-
Notifications
You must be signed in to change notification settings - Fork 10
/
run_doc.sh
1180 lines (856 loc) · 25.9 KB
/
run_doc.sh
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
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
rm -rf doc
mkdir doc
sphinx-apidoc -F -M -d 1 --separate -o doc gators `find ../gators -name *.pyx`
cd doc
rm gators*rst
mkdir _static/css
cp ../doc_data/gators.css _static/css/
cp ../doc_data/GATORS_LOGO.png _static/css/
cp ../doc_data/pandas_logo.png _static/
cp ../doc_data/koalas_logo.png _static/
cp ../doc_data/cython_logo.jpeg _static/
cp ../doc_data/numpy_logo.png _static/
cp ../doc_data/sklearn_logo.png _static/
cp ../doc_data/hyperopt_logo.png _static/
cp ../doc_data/xgboost_logo.png _static/
cp ../doc_data/lightgbm_logo.png _static/
cp ../doc_data/treelite_logo.png _static/
cp -R ../doc_data/benchmarking_pandas_numpy _static/
rm conf.py
cat > conf.py <<EOL
import os
import sys
import gators
sys.path.insert(0, '..')
project = 'gators'
copyright = '2021, the gators development team.'
author = 'The gators team'
version = gators.__version__
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.viewcode',
'sphinx.ext.todo',
'sphinx.ext.imgmath',
'numpydoc',
'nbsphinx',
'sphinx.ext.autosummary',
]
autoclass_content = "class"
autodoc_member_order = "bysource"
templates_path = ['_templates']
language = 'en'
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
todo_include_todos = True
master_doc = 'index'
numpydoc_show_class_members = False
autosummary_generate = True
panels_add_bootstrap_css = False
html_use_index = False
html_domain_indices = False
html_theme = 'pydata_sphinx_theme'
html_css_files = ['css/gators.css']
html_static_path = ['_static']
html_logo = '../doc_data/GATORS_LOGO.png'
html_favicon = '../doc_data/gators_logo.ico'
html_theme_options = {
"logo_link": "index",
"github_url": "https://github.paypal.com/Simility-R/gators/",
}
man_pages = [
('index', 'gators', u'gators Documentation',
[u'the gators team'], 1)
]
EOL
rm index.rst
cat > index.rst <<EOL
.. gators documentation
******
Gators
******
**Gators** is a machine learning library initially developed by the Simility a PayPal service Data Team. While data pre-processing and machine learning models are usually developed in Python, the pre-processing aspect is usually replaced by faster compiled programming languages in the production environment. This change of programming language is an added complexity to the model deployment process but is usually required to cope with the large number of queries per second that can be observed.
The goal of **Gators** is to be able to manage both model building and model serving using only Python, a language that data scientists are generally familiar with. **Gators** is built on top of Pandas, Koalas, NumPy and Cython. Pandas and Koalas are used for model building, while NumPy and Cython are used to speed up the model predictions in real-time. **Gators** was originally built for fraud modelling but can be generalized to many other modelling domains other than binary classification problems.
**Gators** helps to streamline the model building and productionization processes. The model building part can be done using the Pandas library for datasets held in memory, or Koalas for big data. On the model serving side, the pre-processing is carried out directly with Python, using NumPy and Cython. As a result, the speed-up using both NumPy and Cython for pre-processing is around 100 compared to standard Python code. Additionally, the per-sample response time becomes similar to other compiled languages (microsecond scale).
In summary, **Gators** is a package for handling model building, model deployment, and fast real-time pre-processing for a large number of QPS using only Python.
.. toctree::
:maxdepth: 2
about_gators/index
getting_started/index
user_guide/index
reference/index
benchmarking/index
EOL
mkdir about_gators
cat > about_gators/index.rst <<EOL
************
About Gators
************
**Gators** was created to help data scientists to:
* Perform in-memory and out-of-core memory data pre-processing for model building.
* Fast real-time pre-processing and model scoring.
######################
History of development
######################
In 2018, **Gators** development began at Simility and had been open sourced in 2021,
========
Timeline
========
* **2018**: Development of **Gators** started.
* **2020**: Koalas and Cython packages are added to tackle out-of-core memory datasets and fast real-time pre-processing.
* **2021**: **Gators** becomes open source.
##################
Library Highlights
##################
* Data pre-processing can be done for both in-memory and out-of-memory datasets using the same interface.
* Using Cython, the real-time data pre-processing is carried out on NumPy arrays with compiled C-code leading to fast response times, similar to compiled languages.
##########
Our Vision
##########
A world where data scientists can develop and push their models in production using only Python, even when there are a large number of queries per second (QPS).
###################################
Python packages leveraged in gators
###################################
.. centered:: "If I have seen further it is by standing on the shoulders of Giants."
.. centered:: Sir Isaac Newton
**gators** uses a variety of libraries internally, at each step of the model building process.
Below is the list of libraries used.
===================
Data pre-processing
===================
.. image:: ../_static/pandas_logo.png
:width: 170 px
:target: https://pandas.pydata.org/docs/
The most well known package for data analysis is used for data pre-processing during the model building phase. This package should be used as long as the data can fit in memory.
.. image:: ../_static/koalas_logo.png
:width: 170 px
:target: https://koalas.readthedocs.io/en/latest/
koalas has been chosen to replace pandas if the data does not fit in memory. The main advantage of koalas compared to other big data packages such as PySpark and Dask, is the fact that the syntax is close to pandas.
.. image:: ../_static/numpy_logo.png
:width: 170 px
:target: https://numpy.org/doc/
NumPy is used in the production environment when the pre-processing needs to be as fast as possible.
.. image:: ../_static/cython_logo.jpeg
:width: 170 px
:target: https://cython.readthedocs.io/en/latest/
In the production environment, the pre-processing with be done by pre-compile Cython code on NumPy arrays.
==============
Model building
==============
.. image:: ../_static/sklearn_logo.png
:width: 170 px
:target: https://scikit-learn.org/stable/
The most well known package for model building is used for cross-validation and model evaluation.
.. image:: ../_static/hyperopt_logo.png
:width: 170 px
:target: http://hyperopt.github.io/hyperopt/
This package is used for hyperparameter tuning. The three algorithms currently available to perform hyperparameter tuning are:
* Random Search
* Tree of Parzen Estimators (TPE)
* Adaptative TPE
.. image:: ../_static/xgboost_logo.png
:width: 170 px
:target: https://xgboost.readthedocs.io/en/latest/
Decision tree-based package used for model building. XGBoost algorithm applies level-wise tree growth.
.. image:: ../_static/lightgbm_logo.png
:width: 170 px
:target: https://lightgbm.readthedocs.io/en/latest/
Decision tree-based package used for model building. LightGBM algorithm applies leaf-wise tree growth.
.. image:: ../_static/treelite_logo.png
:width: 170 px
:target: https://treelite.readthedocs.io/
Treelite is used to compile the trained models in C before being deployed in production,
and treelite-runtime is used for real-time model scoring.
EOL
mkdir user_guide
cat > user_guide/index.rst <<EOL
**********
User Guide
**********
.. toctree::
:maxdepth: 2
best_practices
titanic
sf_crime
house_price
EOL
cat > user_guide/best_practices.rst <<EOL
**************
Best Practices
**************
Pandas or Koalas?
#################
The choice of using \`Pandas <https://pandas.pydata.org/>\`__ or \`Koalas <https://koalas.readthedocs.io/en/latest/>\`__ will be dictated by your data set.
For in-memory datasets it is recommended to use pandas, koalas otherwise.
Does the transformation order matter?
#####################################
Absolutely! While Pandas and Koalas dataframes hold the datatype of each column,
Numpy does not.
It is then important to group the transformations according to the datatype of the columns
they consider.
1. datetime column transformations
2. object column transformations
3. encoding transformation
4. numerical transformations
.. Note::
After an encoding transformation, all the column datatypes with be set to *np.float32* or *np.float64*,
any datetime columns should then be removed before this step.
What are the models currently supported by gators?
##################################################
**Gators** mainly focuses on data pre-processing in both offline and in real-time but
a submodule uses the package \`treelite <https://treelite.readthedocs.io/en/latest/>\`__ which compiles in C tree-based
models. Only this type of models is currently supported. Note that for deep learning
models, the \`tvm package <https://tvm.apache.org/>\`__ could be interesting to consider.
When using NumPy?
##################
In **gators**, NumPy, by means of the method \`transform_numpy()\` , should only be used in the production environment where the response time of the data pre-processing is critical.
Why the method \`fit_numpy()\` is not defined?
##############################################
The offline model building steps are only done with pandas or koalas dataframes.
First, the excellent \`Sklearn <https://scikit#learn.org/stable/>\`__ package already handle NumPy arrays, second,
NumPy is not suitable for large-scale data.
EOL
mkdir getting_started
ln ../examples/titanic.ipynb user_guide/
ln ../examples/sf_crime.ipynb user_guide/
ln ../examples/house_price.ipynb user_guide/
ln ../examples/10min.ipynb getting_started/
cat > getting_started/index.rst <<EOL
***************
Getting Started
***************
.. toctree::
:maxdepth: 2
install
10min
EOL
cat > getting_started/install.rst <<EOL
************
Installation
************
.. _getting_started:
Prerequisites
=============
**Gators** requires the following dependencies:
* python >=3.6
* numpy == 1.19.5
* cython
* sklearn
* pandas
* pyspark
* koalas
* xgboost
* lightgboost
* treelite
* treelite-runtime
Install
=======
From PyPi or conda-forge repositories
#####################################
Not yet available
From source available on GitHub
###############################
If you prefer, you can clone it and run the setup.py file. Use the following
commands to get a copy from Github and install all dependencies:
>>> git clone git@github.paypal.com:Simility-R/gators.git
>>> cd gators
>>> pip3 install -r requirements.txt
>>> python3 setup.py build_ext --inplace
>>> pip3 install .
To install the dev gators enironment:
Extra packages
>>> git clone git@github.paypal.com:Simility-R/gators.git
>>> cd gators
>>> pip3 install -r requirements.txt
>>> python3 setup.py build_ext --inplace
>>> brew install libomp
>>> brew install pandoc
>>> pip3 install .[dev]
Test and coverage
#################
Test
====
>>> pytest gators -v
Test coverage
=============
>>> coverage run -m pytest gators -v
Contribute
##########
You can contribute to this code through Pull Request on GitHub. Please, make
sure that your code is coming with unit tests to ensure full coverage and
continuous integration in the API.
.. _GitHub: https://github.paypal.com/Simility-R/gators/pulls
EOL
mkdir reference
cat > reference/index.rst <<EOL
*************
API Reference
*************
.. toctree::
:maxdepth: 2
sampling
data_cleaning
binning
clipping
scalers
imputers
encoders
feature_generation
feature_generation_str
feature_generation_dt
feature_selection
pipeline
model_building
converter
transformers
EOL
cat > reference/data_cleaning.rst <<EOL
.. _api.data_cleaning:
*************
Data Cleaning
*************
.. currentmodule:: gators.data_cleaning
These transformers can be used to reduce the number of columns
during the feature selection step.
Base data_cleaning transformer
##############################
.. autosummary::
:toctree: api/
_BaseDataCleaning
Off-line data cleaning
######################
.. autosummary::
:toctree: api/
DropHighCardinality
DropHighNaNRatio
DropLowCardinality
Realtime data cleaning
######################
.. autosummary::
:toctree: api/
DropColumns
DropDatatypeColumns
KeepColumns
Replace
EOL
cat > reference/binning.rst <<EOL
.. _api.binning:
*******
Binning
*******
.. currentmodule:: gators.binning
Categorical variable binning
############################
.. autosummary::
:toctree: api/
BinRareEvents
Numerical variable binning
##########################
Base discretizer
----------------
.. autosummary::
:toctree: api/
_BaseDiscretizer
Discretizers
------------
.. autosummary::
:toctree: api/
Discretizer
QuantileDiscretizer
CustomDiscretizer
EOL
cat > reference/clipping.rst <<EOL
.. _api.clipping:
********
Clipping
********
.. currentmodule:: gators.clipping
.. autosummary::
:toctree: api/
Clipping
EOL
cat > reference/imputers.rst <<EOL
.. _api.imputers:
********
Imputers
********
Four different types of imputers are available depending on the variable datatype,
namely: numerical, integer, float, and categorical (string or object).
.. note::
* *NumericsImputer* imputes numerical variables.
* *FloatImputer* imputes only numerical variables satisfying the condition:
x != x.round().
* *IntImputer* imputes only numerical variables satisfying the condition:
x == x.round()
* *ObjectImputer* imputes only categorical variables.
Base Imputer
############
.. currentmodule:: gators.imputers
.. autosummary::
:toctree: api/
_BaseImputer
Numerical Imputers
##################
.. currentmodule:: gators.imputers
.. autosummary::
:toctree: api/
NumericsImputer
IntImputer
FloatImputer
Categorical Imputer
###################
.. autosummary::
:toctree: api/
ObjectImputer
EOL
cat > reference/encoders.rst <<EOL
********
Encoders
********
.. _api.encoders:
The Encoders transform the categorical columns into numerical columns.
.. note::
The Encoders transform the data inplace. The output of an encoder is then a numerical dataframe or array.
Before calling an encoder, all the transformations on categorical columns should be done and the datetime columns should be dropped.
.. currentmodule:: gators.encoders
.. autosummary::
:toctree: api/
BaseEncoder
###########
.. autosummary::
:toctree: api/
_BaseEncoder
Unsupervised Encoders
#####################
.. autosummary::
:toctree: api/
OrdinalEncoder
OneHotEncoder
Binary Encoders
###############
.. autosummary::
:toctree: api/
WOEEncoder
TargetEncoder
Multi-Class Encoder
###################
.. note::
The *MultiClassEncoder* takes as input a binary encoder and apply it
to each class. For example, if a n-class classification is composed of
*c* categorical columns, the number encoded columns with be *n x c*.
.. autosummary::
:toctree: api/
MultiClassEncoder
Regression Encoder
##################
.. note::
The *RegressionEncoder* takes as input a binary encoder and a discretizer.
First, the discretizer transform the continuous target values into categorical values.
Second, the MultiClassEncoder is applied to the data using the transformed target values.
.. autosummary::
:toctree: api/
RegressionEncoder
EOL
cat > reference/feature_generation.rst <<EOL
.. _api.feature_generation:
******************
Feature Generation
******************
.. currentmodule:: gators.feature_generation
Base Feature Generation Transformer
###################################
.. autosummary::
:toctree: api/
_BaseFeatureGeneration
Numerical Feature Generation
############################
.. autosummary::
:toctree: api/
ClusterStatistics
ElementaryArithmetics
PlaneRotation
PolynomialFeatures
Categorical Feature Generation
##############################
.. autosummary::
:toctree: api/
OneHot
Feature Generation
##################
.. autosummary::
:toctree: api/
IsEqual
IsNull
EOL
cat > reference/feature_generation_dt.rst <<EOL
.. _api.feature_generation_dt:
***************************
Feature Generation DateTime
***************************
.. currentmodule:: gators.feature_generation_dt
Base Datetime Feature Generation
################################
.. autosummary::
:toctree: api/
_BaseDatetimeFeature
Ordinal Datetime Features
#########################
.. autosummary::
:toctree: api/
OrdinalMinuteOfHour
OrdinalHourOfDay
OrdinalDayOfWeek
OrdinalDayOfMonth
OrdinalMonthOfYear
Cyclic Datetime Features
########################
.. autosummary::
:toctree: api/
CyclicMinuteOfHour
CyclicHourOfDay
CyclicDayOfWeek
CyclicDayOfMonth
CyclicMonthOfYear
Delta Time Features
###################
.. autosummary::
:toctree: api/
DeltaTime
EOL
cat > reference/feature_generation_str.rst <<EOL
.. _api.feature_generation_str:
*************************
Feature Generation String
*************************
.. currentmodule:: gators.feature_generation_str
Base String Feature Generation
##############################
.. autosummary::
:toctree: api/
_BaseStringFeature
.. autosummary::
:toctree: api/
SplitExtract
Extract
StringContains
StringLength
LowerCase
UpperCase
EOL
cat > reference/feature_selection.rst <<EOL
.. _api.feature_selection:
*****************
Feature Selection
*****************
.. currentmodule:: gators.feature_selection
Base Feature Selection Transformer
##################################
.. autosummary::
:toctree: api/
_BaseFeatureSelection
Unsupervised Feature Selection
##############################
.. autosummary::
:toctree: api/
VarianceFilter
CorrelationFilter
Supervised Feature Selection
############################
.. autosummary::
:toctree: api/
InformationValue
MultiClassInformationValue
RegressionInformationValue
SelectFromModel
SelectFromModels
EOL
cat > reference/converter.rst <<EOL
.. _api.converter:
*********
Converter
*********
.. currentmodule:: gators.converter
.. autosummary::
:toctree: api/
ConvertColumnDatatype
KoalasToPandas
ToNumpy
EOL
cat > reference/pipeline.rst <<EOL
.. _api.pipeline:
********
Pipeline
********
.. currentmodule:: gators.pipeline
.. autosummary::
:toctree: api/
Pipeline
EOL
cat > reference/transformers.rst <<EOL
.. _api.transformers:
************
Transformers
************
.. currentmodule:: gators.transformers
.. autosummary::
:toctree: api/
Transformer
TransformerXY
EOL
cat > reference/scalers.rst <<EOL
.. _api.scalers:
*******
Scalers
*******
.. currentmodule:: gators.scalers
.. autosummary::
:toctree: api/
MinMaxScaler
StandardScaler
EOL
cat > reference/sampling.rst <<EOL
.. _api.sampling:
********
Sampling
********
.. currentmodule:: gators.sampling
.. note::
**UnsupevisedSampling** should be used for regression problems, and
*SupervisedSampling* should be used for classification problems.
.. autosummary::
:toctree: api/
UnsupervisedSampling
SupervisedSampling
EOL
cat > reference/model_building.rst <<EOL
.. _api.model_building:
***************
Model Building
***************
.. currentmodule:: gators.model_building
.. autosummary::
:toctree: api/
TrainTestSplit
HyperOpt
XGBBoosterBuilder
XGBTreeliteDumper
LGBMTreeliteDumper
EOL
cat > reference/transformers.rst <<EOL
.. _api.transformers:
************
Transformers
************
.. currentmodule:: gators.transformers
.. autosummary::
:toctree: api/
Transformer
TransformerXY
EOL
mkdir benchmarking
cat > benchmarking/index.rst <<EOL
************
Benchmarking
************
Per-sample *transform* benchmarking
===================================
Benchmarking done using the jupyter notebook *%timeit* magic command.
Data Cleaning
-------------
.. image:: ../../_static/benchmarking_pandas_numpy/DropColumns.jpg
:width: 800px
:alt: alternate text
:align: left
.. image:: ../../_static/benchmarking_pandas_numpy/DropDatatypeColumns.jpg
:width: 800px
:alt: alternate text
:align: left
.. image:: ../../_static/benchmarking_pandas_numpy/KeepColumns.jpg
:width: 800px
:alt: alternate text
:align: left
.. image:: ../../_static/benchmarking_pandas_numpy/Replace.jpg
:width: 800px
:alt: alternate text
:align: left
Binning
-------
.. image:: ../../_static/benchmarking_pandas_numpy/BinRareEvents.jpg
:width: 800px
:alt: alternate text
:align: left
.. image:: ../../_static/benchmarking_pandas_numpy/CustomDiscretizer.jpg
:width: 800px
:alt: alternate text
:align: left
.. image:: ../../_static/benchmarking_pandas_numpy/Discretizer.jpg
:width: 800px
:alt: alternate text
:align: left
.. image:: ../../_static/benchmarking_pandas_numpy/QuantileDiscretizer.jpg
:width: 800px
:alt: alternate text
:align: left
Clipping
--------
.. image:: ../../_static/benchmarking_pandas_numpy/Clipping.jpg
:width: 800px
:alt: alternate text
:align: left
Scalers
-------
.. image:: ../../_static/benchmarking_pandas_numpy/MinMaxScaler.jpg
:width: 800px
:alt: alternate text
:align: left
.. image:: ../../_static/benchmarking_pandas_numpy/StandardScaler.jpg
:width: 800px
:alt: alternate text
:align: left
Imputers
--------
.. image:: ../../_static/benchmarking_pandas_numpy/NumericsImputer.jpg
:width: 800px
:alt: alternate text
:align: left