1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
|
%global _empty_manifest_terminate_build 0
Name: python-featuretools
Version: 1.24.0
Release: 1
Summary: a framework for automated feature engineering
License: BSD 3-clause
URL: https://pypi.org/project/featuretools/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/01/c3/a1b500df4cbf1f071458b4d30c962c90a8e1641ede796987d9a5169bfd68/featuretools-1.24.0.tar.gz
BuildArch: noarch
Requires: python3-cloudpickle
Requires: python3-dask[dataframe]
Requires: python3-distributed
Requires: python3-holidays
Requires: python3-numpy
Requires: python3-packaging
Requires: python3-pandas
Requires: python3-psutil
Requires: python3-scipy
Requires: python3-tqdm
Requires: python3-woodwork[dask]
Requires: python3-autonormalize
Requires: python3-featuretools[autonormalize,nlp,sklearn,spark,sql,tsfresh,updater]
Requires: python3-ruff
Requires: python3-black[jupyter]
Requires: python3-pre-commit
Requires: python3-featuretools[docs,spark,test]
Requires: python3-ipython
Requires: python3-jupyter
Requires: python3-jupyter-client
Requires: python3-matplotlib
Requires: python3-Sphinx
Requires: python3-nbsphinx
Requires: python3-nbconvert
Requires: python3-pydata-sphinx-theme
Requires: python3-sphinx-inline-tabs
Requires: python3-sphinx-copybutton
Requires: python3-myst-parser
Requires: python3-nlp-primitives
Requires: python3-autonormalize
Requires: python3-click
Requires: python3-featuretools[sklearn,spark,test]
Requires: python3-nlp-primitives[complete]
Requires: python3-featuretools-sklearn-transformer
Requires: python3-woodwork[spark]
Requires: python3-pyspark
Requires: python3-numpy
Requires: python3-featuretools-sql
Requires: python3-boto3
Requires: python3-composeml
Requires: python3-graphviz
Requires: python3-moto[all]
Requires: python3-pip
Requires: python3-pyarrow
Requires: python3-pympler
Requires: python3-pytest
Requires: python3-pytest-cov
Requires: python3-pytest-xdist
Requires: python3-smart-open
Requires: python3-urllib3
Requires: python3-pytest-timeout
Requires: python3-featuretools-tsfresh-primitives
Requires: python3-alteryx-open-src-update-checker
%description
<p align="center">
<img width=50% src="https://www.featuretools.com/wp-content/uploads/2017/12/FeatureLabs-Logo-Tangerine-800.png" alt="Featuretools" />
</p>
<p align="center">
<i>"One of the holy grails of machine learning is to automate more and more of the feature engineering process."</i> ― Pedro Domingos, <a href="https://bit.ly/things_to_know_ml">A Few Useful Things to Know about Machine Learning</a>
</p>
<p align="center">
<a href="https://github.com/alteryx/featuretools/actions?query=branch%3Amain+workflow%3ATests" target="_blank">
<img src="https://github.com/alteryx/featuretools/workflows/Tests/badge.svg?branch=main" alt="Tests" />
</a>
<a href="https://codecov.io/gh/alteryx/featuretools">
<img src="https://codecov.io/gh/alteryx/featuretools/branch/main/graph/badge.svg"/>
</a>
<a href='https://featuretools.alteryx.com/en/stable/?badge=stable'>
<img src='https://readthedocs.com/projects/feature-labs-inc-featuretools/badge/?version=stable' alt='Documentation Status' />
</a>
<a href="https://badge.fury.io/py/featuretools" target="_blank">
<img src="https://badge.fury.io/py/featuretools.svg?maxAge=2592000" alt="PyPI Version" />
</a>
<a href="https://anaconda.org/conda-forge/featuretools" target="_blank">
<img src="https://anaconda.org/conda-forge/featuretools/badges/version.svg" alt="Anaconda Version" />
</a>
<a href="https://stackoverflow.com/questions/tagged/featuretools" target="_blank">
<img src="http://img.shields.io/badge/questions-on_stackoverflow-blue.svg" alt="StackOverflow" />
</a>
<a href="https://pepy.tech/project/featuretools" target="_blank">
<img src="https://pepy.tech/badge/featuretools/month" alt="PyPI Downloads" />
</a>
</p>
<hr>
[Featuretools](https://www.featuretools.com) is a python library for automated feature engineering. See the [documentation](https://docs.featuretools.com) for more information.
## Installation
Install with pip
```
python -m pip install featuretools
```
or from the Conda-forge channel on [conda](https://anaconda.org/conda-forge/featuretools):
```
conda install -c conda-forge featuretools
```
### Add-ons
You can install add-ons individually or all at once by running
```
python -m pip install "featuretools[complete]"
```
**Update checker** - Receive automatic notifications of new Featuretools releases
```
python -m pip install "featuretools[updater]"
```
**NLP Primitives** - Use Natural Language Processing Primitives:
```
python -m pip install "featuretools[nlp]"
```
**TSFresh Primitives** - Use 60+ primitives from [tsfresh](https://tsfresh.readthedocs.io/en/latest/) within Featuretools
```
python -m pip install "featuretools[tsfresh]"
```
**SQL** - Automatic EntitySet generation from relational data stored in a SQL database:
```
python -m pip install "featuretools[sql]"
```
## Example
Below is an example of using Deep Feature Synthesis (DFS) to perform automated feature engineering. In this example, we apply DFS to a multi-table dataset consisting of timestamped customer transactions.
```python
>> import featuretools as ft
>> es = ft.demo.load_mock_customer(return_entityset=True)
>> es.plot()
```
<img src="https://github.com/alteryx/featuretools/blob/main/docs/source/_static/images/entity_set.png?raw=true" width="350">
Featuretools can automatically create a single table of features for any "target dataframe"
```python
>> feature_matrix, features_defs = ft.dfs(entityset=es, target_dataframe_name="customers")
>> feature_matrix.head(5)
```
```
zip_code COUNT(transactions) COUNT(sessions) SUM(transactions.amount) MODE(sessions.device) MIN(transactions.amount) MAX(transactions.amount) YEAR(join_date) SKEW(transactions.amount) DAY(join_date) ... SUM(sessions.MIN(transactions.amount)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.MIN(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.MEAN(transactions.amount)) SKEW(sessions.MEAN(transactions.amount)) STD(sessions.MAX(transactions.amount)) NUM_UNIQUE(sessions.DAY(session_start)) MIN(sessions.SKEW(transactions.amount))
customer_id ...
1 60091 131 10 10236.77 desktop 5.60 149.95 2008 0.070041 1 ... 169.77 0.610052 41.95 791.976505 175.939423 9.299023 -0.377150 5.857976 1 -0.395358
2 02139 122 8 9118.81 mobile 5.81 149.15 2008 0.028647 20 ... 114.85 0.492531 42.96 596.243506 230.333502 10.925037 0.962350 7.420480 1 -0.470007
3 02139 78 5 5758.24 desktop 6.78 147.73 2008 0.070814 10 ... 64.98 0.645728 21.77 369.770121 471.048551 9.819148 -0.244976 12.537259 1 -0.630425
4 60091 111 8 8205.28 desktop 5.73 149.56 2008 0.087986 30 ... 83.53 0.516262 17.27 584.673126 322.883448 13.065436 -0.548969 12.738488 1 -0.497169
5 02139 58 4 4571.37 tablet 5.91 148.17 2008 0.085883 19 ... 73.09 0.830112 27.46 313.448942 198.522508 8.950528 0.098885 5.599228 1 -0.396571
[5 rows x 69 columns]
```
We now have a feature vector for each customer that can be used for machine learning. See the [documentation on Deep Feature Synthesis](https://featuretools.alteryx.com/en/stable/getting_started/afe.html) for more examples.
Featuretools contains many different types of built-in primitives for creating features. If the primitive you need is not included, Featuretools also allows you to [define your own custom primitives](https://featuretools.alteryx.com/en/stable/getting_started/primitives.html#defining-custom-primitives).
## Demos
**Predict Next Purchase**
[Repository](https://github.com/alteryx/open_source_demos/blob/main/predict-next-purchase/) | [Notebook](https://github.com/alteryx/open_source_demos/blob/main/predict-next-purchase/Tutorial.ipynb)
In this demonstration, we use a multi-table dataset of 3 million online grocery orders from Instacart to predict what a customer will buy next. We show how to generate features with automated feature engineering and build an accurate machine learning pipeline using Featuretools, which can be reused for multiple prediction problems. For more advanced users, we show how to scale that pipeline to a large dataset using Dask.
For more examples of how to use Featuretools, check out our [demos](https://www.featuretools.com/demos) page.
## Testing & Development
The Featuretools community welcomes pull requests. Instructions for testing and development are available [here.](https://featuretools.alteryx.com/en/stable/install.html#development)
## Support
The Featuretools community is happy to provide support to users of Featuretools. Project support can be found in four places depending on the type of question:
1. For usage questions, use [Stack Overflow](https://stackoverflow.com/questions/tagged/featuretools) with the `featuretools` tag.
2. For bugs, issues, or feature requests start a [Github issue](https://github.com/alteryx/featuretools/issues).
3. For discussion regarding development on the core library, use [Slack](https://join.slack.com/t/alteryx-oss/shared_invite/zt-182tyvuxv-NzIn6eiCEf8TBziuKp0bNA).
4. For everything else, the core developers can be reached by email at open_source_support@alteryx.com
## Citing Featuretools
If you use Featuretools, please consider citing the following paper:
James Max Kanter, Kalyan Veeramachaneni. [Deep feature synthesis: Towards automating data science endeavors.](https://dai.lids.mit.edu/wp-content/uploads/2017/10/DSAA_DSM_2015.pdf) *IEEE DSAA 2015*.
BibTeX entry:
```bibtex
@inproceedings{kanter2015deep,
author = {James Max Kanter and Kalyan Veeramachaneni},
title = {Deep feature synthesis: Towards automating data science endeavors},
booktitle = {2015 {IEEE} International Conference on Data Science and Advanced Analytics, DSAA 2015, Paris, France, October 19-21, 2015},
pages = {1--10},
year = {2015},
organization={IEEE}
}
```
## Built at Alteryx
**Featuretools** is an open source project maintained by [Alteryx](https://www.alteryx.com). To see the other open source projects we’re working on visit [Alteryx Open Source](https://www.alteryx.com/open-source). If building impactful data science pipelines is important to you or your business, please get in touch.
<p align="center">
<a href="https://www.alteryx.com/open-source">
<img src="https://alteryx-oss-web-images.s3.amazonaws.com/OpenSource_Logo-01.png" alt="Alteryx Open Source" width="800"/>
</a>
</p>
%package -n python3-featuretools
Summary: a framework for automated feature engineering
Provides: python-featuretools
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-featuretools
<p align="center">
<img width=50% src="https://www.featuretools.com/wp-content/uploads/2017/12/FeatureLabs-Logo-Tangerine-800.png" alt="Featuretools" />
</p>
<p align="center">
<i>"One of the holy grails of machine learning is to automate more and more of the feature engineering process."</i> ― Pedro Domingos, <a href="https://bit.ly/things_to_know_ml">A Few Useful Things to Know about Machine Learning</a>
</p>
<p align="center">
<a href="https://github.com/alteryx/featuretools/actions?query=branch%3Amain+workflow%3ATests" target="_blank">
<img src="https://github.com/alteryx/featuretools/workflows/Tests/badge.svg?branch=main" alt="Tests" />
</a>
<a href="https://codecov.io/gh/alteryx/featuretools">
<img src="https://codecov.io/gh/alteryx/featuretools/branch/main/graph/badge.svg"/>
</a>
<a href='https://featuretools.alteryx.com/en/stable/?badge=stable'>
<img src='https://readthedocs.com/projects/feature-labs-inc-featuretools/badge/?version=stable' alt='Documentation Status' />
</a>
<a href="https://badge.fury.io/py/featuretools" target="_blank">
<img src="https://badge.fury.io/py/featuretools.svg?maxAge=2592000" alt="PyPI Version" />
</a>
<a href="https://anaconda.org/conda-forge/featuretools" target="_blank">
<img src="https://anaconda.org/conda-forge/featuretools/badges/version.svg" alt="Anaconda Version" />
</a>
<a href="https://stackoverflow.com/questions/tagged/featuretools" target="_blank">
<img src="http://img.shields.io/badge/questions-on_stackoverflow-blue.svg" alt="StackOverflow" />
</a>
<a href="https://pepy.tech/project/featuretools" target="_blank">
<img src="https://pepy.tech/badge/featuretools/month" alt="PyPI Downloads" />
</a>
</p>
<hr>
[Featuretools](https://www.featuretools.com) is a python library for automated feature engineering. See the [documentation](https://docs.featuretools.com) for more information.
## Installation
Install with pip
```
python -m pip install featuretools
```
or from the Conda-forge channel on [conda](https://anaconda.org/conda-forge/featuretools):
```
conda install -c conda-forge featuretools
```
### Add-ons
You can install add-ons individually or all at once by running
```
python -m pip install "featuretools[complete]"
```
**Update checker** - Receive automatic notifications of new Featuretools releases
```
python -m pip install "featuretools[updater]"
```
**NLP Primitives** - Use Natural Language Processing Primitives:
```
python -m pip install "featuretools[nlp]"
```
**TSFresh Primitives** - Use 60+ primitives from [tsfresh](https://tsfresh.readthedocs.io/en/latest/) within Featuretools
```
python -m pip install "featuretools[tsfresh]"
```
**SQL** - Automatic EntitySet generation from relational data stored in a SQL database:
```
python -m pip install "featuretools[sql]"
```
## Example
Below is an example of using Deep Feature Synthesis (DFS) to perform automated feature engineering. In this example, we apply DFS to a multi-table dataset consisting of timestamped customer transactions.
```python
>> import featuretools as ft
>> es = ft.demo.load_mock_customer(return_entityset=True)
>> es.plot()
```
<img src="https://github.com/alteryx/featuretools/blob/main/docs/source/_static/images/entity_set.png?raw=true" width="350">
Featuretools can automatically create a single table of features for any "target dataframe"
```python
>> feature_matrix, features_defs = ft.dfs(entityset=es, target_dataframe_name="customers")
>> feature_matrix.head(5)
```
```
zip_code COUNT(transactions) COUNT(sessions) SUM(transactions.amount) MODE(sessions.device) MIN(transactions.amount) MAX(transactions.amount) YEAR(join_date) SKEW(transactions.amount) DAY(join_date) ... SUM(sessions.MIN(transactions.amount)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.MIN(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.MEAN(transactions.amount)) SKEW(sessions.MEAN(transactions.amount)) STD(sessions.MAX(transactions.amount)) NUM_UNIQUE(sessions.DAY(session_start)) MIN(sessions.SKEW(transactions.amount))
customer_id ...
1 60091 131 10 10236.77 desktop 5.60 149.95 2008 0.070041 1 ... 169.77 0.610052 41.95 791.976505 175.939423 9.299023 -0.377150 5.857976 1 -0.395358
2 02139 122 8 9118.81 mobile 5.81 149.15 2008 0.028647 20 ... 114.85 0.492531 42.96 596.243506 230.333502 10.925037 0.962350 7.420480 1 -0.470007
3 02139 78 5 5758.24 desktop 6.78 147.73 2008 0.070814 10 ... 64.98 0.645728 21.77 369.770121 471.048551 9.819148 -0.244976 12.537259 1 -0.630425
4 60091 111 8 8205.28 desktop 5.73 149.56 2008 0.087986 30 ... 83.53 0.516262 17.27 584.673126 322.883448 13.065436 -0.548969 12.738488 1 -0.497169
5 02139 58 4 4571.37 tablet 5.91 148.17 2008 0.085883 19 ... 73.09 0.830112 27.46 313.448942 198.522508 8.950528 0.098885 5.599228 1 -0.396571
[5 rows x 69 columns]
```
We now have a feature vector for each customer that can be used for machine learning. See the [documentation on Deep Feature Synthesis](https://featuretools.alteryx.com/en/stable/getting_started/afe.html) for more examples.
Featuretools contains many different types of built-in primitives for creating features. If the primitive you need is not included, Featuretools also allows you to [define your own custom primitives](https://featuretools.alteryx.com/en/stable/getting_started/primitives.html#defining-custom-primitives).
## Demos
**Predict Next Purchase**
[Repository](https://github.com/alteryx/open_source_demos/blob/main/predict-next-purchase/) | [Notebook](https://github.com/alteryx/open_source_demos/blob/main/predict-next-purchase/Tutorial.ipynb)
In this demonstration, we use a multi-table dataset of 3 million online grocery orders from Instacart to predict what a customer will buy next. We show how to generate features with automated feature engineering and build an accurate machine learning pipeline using Featuretools, which can be reused for multiple prediction problems. For more advanced users, we show how to scale that pipeline to a large dataset using Dask.
For more examples of how to use Featuretools, check out our [demos](https://www.featuretools.com/demos) page.
## Testing & Development
The Featuretools community welcomes pull requests. Instructions for testing and development are available [here.](https://featuretools.alteryx.com/en/stable/install.html#development)
## Support
The Featuretools community is happy to provide support to users of Featuretools. Project support can be found in four places depending on the type of question:
1. For usage questions, use [Stack Overflow](https://stackoverflow.com/questions/tagged/featuretools) with the `featuretools` tag.
2. For bugs, issues, or feature requests start a [Github issue](https://github.com/alteryx/featuretools/issues).
3. For discussion regarding development on the core library, use [Slack](https://join.slack.com/t/alteryx-oss/shared_invite/zt-182tyvuxv-NzIn6eiCEf8TBziuKp0bNA).
4. For everything else, the core developers can be reached by email at open_source_support@alteryx.com
## Citing Featuretools
If you use Featuretools, please consider citing the following paper:
James Max Kanter, Kalyan Veeramachaneni. [Deep feature synthesis: Towards automating data science endeavors.](https://dai.lids.mit.edu/wp-content/uploads/2017/10/DSAA_DSM_2015.pdf) *IEEE DSAA 2015*.
BibTeX entry:
```bibtex
@inproceedings{kanter2015deep,
author = {James Max Kanter and Kalyan Veeramachaneni},
title = {Deep feature synthesis: Towards automating data science endeavors},
booktitle = {2015 {IEEE} International Conference on Data Science and Advanced Analytics, DSAA 2015, Paris, France, October 19-21, 2015},
pages = {1--10},
year = {2015},
organization={IEEE}
}
```
## Built at Alteryx
**Featuretools** is an open source project maintained by [Alteryx](https://www.alteryx.com). To see the other open source projects we’re working on visit [Alteryx Open Source](https://www.alteryx.com/open-source). If building impactful data science pipelines is important to you or your business, please get in touch.
<p align="center">
<a href="https://www.alteryx.com/open-source">
<img src="https://alteryx-oss-web-images.s3.amazonaws.com/OpenSource_Logo-01.png" alt="Alteryx Open Source" width="800"/>
</a>
</p>
%package help
Summary: Development documents and examples for featuretools
Provides: python3-featuretools-doc
%description help
<p align="center">
<img width=50% src="https://www.featuretools.com/wp-content/uploads/2017/12/FeatureLabs-Logo-Tangerine-800.png" alt="Featuretools" />
</p>
<p align="center">
<i>"One of the holy grails of machine learning is to automate more and more of the feature engineering process."</i> ― Pedro Domingos, <a href="https://bit.ly/things_to_know_ml">A Few Useful Things to Know about Machine Learning</a>
</p>
<p align="center">
<a href="https://github.com/alteryx/featuretools/actions?query=branch%3Amain+workflow%3ATests" target="_blank">
<img src="https://github.com/alteryx/featuretools/workflows/Tests/badge.svg?branch=main" alt="Tests" />
</a>
<a href="https://codecov.io/gh/alteryx/featuretools">
<img src="https://codecov.io/gh/alteryx/featuretools/branch/main/graph/badge.svg"/>
</a>
<a href='https://featuretools.alteryx.com/en/stable/?badge=stable'>
<img src='https://readthedocs.com/projects/feature-labs-inc-featuretools/badge/?version=stable' alt='Documentation Status' />
</a>
<a href="https://badge.fury.io/py/featuretools" target="_blank">
<img src="https://badge.fury.io/py/featuretools.svg?maxAge=2592000" alt="PyPI Version" />
</a>
<a href="https://anaconda.org/conda-forge/featuretools" target="_blank">
<img src="https://anaconda.org/conda-forge/featuretools/badges/version.svg" alt="Anaconda Version" />
</a>
<a href="https://stackoverflow.com/questions/tagged/featuretools" target="_blank">
<img src="http://img.shields.io/badge/questions-on_stackoverflow-blue.svg" alt="StackOverflow" />
</a>
<a href="https://pepy.tech/project/featuretools" target="_blank">
<img src="https://pepy.tech/badge/featuretools/month" alt="PyPI Downloads" />
</a>
</p>
<hr>
[Featuretools](https://www.featuretools.com) is a python library for automated feature engineering. See the [documentation](https://docs.featuretools.com) for more information.
## Installation
Install with pip
```
python -m pip install featuretools
```
or from the Conda-forge channel on [conda](https://anaconda.org/conda-forge/featuretools):
```
conda install -c conda-forge featuretools
```
### Add-ons
You can install add-ons individually or all at once by running
```
python -m pip install "featuretools[complete]"
```
**Update checker** - Receive automatic notifications of new Featuretools releases
```
python -m pip install "featuretools[updater]"
```
**NLP Primitives** - Use Natural Language Processing Primitives:
```
python -m pip install "featuretools[nlp]"
```
**TSFresh Primitives** - Use 60+ primitives from [tsfresh](https://tsfresh.readthedocs.io/en/latest/) within Featuretools
```
python -m pip install "featuretools[tsfresh]"
```
**SQL** - Automatic EntitySet generation from relational data stored in a SQL database:
```
python -m pip install "featuretools[sql]"
```
## Example
Below is an example of using Deep Feature Synthesis (DFS) to perform automated feature engineering. In this example, we apply DFS to a multi-table dataset consisting of timestamped customer transactions.
```python
>> import featuretools as ft
>> es = ft.demo.load_mock_customer(return_entityset=True)
>> es.plot()
```
<img src="https://github.com/alteryx/featuretools/blob/main/docs/source/_static/images/entity_set.png?raw=true" width="350">
Featuretools can automatically create a single table of features for any "target dataframe"
```python
>> feature_matrix, features_defs = ft.dfs(entityset=es, target_dataframe_name="customers")
>> feature_matrix.head(5)
```
```
zip_code COUNT(transactions) COUNT(sessions) SUM(transactions.amount) MODE(sessions.device) MIN(transactions.amount) MAX(transactions.amount) YEAR(join_date) SKEW(transactions.amount) DAY(join_date) ... SUM(sessions.MIN(transactions.amount)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.MIN(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.MEAN(transactions.amount)) SKEW(sessions.MEAN(transactions.amount)) STD(sessions.MAX(transactions.amount)) NUM_UNIQUE(sessions.DAY(session_start)) MIN(sessions.SKEW(transactions.amount))
customer_id ...
1 60091 131 10 10236.77 desktop 5.60 149.95 2008 0.070041 1 ... 169.77 0.610052 41.95 791.976505 175.939423 9.299023 -0.377150 5.857976 1 -0.395358
2 02139 122 8 9118.81 mobile 5.81 149.15 2008 0.028647 20 ... 114.85 0.492531 42.96 596.243506 230.333502 10.925037 0.962350 7.420480 1 -0.470007
3 02139 78 5 5758.24 desktop 6.78 147.73 2008 0.070814 10 ... 64.98 0.645728 21.77 369.770121 471.048551 9.819148 -0.244976 12.537259 1 -0.630425
4 60091 111 8 8205.28 desktop 5.73 149.56 2008 0.087986 30 ... 83.53 0.516262 17.27 584.673126 322.883448 13.065436 -0.548969 12.738488 1 -0.497169
5 02139 58 4 4571.37 tablet 5.91 148.17 2008 0.085883 19 ... 73.09 0.830112 27.46 313.448942 198.522508 8.950528 0.098885 5.599228 1 -0.396571
[5 rows x 69 columns]
```
We now have a feature vector for each customer that can be used for machine learning. See the [documentation on Deep Feature Synthesis](https://featuretools.alteryx.com/en/stable/getting_started/afe.html) for more examples.
Featuretools contains many different types of built-in primitives for creating features. If the primitive you need is not included, Featuretools also allows you to [define your own custom primitives](https://featuretools.alteryx.com/en/stable/getting_started/primitives.html#defining-custom-primitives).
## Demos
**Predict Next Purchase**
[Repository](https://github.com/alteryx/open_source_demos/blob/main/predict-next-purchase/) | [Notebook](https://github.com/alteryx/open_source_demos/blob/main/predict-next-purchase/Tutorial.ipynb)
In this demonstration, we use a multi-table dataset of 3 million online grocery orders from Instacart to predict what a customer will buy next. We show how to generate features with automated feature engineering and build an accurate machine learning pipeline using Featuretools, which can be reused for multiple prediction problems. For more advanced users, we show how to scale that pipeline to a large dataset using Dask.
For more examples of how to use Featuretools, check out our [demos](https://www.featuretools.com/demos) page.
## Testing & Development
The Featuretools community welcomes pull requests. Instructions for testing and development are available [here.](https://featuretools.alteryx.com/en/stable/install.html#development)
## Support
The Featuretools community is happy to provide support to users of Featuretools. Project support can be found in four places depending on the type of question:
1. For usage questions, use [Stack Overflow](https://stackoverflow.com/questions/tagged/featuretools) with the `featuretools` tag.
2. For bugs, issues, or feature requests start a [Github issue](https://github.com/alteryx/featuretools/issues).
3. For discussion regarding development on the core library, use [Slack](https://join.slack.com/t/alteryx-oss/shared_invite/zt-182tyvuxv-NzIn6eiCEf8TBziuKp0bNA).
4. For everything else, the core developers can be reached by email at open_source_support@alteryx.com
## Citing Featuretools
If you use Featuretools, please consider citing the following paper:
James Max Kanter, Kalyan Veeramachaneni. [Deep feature synthesis: Towards automating data science endeavors.](https://dai.lids.mit.edu/wp-content/uploads/2017/10/DSAA_DSM_2015.pdf) *IEEE DSAA 2015*.
BibTeX entry:
```bibtex
@inproceedings{kanter2015deep,
author = {James Max Kanter and Kalyan Veeramachaneni},
title = {Deep feature synthesis: Towards automating data science endeavors},
booktitle = {2015 {IEEE} International Conference on Data Science and Advanced Analytics, DSAA 2015, Paris, France, October 19-21, 2015},
pages = {1--10},
year = {2015},
organization={IEEE}
}
```
## Built at Alteryx
**Featuretools** is an open source project maintained by [Alteryx](https://www.alteryx.com). To see the other open source projects we’re working on visit [Alteryx Open Source](https://www.alteryx.com/open-source). If building impactful data science pipelines is important to you or your business, please get in touch.
<p align="center">
<a href="https://www.alteryx.com/open-source">
<img src="https://alteryx-oss-web-images.s3.amazonaws.com/OpenSource_Logo-01.png" alt="Alteryx Open Source" width="800"/>
</a>
</p>
%prep
%autosetup -n featuretools-1.24.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-featuretools -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.24.0-1
- Package Spec generated
|
