UNIQUE(transactions.product_id)) | SUM(sessions.SKEW(transactions.amount)) | SUM(sessions.STD(transactions.amount)) | MODE(transactions.sessions.device) | NUM_UNIQUE(transactions.sessions.device) | label | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| customer_id | time | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 1 | 2014-01-01 04:00:00 | 60091 | 4 | tablet | 3 | 67 | 139.23 | 74.002836 | 5.81 | 4 | 5 | -0.006928 | 42.309717 | 4958.19 | 18 | 17 | 7 | 4 | 0 | 6 | 1994 | 2011 | 25.0 | 85.469167 | 8.74 | 5.0 | 0.234349 | 46.905665 | 1613.93 | 16.75 | 135.01000 | 76.150425 | 6.90500 | 5.0 | -0.126261 | 42.393218 | 1239.5475 | 12.0 | 129.00 | 64.557200 | 5.0 | -0.830975 | 39.825249 | 1025.63 | 1 | 4 | 1 | 2 | 2014 | 1 | 3 | 1 | 1 | 1 | 1.614843 | -0.451371 | -0.233453 | 1.452325 | 0.0 | 1.235445 | 1.197406 | 5.678908 | 5.027226 | 10.426572 | 1.285833 | 0.0 | 0.500353 | 271.917637 | 540.04 | 304.601700 | 27.62 | 20.0 | -0.505043 | 169.572874 | tablet | 3 | True |
| 2 | 2014-01-01 05:00:00 | 13244 | 5 | desktop | 2 | 62 | 146.81 | 83.149355 | 12.07 | 4 | 5 | -0.121811 | 38.047944 | 5155.26 | 18 | 15 | 8 | 4 | 0 | 6 | 1986 | 2012 | 16.0 | 96.581000 | 56.46 | 5.0 | 0.295458 | 47.935920 | 1320.64 | 12.40 | 137.62800 | 83.619281 | 25.41200 | 5.0 | -0.053949 | 38.197555 | 1031.0520 | 8.0 | 118.85 | 76.813125 | 5.0 | -0.455197 | 27.839228 | 634.84 | 1 | 2 | 1 | 2 | 2014 | 1 | 4 | 1 | 1 | 1 | -0.379092 | -1.814717 | 1.082192 | 1.959531 | 0.0 | -0.213518 | -0.667256 | 3.361547 | 10.919023 | 8.543351 | 17.801322 | 0.0 | 0.316873 | 266.912832 | 688.14 | 418.096407 | 127.06 | 25.0 | -0.269747 | 190.987775 | desktop | 2 | True |
| 3 | 2014-01-01 06:00:00 | 13244 | 4 | desktop | 2 | 44 | 146.31 | 65.174773 | 6.65 | 1 | 5 | 0.318315 | 40.349758 | 2867.69 | 21 | 13 | 11 | 8 | 4 | 5 | 2003 | 2011 | 17.0 | 91.760000 | 91.76 | 5.0 | 0.618455 | 47.264797 | 944.85 | 11.00 | 123.26750 | 72.742004 | 31.66500 | 4.0 | 0.286859 | 39.712232 | 716.9225 | 1.0 | 91.76 | 55.579412 | 1.0 | -0.289466 | 35.704680 | 91.76 | 1 | 1 | 1 | 2 | 2014 | 1 | 2 | 1 | 1 | 1 | -1.330938 | -1.060639 | 0.201588 | 1.874170 | -2.0 | 1.722323 | -1.977878 | 7.118052 | 22.808351 | 16.540737 | 40.508892 | 2.0 | 0.500999 | 417.557763 | 493.07 | 290.968018 | 126.66 | 16.0 | 0.860577 | 119.136697 | desktop | 2 | False |
| 1 | 2014-01-01 08:00:00 | 60091 | 8 | mobile | 3 | 126 | 139.43 | 71.631905 | 5.81 | 4 | 5 | 0.019698 | 40.442059 | 9025.62 | 18 | 17 | 7 | 4 | 0 | 6 | 1994 | 2011 | 25.0 | 88.755625 | 26.36 | 5.0 | 0.640252 | 46.905665 | 1613.93 | 15.75 | 132.24625 | 72.774140 | 9.82375 | 5.0 | -0.059515 | 39.093244 | 1128.2025 | 12.0 | 118.90 | 50.623125 | 5.0 | -1.038434 | 30.450261 | 809.97 | 1 | 4 | 1 | 2 | 2014 | 1 | 4 | 1 | 1 | 1 | 1.946018 | -0.780493 | -0.424949 | 2.440005 | 0.0 | -0.312355 | 0.778170 | 4.062019 | 7.322191 | 13.759314 | 6.954507 | 0.0 | 0.589386 | 279.510713 | 1057.97 | 582.193117 | 78.59 | 40.0 | -0.476122 | 312.745952 | mobile | 3 | True |
我们现在可以看到特征矩阵的每一行都是在截止时间数据框中对应的时间计算出来的。因为我们在不同的时间计算每一行，所以有可能出现重复的客户。在这种情况下，我们计算了客户1在“04:00”和“08:00”两个时间点的特征向量。
训练窗口—————默认情况下，使用截止时间之前和包括截止时间的所有数据。我们可以使用“训练窗口”来限制用于计算的历史数据量。以下是使用两小时训练窗口的示例：
[6]:
window_fm, window_features = ft.dfs(
entityset=es,
target_dataframe_name="customers",
cutoff_time=cutoff_times,
cutoff_time_in_index=True,
training_window="2 hour",
)
window_fm
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
[6]:
| zip_code | COUNT(sessions) | MODE(sessions.device) | NUM_UNIQUE(sessions.device) | COUNT(transactions) | MAX(transactions.amount) | MEAN(transactions.amount) | MIN(transactions.amount) | MODE(transactions.product_id) | NUM_UNIQUE(transactions.product_id) | SKEW(transactions.amount) | STD(transactions.amount) | SUM(transactions.amount) | DAY(birthday) | DAY(join_date) | MONTH(birthday) | MONTH(join_date) | WEEKDAY(birthday) | WEEKDAY(join_date) | YEAR(birthday) | YEAR(join_date) | MAX(sessions.COUNT(transactions)) | MAX(sessions.MEAN(transactions.amount)) | MAX(sessions.MIN(transactions.amount)) | MAX(sessions.NUM_UNIQUE(transactions.product_id)) | MAX(sessions.SKEW(transactions.amount)) | MAX(sessions.STD(transactions.amount)) | MAX(sessions.SUM(transactions.amount)) | MEAN(sessions.COUNT(transactions)) | MEAN(sessions.MAX(transactions.amount)) | MEAN(sessions.MEAN(transactions.amount)) | MEAN(sessions.MIN(transactions.amount)) | MEAN(sessions.NUM_UNIQUE(transactions.product_id)) | MEAN(sessions.SKEW(transactions.amount)) | MEAN(sessions.STD(transactions.amount)) | MEAN(sessions.SUM(transactions.amount)) | MIN(sessions.COUNT(transactions)) | MIN(sessions.MAX(transactions.amount)) | MIN(sessions.MEAN(transactions.amount)) | MIN(sessions.NUM_UNIQUE(transactions.product_id)) | MIN(sessions.SKEW(transactions.amount)) | MIN(sessions.STD(transactions.amount)) | MIN(sessions.SUM(transactions.amount)) | MODE(sessions.DAY(session_start)) | MODE(sessions.MODE(transactions.product_id)) | MODE(sessions.MONTH(session_start)) | MODE(sessions.WEEKDAY(session_start)) | MODE(sessions.YEAR(session_start)) | NUM_UNIQUE(sessions.DAY(session_start)) | NUM_UNIQUE(sessions.MODE(transactions.product_id)) | NUM_UNIQUE(sessions.MONTH(session_start)) | NUM_UNIQUE(sessions.WEEKDAY(session_start)) | NUM_UNIQUE(sessions.YEAR(session_start)) | SKEW(sessions.COUNT(transactions)) | SKEW(sessions.MAX(transactions.amount)) | SKEW(sessions.MEAN(transactions.amount)) | SKEW(sessions.MIN(transactions.amount)) | SKEW(sessions.NUM_UNIQUE(transactions.product_id)) | SKEW(sessions.STD(transactions.amount)) | SKEW(sessions.SUM(transactions.amount)) | STD(sessions.COUNT(transactions)) | STD(sessions.MAX(transactions.amount)) | STD(sessions.MEAN(transactions.amount)) | STD(sessions.MIN(transactions.amount)) | STD(sessions.NUM_UNIQUE(transactions.product_id)) | STD(sessions.SKEW(transactions.amount)) | STD(sessions.SUM(transactions.amount)) | SUM(sessions.MAX(transactions.amount)) | SUM(sessions.MEAN(transactions.amount)) | SUM(sessions.MIN(transactions.amount)) | SUM(sessions.NUM_UNIQUE(transactions.product_id)) | SUM(sessions.SKEW(transactions.amount)) | SUM(sessions.STD(transactions.amount)) | MODE(transactions.sessions.device) | NUM_UNIQUE(transactions.sessions.device) | label | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| customer_id | time | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 1 | 2014-01-01 04:00:00 | 60091 | 2 | desktop | 2 | 27 | 139.09 | 76.950370 | 5.81 | 4 | 5 | -0.187686 | 43.772157 | 2077.66 | 18 | 17 | 7 | 4 | 0 | 6 | 1994 | 2011 | 15.0 | 85.469167 | 6.78 | 5.0 | 0.226337 | 46.905665 | 1052.03 | 13.500000 | 135.905000 | 77.802250 | 6.295000 | 5.000000 | -0.302319 | 43.365457 | 1038.830000 | 12.0 | 132.72 | 70.135333 | 5.0 | -0.830975 | 39.825249 | 1025.63 | 1 | 1 | 1 | 2 | 2014 | 1 | 2 | 1 | 1 | 1 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.121320 | 4.504270 | 10.842658 | 0.685894 | 0.000000 | 0.747633 | 18.667619 | 271.81 | 155.604500 | 12.59 | 10.0 | -0.604638 | 86.730914 | desktop | 2 | True |
| 2 | 2014-01-01 05:00:00 | 13244 | 3 | desktop | 2 | 31 | 146.81 | 84.051935 | 12.07 | 4 | 5 | -0.198611 | 36.077146 | 2605.61 | 18 | 15 | 8 | 4 | 0 | 6 | 1986 | 2012 | 13.0 | 96.581000 | 56.46 | 5.0 | 0.130019 | 47.935920 | 1004.96 | 10.333333 | 134.680000 | 84.413538 | 30.116667 | 5.000000 | -0.036670 | 36.500062 | 868.536667 | 8.0 | 118.85 | 77.304615 | 5.0 | -0.314918 | 27.839228 | 634.84 | 1 | 1 | 1 | 2 | 2014 | 1 | 3 | 1 | 1 | 1 | 0.585583 | -1.083626 | 1.659252 | 1.397956 | 0.000000 | 1.121470 | -1.660092 | 2.516611 | 14.342521 | 10.587085 | 23.329038 | 0.000000 | 0.242542 | 203.331699 | 404.04 | 253.240615 | 90.35 | 15.0 | -0.110009 | 109.500185 | desktop | 2 | True |
| 3 | 2014-01-01 06:00:00 | 13244 | 3 | desktop | 1 | 29 | 128.26 | 66.407586 | 6.65 | 1 | 5 | 0.110145 | 37.130891 | 1925.82 | 21 | 13 | 11 | 8 | 4 | 5 | 2003 | 2011 | 17.0 | 91.760000 | 91.76 | 5.0 | 0.531588 | 36.167220 | 944.85 | 9.666667 | 115.586667 | 76.058895 | 39.490000 | 3.666667 | 0.121061 | 35.935950 | 641.940000 | 1.0 | 91.76 | 55.579412 | 1.0 | -0.289466 | 35.704680 | 91.76 | 1 | 1 | 1 | 2 | 2014 | 1 | 2 | 1 | 1 | 1 | -0.722109 | -1.721498 | -1.081879 | 1.566223 | -1.732051 | NaN | -1.705607 | 8.082904 | 20.648490 | 18.557570 | 45.761028 | 2.309401 | 0.580573 | 477.281339 | 346.76 | 228.176684 | 118.47 | 11.0 | 0.242122 | 71.871900 | desktop | 1 | False |
| 1 | 2014-01-01 08:00:00 | 60091 | 3 | mobile | 2 | 47 | 139.43 | 66.471277 | 5.91 | 4 | 5 | 0.047120 | 38.952172 | 3124.15 | 18 | 17 | 7 | 4 | 0 | 6 | 1994 | 2011 | 16.0 | 88.755625 | 11.62 | 5.0 | 0.640252 | 44.354104 | 1420.09 | 15.666667 | 128.146667 | 66.328250 | 8.203333 | 5.000000 | -0.001146 | 35.709633 | 1041.383333 | 15.0 | 118.90 | 50.623125 | 5.0 | -1.038434 | 30.450261 | 809.97 | 1 | 1 | 1 | 2 | 2014 | 1 | 3 | 1 | 1 | 1 | -1.732051 | 0.846298 | 1.344879 | 1.443486 | 0.000000 | 1.612576 | 1.606791 | 0.577350 | 10.415432 | 19.935229 | 3.016195 | 0.000000 | 0.906666 | 330.655558 | 384.44 | 198.984750 | 24.61 | 15.0 | -0.003438 | 107.128899 | mobile | 2 | True |
我们可以看到，在缩短训练窗口后，相同特征的计数较低：
[7]:
fm[["COUNT(transactions)"]]
window_fm[["COUNT(transactions)"]]
[7]:
| COUNT(transactions) | ||
|---|---|---|
| customer_id | time | |
| 1 | 2014-01-01 04:00:00 | 27 |
| 2 | 2014-01-01 05:00:00 | 31 |
| 3 | 2014-01-01 06:00:00 | 29 |
| 1 | 2014-01-01 08:00:00 | 47 |
设置最后时间索引#
Featuretools中的训练窗口限制了在计算特征向量时可以使用的过去数据量。如果数据框中的时间索引值在训练窗口之前或之后，那么该行将被过滤掉。这适用于数据框中的行发生在单个时间点的情况。然而，有时一行可能存在一段时间。例如，客户的会话包含多个可能发生在不同时间点的交易。如果我们试图计算用户在给定时间段内拥有的会话数量，通常我们希望计算在训练窗口期间有任何交易的所有会话。为了实现这一点，我们不仅需要知道会话何时开始，还需要知道何时结束。数据中实例出现的最后时间存储在数据框上的_ft_last_time列中。我们可以比较上面sessions数据框的时间索引和最后时间索引：
[8]:
last_time_index_col = es["sessions"].ww.metadata.get("last_time_index")
es["sessions"][["session_start", last_time_index_col]].head()
[8]:
| session_start | _ft_last_time | |
|---|---|---|
| 1 | 2014-01-01 00:00:00 | 2014-01-01 00:16:15 |
| 2 | 2014-01-01 00:17:20 | 2014-01-01 00:27:05 |
| 3 | 2014-01-01 00:28:10 | 2014-01-01 00:43:20 |
| 4 | 2014-01-01 00:44:25 | 2014-01-01 01:10:25 |
| 5 | 2014-01-01 01:11:30 | 2014-01-01 01:22:20 |
Featuretools可以通过运行EntitySet.add_last_time_indexes()自动向Entityset中的每个DataFrame添加最后时间索引。在使用训练窗口时，如果已设置了last_time_index，Featuretools将检查last_time_index是否在训练窗口的开始之后。这与截止时间结合使用，允许DFS发现哪些数据与给定的训练窗口相关。## 在截止时间排除数据
The cutoff_time is the last point in time where data can be used for feature
calculation. If you don’t want to use the data at the cutoff time in feature
calculation, you can exclude that data by setting include_cutoff_time to
False in featuretools.dfs() or featuretools.calculate_feature_matrix().
If you set it to True (the default behavior), data from the cutoff time point
will be used.
将include_cutoff_time设置为False也会影响如何包含或排除训练窗口边缘的数据。以这段数据为例：
[9]:
df = es["transactions"]
df[df["session_id"] == 1].head()
[9]:
| transaction_id | session_id | transaction_time | product_id | amount | _ft_last_time | |
|---|---|---|---|---|---|---|
| 298 | 298 | 1 | 2014-01-01 00:00:00 | 5 | 127.64 | 2014-01-01 00:00:00 |
| 2 | 2 | 1 | 2014-01-01 00:01:05 | 2 | 109.48 | 2014-01-01 00:01:05 |
| 308 | 308 | 1 | 2014-01-01 00:02:10 | 3 | 95.06 | 2014-01-01 00:02:10 |
| 116 | 116 | 1 | 2014-01-01 00:03:15 | 4 | 78.92 | 2014-01-01 00:03:15 |
| 371 | 371 | 1 | 2014-01-01 00:04:20 | 3 | 31.54 | 2014-01-01 00:04:20 |
观察数据，交易每隔65秒发生一次。为了检查include_cutoff_time对训练窗口的影响，我们可以在交易发生时计算特征，同时使用65秒的训练窗口。这样就创建了一个训练窗口，窗口的两个端点都有一个交易。在这个例子中，我们将找到会话ID为1的所有在训练窗口内的交易的总和。
[10]:
from featuretools.primitives import Sum
sum_log = ft.Feature(
es["transactions"].ww["amount"],
parent_dataframe_name="sessions",
primitive=Sum,
)
cutoff_time = pd.DataFrame(
{
"session_id": [1],
"time": ["2014-01-01 00:04:20"],
}
).astype({"time": "datetime64[ns]"})
使用include_cutoff_time=True参数，训练窗口中最旧的时间点（2014-01-01 00:03:15）被排除在外，而截止时间点被包含在内。这意味着训练窗口中只有交易371，因此所有交易金额的总和为31.54。
[11]:
# Case1. include_cutoff_time = Trueactual = ft.calculate_feature_matrix( features=[sum_log], entityset=es, cutoff_time=cutoff_time, cutoff_time_in_index=True, training_window="65 seconds", include_cutoff_time=True,)actual
当 include_cutoff_time=False 时，窗口中的最旧时间点被包括在内，截止时间点被排除在外。因此，在这种情况下，交易116被包括在内，交易371被排除在外，总和为78.92。
[12]:
# Case2. include_cutoff_time = Falseactual = ft.calculate_feature_matrix( features=[sum_log], entityset=es, cutoff_time=cutoff_time, cutoff_time_in_index=True, training_window="65 seconds", include_cutoff_time=False,)actual
通过舍入截止时间来近似特征———————————————–对于每个唯一的截止时间，Featuretools必须执行操作来选择用于计算的有效数据。如果相对于我们计算特征的实例数量，唯一截止时间的数量很大，那么过滤数据所花费的时间会累积起来。通过减少唯一截止时间的数量，我们可以最小化从搜索和提取数据用于特征计算中的开销。减少唯一截止时间的一种方法是将截止时间舍入到较早的时间点。较早的截止时间对于预测建模始终有效 — 这意味着我们在计算该特征时没有使用可能使用的一些数据。因此，通过丢失少量信息，我们可以获得计算速度的提升。要了解近似何时有用，考虑为模型预测欺诈信用卡交易而计算特征。在这种情况下，一个重要的特征可能是“该卡在过去的平均交易金额”。虽然每次有新交易时这个值可能会改变，但较少频繁更新它可能不会影响准确性。
Note
The bank BBVA used approximation when building a predictive model for credit card fraud using Featuretools. For more details, see the “Real-time deployment considerations” section of the white paper describing the work involved.
The frequency of approximation is controlled using the approximate parameter to featuretools.dfs() or featuretools.calculate_feature_matrix(). For example, the following code would approximate aggregation features at 1 day intervals:
..
fm = ft.calculate_feature_matrix(features=features, entityset=es_transactions, cutoff_time=ct_transactions, approximate="1 day")在这个计算中，可以近似计算的特征将以1天的间隔计算，而无法近似计算的特征（例如“这笔交易发生在哪里？”）将在确切的截止时间计算。
## 次要时间索引
有时数据集中的信息在创建行之后会被更新或添加。这意味着某些列实际上可能在行的时间索引之后才变为已知。我们可以创建一个次要时间索引来指示这些列何时变为已知，而不是删除这些列以避免信息泄漏。
The Flights entityset is a good example of a dataset where column values in a row become known at different times. Each trip is recorded in the trip_logs dataframe, and has many times associated with it.
[14]:
es_flight = ft.demo.load_flight(nrows=100)
es_flight
es_flight["trip_logs"].head(3)
Downloading data ...
/Users/code/fin_tool/github/featuretools/featuretools/demo/flight.py:288: PerformanceWarning: Adding/subtracting object-dtype array to TimedeltaArray not vectorized.
clean_data.loc[:, "dep_time"] = clean_data["scheduled_dep_time"] + pd.to_timedelta(
/Users/code/fin_tool/github/featuretools/featuretools/demo/flight.py:293: PerformanceWarning: Adding/subtracting object-dtype array to TimedeltaArray not vectorized.
clean_data.loc[:, "arr_time"] = clean_data["dep_time"] + pd.to_timedelta(
/Users/code/fin_tool/github/featuretools/featuretools/demo/flight.py:299: PerformanceWarning: Adding/subtracting object-dtype array to TimedeltaArray not vectorized.
clean_data["scheduled_dep_time"] + clean_data["scheduled_elapsed_time"]
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/logical_types.py:897: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
series = series.replace(ww.config.get_option("nan_values"), np.nan)
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/logical_types.py:897: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
series = series.replace(ww.config.get_option("nan_values"), np.nan)
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/logical_types.py:897: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
series = series.replace(ww.config.get_option("nan_values"), np.nan)
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/logical_types.py:897: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
series = series.replace(ww.config.get_option("nan_values"), np.nan)
[14]:
| trip_log_id | flight_id | date_scheduled | scheduled_dep_time | scheduled_arr_time | dep_time | arr_time | dep_delay | taxi_out | taxi_in | arr_delay | diverted | scheduled_elapsed_time | air_time | distance | carrier_delay | weather_delay | national_airspace_delay | security_delay | late_aircraft_delay | canceled | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 30 | 30 | AA-494:RSW->CLT | 2016-09-03 | 2017-01-01 13:14:00 | 2017-01-01 15:05:00 | 2017-01-01 13:03:00 | 2017-01-01 14:53:00 | -11.0 | 12.0 | 10.0 | -12.0 | False | 0 days 01:51:00 | 88.0 | 600.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | False |
| 38 | 38 | AA-495:ATL->PHX | 2016-09-03 | 2017-01-01 11:30:00 | 2017-01-01 15:40:00 | 2017-01-01 11:24:00 | 2017-01-01 15:41:00 | -6.0 | 28.0 | 5.0 | 1.0 | False | 0 days 04:10:00 | 224.0 | 1587.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | False |
| 46 | 46 | AA-495:CLT->ATL | 2016-09-03 | 2017-01-01 09:25:00 | 2017-01-01 10:42:00 | 2017-01-01 09:23:00 | 2017-01-01 10:39:00 | -2.0 | 18.0 | 8.0 | -3.0 | False | 0 days 01:17:00 | 50.0 | 226.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | False |
对于每次航班记录，时间索引是date_scheduled，这是航空公司确定计划的起飞和到达时间以及飞行路线的时间点。我们目前不知道实际起飞/到达时间以及任何延误的其他信息。然而，一旦航班到达后，就可以了解关于航班情况的所有信息，因此我们可以在航班降落后的任何时间使用这些信息。使用辅助时间索引，我们可以告诉Featuretools我们的航班记录中哪些列是在计划航班时已知的，以及哪些是在航班降落时已知的。在Featuretools中，将数据框添加到EntitySet时，我们将辅助时间索引设置为到达时间，如下所示：
es = ft.EntitySet('Flight Data')
arr_time_columns = ['arr_delay', 'dep_delay', 'carrier_delay', 'weather_delay',
'national_airspace_delay', 'security_delay',
'late_aircraft_delay', 'canceled', 'diverted',
'taxi_in', 'taxi_out', 'air_time', 'dep_time']
es.add_dataframe(
dataframe_name='trip_logs',
dataframe=data,
index='trip_log_id',
make_index=True,
time_index='date_scheduled',
secondary_time_index={'arr_time': arr_time_columns}
)
通过设置辅助时间索引，我们仍然可以使用行中的延误信息，但只有在该信息变为已知时才能使用。
Hint
It’s often a good idea to use a secondary time index if your entityset has inline labels. If you know when the label would be valid for use, it’s possible to automatically create very predictive features using historical labels.
航班预测#
让我们使用上面描述的航班示例，在不同的时间点生成一些特征。行程“14”是2017年1月31日从CLT飞往PHX的航班，行程“92”是2017年1月1日从PIT飞往DFW的航班。我们可以在航班计划起飞之前设置任何截止时间，模拟我们在那个时间点进行预测的情况。我们为行程“14”设置了两个不同时间点的截止时间：一个是在飞行前一个月以上，另一个是在飞行前仅5天。对于行程“92”，我们只设置一个截止时间，即计划起飞前三天。我们的截止时间数据框如下所示：
[15]:
ct_flight = pd.DataFrame()
ct_flight["trip_log_id"] = [14, 14, 92]
ct_flight["time"] = pd.to_datetime(["2016-12-28", "2017-1-25", "2016-12-28"])
ct_flight["label"] = [True, True, False]
ct_flight
[15]:
| trip_log_id | time | label | |
|---|---|---|---|
| 0 | 14 | 2016-12-28 | True |
| 1 | 14 | 2017-01-25 | True |
| 2 | 92 | 2016-12-28 | False |
现在，让我们计算特征矩阵：
[16]:
fm, features = ft.dfs(
entityset=es_flight,
target_dataframe_name="trip_logs",
cutoff_time=ct_flight,
cutoff_time_in_index=True,
agg_primitives=["max"],
trans_primitives=["month"],
)
fm[
[
"flights.origin",
"flights.dest",
"label",
"flights.MAX(trip_logs.arr_delay)",
"MONTH(scheduled_dep_time)",
]
]
/Users/code/fin_tool/github/featuretools/featuretools/entityset/entityset.py:1403: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'nan' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.
df.loc[mask, columns] = np.nan
/Users/code/fin_tool/github/featuretools/featuretools/entityset/entityset.py:1403: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'nan' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.
df.loc[mask, columns] = np.nan
/Users/code/fin_tool/github/featuretools/featuretools/entityset/entityset.py:1403: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'nan' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.
df.loc[mask, columns] = np.nan
/Users/code/fin_tool/github/featuretools/featuretools/entityset/entityset.py:1403: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'nan' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.
df.loc[mask, columns] = np.nan
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/entityset/entityset.py:1403: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'nan' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.
df.loc[mask, columns] = np.nan
/Users/code/fin_tool/github/featuretools/featuretools/entityset/entityset.py:1403: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'nan' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.
df.loc[mask, columns] = np.nan
/Users/code/fin_tool/github/featuretools/featuretools/entityset/entityset.py:1403: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'nan' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.
df.loc[mask, columns] = np.nan
/Users/code/fin_tool/github/featuretools/featuretools/entityset/entityset.py:1403: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'nan' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.
df.loc[mask, columns] = np.nan
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/logical_types.py:897: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
series = series.replace(ww.config.get_option("nan_values"), np.nan)
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/logical_types.py:897: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
series = series.replace(ww.config.get_option("nan_values"), np.nan)
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/logical_types.py:897: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
series = series.replace(ww.config.get_option("nan_values"), np.nan)
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/logical_types.py:897: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
series = series.replace(ww.config.get_option("nan_values"), np.nan)
[16]:
| flights.origin | flights.dest | label | flights.MAX(trip_logs.arr_delay) | MONTH(scheduled_dep_time) | ||
|---|---|---|---|---|---|---|
| trip_log_id | time | |||||
| 14 | 2016-12-28 | CLT | PHX | True | NaN | 1 |
| 2017-01-25 | CLT | PHX | True | 33.0 | 1 | |
| 92 | 2016-12-28 | PIT | DFW | False | NaN | 1 |
让我们理解输出：
1. 对于
ct_flight中的每个id-time对，都创建了一行，这些行作为特征矩阵的索引返回。2. 输出按照截止时间进行了排序。由于排序的原因，通常有助于在截止时间数据框中传入一个标签，以便它保持与特征矩阵相同的排序方式。除了
id和cutoff_time之外的任何其他列都不会用于生成特征。3. 列
flights.MAX(trip_logs.arr_delay)并非总是定义的。只有在存在历史航班可聚合时，它才能具有任何实际值。请注意，对于行程14，在我们提前一个月制作特征时，没有任何历史数据，但在缩短到5天时有航班可聚合。这些是强大的特征，通常在手动流程中被排除，因为它们很难制作。创建和展平特征张量—————————————-
The featuretools.make_temporal_cutoffs() function generates a series of equally spaced cutoff times from a given set of cutoff times and instance ids.
这个函数可以与DFS配合使用，以创建并展平一个特征张量，而不是在不同延迟上生成多个特征矩阵。该函数接受以下参数：
instance_ids (list, pd.Series, or np.ndarray)：一个实例的列表。cutoffs (list, pd.Series, or np.ndarray)：一个关联的截断时间列表。window_size (str or pandas.DateOffset)：在创建的时间序列中每个截断时间之间的时间量。start (datetime.datetime or pd.Timestamp)：创建的时间序列中的第一个截断时间。num_windows (int)：在创建的时间序列中要创建的截断时间的数量。
只需要指定三个选项中的两个：window_size、start和num_windows，即可唯一确定一个等间隔的截断时间集，用于计算每个实例。如果您的截断时间与上述使用的时间相同：
[17]:
cutoff_times
[17]:
| customer_id | time | label | |
|---|---|---|---|
| 0 | 1 | 2014-01-01 04:00:00 | True |
| 1 | 2 | 2014-01-01 05:00:00 | True |
| 2 | 3 | 2014-01-01 06:00:00 | False |
| 3 | 1 | 2014-01-01 08:00:00 | True |
然后传入window_size='1h'和num_windows=2，使得每一行代表过去两个小时内的每小时数据，从而生成以下新的数据框。可以直接将结果传递给DFS，在不同的时间点生成特征。
[18]:
temporal_cutoffs = ft.make_temporal_cutoffs(
cutoff_times["customer_id"], cutoff_times["time"], window_size="1h", num_windows=2
)
temporal_cutoffs
fm, features = ft.dfs(
entityset=es,
target_dataframe_name="customers",
cutoff_time=temporal_cutoffs,
cutoff_time_in_index=True,
)
fm
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x1052a5260> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x1052a4a40> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x1052a5b20> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x1052a5c60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x1052a5120> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
[18]:
| zip_code | COUNT(sessions) | MODE(sessions.device) | NUM_UNIQUE(sessions.device) | COUNT(transactions) | MAX(transactions.amount) | MEAN(transactions.amount) | MIN(transactions.amount) | MODE(transactions.product_id) | NUM_UNIQUE(transactions.product_id) | SKEW(transactions.amount) | STD(transactions.amount) | SUM(transactions.amount) | DAY(birthday) | DAY(join_date) | MONTH(birthday) | MONTH(join_date) | WEEKDAY(birthday) | WEEKDAY(join_date) | YEAR(birthday) | YEAR(join_date) | MAX(sessions.COUNT(transactions)) | MAX(sessions.MEAN(transactions.amount)) | MAX(sessions.MIN(transactions.amount)) | MAX(sessions.NUM_UNIQUE(transactions.product_id)) | MAX(sessions.SKEW(transactions.amount)) | MAX(sessions.STD(transactions.amount)) | MAX(sessions.SUM(transactions.amount)) | MEAN(sessions.COUNT(transactions)) | MEAN(sessions.MAX(transactions.amount)) | MEAN(sessions.MEAN(transactions.amount)) | MEAN(sessions.MIN(transactions.amount)) | MEAN(sessions.NUM_UNIQUE(transactions.product_id)) | MEAN(sessions.SKEW(transactions.amount)) | MEAN(sessions.STD(transactions.amount)) | MEAN(sessions.SUM(transactions.amount)) | MIN(sessions.COUNT(transactions)) | MIN(sessions.MAX(transactions.amount)) | MIN(sessions.MEAN(transactions.amount)) | MIN(sessions.NUM_UNIQUE(transactions.product_id)) | MIN(sessions.SKEW(transactions.amount)) | MIN(sessions.STD(transactions.amount)) | MIN(sessions.SUM(transactions.amount)) | MODE(sessions.DAY(session_start)) | MODE(sessions.MODE(transactions.product_id)) | MODE(sessions.MONTH(session_start)) | MODE(sessions.WEEKDAY(session_start)) | MODE(sessions.YEAR(session_start)) | NUM_UNIQUE(sessions.DAY(session_start)) | NUM_UNIQUE(sessions.MODE(transactions.product_id)) | NUM_UNIQUE(sessions.MONTH(session_start)) | NUM_UNIQUE(sessions.WEEKDAY(session_start)) | NUM_UNIQUE(sessions.YEAR(session_start)) | SKEW(sessions.COUNT(transactions)) | SKEW(sessions.MAX(transactions.amount)) | SKEW(sessions.MEAN(transactions.amount)) | SKEW(sessions.MIN(transactions.amount)) | SKEW(sessions.NUM_UNIQUE(transactions.product_id)) | SKEW(sessions.STD(transactions.amount)) | SKEW(sessions.SUM(transactions.amount)) | STD(sessions.COUNT(transactions)) | STD(sessions.MAX(transactions.amount)) | STD(sessions.MEAN(transactions.amount)) | STD(sessions.MIN(transactions.amount)) | STD(sessions.NUM_UNIQUE(transactions.product_id)) | STD(sessions.SKEW(transactions.amount)) | STD(sessions.SUM(transactions.amount)) | SUM(sessions.MAX(transactions.amount)) | SUM(sessions.MEAN(transactions.amount)) | SUM(sessions.MIN(transactions.amount)) | SUM(sessions.NUM_UNIQUE(transactions.product_id)) | SUM(sessions.SKEW(transactions.amount)) | SUM(sessions.STD(transactions.amount)) | MODE(transactions.sessions.device) | NUM_UNIQUE(transactions.sessions.device) | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| customer_id | time | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 1 | 2014-01-01 03:00:00 | 60091 | 3 | desktop | 3 | 55 | 139.23 | 71.501091 | 5.81 | 1 | 5 | 0.140387 | 42.769602 | 3932.56 | 18 | 17 | 7 | 4 | 0 | 6 | 1994 | 2011 | 25.0 | 84.440000 | 8.74 | 5.0 | 0.234349 | 46.905665 | 1613.93 | 18.333333 | 133.650000 | 73.044178 | 6.946667 | 5.0 | 0.108644 | 43.249208 | 1310.853333 | 15.0 | 129.00 | 64.557200 | 5.0 | -0.134754 | 40.187205 | 1052.03 | 1 | 1 | 1 | 2 | 2014 | 1 | 3 | 1 | 1 | 1 | 1.732051 | 0.782152 | 1.173675 | 1.552040 | 0.0 | 0.763052 | 0.685199 | 5.773503 | 5.178021 | 10.255607 | 1.571507 | 0.0 | 0.210827 | 283.551883 | 400.95 | 219.132533 | 20.84 | 15.0 | 0.325932 | 129.747625 | mobile | 3 |
| 2014-01-01 04:00:00 | 60091 | 4 | tablet | 3 | 67 | 139.23 | 74.002836 | 5.81 | 4 | 5 | -0.006928 | 42.309717 | 4958.19 | 18 | 17 | 7 | 4 | 0 | 6 | 1994 | 2011 | 25.0 | 85.469167 | 8.74 | 5.0 | 0.234349 | 46.905665 | 1613.93 | 16.750000 | 135.010000 | 76.150425 | 6.905000 | 5.0 | -0.126261 | 42.393218 | 1239.547500 | 12.0 | 129.00 | 64.557200 | 5.0 | -0.830975 | 39.825249 | 1025.63 | 1 | 4 | 1 | 2 | 2014 | 1 | 3 | 1 | 1 | 1 | 1.614843 | -0.451371 | -0.233453 | 1.452325 | 0.0 | 1.235445 | 1.197406 | 5.678908 | 5.027226 | 10.426572 | 1.285833 | 0.0 | 0.500353 | 271.917637 | 540.04 | 304.601700 | 27.62 | 20.0 | -0.505043 | 169.572874 | tablet | 3 | |
| 2 | 2014-01-01 04:00:00 | 13244 | 4 | desktop | 2 | 49 | 146.81 | 84.700000 | 12.07 | 4 | 5 | -0.134786 | 39.289512 | 4150.30 | 18 | 15 | 8 | 4 | 0 | 6 | 1986 | 2012 | 16.0 | 96.581000 | 56.46 | 5.0 | 0.295458 | 47.935920 | 1320.64 | 12.250000 | 142.322500 | 85.197948 | 26.310000 | 5.0 | 0.011293 | 39.315685 | 1037.575000 | 8.0 | 138.38 | 76.813125 | 5.0 | -0.455197 | 27.839228 | 634.84 | 1 | 2 | 1 | 2 | 2014 | 1 | 3 | 1 | 1 | 1 | -0.169238 | 0.459305 | 0.651941 | 1.815491 | 0.0 | -0.966834 | -0.823347 | 3.862210 | 3.470527 | 8.983533 | 20.424007 | 0.0 | 0.324809 | 307.743859 | 569.29 | 340.791792 | 105.24 | 20.0 | 0.045171 | 157.262738 | desktop | 2 |
| 2014-01-01 05:00:00 | 13244 | 5 | desktop | 2 | 62 | 146.81 | 83.149355 | 12.07 | 4 | 5 | -0.121811 | 38.047944 | 5155.26 | 18 | 15 | 8 | 4 | 0 | 6 | 1986 | 2012 | 16.0 | 96.581000 | 56.46 | 5.0 | 0.295458 | 47.935920 | 1320.64 | 12.400000 | 137.628000 | 83.619281 | 25.412000 | 5.0 | -0.053949 | 38.197555 | 1031.052000 | 8.0 | 118.85 | 76.813125 | 5.0 | -0.455197 | 27.839228 | 634.84 | 1 | 2 | 1 | 2 | 2014 | 1 | 4 | 1 | 1 | 1 | -0.379092 | -1.814717 | 1.082192 | 1.959531 | 0.0 | -0.213518 | -0.667256 | 3.361547 | 10.919023 | 8.543351 | 17.801322 | 0.0 | 0.316873 | 266.912832 | 688.14 | 418.096407 | 127.06 | 25.0 | -0.269747 | 190.987775 | desktop | 2 | |
| 3 | 2014-01-01 05:00:00 | 13244 | 2 | desktop | 2 | 32 | 146.31 | 58.960000 | 6.65 | 1 | 5 | 0.637074 | 41.199361 | 1886.72 | 21 | 13 | 11 | 8 | 4 | 5 | 2003 | 2011 | 17.0 | 62.791333 | 8.19 | 5.0 | 0.618455 | 47.264797 | 944.85 | 16.000000 | 136.525000 | 59.185373 | 7.420000 | 5.0 | 0.575022 | 41.716008 | 943.360000 | 15.0 | 126.74 | 55.579412 | 5.0 | 0.531588 | 36.167220 | 941.87 | 1 | 1 | 1 | 2 | 2014 | 1 | 1 | 1 | 1 | 1 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.414214 | 13.838080 | 5.099599 | 1.088944 | 0.0 | 0.061424 | 2.107178 | 273.05 | 118.370745 | 14.84 | 10.0 | 1.150043 | 83.432017 | desktop | 2 |
| 2014-01-01 06:00:00 | 13244 | 4 | desktop | 2 | 44 | 146.31 | 65.174773 | 6.65 | 1 | 5 | 0.318315 | 40.349758 | 2867.69 | 21 | 13 | 11 | 8 | 4 | 5 | 2003 | 2011 | 17.0 | 91.760000 | 91.76 | 5.0 | 0.618455 | 47.264797 | 944.85 | 11.000000 | 123.267500 | 72.742004 | 31.665000 | 4.0 | 0.286859 | 39.712232 | 716.922500 | 1.0 | 91.76 | 55.579412 | 1.0 | -0.289466 | 35.704680 | 91.76 | 1 | 1 | 1 | 2 | 2014 | 1 | 2 | 1 | 1 | 1 | -1.330938 | -1.060639 | 0.201588 | 1.874170 | -2.0 | 1.722323 | -1.977878 | 7.118052 | 22.808351 | 16.540737 | 40.508892 | 2.0 | 0.500999 | 417.557763 | 493.07 | 290.968018 | 126.66 | 16.0 | 0.860577 | 119.136697 | desktop | 2 | |
| 1 | 2014-01-01 07:00:00 | 60091 | 7 | tablet | 3 | 110 | 139.43 | 69.141182 | 5.81 | 4 | 5 | 0.149908 | 41.018896 | 7605.53 | 18 | 17 | 7 | 4 | 0 | 6 | 1994 | 2011 | 25.0 | 85.469167 | 26.36 | 5.0 | 0.640252 | 46.905665 | 1613.93 | 15.714286 | 133.122857 | 70.491070 | 9.567143 | 5.0 | 0.080330 | 40.060203 | 1086.504286 | 12.0 | 118.90 | 50.623125 | 5.0 | -0.830975 | 30.450261 | 809.97 | 1 | 1 | 1 | 2 | 2014 | 1 | 4 | 1 | 1 | 1 | 1.927658 | -1.277394 | -0.282093 | 2.552328 | 0.0 | -0.755846 | 1.377768 | 4.386125 | 7.441648 | 13.123365 | 7.470707 | 0.0 | 0.471955 | 273.713405 | 931.86 | 493.437492 | 66.97 | 35.0 | 0.562312 | 280.421418 | tablet | 3 |
| 2014-01-01 08:00:00 | 60091 | 8 | mobile | 3 | 126 | 139.43 | 71.631905 | 5.81 | 4 | 5 | 0.019698 | 40.442059 | 9025.62 | 18 | 17 | 7 | 4 | 0 | 6 | 1994 | 2011 | 25.0 | 88.755625 | 26.36 | 5.0 | 0.640252 | 46.905665 | 1613.93 | 15.750000 | 132.246250 | 72.774140 | 9.823750 | 5.0 | -0.059515 | 39.093244 | 1128.202500 | 12.0 | 118.90 | 50.623125 | 5.0 | -1.038434 | 30.450261 | 809.97 | 1 | 4 | 1 | 2 | 2014 | 1 | 4 | 1 | 1 | 1 | 1.946018 | -0.780493 | -0.424949 | 2.440005 | 0.0 | -0.312355 | 0.778170 | 4.062019 | 7.322191 | 13.759314 | 6.954507 | 0.0 | 0.589386 | 279.510713 | 1057.97 | 582.193117 | 78.59 | 40.0 | -0.476122 | 312.745952 | mobile | 3 |